From 4c9d2ed0c801d9f9b0cca1550a433a54835316d9 Mon Sep 17 00:00:00 2001 From: Delpoo Date: Mon, 7 Apr 2025 20:21:30 -0300 Subject: [PATCH 1/9] First commit --- .vscode/launch.json | 37 + ADRpy/analisis/Data/Datos_aeronaves.xlsx | Bin 0 -> 65069 bytes ADRpy/analisis/Modulos/config_and_loading.py | 65 + .../analisis/Modulos/correlation_analysis.py | 108 + .../Modulos/correlation_imputation.py | 224 + ADRpy/analisis/Modulos/data_processing.py | 234 + ADRpy/analisis/Modulos/excel_export.py | 71 + ADRpy/analisis/Modulos/html_utils.py | 71 + ADRpy/analisis/Modulos/imputation_loop.py | 202 + .../analisis/Modulos/similarity_imputation.py | 295 + ADRpy/analisis/Modulos/user_interaction.py | 59 + ADRpy/analisis/README.md | 0 ADRpy/analisis/aaa.ipynb | 9397 +++++++++++++++++ ADRpy/analisis/main.py | 171 + ADRpy/analisis/requirements.txt | 8 + archivo_imputaciones.xlsx | Bin 0 -> 50354 bytes ...-wing Aircraft Design (Keane et al.).ipynb | 12 +- 17 files changed, 10948 insertions(+), 6 deletions(-) create mode 100644 .vscode/launch.json create mode 100644 ADRpy/analisis/Data/Datos_aeronaves.xlsx create mode 100644 ADRpy/analisis/Modulos/config_and_loading.py create mode 100644 ADRpy/analisis/Modulos/correlation_analysis.py create mode 100644 ADRpy/analisis/Modulos/correlation_imputation.py create mode 100644 ADRpy/analisis/Modulos/data_processing.py create mode 100644 ADRpy/analisis/Modulos/excel_export.py create mode 100644 ADRpy/analisis/Modulos/html_utils.py create mode 100644 ADRpy/analisis/Modulos/imputation_loop.py create mode 100644 ADRpy/analisis/Modulos/similarity_imputation.py create mode 100644 ADRpy/analisis/Modulos/user_interaction.py create mode 100644 ADRpy/analisis/README.md create mode 100644 ADRpy/analisis/aaa.ipynb create mode 100644 ADRpy/analisis/main.py create mode 100644 ADRpy/analisis/requirements.txt create mode 100644 archivo_imputaciones.xlsx diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 00000000..85b64e34 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,37 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + + { + "name": "Python Debugger: Current File", + "type": "debugpy", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal" + }, + { + "name": "Python Script Debugging", + "type": "debugpy", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal" + }, + { + "name": "Jupyter Notebook Debugging", + "type": "debugpy", + "request": "launch", + "program": "${workspaceFolder}/mi_notebook.ipynb", + "console": "integratedTerminal" + }, + { + "name": "Custom Task", + "type": "debugpy", + "request": "launch", "program": "${workspaceFolder}/custom_script.py", + "args": ["--custom-arg", "value"], + "console": "integratedTerminal" + } + ] +} \ No newline at 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z<)P_2I0|^z*GClk+&`i`^ne9N0q^qnh~od@k0=j4|G-heTarDZ5SBflfHx=u2LNxT z@(7S!`vCCRS_PaGyf*TY6t&|wDR`wMI0ATT)guDN*aO0UmS_bI1fIk42y{5{p8-8& zw15MF=LS3iElocF{dbuH;DF%K%|}3o`Tqj&G1dtV2=17C1Y}%(00fJ-FoT^x#9zQc z!QKClpl2%&p#KO4fK&hHOM=_@pDgLYa|#X$?znqg(!l0Fmh{J62iynpNZqmXoBHn_ z5%6pO(=dLdg@D-JgMj!qGZ~!xpXSHk$-55!M*f#E0!|IClReV!p8UR>|E8gVLx3wJ ij}UBU4-o$&^+ZV)7S!ANSJER9gc|6H)Or5v?Ee5-5^_}l literal 0 HcmV?d00001 diff --git a/ADRpy/analisis/Modulos/config_and_loading.py b/ADRpy/analisis/Modulos/config_and_loading.py new file mode 100644 index 00000000..de1dced2 --- /dev/null +++ b/ADRpy/analisis/Modulos/config_and_loading.py @@ -0,0 +1,65 @@ +import pandas as pd +import tkinter as tk +from tkinter import simpledialog, messagebox +from openpyxl import load_workbook + + + +def configurar_entorno(max_rows=20, max_columns=10): + """ + Configura el entorno para mostrar más datos en la consola. + :param max_rows: Número máximo de filas para mostrar en consola. + :param max_columns: Número máximo de columnas para mostrar en consola. + """ + pd.set_option('display.max_rows', max_rows) + pd.set_option('display.max_columns', max_columns) + + +def cargar_datos(ruta_archivo='Datos_aeronaves.xlsx'): + """ + Carga los datos desde un archivo Excel y realiza validaciones. + Devuelve el DataFrame cargado y la ruta utilizada. + """ + # Solicitar al usuario la ruta del archivo si no se proporciona + if ruta_archivo is None: + ruta_archivo = input("Ingrese la ruta del archivo Excel original (o presione Enter para usar 'C:/Users/delpi/OneDrive/Tesis/ADRpy-VTOL/ADRpy/analisis/data/Datos_aeronaves.xlsx'): ").strip() + if not ruta_archivo: + ruta_archivo = "C:/Users/delpi/OneDrive/Tesis/ADRpy-VTOL/ADRpy/analisis/data/Datos_aeronaves.xlsxC:/Users/delpi/OneDrive/Tesis/ADRpy-VTOL/ADRpy/analisis/data/Datos_aeronaves.xlsx" # Asignar valor predeterminado + + # Validar el formato del archivo + if not ruta_archivo.endswith(('.xlsx', '.xlsm')): + raise ValueError("El archivo debe estar en formato .xlsx o .xlsm compatible con openpyxl.") + + # Mostrar mensaje de carga + print(f"=== Cargando datos desde el archivo: {ruta_archivo} ===") + + try: + # Cargar el archivo con encabezado e índice configurados + df = pd.read_excel(ruta_archivo, header=0, index_col=0) + + # Validaciones adicionales + if df.empty: + raise ValueError("El archivo cargado está vacío. Verifica el archivo de origen.") + + # Manejar índices nulos + if df.index.isnull().any(): + print("Advertencia: El índice contiene valores nulos. Se reemplazarán por 'indice_desconocido'.") + df.index = df.index.fillna("indice_desconocido") + + # Manejar columnas nulas + if df.columns.isnull().any(): + print("Advertencia: Algunas columnas contienen valores nulos. Se reemplazarán por 'columna_desconocida'.") + df.columns = df.columns.fillna("columna_desconocida") + + # Mostrar información básica del DataFrame cargado + print("\n=== Resumen inicial del DataFrame cargado ===") + print(df.info()) + #print("\n=== Vista previa de índices y columnas ===") + #print(f"Primeros índices: {df.index.tolist()[:10]}") + #print(f"Primeras columnas: {df.columns.tolist()[:10]}") + + return df, ruta_archivo + except FileNotFoundError: + raise ValueError("Error: Archivo no encontrado.") + except Exception as e: + raise ValueError(f"Error al cargar el archivo: {e}") diff --git a/ADRpy/analisis/Modulos/correlation_analysis.py b/ADRpy/analisis/Modulos/correlation_analysis.py new file mode 100644 index 00000000..093a34da --- /dev/null +++ b/ADRpy/analisis/Modulos/correlation_analysis.py @@ -0,0 +1,108 @@ +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +from sklearn.metrics import r2_score +from sklearn.linear_model import LinearRegression +import numpy as np +from .html_utils import convertir_a_html +from .user_interaction import solicitar_umbral + + + + +def calcular_correlaciones_y_generar_heatmap_con_resumen(df_procesado, parametros_seleccionados, valor_por_defecto=0.7): + """ + Calcula las correlaciones completas y filtradas entre variables seleccionadas, + genera tablas en HTML con un resumen agregado, y crea un heatmap. + :param df_procesado: DataFrame procesado con los datos completos. + :param parametros_seleccionados: Lista de variables a incluir en los cálculos y visualización. + :param valor_por_defecto: Umbral predeterminado para correlaciones significativas. + :param devolver_tabla: Si True, retorna la tabla completa de correlaciones. + :return: Tabla completa de correlaciones (opcional). + """ + def agregar_resumen_a_tabla(tabla, titulo): + """ + Agrega un resumen al final de una tabla HTML indicando: + - Cantidad total de valores. + - Cantidad de valores numéricos. + - Cantidad de valores NaN. + """ + total_valores = tabla.size + valores_numericos = tabla.count().sum() + valores_nan = total_valores - valores_numericos + + resumen = pd.DataFrame({ + "Resumen": ["Total de valores", "Valores numéricos", "Valores NaN"], + "Cantidad": [total_valores, valores_numericos, valores_nan] + }) + + convertir_a_html(tabla, titulo=titulo, mostrar=True) + convertir_a_html(resumen, titulo="Resumen de la Tabla", mostrar=True) + + try: + # === Paso 1: Solicitar umbral al usuario === + umbral = solicitar_umbral(valor_por_defecto) + print(f"\nUmbral seleccionado para correlaciones significativas: {umbral}") + + # === Validación de parámetros seleccionados === + parametros_no_encontrados = [v for v in parametros_seleccionados if v not in df_procesado.index] + if parametros_no_encontrados: + raise ValueError(f"Los siguientes parámetros no se encontraron en los datos procesados: {', '.join(parametros_no_encontrados)}") + + # === Tabla completa (sin filtrar) === + print("\n=== Cálculo de tabla completa ===") + tabla_completa = df_procesado.transpose().corr() + agregar_resumen_a_tabla(tabla_completa.round(3), "Tabla de Correlaciones con todos los parametros(tabla_completa)") + + # Filtrar correlaciones por el umbral + tabla_completa_significativa = tabla_completa[ + (tabla_completa.abs() >= umbral) & (tabla_completa != 1) + ] + #agregar_resumen_a_tabla(tabla_completa_significativa.round(3), f"Tabla de Correlaciones Significativas (Umbral >= {umbral})") + + # === Filtrar datos seleccionados === + print("\n=== Filtrando datos seleccionados ===") + + datos_filtrados = df_procesado.loc[parametros_seleccionados].transpose() + + # Tabla filtrada + print("\n=== Cálculo de correlaciones filtradas ===") + tabla_filtrada = datos_filtrados.corr() + agregar_resumen_a_tabla(tabla_filtrada.round(3), "Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)") + + # Filtrar correlaciones por el umbral para la tabla filtrada + tabla_filtrada_significativa = tabla_filtrada[ + (tabla_filtrada.abs() >= umbral) & (tabla_filtrada != 1) + ] + agregar_resumen_a_tabla( + tabla_filtrada_significativa.round(3), + f"Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= {umbral})" + ) + + # Preparar datos para el heatmap + print("\n=== Preparando datos para el heatmap ===") + heatmap_data = datos_filtrados.dropna(thresh=2) # Excluir variables con menos de 2 valores válidos + heatmap_correlaciones = heatmap_data.corr() + + # Generar heatmap + print("\n=== Generando heatmap ===") + plt.figure(figsize=(12, 10)) + cmap = sns.diverging_palette(10, 145, s=80, l=55, n=9, as_cmap=True) + sns.heatmap( + heatmap_correlaciones, + annot=True, + cmap=cmap, + center=0, + linewidths=0.5, + vmin=-1, + vmax=1 + ) + plt.title(f"Heatmap de Correlaciones de Variables Seleccionadas (Umbral >= {umbral})") + plt.show() + + except ValueError as e: + print(f"Error: {e}. Por favor verifica los parámetros seleccionados.") + except KeyError as e: + print(f"Error: {e}. Asegúrate de que las variables seleccionadas existen en los datos.") + + return tabla_completa \ No newline at end of file diff --git a/ADRpy/analisis/Modulos/correlation_imputation.py b/ADRpy/analisis/Modulos/correlation_imputation.py new file mode 100644 index 00000000..05ce634f --- /dev/null +++ b/ADRpy/analisis/Modulos/correlation_imputation.py @@ -0,0 +1,224 @@ +import pandas as pd +import numpy as np +from sklearn.linear_model import LinearRegression +from .html_utils import convertir_a_html + + + + +def Imputacion_por_correlacion( + df_correlacion, + parametros_preseleccionados, + tabla_completa, + min_datos_validos=5, + max_lineas_consola=250, + umbral_correlacion=0.7, + nivel_confianza_min_correlacion=0.5, + reduccion_confianza=0.05 +): + # Lógica de la función + """ + Imputa valores faltantes en un dataframe basado en correlaciones significativas entre parámetros. + + Parámetros: + df_procesado (pd.DataFrame): DataFrame con los datos a procesar. + parametros_preseleccionados (list): Lista de parámetros a imputar. + umbral_correlacion (float): Valor mínimo absoluto de correlación para considerar como significativa. + min_datos_validos (int): Mínimo número de datos válidos requeridos por parámetro para ser incluido. + max_lineas_consola (int): Máximo número de líneas a imprimir en la consola. + + Retorna: + pd.DataFrame: DataFrame con valores imputados. + """ + # Cargar datos simulados + df = df_correlacion.copy() + + # Mostrar df en formato HTML + print("\n=== DataFrame inicial ===") + convertir_a_html(df, titulo="DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())", mostrar=True) + #print("Parámetros disponibles en el índice del DataFrame:") + #print(df.index.tolist()) + + # Convertir todo a numérico + print("\n=== Convertir todo a numérico ===") + df = df.apply(pd.to_numeric, errors='coerce') # Forzar datos no numéricos a NaN + + #afasfasfasfasf no se que hace + parametros_validos = df.index[df.notna().sum(axis=1) >= 5].tolist() + df = df.loc[parametros_validos] + print(type(parametros_validos)) + + + # === PASO 1: CÁLCULO DE CORRELACIONES === + print("\n=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===") + tabla_completa = calcular_correlaciones_y_generar_heatmap_con_resumen(df, parametros_seleccionados, valor_por_defecto=0.7) + correlaciones = tabla_completa.copy() + indices_validos = df.index + + # Filtrar correlaciones para que coincidan los índices y las columnas con parámetros válidos + correlaciones_filtradas = correlaciones.loc[ + indices_validos.intersection(correlaciones.index), # Filtra filas válidas + indices_validos.intersection(correlaciones.columns) # Filtra columnas válidas + ] + + correlaciones_aceptables = correlaciones_filtradas[(correlaciones_filtradas.abs() >= 0.7) & (correlaciones_filtradas.abs() < 1.0)] + + # Mostrar tabla de correlaciones + #convertir_a_html(correlaciones, titulo="Tabla de Correlaciones", mostrar=True) + #print("Parámetros disponibles en el índice del DataFrame:") + #print(correlaciones.index.tolist()) + + # Mostrar correlaciones aceptables en HTML + convertir_a_html( + datos_procesados=correlaciones_aceptables, + titulo="Tabla de correlaciones con filtro de umbral", + mostrar=True + ) + #print("Parámetros disponibles en el índice del DataFrame:") + #print(correlaciones_aceptables.index.tolist()) + + # === PASO 2: IMPUTACIÓN === + print("\n=== PASO 2: IMPUTACIÓN DE VALORES ===") + valores_imputados = 0 + lineas_impresas = 0 + MAX_LINEAS_CONSOLA = 40000000 + + def evaluar_confianza(puntaje): + """Evalúa el nivel de confianza basado en el puntaje.""" + if puntaje >= 0.9: + return "Confianza Muy Alta" + elif puntaje >= 0.75: + return "Confianza Alta" + elif puntaje >= 0.6: + return "Confianza Media" + elif puntaje >= 0.4: + return "Confianza Baja" + else: + return "Confianza Muy Baja" + + + #Declara una variable para crear una lista para registrar las imputaciones + reporte_imputaciones = [] + + + + for parametro in parametros_preseleccionados: + if parametro not in correlaciones_aceptables.index: + print(f"\n=== {parametro}: Sin correlaciones significativas (|r| < 0.7) ===") + continue + + valores_faltantes = df.loc[parametro][df.loc[parametro].isna()].index.tolist() + if not valores_faltantes: + print(f"\n=== {parametro}: No hay valores faltantes para imputar. ===") + continue + + print(f"\n=== Imputación para el parámetro: **{parametro}** ===") + for aeronave in valores_faltantes: + if lineas_impresas >= MAX_LINEAS_CONSOLA: + print("\n--- Límite de impresión alcanzado. ---") + break + + print(f"\n--- Imputación para aeronave: **{aeronave}** ---") + valores_predichos = [] + + correlaciones_parametro = correlaciones_aceptables.loc[parametro].dropna() + + for parametro_correlacionado, correlacion in correlaciones_parametro.items(): + datos_validos = df.loc[[parametro, parametro_correlacionado]].dropna(axis=1) + + if datos_validos.shape[1] < 5: + continue + + # Evitar duplicados + datos_validos = datos_validos.T.drop_duplicates().T + + X = datos_validos.loc[parametro_correlacionado].values.reshape(-1, 1) + y = datos_validos.loc[parametro].values + + # Entrenar modelo de regresión + modelo = LinearRegression().fit(X, y) + r2 = modelo.score(X, y) + desviacion_std = np.std(y - modelo.predict(X)) + varianza = np.var(y - modelo.predict(X)) + incertidumbre = desviacion_std / np.sqrt(len(y)) + puntaje_confianza = 0.4 * r2 + 0.3 * (1 - incertidumbre) + 0.2 * (1 - desviacion_std) + 0.1 * (1 - varianza) + nivel_confianza = evaluar_confianza(puntaje_confianza) + + if pd.notna(df.loc[parametro_correlacionado, aeronave]): + valor_imputado = modelo.predict([[df.loc[parametro_correlacionado, aeronave]]])[0] + valores_predichos.append( + (parametro_correlacionado, round(valor_imputado, 3), round(r2, 3), round(desviacion_std, 3)) + ) + + # Detalle de datos usados + print(f"\n--- Correlación: {parametro_correlacionado} (r = {round(correlacion, 3)}) ---") + print(f"Aeronaves utilizadas: {datos_validos.columns.tolist()}") + print(f"Valores para {parametro_correlacionado}: {X.flatten().round(3).tolist()}") + print(f"Valores para {parametro}: {y.round(3).tolist()}") + print(f"Ecuación de regresión: y = {round(modelo.coef_[0], 3)}x + {round(modelo.intercept_, 3)}") + print(f"Valor del parámetro correlacionado para la aeronave: {round(df.loc[parametro_correlacionado, aeronave], 3)}") + print(f"Predicción obtenida: {round(valor_imputado, 3)}") + print(f"\tR²: {r2}, Desviación Estándar: {desviacion_std}, Varianza: {varianza}, Incertidumbre: {incertidumbre}") + print(f"\tNivel de confianza: {nivel_confianza}") + lineas_impresas += 1 + + if valores_predichos: + valor_final = np.median([pred[1] for pred in valores_predichos]) + df.loc[parametro, aeronave] = round(valor_final, 3) + valores_imputados += 1 + print(f"Valores imputados: {[f'{pred[0]}: {pred[1]}' for pred in valores_predichos]}") + print(f"**Mediana calculada:** {round(valor_final, 3)}") + + # Registro correcto + reporte_imputaciones.append({ + "Aeronave": aeronave, + "Parámetro": parametro, + "Valor Imputado": valor_final, + "Nivel de Confianza": puntaje_confianza + }) + else: + info_imposible = pd.DataFrame([{ + "Mensaje": f"No se pudo imputar el parámetro '{parametro}' para la aeronave '{aeronave}'." + }]) + convertir_a_html(info_imposible, titulo="Imputación no Exitosa", mostrar=True) + + lineas_impresas += 1 + + + # Filtro y generación del reporte final + df_reporte = pd.DataFrame(reporte_imputaciones) + #print("Contenido de reporte_imputaciones:", reporte_imputaciones) + #print("Columnas de df_reporte:", df_reporte.columns) + #print("Contenido inicial de df_reporte:\n", df_reporte.head()) + if "Nivel de Confianza" in df_reporte.columns: + df_reporte = df_reporte[df_reporte["Nivel de Confianza"] >= nivel_confianza_min_correlacion] + else: + print("La columna 'Nivel de Confianza' no está presente en df_reporte.") + # Maneja el caso sin filtro, por ejemplo: + return df_procesado, [] + + + # Resumen de imputaciones + resumen_imputaciones = df_reporte.groupby("Aeronave").size().reset_index(name="Cantidad de Valores Imputados") + total_imputaciones = resumen_imputaciones["Cantidad de Valores Imputados"].sum() + resumen_imputaciones.loc["Total"] = ["Total", total_imputaciones] + + # Mostrar reportes (HTML opcional) + convertir_a_html(df_reporte, titulo="Reporte Final de Imputaciones", mostrar=True) + convertir_a_html(resumen_imputaciones, titulo="Resumen de Imputaciones", mostrar=True) + + # Validar si se realizaron imputaciones + if not reporte_imputaciones: + print("No se realizaron imputaciones con éxito.") + return df, [] + + # Validar si el DataFrame está vacío (seguridad adicional) + if df.empty: + print("El DataFrame de resultados está vacío.") + return df, [] + + # Opcional: convertir reporte_imputaciones a DataFrame si necesario + #df_reporte_final = pd.DataFrame(reporte_imputaciones) + + # Retornar el DataFrame procesado y la lista de imputaciones + return df, reporte_imputaciones \ No newline at end of file diff --git a/ADRpy/analisis/Modulos/data_processing.py b/ADRpy/analisis/Modulos/data_processing.py new file mode 100644 index 00000000..eed26b63 --- /dev/null +++ b/ADRpy/analisis/Modulos/data_processing.py @@ -0,0 +1,234 @@ +import pandas as pd +import numpy as np +import tkinter as tk +from tkinter import simpledialog + + + +def procesar_datos_y_manejar_duplicados(df): + """ + Limpia un DataFrame preservando la estructura original y maneja duplicados en índices y columnas. + Incluye interacción para gestionar duplicados según las elecciones del usuario. + :param df: DataFrame a procesar. + :return: DataFrame limpio y procesado. + """ + import tkinter as tk + from tkinter import simpledialog + + try: + print("=== Inicio del procesamiento de datos ===") + + # Paso 1: Limpieza inicial de encabezados + df.columns = df.columns.str.strip().str.replace('\xa0', ' ', regex=True) + df.index = df.index.astype(str).str.strip().str.replace('\xa0', ' ', regex=True) + + # Paso 2: Eliminar filas y columnas completamente vacías + df.dropna(how='all', inplace=True) # Filas vacías + df.dropna(how='all', axis=1, inplace=True) # Columnas vacías + + # Paso 3: Manejo de duplicados + print("\n=== Comprobación de duplicados ===") + duplicados_fila = df.index[df.index.duplicated()].tolist() + duplicados_columna = df.columns[df.columns.duplicated()].tolist() + + if not duplicados_fila and not duplicados_columna: + print("No se encontraron duplicados en índices o columnas.") + else: + print(f"Índices duplicados: {duplicados_fila}") + print(f"Columnas duplicadas: {duplicados_columna}") + + # Crear ventana emergente para interacción + root = tk.Tk() + root.withdraw() + + # Preguntar manejo global de duplicados + respuesta_global = simpledialog.askstring( + "Manejo global de duplicados", + "Se encontraron duplicados. ¿Deseas aplicar una acción global a todos?\n" + "[1] Eliminar todos los duplicados\n" + "[2] Conservar el primero en todos\n" + "[3] Conservar el último en todos\n" + "[4] Procesar duplicados uno por uno" + ) + + # Aplicar acción global si corresponde + if respuesta_global in ['1', '2', '3']: + if respuesta_global == '1': + print("Eliminando todos los duplicados...") + if duplicados_fila: + df = df.loc[~df.index.duplicated(keep=False)] + if duplicados_columna: + df = df.loc[:, ~df.columns.duplicated(keep=False)] + + elif respuesta_global == '2': + print("Conservando el primero en todos los duplicados...") + if duplicados_fila: + df = df.loc[~df.index.duplicated(keep='first')] + if duplicados_columna: + df = df.loc[:, ~df.columns.duplicated(keep='first')] + + elif respuesta_global == '3': + print("Conservando el último en todos los duplicados...") + if duplicados_fila: + df = df.loc[~df.index.duplicated(keep='last')] + if duplicados_columna: + df = df.loc[:, ~df.columns.duplicated(keep='last')] + else: + # Procesar duplicados uno por uno si respuesta_global es '4' + for duplicado in duplicados_fila + duplicados_columna: + tipo = "Índice" if duplicado in duplicados_fila else "Columna" + respuesta = simpledialog.askstring( + "Duplicado encontrado", + f"{tipo} duplicado '{duplicado}' encontrado. Opciones:\n" + "[1] Eliminar\n" + "[2] Conservar el primero\n" + "[3] Conservar el último" + ) + # Realizar la acción según la elección del usuario + if respuesta == '1': + if tipo == "Índice": + df = df[df.index != duplicado] + else: + df = df.loc[:, df.columns != duplicado] + elif respuesta == '2': + if tipo == "Índice": + df = df.loc[~df.index.duplicated(keep='first')] + else: + df = df.loc[:, ~df.columns.duplicated(keep='first')] + elif respuesta == '3': + if tipo == "Índice": + df = df.loc[~df.index.duplicated(keep='last')] + else: + df = df.loc[:, ~df.columns.duplicated(keep='last')] + + # Paso 4: Convertir valores internos a numéricos + print("\n=== Convirtiendo valores a numéricos donde sea posible ===") + for col in df.columns: + try: + df.loc[:, col] = pd.to_numeric(df[col], errors='coerce') + except Exception as e: + print(f"Advertencia: No se pudo convertir la columna '{col}' a numérico. Error: {e}") + + print("=== Procesamiento completado ===") + return df + + except Exception as e: + raise ValueError(f"Error durante el procesamiento y manejo de duplicados: {e}") + + +def mostrar_celdas_faltantes_con_seleccion(df): + """ + Permite al usuario seleccionar una columna para analizar y muestra las celdas faltantes. + Si el usuario no selecciona ninguna columna, utiliza una columna predeterminada. + Maneja columnas con más de 26 posiciones generando etiquetas en formato Excel. + :param df: DataFrame procesado. + :return: DataFrame con los detalles de las celdas faltantes (si las hay). + """ + def seleccionar_columna(df): + """ + Permite al usuario seleccionar una columna específica para validar. + Si no selecciona ninguna, retorna la primera columna como predeterminada. + """ + # Crear un diccionario para asociar números con las columnas + columnas_dict = {i + 1: col for i, col in enumerate(df.columns)} + opciones_texto = "\n".join([f"{num}: {col}" for num, col in columnas_dict.items()]) + + try: + # Solicitar al usuario seleccionar una columna + columna_numero = simpledialog.askstring( + "Selección de columna", + f"Selecciona el número correspondiente a la columna que deseas validar:\n\n{opciones_texto}" + ) + if not columna_numero: # Si no se selecciona nada, usar la primera columna + print("No se seleccionó ninguna columna. Usando la primera columna como predeterminada.") + return df.columns[0] + + columna_numero = int(columna_numero) + + if columna_numero not in columnas_dict: + raise ValueError("Número ingresado fuera del rango válido.") + + return columnas_dict[columna_numero] + + except ValueError as e: + print(f"Error: {e}. Finalizando la ejecución.") + exit() + + def indice_a_columna_excel(indice): + """ + Convierte un índice numérico de columna en una etiqueta al estilo Excel (A, B, ..., Z, AA, AB, ...). + :param indice: Índice numérico de la columna (0 para A, 1 para B, ..., 25 para Z, 26 para AA, etc.). + :return: Etiqueta de columna en formato Excel. + """ + etiqueta = "" + while indice >= 0: + etiqueta = chr(indice % 26 + ord('A')) + etiqueta + indice = indice // 26 - 1 + return etiqueta + + try: + # Selección de columna + columna_prueba = seleccionar_columna(df) + + # Identificar celdas faltantes en la columna seleccionada + print(f"\n=== Analizando celdas faltantes en la columna: '{columna_prueba}' ===") + missing_indices = df[df[columna_prueba].isna()].index.tolist() + + if not missing_indices: + print(f"No se encontraron valores faltantes en la columna '{columna_prueba}'.") + return pd.DataFrame() # Devuelve un DataFrame vacío si no hay faltantes + + # Crear un DataFrame para almacenar los resultados + resultados = [] + + for idx in missing_indices: + fila_excel = df.index.get_loc(idx) + 2 # +2 para ajustarse al formato Excel (encabezado en fila 1) + columna_excel = indice_a_columna_excel(df.columns.get_loc(columna_prueba)) + celda_excel = f"{columna_excel}{fila_excel}" + resultados.append({ + "Índice": idx, + "Celda": celda_excel, + "Columna": columna_prueba, + "Valor Actual": "NaN" + }) + + # Convertir resultados a DataFrame + df_resultados = pd.DataFrame(resultados) + + return df_resultados + + except Exception as e: + print(f"Error al analizar celdas faltantes: {e}") + raise + + +def generar_resumen_faltantes(df, titulo="Resumen de Valores Faltantes por Columna", ancho="50%", alto="300px"): + """ + Genera un resumen de los valores faltantes por columna en un DataFrame. + También genera una tabla HTML con la sumatoria total de los valores faltantes de todas las columnas. + + :param df: DataFrame a analizar. + :param titulo: Título opcional para mostrar en la tabla HTML. + :param ancho: Ancho del contenedor HTML. + :param alto: Alto del contenedor HTML. + :return: Tuple con dos DataFrames: resumen de valores faltantes por columna y sumatoria total. + """ + # Calcular la cantidad de valores faltantes por columna + faltantes_por_columna = df.isnull().sum() + + # Crear un DataFrame con el resumen por columna + resumen_faltantes = faltantes_por_columna.reset_index() + resumen_faltantes.columns = ["Columna", "Valores Faltantes"] + + # Calcular la sumatoria total de los valores faltantes + total_faltantes = faltantes_por_columna.sum() + resumen_total = pd.DataFrame({"Resumen": ["Total de Valores Faltantes"], "Cantidad": [total_faltantes]}) + + # Mostrar el resumen por columna como una tabla HTML + convertir_a_html(resumen_faltantes, titulo=titulo, ancho=ancho, alto=alto, mostrar=True) + + # Mostrar la sumatoria total como una tabla HTML + convertir_a_html(resumen_total, titulo="Sumatoria Total de Valores Faltantes", ancho=ancho, alto="100px", mostrar=True) + + # Retornar ambos DataFrames para su posible uso posterior + return resumen_faltantes, resumen_total \ No newline at end of file diff --git a/ADRpy/analisis/Modulos/excel_export.py b/ADRpy/analisis/Modulos/excel_export.py new file mode 100644 index 00000000..05216195 --- /dev/null +++ b/ADRpy/analisis/Modulos/excel_export.py @@ -0,0 +1,71 @@ +import pandas as pd +from openpyxl import load_workbook +from openpyxl.styles import PatternFill +from openpyxl.comments import Comment + + + + +def exportar_excel_con_imputaciones(archivo_origen, df_procesado, resumen_imputaciones, archivo_destino="archivo_imputaciones.xlsx"): + """ + Exporta el DataFrame procesado a un archivo Excel manteniendo el formato original. + Agrega colores y comentarios a las celdas imputadas por similitud y correlación. + + :param archivo_origen: Ruta del archivo Excel original. + :param archivo_destino: Ruta del archivo Excel de salida. + :param df_procesado: DataFrame con las imputaciones realizadas. + :param resumen_imputaciones: Lista de diccionarios con detalles de imputaciones. + """ + try: + # Asegurarse de que 'resumen_imputaciones' sea una lista de diccionarios + if isinstance(resumen_imputaciones, pd.DataFrame): + resumen_imputaciones = resumen_imputaciones.to_dict('records') + + # Manejar el caso donde no haya imputaciones + if not resumen_imputaciones: + print("No hay imputaciones para exportar.") + return + + print(f"=== Exportando datos al archivo: {archivo_destino} ===") + wb = load_workbook(archivo_origen) + ws = wb.active + + color_similitud = PatternFill(start_color="FFFF00", end_color="FFFF00", fill_type="solid") + color_correlacion = PatternFill(start_color="00FF00", end_color="00FF00", fill_type="solid") + + imputaciones_por_celda = { + (registro["Parámetro"], registro["Aeronave"]): registro + for registro in resumen_imputaciones + } + + # Recorrer las celdas del archivo original y actualizar según las imputaciones + for fila in ws.iter_rows(min_row=2, min_col=2): # Ajustar filas/columnas según tu estructura + for celda in fila: + aeronave = ws.cell(row=1, column=celda.column).value # Obtener nombre del parámetro + parametro = ws.cell(row=celda.row, column=1).value # Obtener nombre de la aeronave + + if (parametro, aeronave) in imputaciones_por_celda: + registro = imputaciones_por_celda[(parametro, aeronave)] + valor_imputado = df_procesado.loc[parametro, aeronave] + + # Actualizar el valor en la celda + celda.value = valor_imputado + + # Asignar color según el tipo de imputación + if registro["Método"] == "Similitud": + celda.fill = color_similitud + elif registro["Método"] == "Correlación": + celda.fill = color_correlacion + + # Agregar comentario con el nivel de confianza + comentario = f"Nivel de confianza: {registro['Nivel de Confianza']:.2f}" + celda.comment = Comment(comentario, "Sistema") + + # Guardar el archivo con las imputaciones + wb.save(archivo_destino) + print(f"Exportación completada. El archivo se guardó como '{archivo_destino}'.") + + except FileNotFoundError: + print(f"Error: El archivo '{archivo_origen}' no fue encontrado.") + except Exception as e: + print(f"Error al procesar el archivo: {e}") diff --git a/ADRpy/analisis/Modulos/html_utils.py b/ADRpy/analisis/Modulos/html_utils.py new file mode 100644 index 00000000..293ebdfc --- /dev/null +++ b/ADRpy/analisis/Modulos/html_utils.py @@ -0,0 +1,71 @@ +import pandas as pd +import numpy as np +from IPython.display import display, HTML + + + +def convertir_a_html(datos_procesados, titulo="", ancho="100%", alto="400px", mostrar=True): + """ + Convierte un DataFrame o Series a una tabla HTML, redondeando números a 3 cifras significativas. + :param datos_procesados: DataFrame o Series a transformar. + :param titulo: Título opcional para mostrar en la tabla. + :param ancho: Ancho del contenedor HTML. + :param alto: Alto del contenedor HTML. + :param mostrar: Si True, muestra la tabla directamente; si False, devuelve el HTML. + """ + + # Asegurarse de que sea un DataFrame + if isinstance(datos_procesados, pd.Series): + datos_procesados = datos_procesados.to_frame(name="Valores") + datos_procesados.index.name = "Índice" + + # Redondear números a 3 cifras significativas sin notación científica + datos_procesados = datos_procesados.apply( + lambda col: col.map( + lambda x: f"{x:.3f}" if isinstance(x, (int, float)) else x + ) if col.dtypes in [np.float64, np.int64] else col + ) + + # Estilo CSS modificado + estilo_scroll = f""" + + """ + # Generar el HTML con el título y la tabla + tabla_html = estilo_scroll + f"

{titulo}

{datos_procesados.to_html()}
" + + # Mostrar o devolver el HTML + if mostrar: + from IPython.display import display, HTML + display(HTML(tabla_html)) # Muestra directamente + else: + return tabla_html # Devuelve el HTML diff --git a/ADRpy/analisis/Modulos/imputation_loop.py b/ADRpy/analisis/Modulos/imputation_loop.py new file mode 100644 index 00000000..77349906 --- /dev/null +++ b/ADRpy/analisis/Modulos/imputation_loop.py @@ -0,0 +1,202 @@ +import pandas as pd +from .similarity_imputation import imputacion_similitud_con_rango +from .correlation_imputation import Imputacion_por_correlacion +from .html_utils import convertir_a_html +from .data_processing import generar_resumen_faltantes + + + + +def bucle_imputacion_similitud_correlacion(df_procesado, parametros_preseleccionados, tabla_completa, reduccion_confianza=0.05, max_iteraciones=7): + """ + Realiza un bucle alternando imputaciones por similitud y correlación, consolidando los resultados. + Ahora se evita actualizar los DataFrames inmediatamente, y se eligen las imputaciones finales + al final de cada iteración. + + Retorna: + df_procesado_base (pd.DataFrame): DataFrame con imputaciones realizadas. + df_resumen (pd.DataFrame): Detalle consolidado de imputaciones realizadas. + """ + + df_procesado_base = df_procesado.copy() # Copia base del DataFrame original + df_filtrado_base = df_filtrado.copy() # Copia base del DataFrame original + + convertir_a_html(df_procesado_base, titulo="df_procesado_base", ancho="100%", alto="400px", mostrar=True) + convertir_a_html(df_filtrado_base, titulo="df_filtrado_base", ancho="100%", alto="400px", mostrar=True) + resumen_imputaciones = [] # Lista para consolidar detalles de todas las imputaciones finales + + # Configuración inicial para imputaciones por similitud + print("\n=== Configuración Inicial ===") + try: + rango_min = float(input("Ingrese el rango mínimo de MTOW (1-200, predeterminado 85): ") or 85) / 100 + rango_max = float(input("Ingrese el rango máximo de MTOW (1-200, predeterminado 115): ") or 115) / 100 + nivel_confianza_min = float(input("Ingrese el nivel mínimo de confianza (0-1, predeterminado 0.5): ") or 0.5) + + if not (0.01 <= rango_min <= 2.00 and 0.01 <= rango_max <= 2.00): + raise ValueError("Los rangos deben estar entre 1% y 200%.") + if rango_min >= rango_max: + raise ValueError("El rango mínimo no puede ser mayor o igual al rango máximo.") + if not (0 <= nivel_confianza_min <= 1): + raise ValueError("El nivel de confianza debe estar entre 0 y 1.") + except ValueError as e: + print(f"Error: {e}. Usando valores predeterminados (85% mínimo, 115% máximo, 0.5 confianza mínima).") + rango_min, rango_max, nivel_confianza_min = 0.85, 1.15, 0.5 + + print(f"\nValores configurados: Rango MTOW [{rango_min*100:.0f}% - {rango_max*100:.0f}%], Confianza Mínima: {nivel_confianza_min:.2f}") + + # Configuración inicial para imputaciones por correlación + try: + umbral_correlacion = float(input("Ingrese el umbral mínimo de correlación (0-1, predeterminado 0.7): ") or 0.7) + nivel_confianza_min_correlacion = float(input("Ingrese el nivel mínimo de confianza para correlación (0-1, predeterminado 0.5): ") or 0.5) + + if not (0 <= umbral_correlacion <= 1): + raise ValueError("El umbral de correlación debe estar entre 0 y 1.") + if not (0 <= nivel_confianza_min_correlacion <= 1): + raise ValueError("El nivel de confianza debe estar entre 0 y 1.") + except ValueError as e: + print(f"Error: {e}. Usando valores predeterminados (umbral = 0.7, confianza mínima = 0.5).") + umbral_correlacion, nivel_confianza_min_correlacion = 0.7, 0.5 + + # Definir valores predeterminados para correlación + min_datos_validos = 5 # Cantidad mínima de datos requeridos por parámetro + umbral_correlacion = 0.7 + nivel_confianza_min_correlacion = 0.5 + reduccion_confianza = 0.05 + max_lineas_consola = 40000000 + + for iteracion in range(1, max_iteraciones + 1): + print("\n" + "="*80) + print(f"\033[1m=== INICIO DE ITERACIÓN {iteracion} ===\033[0m") + print("="*80) + + print(f"\n=== Iteración {iteracion}: Resumen antes de imputaciones ===") + resumen_antes, total_faltantes_antes = generar_resumen_faltantes( + df_procesado_base, titulo=f"Resumen de Valores Faltantes Antes de Iteración {iteracion}" + ) + + # Crear copias independientes para cada método + df_similitud = df_filtrado_base.copy() + df_correlacion = df_procesado_base.copy() + + # Imputación por similitud (no actualiza todavía) + print("\n" + "-"*80) + print(f"\033[1m*** IMPUTACIÓN POR SIMILITUD - ITERACIÓN {iteracion} ***\033[0m") + print("-"*80) + df_resultado_final, reporte_similitud = imputacion_similitud_con_rango( + df_filtrado=df_similitud, + df_procesado=df_procesado_base, + rango_min=rango_min, + rango_max=rango_max, + nivel_confianza_min=nivel_confianza_min + ) + + if reporte_similitud and len(reporte_similitud) > 0: + print("\033[1m>>> RESULTADOS DE IMPUTACIÓN POR SIMILITUD\033[0m") + # Se guardan las imputaciones de similitud, pero NO se actualiza el DataFrame aún. + # Se agregan la iteración y método aquí. + for registro in reporte_similitud: + registro["Iteración"] = iteracion + registro["Método"] = "Similitud" + registro["Nivel de Confianza"] *= (1 - reduccion_confianza) ** (iteracion - 1) + else: + print("\033[1mNo se realizaron imputaciones por similitud en esta iteración.\033[0m") + + # Imputación por correlación (no actualiza todavía) + print("\n" + "-"*80) + print(f"\033[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN {iteracion} ***\033[0m") + print("-"*80) + df_correlacion_final, reporte_correlacion = Imputacion_por_correlacion( + df_correlacion, + parametros_preseleccionados, + tabla_completa, + min_datos_validos=min_datos_validos, + max_lineas_consola=max_lineas_consola, + umbral_correlacion=umbral_correlacion, + nivel_confianza_min_correlacion=nivel_confianza_min_correlacion, + reduccion_confianza=reduccion_confianza + ) + + if reporte_correlacion and len(reporte_correlacion) > 0: + print("\033[1m>>> RESULTADOS DE IMPUTACIÓN POR CORRELACIÓN\033[0m") + for registro in reporte_correlacion: + registro["Iteración"] = iteracion + registro["Método"] = "Correlación" + registro["Nivel de Confianza"] *= (1 - reduccion_confianza) ** (iteracion - 1) + else: + print("\033[1mNo se realizaron imputaciones por correlación en esta iteración.\033[0m") + + # Combinar las imputaciones de similitud y correlación + imputaciones_candidatas = {} + + def registrar_imputacion(regs): + for reg in regs: + parametro = reg["Parámetro"] + aeronave = reg["Aeronave"] + key = (parametro, aeronave) + if key not in imputaciones_candidatas: + imputaciones_candidatas[key] = [] + imputaciones_candidatas[key].append(reg) + + if reporte_similitud and len(reporte_similitud) > 0: + registrar_imputacion(reporte_similitud) + if reporte_correlacion and len(reporte_correlacion) > 0: + registrar_imputacion(reporte_correlacion) + + # Seleccionar las mejores imputaciones por celda (la de mayor confianza) + imputaciones_finales = [] + for key, candidatos in imputaciones_candidatas.items(): + parametro, aeronave = key + # Si ya hay un valor en df_procesado_base, no imputar. + if not pd.isna(df_procesado_base.loc[parametro, aeronave]): + continue + # Escoger la de mayor confianza + mejor = max(candidatos, key=lambda x: x["Nivel de Confianza"]) + imputaciones_finales.append(mejor) + + # Ahora sí, aplicar las imputaciones finales al DataFrame base + for imp in imputaciones_finales: + parametro = imp["Parámetro"] + aeronave = imp["Aeronave"] + valor = imp["Valor Imputado"] + metodo = imp["Método"] + df_procesado_base.loc[parametro, aeronave] = valor + df_filtrado_base.loc[parametro, aeronave] = valor + resumen_imputaciones.append(imp) + print(f"Imputación final aplicada: {parametro} - {aeronave} = {valor} ({metodo})") + + print(f"\n=== Iteración {iteracion}: Resumen después de imputaciones ===") + resumen_despues, total_faltantes_despues = generar_resumen_faltantes( + df_filtrado_base, titulo=f"Resumen de Valores Faltantes Después de Iteración {iteracion}" + ) + + # Verificar condición de salida + no_similitud = (reporte_similitud is None or len(reporte_similitud) == 0) + no_correlacion = (reporte_correlacion is None or len(reporte_correlacion) == 0) + if no_similitud and no_correlacion: + print("\033[1mNo se realizaron nuevas imputaciones. Finalizando...\033[0m") + # Retornar resultados actuales antes de salir + return df_procesado_base, pd.DataFrame(resumen_imputaciones) + + print("\n" + "="*80) + print(f"\033[1m=== FIN DE ITERACIÓN {iteracion} ===\033[0m") + print("="*80) + + # Si se terminan las iteraciones sin break: + df_resumen = pd.DataFrame(resumen_imputaciones) + print("\n" + "="*80) + print("\033[1m=== RESUMEN FINAL ===\033[0m") + print("="*80) + + convertir_a_html( + df_procesado_base, + titulo="DataFrame Procesado Final (df_procesado_base)" + ) + convertir_a_html( + df_resumen, + titulo="Resumen Final de Imputaciones (df_resumen)" + ) + + print(f"\033[1mTotal de iteraciones realizadas: {iteracion}\033[0m") + print(f"\033[1mTotal de valores imputados: {len(resumen_imputaciones)}\033[0m") + + return df_procesado_base, df_resumen \ No newline at end of file diff --git a/ADRpy/analisis/Modulos/similarity_imputation.py b/ADRpy/analisis/Modulos/similarity_imputation.py new file mode 100644 index 00000000..28706356 --- /dev/null +++ b/ADRpy/analisis/Modulos/similarity_imputation.py @@ -0,0 +1,295 @@ +import pandas as pd +import numpy as np +from sklearn.metrics import r2_score +from .html_utils import convertir_a_html +from .imputation_loop import imprimir_detalles_imputacion # si la función está ahí, si no corregimos + + + + + +def imputacion_similitud_con_rango(df_filtrado, df_procesado, rango_min, rango_max, nivel_confianza_min): + + """ + Ajusta el rango de similitud e imputa valores faltantes en los parámetros de df_filtrado. + Genera un reporte final en HTML con un resumen agregado, filtrando por nivel de confianza. + :param df_filtrado: DataFrame con los parámetros a imputar. + :param df_procesado: DataFrame procesado con todos los datos. + :return: Nuevo DataFrame con los valores imputados. + """ + + # Crear una copia para mantener intacto el DataFrame original + df_resultado_por_similitud = df_filtrado.copy() + + # Función interna para imputar por similitud + def imputar_por_similitud(datos, parametro, aeronave, rango_min, rango_max, numero_valor_imputado): + + """ + Imputa un valor faltante basado en la similitud dentro del rango de MTOW. + Detalla el proceso con mensajes informativos y utiliza la mediana para calcular el valor imputado. + :param datos: DataFrame con los datos originales. + :param parametro: Parámetro a imputar. + :param aeronave: Aeronave con valor faltante. + :param rango_min: Rango mínimo de similitud. + :param rango_max: Rango máximo de similitud. + :return: Tuple (valor imputado, nivel de confianza) o (None, None) si no es posible imputar. + """ + + if df_filtrado.isna().all().all(): + print("Todos los valores en 'df_filtrado' están vacíos. No se puede proceder con la imputación.") + return df_filtrado, [] + + if df_procesado.isna().all().all(): + print("Todos los valores en 'df_procesado' están vacíos. No se puede proceder con la imputación.") + return df_filtrado, [] + + try: + # Verificar si MTOW está presente + if "Peso máximo al despegue (MTOW)" not in datos.index: + print(f"Advertencia: 'Peso máximo al despegue (MTOW)' no está en los datos para la aeronave '{aeronave}'.") + return None, None + + # Obtener el valor actual de MTOW + mtow_actual = datos.loc["Peso máximo al despegue (MTOW)", aeronave] + + # Validar si MTOW es válido + if pd.isna(mtow_actual): + print(f"MTOW faltante para la aeronave '{aeronave}'. Imputación no es posible.") + return None, None + + # Filtrar candidatas dentro del rango ajustado + candidatas_mtow = datos.loc[ + :, (datos.loc["Peso máximo al despegue (MTOW)"] >= rango_min * mtow_actual) & + (datos.loc["Peso máximo al despegue (MTOW)"] <= rango_max * mtow_actual) + ] + + if candidatas_mtow.empty: + print(f"No hay candidatos dentro del rango de {rango_min*100:.0f}% - {rango_max*100:.0f}%.") + return None, None + + # Excluir la misma aeronave de las candidatas + candidatas_mtow = candidatas_mtow.loc[:, candidatas_mtow.columns != aeronave] + + # Verificar que las candidatas tengan valores válidos en el parámetro a imputar + candidatas_validas = candidatas_mtow.loc[parametro].dropna() + + if candidatas_validas.empty: + print(f"Razón: Ninguna aeronave se encuentra dentro del rango MTOW de '{aeronave}'para el parametro '{parametro}'.") + return None, None + + + # Calcular los valores ajustados individualmente en función del MTOW de cada candidata + valores_ajustados = [] + mtow_candidatos = [] # Nueva lista para almacenar los valores de MTOW de los candidatos + detalles_ajustes = [] + + for candidata in candidatas_validas.index: + mtow_candidata = datos.loc["Peso máximo al despegue (MTOW)", candidata] + relacion_mtow_individual = mtow_candidata / mtow_actual + ajuste_individual = (relacion_mtow_individual - 1) / 4 + + valor_candidata = candidatas_validas[candidata] + valor_ajustado = valor_candidata * (1 + ajuste_individual) + + # Guardar en las listas correspondientes + valores_ajustados.append(valor_ajustado) + mtow_candidatos.append(mtow_candidata) + + + # Registrar detalle del ajuste + detalles_ajustes.append({ + "Aeronave": candidata, + "MTOW Candidata": mtow_candidata, + "Relación MTOW": relacion_mtow_individual, + "Ajuste Individual": ajuste_individual, + "Valor Original": valor_candidata, + "Valor Ajustado": valor_ajustado + }) + + + # Usar la mediana de los valores ajustados como valor final imputado + valor_imputado = np.median(valores_ajustados) + + # Calcular la confianza directamente + cantidad_minima = 3 + penalizacion_candidatos = 1 - (cantidad_minima - len(valores_ajustados)) / (3 * cantidad_minima) + penalizacion_candidatos = max(0, penalizacion_candidatos) # Evitar valores negativos + + # Cantidad ponderada basada en calidad + pesos_mtow = np.exp(-np.abs(np.array(mtow_candidatos) - mtow_actual) / mtow_actual) + cantidad_ponderada = np.sum(pesos_mtow) / len(mtow_candidatos) + + # Evaluación de R^2 con normalización + if len(candidatas_validas) > 1: + try: + r2 = r2_score(candidatas_validas.tolist(), valores_ajustados) + ponderacion_modelo = r2 + except ValueError: + print("No se puede calcular R^2: insuficientes datos válidos.") + r2 = None + else: + r2 = None + + # Usar dispersión como respaldo cuando R^2 no se puede calcular + if r2 is None: + dispersion = np.std(valores_ajustados) if len(valores_ajustados) > 1 else 1.0 + ponderacion_modelo = 1 / (1 + dispersion) + + # Validar ponderación del modelo + if ponderacion_modelo is not None and (ponderacion_modelo < 0 or ponderacion_modelo > 1): + print(f"Advertencia: Ponderación del modelo ({ponderacion_modelo:.3f}) fuera de rango. Revisar lógica previa.") + ponderacion_modelo = max(0, min(ponderacion_modelo, 1)) # Forzar a rango válido + + + # Confianza final combinada + peso_candidatos = 0.6 + peso_modelo = 0.4 + confianza_base = ( + peso_candidatos * cantidad_ponderada + + peso_modelo * ponderacion_modelo + ) + + # Aplicar penalización por pocos candidatos + confianza = confianza_base * penalizacion_candidatos + + # Asegurar valores entre 0 y 1 + confianza = min(1.0, max(0.0, confianza)) + + # Detalles del cálculo de confianza + calculos_confianza = { + "Penalización por pocos candidatos": penalizacion_candidatos, + "Cantidad Ponderada (basada en MTOW)": cantidad_ponderada, + "Ponderación del modelo (R² o dispersión)": ponderacion_modelo, + "Confianza Base": confianza_base, + "Confianza Final (tras penalización)": confianza + } + + imprimir_detalles_imputacion( + numero_valor_imputado=numero_valor_imputado, + parametro=parametro, + aeronave=aeronave, + mtow_actual=mtow_actual, + rango_min=rango_min, + rango_max=rango_max, + candidatas_validas=candidatas_validas, + detalles_ajustes=detalles_ajustes, + valores_ajustados=valores_ajustados, + valor_imputado=valor_imputado, + confianza=confianza, + calculos_confianza=calculos_confianza + ) + + + return valor_imputado, confianza + + + except Exception as e: + print(f"Error durante la imputación: {e}") + return None, None + + + + + try: + # Mostrar df_resultado_por_similitud en HTML para verificar valores iniciales + print("\n=== Verificación de 'df_resultado_por_similitud' ===") + convertir_a_html(df_resultado_por_similitud, titulo="Datos Filtrados por aeronaves seleccionadas antes de imputar(df_resultado_por_similitud)", mostrar=True) + + # Reporte de imputaciones + reporte_imputaciones = [] + numero_valor_imputado = 0 + + for parametro in df_resultado_por_similitud.index: + for aeronave in df_resultado_por_similitud.columns: + if pd.isna(df_resultado_por_similitud.loc[parametro, aeronave]): + # Realizar imputación + valor_imputado, confianza = imputar_por_similitud( + df_procesado, parametro, aeronave, rango_min, rango_max, numero_valor_imputado + ) + + # Verificar si se realizó una imputación válida + if valor_imputado is not None and confianza is not None and confianza >= nivel_confianza_min: + # Incrementar el contador SOLO aquí + numero_valor_imputado += 1 + # Asignar el valor imputado al DataFrame + df_resultado_por_similitud.loc[parametro, aeronave] = valor_imputado + # Registrar la imputación en el resumen + reporte_imputaciones.append({ + "Aeronave": aeronave, + "Parámetro": parametro, + "Valor Imputado": valor_imputado, + "Nivel de Confianza": confianza + }) + + elif confianza is not None and confianza < nivel_confianza_min: + print(f"Imputación descartada por baja confianza: {confianza:.3f} < {nivel_confianza_min}.") + else: + print(f"No se pudo imputar: {parametro} para {aeronave}.") + + + + # Generar reporte final en HTML con filtro de confianza + print("\n=== Generando reporte final ===") + if reporte_imputaciones: + df_reporte = pd.DataFrame(reporte_imputaciones) + #print("Contenido de reporte_imputaciones:", reporte_imputaciones) + df_reporte = df_reporte[df_reporte["Nivel de Confianza"] >= nivel_confianza_min] + convertir_a_html(df_reporte, titulo="Reporte Final de Imputaciones", mostrar=True) + else: + print("No se realizaron imputaciones con el nivel de confianza aceptable.") + + + except Exception as e: + print(f"Error durante la imputación: {e}") + + # Generar DataFrame con valores imputados + if not reporte_imputaciones: + print("No se realizaron imputaciones con éxito.") + return df_filtrado, [] + + + # Convertir lista de diccionarios a DataFrame para exportación final + df_reporte_final = pd.DataFrame(reporte_imputaciones) + + # Asegurar estructura del DataFrame imputado + df_resultado_final = df_resultado_por_similitud.copy() + + # Validar resultados + if df_resultado_final.empty: + print("El DataFrame de resultados está vacío.") + return df_filtrado, [] + + # Retornar DataFrame imputado y lista de diccionarios + return df_resultado_final, reporte_imputaciones + +def imprimir_detalles_imputacion(numero_valor_imputado, parametro, aeronave, mtow_actual, rango_min, rango_max, candidatas_validas, detalles_ajustes, valores_ajustados, valor_imputado, confianza, calculos_confianza): + """ + Imprime un resumen claro y organizado del proceso de imputación en consola. + """ + negrita = "\033[1m" + reset = "\033[0m" + + print(f"\n{negrita}======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #{numero_valor_imputado} ========================{reset}") + print(f"{negrita}Parámetro:{reset} {parametro}") + print(f"{negrita}Aeronave a imputar:{reset} {aeronave}") + print(f"{negrita}MTOW actual:{reset} {mtow_actual} kg") + print(f"{negrita}Rango Similitud:{reset} {rango_min*100:.0f}% - {rango_max*100:.0f}%") + print(f"{negrita}Candidatas dentro del rango:{reset} {', '.join(candidatas_validas.index)}") + + print("\nAeronaves Válidas para el Cálculo:") + print("-----------------------------------------------------------------------------------------------") + print("Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado") + print("------------|----------------|------------------------------|-------------------|----------------|---------------") + for detalle in detalles_ajustes: + print(f"{detalle['Aeronave']:12}| {detalle['MTOW Candidata']:14}| {detalle['Relación MTOW']:<30.3f}| {detalle['Ajuste Individual']:<19.4f}| {detalle['Valor Original']:<16.2f}| {detalle['Valor Ajustado']:<13.2f}") + print("-----------------------------------------------------------------------------------------------") + + print("\nCálculo del Valor Final:") + print(f"{negrita}- Se tomó la mediana de los valores ajustados {valores_ajustados} = {valor_imputado:.2f}{reset}") + print(f"{negrita}- Nivel de Confianza calculado:{reset} {confianza:.2f}") + print(f"{negrita}- Valor Imputado Final:{reset} {valor_imputado:.2f}") + + print("\nDetalle del Cálculo de Confianza:") + for key, value in calculos_confianza.items(): + print(f"{negrita}- {key}:{reset} {value:.2f}") + print(f"{negrita}============================================================================================{reset}\n") diff --git a/ADRpy/analisis/Modulos/user_interaction.py b/ADRpy/analisis/Modulos/user_interaction.py new file mode 100644 index 00000000..3af78070 --- /dev/null +++ b/ADRpy/analisis/Modulos/user_interaction.py @@ -0,0 +1,59 @@ +from tkinter import simpledialog + + + +def seleccionar_parametros_por_indices(elementos, predeterminados): + """ + Permite seleccionar parámetros desde los índices usando números en lugar de nombres. + :param elementos: Lista de nombres de parámetros disponibles (índices). + :param predeterminados: Lista de parámetros preseleccionados por defecto. + :return: Lista de parámetros seleccionados válidos. + """ + print("\n=== Selección de Parámetros ===") + print("Parámetros disponibles:") + for i, elem in enumerate(elementos, 1): + print(f"{i}. {elem}") + + # Mostrar preseleccionados + preseleccion_indices = [elementos.index(param) + 1 for param in predeterminados if param in elementos] + print("\nPreseleccionados: ", ", ".join([f"{i}" for i in preseleccion_indices])) + + # Entrada del usuario + seleccion = input("\nIngresa los números separados por coma (o presiona Enter para usar los preseleccionados): ") + + # Manejar casos según la entrada del usuario + if seleccion.strip(): # Si el usuario ingresó algo + try: + indices_seleccionados = [int(num.strip()) - 1 for num in seleccion.split(",")] + except ValueError: + print("⚠️ Entrada inválida. Usando parámetros preseleccionados.") + indices_seleccionados = [i - 1 for i in preseleccion_indices] + else: # Si el usuario presiona Enter sin ingresar nada + indices_seleccionados = [i - 1 for i in preseleccion_indices] + + # Construir la lista de seleccionados a partir de los índices válidos + seleccionados = [elementos[i] for i in indices_seleccionados if 0 <= i < len(elementos)] + + # Validar parámetros seleccionados contra elementos disponibles + seleccionados_validos = [p for p in seleccionados if p in elementos] + if len(seleccionados) > len(seleccionados_validos): + print(f"⚠️ Algunos parámetros seleccionados no son válidos y fueron eliminados: {set(seleccionados) - set(seleccionados_validos)}") + + # Retornar solo los parámetros válidos + return seleccionados_validos + +def solicitar_umbral(valor_por_defecto=0.7): + """ + Solicita al usuario ingresar un umbral para las correlaciones significativas. + Si el usuario no proporciona un valor válido, se usa el valor por defecto. + :param valor_por_defecto: Valor predeterminado del umbral si el usuario no ingresa ninguno. + :return: Umbral de correlación como flotante. + """ + try: + umbral = float(input(f"Ingrese el umbral mínimo de correlación significativa (por defecto {valor_por_defecto}): ") or valor_por_defecto) + if not (0 < umbral < 1): + raise ValueError("El umbral debe estar entre 0 y 1.") + return umbral + except ValueError as e: + print(f"Valor inválido: {e}. Se usará el umbral por defecto de {valor_por_defecto}.") + return valor_por_defecto \ No newline at end of file diff --git a/ADRpy/analisis/README.md b/ADRpy/analisis/README.md new file mode 100644 index 00000000..e69de29b diff --git a/ADRpy/analisis/aaa.ipynb b/ADRpy/analisis/aaa.ipynb new file mode 100644 index 00000000..85b09255 --- /dev/null +++ b/ADRpy/analisis/aaa.ipynb @@ -0,0 +1,9397 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "b70a4a82", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Cargando datos desde el archivo: C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\data\\Datos_aeronaves.xlsx ===\n", + "\n", + "=== Resumen inicial del DataFrame cargado ===\n", + "\n", + "Index: 52 entries, Distancia de carrera requerida para despegue to kjbk\n", + "Data columns (total 37 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Stalker XE 52 non-null object\n", + " 1 Stalker VXE30 52 non-null object\n", + " 2 Aerosonde® Mk. 4.7 Fixed Wing 52 non-null object\n", + " 3 Aerosonde® Mk. 4.7 VTOL 52 non-null object\n", + " 4 Aerosonde® Mk. 4.8 Fixed wing 52 non-null object\n", + " 5 Aerosonde® Mk. 4.8 VTOL FTUAS 52 non-null object\n", + " 6 AAI Aerosonde 52 non-null object\n", + " 7 Fulmar X 52 non-null object\n", + " 8 Orbiter 4 52 non-null object\n", + " 9 Orbiter 3 52 non-null object\n", + " 10 Mantis 52 non-null object\n", + " 11 ScanEagle 52 non-null object\n", + " 12 Integrator 52 non-null object\n", + " 13 Integrator VTOL 52 non-null object\n", + " 14 Integrator Extended Range (ER) 52 non-null object\n", + " 15 ScanEagle 3 52 non-null object\n", + " 16 RQNan21A Blackjack 52 non-null object\n", + " 17 DeltaQuad Evo 52 non-null object\n", + " 18 DeltaQuad Pro #MAP 52 non-null object\n", + " 19 DeltaQuad Pro #CARGO 52 non-null object\n", + " 20 V21 52 non-null object\n", + " 21 V25 52 non-null object\n", + " 22 V32 52 non-null object\n", + " 23 V35 52 non-null object\n", + " 24 V39 52 non-null object\n", + " 25 Volitation VT370 52 non-null object\n", + " 26 Skyeye 2600 52 non-null object\n", + " 27 Skyeye 2930 VTOL 52 non-null object\n", + " 28 Skyeye 3600 52 non-null object\n", + " 29 Skyeye 3600 VTOL 52 non-null object\n", + " 30 Skyeye 5000 52 non-null object\n", + " 31 Skyeye 5000 VTOL 52 non-null object\n", + " 32 Skyeye 5000 VTOL octo 52 non-null object\n", + " 33 Volitation VT510 52 non-null object\n", + " 34 Ascend 52 non-null object\n", + " 35 Transition 52 non-null object\n", + " 36 Reach 52 non-null object\n", + "dtypes: object(37)\n", + "memory usage: 15.4+ KB\n", + "None\n", + "Datos cargados correctamente desde: C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\data\\Datos_aeronaves.xlsx\n", + "\n", + "=== Validando datos cargados ===\n", + "\n", + "=== Continuando con el procesamiento de datos ===\n", + "\n", + "Encabezados iniciales cargados:\n", + "['Stalker XE', 'Stalker VXE30', 'Aerosonde®\\xa0Mk. 4.7 Fixed Wing', 'Aerosonde®\\xa0Mk. 4.7 VTOL', 'Aerosonde®\\xa0Mk. 4.8 Fixed wing', 'Aerosonde®\\xa0Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "\n", + "=== Mostrando datos iniciales en formato HTML ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Datos Iniciales

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Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Distancia de carrera requerida para despegue00Nan0Nan0NanNanNanNanNanNanNan0NanNanNan000000000Nan050060000000
Tasa de ascensoNanNanNanNanNanNan2.49936NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan5Nan5NanNanNanNanNanNanNan5NanNanNan
Altitud a la que se realiza el crucero6000600060006000600060005500600060006000600060006000500060006000600060006000600060006000600060006000600060006000600060006000600060006000600060006000
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NanNan30.406584NanNan18.26582630.62533630.953465NanNan25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288Nan32.8128636.09414730.625336Nan32.8128621.8752421.8752427.34405
Techo de servicio máximo12000120001470097001820015000150009.842NanNanNan1950019500Nan1950020201313.12313.123488001600016000160001600017000NanNanNanNanNanNanNan17000100001300016000
Velocidad de pérdida limpia (KCAS)NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan1415.517NanNanNan101812.52415NanNan25NanNanNan
Área del ala0.871.1582831.551.551.55Nan0.57NanNanNanNanNanNanNanNanNanNan0.84NanNan0.80.52NanNanNanNan0.8811.331.322.6152.6152.615NanNanNanNan
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan
Longitud del fuselaje2.12.5908333Nan1.71.21.21.21.481.712.5Nan2.52.42.50.750.90.90.930.9311.88Nan2.022.052.032.4882.423.53.53.52.9051.5622.34.712
Profundidad del fuselajeNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan
Ancho del fuselaje0.2110.20.2770.2770.277NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan0.3750.3750.375NanNanNanNan
Peso máximo al despegue (MTOW)13.619.95804842.253.554.49313.12055326.526.574.87574.836.361106.26.21012.523.532244015282840901001001009.51891
Alcance de la aeronave370433NanNanNanNan32708001505025NanNanNan500Nan92.6270100100NanNanNanNanNanNanNanNanNan300Nan800NanNanNanNanNan
Autonomía de la aeronave8819.81219.81426824621824161918164.531.831.83344.52.84.515234.5688Nan561220
Velocidad máxima (KIAS)2025.03421133.4388633.4388633.43886Nan30.84572541.7363625.641.246.3Nan46.341.246.3NanNanNan333333333333Nan30Nan3342423850303035
Velocidad de pérdida (KCAS)NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan1415.517NanNanNan101812.52415Nan2425131313
Radio de giroNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan100120150NanNanNanNanNanNanNanNanNanNanNanNanNanNan
envergadura3.6574.87684.44.44.4Nan2.935.24.42.13.14.8Nan4.844.82.692.352.352.152.453.23.53.96.52.62.933.63.65555.1236
Cuerda0.2390.3181950.3520.3520.352Nan0.196552NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan
payload2.4947562.49475614.511.317.722.7NanNan125.5Nan51818188.617.731.21.21.52.2510518461010202515250.61.57
duracion en VTOL2NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan4.53NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan0.050.050.05
Crucero KIAS15.4333216.093422252525NanNan27.8NanNan16.72828.3NanNan23.530.916.5416161820202525253324Nan3033283530202025
RTF (dry weight)NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan4.8NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan611.854
RTF (Including fuel & Batteries)NanNan27.742.236.770.3NanNanNanNanNanNanNanNanNanNanNan6.8NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan8.916.584
Empty weight10.88620817.463292NanNanNanNan10NanNanNanNanNanNanNanNanNanNan4.8NanNan2.653.456.45NanNanNan6.57.111.51132Nan35Nan35.831
Maximum CrosswindNanNanNanNanNanNanNanNanNanNanNanNanNan30NanNanNan455050NanNanNanNanNanNanNanNanNanNanNanNanNanNan151515
Rango de comunicación59161140140140Nan150Nan1505025101.8692.6NanNanNan92.6Nan50303030303030NanNanNanNanNanNanNanNanNanNanNanNan
Wing LoadingNanNanNanNanNanNan23NanNanNanNanNanNanNanNanNanNanNanNanNan12.52425NanNanNanNanNanNanNanNanNanNanNanNanNanNan
Potencia/PesoNanNanNanNanNanNan98NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan
Capacidad combustibleNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan13NanNan11.511.528282825NanNanNan
ConsumoNanNan0.60.6NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan0.96NanNanNanNan1.2NanNan5NanNanNan
Potencia(W)NanNan29802980NanNan1280NanNanNanNanNanNanNanNan170NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan
Potencia(HP)NanNan44NanNan1.74NanNanNanNanNanNanNanNanNan8NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan
PrecioNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan399946796999979998999899922996799499969999999139001599916599NanNanNan
Tiempo de emergencia en vueloNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan0.1080.1080.108NanNanNanNanNanNanNanNanNanNanNanNanNanNan
Distancia de aterrizajeNanNanNanNanNanNanNanNanNanNanNanNanNan0NanNanNan000NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan
Dimensiones de la bahía de carga útilNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan0.2 x 0.2 x 0.11Nan15 x 10 x 9NanNanNanNanNanNanNan560 x 210 x 185 mm560 x 260 x 270 mm560 x 260 x 270 mm920 x 340 x 350 mmNanNanNanNanNanNan
Battery Power SupplyNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanSemi Solid State LiNanion, 22AhNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan2 x 3800mAh 4S Lipo2 x 9000mAh 4S Lipo12 x 16000mAh 4S Lipo
Modelo Motor Fixed WingNanNanELNan005ELNan005NanNanEnya R120NanNanNanBatería recargableMotor de combustible pesado (JPNan5 o JPNan8)EFI con JPNan5/JPNan8NanEFI con JPNan5/JPNan8Heavy Fuel (JPNan5/JPNan8)EFI, JPNan5/JPNan8NanNanNanNanNanNanNanNanEFI de gasolina20–35 cc gasolinaCompatible con motores eléctricos o de gasolina50–100 cc gasolina50–100 cc gasolinaDLA 180cc EFINanNanMotor pistón EFITNanMotor MN501Saito FG21HFE International DA100EFI
Modelo Motor VTOLNanNanNanNanNanNanNanNanNanNanNanNanNanFLARES eléctricoNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanTNanMotor MN501TNanMotor ALTI U7TNanMotor ALTI U15 II KV100
PortabilidadsisisisisisiNanAltaAltamente transportableAltamente transportableSistema transportable en cajas ligerasNanTransportable por barco y aviónOperaciones en espacios reducidosTransportable por barco y aviónAltaOperable desde tierra y marIncluye maleta de transporteMontaje en 2 minutosMontaje en 2 minutosAltaAltaAltaAltaAltaMediaNanNanNanNanNanNanNanNanNanNanNan
CámaraNanNanNanNanNanNanNanNanEO/IR, WAMI, SAR, COMINT, ELINTEO/Cooled IR, WAMI, COMINT, ELINTEO, IR giroestabilizadasEO, MWIR, ViDAREO, MWIR, ViDAR, LRFModular payloadsEO, MWIR, ViDAR, LRFEO, MWIR/EO, ViDAREO, MWIR, LRF, IR MarkerNanRGB, Multiespectral, TérmicaNo aplicaNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan
DespegueBungee, rail, VTOLBungee, rail, VTOLRailVTOLRailVTOLRailRailRailRailRailRailRailVTOLRailRailRailVTOLVTOLVTOLVTOLVTOLVTOLVTOLVTOLVTOLNanVTOLNanVTOLNanVTOLNanVTOLNanNanNan
Datalink banksNanNanL,S,CL,S,CL,S,CL,S,C,KUNanNanLOS, BLOSLOS, AESNan256, RelayDigital, varias frecuenciasEnlace de datos digital encriptadoEnlace de datos encriptadoNanSATCOM, BLOS, robustoEncrypted/UnencryptedEncriptadoNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan
Material del fuselajeNanNanNanNanNanNanNanFibra de carbonoNanNanNanNanNanNanNanNanNanNanNanNanFibra de carbono, Kevlar, PVCFibra de carbono, Kevlar, PVCFibra de carbono y compuestosFibra de carbono, Kevlar, fibra de vidrioFibra de carbono completaNanFibra de carbonoFibra de carbonoFibra de carbonoFibra de carbonoFibra de carbonoFibra de carbonoCarbon FiberFibra de carbono compuestaNanNanNan
Motor recomendadoNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanEFI de gasolinaGasolina 20–35 ccGasolina o eléctricaGasolina 50–100 ccGasolina 50–100 ccGasolina EFI DLA 180 ccNanNanGasolina o diéselNanNanNan
Hélice recomendada VTOLNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanMaster Airscrew 3x Power 13x12TNanMotor 18x6.1 Carbon FiberTNanMotor 40x13.1 Carbon Fiber
Hélice recomendada Fixed WingNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan16 pulgadas16Nan17 pulgadas17Nan18 pulgadasVTOL: 26 pulgadas; Ala fija: 24 pulgadasVTOL: 22 pulgadas; Ala fija: 21 pulgadasNan19 x 8 pulgadasNanNanNan32 x 10 pulgadasNanNanNanNanNanNan
Sistema de controlNanNanNanNanNanNanNanNanNavegación avanzada, redundanteNavegación avanzada, precisiónGPS/INS, navegación manual o automáticaArquitectura abiertaArquitectura abiertaModular y portátilNavegación avanzadaModularModular y portátilNanControlador DeltaQuadControlador DeltaQuadNanNanNanNanLightning X7NanNanNanNanNanNanNanNanNanNanNanNan
Características adicionalesNanNanNanNanNanNanNanAlta fiabilidad y facilidad de usoMultiNanpayload, operable en clima extremoMultiNanpayload, operable en clima extremoModular, navegación spline, ATOLModular, flexibleModular, multiNanmisiónResistente a alta mar y vientosSeguridad aumentadaCapacidad MultiNanINTModularidad y flexibilidadDespegue y aterrizaje vertical, resistente a lluvias ligerasDespegue y aterrizaje vertical, transmisión de video en vivo, integración de múltiples sensoresDespegue y aterrizaje vertical, transmisión de video en vivo, mecanismo de liberación de carga útil personalizableAlta portabilidadAlta portabilidadAlta portabilidadMayor capacidad de cargaLarga autonomíaAutonomía extendidaUso optimizado para misiones prolongadasCompatible VTOL; configuración flexibleDiseñado para vigilancia y cartografíaAlta capacidad VTOL; alcance extendidoTanque de combustible de Kevlar; sistema de freno en las ruedasConfiguración VTOL avanzadaNanDiseño modular de ala tándemNanNanNan
EmpresaLockheed MartinLockheed MartinTextron SystemsTextron SystemsTextron SystemsTextron SystemsTextron SystemsThales GroupAeronautics GroupAeronautics GroupIndra SistemasInsituInsituInsituInsituInsituInsituVertical TechnologiesVertical TechnologiesVertical TechnologiesAirmobiAirmobiAirmobiAirmobiAirmobiAirmobiAirmobiAirmobiAirmobiAirmobiAirmobiAirmobiAirmobiAirmobiAltiAltiAlti
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Procesando los datos ===\n", + "=== Inicio del procesamiento de datos ===\n", + "\n", + "=== Comprobación de duplicados ===\n", + "No se encontraron duplicados en índices o columnas.\n", + "\n", + "=== Convirtiendo valores a numéricos donde sea posible ===\n", + "=== Procesamiento completado ===\n", + "\n", + "✅ Los encabezados se preservaron correctamente.\n", + "\n", + "=== Mostrando datos procesados en formato HTML ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Datos Procesados

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Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Distancia de carrera requerida para despegue0.00.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
Tasa de ascensoNaNNaNNaNNaNNaNNaN2.49936NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5.0NaN5.0NaNNaNNaNNaNNaNNaNNaN5.0NaNNaNNaN
Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.0
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NaNNaN30.406584NaNNaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaNNaN17000.010000.013000.016000.0
Velocidad de pérdida limpia (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaNNaN25.0NaNNaNNaN
Área del ala0.871.1582831.551.551.55NaN0.57NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.712
Profundidad del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Ancho del fuselaje0.2110.20.2770.2770.277NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.3750.3750.375NaNNaNNaNNaN
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0NaNNaNNaNNaN3270.0800.0150.050.025.0NaNNaNNaN500.0NaN92.6270.0100.0100.0NaNNaNNaNNaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaNNaN
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0NaN30.0NaN33.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.0
Radio de giroNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100.0120.0150.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
envergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.352NaN0.196552NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
payload2.4947562.49475614.511.317.722.7NaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
duracion en VTOL2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050.050.05
Crucero KIAS15.4333216.09342225.025.025.0NaNNaN27.8NaNNaN16.728.028.3NaNNaN23.530.916.5416.016.018.020.020.025.025.025.033.024.0NaN30.033.028.035.030.020.020.025.0
RTF (dry weight)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.011.854.0
RTF (Including fuel & Batteries)NaNNaN27.742.236.770.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8.916.584.0
Empty weight10.88620817.463292NaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
Maximum CrosswindNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.0NaNNaNNaN45.050.050.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15.015.015.0
Rango de comunicación59.0161.0140.0140.0140.0NaN150.0NaN150.050.025.0101.8692.6NaNNaNNaN92.6NaN50.030.030.030.030.030.030.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Wing LoadingNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.524.025.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Potencia/PesoNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Capacidad combustibleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.0NaNNaN11.511.528.028.028.025.0NaNNaNNaN
ConsumoNaNNaN0.60.6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.96NaNNaNNaNNaN1.2NaNNaN5.0NaNNaNNaN
Potencia(W)NaNNaN2980.02980.0NaNNaN1280.0NaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Potencia(HP)NaNNaN4.04.0NaNNaN1.74NaNNaNNaNNaNNaNNaNNaNNaNNaN8.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
PrecioNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.0NaNNaNNaN
Tiempo de emergencia en vueloNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.1080.1080.108NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Distancia de aterrizajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Battery Power SupplyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Modelo Motor Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Modelo Motor VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
PortabilidadNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
CámaraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
DespegueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Datalink banksNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Motor recomendadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Hélice recomendada VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Hélice recomendada Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Sistema de controlNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Características adicionalesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
EmpresaNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
kjbkNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parámetros disponibles en df_procesado antes de seleccionar:\n", + "['Distancia de carrera requerida para despegue', 'Tasa de ascenso', 'Altitud a la que se realiza el crucero', 'Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Velocidad de pérdida limpia (KCAS)', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Profundidad del fuselaje', 'Ancho del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Radio de giro', 'envergadura', 'Cuerda', 'payload', 'duracion en VTOL', 'Crucero KIAS', 'RTF (dry weight)', 'RTF (Including fuel & Batteries)', 'Empty weight', 'Maximum Crosswind', 'Rango de comunicación', 'Wing Loading', 'Potencia/Peso', 'Capacidad combustible', 'Consumo', 'Potencia(W)', 'Potencia(HP)', 'Precio', 'Tiempo de emergencia en vuelo', 'Distancia de aterrizaje', 'Dimensiones de la bahía de carga útil', 'Battery Power Supply', 'Modelo Motor Fixed Wing', 'Modelo Motor VTOL', 'Portabilidad', 'Cámara', 'Despegue', 'Datalink banks', 'Material del fuselaje', 'Motor recomendado', 'Hélice recomendada VTOL', 'Hélice recomendada Fixed Wing', 'Sistema de control', 'Características adicionales', 'Empresa', 'kjbk']\n", + "\n", + "=== Selección de Parámetros ===\n", + "Parámetros disponibles:\n", + "1. Distancia de carrera requerida para despegue\n", + "2. Tasa de ascenso\n", + "3. Altitud a la que se realiza el crucero\n", + "4. Velocidad a la que se realiza el crucero (KTAS)\n", + "5. Techo de servicio máximo\n", + "6. Velocidad de pérdida limpia (KCAS)\n", + "7. Área del ala\n", + "8. Relación de aspecto del ala\n", + "9. Longitud del fuselaje\n", + "10. Profundidad del fuselaje\n", + "11. Ancho del fuselaje\n", + "12. Peso máximo al despegue (MTOW)\n", + "13. Alcance de la aeronave\n", + "14. Autonomía de la aeronave\n", + "15. Velocidad máxima (KIAS)\n", + "16. Velocidad de pérdida (KCAS)\n", + "17. Radio de giro\n", + "18. envergadura\n", + "19. Cuerda\n", + "20. payload\n", + "21. duracion en VTOL\n", + "22. Crucero KIAS\n", + "23. RTF (dry weight)\n", + "24. RTF (Including fuel & Batteries)\n", + "25. Empty weight\n", + "26. Maximum Crosswind\n", + "27. Rango de comunicación\n", + "28. Wing Loading\n", + "29. Potencia/Peso\n", + "30. Capacidad combustible\n", + "31. Consumo\n", + "32. Potencia(W)\n", + "33. Potencia(HP)\n", + "34. Precio\n", + "35. Tiempo de emergencia en vuelo\n", + "36. Distancia de aterrizaje\n", + "37. Dimensiones de la bahía de carga útil\n", + "38. Battery Power Supply\n", + "39. Modelo Motor Fixed Wing\n", + "40. Modelo Motor VTOL\n", + "41. Portabilidad\n", + "42. Cámara\n", + "43. Despegue\n", + "44. Datalink banks\n", + "45. Material del fuselaje\n", + "46. Motor recomendado\n", + "47. Hélice recomendada VTOL\n", + "48. Hélice recomendada Fixed Wing\n", + "49. Sistema de control\n", + "50. Características adicionales\n", + "51. Empresa\n", + "52. kjbk\n", + "\n", + "Preseleccionados: 4, 5, 7, 8, 9, 12, 13, 14, 15, 16, 18, 19, 20, 25\n", + "Parámetros seleccionados después de filtrar:\n", + "['Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'envergadura', 'Cuerda', 'payload', 'Empty weight']\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Datos Filtrados por Parámetros (df_filtrado)

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Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NaNNaN30.406584NaNNaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaNNaN17000.010000.013000.016000.0
Área del ala0.871.1582831.551.551.55NaN0.57NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.712
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0NaNNaNNaNNaN3270.0800.0150.050.025.0NaNNaNNaN500.0NaN92.6270.0100.0100.0NaNNaNNaNNaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaNNaN
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0NaN30.0NaN33.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.0
envergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.352NaN0.196552NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
payload2.4947562.49475614.511.317.722.7NaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
Empty weight10.88620817.463292NaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
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Celdas Faltantes Identificadas en df_filtrado (df_celdas_faltantes)

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ÍndiceCeldaColumnaValor Actual
0Alcance de la aeronaveC8Aerosonde® Mk. 4.7 Fixed WingNaN
1Velocidad de pérdida (KCAS)C11Aerosonde® Mk. 4.7 Fixed WingNaN
2Empty weightC15Aerosonde® Mk. 4.7 Fixed WingNaN
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Resumen de Valores Faltantes de df_filtrado

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ColumnaValores Faltantes
0Stalker XE1.000
1Stalker VXE301.000
2Aerosonde® Mk. 4.7 Fixed Wing3.000
3Aerosonde® Mk. 4.7 VTOL3.000
4Aerosonde® Mk. 4.8 Fixed wing3.000
5Aerosonde® Mk. 4.8 VTOL FTUAS9.000
6AAI Aerosonde3.000
7Fulmar X6.000
8Orbiter 47.000
9Orbiter 37.000
10Mantis7.000
11ScanEagle6.000
12Integrator6.000
13Integrator VTOL11.000
14Integrator Extended Range (ER)6.000
15ScanEagle 36.000
16RQNan21A Blackjack5.000
17DeltaQuad Evo4.000
18DeltaQuad Pro #MAP6.000
19DeltaQuad Pro #CARGO6.000
20V213.000
21V253.000
22V324.000
23V356.000
24V397.000
25Volitation VT3706.000
26Skyeye 26005.000
27Skyeye 2930 VTOL4.000
28Skyeye 36006.000
29Skyeye 3600 VTOL3.000
30Skyeye 50004.000
31Skyeye 5000 VTOL5.000
32Skyeye 5000 VTOL octo6.000
33Volitation VT5105.000
34Ascend4.000
35Transition4.000
36Reach4.000
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Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes185.000
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Tabla de Correlaciones con todos los parametros(tabla_completa)

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Distancia de carrera requerida para despegueTasa de ascensoAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Radio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia/PesoCapacidad combustibleConsumoPotencia(W)Potencia(HP)PrecioTiempo de emergencia en vueloDistancia de aterrizajeDimensiones de la bahía de carga útilBattery Power SupplyModelo Motor Fixed WingModelo Motor VTOLPortabilidadCámaraDespegueDatalink banksMaterial del fuselajeMotor recomendadoHélice recomendada VTOLHélice recomendada Fixed WingSistema de controlCaracterísticas adicionalesEmpresakjbk
Distancia de carrera requerida para despegue1.000nan0.0630.467nan-0.5050.363nan0.260nan0.4250.168nan-0.0680.316-0.308nan0.145nan0.229nan0.389nannan0.357nannannannan-0.018-0.240nannan-0.156nannannannannannannannannannannannannannannannannannan
Tasa de ascensonan1.0001.000nan0.870nannannan0.531nannan0.589nan-0.8660.439nannan0.656nannannannannannannannan-1.000nannannannannannannannannannannannannannannannannannannannannannannannannan
Altitud a la que se realiza el crucero0.0631.0001.000nan-0.038nan0.301-0.3510.081nannan-0.095-0.952-0.2800.128nannan0.1360.761-0.183nannannannan0.0600.038-0.325-0.215nannannan0.2770.690nannannannannannannannannannannannannannannannannannannan
Velocidad a la que se realiza el crucero (KTAS)0.467nannan1.0000.0410.1280.587-0.9990.535nan0.9360.6630.4070.3360.8150.2570.8030.4720.8460.696-0.6941.0000.9150.7230.426-0.8550.3590.997nan0.4910.4611.0001.000-0.296nannannannannannannannannannannannannannannannannannan
Techo de servicio máximonan0.870-0.0380.0411.000-0.502-0.152-0.3140.082nan0.3690.1370.4630.079-0.111-0.071-0.8030.0570.0170.087-0.8750.0410.6770.579-0.138-0.961-0.120-0.986nannan0.5150.653-0.933-0.257nannannannannannannannannannannannannannannannannannan
Velocidad de pérdida limpia (KCAS)-0.505nannan0.128-0.5021.0000.097nan0.260nannan0.546nan0.4010.4461.0000.9930.505nan0.536nan0.128nannan0.038nannan0.900nan0.0681.000nannan0.163nannannannannannannannannannannannannannannannannannan
Área del ala0.363nan0.3010.587-0.1520.0971.000-0.8310.867nan0.9840.977-0.3010.0810.7370.423-1.0000.8410.9840.899-1.0000.675nan0.9230.941nan0.595-0.996nan1.0001.0001.0001.0000.899nannannannannannannannannannannannannannannannannannan
Relación de aspecto del alanannan-0.351-0.999-0.314nan-0.8311.000-0.790nan-0.996-0.823-0.998-0.305-0.859nannan-0.349-0.744-0.888nan-0.999nannan0.622nan-0.298nannannannan-1.000-1.000nannannannannannannannannannannannannannannannannannannan
Longitud del fuselaje0.2600.5310.0810.5350.0820.2600.867-0.7901.000nan0.9380.7860.1290.3890.2560.1800.9180.6930.9950.599-0.6170.5760.9660.9400.880-0.7180.6760.263nan0.9290.0360.6860.359-0.210nannannannannannannannannannannannannannannannannannan
Profundidad del fuselajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Ancho del fuselaje0.425nannan0.9360.369nan0.984-0.9960.938nan1.0000.9860.982-0.0900.940nannan0.6710.7600.868nan0.944nannan0.955nan0.323nannannan1.000nannannannannannannannannannannannannannannannannannannannannan
Peso máximo al despegue (MTOW)0.1680.589-0.0950.6630.1370.5460.977-0.8230.786nan0.9861.000-0.0510.4340.6780.5390.9730.7910.8580.875-0.4010.7080.9990.9790.947-0.4640.5140.628nan0.9760.7580.5590.8550.052nannannannannannannannannannannannannannannannannannan
Alcance de la aeronavenannan-0.9520.4070.463nan-0.301-0.9980.129nan0.982-0.0511.0000.578-0.062nannan-0.107-0.7550.554-1.0000.407nannan-0.059-1.0000.508nannan1.000nannan-1.0001.000nannannannannannannannannannannannannannannannannannan
Autonomía de la aeronave-0.068-0.866-0.2800.3360.0790.4010.081-0.3050.389nan-0.0900.4340.5781.0000.297-0.1640.9540.532-0.2010.461-0.5940.3360.9400.6340.428-0.7150.8020.268nan-0.113-0.732-0.391-0.5770.011nannannannannannannannannannannannannannannannannannan
Velocidad máxima (KIAS)0.3160.4390.1280.815-0.1110.4460.737-0.8590.256nan0.9400.678-0.0620.2971.0000.539nan0.4000.5120.715-0.9270.7750.9940.8570.517nan0.094-0.215nan0.7050.910-0.6240.9700.015nannannannannannannannannannannannannannannannannannan
Velocidad de pérdida (KCAS)-0.308nannan0.257-0.0711.0000.423nan0.180nannan0.539nan-0.1640.5391.0000.9930.401nan0.627nan0.417nannan0.321nannan0.900nan0.2301.000nannan0.160nannannannannannannannannannannannannannannannannannan
Radio de gironannannan0.803-0.8030.993-1.000nan0.918nannan0.973nan0.954nan0.9931.0000.992nan0.976nan0.803nannan0.979nannan0.844nannannannannan0.921nannannannannannannannannannannannannannannannannannan
envergadura0.1450.6560.1360.4720.0570.5050.841-0.3490.693nan0.6710.791-0.1070.5320.4000.4010.9921.0000.8850.734-0.2580.5010.9830.9360.924-0.4520.6480.780nan0.2970.0850.5220.8350.032nannannannannannannannannannannannannannannannannannan
Cuerdanannan0.7610.8460.017nan0.984-0.7440.995nan0.7600.858-0.755-0.2010.512nannan0.8851.0000.776nan0.846nannan0.971nan0.354nannannannan1.0001.000nannannannannannannannannannannannannannannannannannannan
payload0.229nan-0.1830.6960.0870.5360.899-0.8880.599nan0.8680.8750.5540.4610.7150.6270.9760.7340.7761.000-0.0240.6940.9150.5590.778-0.1420.5460.707nan0.7110.8460.8410.866-0.008nannannannannannannannannannannannannannannannannannan
duracion en VTOLnannannan-0.694-0.875nan-1.000nan-0.617nannan-0.401-1.000-0.594-0.927nannan-0.258nan-0.0241.000-0.694-0.408-0.402-0.3151.000nannannannannannannannannannannannannannannannannannannannannannannannannannan
Crucero KIAS0.389nannan1.0000.0410.1280.675-0.9990.576nan0.9440.7080.4070.3360.7750.4170.8030.5010.8460.694-0.6941.0000.9150.7230.583-0.8550.3590.997nan0.5810.4611.0001.000-0.243nannannannannannannannannannannannannannannannannannan
RTF (dry weight)nannannan0.9150.677nannannan0.966nannan0.999nan0.9400.994nannan0.983nan0.915-0.4080.9151.0001.0000.995-0.408nannannannannannannannannannannannannannannannannannannannannannannannannannan
RTF (Including fuel & Batteries)nannannan0.7230.579nan0.923nan0.940nannan0.979nan0.6340.857nannan0.936nan0.559-0.4020.7231.0001.0000.996-0.402nannannannannannannannannannannannannannannannannannannannannannannannannannan
Empty weight0.357nan0.0600.426-0.1380.0380.9410.6220.880nan0.9550.947-0.0590.4280.5170.3210.9790.9240.9710.778-0.3150.5830.9950.9961.000-0.3190.8320.530nan0.995nannannan-0.029nannannannannannannannannannannannannannannannannannan
Maximum Crosswindnannan0.038-0.855-0.961nannannan-0.718nannan-0.464-1.000-0.715nannannan-0.452nan-0.1421.000-0.855-0.408-0.402-0.3191.000nannannannannannannannannannannannannannannannannannannannannannannannannannan
Rango de comunicaciónnan-1.000-0.3250.359-0.120nan0.595-0.2980.676nan0.3230.5140.5080.8020.094nannan0.6480.3540.546nan0.359nannan0.832nan1.0000.215nannannan-1.000-0.972nannannannannannannannannannannannannannannannannannannan
Wing Loadingnannan-0.2150.997-0.9860.900-0.996nan0.263nannan0.628nan0.268-0.2150.9000.8440.780nan0.707nan0.997nannan0.530nan0.2151.000nannannannannan0.568nannannannannannannannannannannannannannannannannannan
Potencia/Pesonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Capacidad combustible-0.018nannan0.491nan0.0681.000nan0.929nannan0.9761.000-0.1130.7050.230nan0.297nan0.711nan0.581nannan0.995nannannannan1.0000.377nannan0.817nannannannannannannannannannannannannannannannannannan
Consumo-0.240nannan0.4610.5151.0001.000nan0.036nan1.0000.758nan-0.7320.9101.000nan0.085nan0.846nan0.461nannannannannannannan0.3771.000nannan0.998nannannannannannannannannannannannannannannannannannan
Potencia(W)nannan0.2771.0000.653nan1.000-1.0000.686nannan0.559nan-0.391-0.624nannan0.5221.0000.841nan1.000nannannannan-1.000nannannannan1.0001.000nannannannannannannannannannannannannannannannannannannan
Potencia(HP)nannan0.6901.000-0.933nan1.000-1.0000.359nannan0.855-1.000-0.5770.970nannan0.8351.0000.866nan1.000nannannannan-0.972nannannannan1.0001.000nannannannannannannannannannannannannannannannannannannan
Precio-0.156nannan-0.296-0.2570.1630.899nan-0.210nannan0.0521.0000.0110.0150.1600.9210.032nan-0.008nan-0.243nannan-0.029nannan0.568nan0.8170.998nannan1.000nannannannannannannannannannannannannannannannannannan
Tiempo de emergencia en vuelonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Distancia de aterrizajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Dimensiones de la bahía de carga útilnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Battery Power Supplynannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Modelo Motor Fixed Wingnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Modelo Motor VTOLnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Portabilidadnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Cámaranannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Despeguenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Datalink banksnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Material del fuselajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Motor recomendadonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Hélice recomendada VTOLnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Hélice recomendada Fixed Wingnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Sistema de controlnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Características adicionalesnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Empresanannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
kjbknannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores2704.000
1Valores numéricos700.000
2Valores NaN2004.000
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Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)1.0000.0410.587-0.9990.5350.6630.4070.3360.8150.2570.4720.8460.6960.426
Techo de servicio máximo0.0411.000-0.152-0.3140.0820.1370.4630.079-0.111-0.0710.0570.0170.087-0.138
Área del ala0.587-0.1521.000-0.8310.8670.977-0.3010.0810.7370.4230.8410.9840.8990.941
Relación de aspecto del ala-0.999-0.314-0.8311.000-0.790-0.823-0.998-0.305-0.859nan-0.349-0.744-0.8880.622
Longitud del fuselaje0.5350.0820.867-0.7901.0000.7860.1290.3890.2560.1800.6930.9950.5990.880
Peso máximo al despegue (MTOW)0.6630.1370.977-0.8230.7861.000-0.0510.4340.6780.5390.7910.8580.8750.947
Alcance de la aeronave0.4070.463-0.301-0.9980.129-0.0511.0000.578-0.062nan-0.107-0.7550.554-0.059
Autonomía de la aeronave0.3360.0790.081-0.3050.3890.4340.5781.0000.297-0.1640.532-0.2010.4610.428
Velocidad máxima (KIAS)0.815-0.1110.737-0.8590.2560.678-0.0620.2971.0000.5390.4000.5120.7150.517
Velocidad de pérdida (KCAS)0.257-0.0710.423nan0.1800.539nan-0.1640.5391.0000.401nan0.6270.321
envergadura0.4720.0570.841-0.3490.6930.791-0.1070.5320.4000.4011.0000.8850.7340.924
Cuerda0.8460.0170.984-0.7440.9950.858-0.755-0.2010.512nan0.8851.0000.7760.971
payload0.6960.0870.899-0.8880.5990.8750.5540.4610.7150.6270.7340.7761.0000.778
Empty weight0.426-0.1380.9410.6220.8800.947-0.0590.4280.5170.3210.9240.9710.7781.000
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Resumen de la Tabla

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ResumenCantidad
0Total de valores196.000
1Valores numéricos190.000
2Valores NaN6.000
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Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)nannannan-0.999nannannannan0.815nannan0.846nannan
Techo de servicio máximonannannannannannannannannannannannannannan
Área del alanannannan-0.8310.8670.977nannan0.737nan0.8410.9840.8990.941
Relación de aspecto del ala-0.999nan-0.831nan-0.790-0.823-0.998nan-0.859nannan-0.744-0.888nan
Longitud del fuselajenannan0.867-0.790nan0.786nannannannannan0.995nan0.880
Peso máximo al despegue (MTOW)nannan0.977-0.8230.786nannannannannan0.7910.8580.8750.947
Alcance de la aeronavenannannan-0.998nannannannannannannan-0.755nannan
Autonomía de la aeronavenannannannannannannannannannannannannannan
Velocidad máxima (KIAS)0.815nan0.737-0.859nannannannannannannannan0.715nan
Velocidad de pérdida (KCAS)nannannannannannannannannannannannannannan
envergaduranannan0.841nannan0.791nannannannannan0.8850.7340.924
Cuerda0.846nan0.984-0.7440.9950.858-0.755nannannan0.885nan0.7760.971
payloadnannan0.899-0.888nan0.875nannan0.715nan0.7340.776nan0.778
Empty weightnannan0.941nan0.8800.947nannannannan0.9240.9710.778nan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores196.000
1Valores numéricos64.000
2Valores NaN132.000
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nz55M8+bNBW4nSPnxnjp1iuc9PT09pkePHny3Kz9XleunkSNH8pzX4uJiRlVVlQHAvHz5khOemprKCAsLM7NmzeKE1eXaEFS3V1X+HX3//n0GAPP69WvOe1XLRG0kJCQw//zzD9O+fXuGxWIxampqzOTJk5lHjx7xlL/a3IeU/9XExMSEb1mLi4tjADBr166t03HMnz+fAcAEBwfzfT8yMpIBwOzevbtO6RJCfh31PCOEENIojhw5gpCQEJ6/Dh06cMULCAgAi8XCsGHDuJ4Ga2hooFmzZlzDs5KSkjBhwgQ0adIEIiIiEBUV5UyyHB4eXuu8NW/eHNra2pzXFhYWAMqe+Faet6U8vHJPi5ycHMyfPx/GxsYQERGBiIgIZGRkkJubyzcPQ4cO5Xrdrl076OnpcYYp8pObm4uQkBD07dsXEhISnPDynk6V1eXz+1XleR41ahRXeOvWrWFhYYE7d+5whSsqKqJz58580/Lw8ICoqCjn9adPn/DhwwfO51X5WNzc3BAfH8+3d1K52p6Xa9euoVOnTpxzWxu5ubl4+vQp+vfvz9VTQFhYGMOHD8e3b9948lZ5qCtQ0UuhvCzduHEDJSUlGDFiBNexSkhIwNHRkXPelJSUYGRkhE2bNmHr1q149epVrea/+vjxI+Li4jBkyBCuIXN6enpo164dV9yAgAAoKCjA3d2dKy/NmzeHhoZGjWWIxWJh9OjRePPmDac3S2pqKi5fvox+/fpx5isqKSnB2rVrYWlpCTExMYiIiEBMTAyRkZF8r51+/frVeJzA77kma3udGRsbQ1FREfPnz8eePXu4ehDWxM7ODp8+fcL169exaNEitG3bFnfu3MGIESPg4eHB6Yn2K+erttveunULpaWlmDx5ssC0goKCkJWVhUmTJtVp5cS61iMaGhpo3bo1V5iNjQ3PsNwzZ86gffv2kJGR4Xw/HDx4kKsMlO+7ahkYMmQITz5rW15bt26N169fY9KkSbhx4waysrJq9TmUD+MW1KOyrlgsFte8gyIiIjA2NoampiZsbW054UpKSlBTU+M7rLm214aguv3z588YMmQINDQ0ICwsDFFRUTg6OgKo23c0P+rq6pgyZQoePXqEL1++YN68eZye5wYGBlxDQ93d3fnef/D7q43qynddyn5JSQkOHz4MKysrgXM3lpeH79+/1zpdQkj9oAUDCCGENAoLCwu0atWKJ1xeXh5fv37lvE5MTATDMFBXV+ebjqGhIYCy4UbdunVDXFwclixZAmtra0hLS4PNZsPe3r7GoWWVKSkpcb0uXwVQUHjleVaGDBmCO3fuYMmSJbCzs4OcnBznRwu/PJSvpFU1rLrVtNLT08FmswVuW1ltPz9+VFRUICUlhejoaIFxKivPM795mbS0tHh+jAmav4nfe+XD3ubMmYM5c+bw3YbfELxytT0vycnJdZ68PD09HQzDCDxuADznU1lZmet1+Xxy5XkpP147Ozu++yyf44rFYuHOnTtYuXIlNm7ciNmzZ0NJSQlDhw7FmjVrICsry3f78vwIKkMxMTGc14mJicjIyBC4GmZ1n3u50aNHY/ny5fDx8UHLli1x/PhxFBUVca3UOmvWLOzcuRPz58+Ho6MjFBUVISQkhLFjx/K9dqorP5X9jmuytteZvLw87t+/jzVr1mDRokVIT0+HpqYmxo0bh8WLF3M1GPMjKioKFxcXuLi4ACg7j/3790dAQACuXbsGNze3Xzpftd22fP6z6q6V2sThp671SNVrCSi7niqf2/Pnz2PgwIEYMGAA5s6dCw0NDYiIiGD37t2cRSvK9y0iIsKTJr8yUdvyunDhQkhLS+PYsWPYs2cPhIWF4eDggA0bNvD9DixXnkblByTlREREBA5LLB9qWLUsSUlJ8aQlJibG871WHs5v/rDaXhv8zl1OTg46duwICQkJrF69GqamppCSksLXr1/Rt2/fOn1H1yQzMxMZGRnIzMwEAM65KaekpAR5efl62ZeysjLfuiEtLY2zr9q6evUqEhISMH/+fIFxys9hfX5ehJDaocYzQgghfzQVFRWwWCw8fPiQ74T15WFhYWF4/fo1fH19MXLkSM77nz59+m15zczMREBAAJYtW4YFCxZwwsvn2+EnISGBb5ixsbHA/SgqKoLFYgnctrLafn78CAsLo0uXLrh27Rq+fftW44/g8h+c8fHxPHHj4uKgoqLCFVaXp/Xl2y5cuBB9+/blu42gSbXrcl5UVVVrnI+pqvIfZvHx8TzvlfceqXrsNSmPf/bsWU7vSUH09PQ4k7RHRETAz88Py5cvR1FREfbs2cN3m/JzVdsypKysLHBVVUENdJXp6OigW7duOHHiBLZs2QIfHx8YGxvDwcGBE+fYsWMYMWIE1q5dy7VtSkoKFBQUeNKsTY+O33VN1uU6s7a2xqlTp8AwDN68eQNfX1+sXLkSkpKSXHmsDWVlZcyYMQOBgYEICwuDm5vbL52v2m6rqqoKAPj27ZvA+RArx6mLutYjtXHs2DEYGBjg9OnTXOWmsLCQZ98lJSVITU3lakDjVyZqW15FREQwa9YszJo1CxkZGbh9+zYWLVoEFxcXfP36VeAqlOXHya+cqqurC+x5VB4uqCH3V9T22uB3bd69exdxcXEIDAzk9DYDUOPcb7UVERGB06dP49SpU3j//j2MjY0xePBgDBkyBObm5lxxDx8+jNGjR9cqXaaGifmtra1x8uRJlJSUcM17Vr5wRNOmTWt9DAcPHoSYmBiGDx8uME55efiZ64AQ8mto2CYhhJA/Ws+ePcEwDL5//45WrVrx/FlbWwOouFmv+sN17969PGlW7eVTX1gsFhiG4cnDgQMHBPYSOH78ONfroKAgfPnyBU5OTgL3U77SZ9XVxbKzs3H58mWuuLX9/ARZuHAhGIbBuHHjUFRUxPN+cXExZ5/lw3SqTgwfEhKC8PBwdOnSpdp9VcfMzAwmJiZ4/fo13+No1aqVwEaBupyX7t274969e9UOAa1KWloabdq0wfnz57nKFJvNxrFjx6CjowNTU9M6HC3g4uICERERREVFCTxefkxNTbF48WJYW1vj5cuXAtM3MzODpqYmTp48yfXj8MuXLwgKCuKK27NnT6SmpqK0tJRvPmq7EqCXlxfS09OxdOlShIaGYvTo0Vw/slksFs85unLlyi8NT/pd1+TPXGcsFgvNmjXDtm3boKCgUO35Ki4uFtjzrXy4W3kvx185X7Xdtlu3bhAWFsbu3bsFptWuXTvIy8tjz549dVoZsCHqERaLBTExMa7ylpCQwLPaZvnk/FXLwIkTJ/imWdfyqqCggP79+2Py5MlIS0vj6uFZVfnQ8aioKJ73unbtirCwML7Dfv38/CAjI4M2bdoITPtn/cy1Ua4u39G1lZSUhA0bNsDW1hZmZmbYs2cPunXrhmfPniEyMhIrV67kaTgD6nfYZp8+fZCTk4Nz585xhR8+fBhaWlq1Pg8JCQm4evUqevfuzbc3Zbny1U0tLS1rlS4hpP5QzzNCCCF/tPbt22P8+PEYPXo0nj9/DgcHB0hLSyM+Ph6PHj2CtbU1Jk6cCHNzcxgZGWHBggVgGAZKSkq4fPkybt26xZNm+Q/Z7du3Y+TIkRAVFYWZmVmtetBUR05ODg4ODti0aRNUVFSgr6+P+/fv4+DBg3x7zgDA8+fPMXbsWAwYMABfv37F33//DW1tba6VJ/lZtWoVXF1d4ezsjNmzZ6O0tBQbNmyAtLQ0V0+F2n5+gpSvXjpp0iS0bNkSEydOhJWVFYqLi/Hq1Svs27cPTZs2hbu7O8zMzDB+/Hj8888/EBISQvfu3RETE4MlS5agSZMmmDlz5k99ruX27t2L7t27w8XFBaNGjYK2tjbS0tIQHh6Oly9f4syZM3y3q8t5WblyJa5duwYHBwcsWrQI1tbWyMjIwPXr1zFr1iy+P8QAYN26dXB2dkanTp0wZ84ciImJYdeuXQgLC8PJkyfrNO8NAOjr62PlypX4+++/8fnzZ7i6ukJRURGJiYl49uwZpKWlsWLFCrx58wZTpkzBgAEDYGJiAjExMdy9exdv3ryptheTkJAQVq1ahbFjx6JPnz4YN24cMjIysHz5cp6hWYMGDcLx48fh5uaG6dOno3Xr1hAVFcW3b99w79499OrVC3369KnxmDw8PKCiooJNmzZBWFiYq4coUNZw4+vrC3Nzc9jY2ODFixfYtGlTnYf9Vfa7rsnaXmcBAQHYtWsXevfuDUNDQzAMg/PnzyMjIwPOzs4C08/MzIS+vj4GDBiArl27okmTJsjJyUFgYCC2b98OCwsLTo/MXzlftd1WX18fixYtwqpVq5Cfn4/BgwdDXl4e79+/R0pKClasWAEZGRls2bIFY8eORdeuXTFu3Dioq6vj06dPeP36Nf7991++eWiIeqRnz544f/48Jk2ahP79++Pr169YtWoVNDU1ERkZyYnXrVs3ODg4YN68ecjNzUWrVq3w+PFjHD16lG+atSmv7u7uaNq0KVq1agVVVVV8+fIF3t7e0NPTg4mJicA86+jowNDQEE+ePMG0adO43ps+fTqOHDkCJycnTj2Vnp6O06dP4+zZs9i6desvf5/x87PfV0BZY6qioiImTJiAZcuWQVRUFMePH8fr169/Oj9Xr17F+vXr0a9fP2zevBmdOnXiGp4piLKycrUNVHXRvXt3ODs7Y+LEicjKyoKxsTFOnjyJ69ev49ixYxAWFubE9fLywuHDhxEVFcXTo/jw4cMoKSnB2LFjq93fkydPOEN/CSG/2e9eoYAQQsj/tvJVrkJCQvi+36NHD65V7ModOnSIadOmDSMtLc1ISkoyRkZGzIgRI5jnz59z4rx//55xdnZmZGVlGUVFRWbAgAFMbGws31XSFi5cyGhpaTFCQkJcq/MJWsUMADN58mSusPJVFzdt2sQJ+/btG9OvXz9GUVGRkZWVZVxdXZmwsDBGT0+PGTlyJM/ncPPmTWb48OGMgoICIykpybi5uTGRkZE1fIplLl26xNjY2DBiYmKMrq4us379eoGrlNXm86tOaGgoM3LkSEZXV5cRExNjpKWlGVtbW2bp0qVMUlISJ15paSmzYcMGxtTUlBEVFWVUVFSYYcOGMV+/fuVKz9HRkbGysuLZD7/PtLLXr18zAwcOZNTU1BhRUVFGQ0OD6dy5M7Nnzx5OHH4rLtb2vDBM2QqJY8aMYTQ0NBhRUVFGS0uLGThwIJOYmMiVx8qr2TEMwzx8+JDp3Lkz5zO2t7dnLl++zBVHUPkXtErkhQsXmE6dOjFycnKMuLg4o6enx/Tv35+5ffs2wzAMk5iYyIwaNYoxNzdnpKWlGRkZGcbGxobZtm0bU1JSwvczrOzAgQOMiYkJIyYmxpiamjKHDh3iWUmSYcpW5tu8eTPTrFkzRkJCgpGRkWHMzc2Zv/76q9bllWEYZubMmQwAxs3Njee99PR0xsvLi1FTU2OkpKSYDh06MA8fPuRZEbL8szpz5gxPGr9y7utyTfL7jBim5uvsw4cPzODBgxkjIyNGUlKSkZeXZ1q3bs34+vpW+7kVFhYymzdvZrp3787o6uoy4uLijISEBGNhYcHMmzePSU1N5Ypf2/NV9bOty7YMwzBHjhxh7OzsOPFsbW15rourV68yjo6OjLS0NCMlJcVYWloyGzZs4LzPr8761XqE3/lZv349o6+vz4iLizMWFhbM/v37+e47IyODGTNmDKOgoMBISUkxzs7OzIcPH3i+R2pbXrds2cK0a9eOUVFR4dTVXl5eTExMDE++q1qyZAmjqKjIFBQU8LyXkJDATJw4kdHV1WVEREQYWVlZpkOHDnyvi5EjRzLS0tI84YI+v6rfg3W5NgSlyTAMExQUxLRt25aRkpJiVFVVmbFjxzIvX77kqU9ru9pmSkoKU1hYWGO8hpadnc1MmzaN0dDQYMTExBgbGxvm5MmTPPHKVz3lt9K3qakpo6+vX+3KtAzDMB07dmTc3d3rK+uEkDpgMUwd+lETQgghpF74+vpi9OjRCAkJqXbSaEIIIf+b4uLiYGBggCNHjsDT07Oxs0MaWVRUFExMTHDjxo1qe6oSQhoGzXlGCCGEEEIIIX8YLS0tzJgxA2vWrAGbzW7s7JBGtnr1anTp0oUazghpJDTnGSGEEEIIIYT8gRYvXgwpKSl8//5d4Mqm5L+vpKQERkZGWLhwYWNnhZD/WTRskxBCCCGEEEIIIYQQAWjYJiGEEEIIIYQQQgipdw8ePIC7uzu0tLTAYrFw4cKFGre5f/8+WrZsCQkJCRgaGmLPnj08cc6dOwdLS0uIi4vD0tIS/v7+DZD7CtR4RgghhBBCCCGEEELqXW5uLpo1a4Z///23VvGjo6Ph5uaGjh074tWrV1i0aBGmTZuGc+fOceIEBwfD09MTw4cPx+vXrzF8+HAMHDgQT58+bajDoGGbhBBCCCGEEEIIIaRhsVgs+Pv7o3fv3gLjzJ8/H5cuXUJ4eDgnbMKECXj9+jWCg4MBAJ6ensjKysK1a9c4cVxdXaGoqIiTJ082SN6p5xkhhBBCCCGEEEIIqVFhYSGysrK4/goLC+st/eDgYHTr1o0rzMXFBc+fP0dxcXG1cYKCguotH1XRapuEEEIIIYQQQgghf5D2Oyc0dhb4ck7WwIoVK7jCli1bhuXLl9dL+gkJCVBXV+cKU1dXR0lJCVJSUqCpqSkwTkJCQr3kgR9qPCOEkHr0p37J/azHk/cgLiOpsbNR77QU1LD6lk9jZ6NeLXYejXTnPo2djXqleMsfW+43TNf7xjLbcTDmX9nV2NmoVxt6TMKxF9cbOxv1alhLV1wKe9jY2ahXHk07YuiJ5Y2djXp3fMhyBH560djZqFdOxi2xN/hCY2ejXv3VtjcGHl3c2NmoV37DV2PGRe/Gzka98u41A0dCrjZ2NurVCDu3xs7Cf87ChQsxa9YsrjBxcfF63QeLxeJ6XT7bWOVwfnGqhtUnajwjhBBCCCGEEEIIITUSFxev98ayyjQ0NHh6kCUlJUFERATKysrVxqnaG60+0ZxnhBBCCCGEEEIIIX8Q1h/6r6G1bdsWt27d4gq7efMmWrVqBVFR0WrjtGvXrsHyRT3PCCGEEEIIIYQQQki9y8nJwadPnzivo6OjERoaCiUlJejq6mLhwoX4/v07jhw5AqBsZc1///0Xs2bNwrhx4xAcHIyDBw9yraI5ffp0ODg4YMOGDejVqxcuXryI27dv49GjRw12HNTzjBBCCCGEEEIIIYTUu+fPn8PW1ha2trYAgFmzZsHW1hZLly4FAMTHxyM2NpYT38DAAFevXkVgYCCaN2+OVatWYceOHejXrx8nTrt27XDq1Cn4+PjAxsYGvr6+OH36NNq0adNgx0E9zwghhBBCCCGEEEL+IEINOPn97+Tk5MSZ8J8fX19fnjBHR0e8fPmy2nT79++P/v37/2r2ao16nhFCCCGEEEIIIYQQIgA1nhFCCCGEEEIIIYQQIgAN2ySEEEIIIYQQQgj5g7D+I8M2/yuo5xkhhBBCCCGEEEIIIQJQ4xkhhBBCCCGEEEIIIQLQsE1CCCGEEEIIIYSQP8h/ZbXN/wrqeUYIIYQQQgghhBBCiADUeEYIIYQQQgghhBBCiAA0bJMQQgghhBBCCCHkD8ICDdv8k1DPM0IIIYQQQgghhBBCBKDGM0IIIYQQQgghhBBCBKBhm4QQQgghhBBCCCF/EBattvlHoZ5nhBBCCCGEEEIIIYQIQI1nhBBCCCGEEEIIIYQI8Nsbz/T19eHt7f1b02OxWLhw4cIv7Wf58uVo3rz5L6VRVWBgIFgsFjIyMuo1XcKt6ufs6+sLBQWFRs3TqFGj0Lt370bNw58gNTUVampqiImJaeyscPn333/h4eHR2NkghBBCCCGE/I8SYrH+yL//VbWe88zd3R35+fm4ffs2z3vBwcFo164dXrx4gRYtWtRrBmsSEhICaWnp37pP8v+bp6cn3NzcGjsbBMC6devg7u4OfX19AEBMTAwMDAzw6tUrTmN1dnY23N3dkZCQgI8fP1abXnR0NPT19REUFISOHTvC2dkZ169f54l37tw5bNy4ER8+fACbzYauri5cXV2xZcsWAMC4ceOwZs0aPHr0CB06dKjXY/4VzTSNMcS2G8zVdKEirYAFV3fjYfTrxs4WAODCWX+cPnYSqamp0DfQx5SZ02Bj20xg/NCXr7DL+1/ERMdARUUZg4YPgUff3nzj3r15G6uWrEB7hw5YvWkdJ/z1q1CcPnYSER8+IjUlFas2rkEHR4f6PjQuHx+8xLs7T5GfmQMFTRW06tcV6sZN+MZNiPiCWztO8oR7LB4HeQ1lAAC7tBRhN4MR9TQMeRnZkFdXgm2vTtC2NGzQ4/hZoh3sId6jG4RNjCAkL4esCTNRGhXT2Nni8S7wGd7cCEJeZjYUtdTQ1tMVmiZ6fOPGfYxGwJbDPOEDV0yGgqYqT/inZ29x98A56DUzg8vkwfWed0Hs9azgaGgLWXEpJOak4fK7x4hJjxcYX1hICF1N7GCrZQpZcSlkFuTg7qcXeP7tAyeOhIgYXMzaoKmGISRFxZGen42A94/xMTn2dxwSnt96iOCAu8jOyIKqtgZcRvSFrrlRjdt9/fgZh1f9A7Ummhi/bh4nPOlbPO6fuYr46G/ITElDt+F90Ka7UwMeAa+g6/cQePEGstMzoN5ECx6jB8HQ0pRv3OjwSFw5ehbJ3xNQVFQERRVl2HdzgIN7N77xQx89w/Ft+2Bl1xyjFkxpyMPg0tXEDj0s2kFBUhbfM5Nw9MX1astIO31r9LRoDw1ZZeQVF+BN3CeceHUTOUX5AIBORi3QwaAZmiioAQCi0+Jx+vUdfE79/luOp1xgwC3cPB+AzLQMaOlqY+D4ETBpas437svHz/Dg6m18/fwFJcUl0NTThvuQfrBqyf09l5eTiwtH/PAqKAR5OblQUVdF/7FDYW1n+zsOCaF3gvH82n3kZmRDWVsdTkPcoWNmUON23yNj4LduL1S01TF81QxOuN+6vfj28TNPfAMbc/SZNbo+sy5QN9PW8LDqCAVJGXzLSILv86v4kPRFYPwOBs3gYdkBmnLKyCsqRGhcJI6+uMYpf46Gtpjcvh/PdkOPL0cxu6TBjqM67fVt0Nm4JeQkpJGQnQr/t/fxOS2Ob9whtt3QWteSJzw+KxUb7h1t6KwK9PzWIzy5eg85P+pz52G9a1efR3zG0dU7oaqjgXFr53LCP4S8weNLt5CemAJ2KRuK6iqwd3OCdQe7hjwMQqpV68YzLy8v9O3bF1++fIGeHvcN6KFDh9C8efPf3nAGAKqqvDe25L+hqKgIYmJi9Z6upKQkJCUl6z3dxtRQn1VtFBcXQ1RUtM7b5efn4+DBg7h69arAOMnJyejevTsA4Pbt2xARqaiy7OzsMH78eIwbN44TVl4fHDp0CFOnTsWBAwcQGxsLXV1dTpzbt29j0KBBWLt2LTw8PMBisfD+/XvcuXOHE0dcXBxDhgzBP//880c1nkmKiuNT6jdc/RCEtd0nNHZ2OO7euoOd23ZgxrxZaGpjjcv+lzB/5lz4njoKdQ11nvjxcXFYOHMeevRyx98rliDszVt4b9wKeQUFOHZ24oqbEJ+A3Tt2waY5b0NcQX4BjEyM4drTDcsWLG6ow+OIeRGO5+duo7WnC9QMtRHxKBR3d/nBY/FYSCvJC9yu15LxEJWsuD7FZaQ4/w+9/ACfQ96h7ZDukFNXRlz4Z9zffx6us4ZBqYlGgx7Pz2BJiKPk3QcUPQiC9KzJjZ0dvqJCwhB8+jo6DOkBdWNdhD94jms7jmHg8smQUVYQuN3AVVMgJiHOeS0hy/tgLjs1A0/P3oSGiS7Pew3JRtMY7pYdcCHsAb6kJ6CNriXGtO6JrfdPIqMgh+82Q21dICsuibNv7iE1LxPSYpIQFqoYcCDMEsLYNh7IKcrHsZc3kFmQAwUJGRSWFP+WY3oX/BI3jvjDbcwA6Jga4OWdIJzYsAcTNy2EvIqSwO0K8vJxcfcxGFiZIjcrm+u9ksIiKKqpwKKNLW4d82/oQ+AR+vgZLvmcQp9xQ6FvbownNx/g4JrtmOO9EoqqyjzxxcTF0b57Z2jq6UBMQhzR4ZE4t/coxMTFYd/NkStuelIqAg6fgYGFye86HACAva4Vhrdwhc/zK4hIjkVn41aY5zQM867sRGpeJk98U1VdTLTvg2Mvb+Dl949QlJLDGLueGNvGA94PTwMALNT1EfwlDEeef0URuwQ9LdpjQafhmH9lJ9Lzs3nSbAghD4Lht/8IhkwaAyMLUzy4fgf/LNuA5bs3QUlNhSd+5LsPsLC1Ru+RnpCUlkLQ7fvYuXIzFmxdBV0jfQBASXEJvBevg6y8HP5aNB2KKkpIT06F+G+65/z49DUCT1xGlxG9oWWihzf3nsJ/6yGMXDsLcsqKArcrzMvH9X2noWtphLxM7vrEfepwsEtKOa/zc3NxdMl2mNpZN9hxVNZWrylGtXLDgWeX8TEpFl1N7bCo8wjMvLSDb/kzU9XDlHb9cPjFVTz/9hFKknIYZ++BCW37YPP9E5x4eUUFmH7Rm2vbxmo4s9UyRR9rR5x9fRfRaXFop2+Dv9r2xrq7R5HB53o4/zYQl98/4rwWYglhXqeheB0X+Rtzze39k1e4dewCXEf1RxNTA7y8G4RTm/bhrw0LIK8iuOwV5OXj0p4TMLAyQU4m97FKSkuhvYczVLTUISwijMhX73B53ylIycnCyIZ/IzchDa3WwzZ79uwJNTU1+Pr6coXn5eXh9OnT8PLyAgAEBQXBwcEBkpKSaNKkCaZNm4bc3FyB6cbGxqJXr16QkZGBnJwcBg4ciMTERK44ly5dQqtWrSAhIQEVFRX07duX817VYZuRkZFwcHCAhIQELC0tcevWLZ59zp8/H6amppCSkoKhoSGWLFmC4mLum8X169dDXV0dsrKy8PLyQkFBQbWfT2lpKby8vGBgYABJSUmYmZlh+/bt1W5TVWpqKgYPHgwdHR1ISUnB2toaJ0/y9lqoytfXF7q6upCSkkKfPn2wZcsWrmGJ/IYIzpgxA05OTpzXDMNg48aNMDQ0hKSkJJo1a4azZ89Wu99du3bBxMQEEhISUFdXR//+/X8pPX19faxevRqjRo2CvLw8p1GkpjJ17NgxtGrVCrKystDQ0MCQIUOQlJRU7edV+fPR19cHi8Xi+StXm/JS1ffv3+Hp6QlFRUUoKyujV69edR6a+PjxYzg6OkJKSgqKiopwcXFBeno6AMDJyQlTpkzBrFmzoKKiAmdnZ8TExIDFYiE0NJSTRkZGBlgsFgIDAzlh7969Q48ePSAnJwdZWVl07NgRUVFRnPd9fHxgYWEBCQkJmJubY9euXZz3yvfh5+cHJycnSEhI4NixY2Cz2Vi5ciV0dHQgLi6O5s2b8+3xVdm1a9cgIiKCtm3b8n3/69ev6NixI2RlZXHv3j3o6OhAQ0OD8ycsLMw555XDcnNz4efnh4kTJ6Jnz548dVZAQAA6dOiAuXPnwszMDKampujduzf++ecfrngeHh64cOEC8vPzqz2O3+lJ7Dvsf3oJ9z+HNnZWuJw5eRpuHj3Qo5c79Az0MWXWNKipq+HSOf4/Xi+dvwg1DXVMmTUNegb66NHLHd3de8Dv+CmueKWlpVizdCVGjR8DTW1NnnTatLOH14RxcOjkyPNeQ3h/9xmM2zaDSbtmkNdQgV3/rpBSlMPHh6+q3U5CVgqScjKcP6FKDRifn72Ddbe20LYygqyKAsw6toCmhQHe3w1p6MP5KUW376PgmB9KXv4ZPR75eXMrGGYdWsC8Y0soaqqinWd3yCjK4/3959VuJykrDSl5Wc5f5fMEAGw2G3cPnENLj06Qq+bHQEPoaNAMIV/DEfI1HEk56bj8/jEyC3Jgr9eUb3xT1SYwVNbCoZAr+JT6Den52fiWmYQv6QmcOK2aWEBKVBxHnl/Dl/QEZOTnICY9AfHZqb/lmJ5cDYStkz1sO7Xl9DqTU1bE89uPq93uyoHTsGrXEjom+jzvaRnpoevQXmjargWERX7/gvIPLt+CXecOaNPVAeo6Wug1ZhAUlBURfCOQb3xtQ13YdmwDDV1tKKmpoKVjW5g1t0J0OPePYHYpGye270c3Tw8oqf/eh8bdzdsi8PNLBEa9RFxWCo69vI7UvEx0NWnFN76xsg6SczNwI+IpknMzEJEci7ufnsNQSYsTZ1fQedyODMGXjATEZ6XgwLNLEGKxYKXx+3rc3va/ivbdnNDBpRM0dbXhOX4EFFWUcf8q7wgbAPAcPwIu/d2hb2oEdW1N9Bk5CGpaGnjz9CUnzuNbgcjNzsGkJbNgbGkGZTVVGFuZo4kh/16v9e3FjYdo6mAHa8fWUNZSR6ehHpBVksfru0+q3e6273mY2zeHphFvPiVlpCCtIMv5iw2LhKiYKExb2zTUYXDpadkedz+9wN1PL/A9KxmHn19FSl4mupm15hvfVFUHSbkZuPbhCZJz0vEx+QtuR4TAUFmbKx4DBpkFOVx/jcXJuAWefnmHJ7HvkJiTDv+w+8jIz0EHff6fcUFJEbIL8zh/ugrqkBSVwNPYd7855xWeXgtEc6c2sO1kDxVtdXQb3gdyygp4eaf6+vzaoTOwatsC2sb6PO/pWRrD3M4GKtrqUFRXQWtXR6g10cRXPj0h/8v4/Ub9E/7+V9W68UxERAQjRoyAr68vGIbhhJ85cwZFRUUYOnQo3r59CxcXF/Tt2xdv3rzB6dOn8ejRI0yZwr9rOcMw6N27N9LS0nD//n3cunULUVFR8PT05MS5cuUK+vbtix49euDVq1e4c+cOWrXi/4XNZrPRt29fCAsL48mTJ9izZw/mz5/PE09WVha+vr54//49tm/fjv3792Pbtm2c9/38/LBs2TKsWbMGz58/h6amJlcDgqB96+jowM/PD+/fv8fSpUuxaNEi+Pn5VbtdZQUFBWjZsiUCAgIQFhaG8ePHY/jw4Xj69KnAbZ4+fYoxY8Zg0qRJCA0NRadOnbB69epa77Pc4sWL4ePjg927d+Pdu3eYOXMmhg0bhvv37/ON//z5c0ybNg0rV67Ex48fcf36dTg4OPx0euU2bdqEpk2b4sWLF1iyZEmtylRRURFWrVqF169f48KFC4iOjsaoUaNqfewhISGIj49HfHw8vn37Bnt7e3Ts2JHzfk3lpaq8vDx06tQJMjIyePDgAR49egQZGRm4urqiqKioVnkKDQ1Fly5dYGVlheDgYDx69Aju7u4oLa14+nf48GGIiIjg8ePH2Lt3b63S/f79O6dx+e7du3jx4gXGjBmDkpKyp2379+/H33//jTVr1iA8PBxr167FkiVLcPgw95Cm+fPnY9q0aQgPD4eLiwu2b9+OLVu2YPPmzXjz5g1cXFzg4eGByEjBT8EePHgg8Fr++PEj2rdvD3Nzc1y/fh2ysrK1Oj4AOH36NMzMzGBmZoZhw4bBx8eHq87S0NDAu3fvEBYWVm06rVq1QnFxMZ49e1brff8vKi4uRsSHCLRqw30j26q1HcLe8v+M3799h1atubvd29m3xsfwD5yyCABHDvpCQVEBPTx61n/G66i0pBRpXxOgaaHPFa5loY/k6OqHGgVs8MHZRf/g1o6TSIjgHmpSWlICIVHuH/kioiJIivpaL/n+X1NaUoKU2DjoWHIPFdGxNEJiDZ/p+VV7cXTOZgRsPYy4D9E8778MuA9JWWmYd/i9veyFWULQlldFZDJ3/iOSv0JPkbdnJwBYqhvgW2YSHA1tsajLCMxxHIIeFu0gIiRcKY4+vmQkonfTjljcdRRmOniik1ELsNDwN8WlJSWIj/4KQxszrnAjazN8i+D97MuFBj5BelIKHPu5NnQW66ykuATfo77AtLkVV7hpMyt8+RglYCtu3z/HIuZjFAytuId53jpzGdJysmjdtaOALRuGsJAwDJS08DaeO/9vE6JgosJ/uHpkylcoScmhmVZZDzk5CWm0bmKJ0Gp6xYgLi0KYJYTcwt/zsKqkuASxn6JhacvdOGHZwhpR4RG1SoPNZqMgvwDSlXqovnn6AobmJjixywdzhk7AiknzcPX0BbBL2fWaf35KS0qQGPMdek25eybqNTVF3CfBQxzDHoYgIzkNbXt3rdV+3j58DrM2zSAq3vCjHYSFhGGopIXX8Z+4wt/EfYKZKv/evx+TY6EsJQdbrbJrSF5CGvZ6Vnj1nXvaDwkRMezsMwe7+87F/E7DoK/I+4DudxBmCUFHXg0fkrnP0YekL9BXql2e2uhZISI59rf12qyqrD7/BoOm3PW5YVMzfIuMEbjd6/tPkZ6YAoe+LjXug2EYRIdFIC0huVZDQQlpKHV6LDdmzBhs2rQJgYGB6NSpE4Cy4VF9+/aFoqIipk+fjiFDhmDGjBkAABMTE+zYsQOOjo7YvXs3JCQkuNK7ffs23rx5g+joaDRpUvYlfPToUVhZWSEkJAR2dnZYs2YNBg0ahBUrVnC2a9aM/zw6t2/fRnh4OGJiYqCjowMAWLt2LWfYV7nFiyuG9+jr62P27Nk4ffo05s0rmzfD29sbY8aMwdixYwEAq1evxu3bt6vtfSYqKsqVRwMDAwQFBcHPzw8DBw4U/KFWoq2tjTlz5nBeT506FdevX8eZM2fQpk0bvtts374dLi4uWLBgAQDA1NQUQUFBNfb6qSw3Nxdbt27F3bt3Ob2ADA0N8ejRI+zduxeOjry9OmJjYyEtLY2ePXtCVlYWenp6sLW1/en0ynXu3JnrMxgxYkSNZWrMmDGc+IaGhtixYwdat26NnJwcyMjI1Hj8lYf+Tp8+HfHx8QgJqej1UVN5qerUqVMQEhLCgQMHOC3zPj4+UFBQQGBgILp14z+XSWUbN25Eq1atuBptray4b8iNjY2xceNGzuva9GzbuXMn5OXlcerUKc5QS1PTipv0VatWYcuWLZzenQYGBnj//j327t2LkSNHcuLNmDGDqwfo5s2bMX/+fAwaNAgAsGHDBty7dw/e3t7YuXMn37zExMRAS0uL73sjRoxAu3btcO7cOQgLC/ONI8jBgwcxbNgwAICrqytycnJw584ddO1admM4depUPHz4ENbW1tDT04O9vT26deuGoUOHQly8YsiWtLQ0FBQUEBMTw7fMFhYWorCwkCus8vb/KzIzMsEuLYWiEndPHEVlRaQ/SeO7TVpqKhSVuRvbFJUUUVpaisyMDCirqODt6ze4eukKDhw71GB5r4vCnDwwbIZnKJ+ErDQKsvj3rpaUl4H9YFco6WqAXVyKzyFhuPXPSXSbPgTqxmU3/loWhgi/GwJ14yaQVVFE/McYfH0TydXgS2qv4Md5kpTjPk+SctLIy+Lfs0BKXhYdh7tDVVcTpSWliHzyGgHbDsN99ihomuoDABI+xeLjo5fot+T3D5eWEpOAsJAQZ76ecjmFeZAV59+AoSQpB31FTZSUluLI8+uQFpNA76YOkBQVx9k398riSMnBSFIWoXGR8Hl2BSrS8ujV1AFCLCHc+VR9L71flZedC4bNhrS8HFe4tLwsz9CdcqnxSbh76jJGLpsOoTp+L/wOudk5YLPZkK1yTDIKcsjO4B1eVtnqcXORk5UNNrsUzgM90KZrxcPI6A+RCLnzCDO3LG2QfFdHVlwKwkJCyCzgruMy83Mhr8n//ioy5St2BZ3H1Pb9ISosAhEhYbz49gGHnwueomFQ865Iy89GWMLv6VFS9lmzIafAPdxeVkEeWenVn6tyt/yvoKigEC072nPCkhOSkJr4Hm2c2mPq8nlIikvAyd2+YJey0XNI32pS+3X52Xll15Qc93mRkpNBnoBrKj0hBY/OXIfnogm1uqbiP39F6rcEdBvTv8a49UFOXArCQsI8vcIyC3KhIMG//EUkf8WOR2cww8GTU/5Cvobj0LMATpy4rGTsCjqP2IxESIqKw828LVa5jsPcgJ1I+E09b8tJi5cNp88uyOMKzy7Mg5yElICtKsiJS8FCTR9HX1xrqCzWqLw+l5HnftAtLS+LnIwsvtukJSTj3ukADF8ytdqyV5CXjx1Tl6O0pAQsISG4juoPQ2szgfEJaWh1ajwzNzdHu3btcOjQIXTq1AlRUVF4+PAhbt68CQB48eIFPn36hOPHj3O2YRgGbDYb0dHRsLCw4EovPDwcTZo04TScAYClpSUUFBQQHh4OOzs7hIaGcs1pVJ3w8HDo6upyGs4A8B0SdvbsWXh7e+PTp0/IyclBSUkJ5OTkuNKZMIH7Brlt27a4d+9etfvfs2cPDhw4gC9fviA/Px9FRUV1WqGztLQU69evx+nTp/H9+3fOj/PqFkQIDw9Hnz59ePJal8az9+/fo6CgAM7OzlzhRUVFnAaxqpydnaGnpwdDQ0O4urrC1dUVffr0gZSU1E+lV65qT6TalKlXr15h+fLlCA0NRVpaGtjssid8sbGxsLTknVBTkH379uHgwYN4/PgxV4NaTeWlqvI8V+0tVVBQwDU8sjqhoaEYMGBAtXEE9dqqKd2OHTvynaMsOTkZX79+hZeXF9c1V1JSAnl57hvMyvvOyspCXFwc2rdvzxWnffv2eP1a8NCu/Px8ngb1cr169YK/vz/OnTtX68ZnoKzH2rNnz3D+/HkAZT1mPT09cejQIU7jmbS0NK5cuYKoqCjcu3cPT548wezZs7F9+3YEBwdDSqriZkVSUhJ5eXl897Vu3TquBnMAWLZsGfA/Og0jTxduBkA13bqr9m4pbyxisVjIy83D2mWrMWfRPMg38sq4VfH0ymEAQR115NWVIa9eMc+RqqE2ctOz8P72M07jmV3/rgg+eQ2XVu0HWICsiiKM7G0Q9eRNAx1B7Yl1doDUjIrvwpxFq1ASFt6IOao93vIlOK6ChgoUNCrmOFI3aoKc9Cy8vhkETVN9FBUU4t7B8+g43IPvPGi/C0+DKosFQYdVfj2eCr2NgpKyHs8B4UEY1sIFF8IeoIRdChZYyC3Kx7k3gWDA4HtWMuQkpOFg2LzBG884+azyWtDlxGaz4b/zCBz7d4eyptpvyNkv4KkLq6kkfpi0eh4KCwoRG/EZV4+dg4qGGmw7tkFBfgFObj+I/hNHQFqu9j2w6xtTpaSxWICgwqctp4oRLbvDP+w+3sRHQUFSBkOad8OY1j2x/+klnvg9LdqjrZ41Vt/x/f1zTtW2AFbxLDAIAcfPY9KSWVwNcAybgayCHIZNHQshYSHomRgiIy0dN89dafDGMw5+38V8DorNZuPq3pNo29sZihq1u3EJe/AMyjoa0DTk32jfUPjV34LqPm15VYy264Gzb+7hdVwkFCVlMaylK8bZ98Ke4LKpJCJTviEy5Rtnm49JsdjQYxK6m9vDJ+RKAxxB3bFY1X9vlWuta4X84kKe3qGNokrZK7sN5F/2Luw8io79XGusz8UlxDF2zRwUFRYh5l0Ebh+/AEVVZehZGtdnzv9oQr+hNzipvTpPCOHl5YUpU6Zg586d8PHxgZ6eHrp06QKg7GL466+/MG3aNJ7tKk/YXY5hGL4XVeXwukzszu9JfdX0nzx5wunJ5uLiwumFU77K3s/y8/PDzJkzsWXLFrRt2xaysrLYtGlTtUMuq9qyZQu2bdsGb29vWFtbQ1paGjNmzKh2qF9teicICQnxxKs8Z1d5Y9OVK1egrc09J4CgnjSysrJ4+fIlAgMDcfPmTSxduhTLly9HSEjIT6VXrmpDYU1lKjc3F926dUO3bt1w7NgxqKqqIjY2Fi4uLrUeIgkAgYGBmDp1Kk6ePMnVs/FnygubzUbLli25GvzK1XaBi9qU+6qfVfn8PJXPddW52apLt/y87d+/n6enY9XeX/wadKtea4Ku73IqKiqcOdyqWrRoEWxsbDB06FAwDMM1lLs6Bw8eRElJCVe5YxgGoqKiSE9Ph6JiRe8oIyMjGBkZYezYsfj7779hamqK06dPY/ToitWj0tLSBJ6zhQsXYtasWVxh4uLiuHVgeq3y+l8hryAPIWFhpKVy9zJLT0vn6Y1WTklZmSd+RnoGhIWFIScvj5jP0UiIj8eiOQs47zM/ymeXdk444ncc2jrcdUtDE5eRAkuIhfxs7ifgBTm5dWpQUdXXxueQinlJJGSl0Gl8P5QWl6AwNx+S8jJ4dTGw2ontf5ei4Gco+VAxhImdwr8n4Z9E4sd5qtrLrCA7F1JyNfdELqdmoINPT8saMLOS05CdmoEbOysmmy6vZ/dPWAHPlVMhpyZ4gvtflVdUgFI2G7Li3L0QZMQkkVPIv3E/uzAXmQW5nIYzAEjOSYcQiwV5CRmk5mUiuzAXpQybq2EkKScdchLSEGYJoZRpuKFmUrLSYAkJISeTu1dCXmY2pOV5G4mK8gsQ//krEmK+45rvOQA/zgHDYPWwmRi6cCIMrPivaPm7SMuWzWdYtZdZTmY2ZBUEP3ADwJnHTFNPB9mZWbjldwm2HdsgNSEJ6Ukp8FlXMSdnedmbP2A85v6zGioaDdeYmF2Yh1I2m6eXj5yEtMA5ojysOiAiJRZXwoMAAF8zEuFTcgXLnMfgzOu7XAtcuJm3g4dVR6y7ewRfMxL5ptcQZOTK5jSs2sssOzOTpzdaVSEPgnFkxz78tWA6LGy5J82XV1KAsLAwhIQrZsXRbKKNrPQMlBSXQES0zj+7ak1SVgosISHkVulllpedAyl53rqvKL8QidHfkPQlDnePXQRQcU1tG7MQ/eZ4QbdSA0VxYRE+Pn2Ndn1qHj1RX7IK81DKLoWCJHf+5aspf32aOuJjcixnQv3YjEQUPL2MVa7jcCr0FjLyebdjwCAq9Ts0ZHkX9WhouYX5ZfW7RNX6XQrZAur3ytroWuL5t/AGra9rwqnPM2pbnxciPvorEr58x43DZQ+7y8ve2hGzMWT+BOhblQ0/ZgkJQelH466GnjZSvici6PLt/6nGM/JnqXMtPnDgQEyfPh0nTpzA4cOHMW7cOM4P5BYtWuDdu3cwNq5dgba0tERsbCy+fv3K6X32/v17ZGZmcnqp2djY4M6dO1w/aGtKLy4ujjMcLDg4mCvO48ePoaenh7///psT9uUL9zhzCwsLPHnyBCNGjOCEPXlS/WSbDx8+RLt27TBp0iROWG17GVVOo1evXpwhZ2w2G5GRkTw99iqztLTkyVvV16qqqjzzO4WGhnJ6H1laWkJcXByxsbHVDqmsSkREBF27dkXXrl2xbNkyKCgo4O7du3B2dv6p9PipqUy9ffsWKSkpWL9+PacMPX9etyfmnz59Qr9+/bBo0SKuoYhA7coLvzyfPn0aampq1fZQq055ua/as6k65Y088fHxnB5+lRcPKE/38OHDfFfIVFdXh7a2Nj5//oyhQ4fWer9ycnLQ0tLCo0ePuOa9CwoKQuvW/Cd0BQBbW1scO3ZM4PuLFy+GiIgIhg4dCjabjcGDB1ebj5KSEhw5cgRbtmzhGRrbr18/HD9+XOD8i/r6+pCSkuJaiCIqKgoFBQUCe0uKi4v/Tw7TrEpUVBSm5qZ4/iwEHZ0qzv+LZyFo78B/pVJLaysEP+SeRPb502cwszCHiIgIdPV0cegE9zx7B/fsR15eHqbOmg419d/f80RYRBhKTTQQ/yEGus0qhgzEf4iBjnXtV79L+5YISXnexjZhURFIKciCXVqK2NCP0GshuN7/bfILwM5PqDneH0RYRAQqulr4/j4KBrYVn+G38CjoN6v96lypX+M5PzgVNFTQf9lErvdDLtxFcWER2nm6Qlrp5+r52ipl2PiemQwT1SZ4l1gxH5iJig7eJ8bw3SYmLQHWmkYQExZBUWlZbx4VaXmwGTbnR2dMegKaa5mgciciFWkFZBXkNvgPMWEREWgaNMHntx9hblfxwOpz2EeYtuRdxU9cUgJ/beCew/b5rUeIeReJ/jNGQ4HPSpa/m4ioCLSN9BD5+j2s21TMixfx5j2s7JrXOh2GYVBSXHbO1LQ1MXsb933A9RP+KCwoQK8xg6Gg3HCNtgBQyi5FdFocmmoY4fm3D5xwaw0jvKj0ujIxYVGwq5QfzutKD9R6WLRDbysHbLh3DNFpcfWf+WqIiIpA19gA4a/ewrZdxfyb4a/C0My+pcDtngUG4cj2vRg7bwqsW/PeGxhZmiIkMAhsNpvzQDPxezzklRQatOEMKLum1PW1EfsuEiYtKxYS+fIuEka2vKMwxCXFMWL1TK6w13eDEfs+Cu5ThkFelbtsRTx7g9LiUli0q34ESX0qZZfic1ocbDSNEfK1otezjaYxQr7x7wUtLizKU3+Vl7/q5nPUU9T4rQ245UoZNr5lJsFMVZer95iZmi7C4qsfxmysrANVGUU8qTQktTGU1ec6iA6LgLldxTyC0WERMG3Ju6iNuKQ4xq3jnvrmxe3H+PI+En2njYKCavX1Wnn9SEhjqHNNLiMjA09PTyxatAiZmZlcE7PPnz8f9vb2mDx5MsaNGwdpaWmEh4fj1q1bPCvZAUDXrl05vUu8vb1RUlKCSZMmwdHRkTMsbNmyZejSpQuMjIwwaNAglJSU4Nq1a3znm+ratSvMzMwwYsQIbNmyBVlZWVyNHkDZPFGxsbE4deoU7OzscOXKFfj7c68IN336dIwcORKtWrVChw4dcPz4cbx79w6GhoJXATI2NsaRI0dw48YNGBgY4OjRowgJCYGBgUGtP1tjY2OcO3cOQUFBUFRUxNatW5GQkFBt49m0adPQrl07bNy4Eb1798bNmzd5hmx27twZmzZtwpEjR9C2bVscO3YMYWFhnEYBWVlZzJkzBzNnzgSbzUaHDh2QlZWFoKAgyMjIcM11VS4gIACfP3+Gg4MDFBUVcfXqVbDZbJiZmf1UeoLUVKZ0dXUhJiaGf/75BxMmTEBYWBhWrVpV6/Tz8/Ph7u6O5s2bY/z48UhIqPihqKGhUavyUtXQoUOxadMm9OrVi7MCZWxsLM6fP4+5c+dyDSsWZOHChbC2tsakSZMwYcIEiImJ4d69exgwYABUVHiXUAfKepXZ29tj/fr10NfXR0pKCtd8bQAwZcoU/PPPPxg0aBAWLlwIeXl5PHnyBK1bt4aZmRmWL1+OadOmQU5ODt27d0dhYSGeP3+O9PR0nl5Wlc2dOxfLli2DkZERmjdvDh8fH4SGhvLtfVfOxcUFCxcu5OkRVtmCBQsgLCyM4cOHg81mV9uoFxAQgPT0dHh5efEMM+3fvz8OHjyIKVOmYPny5cjLy4Obmxv09PSQkZGBHTt2oLi4mGuo8cOHD2FoaAgjoz9nYlJJUXHoyFf0hNOSU4GJig6yCnKRmMO/F9/vMGCwJ9YtXw0zc3NYWVsh4MIlJCYmwb1vbwDA/p17kJycgkXLy8qjR99euHDmPHZ6/4Oevdzx7u07XL10BYtXLQMAiImLw8CIu76VkS1ryKgcnp+Xh+/fKibrj4+Lx6eISMjKyUFdg/9E6r/CsnNrPD5yGcq6GlA10EbE41DkpmXBtGNZXfryYiDyM7PRfoQ7ACD8XgikleShoKkCdmkpPj97h9jQj3AcWzHUPjkmDvkZ2VDUUUdeRjbeXH0EhmFg1ZX/PJeNjSUrAyE1FbB+/GgX+tEDkJ2WASY9oxFzVsHGuS3uHToPFT0tqBs1QfiDF8hJy4SFY9l9xbPzt5GbkYVOY8oelry9HQxZZQUoaqmhtLQUn568QfTLcDhPKBsyLiIqCiVt7vIkLlU25LxqeEN5GP0ans274FtGEmIzEtG6iSUUJGXxJLbswZirmT3kJKTh9/oOACA0LgJdTFphQLPOuBURAmkxCbiZt8Pzrx9Qwi5beObJl3dor28Nd6sOCIp5CxVpBXQyboHHMW9/yzHZuznhwq5j0DLUhbaJPl7dDUJmSjpadimbAuDOqcvITstE70nDwBISgloT7jkypeVkISImyhVeWlKC5G8JnP9np2UiIeYbxCTEOb0XGpKDuzNO7TgIHSN96JkZ4umtB8hISUPbbk4AgKvHziEzLQODp5WtUP/42l0oqihB9cdqwjEfIvHg0k20794ZACAqJgoNXe5ethLSZT1UqoY3lGsfgjGxbV9Ep8UhMuUrOhu3hLKUPO5Elj2o9GzWBYpScpwhca++R8CrjTu6GLfCm/goKErKYFhLV3xK+YaMHxOa97Roj/42nbAz6BySczMg/6NnW0FJEQpLaj9q4Fd07eMGny27oGdiCENzEzy8fhdpySlwcCsbTePvewoZqWkYPbvsofizwCD4bN0Nz/EjYGBmgsy0DACAmLgYJH+cE0c3Z9y7fBOn9x5BZw8XJH1PwDW/i+js/nsWuGjp0hHX9p2Gur4ONI118TbwGbJTM9CsU9m8bA/PXENOeha6j/cES0gIKjoaXNtLyspARFSEJxwoW1jAuIUlJGV+79D1gPePMbV9f3xO/Y6I5K/oatoKKtLyuBVRNjfxYFtnKEnKYWdQWY/U598+4K+2veFs2pozbHNkKzdEpnzlTKjf36YTIpO/Ij47lTPnmb6SJg4+u/xbj61c4KeXGNrSBV8zEhGTFo+2+tZQlJTF45iy3s89LdpDXlIax1/e5NqujZ4VYtLif/s8bfy06e6Ei7uPQ9OwCXSM9fHqXhAyU9PRoks7AMC90wHITs+Ex4ShP+pz7sUQpOVkICwqwhX++NJtaBo0gaK6MkpLShEVGo63j0LgOqr6aW3+a/6XV7b8E/3UYxAvLy8cPHgQ3bp14xqOaWNjg/v37+Pvv/9Gx44dwTAMjIyMBA65YrFYuHDhAqZOnQoHBwcICQnB1dWVq6HNyckJZ86cwapVq7B+/XrIyclx9W6pTEhICP7+/vDy8kLr1q2hr6+PHTt2wNW14kurV69emDlzJqZMmYLCwkL06NEDS5YswfLlyzlxPD09ERUVhfnz56OgoAD9+vXDxIkTcePGDYGfyYQJExAaGgpPT0+wWCwMHjwYkyZNwrVrtZ/AccmSJYiOjoaLiwukpKQwfvx49O7dG5mZgicvtbe3x4EDB7Bs2TIsX74cXbt2xeLFi7kakFxcXLBkyRLMmzcPBQUFGDNmDEaMGIG3bytukFetWgU1NTWsW7cOnz9/hoKCAlq0aIFFixbx3a+CggLOnz+P5cuXo6CgACYmJjh58iRnUvu6pidITWVKVVUVvr6+WLRoEXbs2IEWLVpg8+bN8PDwqFX6iYmJ+PDhAz58+MAzeT3DMLUqL1VJSUnhwYMHmD9/Pvr27Yvs7Gxoa2ujS5cute6JZmpqips3b2LRokVo3bo1JCUl0aZNmxp7Xx06dAhjxoxBq1atYGZmho0bN3L1wlJWVsbdu3cxd+5cODo6QlhYGM2bN+fMVzZ27FhISUlh06ZNmDdvHqSlpWFtbc1ZsEGQadOmISsrC7Nnz0ZSUhIsLS1x6dIlmJgI7pFjbW2NVq1awc/PD3/99ZfAeHPnzoWwsDBGjhwJNpuN4cOH84138OBBdO3alafhDCjrebZ27Vq8fPkSjo6O2LlzJ0aMGIHExEQoKirC1tYWN2/ehJlZRY+ikydP1nq+xd/FXFUP//apaMSc1qHsBuJqeDDW3D0saLMG19m5C7Iys3DkkC/SUlKhb2iA9ds2QkOz7AY8NTUVSYkVT3Q1tbSwbttG7PL+BxfP+kNZRQVTZ0+HY2enOu33Y/hHzJxUMaR7l/e/AACXHq5YsPRvQZv9NP2WFijMzceba4+Rn5ULBU0VdJ40ADJKZWUuPysHuWkVwxbYJaV46X8XeZk5EBYVKYs/cQC0rSoaZNnFJQgNeIDslAyIiotB28oQ7Uf0hJgU//kAG5toWztIz634zGUWly3wkn/kFAqOnm6sbHExsmuKgtw8vLxyH3mZOVDSUkP3qUMh+2MobF5mNnLSKr5XS0tK8eTsTeRmZENEVASKWmpwnToEutaNOwywsjfxnyAlJo4uJq0gJy6NhJxU+IQEcIYgyYpLcQ1tKiotwYGnl9DLqiOmduiPvKJCvIn/hBsfK6aSyCzIwYGnl+Fu2R4zOnoiqyAXj6PfIDDq1W85Jqu2LZCfk4sH528gJyMTqjqaGDzvL06vg5yMLGSl1u2hQHZ6JvYv2sR5HXzlLoKv3IWehTFGLJlar/nnp3n71sjLzsXtM5eRlZ4JDV0teC2aDkW1sp5xWemZyEip+KHLMAyuHj+PtKQUCAsLQ1ldFd2H9oV9t1/rtV+fnsS+g4y4FPo0dYSCpAy+ZSZhU+BxpOSVXUMKkrJQlqr43n0QHQoJUTF0M22NoS1ckFdUgHeJ0TgVeosTp6uJHUSFRTCjI/fvg3NvA3H+beBvOCrAzqEtcrNycOXkeWSmZUBLTwdTVsyDslpZI2tmWgbSkivO1cPrd8AuLcXJ3T44uduHE962iwNGzSqbG1JJVRnTVy3Amf3HsHLyAigoK6Kzhytc+9funvRXmbVphvycPDy5eAe5mVlQ1tZAn1mjIadS9oAyNyMb2akZdU43PSEZ3yNi0G+OVz3nuGbBX8IgKy6FfjadoCgpi68ZiVh39yhScjMAAIqSslCRVuDEv//5FSRFxeFq1gYjWroit6gA7xI+49jLit9w0mISGG/fGwqSMsgrLkB0WjyW3TiAqNTqV85uKK/iIiAlJgEXM3vIiUshPjsVe59c5DT2yUlIQ1GS+/eDhIgYmmka43zY/cbIMg9Le1vkZefikf8N5GRkQVVHE4Pmjoe8SkV9nplSt/q8uLAI133PIjstEyJiolDWUkOvicNgaf/7ej8SUhWLoSW9/nN8fX0xY8YMZGRkNHZWCKnW1atXMWfOHISFhXGGOPwJwsLC0KVLF0RERPBtjKtO+52/fzW+hvR48h7EZSQ1djbqnZaCGlbf8qk54v8ji51HI925T80R/x9RvOWPLfdPNnY26tVsx8GYf2VXzRH/H9nQYxKOvaj9QkX/Hwxr6YpLYQ8bOxv1yqNpRww9sbyxs1Hvjg9ZjsBPLxo7G/XKybgl9gZfaOxs1Ku/2vbGwKOLa474/4jf8NWYcdG7sbNRr7x7zcCREMGr4/5/NMLOrbGz8NO6HxA88qcxXRu7tbGz0CgadgA+IYRUw83NDZGRkfj+/TvXqruNLS4uDkeOHKlzwxkhhBBCCCGE1AcW68/pXECo8YwQ0simT//zVqesuuAAIYQQQgghhJD/XdSU+R80atQoGrJJCCGEEEIIIYQQUg+o5xkhhBBCCCGEEELIH0SIVtv8o1DPM0IIIYQQQgghhBBCBKDGM0IIIYQQQgghhBBCBKBhm4QQQgghhBBCCCF/EBZo2OafhHqeEUIIIYQQQgghhBAiADWeEUIIIYQQQgghhBAiAA3bJIQQQgghhBBCCPmD0GqbfxbqeUYIIYQQQgghhBBCiADUeEYIIYQQQgghhBBCiAA0bJMQQgghhBBCCCHkD8KiYZt/FOp5RgghhBBCCCGEEEKIANR4RgghhBBCCCGEEEKIADRskxBCCCGEEEIIIeQPQsM2/yzU84wQQgghhBBCCCGEEAGo8YwQQgghhBBCCCGEEAFo2CYhhBBCCCGEEELIH0QINGzzT0I9zwghhBBCCCGEEEIIEYAazwghhBBCCCGEEEIIEYCGbRJCCCGEEEIIIYT8QWi1zT8Li2EYprEzQQghhBBCCCGEEELK9D/yd2Nnga+zI9Y0dhYaBfU8I4SQehSXkdTYWahXWgpqaL9zQmNno949nrwH2R8iGjsb9UrW3BRZt+81djbqlVzXTohO+d7Y2ahXBiraeBcf1djZqFdWmkbY/+RiY2ejXo2z7/WfLHvZ0TGNnY16J2ugj+y4uMbORr2S1dLC1/SExs5GvWqiqIGkrNTGzka9UpNTxueUb42djXplqKKD7MhPjZ2NeiVrYtzYWSD/EdR4RgghhBBCCCGEEPIHEaJhm38UWjCAEEIIIYQQQgghhBABqPGMEEIIIYQQQgghhBABaNgmIYQQQgghhBBCyB+EVtv8s1DPM0IIIYQQQgghhBBCBKDGM0IIIYQQQgghhBBCBKBhm4QQQgghhBBCCCF/EBZo2OafhHqeEUIIIYQQQgghhBAiADWeEUIIIYQQQgghhBAiAA3bJIQQQgghhBBCCPmDCNFqm38U6nlGCCGEEEIIIYQQQogA1HhGCCGEEEIIIYQQQogANGyTEEIIIYQQQggh5A/ComGbfxTqeUYIIYQQQgghhBBCiADUeEYIIYQQQgghhBBCiAA0bJMQQgghhBBCCCHkD0Krbf5ZqOcZIYQQQgghhBBCCCECUOMZIYQQQgghhBBCCCECUOMZIeSPkZeXh1WrVuHLly+NnRVCCCGEEEIIaTSsP/Tf/ypqPCP/L/n6+kJBQeG37MvJyQkzZsz4LfuqT3X9jPT19eHt7d1g+amNqVOnIi4uDnp6enXa7v/rOSKEEEIIIYQQ8uejBQNIg2HVMMHhyJEj4evr+3sy8z/I09MTbm5utY4fEhICaWnpBsxR9U6ePInExERcvHixztueP38eoqKiDZCrhnHhrD9OHzuJ1NRU6BvoY8rMabCxbSYwfujLV9jl/S9iomOgoqKMQcOHwKNvb75x7968jVVLVqC9Qwes3rSOE/76VShOHzuJiA8fkZqSilUb16CDo0N9H1qdNdM0xhDbbjBX04WKtAIWXN2Nh9GvGztbAjEMg32nTsL/xg1k5+bAytQU8/+aACNdwQ2+UbFfsOfEcXyIikJ8UhJmeY3FEI9eXHH2njyB/adOcoUpKyjgxuGjDXIclTEMg/1XA+D/+BGy8/Jgpa+PeQMHw0hLS+A2d0NfwffGNXxNTkZJaSmaqKphWJeucGtjz4nzMjISR2/fxIevsUjJzMSm8RPg1Kx5vef/8vmLOHviNNJSU6FnoI8J0yajaXMbgfHfvHqNff/swpfoGCirqGDAEE/06OPBFcf/9FkE+F9CcmIS5BTk0dHJAaMnjIOYuBgA4NSRE3h8/yG+fYmFmLg4LK2tMGbiODTR06334yt37UIALp46h/TUNDQx0MOYKeNhadOUb9y01DQc3rUfURGfEP8tDm59PeA19S+BaT+6cx9bV21A6/b2WLBmaUMdAo9Xd4IQcvU+cjOzoaKljk5DPaBjZlDjdt8jYnBq3R6o6Khj5KqZXO8V5Obj0bnriHwehoK8fMirKMFpcA8YNrNokGOo7/I3d8pMvH3FWwfatW2DVZvL6vS83Dwc2X8IQQ8eISM9A0amxpgwYwrMLMzr/wB/YBgG+44dg/+1q8jOyYGVmTnmT54MI319gdtExcRgz9Ej+BD5CfFJiZj1118Y0qcvV5yXb9/i6NkzCI+MREpaGjYvXQandu0aJv+HD8M/IADZ2dmwsrDA/OnTYWRQfXm7c/8+9vj44FtcHHS0tDDJywudOnbkvO8+aBDiExN5thvQqxfm/3iod/fBA5y/fBnhERHIzMrC8f37YWZs/MvHdPGsP84cP4XU1DToG+hj0swpsG4u+F7i9ctQ7Nm+EzHRMVBWUYbnsMFw71vxXXQj4Bo2rV7Ps93V+zchJi4OABja2xOJCQk8cTz69ca0uTN5wuvK/8w5nDx2AqkpqdA3NMC0WdPRzLa5wPivXrzCv947EPM5GsoqKhgyYih69+vDeT866jMO7j2Ajx8+ICE+AVNnTsfAIZ5caQzw6IuEeN5j6tO/L2bNn/PLxwQAAecv4uwJP0498de0STXWE/v/2c2pJ/oP8USPPu5ccfxPn8OVSt9THZwcMHrCWM731Mh+Q5CUwFs2e/b1wOTZ0+vluCpjGAb7TpyA/43rZXWEqRnmT5wIo2oeiEd9+YI9x4/hw6dPZfdH48ZhSK/eAuP7+Plh55HDGOzRC7PHj6/3YyCkJtR4RhpMfHw85/+nT5/G0qVL8fHjR06YpKRkY2Tr/73S0lKwWCwICVXfcVRSUrJOn7GqquqvZu2XDB48GIMHD/6pbZWUlOo5Nw3n7q072LltB2bMm4WmNta47H8J82fOhe+po1DXUOeJHx8Xh4Uz56FHL3f8vWIJwt68hffGrZBXUIBjZyeuuAnxCdi9Yxds+Nw8F+QXwMjEGK493bBsweKGOrw6kxQVx6fUb7j6IQhru09o7OzU6PD5czhx8QKWTZ8BXS1tHPQ7jclLl+Lcrt2QlpLiu01BYSF01DXQtV0HbD10QGDahrq62LVyNee1cA3XeH05cusmTty9g6XDR0JXTQ2Hrl/DlH+34+zSFZCWkOC7jbyUFEa7dIe+hgZEhUXwMOwNVh47AkVZWbS1tAIA5BcVwlRHB+5t22H+/r0Nkvf7t+9h7/admDx7OqxsmuLqhctYPGcB9h3zgRqf6ykhLh5L5ixEd3c3zFu6CO/ehGHnlu2QV1BAh05ljcl3b9zGoT37MWvhPFhYW+F77FdsWbMRAPDX9MkAgLehr+HetxdMLczALmXDd99B/D1zHvYd94FEA3y3Pbp7Hz7/7sO4GZNgYW2JG5euYfW8pdh+eA9U1dV44pcUFUNOQR79hg1CwBn/atNOSkiE7+4DsLSxqvd8V+fD01DcO34ZXUf0hrapPl7fe4pzWw5i9LrZkFNWFLhdYV4+ru47BT1LY+RmZXO9V1pSgjOb9kNKTgYeU4ZDRkke2WkZEJMQb5BjaIjyt3TtChQXl3C2ycrMxKRR49CxkyMnzHv9ZsR8jsbcpQuhrKKCOzduYeH0udh3/BBUGui7/PAZP5zwP49ls2ZDV0cHB0+ewORFC3HuwMHq6z4NTXTt6ICte/nXAfkFBTAxMIS7czfMW72qQfIOAIdPncKJM2ewbP586DZpgoNHj2Ly3Lk4d+SIwPy/efcOi1auxIQxY9CpY0fce/gQC1aswMEdO9DU0hIAcGTPHpSy2ZxtoqKjMXnOHHRxcuI6xmZNm6KrkxNWb95cL8dz79Zd7Pb+F9PmzoSVTVNcuXAZC2fOx8GThwXcS8Tj71nz4darJxYs/xvv3oRhx6ZtkFdQgEPnirIlJS0NXz/uhzblDWcAsNNnL9jsUs7r6KhozJ82Gw5V7kd+xp2bt7Fj63bMmj8H1s1scOn8BcydPhtH/Y5DXUODJ37c9zjMmzEb7r09sGTlMrx9/QZbN2yGgqICnDp3AgAUFBRAU1sLTl074Z+tO/jud9/hg2CXVpzD6KjPmDllOjp17fzLxwSU1xO7MHn2NFjaNMXVCwFYMmch9h47JLCeWDpnEVzd3TB36UK8fxOGnVt2QF5Bnut7ymfPfsxcOBeW1lb4FvsNWznfU5MAANsP7AK7Utn88jkai2bM46pL6tPhc2dx4oI/ls2cWXZ/dPo0Ji9ZjHN79tZQR2iga/sO2Hpgf7Xpv4uIgP+N6zDRr/kBy39JTZ1RyO9FwzZJg9HQ0OD8ycvLg8VicYU9ePAALVu2hISEBAwNDbFixQqUlFTcMGZkZGD8+PFQV1eHhIQEmjZtioCAAK593LhxAxYWFpCRkYGrqytXgx2bzcbKlSuho6MDcXFxNG/eHNevX682z7m5uRgxYgRkZGSgqamJLVu28MQpKirCvHnzoK2tDWlpabRp0waBgYHVprt8+XLo6upCXFwcWlpamDZtWq3TKx9+GRAQAEtLS4iLi2P//v2QkJBARkYG136mTZsGR0dHru0qu3TpElq1agUJCQmoqKigb9+KJ8BVh23GxsaiV69ekJGRgZycHAYOHIhEPk9Xy8XExIDFYsHPzw8dO3aEpKQk7OzsEBERgZCQELRq1YpznpKTkznbhYSEwNnZGSoqKpCXl4ejoyNevnzJeT8wMBBiYmJ4+PAhJ2zLli1QUVHhnO+qwzb19fWxevVqzrnU09PDxYsXkZyczDkma2trPH/+nOsYzp07BysrK4iLi0NfX5/v+f9VZ06ehptHD/To5Q49A31MmTUNaupquHSO/w/cS+cvQk1DHVNmTYOegT569HJHd/ce8Dt+iiteaWkp1ixdiVHjx0BTW5MnnTbt7OE1YRwcGuim6Wc9iX2H/U8v4f7n0MbOSo0YhsHJy5cwesBAdG7bDsZ6elgxYyYKigpx/cF9gdtZmZhi+ugxcHFwgFg1PSRFhIWhoqjI+VOUl2+Iw+DCMAxO3ruD0S7d0bm5LYy1tLF8+EgUFBXhRsgzgdu1NDVDp+a2MNDQhI6qKgZ36gJjbW2ERkVx4rS3aoqJ7r3Qubltg+X//OkzcOnZHd09ekBXXw8TZkyBqpoaAvwv8Y1/5cJlqKmrYcKMKdDV10N3jx7o1qM7zp7048QJD3sHK+um6NStCzQ0NdCyjR2cnDsj4kMEJ86arRvQrYcr9A0NYGhihFmL5iEpMQmRHyP47faXXT7jjy5u3eDc0xU6errwmvoXlNVUcePiFb7x1TTV4TV1Ajq5dIFUNT2KS0tL4b16EwaNHgZ1Td56oyE9v/4Q1g52sHFqA2UtdXQe6gFZJQWE3nlS7XY3fc/Doq0tNI15e/m9fRCCgpw89J42Etqm+pBXUYSOqQHUdAX3ovwVDVH+ZOXkoKSsxPl7FfICEuISnAaOwsJCPLr/AF6T/4J182bQ0tHGcK9R0NDUELjfX8UwDE76X8DoQYPQuUMHGOvrY8XsOSgoLMT1e/cEbmdlZobp48bBxclJYN3X3s4Ok0aNQucOHRok78CP/J89i9HDhqGzgwOMDQywYsECFBQU4Prt2wK3O3n2LNq0aoXRQ4dCX1cXo4cOResWLXDi3DlOHEUFBagoKXH+HgUHQ0dLCy2bVTzE6tGtG8aNHInWLVvW2zGdO+kHV3c3uPXqCT0DfUyaORVqaqq4fJ5/7/2A8xehpq6GSTOnQs9AH269esLV3Q1nTnDfS7BYLCgpK3P9VaagqMD13tPHwdDS0UazFs1/+ZhOnziFHr3c4d7bA/oG+pg2ewbU1NXgf5b//dHF8/5Q11DHtNkzoG+gD/feHujh0ROnjp3gxLGwssTk6VPQtZszxMT4l0FFRUUoqyhz/oIePYa2jjaat6if7y7/02fRrWd3uHLqiclQVVPDFf/LfONX1BOToauvB1ePHujWwxXnKtUTH8Lew/LH95S6pgZatmkFJ+dOiPxQ0Umh7FxV1CVPHz+BprYWrKsZ6fCzGIbByYsXMdrTE53btS+rI2bNKqsj7ldzf2RqiuljvODi6Fjt/VFefj6WbN6Ev6dOhayMTL3nn5DaosYz0ihu3LiBYcOGYdq0aXj//j327t0LX19frFmzBkBZw1f37t0RFBSEY8eO4f3791i/fj2EhYU5aeTl5WHz5s04evQoHjx4gNjYWMyZU9G9evv27diyZQs2b96MN2/ewMXFBR4eHoiMjBSYr7lz5+LevXvw9/fHzZs3ERgYiBcvXnDFGT16NB4/foxTp07hzZs3GDBgAFxdXQWme/bsWWzbtg179+5FZGQkLly4AGtr6zqll5eXh3Xr1uHAgQN49+4dhg0bBgUFBZyrdANXWloKPz8/DB06lG8+rly5gr59+6JHjx549eoV7ty5g1atWvGNyzAMevfujbS0NNy/fx+3bt1CVFQUPD09+cavbNmyZVi8eDFevnwJERERDB48GPPmzcP27dvx8OFDREVFYenSimFBWVlZGDFiBB4+fIjg4GAYGhrCzc0N2dllPQrKG8aGDx+OzMxMvH79Gn///Tf2798PzWp+7G3btg3t27fHq1ev0KNHDwwfPhwjRozAsGHD8PLlSxgbG2PEiBFgGAYA8OLFCwwcOBCDBg3C27dvsXz5cixZsqRehxYXFxcj4kMEWrVpzRXeqrUdwt6G8d3m/dt3aNXajivMzr41PoZ/4GpsPnLQFwqKCujh0bPe8ku4fU9MRGp6OuxtK26oxURF0cKqKd58+PDL6cfGxcF11Eh4jPPCwk0b8Y3P0Jj69j01BalZWbC3qBjSJiYqihbGJngT/blWaTAMg2cfPuBLYiJa1MNQpNoqLi5G5McItGjNXY+1aN0K4WHv+G4THvaOJ37LNq0Q+eEj53qyamaNyI8R+Pg+HAAQ/z0OIcFP0bpdG4F5ycvNBVDW8FHfiouLEfXxE5rZteAKb25niw/vwn8p7TNHTkJOQR5de7j8Ujp1VVpSgsSY79BvasoVrt/UBHGfYgRu9/ZBCDKSUtGud1e+70e9eg8tYz3cOeKPXVNXwmfRFjy5fJer90V9aajyV9WNgGtw7NqJ06OxtKQU7FI2xMTEuOKJiYvj3Rv+3yO/6ntCAlLT02DfoqLxR0xMDC2srfEm/H2D7LM+fY+PR2paGuwr3fOIiYmhRbNmePOO/7kCgDfv36NNlfskezs7gdsUFxfj6q1b8OjevUF7ixQXFyPiYwRateG+N2jZxg7vBd1LhL1DyyrxW7WxQ0Q4d9nLz8/HkN4DMci9P/6evaDaBwLFxcW4ff0WXHv++vGW3R99ROsq90d2bVoj7M1bvtu8exsGuyrxW9u3wYf3HwReT7XJx81rN+Dm0bNezqHgeqIl3guoJz6EvUeL1twNrS3a2CHyQwTnuCybNcWnjxH4+L7s3qPse+oZWrez50mvPB/3bt5Gtx6uDVI2vycm/Lg/qvieEhMVRYumTfEm/Ne+pwBgw+7daG9nhzYN+DCOkNqgYZukUaxZswYLFizAyJEjAQCGhoZYtWoV5s2bh2XLluH27dt49uwZwsPDYWpqyolTWXFxMfbs2QMjIyMAwJQpU7By5UrO+5s3b8b8+fMxaNAgAMCGDRtw7949eHt7Y+fOnTx5ysnJwcGDB3HkyBE4OzsDAA4fPgwdHR1OnKioKJw8eRLfvn2D1o/5gObMmYPr16/Dx8cHa9eu5Uk3NjYWGhoa6Nq1K0RFRaGrq4vWrVvXKb3i4mLs2rULzSo9yfT09MSJEyfg5eUFALhz5w7S09MxYMAAgZ/5oEGDsGLFCk5Y5fQqu337Nt68eYPo6Gg0adIEAHD06FFYWVkhJCQEdnZ2fLcrz7+LS9kPsenTp2Pw4MG4c+cO2rdvDwDw8vLiapDq0qUL1/YHDhyAgoIC7t+/j549yxqCVq9ejdu3b2P8+PF49+4dhg8fjj59+qA6bm5u+Ouvsjl+li5dit27d8POzo7z+cyfPx9t27ZFYmIiNDQ0sHXrVnTp0gVLliwBAJiamuL9+/fYtGkTRo0axZN+YWEhCgsLucLExasfGpSZkQl2aSkUlbiHJCkqKyL9SRrfbdJSU6GozH1zqKikiNLSUmRmZEBZRQVvX7/B1UtXcODYoWr3T35Nano6AEBZXoErXFlBAfFJSb+UdlNTU6yYMRN6WtpIzcjAwTOn4TV/Lk7/sxMKDdAgUy41KwsAoCTLvQ8lOTkkpPEvk+Vy8vPhtmgBikqKISwkhPmeg9HGwrLB8lpVVkYm2KVs3utJURFpqfzznp6WDkXFKvE511MmlFWU4dS1MzLTMzB74nQwDIPS0lL07OMBz+FD+KbJMAz27tgFKxtr6BvW/3CS7MwssNlsKCgqcIXLKyoiIy39p9MNf/sOt6/cwNYD//5iDusuPzsXDJsNKXnuHgRS8rLIzczmu016QjIenrmGQX9PhFClB2mVZSanITY8ChZtbdF31hhkJKbg9pELYJeWol1v53o9hoYqf5V9fB+OmM/RmLmw4sGglLQULJpa4oTvUejq6UJBSRGBt+/i4/twaOlo19PRcUtNLzse5Sp5V1ZURHzir9V9v0NqWnX5F9yjPjUtje82qQLqxsBHj5CTkwN3V9dfzHH1Ku4luKesUFQSXPbSUtN4y6qSElfZa6Kvi3mLF8DA2BB5ubk4f/ocZoyfgr1HD0FHV4cnzcf3HyInJwfdenSvh2PKQCm/Y1JWEnhMqalpaK1c9TMoO6aMjAyoqKjUOR8PAx8gJycHbj1rP2dwdQTVEwqKikgXWE+kQUFAPZGVkQklzvdUJuZU+p7q0ccDA4fzn/4k+MFj5OTkwNmtYR6UcO6Pqox4Kbs/SuazRe3duH8fH6I+4cg2719K5/8rIRb1dfqTUOMZaRQvXrxASEgIp6cZUNZzqqCgAHl5eQgNDYWOjg6n4YwfKSkpTsMZAGhqaiLpxw/YrKwsxMXFcRpsyrVv3x6vX/OfjDwqKgpFRUVo27YtJ0xJSQlmZmac1y9fvgTDMDz5KiwshHKVru3lBgwYAG9vbxgaGsLV1RVubm5wd3eHiIhIrdMTExODjQ33xKJDhw5F27ZtERcXBy0tLRw/fhxubm48N+blQkNDMW7cOL7vVRUeHo4mTZpwGs4AwNLSEgoKCggPD6+28axyPtXVy+ZyqNzTTl1dnXOeACA1NRWrV69GYGAgkpKSUFpairy8PMTGxnId/7Fjx2BjYwM9Pb1arQpam3wAQFJSEjQ0NBAeHo5evbgncW/fvj28vb1RWlrK1esRANatW8fVEAmU9bobP2NSjXnjeerHAKjmSWDVJaHLe8uxWCzk5eZh7bLVmLNoHuR/0wq0/yuuBQZi7e6KhnbvJWU9JqueP4ZhfvlJbvuWFU+ljQHYmJuj91/jEHDvLoZVM3luXV179hTrTlYMadk2qWwOL37HVBMpcXEcX/g38goLEfLxA7adPwttFRW0NDWrcdt6VTXvqOF88BxreXBZ+OuXoTh15Dgmz54OcysLxH37jj3bd0LR5yiGjh7Ok9zOrTsQHfUZW3bzn0+nvvDWG8xPLxefn5eH7Ws2Y9LcaZBTaPjhwYLwPSY+547NZiNgz0m06+MMJQ3Bc3oxbAZSsjLoNrofhISEoGGgg5yMLIRcvV/vjWcc9Vz+KrsecA36hgYws+Re7GDukoXYtm4ThvYeCCFhIRibmsDJuQuiIgT3rK+La3fvYu2O7ZzX3ivL5iKrmsOyuq9edlmvrt26hbVbt3Jee68rW2iBp57jE8aDX30vIOrFq1fRrk0bqP5Eo83P4HP51LHsMVzBlk2tYNm0Yu5DKxtrTBw5DhfOnMMUPhPMX7t8Fa3tW0NFtf6Olyf7NZQxnvsjMHzDayvg0mW0aWtf73MH8r/tq+a+T9B38o/wNy9DcfrIcUyePQ1mVhaI+xaHvdt34oSPEobw+Z66EXANrexbQ7meztW1e/ewdmfFgxfvZcsF5Lva29saJSQnY8v+ffh35SqIV+ltS0hjoMYz0ijYbDZWrFjBNedWOQkJiVpNdF91dUUWi8Xzg68uP3Jr82ORzWZDWFgYL1684GlMkREwBr9Jkyb4+PEjbt26hdu3b2PSpEnYtGkT7t+/X+v0JCUlefLdunVrGBkZ4dSpU5g4cSL8/f3h4+MjMO91WTxA0OdUm0aCyuelPG7VsMpDaEaPHo3U1FRs374denp6EBcXh7W1NYqKirjSDQoKAgCkpaUhLS2txpVBa5MPAJy88Du26srEwoULMWvWLK4wcXFxpOZnCtxGXkEeQsLCPE9R09PSeZ5KllNSVuaJn5GeAWFhYcjJyyPmczQS4uOxaM6Cinz/OKYu7ZxwxO84tBuoN8J/nUPr1mhqVtGwXVRcDABIyUiHSqWn42mZmVCq54ZLSQkJGOnp42tcXL2m62DTDE0rTbZb9GMISGpWJlQqzbGWnp0N5Rp6vAkJCaGJWtlk9WZNmiAmMQG+N2/8tsYzOQV5CAkL8Ty9z0jPEHg9KSopIj2tavz0H9dT2fEe2e+Dzi7O6O7RAwBgYGSIgoIC7NiwFYNHDuVarGXX1h148igIm3d6Q1WtYSZql5WXg5CQENKr9DLLzMiAvJLCT6WZ8D0eSQmJWLuw4gFAeX3Xv3NP/Ht0PzT4zJ1YXyRlpcESEkJuBncvs7ysHEjJ8X6XFuUXIjH6G5K+xOHO0YsV+WUYbBm9AAPmjoWupTGkFWQhJCzMdY6UNNWQm5mN0pISCIvU321vQ5W/cgUFBbh/+x5GjB3Fk46WjjY27fRGQX4+cnPzoKyijLVLVkJdk3dS9Z/hYG+PpuYV13FR0Y+6Lz0dKpUe7KVlZEBJwAO7xuTQvj1nQn8AnPuJlLQ07vynp1ebf2UlJZ5eZmkZGXwXKYpPSMCzly+xscpDtYYg6F4iI726ewklPmU1nXMvwY+QkBBMLczw/es3nvcS4xPwKuQFlq2vn0Ue5BUUICzw/oj/olDKykpIS03lCstIKzsm+Z94KJAQH48Xz55j9UbeUSQ/q7yeSEutUn+np0NBYD2hxKeeyOD7PeVa6XuqsCAfOzZsw6Aq31OJCYkIff4Si9cur7fjcmjTBk0rdS7g3B+lV70/yoCSws/XER8+fUJaRgaGz6hovC1ls/HqXRj8Ai4jyP8Cz+8nQhoSNZ6RRtGiRQt8/PgRxgLmx7GxscG3b98QERFRbe8zQeTk5KClpYVHjx7BwcGBEx4UFMQZMlmVsbExREVF8eTJE+jqlk1EnJ6ejoiICM4k/La2tigtLUVSUhI6VlqqvCaSkpLw8PCAh4cHJk+eDHNzc7x9+/an0ys3ZMgQHD9+HDo6OhASEkKPHj0ExrWxscGdO3cwevToGtO1tLREbGwsvn79yul99v79e2RmZsLCwqKGrevm3r172L17N+c8ffnyBSkpKVxxoqKiMHPmTOzfvx9+fn4YMWIE7ty5U+OKo3VhaWmJR48ecYUFBQXB1NSU7xezuLg4/2Ga+YL3ISoqClNzUzx/FoKOThXl8sWzELR34D9ZsqW1FYIfPuYKe/70GcwszCEiIgJdPV0cOnGY6/2De/YjLy8PU2dNhxqflfhI7UhLSXGtEMUwDJQVFfE0NBTmhmW9XouLi/HyXRimjhhZr/suKi5GzLevsLWs32GQ0hISXCtoMgwDZTk5PP0QDrMmZfVecUkJXn6KxNRe1Q+NrophGBSVFNdrfqsjKioKEzNTvAp5gfaOFfXnq5AXsO/Qju82Fk2t8PRxMFfYy2fPYWJuBpEfDSuFhQU8dYuQkBAYhuE0MDEMg11bdyDowSNs/HcbNLQarqFJVFQURmbGeP38Few7VhzX6+ev0Lo9//ltaqKt2wTbDu3iCjt58Ajy8/MxZspfUFZr2J4zwiIiUNfXRsy7SJi0asoJj3kXCWNb3lU/xSXFMXIN98OK0DvB+Br+Ce5ThkNetezHmraJPsKfhIJhs8H6cQ7TE1MgrSBbrw1nQMOVv3IP7gSiuLgInV34z+8GABKSkpCQlER2VjZePAuB16S/fuGIKvCv+5Tw9NVLmP+4bysuLsbLt28xdYxXveyzPvHNv5ISnj5/DnMTEwA/8v/6NaaOHy8wHRtLSzx98QJDK02H8fT5c9hY8ZbRS9evQ1FBAR0qjV5oKKKiojA1M8WLZ8/Rgete4jnaCbqXaGqF4EdBXGHPn4bA1IK37JVjGAZRkZ9gYGTI8971gGtQUFSAvYA5tuqq7P7IDCFPn3EtbBTyLAQdHPjfH1tZN8XjKvdHz54+g7mlucBjqs7Vy1egoKiItu35X78/g7ueqDg3L0NeoG2H9ny3MW9qKaCeMK30PVUIlhD3A18hIWGu76lyt65ch7yiAlq3rZ9zBVRzf/TqFcyNKt0fhYVh6qiaf3cIYtesGU79yz3Vzsrt3tDT0cHIfv3/JxrOaLXNPws1npFGsXTpUvTs2RNNmjTBgAEDICQkhDdv3uDt27dYvXo1HB0d4eDggH79+mHr1q0wNjbGhw8fwGKx4FrLuSTmzp2LZcuWwcjICM2bN4ePjw9CQ0Nx/PhxvvFlZGTg5eWFuXPnQllZGerq6vj777+5fkSZmppi6NChGDFiBLZs2QJbW1ukpKTg7t27sLa2hpsb7xwJvr6+KC0tRZs2bSAlJYWjR49CUlISenp6UFZWrnN6lQ0dOhQrVqzAmjVr0L9/f0hU+lFc1bJly9ClSxcYGRlh0KBBKCkpwbVr1zBv3jyeuF27doWNjQ2GDh0Kb29vlJSUYNKkSXB0dBS4yMDPMjIywuHDh9GyZUtkZmZizpw5XL3kSktLMXz4cHTr1g2jR49G9+7dYW1tjS1btmDu3Ln1lo/Zs2fDzs4Oq1atgqenJ4KDg/Hvv/9i165dNW9cBwMGe2Ld8tUwMzeHlbUVAi5cQmJiEtz79gYA7N+5B8nJKVi0fDEAwKNvL1w4cx47vf9Bz17uePf2Ha5euoLFq5YBKJskuuqNrYxsWc+NyuH5eXn4/u0753V8XDw+RURCVk6O77L2v4ukqDh05Ct67GjJqcBERQdZBblIzPn5+ZwaAovFwmB3D/icPQNdTS000dKCz1k/SIiJw9Wh4mZ/6batUFNWxpQfDWrFxcX4/PXrj/+XIDk1FR8/f4aUpASaaJbNdejtcxAd7VpDQ1UV6RmZOHjmNHLz8tCzcxfejNT3MXXqAp8b19FEVQ1N1NTge+M6JMTE4GJX8aBh2WEfqCooYMqPBjWfG9dhqasLbVVVlJSU4vG7MFx5+gQLBlXMC5ZXUICvlVbWjUtNwcevXyEvLQ0NAT0J6qqv5wBsWrUOJuZmsGhqiWsXA5CUmIgefdwBAId270dqSgrmLlkIAOjR2x2Xzl3A3h270N2jB8LD3uNGwDUs+HG9AUCb9m3hf+osjEyNYW5ZNmzzyH4f2Hdox7lR37llO+7duoNl61dDUkqK01tCWka6xrkPf4b7gD7YsXYLjM1MYGZljpuXryMlMRndPMq+I47t80FqSiqmL6qYGys6smzl04L8fGRlZiI6MgoioqJooq8LMXEx6Bnqc+1D+kdv56rhDaWVa0dc3XsaGgY60DLWxZt7T5GdmoFmnct+4D3wu4ac9Ey4/TUILCEhqOpw96qSkpOGsKgIV3izzm3x8vZj3D1+CbbO7ZGekIKnl++ihTP/H6m/qiHKX7kbAdfQrmMHvr2Cnj8NARgGOrpNEPftOw7s3Asd3Sbo1qNh5tpisVgY3Kc3fE6dgq6WNppoa8Pn1ElIiIvDtVMnTrylmzZCTVkFU8aMAfCj7vsxBUNxSTGSU1LxMSqqrO7TKusRnZefz9XD9ntCAj5GRUFeVhYaavXz8IfFYmFw//7wOX4cujo6aKKjA59jxyAhIQHXrhWNk0vXroWaqiqm/JjiYlC/fhg/fTp8T56EU/v2CHz8GE9fvMDBHdxDtNlsNi5fv46eLi4Q4fNjPjMrCwlJSUj+8WDwy4/PRPnHCp0/o9/ggdiwYg1MLcxg2dQKVy4GICkxCe59PAAAB3btQ0pyMhYs+xsA0LNvL1w864/d3v/CrVdPvA97h+uXr2LRyooFnI4c8IVFU0toN9FBXm4u/P3OISriE6bNmclzvDeuXIOzm2u9Nkp7DhmE1ctWwtzSAlbWTXHJ/yKSEhLRu19vAMCef3cjJTkZi1eU5blX3z4473cO/2zbDvfevfDubRiuXLyMZWsqev8VFxcj5nP0j/+XIDk5GZEfIyApJQWdJhXzuLHZbFy9fAXde3T/qYa36vTx7I/Nq9bDxNz0Rz1xBcmJSXD7UU/47D6A1JQUzFlSNoKgR293XD53Eft27ILrj3riZsA1zF/+NyfNNu3b4nwN31Plx3XrynV07d4NwiIN19DEYrEwuFcv+Jzxg67Wj/ujM35ldYRjpfujLVvK7o9+zCVcdn9UXkeU3x9FQUpCEk20tCAtJQVjfX2ufUmIS0BBVo4nnJDfgRrPSKNwcXFBQEAAVq5ciY0bN0JUVBTm5uYYO3YsJ865c+cwZ84cDB48GLm5uTA2Nsb69etrvY9p06YhKysLs2fPRlJSEiwtLXHp0iWY/HjqyM+mTZuQk5MDDw8PyMrKYvbs2cjM5B6G5+Pjg9WrV2P27Nn4/v07lJWV0bZtW4ENXQoKCli/fj1mzZqF0tJSWFtb4/Lly5w5zeqaXmUmJiaws7NDSEhIjfOAOTk54cyZM1i1ahXWr18POTk5rl55lbFYLFy4cAFTp06Fg4MDhISE4Orqin/++afGPNWVj48Pxo8fD1tbW+jq6mLt2rVcq6auWbMGMTExuHy5bElvDQ0NHDhwAAMHDoSzszOaN29eL/lo0aIF/Pz8sHTpUqxatQqamppYuXIl38UCfkVn5y7IyszCkUO+SEtJhb6hAdZv2wiNH8NtUlNTkVRpAmNNLS2s27YRu7z/wcWz/lBWUcHU2dPh2NmpTvv9GP4RMydN47ze5V02V4VLD1csWPq3oM0anLmqHv7tU9GjZFqHsif8V8ODsebuYUGbNZqRffuhsKgI6/fuRnZODpqamuLfFSu5nsAmpCRDqNIT4eS0NAydWTHk4OgFfxy94I8WTZti35qyuXgSU1Lx9+bNyMjOgqKcHJqamcFn42Zo1tOPx+qMcO6GwuIibDh9Etl5ebDSN8A/U6Zx9VBLSE/jevpZUFSIDadPIikjA+KiotBT18DKUWPQrdLcbeGxXzBh+zbO623nzgIAerSxx/IRo+ol745dOyErKwvHfY4gPTUNeob6WLV5HdQ1yq6ntNQ0JFWa0FxDSxOrNq/D3h07EXD+IpRUlDFxxhR06FRRFw4ZORwsFguH9x1CanIK5BUV0KZ9W4waX9HDJsD/EgBg3hTuH5WzFs1rkAaMDp0dkZ2VDb/DJ5CelgZdA338vWEF1H40fKenpiMlkXtS5tnjpnL+HxXxCQ9vB0JVXQ17T/vWe/5+hnmb5sjPyUPwxdvIzciCirYG+s4aA3mVsiE+uZlZyErLqFOacsoKGDB3HO6duIzDi7dBRkEOLbp1QOseTvV/AGiY8gcA32K/4t2bt1i7bSPf/ebl5MJnz36kJKdARk4WHRw7YtRfXvX+o7+ykQMGorCwCOv//RfZOdloam6Of9eu4677kpK5JrdOTk3F0MkV84AePXcWR8+dRQtrG+zbtAkA8D4iAhPmVzzE27ZvLwCgZ1dnLK90L/DL+R80CIWFhVjv7Y3s7Gw0tbDAv5s2Vcl/EtcD02ZNm2LN0qXYffAg9hw6BB0tLaxbupRrSCgAPHvxAgmJifDozn/i/AdBQVixYQPn9aJVZUMdx40cib9+8h6jk3NnZGVm4tjBI0hLLbuXWLt1A2foblpKKpISKsqeppYm1mzdgN3e/+LSuQtQVlHG5FnT4NC5omEjJycH29ZvRnpqGqRlpGFkaoJte3bA3Ip7xMHLkBdISkhEd/f6mVS/XJduXZGVmQnfA4eQmpIKAyNDbPTeDI0fK6unpqQiMaHi/khLWwsbvbfgn23b4X/mPFRUVTB9zkw4da5o0E1JTsGYYaM4r08dO4FTx06geQtb/LO3okfT82chSExIhFsDrFju2LUTsrOycMLnKNJS06BvqI+Vm9dxHlympaby1BMrN6/Fvh27cPn8JSirKGNClXpi8MhhYLFYOLLPp9L3lD1GjufuCfoq5CWSEpMarGG9spH9+pfVEbt3ld0fmZnh35WruK+xZD73R9Mq7k2Pnj+Po+fPo0VTa+yrw28+Qn4XFlObiZ4IIYTUSlzGn7/yWF1oKaih/c4JjZ2Nevd48h5kf4ho7GzUK1lzU2TdvtfY2ahXcl07ITrle80R/x8xUNHGu/ioxs5GvbLSNML+JxcbOxv1apx9r/9k2cuOjmnsbNQ7WQN9ZNfz/JCNTVZLC1/TExo7G/WqiaIGkrJSa474/4ianDI+p/DOCff/maGKDrIjPzV2NuqVrAn/aYL+Pxjrt66xs8DXgYELGzsLjYLWPiWEEEIIIYQQQgghRABqPCOEEEIIIYQQQgghRACa84wQQgghhBBCCCHkD0Krbf5ZqOcZIYQQQgghhJD/Y+++w5uq3gCOf5N07z0ohRYKbSl77703gvADWQoqojhwIE4UBVEZooKKbJG9lL33KqPMslpKGd17jyS/PwopaROGtrTq+3mePE9ze+7NOcnNSfLe95wjhBDCCAmeCSGEEEIIIYQQQohSMWfOHHx9fbGwsKBBgwYcPHjQaNmRI0eiUCiK3YKCgnRlFi1aZLBMdnZ2qbVBgmdCCCGEEEIIIYQQ5Yih4FB5uD2plStX8uabb/Lhhx9y5swZWrVqRbdu3YiMjDRY/rvvviMqKkp3u3XrFk5OTjz77LN65ezs7PTKRUVFYWFh8Zee68chwTMhhBBCCCGEEEIIUeJmzJjBqFGjGD16NIGBgcyaNQtvb2/mzp1rsLy9vT0eHh6628mTJ0lKSuL555/XK6dQKPTKeXh4lGo7JHgmhBBCCCGEEEIIIR4pJyeH1NRUvVtOTo7Bsrm5uZw6dYrOnTvrbe/cuTNHjhx5rMebP38+HTt2pHLlynrb09PTqVy5MhUrVqRnz56cOXPmrzXoMUnwTAghhBBCCCGEEKIcUaIol7epU6dib2+vd5s6darBNsTHx6NWq3F3d9fb7u7uTnR09COfg6ioKLZu3cro0aP1tgcEBLBo0SL++OMPli9fjoWFBS1atODatWt//Ql/BJNSO7IQQgghhBBCCCGE+NeYOHEi48eP19tmbm7+0H2KzpWm1Wofa/60RYsW4eDgQN++ffW2N23alKZNm+rut2jRgvr16/P9998ze/bsRx73r5DgmRBCCCGEEEIIIYR4JHNz80cGy+5zcXFBpVIVyzKLjY0tlo1WlFarZcGCBQwbNgwzM7OHllUqlTRq1KhUM89k2KYQQgghhBBCCCFEOVLWq2qWxGqbZmZmNGjQgJ07d+pt37lzJ82bN3/ovvv37+f69euMGjXqkY+j1WoJCQnB09Pzier3JCTzTAghhBBCCCGEEEKUuPHjxzNs2DAaNmxIs2bN+OWXX4iMjGTMmDFAwTDQO3fusGTJEr395s+fT5MmTahZs2axY3722Wc0bdqUatWqkZqayuzZswkJCeHHH38stXZI8EwIIYQQQgghhBBClLhBgwaRkJDA559/TlRUFDVr1mTLli261TOjoqKIjIzU2yclJYW1a9fy3XffGTxmcnIyL730EtHR0djb21OvXj0OHDhA48aNS60dEjwTQgghhBBCCCGEKEeUTzhEsjwbO3YsY8eONfi/RYsWFdtmb29PZmam0ePNnDmTmTNnllT1HovMeSaEEEIIIYQQQgghhBESPBNCCCGEEEIIIYQQwggZtimEEEIIIYQQQghRjjzpypaidEnmmRBCCCGEEEIIIYQQRkjwTAghhBBCCCGEEEIII2TYphBCCCGEEEIIIUQ5okCGbZYnknkmhBBCCCGEEEIIIYQRCq1Wqy3rSgghhBBCCCGEEEKIAuPWzyjrKhj0fb/xZV2FMiHDNoUQogR9sXNhWVehRH3U6XnSLl8t62qUONuA6rT4cUxZV6NEHX71J5JfeK2sq1GiHBb8QP/FH5R1NUrU2hFTaD1nbFlXo0QdGDuH1D0HyroaJcqufWtuxN8p62qUKF8XL15Z+01ZV6PEze3/LkuCt5R1NUrU8EbdGfTbx2VdjRK1cujkf2Xf13PBu2VdjRK16YVv6PTLG2VdjRK186XvyroKf5lSVtssV2TYphBCCCGEEEIIIYQQRkjwTAghhBBCCCGEEEIII2TYphBCCCGEEEIIIUQ5opBhm+WKZJ4JIYQQQgghhBBCCGGEBM+EEEIIIYQQQgghhDBChm0KIYQQQgghhBBClCMybLN8kcwzIYQQQgghhBBCCCGMkOCZEEIIIYQQQgghhBBGyLBNIYQQQgghhBBCiHJEiQzbLE8k80wIIYQQQgghhBBCCCMkeCaEEEIIIYQQQgghhBEybFMIIYQQQgghhBCiHJHVNssXyTwTQgghhBBCCCGEEMIICZ4JIYQQQgghhBBCCGGEDNsUQgghhBBCCCGEKEeUMmyzXJHMMyGEEEIIIYQQQgghjJDgmRD/EdnZ2XzxxRfExMSUdVWEEEIIIYQQQoh/DBm2KcR/xMcff0xqairu7u5lXRUhhBBCCCGEEA+hUEiuU3kir4YQ/wE5OTk4Ozsze/bssq6KEEIIIYQQQgjxjyLBMyH+A8zNzXn//fcxNzcv66oUs2/fPhQKBcnJyY+9j4+PD7Nmzfpbj6tQKNiwYcPfOoYQQgghhBBCiH8/GbYpxH/AkSNHaNWqFZ06dWLbtm1lXR1RxJUDp7m4+zhZKek4eLrQsH9H3P28DZaNvnqTnbOXF9ve+6MXsfdwBkCjVnNhx1HCjl8gMzkNe3cn6vVph1eNKqXajqK0Wi2/rFjO+u3bSctIJ6h6dSa8PIaqlSob3Scs8iY//b6My2FhRMXGMn7UaIb07qNX5uflvzNvhf5z4OzgwPbFS0ulHU+qjqcfQ+p1JsCtEi7WDry/ZS4Hb5wt62o9lEWf7pi1aYHCyhJ1+E0yf1uJ5m70Q/cx79QWs3atUDo5ok3PIPfkGbLX/AH5+QCYtW2JebtWKF2cAFDfiSb7z63kn79U6u3p4t+EPkGtcLSy5VZyLAtPbCY0NsJo+Va+dehbszWeds5k5mZz5u41Fp/cQnpOFgCfdRlNTY/i759Tty8zZfeS0mqGnr5BrRlcryNOVvZEJEbx/eHVnIsKM1q+X83WPFOrLR62TsSkJbH09Da2XzmuV+bZ2u3oE9Qad1tHUrIz2Bd2ml+ObSRXnV/azTFKq9Uyb/OfrD90gLTMTIJ8fHnvf0OoWsHL6D57zpxm0bYt3IqLJV+txtvNjaEdO9O9SbNSr++f6zay5veVJCYkUNnXhzGvv0rNurWNlj935iy/fD+HmzcicHZx4dkhg+jRr7demfUr17Bp/R/ExcRi52BPq7ateX7Mi5iZmwFwPuQsa35fybXL10hMSOCTqZ/TvHXLUmtj6yp16VS9EfYWNkSlxrP67B6uJ9wxWHZ4g24086lZbPvd1Hgm71xYbHvDigGMatKLkLvX+PnohpKu+kOd3HmIY1v2kp6ciquXB52G9qVSQNVH7nfrajhLv/gR14oevDjlXd32M3uPcv5gMHG3C/pOD9+KtB3YA6+qxj/zSlrn6o3pVaMlDpY23E6OZfHJrVyOu2m0fEuf2vQOaoWHrROZeTmcvXuNpae2kZ6bVaxs88q1eKPVQIJvhfLt/t9Lsxl6/gt9X/eAZjxTqy1OlrZEJscw7/gfXIy5YbR8j8Dm9AxsjpuNE3EZSaw6u4c91089xRoX16tGS56t3R5nKzsikqKZe3QdF6LDjZbvXaMlfYJa4W7rRGx6Er+f2cmua8EGy7atWo8PO4zkcMQ5Ju2YX1pNKJcUyGqb5YkEz4T4D1iwYAHjxo3j119/JTIykkqVKhktq9VqUavVmJhI9/A0RJwK5eTaXTQe1AW3Kl5cPRTCnjmr6P3RaKyd7I3u1+fjlzC1NNPdN7ex0v0d8ucBwoMv0mxIN+zcnbkbGs7+eevoOn4oTt4epdqeBy1et5bfN27g0zfepFIFL+avWsmrn3zC2jlzsbayMrhPdk4OFd096Ni8JTMW/Gr02FUqVWLO51/o7quU5SeR2tLUnOsJt9ly+QhTuo0p6+o8knm3jph3bkfm/N9Qx8Ri0bMrNu+MI/WDzyE7x+A+pk0bYjGgD5kLlqG+Ho7Sww2rUcMAyF6xDgBNUjJZazaiiY0HwKxFE6zHvUTapK8eGZj7O5r71OL5Rj2Yd/wPLsfepHP1xnzYcQRvbpxFfEZKsfIBbpUZ1/JZFgVv5uTtyzhZ2fFy076Mbf4MX+9dBsA3e5dholTp9rG1sGJ6r3EcjbhQau14UHu/BoxrOYAZB1ZwITqc3jVa8nXPVxm+fDKx6UnFyvcJasVLTfvwzb7fCY2NINDNh/faPkdadiZHbp4HoFO1RrzUtC/T9i7lQnQ43g7uTGxf8Br+cHjtU2mXIUt2bOP33Tv5ZPjzVHJzZ8HWzbw2eyZrJn2BtYWFwX3sra15vlt3fNw9MTVRcfD8OT5fsghHW1ua1SgeyCkp+3ft5efvfuTVt98gqHZNtmz4k4/eeZ9ffluIm0fx+UWj70bx8TsT6darO+998gEXz13gx+nfYe/gQMt2rQHYs30XC36ax/iJ7xFYK4g7kbeY/uXXALz8xqsAZGdl4+tXlU7du/LFh5NKrX0ADSr682yd9qw4s5OwhDu08q3Dqy0H8PmOBSRlpRUrv+rsbjZcOKC7r1Qq+LDDSE7fvlKsrJOVHc/Uasu1uFul2gZDLh07w87fNtB15AC8q/tyes8RVnzzCy9Pex97F0ej+2VnZvHHT7/jG1SN9BT99t8MvU6NZvWpWN0XE1MTjm7aw/JpP/HSVxOwc3Io5RZBs8o1GdGgG/ODN3ElNpKO1Roysf0wxv/5PQmZxfs+f9dKvNq8P4tPbeXUvb7vxSa9eblpX6Yf0L9A5WJtz9D6XQiNiSj1djzov9D3tfKtw4tNejP36HouxUTQLaApkzqPYuy6b4nLSC5WvltAM0Y06Mb3h9dwNf4W/i7evNZyAOk5mZy4FfrU6w/Qpko9XmnWj+8PreZizA16BDZnSrcxjFo1lbiM4q9Tz8AWvNC4FzMPrOBKXCQBbpV4q9X/SM/J5FjkRb2ybjaOvNSkL+eirj+t5ghhVPn5tSGEKBUZGRmsWrWKV155hZ49e7Jo0SK9/98fNrl9+3YaNmyIubk5Bw8eRKvV8vXXX1OlShUsLS2pU6cOa9as0e2nVqsZNWoUvr6+WFpa4u/vz3fffffI+mzZsoXq1atjaWlJu3btiIiIKFbmyJEjtG7dGktLS7y9vXn99dfJyMh47DYHBwfTqVMnXFxcsLe3p02bNpw+ffqh+0yYMIHq1atjZWVFlSpV+Pjjj8nLy3vsx/yrLu05gV+zOlRrXgd7DxcaDeiIlaMdVw6eeeh+FrZWWNrZ6G7KB4JH4ScuUqtzM7yCqmLr4oB/q/p4BvpyaY/hK3qlQavVsvzPP3j+2YG0b9Ycv8qV+ezNt8jOzWHbgf1G9wuqVp03nn+BLq1bY2ZqarSciUqFi6Oj7uZobzzQ+LQdi7zIvON/sD88pKyr8ljMO7Uje9N28k6fRXMnisz5S1GYmWLWpKHRfUyq+pJ/LZy84yfRJCSSf/EyucdPYuJTGJjPP3uB/POX0MTEoomJJXvdn2izczCp6luq7elVoyV7rp9i97WT3EmJY2HwZhIyUuji38Rg+equ3sRlJLHl8lFi05O4HHuTHVdPUNW5MNMpPTeL5Ox03a22px85+Xm6H2OlbWCd9mwOPcLm0CPcTIrm+8NriEtPpm/N1gbLd/Fvwh8XD7Hn+imiUhPYc/0Um0OPMKR+J12ZIA9fLkSHsevaSaLTEgm+Fcruayfxd316WTJFabValu/ZzfNdu9O+Xn38vLyYNOJ5snNz2R583Oh+Dar7065ufXw9Pano6sbg9h3x86pIyPXS/bG1buVquvTsRrfePajkU5kxb76Gq5sbm9b/YbD85g1/4ubuxpg3X6OST2W69e5B5x7dWLN8la5M6IWLBNWqSbvOHfDw9KBBk0a07dSeq5ev6so0ataEkS+NomVbw69/SepQrSFHIs5zOOI80WmJrD63l6TMNFpXqWuwfHZ+Lqk5GbpbZUcPrMwsOHpTP9CsQMHzjXqwKfSwwaB2aTu+dR912zahXrumuHi503lYP+ycHTi9+/BD99u6YDVBzerj5edT7H99xw6jYaeWeFT2wqWCOz1GD0Kr0RJx8VoptUJfj8Dm7Ak7zZ7rp7iTGsfiU1tJyEylc/XGBstXc/EmNiOZbVeOEZeRzJW4SHZdC6aKs36Wp0KhYFyLZ1l9bg8x6YlPoyk6/4W+r2/N1uy8GsyOqye4nRLLvON/EJ+RTPcAw5mz7avWZ+uVYxy8cZaYtEQO3DjLzqvB9K/d7inXvFD/2m3ZduUYW68cIzI5hrlH1xOXnkSvGi0Mlu9YrRGbQw+zP/wM0WkJ7As7w7YrxxhUt6NeOaVCwcT2w1lyaivRqQlPoylCPJQEz4T4l1u5ciX+/v74+/szdOhQFi5ciFarLVbuvffeY+rUqYSGhlK7dm0++ugjFi5cyNy5c7l48SJvvfUWQ4cOZf/+gsCHRqOhYsWKrFq1ikuXLvHJJ5/wwQcfsGrVqmLHvu/WrVs888wzdO/enZCQEEaPHs3777+vV+b8+fN06dKFZ555hnPnzrFy5UoOHTrEa6+99thtTktLY8SIERw8eJBjx45RrVo1unfvTlpa8avk99na2rJo0SIuXbrEd999x7x585g5c+ZjP+Zfoc5Xk3grGs9AH73tFQJ9iLtheEjMfZumLWTNB9+zc/Zyoq/qD8lQ5+ejNNXPHDQxNSE27Old3b8TE0NCUhJN69XTbTMzNaV+UE3OXb78t48fefcuXUeOoPeLo5j4zdfcji69TKZ/M6WrM0oHe/IvPvCa5OeTf+U6Jn7Gh/nmXwvHxMcblW9l3XFMawWRd+6i4R0UCkwbN0BhbkZ+mPGhKH+XiVJFVecKhNzV/7F69u51oz+MrsRG4mxlT32v6gDYW9jQrHJNThnIlLmvQ7WGHI44R05+6QfYTZQqqrtWIrhIRkHwrVBquht+jUyVJuSq9euWo84j0M1Hl6V5LiqM6q6VCHQreF487ZxpWrkmx24+nWw6Q+7Ex5OQmkLTGkG6bWamptSvVp1zYcaHaT1Iq9Vy4nIoN2OiqV+temlVlby8PK5duUr9xvpB5vqNGxJ6wfD7IPTCxWLlGzRpyLXLV8i/N9w5qE4trl25ypVLBa931J27BB89TuPmhoO/pUmlUFLJwYNLRbKNQmMjigVYjGnuU4vLsTdJzEzV294jsDnpOVkciXg6AegHqfPzibpxG9+a/nrbq9T05/a1CKP7nd1/nKSYeFo/0+WxHicvJxeNWoOljeFM65KkUqqo4lShWHbO2ajrVHc1PA3E1bhInK3sqFuhGgD2FtY0qRTEmTv6fd+AWu1Izc5gb9jDL0KWtP9C32eiVOHn7MWZu1f1tp+5c5UAN8OfWaYqE/KKDC/Nzc+juos3qjJYmdFEqaK6i3exz8xTt68Q5G74YpmpyqTYENmc/Dz8XSvptWFo/a4kZ6Wz7cqxkq/4P4RSoSiXt/8qGZclxL/c/PnzGTp0KABdu3YlPT2d3bt307Gj/tWdzz//nE6dCq7MZWRkMGPGDPbs2UOzZgVXvqpUqcKhQ4f4+eefadOmDaampnz22We6/X19fTly5AirVq1i4MCBBusyd+5cqlSpwsyZM1EoFPj7+3P+/HmmTZumK/PNN98wZMgQ3nzzTQCqVavG7NmzadOmDXPnzsXCyLCdB7Vv317v/s8//4yjoyP79++nZ8+eBvf56KOPdH/7+Pjw9ttvs3LlSt577z2D5XNycsjJ0R/S9qQLMuSkZ6LVaLGwtdbbbmFrTXaq4Uw7S3sbmg7uilMlDzR5asKDL7Dz++V0fmMI7n4FWT8VAqsQuicYdz9vbF0ciboSwa1z1wwGTUtLQlJBmr6zvYPedmcHB6JiY//WsWtWr85nb75F5QpeJCQnM3/1SkZNeJeV3/+Ig53d3zr2f43i3vOlSdUPLGtS01A6OxndL+/EKbJsbbCZ+BagQGGiImfPAXK27NQrp/SqgO2Hb4OpCeTkkPHDvFIdsmlrboVKqSIlK11ve3J2Gg6W1QzucyUuklkHVzG+zWBMVSaYKFWciLzE/ON/Gizv51KRyo4ezDmyrsTrb4i9hQ0mSlWxIXKJmak4eRs+30/cukTPwBYcvHGWq3G38HetRPeAZpiqTHCwsCEhM5U910/hYGnLD/3eRoECE5WK9RcOsOzMjqfRLIMSUgsykJxs9dvlZGdHdMLDsw7SszLpPvE9cvPyUSkVTBj8HE0Ca5RaXVOTU9CoNTg66Q/xc3R0JDHBcHZOUmISjo5Fyjs5olarSUlOwdnFmbYd25OSlMzbr7yhm0ahZ7/eDBo2pNTaYoyNuSUqpZK0bP3Po7TsDOzdrY3sVcjOwpog9yosOLFJb3sVZy+a+9Tiy92LS7S+jyszLQOtRoONva3edmt7W9KTUw3ukxgdx96Vmxj28TiUKpXBMkXtXbkJW0d7fINKL4h7n52Rvi8lKx2HCrYG97kaf4vvD6/hzVaDdH1f8K1QFgZv1pXxd61Eu6r1mbBlTqnW35D/Qt9nZ26NykAbk7LSqW9l+HU7fecKnas35ujNC4Ql3MHPuSIdqzfCVGWCnYW1weHUpcne4n4b9N87SVlpOBppw6nbl+kW0JQjEee4Fn+b6i7edPVviqnKBHsLGxKzUgly96Wrf1PGrP36aTRDiMciwTMh/sWuXLnCiRMnWLeu4EeeiYkJgwYNYsGCBcWCZw0bFl4Nv3TpEtnZ2bpg2n25ubnUeyCT6KeffuLXX3/l5s2bZGVlkZubS926dY3WJzQ0lKZNm6J44IrF/eDcfadOneL69essW7ZMt02r1aLRaLhx4waBgYGPbHdsbCyffPIJe/bsISYmBrVaTWZmJpGRkUb3WbNmDbNmzeL69eukp6eTn5+P3UMCMVOnTtULHgJ8+umnmLR48rT/YpOBasHY/KD27s7Yuzvr7rtW8SIjKZVLu07ogmeNBnTk6PKt/DF5HijA1sWRqk1rE3bs3BPX7XFt3bePKXN/1N2f9fEnAHqvNRS8lkW3PakWDQrPVT+gdkAAfV9+kU179zC0T9+/dex/O9OmDbEaPlh3P33W3II/DAVWHxJsNfGvhkXPLmQtXUl++E1U7i5YDh6AJiWVnD8LFyXRRMeQNmkqCisrTBvUxWr0MNKnfVeqATQALfp1f9iEuxXt3RjVuCerz+4h5O41HC1tGd6gKy8362swQNbBryE3k6K5Hn+7xOv9MEWD3wqFolg771t8citOVnb89Mx7oICkzDS2XT7GkPqdUWs1ANStUI1hDbow48AKQmMi8LJ35fWWz5LQoBtLTm0t9fYAbD1xjKm//6a7P3PsOACKdhFarYGNRViZW7Dsg0/IzMkm+MplZq5ZhZeLKw2q+z90v7+taB/HI/q4Yn3i/c0F28+eDmHFkmW8+vYbBAQFcvf2HX767kccFy7lueeHlWjVH1fxs8z4ufegZpVrkpWXzdkHMkHNTUx5vlF3lp3eToaBSemfqmKvXfHPLCjItN/w41Ja9e+Ks6fbYx366KbdXDx6hqEfvoqJmfEpCEpa0VdFoVAYvXDmZe/KyIbdWXt+L2fvXsfR0pbn6ndhdJPe/HxsAxYmZrzWYgC/HN9IWk5m6VfeiH9j31dMkeYoFMY/gleE7MLR0pbpvcahAJKz0tl97SQDardDc6+NZaFofRUKQ31Hgd9Ob8fRypbZfcejoCDQtuPqcQbV7YhGq8HS1JwJ7YYx8+AKUnMef9oWIUqbBM+E+BebP38++fn5eHkVDq/QarWYmpqSlKR/BdzauvAqskZT8OG7efNmvX2hMLtq1apVvPXWW0yfPp1mzZpha2vLN998w/HjxueleZzMJ41Gw8svv8zrr79e7H8PW+jgQSNHjiQuLo5Zs2ZRuXJlzM3NadasGbm5uQbLHzt2jP/973989tlndOnSBXt7e1asWMH06dONPsbEiRMZP3683jZzc3O+OfD4K1CZ21ihUCrIStO/UpydnlEsG+1hXH28CA8uHCZkYWtFu5f6o87LJycjC0t7G85s3IeNs8NjH/NJtW7cmJr+hVfXc+/NFxefnISLU2EGU2JKCk4OJVsPSwsLqlb24dbduyV63H+jvJDzpIVHFG64tzCI0t4OdUrhVWOlnS3aVONXry369SD3yAlyDx4FQHPnLpiZYzViMDmbthd+i1ardQsGqCMiUflWwrxjW7KWrCjZht2TlpOJWqPGwVL/are9hQ3JRTIy7numVhsux95k48WDANxMiiY7P5cvu73M72d2kvzAVXwzlSktfGuzMmRXqdTfkJTsdPI1apys9IP5jpa2JGUafo1y1XlM2/sb3+7/HSdLOxIyU+hVoyUZuVmkZBX8EBnVuBc7rpxgc+gRAMIT72Jhas67bYaw9NS2xwqO/F2ta9elpk/h8Kvce8NgE1JTcXkgazUpLRVn24dnlSqVSrzdCgIb/t6ViIiKYtG2LaUWPLNzsEepUpJUJMssOSm5WDbafY5OjiQlFi2fhEqlws6+oH1L5i2kfZdOdOvdAwDfqlXIzs5m9rQZDB7xnN78lqUtPScLtUaDnYX+55GthRWp2Y8OpjT3qcXxyEu6oAWAq7UjLtYOvNL8Gd22+wGrH/q9zaQd84k3MEl6SbKytUahVBbLMstMScPavnimTG5WDlE3bhF98w7bFxcE1LVaLWi1TBn+NkMmjMEnqDCz9djmvRz+YxdD3n8F90oVSrUt96Xq+j4bve12FtakZBvu+/oGteZqXCR/XiqY5y0yOYbsE7l83uVFVp7dhb2FDW42jrzX9jndPvdfq9+HTOKtP74jxsCk/SXl39z33Zeak4Faoy6WoeVgYaP32fOgXHU+3x1azQ+H1+JgaUtSVipd/JuSmZv9WO/LkpaSXdCGoq+Tg4UtyQ95nabvX86sAytxtLIlMTOV7gHNycjNJiU7gyrOFfC0c2Zylxd1+9w/97aNnsHzK78kKu2/MQfa373gLEqWBM+E+JfKz89nyZIlTJ8+nc6dO+v9r3///ixbtszoPGI1atTA3NycyMhI2rRpY7DMwYMHad68OWPHjtVtC3vEnDQ1atRgw4YNetuOHdOfx6B+/fpcvHgRPz+/hx7rYQ4ePMicOXPo3r07UDDXWnx8vNHyhw8fpnLlynz44Ye6bTdvGl/aHQoCZU86TLMolYkKJ28Poi5HUKlO4Q+8qMsRVKxleIiZIYm3Y7C0Lx5sU5maYOVgi0atJjLkCpXrPzpr76+ytrLSW0FTq9Xi7OjI8ZAQAqpUBQrmCDp98QLjho8o0cfOzcsj4vYt6tUovSFa/xrZOWiKrKCpSU7BpEYA6sh7mVQqFSb+fmSt3mj8OGZmxS8zazVGMyYLKVCU4kq++Ro1YQl3qePpx4nIS7rttSv4EXzrksF9zE1MUWv0r9bfv3pftDktfGphqlKxP/zhC3qUpHyNmqtxkTT0DuTgjbO67Q0rBnAo4uHZpGqNRrdaWwe/hhyJuKD7YWhhYlbsgoZGo0GheHjWQ0mytrDQW0FTq9XibGfP8dBL+HsXXCzJy8/n9LWrjOvX/4mOrUVLbn7+owv+RaamplTzr86Z4FO0aNNKt/1M8CmatmxucJ/AmkEcP3xUb9vpEyepFuCvW+E6Jye7WIBMqVSi1Wqf6tB7ALVWQ2RyNIFulfWyxwruP3wxhmou3rjZOBab0yw6LYHJOxfqbesV1BILEzNWn91DUqbhYZMlSWVigqdvRW5cuEpAo9q67TcuXKV6g+Krs5pbmvPiVP0pHE7tOszNS9d45vWROLgWXiA6umkPhzfuZPCEl6lQ5fEu+JUEtUZNeOJdantU1ZsjrLZHVU7eNjzPqOG+r+AcU6Dgbko87/z5vd7/B9XtiIWJGYtPbiG+lF+rf3Pfd1++Rs31hDvUrVBNb1GNuhWqczzSyByi96i1Gt0qqq1963DiVuhTDfzdl69RczX+FvW9/Dn8wOtSv6L/I+c0VGs1ugVD2lWtz/HIi2jREpkcw4urv9IrO7JRd6xMLZhzZJ3BVUiFeBokeCbEv9SmTZtISkpi1KhR2BdZiXDAgAHMnz/faPDM1taWd955h7feeguNRkPLli1JTU3lyJEj2NjYMGLECPz8/FiyZAnbt2/H19eXpUuXEhwcjK+v8ZX0xowZw/Tp0xk/fjwvv/wyp06dKrb654QJE2jatCmvvvoqL774ItbW1oSGhrJz506+//57wwcuws/Pj6VLl9KwYUNSU1N59913sbS0fGj5yMhIVqxYQaNGjdi8eTPr169/rMf6u2q0b8zhJX/iXMkDV18vrh4OISMxleqtCobHnt64j6yUNFoM7wVA6N5grJ3scfB0QaNWE37iIpEhV2gzup/umHERd8lKTsOxojuZyWmc23IIrVZLUMenN+G0QqFgcK/eLFyzmkqeFfCuUIGFa1ZhYWZO19aFAdlPZs7AzdmZ1+4F1PLy8gi/deve3/nEJSRwJTwcK0sLvD0LruDPWjifVo0a4+HqSlJyCvNXryQjM5Oe7Ts8tfY9jKWpORXtXXX3K9i5UM2lIqnZGaV6lf6vytm5F4uendHExqKOicOiRxe0uXnkHj+pK2M1ehiapBSy1xasIph/9gLmnduhjryNOjwCpZsrFn17khdyXvfLw+KZXuSdv4Q2MQksLDBr0gCTgGpkzCjduXP+vHSI11s+S1jCHa7ERdKpeiNcrO3ZceUEAM/V74yTlR3fHypYPfjkrcuMad6PLv5NCLlzFQdLW15o3JOrcbeKzR3TvlpDTkSGkp7zdIebrTq7hw87jOBK7E0uxtygV40WuNk6svFCQbbcS0374GLtwJR7c0hVtHcj0N2H0Jgb2JpbMbBOB3ydPZmyp3COqSM3zzOwTnuuxt/SDV0a1aQnhyPO635AP20KhYLB7TuwcNsWvN3c8HZ1Z9G2LViYmdGlUWH/9emi+bg6OPJa34LspYXbtlCjsg9eLq7kq/M5fOE8m48d4/3Bzxl7qBLxzKBn+WbyVKoF+BNYswZbN24iNiaGHv0K+usFc+eREB/Pux9PBKBH3178sXYDP8+eQ7fePQi9cIntm7by/qTCOTebtGjG+hVrqFrdj4AaBcM2l8xbSNOWzVHdm2srKzOLu7cLF5WJvhtF2NXr2NrZ4ubhXqJt3H3tJCMb9eBmUjQ3Eu/S0rcOjlZ2umBGn6BWOFjasvjkFr39WvjU4kbCXe6m6l+4yteoi23Lyi0I6BfdXpqadGvLxrnL8KziTUU/H87sPUJKQhL1OxQEPveu3ERaUgq9xzyHQqnEzdtTb39rOxtUpiZ6249u2s3+NVvpO3YY9i5Ousw2MwtzzCz+3sW2x7E59AivNe9PWOJdrsXdokO1hrhY27PzWkHfN7huJ5ys7PjxyFqgYEL3l5r2oVO1RpyNKhi2OaJhN67FF/Z9t1L05yi9P9S26PbS8l/o+zZcOMD41v/jevxtQmNv0tW/Ca42Dmy5XBBoH9GgG87W9sw4UJCxXcHOhequlbgaF4mNmSV9a7amsqMHMw+ufOp1v2/tuX1MaDeUq/GRhMZE0D2wOW42jmwKLchqfKFRT1ys7fl6X8GULF72rgS4VuZy7E1szC3pX7sdPk6euv/nqfOJSIrSe4yMe5+7RbcL8TRJ8EyIf6n58+fTsWPHYoEzKMg8mzJlCqdPG185afLkybi5uTF16lTCw8NxcHCgfv36fPDBB0BBICwkJIRBgwYV/OAZPJixY8eydavx+SIqVarE2rVreeutt5gzZw6NGzdmypQpvPDCC7oytWvXZv/+/Xz44Ye0atUKrVZL1apVGTRo0GO3fcGCBbz00kvUq1ePSpUqMWXKFN555x2j5fv06cNbb73Fa6+9Rk5ODj169ODjjz9m0qRJj/2Yf5VPg0ByMrI4t/UwWakZOHi60H7ss9g4FbxuWanpZCQWXt3V5Ks5vX4PmSnpqExNCsq/8ixeQVULy+TlE7LpAGnxyZiam+EVVIUWw3tiZvXoxRZK0ohn+pOTm8tXP88lLT2dmtWr88Nnn+tlqEXHx6FUFub3xCUm8txbb+juL92wnqUb1lO/Zk1++XIqADHxCXz47bckp6XiaGdHTX9/Fn79LZ5ujzcXTWkLcK3MD/0Kh/S+3vJZALaEHuXLB77Alxc5W3ehMDPDcuggFNZWqMMjSJ/+AzyQoaZ0cgJN4Y+K7D+3odVqsejXE6WjPdq0dPLOXiB7beEk+wp7W6xfHI7C3g5tVjbq23fImDGH/Et/f7XVhzkScR5bcyuerdMeR0tbIpNjmLJ7se5KtaOlLS7WDrrye8NOY2FqTreApoxo2I2M3GzOR4Xx2+ntesf1tHOmhrsPn+1YUKr1N2TP9VPYmVszomF3nK3tuJEQxYRNc4hJLxgC6Gxlh7tN4VBBlVLJoDodqOTgTr5GzZk7Vxm77lui0wqHDC45uRWtVsvoJr1wtXYgOSudIxHnmXf8j6fevgcN79yVnLw8pi3/nbTMDIJ8q/D9uLf0MtSiExP1hrJk5+QwbfkyYpOTMDc1pbKHJ58/P4rODRuVal3bdGxHamoqyxYuISkhkcpVfJj87VTcPTwASExIJDamMMjgUcGTyd9O5efZP7Jp3UacXJx55c3XaNmuta7MkBHDUCgULP5lAQlx8dg7OtCkRTNGvjRKV+bq5StMGFfYx/zyfcHchR27deGdjyaUaBtP3b6CtZklPQKbY2dhTVRqPD8eXqtbPdPewganIkPOLEzMqOdVnVVn95RoXUpSjab1yEzL4ND67aQnp+Ja0ZP/vfsS9i4FWWTpyamkxD/ZxY5Tuw6jzlezdvYive2t+nWhdf+uJVV1o47evICtuRX9a7XF0dKWW8kxfLV3qS6zx8HSBmfrwu+E+8PPYGlqRhf/pgxr0JWM3GwuxtxgWZG+ryz9F/q+gzfOYmtuxf/qdsTJyo6bSdFM2jG/8DPLyg7XBz6zlAol/Wq2xsveFbVGzbmoMN7d9COxZXhxbn/4GewsrBlavwtOVvZEJEbx4dafdXVytrLD7cHXSaFkQO12VHRwQ61RE3L3Gm9snKV7XUWh//LKluWRQvu0c8CFEOJf7Isiw1H+6T7q9Dxpl68+uuA/jG1AdVr8OKasq1GiDr/6E8kvGM4m/adyWPAD/Rd/UNbVKFFrR0yh9Zyxjy74D3Jg7BxS9xwo62qUKLv2rbkRf+fRBf9BfF28eGXtN2VdjRI3t/+7LAne8uiC/yDDG3Vn0G8fl3U1StTKoZP/lX1fzwXvlnU1StSmF76h0y9vPLrgP8jOl74r6yr8ZR9v+6Wsq2DQ5K4vlXUVysTTm3VUCCGEEEIIIYQQQoh/GBm2KYQQQgghhBBCCFGOKB69EpN4iiTzTAghhBBCCCGEEEIIIyR4JoQQQgghhBBCCCGEETJsUwghhBBCCCGEEKIcUchqm+WKZJ4JIYQQQgghhBBCCGGEBM+EEEIIIYQQQgghhDBChm0KIYQQQgghhBBClCNKGbZZrkjmmRBCCCGEEEIIIYQQRkjwTAghhBBCCCGEEEIII2TYphBCCCGEEEIIIUQ5Iqttli+SeSaEEEIIIYQQQgghhBESPBNCCCGEEEIIIYQQwggZtimEEEIIIYQQQghRjiiRYZvliWSeCSGEEEIIIYQQQghhhATPhBBCCCGEEEIIIYQwQoZtCiGEEEIIIYQQQpQjstpm+SKZZ0IIIYQQQgghhBBCGCHBMyGEEEIIIYQQQgghjJBhm0IIIYQQQgghhBDliAzbLF8k80wIIYQQQgghhBBCCCMkeCaEEEIIIYQQQgghhBEKrVarLetKCCGEEEIIIYQQQogCX+1ZUtZVMOj99sPLugplQuY8E0KIEpTUqV9ZV6FEOe5cT+quvWVdjRJn17EdyS+8VtbVKFEOC36gxY9jyroaJerwqz+RPGZ8WVejRDn8NIPkUePKuholymH+9//Ovm/3/rKuRomy69CG5OH/rj4CwGHJT2VdhVKRumVHWVehRNl170zyWx+UdTVKlMPMKST/b1RZV6NEOayYzyfb5pV1NUrU511fLOsqiH8JGbYphBBCCCGEEEIIIYQRknkmhBBCCCGEEEIIUY4okNU2yxPJPBNCCCGEEEIIIYQQwggJngkhhBBCCCGEEEIIYYQM2xRCCCGEEEIIIYQoR5QKGbZZnkjmmRBCCCGEEEIIIYQQRkjwTAghhBBCCCGEEEIII2TYphBCCCGEEEIIIUQ5opBhm+WKZJ4JIYQQQgghhBBCCGGEBM+EEEIIIYQQQgghhDBChm0KIYQQQgghhBBClCMKheQ6lSfyagghhBBCCCGEEEIIYYQEz4QQQgghhBBCCCGEMEKGbQohhBBCCCGEEEKUI0pktc3yRDLPhBBCCCGEEEIIIYQwQoJnQgghhBBCCCGEEEIYIcM2hRBCCCGEEEIIIcoRhUKGbZYnknkmhBBCCCGEEEIIIYQREjwT/wknT55k5syZaDSasq6KEEIIIYQQQggh/kEkeCbKREREBAqFgpCQkBI7pkKhYMOGDcW2x8fHM3DgQGrWrIlSWfKn/KRJk6hbt26JH/e/ZOTIkfTt2/exy+/btw+FQkFycvJffsxFixbh4ODwl/cXQgghhBBCiNKiVCjK5e2/SuY8E09s5MiRLF68GACVSkWFChXo0aMHU6ZMwdHRsczqFRUVVezxtVotw4cP55NPPqFTp05lVLN/B4VCwfr1658oyCVKh2nLppj36IyqWlWU9nakjnkLdVhEWVerGK1Wy7wtm1h/+BBpmZkE+fjw3sDBVK1Qweg+e0LOsGj7Vm7FxZGvVuPt6sbQDh3p3qSprszpa9dYumsHl29FEp+SwjcvjaFtnbpPoUUFLPp0x6xNCxRWlqjDb5L520o0d6Mfuo95p7aYtWuF0skRbXoGuSfPkL3mD8jPB8CsbUvM27VC6eIEgPpONNl/biX//KVSb8/jqOPpx5B6nQlwq4SLtQPvb5nLwRtny7paD2XRswtmLZuisLJCHXGTzOVr0UTFGC1vM34sJtX9im3PO3+JjB9/Lbhjbo5l726Y1q2JwtYW9a3bZK3agPrmrdJqhh6L3t30z71lqx597nVsi1m7lg+ceyFkry0898y7d8K0fh1Unu5oc/NQh90ga/VGNDGxT6NJT+Sf0vcVpdVqmbf5T9YfPnivL/TlvUFDHt4Xnjl9ry+MLegL3dwY2qET3Zs0e4o112fRrydmbVuisLZCHRZB5pLlaO5EPXQf8y7tMWvfGqWzE9q0dHKDz5C9ej3k5evKKBwdsBzYD5M6QShMzdBEx5A5fynqiMjSbtK/jlarZd72raw/epi0rCyCKlXmvf4DqerpaXSfPedCWLRzB7fi48nXqPF2cWVo2/Z0b9RYV+aXbVuYt32r3n5OtrZs/3xKqbXlQRZdOmDWrBEKS0vUkbfIXPsHmmjjfZTNq6Mx8atSbHvepctkzFtSbLt5hzZY9uxCzv7DZG3YXKJ1N8ZiQG/M2rdBYWOF+no4mQuWobl996H7mHfriFmndihd7r2fjp8ke/nawveThQWWA/ti2qg+Cntb1BGRZC1ajjo8otTb08g7kJa+dbAxtyQuPYmtl49xM8n455NKoaStX33qVPDDxtyK1OwM9oed4cydqwA0qOhP3QrVcbMt+G13NyWeXdeCuZMSV+ptEcIYCZ6Jv6Rr164sXLiQ/Px8Ll26xAsvvEBycjLLly8vszp5eHgU26ZQKNiyZUsZ1EaI0qOwMCf/4mVyDxzBevyrZV0do5bs3MHve3bzybARVHJzY8G2rbz2w3es+eQzrC0sDO5jb2XF81264ePhganKhIMXzvH5b0twtLWlWY0gALJyc6hesSK9mjVnwryfn2aTMO/WEfPO7cic/xvqmFgsenbF5p1xpH7wOWTnGNzHtGlDLAb0IXPBMtTXw1F6uGE1ahgA2SvWAaBJSiZrzUY0sfEAmLVogvW4l0ib9NUjgyNPg6WpOdcTbrPl8hGmdBtT1tV5JPPO7THv0IbMxctRx8Zh0a0TNm+MIfXTryDH8OuU8dMiMFHp7iusrbD96B3yThcGCa2GDURVwZOMhb+jTUnFrEkDbN4cQ+pnX6NNTindNt0/9xYsu3fudcHm7ddI/XCy8XOvSUMsBvQmc+Ey1NdvFJx7LwwFIHtlwblnUt2P3L0Hyb9xE5QqLJ/pic3br5L60ZeQm1uqbXpS/5S+r6glO7fz+55dfDJsJJXc3VmwdTOvfT+TNZ9ONt4XWlvzfNfu+Lh7YGqi4uD583y+dDGOtna6vvBpMu/RGfOuHcictxh1VCwWfbph894bpE741Pj516wxFs/2I3P+EtTX7vV9L44AIPv31QAorKyw/ehd8kKvkPHtD2hT01C6uaDNzHxqbfs3WbJnF7/v28snQ56jkqsbC3Zu57WffmDNxI8f8rlrzfOduuDj7o6pSsXBixf5fMWygs/dgEBduSoenvz4ymu6+yrl08k+MW/fGvO2Lcj8fS3quHgsOrXDZswLpE6dATmG+6iMhctAVaQ/f2cceSEXipVVeXth1qwR6kcEgkuSee9umHfvTObcBaijYrB4pic2H7xN6vgPITvb4D6mLZpgMXgAmT8vRH31OkpPD6zGvABA9pKVAFi9PAJVRS8yfvwVbVIyZq2aYvPR26S+/THapORSa09Njyp0C2zGpkuHiUyKoZF3AEMbdOWHQ6tJyc4wuM/Auh2wMbdkw4UDJGamYm1mqZfR5ONUgXNR17kVGkO+Rk1L3zoMb9iNHw6tIS1H+od/ojlz5vDNN98QFRVFUFAQs2bNolWrVgbL7tu3j3bt2hXbHhoaSkBAgO7+2rVr+fjjjwkLC6Nq1ap8+eWX9OvXr9TaIMM2xV9ibm6Oh4cHFStWpHPnzgwaNIgdO3bolVm4cCGBgYFYWFgQEBDAnDlzjB5PrVYzatQofH19sbS0xN/fn++++65YuQULFhAUFIS5uTmenp689lrhh3jRYZvnz5+nffv2WFpa4uzszEsvvUR6erru//eHCn777bd4enri7OzMq6++Sl5e3kPb/tVXX+Hu7o6trS2jRo0i28CH3JO0HWDbtm20bNkSBwcHnJ2d6dmzJ2FhYbr/5+bm8tprr+Hp6YmFhQU+Pj5MnTpVr+1z586lW7duWFpa4uvry+rVq/Ue486dOwwaNAhHR0ecnZ3p06cPERERj/X8+vj4ANCvXz8UCoXuPsDcuXOpWrUqZmZm+Pv7s3Tp0oe2Va1WM378eF1b33vvPbRarV4ZrVbL119/TZUqVbC0tKROnTqsWbPmocctasaMGdSqVQtra2u8vb0ZO3as3utfVFhYGH369MHd3R0bGxsaNWrErl27nugxn5bcXfvJ/m0V+afLb+aPVqtl+d7dPN+lG+3r1sOvgheTho0gOzeX7cEnjO7XoLo/7erWw9fDk4qurgxu1wE/Ly9CHng/tAiqySu9+tC+br2n0RQ95p3akb1pO3mnz6K5E0Xm/KUozEwxa9LQ6D4mVX3JvxZO3vGTaBISC378Hz+JiU8lXZn8sxfIP38JTUwsmphYstf9iTY7B5Oqvk+jWY90LPIi847/wf7wkLKuymMx79Ca7K27yAs5j+ZuNJmLf0dhZoZZ4/pG99FmZqJNTdPdTAP9ITeP3FP33memppjWq03Wuj9RXw9HExdP9qbtaOITMW/dvPTb1LEt2Zt3PHDu/fZ45971cPKOn3rg3Duld+5lzJpL7uHjaO5Go7l9h8wFy1A6O6Hy8S71Nj2pf0LfV5RWq2X5nl0837U77evVL+gLhz9/ry88bnQ/XV/o6UlFVzcGt7/fF15/irUvZN6lA9l/bCXvZAiaO3fJ/GVxwXuqWWOj+5j4VSH/Whh5R4PRxCeQfyGU3GPBmPgWnn/mPTujSUwk69clqMMjCspduqK7kCAen1arZfn+fTzfqTPta9fFz7MCk4YMJTs3j+2nTxrdr4FfNdrVroOvuwcVXVwZ3KYtfp4VCAkP0yunUipxsbPT3RxtbEu7SQCYt2lO9s595J2/WJCV+Pvqgr6vfl2j+2gzs9CmpetuptX9IC+P3LPn9QuamWE1dBBZq9ajzcoq3YY8wLxbR7I3bCYv+HRBvztnPgpzM8xaNDG6j0n1quRfvU7e4eNo4hLIP3eR3CPHManiU1DA1BTTxg3I+n0N6stXC75LrPkDTWw85p2KByFKUnOfWpy+fYXTt68Qn5HM1svHSM1Op1GlGgbL+7lUxMfJk99ObSc84S7JWencSYnjVnJhNuHac3sJvhVKdFoi8RkpbLxwEIVCQRVnr1JtS3mjUCjK5e1JrVy5kjfffJMPP/yQM2fO0KpVK7p160Zk5MMzjK9cuUJUVJTuVq1aNd3/jh49yqBBgxg2bBhnz55l2LBhDBw4kOPHjX+2/l0SPBN/W3h4ONu2bcPU1FS3bd68eXz44Yd8+eWXhIaGMmXKFD7++GPdcM+iNBoNFStWZNWqVVy6dIlPPvmEDz74gFWrVunKzJ07l1dffZWXXnqJ8+fP88cff+DnV3yIDUBmZiZdu3bF0dGR4OBgVq9eza5du/SCbQB79+4lLCyMvXv3snjxYhYtWsSiRYuMtnXVqlV8+umnfPnll5w8eRJPT89igbEnbTtARkYG48ePJzg4mN27d6NUKunXr59ugYPZs2fzxx9/sGrVKq5cucJvv/2mF8AC+Pjjj+nfvz9nz55l6NChDB48mNDQUN3z0a5dO2xsbDhw4ACHDh3CxsaGrl27knsvs+Bhz29wcDBQEBSMiorS3V+/fj1vvPEGb7/9NhcuXODll1/m+eefZ+/evUbbOn36dBYsWMD8+fM5dOgQiYmJrF+/Xq/MRx99xMKFC5k7dy4XL17krbfeYujQoezfv9/ocYtSKpXMnj2bCxcusHjxYvbs2cN7771ntHx6ejrdu3dn165dnDlzhi5dutCrV69HdurCsDsJ8SSkptI0sPCqtZmpKfX9qnHuRvhjHUOr1XLi8mVuxsRQ38h7/WlSujqjdLAn/+Llwo35+eRfuW5weIiuyLVwTHy8UflW1h3HtFYQeecuGt5BocC0cQMU5mbkh90oySb8JyhdnFDa25EfeqVwY76a/GthhT8yHoNZiybknjxTmH2lVKJQqfSGmwFo8/Iw8SvdIKfS5SHn3kMCrPnXwzCp/MC55+KMaa0axs89QGFVkJ2izZAr+yWhsC8s/BFpZmpK/WrVORf+JH1h6L2+sNqjdyhhSleXgvPvQmjhxvx88q9cw6TaQ/q+q9cx8amE6t77TunqgmmdmuSdLcz+Ma1Xh/wbkVi99iJ2P3yNzeQPMGvbsrSa8q92JyGBhLRUmvoXZmWYmZhS38+Pczce77NEq9Vy4uoVbsbFUr+q/ufurfg4un36IX0mf8oHSxZyO770A5xKZ0eUdnbkX7lWuFGtJv/6Db0g7KOYNWlI7plzkKt/gdxqQG/yQi+TfzXMyJ4lT+nmgtLRgfwH++H8fPJDr2BSvarR/fIvX8fEtzKqe32+0s0F03q1yDt9rqCASnXvM0q/jdrcPEwCSu87lEqhxNPOhbD4O3rbr8ffoZKDu8F9Atwqczclnpa+tXmn7RBebzWQLv5NMFGqDJYHMFWZoFIoycoznOkqyrcZM2YwatQoRo8eTWBgILNmzcLb25u5c+c+dD83Nzc8PDx0N9UDGaWzZs2iU6dOTJw4kYCAACZOnEiHDh2YNWtWqbVDhm2Kv2TTpk3Y2NigVqt1mVczZszQ/X/y5MlMnz6dZ555BgBfX18uXbrEzz//zIgRI4odz9TUlM8++0x339fXlyNHjrBq1SoGDhwIwBdffMHbb7/NG2+8oSvXqFEjg/VbtmwZWVlZLFmyBGtrawB++OEHevXqxbRp03B3L+jMHR0d+eGHH1CpVAQEBNCjRw92797Niy++aPC4s2bN4oUXXmD06NG6Ou3atUsv++xJ2w7Qv39/vfvz58/Hzc2NS5cuUbNmTSIjI6lWrRotW7ZEoVBQuXLlYsd49tlndfWaPHkyO3fu5Pvvv2fOnDmsWLECpVLJr7/+qrtasHDhQhwcHNi3bx+dO3d+6PPr6uoKgIODg97w2G+//ZaRI0cyduxYAMaPH8+xY8f49ttvDaba3n8OJ06cqGvzTz/9xPbt23X/z8jIYMaMGezZs4dmzQrmdqlSpQqHDh3i559/pk2bNgaPW9Sbb76p+9vX15fJkyfzyiuvGM0CrFOnDnXq1NHd/+KLL1i/fj1//PFHsaArQE5ODjlFhn+Zm5s/Vt3+CxJSUwFwsrXT2+5kZ0d0YuJD903PyqL7B++Tm5+HSqlkwqDBNAk0fPXyaVLYFbRFk5qmt12TmobS2cnofnknTpFla4PNxLcABQoTFTl7DpCzZadeOaVXBWw/fBtMTSAnh4wf5pWLIZv/NA99nZweb15OlU8lVF6eZC5dWbgxJ4f8sBtY9OhERnRMQXZao/qofCqVepaMwv5+m1L1tj/63DtNlo0NNu+/ie7c23uQnK07je5jOegZ8q+GPXIuK/F4ElKM9IW2dkQnJjx03/SsTLp/MIHcvHt94f+GlElfqDv/UoqcfympunkaDck7fpIsOxtsPnoH3fm3ez85mwo/85WuLpi3b03Otl1k/LkNVRUfLIcORJuXR97h0sse+DdKSDNyrtnYEp30GJ+7kz4iNz+/4FwbMJAmDwThgipX5rMhw6jk6kZCWioLdm5n1OwZrJzwIQ73vmeXBoVtQXabJk1/5IAmPR2lo8NjHUNVqSKqCh5k3huqfp9pvdqovCqQOfPho0NKmsLBHjD2fnI2ul/e0RMF76fP3i84jokJOTv2kvPHvbnosrPJv3odi2d6knEnCm1yCqYtmqDy833o/HB/l5WZBSqlkvRc/QsuGblZ2JhbGtzH0dKWSo7u5GvULD+zEytTC3oGtcDS1JwNFw4Y3KdT9UakZmcQnnDH4P/F02Xsd5Ch30K5ubmcOnWK999/X297586dOXLkyEMfp169emRnZ1OjRg0++ugjvd+XR48e5a233tIr36VLFwmeifKnXbt2zJ07l8zMTH799VeuXr3KuHHjAIiLi+PWrVuMGjVKLwiVn5+Pvb290WP+9NNP/Prrr9y8eZOsrCxyc3N1q1jGxsZy9+5dOnTo8Fj1Cw0NpU6dOrrAGUCLFi3QaDRcuXJFFzwLCgrSi2B7enpy/vz5Ysd78LhjxujP99OsWTNdptVfbXtYWBgff/wxx44dIz4+XpdxFhkZSc2aNRk5ciSdOnXC39+frl270rNnTzp37lysHkXv31/N9NSpU1y/fh1bW/0U++zsbMLCwp74+X3w+XjppZf0trVo0cLgkFuAlJQUoqKi9OpqYmJCw4YNdUM3L126RHZ2drEFHnJzc6lX7/GH6e3du5cpU6Zw6dIlUlNTyc/PJzs7m4yMDL3z4r6MjAw+++wzNm3axN27d8nPzycrK8to5tnUqVP1Ar4An376KW8YLP3XmbVvjdWbhedc+geT9a/+lxNbTxxn6vLfdfdnji2Yj6hoanfRIbqGWJmbs2zih2Tm5BB85TIz163By8WFBtX9S7bSj2DatCFWwwfr7qfPund1zFAbHtIuE/9qWPTsQtbSleSH30Tl7oLl4AFoUlLJ+XObrpwmOoa0SVNRWFlh2qAuVqOHkT7tOwmgPYJp4/pYDXlWdz/9/uT+RV+TJxhmYNa8Ceo7UcUmLM9c+DtWw/+H/bRJaNVq1LfukBd8BlWlkh1GYtqkIVbD/6e7n/7dTwV/FD3NFIpHnHt+Befeb6vID49A5eaK5eD+aHp20Qtg3Gf53LOoKlYg7atZJdCKv+ef0vcVVdAX/qa7P/PeHFFFTz8t2keek1bmFiyb+PG9vjCUmWtX4+XiWup9oWmzxlg9P0R3P336jwV/GHpPPaRLNwmojkWvbmQtXk5+2A1U7m5YDh2IJjmFnI335qNVKlDfuEn2mo0AqG/eQuXliXmHNhI8e4Stp4KZumqF7v7MFwveL0XPqsc718xZ9s77ZObmEHz1CjM3rMfL2YUG9zIdWwQWzrPnRwVq+/jS98vP2Bx8nOfati+ZBgGm9etgNbCv7n66gcn9Czz83HuQWZOGqO9Go468Xbi3gz2W/XqS/tMC3eIppcW0RROsXhyuu58+7d535GJ99yP68xr+WPTrSdb838i/Ho7Kww3LEYPRJPckZ90mADJ//BWrl5/Hfu70gs+oGzfJO3xcl338tBlrzv3vhmvO7SEnvyBTbtvlYwyq25FNlw6Tr1HrlW/pW5tanlVZeGJzsf/925XXlS2N/Q6aNGlSsbLx8fGo1Wrd7+/73N3diY42/B3X09OTX375hQYNGpCTk8PSpUvp0KED+/bto3Xr1gBER0c/0TFLggTPxF9ibW2tG9I3e/Zs2rVrx2effcbkyZN1gZ958+bRpIn+2P0HA1UPWrVqFW+99RbTp0+nWbNm2Nra8s033+jGLFtaGr5yYYxWqzU6HvvB7Q8ONb3/v/v1/yv+StsBevXqhbe3N/PmzaNChQpoNBpq1qypG1JZv359bty4wdatW9m1axcDBw6kY8eOj5wH7H5bNRoNDRo0YNmyZcXKuLq6olT+9RHchoIjf2Us/H33n8PNmzfj5aX/g/RxM7tu3rxJ9+7dGTNmDJMnT8bJyYlDhw4xatQoo3Pavfvuu2zfvp1vv/0WPz8/LC0tGTBggO41KGrixImMHz++WP0ye/7PYPm/KvfoCfIvX9Xd18Q//OpxWWlduw41fQqHj+Xe+zKakJqCywOB46S0NJzt7Irt/yClUom3mxsA/t7eRMREs2jH9qcePMsLOU/agytUmRR8ZCrt7VA/cMVYaWeLtkiW04Ms+vUg98gJcg8eBUBz5y6YmWM1YnBBAOP+t0u1WpfBpI6IROVbCfOObclassLYoQWQd/YiaTceCHLdm/RfaW+H+oHXRWlr89DXScfUFLNGdcl6ILB5nyY+gfQZP4KZGQoLc7SpaViNHlbi78u8s+dJ+yyicIOxc+8RbbLo25Pcow+ee1FgbobV8MHkbN6h98vGcsgATOvWIn3ad6U6sfTj+qf0fUUZ7wtTcbF30G1PSkvD2fYJ+8LoaBZt31rqfWHembOkPThk3PTe+edgb6DvSy26u45F/17kHjlO7v7DAAUrCZqbYfX80IJsGa0WbXJKsYna1XejMW1ofH5CUaB1UC1qvuOju68719JS9T9309NxfsT8ZEqlEu97owz8vSoSERPDol07dMGzoizNzfHzrMCtuJJd+TDvYihp3z6wevH9vs/WRr8/t7FG+5B5bHVMTTGrV5usbfpz2JpUrIDS1gbbBxYeUahUqKr4YNayKSnvfvLQQNaTyDt1lrTrDwQZHnw/PbDQjNLeFm3KQ95PA/uSe/AouXsPAqC5dQfMzbF6cTg56zeDVosmJo70z78GczMUlpZok1OweuPlUs2OzszNRq3RYGNmpbfd2sySjFzD88il5WSSmp2hC5wBxKUno1QosLOwJjGz8Hlo4VOLVlXqsjh4CzHp/4zPgf8CY7+DHuZJfjP6+/vj71/4WdesWTNu3brFt99+qwuePekxS4IEz0SJ+PTTT+nWrRuvvPIKFSpUwMvLi/DwcJ577rnH2v/gwYM0b95cN/wP0Jsw39bWFh8fH3bv3m10OOCDatSoweLFi/WyjA4fPoxSqaR69epP2LpCgYGBHDt2jOHDC68gHTt2TPe3u7v7E7c9ISGB0NBQfv75Z92KI4cOHSpWzs7OjkGDBjFo0CAGDBhA165dSUxMxMnJSVePovW6n6lVv359Vq5ciZubG3ZGAhePen5NTU1Rq/Wv9gQGBnLo0CG9xz1y5AiBD8xz9SB7e3s8PT05duyYruPLz8/n1KlT1K9f8EW5Ro0amJubExkZ+dhDNIs6efIk+fn5TJ8+XRcYfHD+PEMOHjzIyJEjdSu0pKenF1tQ4UHGUpNLfJagrGw0WeU/88jawkJvJS+tVouznR3HL4fi710wL0lefj6nr19jXJ8nWwVHq9WSm//whTxKRXYOmiKryGmSUzCpEVB4BVulwsTfj6zVG40fx8ys+JdwraZ4ekAxChQm8jH9SDk5aOKKvE4pqZgEVkd9697wDpUKk2pVyVq/6ZGHM2tYF0xMyDt+ynih3Fy0ubkorCwxrRFA1ro//0YDDDB67vkXP/fW/GH8OGamxc89jbbYuWc55FlM69cm/evZaOIfPpTwqfmH9H1FGe0LQy/p94XXrjKu7zNPdOyCvrB0s2SAe+efflBEk5yCSVAg6pv3AhsqFSb+1chatd7AAe4xMys43/QOpN/35V8LQ+Wpnzmg9HBHk1BOzsNyzOC5ZmvH8StX8K9YsOBHwefudcb16v1Ex9by8HMtNz+PiJgY6lYxPkfXX5KTiyZHP0CiSU3FxN+vMMiqUmHi50vWn8WzZ4syq1sLTFTknTyjtz3vWhip9zPA7rEa3B9NbBzZuw+UWOAMgOxsNEUWF9MkJWNSq0ZhdrNKhUmgP1m/P+SiuKHvEhqN4azCnFy0ObkorK0wrV2TrN9XFy9TQtRaDVGp8VR18SI0NkK3vaqLF5djbxrcJzIphiCPKpipTMhVF5xnLtb2aLQaUh9YnbOFT23aVK3HkpNbuZsqi4iUJ8Z+Bxni4uKCSqUqlhEWGxtbLHPsYZo2bcpvvxVmdnt4ePztYz4p+VYuSkTbtm0JCgpiypQp/PDDD0yaNInXX38dOzs7unXrRk5ODidPniQpKalYlBrAz8+PJUuWsH37dnx9fVm6dCnBwcH4+hZevZ00aRJjxozBzc2Nbt26kZaWxuHDh3XDRR/03HPP8emnnzJixAgmTZpEXFwc48aNY9iwYX/rDfXGG28wYsQIGjZsSMuWLVm2bBkXL16kSpXCCXOftO33V7/85Zdf8PT0JDIystiY8JkzZ+Lp6UndunVRKpWsXr0aDw8PHBwcdGVWr16tV68TJ04wf/583fPxzTff0KdPHz7//HMqVqxIZGQk69at491336VixYqPfH7vB9datGiBubk5jo6OvPvuuwwcOJD69evToUMH/vzzT9atW/fQVSrfeOMNvvrqK6pVq0ZgYCAzZswgOTlZ939bW1veeecd3nrrLTQaDS1btiQ1NZUjR45gY2NjdN64B1WtWpX8/Hy+//57evXqxeHDh/npp58euo+fnx/r1q2jV69eKBQKPv7447+VhViaFLY2KN1cUNyb60hZsSBDT5OYXC4yRqDgStDgdh1YuH0b3q5ueLu5sWj7NizMzOjSqHB1tk8XL8TVwYHX7gXUFm7fRo1KlfBydSU/X83hixfYfPwY7/+vcAhRZna23tXuuwnxXLl1C3trazycjM+/UxJydu7FomdnNLGxqGPisOjRBW1uHrnHC1cysxo9DE1SCtlrC4Ia+WcvYN65HerI26jDI1C6uWLRtyd5Ied1X4QtnulF3vlLaBOTwMICsyYNMAmoRsaMpzsXizGWpuZUtHfV3a9g50I1l4qkZmcQk55UhjUzLGf3ASy6dkQTG486Ng6Lrh3R5uaSe+K0rozVyMFoklPJ3rBZb1+z5k3IC7lgcMJ8kxr+gAJNTCxKNxcsn+mFOiaW3CPGV5AtKTm79mHRozOamLiCNnXvXPzcGzUMTVIy2feCefrn3k2Ubi5Y9O1BXsgF3blnOXQgZk0akP79PLTZ2SjsCjJUtFnZxSaeLmv/hL6vKIVCweD2HVm4fSvebu4FfeG2rff6wsIM9U8XLSjoC+8F1BZu20qNypXv9YX59/rCo7w/+PEuzJW0nO27sejVFU1MLOroWCx6dy14Tx0tPPetXhpZcP6t3gBAfsh5zLt2QH3zFuqwGyjd3bDo35u8M+d051/Ott3YfPwe5r26knf8FKqqPpi3a0nmguKZ8uLhFAoFg9u0ZeGuHXi7uuLt6sqiXTuwMDOlS/3CVXk/XbYEV3sHXutZEFBbuGsHNbwr4eXsQr46n8Ohl9gcfIL3nx2k22fWxvW0CqqJh6MjSenpzN+xnYzsbHo2alKsHiUtZ/8RLDq2RROXgDouAYuObQv6vtMhujJWQwqmQsjevENvX7OmDck7H4o2s0gGVE4umugY/W25uWgzMotvLwU5W3dh0bcHmugY1FGxWPTrjjYnl9wHhipbjR2FJjGJ7BUFc7Xlnz6LeffOqG9Eor4ejtLDDYuBfck7FaJ7P5nUDgKFAs3daJQeblg+9yzqqGhy9x0u1fYciTjPM7Xb6lbMbOgdgL2FDcGRBUPtO1ZvhJ25NevO7wPgfNR12latR99abdh77RRWZhZ09m/C6dtXdcMyW/rWpn21hqw5u4fkrDRszApGIeWq83QBt/+C0syielrMzMxo0KABO3fu1CUpAOzcuZM+ffo89nHOnDmDp6en7n6zZs3YuXOn3rxnO3bsoHnz0lsBXYJnosSMHz+e559/ngkTJjB69GisrKz45ptveO+997C2tqZWrVp6k7g/aMyYMYSEhDBo0KCCD//Bgxk7dixbt27VlRkxYgTZ2dnMnDmTd955BxcXFwYMGGDweFZWVmzfvp033niDRo0aYWVlRf/+/fUWNfgrBg0aRFhYGBMmTCA7O5v+/fvzyiuv6E14/6RtVyqVrFixgtdff52aNWvi7+/P7Nmzadu2ra6MjY0N06ZN49q1a6hUKho1asSWLVv0hlt+9tlnrFixgrFjx+Lh4cGyZcuoUaOG7vk4cOAAEyZM4JlnniEtLQ0vLy86dOigy0R71PM7ffp0xo8fz7x58/Dy8iIiIoK+ffvy3Xff8c033/D666/j6+vLwoUL9epe1Ntvv01UVBQjR45EqVTywgsv0K9fP1JSClPXJ0+ejJubG1OnTiU8PBwHBwfq16/PBx988FivU926dZkxYwbTpk1j4sSJtG7dmqlTp+plyBU1c+ZMXnjhBZo3b46LiwsTJkwg9SHDUcqSabNGWL/7uu5+wWTMkLVkBdkPTnBexoZ36kxOXi7TVi4nLTOTIB9fvn/tdb0r5dFJiXpfDLJzc5i2cjmxycmYm5pS2d2Dz0e+QOcGhV/8QyNvMua7mbr7M9cWXKnt0aQpk4aPLNU25WzdhcLMDMuhg1BYW6EOjyB9+g/wQJaQ0slJL9si+89taLVaLPr1ROlojzYtnbyzF8heW5itpLC3xfrF4Sjs7dBmZaO+fYeMGXPIv3SZ8iDAtTI/9CsM/r/esmCOsS2hR/lyj/GVhMtKzo49KMxMsRzcH4WVJeobkaTP/hlyHnydHItdxVe6uWJSrUrhHGNFKCwtsOjbA6WDA9rMTPLOnCNrw5aCq/+lLGfrLhSmplgOHVh47s34sci5p9+m7E3b0VIwfFPv3FtXmIFn3q4g49l2gv6MjZkLftP7IVce/FP6vqKGd+pCTm4u01YsK+wLx71ZvC9UFukLV/xObHLSA33hKDo3NLxQUmnL2byjoO8bMRiFlRXq8Bukfz1b//xzdtI//zZuKej7BvRG6ehQcP6dOaeb3wxAfeMmGbN/wvLZvlj06YEmPp6sZavJO1r6Ael/o+HtO5KTl8e0NatIy8okqLIP3495tci5llTkczeXaWtWEZty73PXzZ3Phw6nc70GujKxKcl8tHQRyRkZONrYULOyDwveHI9nKV+wAsjZc6Cg7xvQG4WlJeqbt0n/aSHkFE6roXR0KN6fuzpjUsWH9LkLSr2OTyrnj60Fn1EvDEVhbY36ejjpU2bAAxlqSpci76d1m9BqwWJQX5ROjmhT08g7dZbsBxZCUFhZYjG4f8H/0zMKFixasR7UpTtP2IXocCxNzWnrVx9bcyti0xL57dQ2UrILhtbamlthb1k433CuOp/FJ7fQI7A5LzfvR1ZuNheiw9l9rfBiUKNKNTBRqvhfPf05kPdeP8Xe66cR/yzjx49n2LBhNGzYkGbNmvHLL78QGRmpm0t84sSJ3LlzhyVLCuY5nDVrFj4+PgQFBZGbm8tvv/3G2rVrWbt2re6Yb7zxBq1bt2batGn06dOHjRs3smvXLoMjuEqKQvs4szcLIco1hULB+vXr6du3b1lX5T8vqdOTDUks7xx3rid1196yrkaJs+vYjuQXiq+i+k/msOAHWvw45tEF/0EOv/oTyWOKZ+z+kzn8NIPkUcUzpv/JHOZ//+/s+3bvL+tqlCi7Dm1IHv7v6iMAHJY8PLP8nyp1y45HF/oHsevemeS3Hu8i6D+Fw8wpJP9vVFlXo0Q5rJjPJ9vmlXU1StTnXV98dKFyas6RtY8uVAbGNu//xPvMmTOHr7/+mqioKGrWrMnMmTN10/iMHDmSiIgI9u3bB8DXX3/NL7/8wp07d7C0tCQoKIiJEyfSvXt3vWOuWbOGjz76iPDwcKpWrcqXX37JM8882ZQIT0Iyz4QQQgghhBBCCCHKESV/fVG38mbs2LF685s/aNGiRXr333vvPd57771HHnPAgAFGR6KVhn/PqyGEEEIIIYQQQgghRAmTzDMh/gVk9LUQQgghhBBCCFE6JHgmhBBCCCGEEEIIUY78G1bb/DeRYZtCCCGEEEIIIYQQQhghwTMhhBBCCCGEEEIIIYyQYZtCCCGEEEIIIYQQ5YhShm2WK5J5JoQQQgghhBBCCCGEERI8E0IIIYQQQgghhBDCCBm2KYQQQgghhBBCCFGOKJBhm+WJZJ4JIYQQQgghhBBCCGGEBM+EEEIIIYQQQgghhDBChm0KIYQQQgghhBBClCMKWW2zXJHMMyGEEEIIIYQQQgghjJDgmRBCCCGEEEIIIYQQRsiwTSGEEEIIIYQQQohyRCnDNssVyTwTQgghhBBCCCGEEMIICZ4JIYQQQgghhBBCCGGEDNsUQgghhBBCCCGEKEdktc3yRTLPhBBCCCGEEEIIIYQwQoJnQgghhBBCCCGEEEIYIcM2hRBCCCGEEEIIIcoRJTJsszxRaLVabVlXQgghhBBCCCGEEEIUWBK8payrYNDwRt3LugplQjLPhBCiBE3fv7ysq1Ci3m4zmBvxd8q6GiXO18WL/os/KOtqlKi1I6aQPGZ8WVejRDn8NIMWP44p62qUqMOv/sTzK78s62qUqIWDPiQ0Orysq1GiAj2qsPLMzrKuRokaVK8T+66fKutqlLi2fg04dSu0rKtRohp4B/LWH9+VdTVK1Mzeb/wr+/PPdy4o62qUqE86vUDfRe+XdTVK1IaRX5V1FcS/hATPhBBCCCGEEEIIIcoRWW2zfJEFA4QQQgghhBBCCCGEMEKCZ0IIIYQQQgghhBBCGCHDNoUQQgghhBBCCCHKERm2Wb5I5pkQQgghhBBCCCGEEEZI8EwIIYQQQgghhBBCCCNk2KYQQgghhBBCCCFEOaKUYZvlimSeCSGEEEIIIYQQQghhhATPhBBCCCGEEEIIIYQwQoZtCiGEEEIIIYQQQpQjCmTYZnkimWdCCCGEEEIIIYQQQhghwTMhhBBCCCGEEEIIIYyQYZtCCCGEEEIIIYQQ5Yistlm+SOaZEEIIIYQQQgghhBBGSPBMCCGEEEIIIYQQQggjZNimEEIIIYQQQgghRDmikGGb5YpkngkhhBBCCCGEEEIIYYQEz4QQQgghhBBCCCGEMEKGbQohhBBCCCGEEEKUIzJss3yRzDMhnqKIiAgUCgUhISGlcnyFQsGGDRueaJ+2bdvy5ptvPnb5RYsW4eDg8NAyly9fpmnTplhYWFC3bt0nqs9f9aTtKO3XQgghhBBCCCHEv4Nknon/jJEjR5KcnPzEwaWS5O3tTVRUFC4uLgDs27ePdu3akZSU9MiA1D/Jp59+irW1NVeuXMHGxqasq2NQ0deiLF3cd4Jz24+QmZKGYwU3mg3qime1ygbL3r1yg03TFxfbPvCzV3HwdC22/fqJ8+z5dS2V6/jT5dXBJV73+/5ct5E1v68kMSGByr4+jHn9VWrWrW20/LkzZ/nl+zncvBGBs4sLzw4ZRI9+vfXKrF+5hk3r/yAuJhY7B3tatW3N82NexMzcDIAVS37n8P6D3L4ZiZm5OTVqBfHCKy/iXblSqbWzi38T+gS1wtHKllvJsSw8sZnQ2Aij5Vv51qFvzdZ42jmTmZvNmbvXWHxyC+k5WQB81mU0NT2qFNvv1O3LTNm9pLSaUYxFzy6YtWyKwsoKdcRNMpevRRMVY7S8zfixmFT3K7Y97/wlMn78teCOuTmWvbthWrcmCltb1Lduk7VqA+qbt0qrGU+sjqcfQ+p1JsCtEi7WDry/ZS4Hb5wt62oZ1M6vAd38m+JgacOdlDh+P7OTa/GGn8tRjXvS0rdOse13UuL4aNsvAKgUSnoENqeFb20cLW2JSktg9dk9XIgOL9V2PGjL+k1sWLGGpMREvH0qM+q1lwmqU9Ng2cSERBb+OI+wq9eIun2XHv17M3rcGL0yRw8cZs1vK4m6cxd1fj6eFb3oM/AZ2nXp8DSaA8CJHQc49Odu0pNTcK3oSbfh/fEJLP5eAbh5OYwdv28k/m40eTl5OLg60bBDC5r3aK8ro85Xc2DjDkL2HyctKRlnT3c6D+lDtbo1nlaT2LdpJzvWbSIlMZkKlbwY+NJwqtUMMFj29OETHNiyi1vhN8nPy8ezshe9hvQnqIH++ZiZnsGGJas4cySYzPQMXNxdGTD6OWo1qvc0mgTAzo1b2LR6A8kJSXj5eDN87CgCagUZLJuUkMiynxZy41oY0Xei6NKvB8PHji5WbuvaP9j15zbiY+OxtbelSavmDBo9DDMzs9JuDgAtfGrTrmp97CysiU5LYMOFA4Qn3jVaXqVU0aV6YxpUDMDO3Irk7HR2Xg3mxK1LACgVSjpWa0gj70DsLWyITU9i06XDXI67+VTaA9CvZhuG1OuEs5U9NxLvMvvQas5GXTda/pmabehfqy2eds7EpCWy+NRWtl05rvu/SqlkeP2udAtohou1A5HJMcw9uo7jkZeeRnMMunrgNJd2nyArJR0HTxca9O+Am5+30fLqvHzObz3CjeCLZKdlYOVgS80uzajazPj3rtLWzb8pfWu2Lvh+lBTD/BObuPSQ70etq9SlX802VLBzJiM3mzN3rrLo5BbScjJ1ZXrVaEFX/6a4WDuQlpPBkYgLLD29jTx1/lNokRDFSfBMiKdIpVLh4eFR1tUodWFhYfTo0YPKlQ0HgMqD8vJahAVf4OjKbbQc0gN3v0qEHjjJ1tm/MXDSq9g4Oxjdb+Dk1zCzMNfdt7C1LlYmLSGZ42t24FGt9IJJAPt37eXn737k1bffIKh2TbZs+JOP3nmfX35biJuHe7Hy0Xej+PidiXTr1Z33PvmAi+cu8OP077B3cKBlu9YA7Nm+iwU/zWP8xPcIrBXEnchbTP/yawBefuNVAM6HnKXXM32oHuiPRq1h0S/z+fCt9/hl2UIsLC1LvJ3NfWrxfKMezDv+B5djb9K5emM+7DiCNzfOIj4jpVj5ALfKjGv5LIuCN3Py9mWcrOx4uWlfxjZ/hq/3LgPgm73LMFGqdPvYWlgxvdc4jkZcKPH6G2PeuT3mHdqQuXg56tg4LLp1wuaNMaR++hXk5BjcJ+OnRWBSWG+FtRW2H71D3unCwJPVsIGoKniSsfB3tCmpmDVpgM2bY0j97Gu0ycWfr7JgaWrO9YTbbLl8hCndxjx6hzLS2DuQIXU7sfT0Nq7F3aKtX33Gt/4fH277mcTM1GLlfz+zk9Xn9uruqxRKPu8ymuBbobptz9RqQ7PKtVh0cjNRqQnU9KjCuBYD+HL3YiKTjQdOS8qhPftZ8MPPvPzWqwTUrMH2P7cwecLHfL/4Z1zd3YqVz8vNw97BnmeH/o8/Vq83eEwbW1ueHToIr0remJiacPLoCb6fNgMHRwfqNW5Q2k3i/JFTbF28lp6jBlHJvwrBuw7x21dzeG36Rzi4OBUrb2ZuRpMurfGo5IWpuRmRV8L449cVmJmb0bBjSwB2r/yTs4eC6fPSEFwquHP9bCjLp8/jxc/H4+lr/Md1SQk+cJRV85YwZOwLVA2szoFtu/n+02lMmvsNTm7FLz5du3iZwHq16DtiEJbWVhzZtZ8fP/+W92dMplJVHwDy8/KZ9dFUbO3tePmDN3B0cSIpLgHzUui3jTm69xBL5i7ghddfpnpQALs3b2faxMl8M/97XNyLX4jKz8vD1sGePkOeZevaPwwe89Du/az4dSkvvfMa1YMCiLp9l5++mQ3AsLGjSrU9AHUrVKNvzdasObeXG4l3aV65Fi817cNXe38jOSvN4D4jGnTD1tyKlSG7iMtIxtbcCqWicGBS94BmNKgYwKqzu4lNT8TfrTLPN+7J7IOruJMaV+pt6uDXgDdaPsv0/cs5Fx1G36BWfNvrNYb+/hkx6UnFyvcNas2YZn2Ztvc3QmNvEujmw/vthpKWk8nhiPMAvNSkD12qN2Havt+4mRRNY+8aTO02hpfXfmP0gkRpijgVyqm1u2k0qDOuVby4diiEvXNW0/Oj0Vg72Rnc59CCjWSlZdD0uW7YujqSnZaBVqN5yjUv1MKnNi807snPxzZyOTaCLv5N+LjT84zbMMPg96NAt8q80XIgC4I3EXwrFGcrO8Y068erzfvz1d6lQEFwbViDrvxwaA2X4yKpYOfC6y2fBWBB8Kan2r6ypESGbZYnMmxTiHv2799P48aNMTc3x9PTk/fff5/8/MIrG23btuX111/nvffew8nJCQ8PDyZNmqR3jMuXL9OyZUssLCyoUaMGu3bt0htK+eBQwYiICNq1aweAo6MjCoWCkSNHAuDj48OsWbP0jl23bl29x7t27RqtW7fWPdbOnTsf2caMjAyGDx+OjY0Nnp6eTJ8+vViZ3Nxc3nvvPby8vLC2tqZJkybs27fvkce+T6FQcOrUKT7//HMUCgWTJk1i3759KBQKkpOTdeVCQkJQKBREREQAcPPmTXr16oWjoyPW1tYEBQWxZcsWXflLly7RvXt3bGxscHd3Z9iwYcTHxxutx2+//UbDhg2xtbXFw8ODIUOGEBsbq/u/oWGbT/oYJeHczqP4t6xPQKsGOHq60nxQN2wc7bm0/+RD97O0tcbK3lZ3Uyr1u3ONRsOeX9fSoHc77FwcS7MJrFu5mi49u9Gtdw8q+VRmzJuv4ermxqb1hn9gbN7wJ27ubox58zUq+VSmW+8edO7RjTXLV+nKhF64SFCtmrTr3AEPTw8aNGlE207tuXr5qq7MlzOm0blHV3yq+FKlWlXGf/AesTGxXLty1dDD/m29arRkz/VT7L52kjspcSwM3kxCRgpd/JsYLF/d1Zu4jCS2XD5KbHoSl2NvsuPqCao6e+nKpOdmkZydrrvV9vQjJz+PIzfPl0obDDHv0JrsrbvICzmP5m40mYt/R2Fmhlnj+kb30WZmok1N091MA/0hN4/cU/eCZ6ammNarTda6P1FfD0cTF0/2pu1o4hMxb938KbXs0Y5FXmTe8T/YHx5S1lV5qM7+TThwI4QD4SFEpSWw/MxOErNSaV/V8GuUlZdDanaG7ubj5ImVmSWHHsiqa+ZTi02hhzkXFUZcRjJ7w05zITqcrkbO55K2cdV6OnbvTKeeXfH2qcTocWNwcXVl28bNBsu7e7oz+vUxtOvaESub4hcLAGrVq03T1i3w9qmEp1cFeg3oi08VXy6dv1iaTdE5snkP9ds1o0H75rh6edB9xADsnB0J3nnQYHlPX29qt2iIm7cnjm7O1GnVGL/agdy8HKYrc/bQCVr37Uz1ekE4ubvQuHMr/OoEcnjznqfSpl3rt9Cic1tadmmHZyUvBr00HEcXZ/Zv2WWw/KCXhtNlQC98qlfF3cuTfiP+h1sFD84dP60rc3jnPjLS0hn78Xj8avjj7OaKX1AA3lWe3gW3LWs30rZrR9p174RXZW+Gjx2Ns5sLu/7cZrC8q4c7I14dTevO7bCytjJY5tqlK1SvGUCLDm1w9XCndsN6NG/XivCrxrOkSlLbqvU5HnmR45EXiU1PYsPFAyRnpdPCp5bB8gGulfFzqci84xu5Gn+LpKw0IpNjiEiK0pVp6B3ArmvBhMZGkJCZypGI81yJvUlbP+OfDyVpUN2ObAo9zJ+hh7mZFM13h1YTm5ZEv5ptDJbv6t+EjRcPsvv6Ke6mxrP7+kk2hR7muXpd9MosObWVozcvcDc1ng0XD3A88hKD63Z8Km0q6vKeYKo2q41f8zrYe7jQcEBHrBxtuXrwjMHydy+FE3P9Fu1eeRbPAB9snO1x8amAa5WKT7nmhfoEtWTXtZPsuhbM7ZQ45p/YRHxGCl39mxosX921EnHpSWwOPUJsehKhsTfZceUEfi6F34/8XStxOeYmB26cJTY9iZC71zgYflavjBBPmwTPhADu3LlD9+7dadSoEWfPnmXu3LnMnz+fL774Qq/c4sWLsba25vjx43z99dd8/vnnuqCVRqOhb9++WFlZcfz4cX755Rc+/PBDo4/p7e3N2rVrAbhy5QpRUVF89913j1VfjUbDM888g0ql4tixY/z0009MmDDhkfu9++677N27l/Xr17Njxw727dvHqVOn9Mo8//zzHD58mBUrVnDu3DmeffZZunbtyrVr1x6rblFRUQQFBfH2228TFRXFO++881j7vfrqq+Tk5HDgwAHOnz/PtGnTdEM+o6KiaNOmDXXr1uXkyZNs27aNmJgYBg4caPR4ubm5TJ48mbNnz7JhwwZu3LihC04aq/eTPsbfpc7PJz7yLhVrVNXbXrFGVWLCHn71c93kn1n6zrdsmrGYu5dvFPv/6U37sbS1JqBl6X7BzcvL49qVq9Rv3FBve/3GDQm9YPgHa+iFi8XKN2jSkGuXr+gC1kF1anHtylWuXCrIlIm6c5fgo8dp3Nz4D/vMjAwAbO0MX6n9O0yUKqo6VyDkrv774Ozd6/i7Gv7BdyU2Emcre+p7VQfA3sKGZpVrcur2FaOP06FaQw5HnCMnP6/kKv8QShcnlPZ25Ic+UKd8NfnXwjCp4vPYxzFr0YTck2cgN/fegZUoVCrI0x9aoc3Lw8TPtwRq/t+hUirxcfTkYrT++/xidDhVXR7vx1Jr37pcirlBwgNZaqZKVbGhL7nqfKq5ln42U15eHmFXr1G3kX7/VLdRfS5fKJmhU1qtlrOnznDn1m2CahseClqS8vPzibpxi6q1A/W2+9UOJPJq8T7akKgbt7h1NRyfGtUKj5uXj4mpqV45UzNTIh8IsJWW/Lx8Iq/foEY9/aFgNerXIiz08S5SaDQasrOysX4gO/rc8VNUCajG73MW8s5zY/hs7HtsWbkBjfrpZM7k5+Vx42oYtRvW1dteq0Fdrl66/JeP618zkBtXw7h+7yJPzN1oQk6cpl6Tho/Y8+9TKZRUtHfjSmyk3vYrcTfxcfQ0uE+QRxVuJcfQ3q8hn3YaxcT2w+ldoyWmD2RDmyhV5GvUevvlqfOp4lSh5BtRhIlShb9rJU5EhuptP3Er1OB0BwCmKhNyi3x+5uTnUcPdB9W9i4ymKhNy1cXL1PY0PLy6NKnz1STeisYzUP9z0TPQl/gbdwzuc/v8dZwreXBp13HWffgjf3z2C6fX7SE/9+l8byiq4PuRV7HvRyF3rxHgZvj70eXYmzhb29PAyx+49/3IpyYnbxe+/0JjI6jq4kW1e59z7jZO1K/or1dGiKdNhm0KAcyZMwdvb29++OEHFAoFAQEB3L17lwkTJvDJJ5/osnpq167Np59+CkC1atX44Ycf2L17N506dWLHjh2EhYWxb98+3XDAL7/8kk6dOhl8TJVKhZNTwTAONze3J5rzbNeuXYSGhhIREUHFigUfKlOmTKFbt25G90lPT2f+/PksWbJEV6fFixfr9oeC4ZbLly/n9u3bVKhQ8MXonXfeYdu2bSxcuJApU6Y8sm4eHh6YmJhgY2PzRMMiIyMj6d+/P7VqFVwhrVKl8IvR3LlzqV+/vt7jL1iwAG9vb65evUr16tWLHe+FF17Q/V2lShVmz55N48aNSU9PNzgP25M+Rk5ODjlFhrSZm5vzJLLTM9FqtFja6WdRWNpZk5mabnAfK3tbWg3rhWslT9T5aq4dO8ummYvp9fZIPKv7ABB9PZIrh07T/+PSH4aWmpyCRq3B0Uk/u83R0ZHEhESD+yQlJuHoWKS8kyNqtZqU5BScXZxp27E9KUnJvP3KG2i1WtRqNT379WbQsCEGj6nVavl59hyCatfCp0rJB2dsza1QKVWkZOm/LsnZaThYVjO4z5W4SGYdXMX4NoMxVZlgolRxIvIS84//abC8n0tFKjt6MOfIuhKvvzGKe4FGTar+kB5NahpKp8fLWFT5VELl5Unm0pWFG3NyyA+7gUWPTmRExxRkpzWqj8qnEprY0s3m/LexNbNCpVSSmq1/7qVkZ1DT4tFzStpb2FDLsyo/H9ugt/1CdDhd/JtwNS6S2PQkAt19qedVHeVTWNkrLSUVjVqDQ5FzzN7RgaTE4kOxnkRGegajBgwlLzcPpUrJy2++WixIVxoyU9PRaDTY2Nvqbbe2tyU9ufjQ2gd9O/YjMlLT0ajVtBvQnQbtC7Mz/WoHcmTLHnwC/XB0dyH8whUunzyHRqMtlXY8KD01DY1Gg52Dvd52Wwd7UpMeb+j1zvWbyc3OoUGrwgyUuOhYEmIu0aRtC8ZNeo/Yu9Esn7sIjVpDzyHPlGgbDElLKWiXvaOD3nZ7R3tS/sb517xdK9KSU/jszQ/g3udWx15d6T24/9+s8aNZm1miUir15osCSMvJws7CcKams7Udvk4VyFOrWRi8CWszSwbUboeVmQUrQgoyCy/HRtK2Sj3CEu6QkJFMNddK1PSo8lT6CQcLG0yUKhKz9N8/SVmpOFsZvkh24tYletZoyYEbZ7kSF0mAayV6BDbHVGWCg4UNCZmpHI+8xP/qdiTk7nXupMTRsGIArXzroFQ+/eFxOfe+B1rY6mczWthak5WaYXCf9PhkYsNuozQxofWL/cjJyCJ45Q5yMrNpNrT706i2nvvfj4oODU7JSsPRsvj3cyj4fjTjwAreaTtE9/3oeOQl5h0rHLFw6MY57M1tmNJtDAqFAhOliq2Xj7Lu/P5SbU95o1BIrlN5IsEzIYDQ0FCaNWumtxxwixYtSE9P5/bt21SqVDBnVO3a+ldfPT09dUMBr1y5gre3t17AqHHjxqVW30qVKukFvpo1a/bQfcLCwsjNzdUr5+TkhL+/v+7+6dOn0Wq1BgNFzs7OJVR7w15//XVeeeUVduzYQceOHenfv7/u+T516hR79+41GPQKCwszGDw7c+YMkyZNIiQkhMTERDT35oKIjIykRo3iEy0/6WNMnTqVzz77TG/bp59+im07f56Uosh8BtqH/CZy8HDBwaNwnhn3qt6kJ6VydscRPKv7kJudw97562g1rLfBedBKTZEv0lq0D19eu2h57f3NBdvPng5hxZJlvPr2GwQEBXL39h1++u5HHBcu5bnnhxU73I8zZnMjLJzpc2f/vXY8ghb9F6foa/egivZujGrck9Vn9xBy9xqOlrYMb9CVl5v1NRgg6+DXkJtJ0VyPv13i9b7PtHF9rIY8q7uffn9y/6In3RP8MDJr3gT1nSjUEfoZD5kLf8dq+P+wnzYJrVqN+tYd8oLPoKokQy7+iqLdQsG59+gASkvf2mTmZXP6jn7G4+9ndjKyYXemdBuDFohNT+LQjbMGFxooPUXPs0f0G4/B0sqSmb/+SFZWFudOh7BgzjzcK3hSq95Tmki7WPUf3aZRk94kNzuHW9ci2Ll8I04ertRuUZCt1H3kADb+spzZ4yejUChwdHehXtumnNl3rHTqb0jxl8lAO4s7se8Im5atY+zH4/UCcFqNFlsHO4aOG41SpaRytSokJyaxY+3mpxI80zHYrr9+/l0KOc+G39fwwusvUzWgGjF3o1ny46+sc17JM0MH/a2qPq6in1FQcHHJECUKtMBvp7eRnV+QNbzh4gFGNuzB2nN7ydOoWX9hP4PqdGBi+2FotZCQmcKJW5do7P30FqwoXn+F0Z5vYfAWnKzs+KX/BFBAUmYaWy4fZWj9LqjvHee7g6uY0G4ovw+ZhBYtd1Pi2Hz5CD0CynJKgSLnnVZr9FTUagv6lBYje2FmWXDRVv1Mew7O30CjgZ0wMTM1vOPTplAYPB+h4PvRi016szJkN2fuXsXR0paRDbvzSrN+/HCkYFROTY8qDKjTjp+PbeRaXCQedi6MbtyLpNpprDr3dIatC1GUBM+EoPCDqOg2QG+7aZGhEwqFQheUMXSMv0qpVBb7spCXV5iObeiL0KMe29iXpwdpNBpUKhWnTp1CpVLp/e/vrJp5P3PvwTo82B6A0aNH06VLFzZv3syOHTuYOnUq06dPZ9y4cWg0Gnr16sW0adOKHdvTs/hwhIyMDDp37kznzp357bffcHV1JTIyki5dupB7f1hZEU/6GBMnTmT8+PF628zNzfnh2ONnDVnYWKFQKoplmWWnZWBl9/jPt5tvRa4fPwdAalwiaQnJbP/xd93/7z/v88Z8xqDPx2HnVnzi6r/KzsEepUpJUpEss+Sk5GLZaPc5OjmSlFi0fBIqlQo7+4KryUvmLaR9l050690DAN+qVcjOzmb2tBkMHvGc3hxvc2bM5tihI3z74yxc3YpP9FwS0nIyUWvUOFjqZ5XYW9iQnGU4S/CZWm24HHuTjRcL5ju6mRRNdn4uX3Z7md/P7NS7SmumMqWFb21WhhieR6ik5J29SNqNB4Jc9yb9V9rboX4g+0xpa4M21fAE03pMTTFrVJcsA/MEaeITSJ/xI5iZobAwR5uahtXoYWjiDWckCsPScjNRazTYF8kys7OwIiXbcGbCg1r51uFIxHnURSaTTsvJ5PvDazBRqrAxtyI5K41na7cjPiO5JKtvkK29HUqVkuQi/UBKUgoORbKBnpRSqcSzYkHmdJVqVbl98xZrl60s9eCZlZ0NSqWS9GT9901GSjrWRbLRinK8N/G+eyUv0lPS2Ltmiy54Zm1ny5B3XiIvN4+s9AxsHe3Z+ftGHNxK94IWgI1dwXyaRbPM0lJSimWjFRV84ChLZv/Cy++/QWA9/Tm37J0cUKlUKFWF/bintxepScn3hqmW7s8T23vzhKYkJuttT0lOKZaN9iRWL/qdlh3b0q57QXZ/pSo+5GRn8+vMOfQd8myxuUlLUkZuFmqNBjtz/YtmtuaWxbLR7kvNySAlO10XOAOISUtEqVBgb2lLfEYyGblZLAjehIlShbWZBSnZGfQMbGFwoZKSlpydTr5GjbOV/rnmaGlr9PFz1XlM3bOUr/ctw8nSjoTMFHrXaEVGbpYuczw5O52JW3/CTGWCnYUN8RnJvNKsH1FpTz8r2vze98DsNP2+PDs90+gFUEs7GyztbXSBMwB7D2fQQmZyWol+x3scf+X70YDabQmNjWDDxQNAwfejn49tYGr3V1h2ZgdJWWkMqdeJfWGn2XUtTy5RrwABAABJREFUuKBMcgwWJqaMbf4Mq8/tNRqYE6I0SR6gEECNGjU4cuSIXnDnyJEj2Nra4uX1eFkSAQEBREZGEhNTuEpZcHDwQ/e5v3S5Wq0/n4SrqytRUYUTtqampnLjRuGcKTVq1CAyMpK7dwuXHz969OhDH8vPzw9TU1OOHSu8Wp2UlMTVq4XzltSrVw+1Wk1sbCx+fn56t7+zMqWra0FA48E2PThR/33e3t6MGTOGdevW8fbbbzNv3jwA6tevz8WLF/Hx8SlWL2vr4l8uLl++THx8PF999RWtWrUiICBAb7EAQ570MczNzbGzs9O7PemwTZWJCS6VKnDnkv7cNbdDw3Cv+vjzDiXcisLKvuCHtYOHCwM+fYX+H4/R3SrX9qeCvy/9Px5jdOWmv8rU1JRq/tU5E6w/d96Z4FME1gwyuE9gzaBi5U+fOEm1AH9MTAp+NOXkZBf7oXE/qHz/farVavlx+ncc3n+QabOn41HB8LwuJSFfoyYs4S51isyJUruCH1fibhrcx9zEtFjQWqMtCGAUDXW38KmFqUrF/nDDEwSXmJwcNHHxhbeoGDQpqZgEPpBZqVJhUq0q+eERjzycWcO6YGJC3vFTxgvl5qJNTUNhZYlpjQDyzj69lUT/DdQaDRFJUQR56A9HruHuS9gjshT9XSvhbuvEwQcWCigqX6MmOSsNlUJJg4oBnLlTOgtuPMjU1JSq1asRclL/fA85eZqAmiWb0aLVaotdrCkNJiYmePp6E3Zefz6esPOXqVT9CYaSa7Woi8wVCAXznNk5OaBRa7h0IoSABqWfSWdiakIlP19Cz+gvYBJ65gJVAw0Px4KCjLPFM39i9LuvUqtxvWL/r1qjOnFRMbqLjwAxd6Kwd3Io9cAZgImpKb7Vq3L+VIje9gunQqheI+AvHzcnJ6fY0L+Czy0enlJeAtRaDbdTYqnuqr+6dnXXSnoLADzoRmIU9ubWmKkKLwy72Tii0WpIKTIEL1+jJiU7A6VCSe0KfpyPDi/5RhSRr1FzJS6SRt768wg28g7kwiMeX63REJeRjEarpWO1hhyOOF8s2JKrzic+IxmVUknbqvUe2k+WFpWJCidvD6IuR+htj7ocgYuv4d8frlW8yEpJJy+nMOiZGpuEQqHAyuHhgfrSUPD96A51K+h/P6pbwY/LsUa+H6nMDHw/0hYpY+g7VEHa61MYNVxuKBWKcnn7r5LgmfhPSUlJISQkRO8WGRnJ2LFjuXXrFuPGjePy5cts3LiRTz/9lPHjxz/2lcJOnTpRtWpVRowYwblz5zh8+LBuwQBjWWGVK1dGoVCwadMm4uLiSE8vuELTvn17li5dysGDB7lw4QIjRozQywTr2LEj/v7+DB8+nLNnz3Lw4MGHLk4ABZljo0aN4t1332X37t1cuHCBkSNH6rWvevXqPPfccwwfPpx169Zx48YNgoODmTZtmt7Kl0/Kz88Pb29vJk2axNWrV9m8eXOxlT7ffPNNtm/fzo0bNzh9+jR79uwhMLDgC9Orr75KYmIigwcP5sSJE4SHh7Njxw5eeOGFYoFHgEqVKmFmZsb3339PeHg4f/zxB5MnT35oHZ/0MUpK7U7NuHzoNJcPnSYpKo4jK7eRnphCYJuCrIMT63axd0FhNtv5XUeJOBNKSkwCiXdjObFuFzdOhxLUrmCIsImpKU5e7no3cysLTM3NcPJyR2VS8j9Mnhn0LNv+3ML2TVuJjLjJz9/9SGxMDD369QJgwdx5fDN5qq58j769iImO4efZc4iMuMn2TVvZvmkrAwYXLs7QpEUzNq//g3279hB9N4rTJ06yZN5CmrZsrnsv/Dj9O/bs2MWESR9haWVFYkIiiQmJxeaiKyl/XjpEh2oNae/XAC97V0Y26o6LtT07rpwA4Ln6nRnXcoCu/Mlbl2lSOYgu/k1wt3HE37USoxr34mpcwapmD2pfrSEnIkNJz8kqlbo/TM7uA1h07Yhp3VooK3hgNWIw2txcck8Uro5nNXIwFn17FNvXrHkT8kIuoM0ontlgUsMfkxoBKJ2dMAmsjs1bY1HHxJJ75ESptudJWJqaU82lom5C4gp2LlRzqYi7TemuUPukdlw5TmvfurTyrYOnrTP/q9sRZyt79oYVvEYDarVldJNexfZrXaUuYQl3uJMSV+x/VZwq0MDLH1drB6q5eDO+zf9QKBRsufzwCzElpc/AfuzavJ1dm7dzKyKS+T/8THxsHF16F8zZs/SXhcz68lu9fcKvhRF+LYzsrGxSk1MIvxbGrYjCH2drfltJSPBpou9GcfvmLTauXMe+7btp26n9U2lT8x7tOb3nCKf3HiXuTjRbF68lJT6RRh1bAbBz+UbW/rhEV/749v1cPnWehKhYEqJiOb3vKIc37aZ2q0a6MreuRXDpRAiJMfFEhF5nydQf0Wq1tOz9dFYG7NivO4d27OXwjn1ERd5h1S9LSYyLp3X3DgCsX7SChdPn6Mqf2HeEhTPmMmDUUHz9q5GSmExKYjJZD/QRbbp3Ij0tnZU/LyHmThTnT5xh66qNtO3R+am0CaB7/z7s3bqLfVt3cefmLZbOmU98bDwdehWsyrji16XM+WqW3j4R18OJuB5OdnY2qcmpRFwP5/bNwsV96jdtxK4/t3Fk70Fio2I4fyqE1Yt+p0GzRiiLZPOXhn1hp2laOYjG3jVws3Gkb1BrHC1tORJREPzsEdicIfUKn+NTt6+QkZfN4HqdcLdxoopTBXrVaMnxyEvk3VskoJKDO7U8q+JsZUcVpwq83LQvShTsuf7wFcFLysqQXfSq0YIegc2p7OjB6y2exd3WkfX3MpbGNO3LRx1G6sp727vRuXpjKtq7Eejmw2edR1HFuQI/H9uoK1PD3Yc2VepSwc6FOp5+zOj1OgoULDu946m0qaiA9o0IO3KWsKPnSImO59Ta3WQmplKtVV0Azmzcz5Elm3TlfRrVwNzakmO/bSElKp6Y67c4s34vVZrVKrMhmxsvHqJjtUZ08GtIRXtXXmjUExdrB7ZfOQ7A0PpdeKNl4Xe84NuhNK1ck67+TXC3cSLArTKjm/Tialyk7vtR8O3LdPVvSkvf2rjZOFLH048h9ToRfOtSsUCbEE+LDNsU/yn79u2jXj39q6AjRoxg0aJFbNmyhXfffZc6derg5OTEqFGj+Oijjx772CqVig0bNjB69GgaNWpElSpV+Oabb+jVqxcWFhYG9/Hy8uKzzz7j/fff5/nnn2f48OEsWrSIiRMnEh4eTs+ePbG3t2fy5Ml6mWdKpZL169czatQoGjdujI+PD7Nnz6Zr164PreM333xDeno6vXv3xtbWlrfffpuUFP3hGAsXLuSLL77g7bff5s6dOzg7O9OsWTO6d//rk5CampqyfPlyXnnlFerUqUOjRo344osvePbZwrmX1Go1r776Krdv38bOzo6uXbsyc+ZMACpUqMDhw4eZMGECXbp0IScnh8qVK9O1a1eDwU1XV1cWLVrEBx98wOzZs6lfvz7ffvstvXv3NlrHJ32MklK1UU2yMzI5vXk/mSnpOFVwo9u457B1dgAgMyWN9MTC10idr+bYmh1kJKdhYmqCYwU3uo4bQqVaxrMASlubju1ITU1l2cIlJCUkUrmKD5O/nYr7vWzFxIREYmMKM/88Kngy+dup/Dz7Rzat24iTizOvvPkaLdu11pUZMmIYCoWCxb8sICEuHntHB5q0aMbIl0bpymxaXzCx7HuvvaVXn/EfvEfnHg9/L/wVRyLOY2tuxbN12uNoaUtkcgxTdi8m7t4wN0dLW1z+z959R0VxvQ0c/y69914UEAUEe+9d7JrE3o2JMRqNMZpE/RljjJqiiSaxx967xl6xN+wNG4pYQJDe675/oIsrLGoEIXmfzzl7Djt77+y9u8PM7DPPvWNsoSofEHweA119WnvXpl/11iSlp3IlLJgV5/eordfRzJry9m5M3Luo0Nv8OtL2HkShp4thjw9QGBmSdS+UxN/nwQtBSC0ryzyZE1p2tuiU9SBx5tx816swNMCgU1u0LCxQJieTceEyKVt2wkvDB4uTt21p/nwvd/j18Po5+6SdQSeZfHBpcTUrjzMPgjDWN6KDb33MDUx4FBfJb0fXqO6eaW5okmdok6GuPtVcvFl1If8fhLraOrxXoRF2JpakZqZzOewOC079TUpG0QSfX1a/aSPi4xJYu2wVMVHRlHJ3Y/xP32PnYA/k7DciX8oYHvnRZ6q/g2/e5sj+Q9g62LFgbc53lZaayrzfZhEV+RQ9fT2cS7nyxf9GU79po3fSpwp1q5GSmMShjbtIiI3HztWR3t8MwcI2ZxhVQkw8cS8MW1Yqlexf/TcxkVFoaWlhZW9Dix4dqd68nqpMZkYGB9ZuJybiKXoG+pSt7MsHQ/tiaGyU5/2LQo2GdUiKT2TH6k3ERcfiVNqFzyZ+hfWzIfJx0bFER0apyh/dfYDsrCxWz1nM6jmLVcvrNGtI/5E5N7GxsrXm80nfsH7BCr4f+g0W1pY07dCKVp01H6MLW50m9UmMj2fTirXERsfg4laKr6aMx9beDoDY6GiiItSDzmMH5+4r7t0K5sTBI9jY2/L7ypws+fd6d0WhULB+8Uqin0ZjZm5G1To16Pphr3fSp4uPb2OsZ4i/Vy3M9I0IS4hi/qmtqmCEmb4xli8MrUvPymDuyc28X6ExIxt2JykjlYuPb7Mr6ISqjK62Dm2862BtZE5aZgZBESGsPL9HbahnUTpw5xxmBiYMqN4Wa2Mz7kY9ZtS2P3mSkPN/ZG1kjr1p7jBFLS0telRuTikLBzKzszj/6CaDN/5CeELuNqqnrcvHtTriZGZDSkYaJ+9fZdK+xSSmv/uLVwBu1XxIT0rhyq7jpMQnYeFoQ+MhXTCxytmnp8YnkhT9wp2S9fVo+lk3zq7fx66fl6JvbEipqt5UategWNoPcDzkMmb6RnSr3Czn/CgmnEn7l6jOj6yMzLA1sVCVP3jnHIY6+rTxrsuAGm1JSk/lclgwy87tUpVZd+kgSqWSXlVaYmVkTnxqEoEPglh5YQ9CFBeF8nUmQhJC/CPHjx+nfv363LlzhzJlyhR3c8QLbt68ibe3N7dv38bTs/BuTz798OpCW1dJ8GWjHtx7mv/t0v/N3G2c+WDp2OJuRqHa2G8KsYNHvrrgv4jF3F+pN6vo7xr7Lh0fOpcBaycXdzMK1eJu4wh6B8O43iUfBw/WXthX3M0oVN2qtODQnQKGWP9LNfasxrkHQcXdjEJVzdWHL/6eWdzNKFS/dfj8P7k//35f8Vz4KirftviQTku+Ke5mFKot/X8s7ib8Y1uvHinuJuSro1/DVxf6D5LMMyEK0ebNmzExMaFs2bLcuXOHzz//nHr16kngrISJjo5mw4YNmJmZ4er6+nOLCSGEEEIIIYT4/0eCZ0IUooSEBL766isePHiAjY0NzZs3zzO3lyh+AwcO5Ny5c8yZM+eNJ/kXQgghhBBCCPH/iwTPhChEffv2pW/fvsXdDPEKmzdvLu4mCCGEEEIIIYRGijz3ZxfFSe62KYQQQgghhBBCCCGEBhI8E0IIIYQQQgghhBBCAxm2KYQQQgghhBBCCFGCaClk2GZJIplnQgghhBBCCCGEEEJoIMEzIYQQQgghhBBCCCE0kGGbQgghhBBCCCGEECWIQoZtliiSeSaEEEIIIYQQQgghhAYSPBNCCCGEEEIIIYQQQgMZtimEEEIIIYQQQghRgsiwzZJFMs+EEEIIIYQQQgghhNBAgmdCCCGEEEIIIYQQQmggwzaFEEIIIYQQQgghShAtZNhmSSKZZ0IIIYQQQgghhBBCaCDBMyGEEEIIIYQQQgghNJBhm0IIIYQQQgghhBAliNxts2SRzDMhhBBCCCGEEEIIITSQ4JkQQgghhBBCCCGEEBrIsE0hhBBCCCGEEEKIEkRLhm2WKJJ5JoQQQgghhBBCCCGEBgqlUqks7kYIIYQQQgghhBBCiBz7bp4u7ibkq4VXreJuQrGQYZtCCFGIvt4xu7ibUKh+ajuEa2HBxd2MQufrWIaGs4cUdzMK1ZEhs4kdOKy4m1GoLBb+wYC1k4u7GYVqcbdx1Js1uLibUaiOD51LQkREcTejUJna2bEkcEdxN6NQ9a/RllHb/ijuZhS6ae2HMf3w6uJuRqH6slEP+q2ZVNzNKFRLu4+n87Jxxd2MQrWh72Q6LfmmuJtRqLb0/5Fm84YXdzMK1YFPfi/uJvxjcrfNkkWGbQohhBBCCCGEEEIIoYEEz4QQQgghhBBCCCGE0ECGbQohhBBCCCGEEEKUIApk2GZJIplnQgghhBBCCCGEEEJoIMEzIYQQQgghhBBCCCE0kGGbQgghhBBCCCGEECWIltxts0SRzDMhhBBCCCGEEEIIITSQ4JkQQgghhBBCCCGEEBpI8EwIIYQQQgghhBCiBFEoFCXy8U/Mnj0bd3d3DAwMqFatGkePHtVYdtOmTbRo0QJbW1vMzMyoU6cOe/bsUSuzZMmSfNuWmpr6j9r3OiR4JoQQQgghhBBCCCEK3dq1axkxYgTjxo3jwoULNGjQgNatWxMaGppv+SNHjtCiRQt27tzJuXPnaNKkCe3bt+fChQtq5czMzAgLC1N7GBgYFFk/5IYBQgghhBBCCCGEEOKV0tLSSEtLU1umr6+Pvr5+vuV//fVXBg4cyEcffQTAjBkz2LNnD3PmzGHq1Kl5ys+YMUPt+ZQpU9i6dSvbtm2jSpUqquUKhQIHB4e37M3rk8wzIYQQQgghhBBCiBJES6EokY+pU6dibm6u9sgvCAaQnp7OuXPnaNmypdryli1bcuLEidf6HLKzs0lISMDKykpteWJiIqVLl8bFxYV27drlyUwrbJJ5JoQQQgghhBBCCCFeacyYMYwcOVJtmaass6dPn5KVlYW9vb3acnt7e8LDw1/r/aZPn05SUhJdu3ZVLfP29mbJkiVUqFCB+Ph4Zs6cSb169bh06RJly5Z9wx69HgmeCSGEEEIIIYQQQohXKmiIpiYv32hAqVS+1s0HVq9ezXfffcfWrVuxs7NTLa9duza1a9dWPa9Xrx5Vq1bljz/+4Pfff3+jtr0uCZ4JIYQQQgghhBBClCAK/tmdLUsSGxsbtLW182SZRURE5MlGe9natWsZOHAg69evp3nz5gWW1dLSokaNGty+ffut26zxPYpszUIIIYQQQgghhBDi/yU9PT2qVavGvn371Jbv27ePunXraqy3evVq+vfvz6pVq2jbtu0r30epVHLx4kUcHR3fus2aSOaZEEIIIYQQQgghhCh0I0eOpE+fPlSvXp06deowf/58QkNDGTx4MJAzh9qjR49YtmwZkBM469u3LzNnzqR27dqqrDVDQ0PMzc0BmDhxIrVr16Zs2bLEx8fz+++/c/HiRWbNmlVk/ZDgmRBCCCGEEEIIIUQJolD8NwYKduvWjaioKL7//nvCwsLw8/Nj586dlC5dGoCwsDBCQ0NV5efNm0dmZiZDhw5l6NChquX9+vVjyZIlAMTGxjJo0CDCw8MxNzenSpUqHDlyhJo1axZZPyR4Jv6RJ0+eMG/ePIYNG4alpWVxN0cIIYQQQgghhBAl0JAhQxgyZEi+rz0PiD136NChV67vt99+47fffiuElr2+/0YoU7xTSqWSPn36oK+v/8aBMzc3N2bMmFE0DXtLCoWCLVu2vFGdxo0bM2LEiCJpz79Vnz59mDJlSnE3462MGjWK4cOHF3czhBBCCCGEEEKUAJJ59g7079+fpUuXAqCjo4Orqyvvv/8+EydOxNjYuJhb9+amTp2Kh4cHX3/99RvXDQwM/Ff2Wbyey5cvs2PHDmbPnq1a1rhxYw4fPszUqVP55ptv1Mq3adOGXbt2MWHCBPr374+7u3uB658wYQLfffcdAEuXLmXWrFlcu3YNLS0tqlSpwldffUW7du0ASExMxNLSkhUrVtCtWzfVOrp168a6deu4c+cOZcqUUS0vU6YM3bp1Y8qUKXz11VeUKVOGL7744pVtKgy1S/vSyKMKpvpGPEmMZtu144TEhGksr62lRfOyNajiVA5TfSPiUhM5eOccZx/eUJUx0NHD36sWfg4eGOrqE5OSwPbrx7kZGapxvYVp15btbF2zkZioaFzdS/PhZ4MoX9Ev37LRUdEsnb2A4Ft3CHv4mDbvd2DgsE80rvvYgcP8OuknatarzTeTvy2qLuSrk29DelRpjpWROSHRYfxxfD2Xw4I1ln/PryHvV2iMg6kVTxJiWH5+N3tunlYr06ViEzr6NsTe1JK41CQOBZ9n/qmtpGdlFnV3VAw6tEavUT0URoZk3b1P8sp1ZD8OL7COfvPG6DWpj5aVJcrEJNLPXiR149+QmdNu/TYt0K1aCW1He5TpGWQF3yNl/Vayn0QUeX+aeFajtVdtLAxNeBQXyaoL+7j99EG+ZQfWbEd990p5lj+Ki+R/u+cDoK3Qoq1PXeq5V8TS0JSwhCjWXzrI1fC7RdqPf6KSoyc9q7TE264UNsYWfLNzDkfvXSruZgE5F9/mL17M5r//JiEhAd/y5fl65EjKvGI/e+DQIeb+9RcPHz/GxcmJIYMG0aRhQ9Xr8xYtYsHixWp1rK2s2LN1q+r5wcOH2bR1K0G3bhEXF8fKRYvwKlu2cDsInNt3nNM7A0iMjcfW2YHmvTvh6u3xynoPb91jxQ+zsHVxYOCUUarlNwMvc+Lv/cQ8eUp2VjaW9jbUbNOYCvWrF3rbNalbugKNPatgqm/Mk4Rotl47yr3oxxrLa2tp0aJcTao5e2Gqb0xsaiIHbgcS+CBIVaaBeyXquFXA0tCUpPQULofdYWfQSTKzs95FlwC4dugMl/ecIDkuAUsnO+p0a4Vj2dL5ln188x7bpy/Ns7zrxKFYONrmWX7nzBUO/rWR0pW88B/ao9DbrklTz2q08a6DuaEpj+MiWXlhD7ci89/3fVSrAw007PvG7poL5Oz72pWvR333ilgYmhEeH8W6Swe4Eq75uFfY/L1q0aF8fSyNTHkQG8GSwB0ERdzXWL6BeyU6+jbA0cya5PQ0Ljy+xbJzu0hMS1GVMdI1oGeVFtQq5YuxvgERCTEsPbeLC49uvYsu0dqrNp38Gub0KeYJC89s53pEiMbyDT0q855fI5zMrElKT+XCo1ssObuThLRkVZn25evRyqs2NsYWJKQlcSLkKsvP7ybjHZ1LdChfn66VmmFtZEZITDizT2zkSgHHyI6+Dejo2wAHUysiEmNYeX4v+24Hql73L1eTr5r0zlOv1V8j31mfSgItxb//bpv/JRI8e0datWrF4sWLycjI4OjRo3z00UckJSUxZ86c4m7aGxs7duw/rmtrm/cEQ/x3/Pnnn3Tp0gVTU1O15a6urixevFgtePb48WMOHjyouiOKq6srYWG5AaNp06axe/du9u/fr1pmYmIC5GSG/fnnn/zwww906tSJjIwMVqxYQceOHZk5cyafffYZJiYmVK9enYCAALXg2eHDh3F1dSUgIEAVPHv48CF3796lSZMmANjZ2dGyZUvmzp3LTz/9VMifkrqKjp60L1+fLVePcD8mnFqlyvNhzXb8eng1samJ+dbpVcUfU31DNlwOICo5DmM9Q7S1chOJtRVafFSrA4npKaw4v4e41EQsDExIy8wo0r48d+zgYRb/OZ+PRwzBp0J59vy9ix+++paZS+dia2+Xp3xmegZmFuZ80Ls729dvLnDdEeFPWDLnL8pX9C2q5mvU1LMaw+p35tcja7gafpcO5evzc7uh9F09iYjEmDzlO/o2YFDtjvxyaBVBESH42LnxVeNeJKQmc+L+FQBalK3BoNqd+ClgOVfD7+JqYc+Ypn0A+PP4xnfSL/3WzdFv2YTkRSvJehKBQTt/TL78jPhxkyA1Ld86urWqY9C5A8mLV5J15x5aDnYYfZhzkpu6dhMAOuU8SQ84Sua9+6CljeH77TD5cijx/5sM6elF1p+arj70rNyC5ed3czvyAY09qzKyYXfG7Z5HdHJ8nvKrLuxj/eUA1XNthRbf+3+k9kP//QqNqFO6AkvO7iAsPgo/Bw+G1evM5ANLCY19UmR9+ScMdfW5E/WQnTdOMKX14OJujpqlq1axau1aJowdSylXVxYuXcrQL75g46pVGBsZ5Vvn8tWrjP3uOwYPHEiThg0JOHKEb779loWzZuHnm7sf8HB3Z/YLwzde3CcCpKSkUKlCBZo3acIPP/9cJP27fuoC+1dswb//B7iUc+fCwROs/WU+H//0NeY2mjP1U5NT2DZ3FW6+ZUmKS1B7zcDYiLodmmPtZI+2jjZ3Llxnx/w1GJuZ4FHRu0j68aJKTmXp4NeATVcOERIdRu3SfnxUqz2/HFpJbEr+x6g+1Vpjqm/EuksHeZoUi4m+kdoPwCrO5WjjU5d1lw4QEh2GrYkF3So3B+Dva8eKvE8AwYFXObl2N/V7tsXesxRBR86y6/cVdP1uKCbWFhrrdZ30GXoG+qrnBqZ5LwgnRMVyesNeHMqWKoqma1TTtTy9qviz7NxObj19SJMyVfmyYU/G7JqT775v5fk9rL90QPVcS6HFD60GcebBddWyDyo2oW5pPxYF7iAs/ikVHMswvH4XJu1fQmhswRdYCkNdtwr0r96Gv05v40bkfVqUrcHYZv344u+ZPE2Ky1Pe2640n9XrzNKzOzn78AZWRmYMqtWRT+u8zy+HVgKgo6XNty0GEJeaxLTDq4hKjsfG2JyUjPyPd4WtnltFPqzZjnmntnIjIgR/r1qMbzGAYVt+zbdPPnal+bx+VxYFbifwQRDWRmYMrvMeQ+t+wI8By4Gc4Fqfaq3489gGbkSG4mRmw/D6XQBYFLi9yPvUuEwVhtR9n9+Predq+F3ala/H1Daf8uG6KfmeH7UvX5+BNdvz65HV3IwIxduuNCMbdicxPYWT96+qyiWmpdB/7Q9qdf8/Bc5EySPDNt8RfX19HBwccHV1pWfPnvTq1Us1RFCpVPLzzz/j4eGBoaEhlSpVYsOGDaq6MTEx9OrVC1tbWwwNDSlbtiyLX7jCeuXKFZo2bYqhoSHW1tYMGjSIxMT8T2ggZwyxQqFgz549VKlSBUNDQ5o2bUpERAS7du3Cx8cHMzMzevToQXJy7hWN3bt3U79+fSwsLLC2tqZdu3YEB+deeVq2bBkmJibcvn1btWzYsGGUK1eOpKQkIO+wTYVCwbx582jXrh1GRkb4+Phw8uRJ7ty5Q+PGjTE2NqZOnTpq7wMwZ84cypQpg56eHl5eXixfvrzAzz8wMJAWLVpgY2ODubk5jRo14vz58wXWeVlSUhJ9+/bFxMQER0dHpk+fnqdMeno6X331Fc7OzhgbG1OrVi21Mdv379+nffv2WFpaYmxsjK+vLzt37gRyv5cdO3ZQqVIlDAwMqFWrFleuXFF7jxMnTtCwYUMMDQ1xdXVl+PDhqs8XciZcbNu2LYaGhri7u7Nq1Sq1zz0kJASFQsHFixdVdWJjY1EoFGptvX79Om3atMHExAR7e3v69OnD06dPNX4+2dnZrF+/ng4dOuR5rV27dkRFRXH8+HHVsiVLltCyZUvs7HKCKdra2jg4OKgeJiYm6Ojo5Fl26tQppk+fzi+//MKoUaPw9PTEx8eHyZMnM2LECEaOHMmDBzlXWZs0aaLWp6CgIFJSUhgyZIja8oCAAHR1dalXr55qWYcOHVi9erXG/haWBu6VCHwQROCDICISY9h2/ThxqYnULp1/llY5W1c8rJ1YFLiDO1EPiUlJ4GFcBPdjck9iq7v6YKSrz7Kzu7gfE05sSiIhMeGEJUQVeX8Atq3fTLM2LWnRrhUupUsxcNgnWNvZsmfrjnzL2znaM3DYYJr4N8OogMzUrKwsZvzwC90H9Ma+CG9DrUnXSk3ZEXSCHUEnuB8Tzh/HNxCZGEsnv4b5lvf3qsXf145x8M45wuKjOHjnHDuCTtCzagtVGV8Hd66GB7P/9lnCE6IJfBDEgdtn8bLNP/uhKOg3b0zqjr1knL9E9qMwkheuQKGni14tzZktOmXcybxzl4zT58iOiibz2g3ST59Dxy33x2LSjDmkHz9N9uNwsh8+InnRSrSsrdB2cy3S/rT0qsWRexc5cvciYQlRrL6wj+iUeJqWqZpv+ZSMNOJTk1QPNytHjPQMOfZCtlYdtwpsDzrO5bBgIpNiCQg+z9Xwu7TyqlWkffknToVeY8Hpvzl892JxN0WNUqlk9bp1DOjbl6aNGuHp4cHEceNITUtj90u3r3/R6vXrqVW9OgP69MGtdGkG9OlDzWrVWLV+vVo5HW1tbKytVY+Xp5Vo26oVHw8YQM3qRZexdWbXYSo1rkXlJrWxcbanRZ/3MLO24MKB4wXW271oPeXrVMXZM+//fenynnjVqIiNsz2W9jbUaNUQO1dHHty8V1TdUNPIozJnQq9zJvQ6EYkx/H3tKLEpidQpXSHf8l62pShj7cxfp//m9tMHxKQk8CD2idoxys3SkZDoMC48ukVMSgK3Ih9w8dFtXMzt30mfAC7vO4lX/ap4N6iGpaMtdbu1xsTSnOuHzxZYz9DUGCNzU9VD66UgbXZ2Ngf/2ki1Dk0wKyBgWhRaedfmyN0LHL57kbD4p6y6sJfo5Hiaeea/zadkpBGXmqR6uFs5YaRnyNG7ufu+um4V2Hb9OJfD7hCZFMvBO+e4En6X1t6130mf2vvU4+Cdcxy4c5ZHcZEsObuTqKQ4WpbLf99bzsaVyKQYdt44SURiDDci7rPv9hnKWDupyjT1rIaJviE/B6zgZmQoT5NiuRFxX20bLUodfeuz//ZZ9t8O5GFcJAvPbOdpUhytvPL/TMvZliIyMYYdQSeISIwhKOI+e2+ewdPGWVXGy7YUN57c58i9S0QkxnDx8W2O3r2kVqYoda7QhF03TrHzxklCY58w+8QmIhJjaF++fr7lW5Stwfag4xwKvkBYQhQBwefZdfMU3So1e6mkkpiUBLWHEMVJgmfFxNDQkIyMnCyQ//3vfyxevJg5c+Zw7do1vvjiC3r37s3hw4cBGD9+PNevX2fXrl0EBQUxZ84cbGxsAEhOTqZVq1ZYWloSGBjI+vXr2b9/P5999tkr2/Ddd9/x559/cuLECR48eEDXrl2ZMWMGq1atYseOHezbt48//vhDVT4hIYEvvviCwMBA9u/fj1Kp5L333iM7OxuAvn370qZNG3r16kVmZia7d+9m3rx5rFy5ssChmpMmTaJv375cvHgRb29vevbsySeffMKYMWM4ezbnJObF/mzevJnPP/+cL7/8kqtXr/LJJ58wYMAAAgICNL0FCQkJ9OvXj6NHj3Lq1CnKli1LmzZtSEh4/Z3w6NGjCQgIYPPmzezdu5dDhw5x7tw5tTIDBgzg+PHjrFmzhsuXL9OlSxdatWqlCigOHTqUtLQ0jhw5wpUrV/jpp59U2VQvvs+0adMIDAzEzs6ODh06qLaVK1eu4O/vz/vvv8/ly5dZu3Ytx44dU/t8+vbty+PHjzl06BAbN25k/vz5RES82TCpsLAwGjVqROXKlTl79iy7d+/myZMndO3aVWOdy5cvExsbS/V8fpjo6enRq1cvtaDvkiVL+PDDD9+oXZBz62ITExM++STvsL4vv/ySjIwMNm7Mydhp0qQJN2/eVGW0BQQE0KBBA5o2bZoneFarVi2MXsh8qFmzJg8ePOD+fc1DA96WtkILZ3Nbbr80pOJW5ANKW+b/I6K8vTsP4yJo5FGFsc36MqpRT9r61EVHS/uFMm7cj31CJ78G/K95f75o2I0mZaqioOhTvzMyMgi+eYdKNdQDFZVrVOHGtSANtV7P+mWrMbMwp3lb/7dazz+ho6VNOdtSatlIAIEPgvCzz39Ilq6WDulZ6tl+aVkZ+Ni5qbJiLocFU862FD52OT+aHc2sqV3aj1MvXHktSlo21mhZmJN5LXfIL5mZZN68g04ZzUPpMu8Eo1PaFW330qr16FYoT8blaxrrKIwMAFAmJWss87a0tbRws3TkWrh6YOFa+F3K2Li81joaulfm+pN7RL2QqaGrpZ3nand6ViZlbYs2EPhf8igsjKjoaGrXqKFapqenR9XKlbl8VfP2fvnqVWq9UAegds2aeeqEPnxIq06d6NC1K2MmTODhY83DCotCVmYm4fce4u5XTm25u58XD2+HaKx3+fAZYp5E0eD9lq98D6VSScjVW0SHR1LqNYaCvq2cY5Qdt14a7n8rMhQ3q/wvYPg6uPMgNoImZaoxvvkAvm7Sm3bl66kdo+5FP8bFwg5Xi5zjnJWRGd52pQkqYNhaYcrKzORp6GNcypdRW+5SvgxPgvMf4vjcpknzWD5qGtt/XcrjG3kDmOe3H8bQ1Bjv+vkH64vK833fy0PJr4YH4/m6+z6Pylx/cpeo5NzsJ10tbTKy1fd9GVkZ72Tfp6OljYe1E5ce31FbfinsDl62+Wf13YwMxdrInCrOOf+H5gbG1C7lx/mHucMxq7t4cyvyAR/V6sBfXcbwa/vhvO/X6J0Mj9PR0qaMtTMXH99WW37x8W287fK/aHYj4j7WxuZUc/YCwNzAhDpufmpTdQRFhFDGxpmyz75rexMrqrp4qZUpKjnnR6553uvcwxv42ud/HqGrrUP6S6Mh0jIz8LYrrZY1bKirz6qe37Gm1/dMbjUIT+vX25b/SxQKRYl8/H8lwzaLwZkzZ1i1ahXNmjUjKSmJX3/9lYMHD1KnTh0APDw8OHbsGPPmzaNRo0aEhoZSpUoVVVDCzc1Nta6VK1eSkpLCsmXLVAGqP//8k/bt2/PTTz9hb6/5Kt4PP/ygyrQZOHAgY8aMITg4GA+PnBOyzp07ExAQoJrbrEuXLmr1Fy1ahIODA9evX8fPLydLZt68eVSsWJHhw4ezadMmJkyYQI2XTnpfNmDAAFVQ5uuvv6ZOnTqMHz8ef/+cH8iff/45AwYMUJWfNm0a/fv3V92tY+TIkZw6dYpp06apht29rGnTpmrP582bh6WlJYcPH1bNkVWQxMREFi5cyLJly2jRIidrZOnSpbi45O7Eg4ODWb16NQ8fPsTJKecK16hRo9i9ezeLFy9mypQphIaG8sEHH1ChQs4V2+ef9YsmTJiQ5z02b95M165d+eWXX+jZs6fqJgVly5bl999/p1GjRsyZM4eQkBD2799PYGCganv566+/KPuG87rMmTOHqlWrqk38v2jRIlxdXbl16xblypXLUyckJARtbW1VJtnLBg4cSP369Zk5cybnzp0jLi6Otm3bquYwe123bt1SZR2+zMnJCXNzc27dyjlJqlevHrq6uhw6dIgePXpw6NAhGjVqRNWqVYmLi+P27duULVuWQ4cO0bu3+rwKzs7Oqn49v43yi9LS0khLU0/x19fXz1OuIEZ6BmhraZGYnqK2PDEtGVP9/E9MrQzNcLN0JDMri2Vnd2OsZ0Anv4YY6uqz4dnQMysjM8oYmnLx8W0Wn9mBjbE5Hf0aoqXQ4sCdgq+qv62EuHiys7OxsLRQW25uaUlsdN7U/dcVdOUa+3fs4de//nzLFv4z5gYm6Ghp57nqGZ0cj5WrWb51zjy4Tjufehy9d4lbkQ/wsi1FG+866GrrYGFgQlRyPAfvnMPC0JQ/3/sSBQp0tLXZfPUIKy/sfRfdQmGe0/bsePUhPdnxCWhZW2msl3HmPCkmJph8MwJQoNDRJi3gKGm7NGcQGXZ7n8xbwWQ/0jyf39sy1TNCW0uL+JeGPMelJuFnYKKhVi5zAxMqOJZh3qktasuvht/F36sWtyJDiUiMwcfenSrO5WQukjcQFZWT+Wptpb5dWVtaEhauOeMjKjo6bx0rK6Kio1XP/cqXZ+K4cZR2dSUqJoaFS5cy8NNPWbtsGRbm5oXYC82SE5JQZmdjbK4+bYGxuSlJsflfqIsOjyRg7XZ6j/8MLW3tfMtAzrDOP4dNJCszE4WWFv79P8C9glehtj8/z6cEeHFeJYCEtBRM9fMfZmtlZI67lSOZ2ZksObsTYz0D3q/QGCNdA9Y9GyJ48fFtjPUMGVrvAxSAtpY2J0IuE3DnXL7rLGypickos5UYmqlf2DU0MyY5Pv+RG0bmpjTo0x7bUo5kZWZx+9Qltv+2lPZf9sexnBsA4XdCuXnsPB+Mf/fDpZ/v++JSk9SWx6UlYf6a+76Kjp7MPak+dcKV8Lu08qrNzYhQIhKjKW/vThVnr3ey7zPVN0JbS5u4l/fnKYlYOOXfp5uRocw8uo6RDbujq62DjpY2gQ+CWHhmm6qMvakVfiYWHL17iSkHluJoZs1HtTqgpaWlOo8q6j7FvnQuEZeSgKVh3nPr53369cgaRjXuqerT6dDrLDj1t6rMsXuXMdc3YUrrwSgUCnS0tNl14ySbrhwu0v5AToBSO5/zo5iUBKyMTPOtc/ZhEG2863A85Aq3nz6gnI0rrb1qoautg7mBCdHJ8YTGRvDzoZXcjXqs2o/M7DiCQRt+4lF8ZJH3S4j8SPDsHdm+fTsmJiZkZmaSkZFBx44d+eOPP7h+/TqpqamqYMlz6enpVKlSBYBPP/2UDz74gPPnz9OyZUs6depE3bp1gZxhaJUqVVLL7KpXrx7Z2dncvHmzwOBZxYoVVX/b29tjZGSkFsyxt7fnzJkzquehoaFMmjSJ06dP8/TpU1XGWWhoqCp4ZmlpycKFC/H396du3bp5Joh/nXYAquDS82WpqanEx8djZmZGUFAQgwYNUltHvXr1mDlzpsb3iIiI4Ntvv+XgwYM8efKErKwskpOTCQ19vcnTg4ODSU9PVwU4AaysrPDyyj15PX/+PEqlMk9gKS0tDWtrawCGDx/Op59+yt69e2nevDkffPCBWv+BfN8jKCgn2+XcuXPcuXOHlStXqsoolUqys7O5d+8et27dQkdHh6pVc694enp6vvFdUc+dO0dAQECerLjnn0V+wbOUlBT09fU1Xo2oWLEiZcuWZcOGDQQEBNCnTx90dXXfqF2vQ6lUqtpgZGREzZo1VcGzw4cPM3r0aHR0dKhXrx6HDh1CX1+fe/fu5QmwGhoaAqgNXX7R1KlTmThxotqyCRMmQI38g4evarMahQJl/kVVfVtzcT+pmTnzRm0POkHvqv5suXqEzOwsFChISk9h4+VDKFHyKD4SMwNjGnpULvLg2cvtVFEq/3HmW0pyMjMnT2PI6OGYWbybH8KavPxdKRQKlBq+raVnd2FlZMbc978CBcQkJ7D7xil6Vm1JljJn/1nZqSx9qvnz65E1BD0JwdncluH1uxBVrTXLzu0q9Pbr1qqOUd/uqueJM+c+69hLBRUKeHm7fIGOlycG7fxJWbGOzLshaNvZYtjjA7Lb+ZO2fU+e8oa9uqDt4kTCjzMKoRevlqc7KPJZmld994okZ6Ry/tFNteWrLuyjf/U2TGk9GCUQkRjDsXuX8r3RgMixa+9epkybpno+49n8kS/vBV7cZ2v00usv16lXO3e4kydQ0deXTt27s33XLnp378479XJbUeZZBjnD+7bOWkGDD1ph7VjwcUPfQJ8PJ39JRlo6Idduc2DlVixsrSld3rNQm/66Cvq6nn8vq87vVR2j/r52jL7VW7PpyiEys7MoY+1Ms7LV2XTlEKExT1QXeJqnJrP/hQnDi9rLx6QCdnlYONhg4WCjem5fxpXEmHgu7T2BYzk30lPTCFi4iQZ9OuQ7D9q78vLxSIHmY9SLGrhXIjkjlXOP1LOHVp7fw4Aa7fixzaeqfd/Rexdp4F65EFtdsPzOkTRxMbflw5rtWH/5IJce3cbCyJS+1VozqHZH5jwLDCoUCuJSk5h3agvZSiV3ox9jaWhGR98GRR4806iAcwkXczs+rtWBtRcPcOHxLSwNTelfvQ2f1nmPP0/kjLTwc/Cgc6UmzDu1lduRoTiY2fBRzfbEVExg3eWD76gTeY+8mv6nlp/bg6WhGX92GolCkRNo23PrDN0rN1f9vgyKCFHLRr0afo+5H4ymk19DZp14N3PCCvEyCZ69I02aNGHOnDno6uri5OSkChrcu5eT8r1jxw5Vpstzz7NYWrduzf3799mxYwf79++nWbNmDB06lGnTphV40vmqk9EXAxcKhSJPIEOhUKh2YJAzb5W7uzsLFizAycmJ7Oxs3NzcSH9p4ucjR46gra3N48ePSUpKwsws/6wMTe3QtOzFtrzct1edfPfv35/IyEhmzJhB6dKl0dfXp06dOnnarkmeA3c+srOz0dbW5ty5c2i/dAX5eRDqo48+wt/fnx07drB3716mTp3K9OnTGTZsWIHrfvEz+OSTTxg+fHieMqVKleLmzZt5lr/c/ufzc7y47Pmw0Bf78jx78WWOGuaasrGxITk5mfT09HyzwgA+/PBDZs2axfXr19UCs2+iXLlyHDt2LN/3efz4MfHx8WqZdk2aNGHt2rVcu3aNlJQUVWCxUaNGBAQEoKenh4GBAbVrq881Ef0sq0HTTS7GjBnDyJEj1Zbp6+vz7f6Fr92X5PRUsrKz81zBN9EzJDEt/6BdQlrO3CTPf5QARCbGoKVQYG5gQlRyHAlpSWQps9VOxCISYzAzMEZboaUK3BQFU3MztLS0iHkpyywuNhZzK4t/tM7wR2FEhD9hypjcYOXz7bdz03b8uXwBDs5FOwdaXGoimdlZWBmp788sDU2JSc4/qyQ9K4OfAlYw7fAqrAzNiEqOo335+iSlpxCXkpMdMLBme/bePMOOoBMA3I1+jIGuPqMb9WT5ud2v9aPnTWRcukLCxJDcBTo5pwFa5mZkxeVmn2mZmqCM1zys3aBTO9JPniH96EmAnGwyfT2M+vYgbcdetV+hhj07o1u5Aok/zUQZE1uo/XlZQnoyWdnZeTItzAyM8mRk5KeBeyVOhFwhK1v9fyQhLZk/jm9AR0sbE30jYlMS6FKxCU+TYguz+f8pDevXx698edXz9GfHmafR0aqpJwCiY2OxstKc5WhtZaXKWlPViYnBqoCLQoaGhpTx8ODBw4f/tPlvzMjUGIWWFkmx6lmcyXGJGJvnvRCVnpJG+L0HPLn/iL1Lc26yoVQqQankx76j6P71J7j55hzLFFpaWDnkHIvsSzsT9egJJ7cdKPLgWVJ6isZj1MvZaM8lpCYRl5qodoyKSIxGS6HAwtCEp0lx+HvV5vzDm5wJzZmYPjwhCj1tXTpXasKB24GFvNfLy8DECIWWIk+WWWpCEkZmr87Ses7O3YU7py8DEB8ZTUJULHtmrVK9/vw4tWDwRLp9PwwzO83b+dt6vu+zeHnfp29E/Ovs+zwqcSLkcr77vt+PrUP32b4vJiWBrpWavZN9X0JaMlnZWVgYqmcvmRsYa7xZxXt+jbgZcV9144n7sU9YkPk3P7QaxOqL+4lNSSAmOYEsZRbZLxynHsVFYmlkio6WdpHe8VVzn0w09qlzxcYERYSw5dqRnD7FhDPv1BamtvmUlRf2EpOSQM8qLTgUfF4VfL4f+wQDHV2G1H2f9ZcDCv1c4kVxqUlkZWdhafjy+ZGJxjnK0rMymHZ4Fb8dXYOloRnRyXG09alHUnqqxmO1EiU3I0NxMf//dfM5rXcw5Yp4fTLn2TtibGyMp6cnpUuXVgsMlS9fHn19fUJDQ/H09FR7uLrmDtuytbWlf//+rFixghkzZjB//nxV/YsXL6pNGH/8+HG0tLTyzQ76p6Kiorhy5QqjR4+mVq1auLq6EhISkqfciRMn+Pnnn9m2bRtmZmavDAr9Ez4+Phw7pn43phMnTuDj46OxztGjRxk+fDht2rTB19cXfX39Aie/f5mnpye6urqcOnVKtSwmJkY1PBCgSpUqZGVlERERkee7dHBwUJVzdXVl8ODBbNq0iS+//JIFCxaovVd+7+HtnXNHrapVq3Lt2rU86/f09ERPTw9vb28yMzO5cOGCah137twhNjZW9fx5MOjFO1u+ePOAF9/Hzc0tz/tomr+ucuXKQM6NBjTp2bMnV65cwc/Pj/Iv/Kh6E927dycxMZF58+bleW3atGno6urywQcfqJY1adKE27dvs2rVKurXr68KbDZq1IhDhw5x6NAh6tSpg4GBgdq6rl69iq6uLr6++d/VUV9fHzMzM7XHmw7bzFJm8yguMs/cIWVtXLgfk/8d/EKiwzEzMEJPO/fah42xOdnKbNXQhpCYcKyNzNUOtzbGFsSnJhVp4AxyAt9lvDy5dPaC2vJLZy/g7av5f7QgzqVc+W3RbKb/9afqUaNuLfyqVGT6X39ibWfz6pW8pczsLG5FhlLdVb0P1V28ufpE863YAbKys4lMiiVbqaSZZ3VOhFxVncga6OjlCc5nZ2ejUBSc3fGPpaaRHfE09/E4nOzYOHTKvzAETFsbHS9PMoMLmJBcTzdvmka2Mk9akWHPLuhWrUTiL3+Q/bTob1iRlZ1NSEwYvg7q86yUt3cn+GnBgRQv21LYm1px9IUbBbwsMzuL2JQEtBVaVHPx5sKjWxrL/n9nbGSEq4uL6uHh5oa1lRWnA3MzizIyMjh/8SIV/fK/QQpART8/Tp9Vz5g9HRhYYJ309HRC7t/H5lnW97ugraODg7sL966qbxP3rt7CpaxbnvL6hvp8NHU0Ayd/qXpUaVoHK0c7Bk7+Eqcymu/UqERJVkbR33Eu5xgVQbmXjlHlbEsREp3/8Ot70WGYGRijp517rmtrbEG2MlsVHNDT1snzYz5bmf0sE6zofyhq6+hgU8qJR9fVb0b1MCgY+zKvP5dX1IMwjJ4FRi0cbOg84VM+GD9Y9Shd0QsnL3c+GD8YY6uCLyS/rdx9n/p0IL4OHtx5xb7P2640DqbWBd5kJCM7i5hn+77qLt55snOLQmZ2FnejHlPRST1IXNHRk5uR+Y8c0dfRJfvlbetZQPD5lnUz8j4OptZqmYeOZtZEJ8cXaeAMcvoUHPWIyi/1qbKTJzci8p9jV187n/OEl57ra+tqKKMomnOJF+ScHz2gmov6UPJqLt5ce1LwjU2ysrN5+uz8qEmZqpy6f7XAQF8Za2e1+UiFeNck86yYmZqaMmrUKL744guys7OpX78+8fHxnDhxAhMTE/r168e3335LtWrV8PX1JS0tje3bt6sCRb169WLChAn069eP7777jsjISIYNG0afPn0KHLL5piwsLLCysmLu3Lk4ODhw//591VxozyUkJNCnTx+GDRtG69atKVWqFNWrV6ddu3Z55kt7G6NHj6Zr165UrVqVZs2asW3bNjZt2sT+/fs11vH09GT58uVUr16d+Ph4Ro8erRqW9zpMTEwYOHAgo0ePxtraGnt7e8aNG6d2l6Vy5crRq1cv+vbty/Tp06lSpQpPnz7l4MGDVKhQgTZt2jBixAhat25NuXLliImJ4eDBg3mCft9//73ae9jY2NCpUycgZ0642rVrM3ToUD7++GOMjY0JCgpS3dzB29ub5s2bM2jQIFWm45dffomhoaEqe83Q0JDatWvz448/4ubmxtOnT/nf//6n1oahQ4eyYMECevTowejRo7GxseHOnTusWbOGBQsW5Mmsg5ygXNWqVTl27JgqkPYyS0tLwsLC3mq4Zp06dfj8888ZPXo06enpdOrUiYyMDFasWMHMmTOZMWOGWuC5bt266Ovr88cffzBu3DjV8ho1ahAXF8fGjRsZPXp0nvc5evQoDRo0eKPt5J84eu8S3So342FsBKGxT6jpWh4LQ1NOheZMht3KqzZmBsYvzBVzi2Zlq9OlUlP23QrEWM+ANt51Ofvghuqk79T9a9Rzq0B73/qcCLmCjbEFTTyrcjzkisZ2FKb2Xd7j9ynT8fQqi5evN3u37ebpk0hadmgDwIr5i4l6GsXnY0ep6ty7nfMjJjUlhfi4OO7dDkZHVxdXt1Lo6etR2sNN7T2Mn2Vzvry8KK27dJBxzfpxM+I+157co335etiZWrL16lEABtXuiI2xBVMOLAVyhlr42LsR9OQepvpGdK3UDHdrR6YcXKpa54n7V+haqSm3nj5QDdscWKsdx0Ou5Dk5Lipp+w9h0LYl2U8iyYqIxKBNS5TpGaSfzg1YGA3sQ3ZMLKmbcuaNybx0Ff2WTcgKfUjW3fto2dlg0KktGRevqoJqhr27olerGol/LECZmorCLOdKuzIlFV7Kdi1Me2+e5uNaHQmJDuPO04c0KlMFayNzAoJz7rDcuUJjLIxM+ev0NrV6DT0qExz1iEdxeedS8bBywtLQlNDYJ1gYmtLJrwEKhYKdN04WWT/+KUNdfbUr805mNpS1cSE+NYknif983sG3pVAo6NG1K4tXrKCUqyuuLi4sXr4cA319Wr0wdcW3P/yAnY0Nnw3OmTeqe+fODBo2jCUrV9K4fn0OHTvG6bNnWThrlqrOjFmzaFC3Lg729sTExLBw2TKSkpJo17q1qkxcfDzhT54Q+ezC2f1n0zZYW1kVWpCtZutGbJuzCkcPV5w93bgYcJL4qBiqNMuZauPQ2u0kxMTTfnBPFFpa2LqqZ8wam5mgo6ujtvzE3/txdHfFwt6G7MxMgi8GcfXYWfz7dy6UNr/K4bsX6VGlBQ9ic+7qXLu0LxaGJqqbmrT2roO5gQlrLubMd3jh0S1alKtBt8rN2HvzNMZ6hrQrX48zoUGqY9T1J/do6FGFR3GRhMY8wdrYnFbetbkWfq9IM2ReVLFFHQIWbcKmtBP2ZVwJOnKOxOg4fBrlzBd7ZtN+kmLjafLh+wBc2X8SU2sLLJ3syMrK4s6py9w7H0SLwTlz9uro6mLlrH7erf/sJikvLy8qu2+c4pPanbgX/Zg7UY9o8mzfd/DZXHJdKjbF0tCU+ae3qtVr6FGZO08fat73GZkRGhOOpZEpnfwa5ez7nmVLF7VtQccZVq8zd6MecTMylBZla2BjbM7eWzmjF3pWaYm1kRl/HN8AwNmHNxhc5z1alqvJxce3sTQ0ZUCNttyOfKDKgtpz8wytveswoGZbdgWdxNHMhvcrNH5n+/Ot144xokFX7jx9xM3I+7QsVwsbYwv23DwNQO+q/lgbmTPz2DoAAh8GMaTu+7TyqsWFR7exNDJlYM123IoMVfUp8OENOpSvz93ox9yKfICjqTU9q7Qg8MH1d3IuseFKAN806cOtyAdcf3KPtj51sTOxZNv1nGSHgTXbY2Nszk8BK4Cc4bXedqUJenIfE31DulRsgruVo+p1gD7VWhH0JIRHcZEY6Rnwnl8jPK1d+P3Y+nzbIMS7IMGzEmDSpEnY2dkxdepU7t69i4WFBVWrVmXs2LFAzt2oxowZQ0hICIaGhjRo0IA1a9YAOXM67dmzh88//5waNWpgZGTEBx98wK+//lqobdTW1mbt2rUMHz4cPz8/vLy8+P3332ncuLGqzOeff46xsbFqknlfX19++uknBg8eTN26dfMMS/2nOnXqxMyZM/nll18YPnw47u7uLF68WK0tL1u0aBGDBg2iSpUqlCpViilTpjBq1CiN5fPzyy+/kJiYSIcOHTA1NeXLL78kLi5OrczixYv54Ycf+PLLL3n06BHW1tbUqVOHNm1yggZZWVkMHTqUhw8fYmZmRqtWrfjtt9/U1vHjjz/y+eefc/v2bSpVqsTff/+tGp5YsWJFDh8+zLhx42jQoAFKpZIyZcrQrVs3Vf1ly5YxcOBAGjZsiIODA1OnTuXatWtqmVWLFi3iww8/pHr16nh5efHzzz/TsmXunb6cnJw4fvw4X3/9Nf7+/qSlpVG6dGlatWqV57bsLxo0aBBLliwp8G6vFhYWr/6wX2HGjBlUrFiROXPmMH78eBQKBVWrVmXLli20b99erezzIZmHDx9W20Z0dXWpU6cOBw4cyPdGE6tXr84zp1lRuBx2ByM9fZqVrY6ZvjHhiVEsDtyuukJvqm+EhWHuMIz0rEz+Ov03HX0bMKx+Z5LT07gcdkd10gU5Qwz/Or2N9uXrMaJBN+JTkzh+7zKHgi/kef+iUL9pIxLiE1i3dBUx0dGUcndj3E8TsXPI+QERExXD0yfqJ+lffpybpRp86w5H9x/C1t6OeWuXvJM2v46Dd85hpm9Mv+ptsDY2415UGF9vn82TxJwhvtZGZtib5A4l09bSolulZpSysCczO4sLj24xZNM0whNyJzpfdnYXSqWSj2q1x9bYgtiURE6EXGHB6b/zvH9RSdu1H4WuLoa9u6IwNiLrbgiJv86C1NwbYmhZWaplmqVu34OSnOGbWpbmKBMSybh0ldRN21Vl9Js0AMD068/V3i950QrSj5+mqJx5EISxvhEdfOtjbmDCo7hIfju6RnW12tzQBGsj9bnzDHX1qebizSoNN2rQ1dbhvQqNsDOxJDUzncthd1hw6m9SMtLyLV+cvG1L8+d7uUPKh9fPuXi1M+gkk18I3BaHfj17kpaWxo/Tp5OQmIifjw9//vorxi/c6Tj8yRO1ycgrVajA5AkTmPPXX8z96y9cnJ2ZOnEifi9kBT+JiGDcxInExsVhaWGBn68vi+fOxfGFrO8jx44xcepU1fOxz25W8/GAAXzyD+78nJ/ytauQkpDM8c17SYyNx9bFka6jP8bcJme4XmJsAvFP3yyAmZGWzp4lG0mIjkVHTxdrJ3vaf9qL8rWrFEqbX+XS49sY6xrQolzNnGNUQhQLT29T/Wg3MzDGUu0YlcG8U1t5z68hnzfsRnJ6Kpce32HXC4GJ/c+GZrbyro25gQmJ6SlcD7+nVqaolanhR2pSMud3HCY5LhErJztaD+uFqbUFAMlxCSRG557jZWVmcWrDXpJiE9DR1cHSyY5Ww3pSqkLhjfJ4W2ceXMdE35COfg2xeLbv+/XIatXdM80NTbAyVs+AM9TVp7qLDyvP552rEnL2fR9UaIytiSVpmelcfnyH+Se3kPyO9n0nQq5gqm9E54pNVBcwphxYpho2amloio1x7v78UPAFDHX1ae1dm37VW5OUnsrV8LusOJfbv6jkOCbtW0z/Gm2Y3mEY0cnx7Aw6oRoWWdSOh1zGTN+IbpWb5fQpJpxJ+5cQ+axPVkZm2JpYqMofvHMOQx192njXZUCNtiSlp3I5LFhtXtR1lw6iVCrpVaUlVkbmxKcmEfggiJUX8v9eC9uh4AuY6RvTp5o/VkbmhESHMWbXXCKeXbCxNjLD7oXzIy2FFp0rNsXV3I7M7CwuPb7NsC2/qc6nIGd4+MiG3bE0MiMpPYU7Tx/yxbaZGrMO/6v+P9/ZsiRSKF9nMichRJE7dOgQTZo0ISYmplACTM89fPgQV1dX1Xx5RSk1NRUvLy/WrFmjduODf5sdO3YwevRoLl++jI7Om11j+HrH7CJqVfH4qe0QroUFv7rgv4yvYxkazh5S3M0oVEeGzCZ2YOEPlS9OFgv/YMDaycXdjEK1uNs46s1693fjK0rHh84lISKiuJtRqEzt7FgSuKO4m1Go+tdoy6htfxR3MwrdtPbDmH54dXE3o1B92agH/dZMKu5mFKql3cfTedm4Vxf8F9nQdzKdlrz65mj/Jlv6/0izeXnnVv43O/DJ78XdhH/szP1rxd2EfNUsnf+0Nv91knkmxH/MwYMHSUxMpEKFCoSFhfHVV1/h5uZGw4YNi/y9DQwMWLZs2RvNJ1cSJSUlsXjx4jcOnAkhhBBCCCGE+O+RX4ZC/MdkZGQwduxY7t69i6mpKXXr1mXlypVvNc/Ym2jUqNE7eZ+i1LVr1+JughBCCCGEEOL/MRm2WbJI8EyIEqJx48Z57pTzT/j7++Pv718ILRJCCCGEEEIIIYTmmb+FEEIIIYQQQgghhPh/TjLPhBBCCCGEEEIIIUoQLRm2WaJI5pkQQgghhBBCCCGEEBpI8EwIIYQQQgghhBBCCA1k2KYQQgghhBBCCCFECaJAhm2WJJJ5JoQQQgghhBBCCCGEBhI8E0IIIYQQQgghhBBCAxm2KYQQQgghhBBCCFGCyN02SxbJPBNCCCGEEEIIIYQQQgMJngkhhBBCCCGEEEIIoYEM2xRCCCGEEEIIIYQoQRQybLNEkcwzIYQQQgghhBBCCCE0kOCZEEIIIYQQQgghhBAayLBNIYQQQgghhBBCiBJEhm2WLJJ5JoQQQgghhBBCCCGEBhI8E0IIIYQQQgghhBBCAwmeCSGEEEIIIYQQQgihgcx5JoQQQgghhBBCCFGCaCFznpUkknkmhBBCCCGEEEIIIYQGCqVSqSzuRgghhBBCCCGEEEKIHFcf3ynuJuTLz8mzuJtQLGTYphBCFKIV53YXdxMKVe9qrVhwamtxN6PQfVy7I/EHjxR3MwqVWdOGxLR4r7ibUags920mKPxucTejUPk4eJAQEVHczShUpnZ21Js1uLibUaiOD53LlANLi7sZhWpss34khD8p7mYUOlMH++JuQpFICLlf3E0oVKZupYn9/Jvibkahspj5I78ErCjuZhSq0U16/+f2E//mfYRCIcM2SxIZtimEEEIIIYQQQgghhAYSPBNCCCGEEEIIIYQQQgMZtimEEEIIIYQQQghRgmjJsM0SRTLPhBBCCCGEEEIIIYTQQIJnQgghhBBCCCGEEEJoIMM2hRBCCCGEEEIIIUoQudtmySKZZ0IIIYQQQgghhBBCaCDBMyGEEEIIIYQQQgghNJBhm0IIIYQQQgghhBAliAIZtlmSSOaZEEIIIYQQQgghhBAaSPBMCCGEEEIIIYQQQggNZNimEEIIIYQQQgghRAmipZBcp5JEvg0hhBBCCCGEEEIIITSQ4JkQQgghhBBCCCGEEBrIsE0hhBBCCCGEEEKIEkShkLttliSSeSaEEEIIIYQQQgghhAYSPBNCCCGEEEIIIYQQQgMZtimEEEIIIYQQQghRgmjJsM0SRTLPhBBCCCGEEEIIIYTQQIJn4j/h0KFDKBQKYmNji7sphc7NzY0ZM2b84/ohISEoFAouXrxYaG0SQgghhBBCCCH+v5Bhm+Jf48SJEzRo0IAWLVqwe/fu4m6OEIXm7L6jnNx+kITYeGydHfDv+z6lvMu8st6Dm3dZOukP7FwdGTT1K9XyiIdhHF6/k7B7D4l7Gk3LPu9Rq3XjIuxBXhcOnCBw52GS4hKwcbKnSa8OuHi5v7Leo1shrJk6FxsXe/pN+kLttdSkFI5t3M3ts1dJTU7B3MaKxj3a4lHJp6i68UpKpZIFO7ax+dgREpKT8XVz56vuPSnj5KyxzsEL51myeycPIiPIzMrC1c6O3s1b0qZWnXfY8n9Gt35t9Nu2RLtsGbTMzYgf/AVZwSHF3aw8dm7ezpY1G4iJjsbVrTQDP/sE30p++ZaNjopm8awFBN+6TdjDx7T9oAMfDRusVubkkeNsWLGWsEePycrMxNHFmY5d36eJf7Mi64NSqWT+4sVs/vtvEhIS8C1fnq9HjqSMe8H/RwcOHWLuX3/x8PFjXJycGDJoEE0aNlS9Pm/RIhYsXqxWx9rKij1bt6qeHzx8mE1btxJ06xZxcXGsXLQIr7JlC7eDb6CSoyc9q7TE264UNsYWfLNzDkfvXSq29hTkxuFzXNt/iuS4RCwcbanZpTn2nqXyLRt+6z57ZqzMs7zTt4Mwd7BRPb9+8Aw3j5wnKSYefWNDSlf1plrHJmjrFs1pvFKpZP6SxWzeti132xvxxau3vcOHmLtwYe6299HHatseQERkJH/Mm8uJ06dJTUujtKsr47/6Gh8vLzIzM5n91wKOnzrFo7AwTIyNqVmtOsM++QRbGxsN7ypepFQqmb9iOZt37iQhMRFfb2++HvoZZdzcNNYJDglh7rJl3Lhzm7AnTxj5yWB6vv++WpnzVy6zfP16gm7f5ml0NNMmTKBx3XpF3JtcBq2ao1e3JgpDQ7LuPyB5wxaywyM0ljf5bBA6ZT3yLM+4doOk+UtU6zRo3Vzt9ez4BOLHTy7Utufn+qGzXN53kpS4BCycbKnTxR+HsvnvJx7fDGHnb8vzLO/83adYPNtP3DpxiSPL/s5Tpv8fY9D5l+0n5i1exIIlS9TqWFtZsWfzFtXzqOho/pg3l1OBgSQkJlK1UiVGf/45pVxcC7OLJYICGbZZkkjwTPxrLFq0iGHDhvHXX38RGhpKqVL5H2TEv0dGRga6urrF3Yxide3kefYs20ybD7vgUs6d8wdOsOqnuXz6yxjMbaw01ktNTmHrnBW4+5YjKT5B7bXMtHQs7WzwqVWFfSs2F3UX8rhx+iIBK7fRvG8nnMu5cSngNBunL2TA1C8xs7bUWC8tOYWd89dQurxnnj5lZWay/pcFGJmZ0OGzPphYmZMQHYuegX5Rd6dAy/buZtWBfXzbdwCl7OxZtGsHn/3+Gxu++wFjA4N865gbGzOgdRvc7B3R1dHm6JXLfL9sCZamptQpn3+Ap6RQGOiTee0G6UdOYDxyaHE3J1/HDh5m0Z/z+OSLoXj7lWfPtp1M+no8fyydh629XZ7yGekZmFuY06V3d/5en///i4mpKV16d8O5lCs6ujqcPXmGP376FQtLC6rUrFYk/Vi6ahWr1q5lwtixlHJ1ZeHSpQz94gs2rlqFsZFRvnUuX73K2O++Y/DAgTRp2JCAI0f45ttvWThrFn6+vqpyHu7uzP7tN9VzbS31gQgpKSlUqlCB5k2a8MPPPxdJ/96Eoa4+d6IesvPGCaa0HvzqCsXk3tnrBG7YR63urbDzcOHWsQvsn7WWjuMHYWJlrrFepwmfqO3L9E1zv9+7Z65ybksA9fq0w87Dmbgn0Rxfvh2Amp1bFEk/lq5exap165gwZgylXFxZuHwZQ78cycYVKwve9iZOZPCHA2nSoAEBR4/yzXcTWPjnLPzKlwcgPiGBgZ8NpXrlKsz8+WesLCx5+PgxpiYmAKSmpnLj1m0+6tuPsp6eJCQkMP3PPxg5dgzL5y8okr7+1yxdt45VmzYx4ctRlHJxZuGqVQwd8w0bFy7S+N2lpqXh4uhA84YN+HXevHzLpKSmUtbDg/Yt/flq0vdF2YU89Js1Qr9JfZJXricr8ikGLZtiMuQj4idPg7T0fOskLVoO2tqq5wpjI0y/+pyMi1fUymWFhZM466/cBdnKIunDi4LPXuPU+j3U7dEG+zIu3Dh6nt1/rqLzhE8L3E90mTgE3Rf2Ewam6t+nroE+XSYOUVtWVIEzKLr9BDw7Rk3/VfVc+4XvUqlUMmrcOHR0tJk+eQrGxsasXLeWISNHsn7pMgwNDYusz0LIsE3xr5CUlMS6dev49NNPadeuHUteuiKRn+PHj9OoUSOMjIywtLTE39+fmJgYAHbv3k39+vWxsLDA2tqadu3aERwcrKr7fKjjpk2baNKkCUZGRlSqVImTJ0++9nsolUp+/vlnPDw8MDQ0pFKlSmzYsKHANkdERNC+fXsMDQ1xd3dn5cq8V6Tj4uIYNGgQdnZ2mJmZ0bRpUy5dev0r8FlZWQwcOBB3d3cMDQ3x8vJi5syZhVJn8eLF+Pj4YGBggLe3N7Nnz1a99vwzXbduHY0bN8bAwIAVK1aQnZ3N999/j4uLC/r6+lSuXFkts/B1vouoqCh69OiBi4sLRkZGVKhQgdWrV6tenzdvHs7OzmRnZ6u1t0OHDvTr10/1fNu2bVSrVg0DAwM8PDyYOHEimZmZr/3Z/hOndh6iSuPaVGlSR5V1ZmZtydn9xwust+OvtfjWrYZLWbc8rzmVKU3zXh3xq1sVbZ13f43k7O6jVGhYg4qNa2HtZE/TXh0wtbLg4oFTBdbbu2QTPnWq4JhPlsaVI4GkJibTaXg/nMu5YW5jiUs5d+xKORVVN15JqVSy+uABBrRqQ9MqVfF0dua7fgNITU9nT+BpjfWqlfOiSeWquDs64mJrR4+mzfF0duHinTvvsPX/TPr+w6SuWEfm+ZKZ9QOwdd1mmrdpSYt2rXB1K8VHwwZjY2vL7q078i1v72jPR8MH06RVc4xMjPMtU6FKRWo3rIerWykcnZ1o37kTbh7uXL9yrUj6oFQqWb1uHQP69qVpo0Z4engwcdw4UtPS2L1vn8Z6q9evp1b16gzo0we30qUZ0KcPNatVY9X69WrldLS1sbG2Vj0sLdWD2m1bteLjAQOoWb16kfTvTZ0KvcaC039z+O7F4m5Kga4fPINn3UqUq1cZC0cbanZpgbGFGTePnC+wnqGpMYbmJqqH1gvBzIh7j7Ar44JHDV9MrC1wLu+Be/XyRN0PK5I+KJVKVq9fz4A+fWja8Nm2N2Zszra3v4Btb8N6alWrzoDevXO2vd6982x7S1etxN7WjgljxuDnUx4nR0dqVquGi3NOpq6JiQmzf/2VFk2b4laqFBV8fRk9/HOCbt4k/MmTIunvf4lSqWT1ls0M6N6DpvXr4+nmzsRRo3O+u4CDGuv5ennx+ceD8G/cBD0NFzTr1ajJkP4DaFq/flE1XyP9RvVI3RtAxuVrZIc9IXnFOhS6uuhVq6yxjjI5BWVCouqh61UWMjJIv3hZvWBWtlo5ZVJS0XYGuLr/FOXqVcG7fhUsHW2p09UfY0szgg6fLbCegakxRuYmqofWSxc9FArUXjcyNymyPhTlfgLyOUZZWKheC334kCvXr/HNyC/x9fHBrVQpvvliJCkpKew5cKCouiwEIMEz8S+xdu1avLy88PLyonfv3ixevBilUvPVoYsXL9KsWTN8fX05efIkx44do3379mRlZQE5wbiRI0cSGBjIgQMH0NLS4r333ssTXBk3bhyjRo3i4sWLlCtXjh49eqiCKa96j//9738sXryYOXPmcO3aNb744gt69+7N4cOHNba7f//+hISEcPDgQTZs2MDs2bOJiMhNS1cqlbRt25bw8HB27tzJuXPnqFq1Ks2aNSM6Ovq1Psvs7GxcXFxYt24d169f59tvv2Xs2LGsW7fureosWLCAcePGMXnyZIKCgpgyZQrjx49n6dKlauv6+uuvGT58OEFBQfj7+zNz5kymT5/OtGnTuHz5Mv7+/nTo0IHbt2+/9neRmppKtWrV2L59O1evXmXQoEH06dOH06dzAhhdunTh6dOnBAQEqNYXExPDnj176NWrFwB79uyhd+/eDB8+nOvXrzNv3jyWLFnC5MlFl76flZlJ2L0HeFT0UltepoIXD2/d01jv4qFTxEQ8pdEHrYqsbf9UVmYmT0Ie4eZXTm25m19ZHt8J0VjvypFAYiOiqNupeb6vB1+4jpNnaQ4s28zsYd+zeOx0Tm07mOd/9l169PQpUfFx1C6fm9Gjp6tL1bLluPxCML4gSqWSMzeCuP8knKply726gihQRkYGwbduU7lGVbXllWtU5cbV64XyHkqlkkvnLvDowUN8KxZNpuCjsDCioqOpXaOGapmenh5VK1fm8tWrGutdvnqVWi/UAahds2aeOqEPH9KqUyc6dO3KmAkTePj4ceF24P+hrMwsokLDcPJRHyrm5ONO5N2HBdbdNnUh676ZyZ6ZKwm7GaL2mn0ZF6JCw4kMyfmOEp7G8OhqMC5+noXa/udU2171l7a9SpUK3vauXcu77dWoyeVruXWOHD+Oj7cXX3/7LS06dqDnwIFs3ratwPYkJiWhUCgwMSm6QMB/xaPw8JzvrlpuNqyenh5VK1Tk8vXC2f+9a1rWVmiZm5F544VzwqwsMoPvoeNe+rXXo1e7BunnL0F6hvr6bW0w+34spt9+hVG/HmhZa874LwxZmVk8DQ3D5aX9hItPGZ68Yj+xefICVn71Gzt/W87jl/YTABlp6awZ+zurvpnBnllreBpaNAF2KNr9BDw7Rr3/Hh26dWXMxO/UjlEZ6TnZhvp6eqpl2tra6OjocPHKS8HR/wCFQlEiH/9fybBN8a+wcOFCevfuDUCrVq1ITEzkwIEDNG+e/w/tn3/+merVq6tlPvm+MGTlgw8+yLN+Ozs7rl+/jp9f7o+hUaNG0bZtWwAmTpyIr68vd+7cwdvbu8D3SEpK4tdff+XgwYPUqZMzj5GHhwfHjh1j3rx5NGrUKE+bb926xa5duzh16hS1atVStcvHJ3c+p4CAAK5cuUJERAT6+jmp29OmTWPLli1s2LCBQYMGveqjRFdXl4kTJ6qeu7u7c+LECdatW0fXrl3/cZ1JkyYxffp03n82T4a7u7sqCPVidteIESNUZZ63/+uvv6Z79+4A/PTTTwQEBDBjxgxmzZqlKlfQd+Hs7MyoUaNUZYcNG8bu3btZv349tWrVwsrKilatWrFq1SqaNcuZo2j9+vVYWVmpnk+ePJlvvvlG1VYPDw8mTZrEV199xYQJE/J8JmlpaaSlpakte/6dvK7khCSU2dkYm5upLTc2NyUxLiHfOlFhERxcs41+Ez5H64U09pIi5VmfXr7iaWRuSpKGPsWER3J0/S66j/tUY5/iIqMJDQrGp04V3h/5IbFPnrJ/2Rays7Ko26lohi69SlR8HABWpurfn5WZGeFRUQXWTUxJps2Yr0jPyERbS8HXPXpRy6d8gXXEqyXExZOdlY2FlXomlbmlBTHRMW+17qTEJAZ27k1GegZa2lp8MmJoniBdYYl6tv1YW6n/kLO2tCQsPFxzvejovHWsrIh64eKKX/nyTBw3jtKurkTFxLBw6VIGfvopa5ctw8Jc85AhUbC0xGSU2UoMTdWzFw3MjEmJzz+bxdDchDo9W2NdypGszEzunr7K3t9X4T+it2r+I/fqvqQmJLN7+jKUSlBmZ+PVoCoV/OsWST+iojVte1aEPXnFtvdSBqO1paXatvcoLIyNW7fSq0tXBvTuzbUbQUz7fSa6urq0a5X3YlBaWhp/zp9Hq+bNMTHOPytU5Hr+Wef9HiwIi9A8P1hJpjDNOZfITlA/f8hOSEDLUvM0EC/SLuWCtpMDyavVR39k3g8la+U6siIi0TI1zRkOOuJTEqb+hjI5uXA68JLU5/sJM/Xt2dDMmJT4xHzrGJmbUL9XW2xKO5KVmcWdU5fZOWM5bUf2xbFsTgDRwsGahv06YOVsR0ZKOlcPnmbbL0t4/3+DMLe3LvR+FOV+ws+nPBPHjqW0y7Nj1PJlDBw6hLVLlmJhbo5b6dI4Ojjw5/z5jB01CkMDA1auW0tUdDRPX3HuJcTbkuCZKPFu3rzJmTNn2LRpEwA6Ojp069aNRYsWaQyeXbx4kS5dumhcZ3BwMOPHj+fUqVM8ffpUlb0SGhqqFjyrWLGi6m9HR0cgZ2ilt7d3ge9x/fp1UlNTadFC/Ud9eno6VapUybdOUFAQOjo6VH9hmIy3tzcWL6Qqnzt3jsTERKyt1Q+EKSkpasNOX2Xu3Ln89ddf3L9/n5SUFNLT06lcufI/rhMZGcmDBw8YOHAgH3/8sapOZmYm5i/9GHuxf/Hx8Tx+/Jh69dQnna1Xr16eoagFfRdZWVn8+OOPrF27lkePHqkCW8YvnGz36tWLQYMGMXv2bPT19Vm5ciXdu3dXzaNw7tw5AgMD1TLNsrKySE1NJTk5GaOX5m+YOnWqWkARYMKECXi2r13g55ifl6/fKPNZBjkZgJtnLaNR59ZYO+adu6kkyXNVSqnM90pVdnY22+eupu57LbBysNW4PmW2EiNTE1oO+AAtLS0c3F1IjI0ncOfhdxY823XmFFNXrVA9/23IMCBnqIRaW5X5LHyJkb4BK8d+S3JaKoE3b/DbhnU429hSrZxXgfXeJb2mDTEakTu/VOLYSWReDSrGFr2JvP9Vb3ul1NDIkN/+mkVKSgqXz19k0ewF2Ds5UqFKxVdXfoVde/cyZdo01fMZP/0E5NMLDf9Hal56/eU69Wrn7qM8gYq+vnTq3p3tu3bR+9lFDPEWXneHDpjbW6v9sLXzcCEpJp5r+0+rgmfht+5zec8JanVvha2bE/GRMQSu38elnceo1Obth9Dt2reXKdOnq57P+PHZtpdnv/YPtr2X/u+ys7Mp7+XF0GcX+rzLlePuvRA2bt2aJ3iWmZnJ2O8nkp2dzddfjHzTbv2/sOvgAaa8MIXGjEk/APntN/49k47rVquMUbf3VM8T5y3RUFJBzj/Xq+nVrkHW43CyQtUzuzKDbqn+zg57QmLIfczGf4VezaqkHTr2hi1/Q/nspzXtKCwcbFQ3BgCw93AhMSaeK/tOqoJndh4u2Hm45JYp48rmKQu4diiQut3efpTCu9xP5HuM6tmD7bt307tbN3R0dPj5+0lM+vknmrZri7a2NjWrVaPus8QDIYqSBM9Eibdw4UIyMzNxds69e51SqURXV5eYmJg8c7UAr5wssn379ri6urJgwQKcnJzIzs7Gz8+P9HT1iUdfnMz++Y79eaCtoPd4XmbHjh1q7QbN2UnPh6EWdNDJzs7G0dGRQ4cO5XntxSBbQdatW8cXX3zB9OnTqVOnDqampvzyyy+qIY7/pM7z/i5YsECVNfec9kuZRMb5XD1+uc/5HXwL+i6mT5/Ob7/9xowZM6hQoQLGxsaMGDFC7fts37492dnZ7Nixgxo1anD06FF+/TV3MtLs7GwmTpyolhX3nEE+E7+PGTOGkSPVT+j19fVZfzUgT1lNjEyNUWhpkRgXr7Y8OS4BY3PTPOXTU1IJu/uA8JBH7FqyEXi23SiV/ND7C3qN+RR33+Id+mf4rE9JsepXiZPjEzEyyzvsJj0ljSf3HhJx/zEHlufc7e95n6YP+IYuoz+iVHlPjC1M0dLWVpvjw8rRjqS4BLIyM9/J3G4NK1bGzy13qEV6Zs7wj6j4eGzMLVTLYxLisX4pG+1lWlpauNrlBEC9XEsREhbGkt07S1TwLP3kGTJvvPDj4unrDQ0vTqbmZmhpaxH70jD2uJg4LCwt3mrdWlpaOLrkzLHnUbYMD+8/YOPKtYUSPGtYv77aZMnpGTnb1tPoaGxeuMtgdGwsVlaahxVZW1mpstZUdWJisCogQ8PQ0JAyHh48eFjwkCFRMH0TIxRaijxZZqkJSXmy0Qpi6+7M3TO5Q5gubDtMmZp+lKtXGQBLZzsy0zI4uWonFVvVQ6H1dkGRhvXq4+eTz7YXFY2N9YvbXsHb0csZjgDRMbFqdWysrXF/6a6P7qVLc/CI+nQWmZmZfDNhAo/Dwpjz2wzJOtOgYe06+Hl5q56rvruYGGxeuMgaHRuL1Vvu/96VjKvXSbj/IHeBTs45pJapKVkv3EhIy9QEZUL+mVpqdHXRq1qJlF2a5+FSSc8gKywcLduiu7OrwfP9RJx621MTkvNkoxXEzt2ZO2euaHxdoaXAtrQT8RGFc9x+l/uJlxkaGlLGXf0Y5ePlxaqFi0hMTCQjMxNLCwv6Df6E8l4l5xyqsGj9Px4iWRJJ8EyUaJmZmSxbtozp06fTsmVLtdc++OADVq5cyWeffZanXsWKFTlw4ECezCDIGQ4TFBTEvHnzaNCgAQDHjr35FaaC3qN8+fLo6+sTGhqa7xDN/Pj4+JCZmcnZs2epWbMmkJN1FxsbqypTtWpVwsPD0dHRwa2A244X5OjRo9StW5chQ3LvyPOqrLVX1bG3t8fZ2Zm7d++q5hB7HWZmZjg5OXHs2DEavnCb6hMnTqg+g9dx9OhROnbsqBram52dze3bt9WGvBoaGvL++++zcuVK7ty5Q7ly5aj2wrwgVatW5ebNm3h6vt48Mvr6+m88TPNl2jo6OLq7cvfKTbxrVFItv3v1JuWqVcj7noYGfPLT12rLzu47Rsi123QeMQAL28JPzX9T2jo62Ls5E3LtNmWr52Zxhly7jWcV3zzl9Q316TdZPQh58cBJHgTdof1nfTC3zQkUOJd1I+jURZTZ2SieBdBinjzF2ML0nd0UwdjAQO0OmkqlEmszc04HXcfLNSdLJCMzk/O3bzHsvQ80rSZfSpSkF/HNKd5YSirZKZqHX5REurq6lClXlotnL1C7YW5G68Wz56lVv06hvpdSqSQjI+PVBV+DsZGR2t3JlEol1lZWnA4MxLtcTkA8IyOD8xcvMmyw5rtNVvTz4/TZs/Tq1k217HRgIBX9NM/Nlp6eTsj9+1Sp+PZBwP/PtHW0sS7lSFjQPUpXzv0B9/jGPVwrvv5FjeiH4Ri+MOw9Mz0zz8Wk5wEzJcq3zijSuO2dPau+7V26xLBPPtG4noq+vpw+G0ivF6Z/OB0YSEXf3G2vkl8F7oc+UKt3/+EDHO3tVc+fB85CHz1k3oyZMpS4ABq/u/Pn8X52LpORkcH5K5cZNnBgcTXzzaSlk52mfgEgOy4eHS9Psh49m/dKWxudMu6kbNv1ytXpVakIOtpkBF549Xtra6Ntb0dmcMg/aPjr0dbRxqaUI4+C7uJWJTfw+SjoLqUrvf5+IupBOEb5XGR9TqlUEvUwHCvnwhml8C73Ey9LT08nJDT/Y9TzuRBDHz4g6OZNPv23bOfiX0uCZ6JE2759OzExMQwcODDP8L/OnTuzcOHCfINnY8aMoUKFCgwZMoTBgwejp6dHQEAAXbp0wcrKCmtra+bPn4+joyOhoaF88803b9y2gt7DxsaGUaNG8cUXX5CdnU39+vWJj4/nxIkTmJiYqM0B9pyXlxetWrXi448/Zv78+ejo6DBixAi1DLfmzZtTp04dOnXqxE8//YSXlxePHz9m586ddOrUSW1IpCaenp4sW7aMPXv24O7uzvLlywkMDMTd3f2t6nz33XcMHz4cMzMzWrduTVpaGmfPniUmJiZPhtaLRo8ezYQJEyhTpgyVK1dm8eLFXLx4Md87jRbUvo0bN3LixAksLS359ddfCQ8PVwueQc7Qzfbt23Pt2jVVoO25b7/9lnbt2uHq6kqXLl3Q0tLi8uXLXLlyhR9++OG12/KmardpzJbZK3DyKIVzWTcuHDxB3NMYqjXL+eF/YM02EqLj6DSkNwotLexc1e8uaWxmio6ertryrMxMIh+Gq/5OiI4jPOQhegb6BQ6NLCzVWzVg57y1OLi74ORZissBp0mIiqVS05xU/CPrdpEYE0ebT7qj0NLC1sVBrb6RmTHaujpqyys1rcP5/cc5uPJvqrSoR0z4U05vO0jVFupDft8lhUJBj6bNWLx7J652drja2rNk904M9PTwr5GbgTlhyUJsLSz5rFNOVuPi3TspX9oNZxtbMrMyOX71CjtOneKbHq8feC4uClMTtOxsUDybVFnLJSezNjs6FmVMbDG2LFfHru8xY/I0PL3K4uXrw97tu3gaEYl/hzYALJ+/mKjIKEaMy50n8e7tnIsBqSmpxMfGcfd2MLq6Ori65QyJ2bBiLZ5eZXFwdiQzI5NzpwI5tOcAg0fmPf4UBoVCQY+uXVm8YgWlXF1xdXFh8fLlGOjr0+qF6QC+/eEH7Gxs+OxZQK17584MGjaMJStX0rh+fQ4dO8bps2dZ+ML8kTNmzaJB3bo42NsTExPDwmXLSEpKol3r1qoycfHxhD95QuTTpwDcDw0FcrIGbKzffZDeUFcfF/PcfZeTmQ1lbVyIT03iSeLbzWVXmMo3rcmxpX9jXdoRW3dnbh2/QFJMPF4NcubGO7clgOTYBBr07wDk3J3TxMocCydbsjKzuHvmKvcv3KTxx7kZ0K4VPLl+8AxWrvbYuDmTEBnDxe1HcK1QNs/d9gqDQqGgR5cuLF65glIuLjnb3ooVOdte8xe2vcmTsbO14bNBOT+Uu3fuzKDhw1myaiWN69Xn0PFjnD53loV/5m57Pbt04cOhQ1i0fDktmjThWlAQm7dtY9yzOUszMzP56tvx3Lx1i99+/ImsrCzVHEbmZmZqGegiL4VCQY9O77F4zWpKOTvh6uzM4tVrcr67Jk1V5b79+WfsbKz57MOcQENGRgZ3n/2PZ2RkEBn1lJvBwRgZGOD6bPREckoKD16YtP1ReDg3g4MxNzXFwa5op5FIO3wcgxZNyH4aRVbkUwxaNEGZkUH6uYuqMka9upIdF0fq9j1qdfVqVyfjyvV85zAz6NiGjKtBKGNiUZiaYNCyKQoDfdLPnCvS/vg1r83hxVuwKe2EnYczN49eIDEmDu+GORd0AzcfICk2gcYDOgFw9cBpTKzNsXS0JTsrizunrxBy4QbNPumsWuf57Yexc3fBzM6KjNQ0rgWcIerBE+p2b51fE95aUe4nZsyeRYO69XCwtyMmJjb3GPXC0O79AQFYWFjgYG/PnbvBTP/jDxrVr0/tGq9/4V2If0KCZ6JEW7hwIc2bN88TOIOczLMpU6Zw/nzeW8CXK1eOvXv3MnbsWGrWrImhoSG1atWiR48eaGlpsWbNGoYPH46fnx9eXl78/vvvNG7c+I3aVtB7QM4E+nZ2dkydOpW7d+9iYWFB1apVGTt2rMZ1Ll68mI8++ohGjRphb2/PDz/8wPjx41WvKxQKdu7cybhx4/jwww+JjIzEwcGBhg0bYv/ClduCDB48mIsXL9KtW7ecg1+PHgwZMoRduzRfwXudOh999BFGRkb88ssvfPXVVxgbG1OhQgVGjBhRYHuGDx9OfHw8X375JREREZQvX56///6bsmXLvlZ/AMaPH8+9e/fw9/fHyMiIQYMG0alTJ+Li4tTKNW3aFCsrK27evEnPnj3VXvP392f79u18//33/Pzzz+jq6uLt7c1HH3302u34J3zrVCUlMYkjm/aQGBuHrYsjPb76BItnGVeJsfHER73Zj8OEmDgWjP1F9fzkjoOc3HGQ0j6e9B0/rFDbnx/vWpVJSUzm5Nb9JMXGY+PswPsjP8TcJictPykunvjo2Ddap5m1BV1Gf0zAqm0s/d9vmFiYUbVlfWq2bVz4HXgDfVu2Ii0jg59WryIhOQlfdw/+GPaFWoZaeHS0WuZIaloaP61eSURsDPq6upR2cOT7AQNpWb1Gfm9RoujWqYHx6OGq5yb/y/nRm7JsDanL1xZXs9TUb9qI+LgE1i5bRUxUNKXc3Rj/0/fYOeTsI6Ojool8afLskR/lBsGCb97myP5D2DrYsWBtzt2C01JTmffbLKIin6Knr4dzKVe++N9o6jd9vczif6Jfz56kpaXx4/TpJCQm4ufjw5+//qp29T/8yRO1IR2VKlRg8oQJzPnrL+b+9Rcuzs5MnTgRvxdumPMkIoJxEycSGxeHpYUFfr6+LJ47F0eH3GD1kWPHmDh1qur52O++A+DjAQP45MMPi6zPmnjblubP93IvwgyvnzPf6M6gk0w+uFRTtXfOvXp50pJSuLTzGCnxiVg42tJsSDdMrHPOYVLiE0mKyR2mn52ZxdnNB0mOTUBbVwcLRxuaDemqdifNiq3rg0LBhW1HSI5NwMDECJcKnlTt0LjI+tGvx7Nt77dfc7e9adPVt72IJ2i9MGS0kl8FJn87gTkL/2LuwoW4ODkx9bvv1IYj+/r4MO2Hyfw5fx5/LVuKk4MDX342jNYtckYWRERGcuT4cQB6DlTfzubOmEl1DXPGilz9unYlLT2NH//8k4SEBPy8vflz6lT17y4yQu27i4yKoteQT1XPl2/YwPING6hasSLzf8mZi/H6rVsM/mq0qsxv8+YB0K5FC74blbu8KKQdOIxCVxfDzh1RGBmSdf8BiXMWQlru1BxalhbPJhzNpWVrg04ZdxJn/5XverUszDHu1wOFsRHKxCQy7z8g4dfZRX4hqEx1X9ISU7iw4wjJ8YlYOtni/1kPTK0tAEiOSyQxOnc/kZWZxZmN+0mKTUBHVwcLJ1v8h3bHtULueXJ6chrHVu4gOT4RPUN9rF0daDeqH3buzi+/faEpqv3Ek8hIxn3/wjGqfHkWz1E/Rj2NiuK3WX8S9WyIclt/fz7qmzcx4b/g//OdLUsihVKpfL3ZFoUQQrzSinO7i7sJhap3tVYsOLW1uJtR6D6u3ZH4g0eKuxmFyqxpQ2JavPfqgv8ilvs2ExR+t7ibUah8HDxI+Jfe+U4TUzs76s3SPJz03+j40LlMOVByAnOFYWyzfiSEPynuZhQ6U4fXu3j4b5MQcr+4m1CoTN1KE/v5m4/0KMksZv7ILwErXl3wX2R0k97/uf3Ev3kf8SCmZE6f4Wrp8OpC/0GFn+8thBBCCCGEEEIIIcR/hAzbFEIIIYQQQgghhChB3vamMKJwSeaZEEIIIYQQQgghhBAaSPBMCCGEEEIIIYQQQggNZNimEEIIIYQQQgghRAmiJXfbLFEk80wIIYQQQgghhBBCCA0keCaEEEIIIYQQQgghhAYybFMIIYQQQgghhBCiBFHIsM0SRTLPhBBCCCGEEEIIIYTQQIJnQgghhBBCCCGEEEJoIMM2hRBCCCGEEEIIIUoQudtmySKZZ0IIIYQQQgghhBBCaCDBMyGEEEIIIYQQQgghNJBhm0IIIYQQQgghhBAliAIZtlmSSOaZEEIIIYQQQgghhBAaSPBMCCGEEEIIIYQQQggNZNimEEIIIYQQQgghRAmikLttliiSeSaEEEIIIYQQQgghhAYSPBNCCCGEEEIIIYQQRWL27Nm4u7tjYGBAtWrVOHr0aIHlDx8+TLVq1TAwMMDDw4O5c+fmKbNx40bKly+Pvr4+5cuXZ/PmzUXVfECCZ0IIIYQQQgghhBAlii7aJfLxptauXcuIESMYN24cFy5coEGDBrRu3ZrQ0NB8y9+7d482bdrQoEEDLly4wNixYxk+fDgbN25UlTl58iTdunWjT58+XLp0iT59+tC1a1dOnz79jz/vV5HgmRBCCCGEEEIIIYQodL/++isDBw7ko48+wsfHhxkzZuDq6sqcOXPyLT937lxKlSrFjBkz8PHx4aOPPuLDDz9k2rRpqjIzZsygRYsWjBkzBm9vb8aMGUOzZs2YMWNGkfVDgmdCCCGEEEIIIYQQ4pXS0tKIj49Xe6SlpeVbNj09nXPnztGyZUu15S1btuTEiRP51jl58mSe8v7+/pw9e5aMjIwCy2haZ2GQu20KIUQh6l2tVXE3odB9XLtjcTehSJg1bVjcTSh0lvuKdq6H4uDj4FHcTSh0pnZ2xd2EQnd8aN65SP7txjbrV9xNKHSmDvbF3QTxmkzdShd3Ewqdxcwfi7sJhW50k97F3YRCJ/uJkkNbWTLvtjl16lQmTpyotmzChAl89913eco+ffqUrKws7O3Vtyt7e3vCw8PzXX94eHi+5TMzM3n69CmOjo4ay2haZ2GQ4JkQQhSiv68WPPnlv00Hvwbce/qouJtR6NxtnP9z/XK3cSb+wOHibkahMmvWiLUX9hV3MwpVtyotWBK4o7ibUaj612jLlANLi7sZhWpss37UmzW4uJtRqI4PncvuoJPF3YxC18qnDglBN4u7GYXK1MeL6YdXF3czCtWXjXqQcPFycTejUJlWrsiCU1uLuxmF6uPaHVkWuLO4m1Go+tZoU9xN+M8ZM2YMI0eOVFumr69fYB2FQj0QqFQq8yx7VfmXl7/pOt+WBM+EEEIIIYQQQgghxCvp6+u/Mlj2nI2NDdra2nkywiIiIvJkjj3n4OCQb3kdHR2sra0LLKNpnYVB5jwTQgghhBBCCCGEKEmys0vm4w3o6elRrVo19u1TH0mwb98+6tatm2+dOnXq5Cm/d+9eqlevjq6uboFlNK2zMEjmmRBCCCGEEEIIIYQodCNHjqRPnz5Ur16dOnXqMH/+fEJDQxk8OGd6hDFjxvDo0SOWLVsGwODBg/nzzz8ZOXIkH3/8MSdPnmThwoWsXp07nP3zzz+nYcOG/PTTT3Ts2JGtW7eyf/9+jh07VmT9kOCZEEIIIYQQQgghhCh03bp1Iyoqiu+//56wsDD8/PzYuXMnpUvn3BglLCyM0NBQVXl3d3d27tzJF198waxZs3BycuL333/ngw8+UJWpW7cua9as4X//+x/jx4+nTJkyrF27llq1ahVZPyR4JoQQQgghhBBCCFGSPJsk/79gyJAhDBkyJN/XlixZkmdZo0aNOH/+fIHr7Ny5M507dy6M5r0WmfNMCCGEEEIIIYQQQggNJHgmhBBCCCGEEEIIIYQGMmxTCCGEEEIIIYQQoiT5Dw3b/C+QzDMhhBBCCCGEEEIIITSQ4JkQQgghhBBCCCGEEBrIsE0hhBBCCCGEEEKIkiRbhm2WJJJ5JoQQQgghhBBCCCGEBhI8E0IIIYQQQgghhBBCAxm2KYQQQgghhBBCCFGSKLOLuwXiBZJ5JoQQQgghhBBCCCGEBhI8E0IIIYQQQgghhBBCAxm2KYQQQgghhBBCCFGSKOVumyWJZJ4JIYQQQgghhBBCCKGBBM+EKEQ3b95k6tSppKWlFXdThBBCCCGEEEIIUQgkeCZEIUlISOC9997D3d0dfX39Qluvm5sbM2bM+Mf1Q0JCUCgUXLx4sdDaJIQQQgghhBCiCGUrS+bj/ymZ80wUiRMnTtCgQQNatGjB7t2737j+d999x5YtW/5VAZ9+/frx0Ucf0b179+JuiviXObE7gENb95AQE4u9qxMdBnTHo3y5fMveC7rNjuUbiHwUTnp6OpY21tRu2ZCG7VvmW/7isTOs/G0+vjUq0/+bz4qsD9s2bWXDqrVER0VR2t2NwcOH4le5osbyly9cYv4fs7l/LwRrGxu69OxG2/c6qF4f/dkXXLlwKU+9GnVqMWnaVACSk5JZtmARJ44cIzYmljLlPBk84jO8fLxLZJ8ANq/dwPbNfxP5JAIzC3MaNG7IgMEfo6evB8CVi5fYsGott2/cJjoqim+nfk/dhvULpT+vS6lUsmDHNjYfP0pCcjK+bu581a0nZZycNNY5eOE8S/bs4kFkBJlZWbja2dG7WQva1KrzDlue68zeIxzbdoDE2DhsXRxp3fcD3Hw88y17/0Ywe1dt5enjcDLSMrCwtaJ6s3rUbdtUVSYrM4sjW/dy8fBpEmJisXa0p2XPjpStXP5ddYlz+45zemcAibHx2Do70Lx3J1y9PV5Z7+Gte6z4YRa2Lg4MnDJKtfxm4GVO/L2fmCdPyc7KxtLehpptGlOhfvWi7IaaG4fPcW3/KZLjErFwtKVml+bYe5bKt2z4rfvsmbEyz/JO3w7C3MFG9fz6wTPcPHKepJh49I0NKV3Vm2odm6CtW7JOeSs5etKzSku87UphY2zBNzvncPRe3n1eSXV05wEObtlFfEwsDq7OvD+wJ2V8vfItG3z9FtuWrePJozAy0tKxtLWmrn8TmnTwf8etVqdUKpm/ZjWb9+4lISkR37Ll+PqTwZQplf82CBAcGsrcVSu5ERxMWGQEIz8cSM8OHfOUi4iK4o9lSzhx/jypaWmUdnJm/GfD8PHMfz9UWK4dOsPlPSdIjkvA0smOOt1a4Vi2dL5lH9+8x/bpS/Ms7zpxKBaOtgDcO3+dC7uOEh8RTXZWNuZ2VlRoUZdydSoVaT9eRalUMn/DejYf2E9CYiK+Zcvy9YcfUcbVVWOdzQf2s+PIYYIfPADAx92DIT164OdZ9l01W+XCgRME7jxMUlwCNk72NOnVARcv91fWe3QrhDVT52LjYk+/SV+ovZaalMKxjbu5ffYqqckpmNtY0bhHWzwq+RRVN9Sc3XeMUy8co1r07kQp7zKvrPfg1l2WPztGfTxltGr5jcDLHP97n9oxqnabxlSoX6MouyFEgUrWmYT4z1i0aBHDhg3jr7/+IjQ0lFIFnIj8V2zatKm4m/Cvk5GRga6ubnE3o1hdPH6Gvxev4b2Pe+Hm7cmpvUdYOHkmo2Z8j6WtdZ7yevr61GvdFMfSLugZ6HMv6DYb5y1HT1+f2i0bqZWNiYhi+9L1uPsU7Ynh4f0BzJs5i6Fffo5vRT92btnG/0Z9w/wVi7FzsM9TPvxxGONHjaF1+zZ89e1Yrl2+yqzpMzG3sKB+k4YAfDtlIhkZmao68XFxDOn/MQ2a5PZxxo/TCLl7j9HfjsHaxoYDe/Yx5vPRzF+5CBtb2xLXp4N79rNo7gJGjvkKnwq+PAp9wPTJPwPwyedDAUhNScXdswwt2rTih3HfvVUf/qll+/aw6uB+vu3Tn1L29izatYPP/viNDRMmYWxgkG8dc2NjBrRqg5u9A7o62hy9coXvly/F0tSMOuV932n7r5w4x66lG2k3sBulvDwI3H+MFT/O5rPp/8PCxipPeT19PWr5N8ShlDO6+nqE3gzm77/WoKevR/XmOYHLA2u3celYIB0H9cTGyZ47l4JYPX0BH38/Ekd3zT/WCsv1UxfYv2IL/v0/wKWcOxcOnmDtL/P5+KevMbex1FgvNTmFbXNX4eZblqS4BLXXDIyNqNuhOdZO9mjraHPnwnV2zF+DsZkJHhULJwBdkHtnrxO4YR+1urfCzsOFW8cusH/WWjqOH4SJlbnGep0mfIKeQW52t76pkervu2eucm5LAPX6tMPOw5m4J9EcX74dgJqdWxRdZ/4BQ1197kQ9ZOeNE0xpPbi4m/NGzh87zeZFq+jySV/cvctyYk8Acyf9ypg/pmCVz3FL30CfBm2a4+Tmip6+HneDbrNuzhL09fWp69/43XfgmaWbN7Hq761MGP45pZycWbh+HUMnfMvG2bMxNjTKt05qWhouDg40r1ePXxctzLdMfGIiA7/5muoVKjBz/ASszM15GB6OqbFxUXaH4MCrnFy7m/o922LvWYqgI2fZ9fsKun43FBNrC431uk76TO1/ysA0t536xoZUadMQCwcbtLW1uX/lFoeXbsHQzBhX36INBBZk6d9bWbVjOxM+HUopR0cWbtrI0MmT2PjbTIwNDfOtc+7aNfzr1qeiVzn0dfVY+vdWPpv8A+um/4qdVd7ttqjcOH2RgJXbaN63E87l3LgUcJqN0xcyYOqXmFlr3p+nJaewc/4aSpf3JClefX+elZnJ+l8WYGRmQofP+mBiZU5CdKza91qUrp+6wL4VW2jVvzOu5dw5f/AEa36Zzyc/ffPKY9Tfc1fh7luWxJeOUYbGRtTr0AKbZ8eo2xeusW3+GozMTCnzDo5RQuRHhm2KQpeUlMS6dev49NNPadeuHUuWLFF7fcmSJVhYWKgt27JlCwqFQvX6xIkTuXTpEgqFAoVCoVpHaGgoHTt2xMTEBDMzM7p27cqTJ09U6/nuu++oXLkyy5cvx83NDXNzc7p3705CQu4OOS0tjeHDh2NnZ4eBgQH169cnMDBQ9fqhQ4dQKBTs2bOHKlWqYGhoSNOmTYmIiGDXrl34+PhgZmZGjx49SE5OVtVr3LgxI0aMUD1fsWIF1atXx9TUFAcHB3r27ElERESBn11ERATt27fH0NAQd3d3Vq7Me5U9Li6OQYMGYWdnh5mZGU2bNuXSpde/Wp2VlcXAgQNxd3fH0NAQLy8vZs6cWSh1Fi9ejI+PDwYGBnh7ezN79mzVa8+Hj65bt47GjRtjYGDAihUryM7O5vvvv8fFxQV9fX0qV66slq34vN6mTZto0qQJRkZGVKpUiZMnT6rKREVF0aNHD1xcXDAyMqJChQqsXr1a9fq8efNwdnYmOztbrb0dOnSgX79+qufbtm2jWrVqGBgY4OHhwcSJE8nMzKQoHdm2jxpN61OreUPsXZzo+GF3LKwtObnnUL7lnT1KUaVBLRxKOWNlZ0O1RnXwquzLvaDbauWys7JZNXMBLbt1wMr+7QJJr7Jp7Xr827WmdYe2lHIrzeARn2FrZ8f2zX/nW37Hlm3Y2dsxeMRnlHIrTesObWnZtjUbVq9TlTE1M8PK2kr1uBB4DgN9Axo2zQmepaWlcezwEQYO/YQKlSvh5OJMn4H9cXB00Pi+xd2noKvX8K3gR5OWzXBwdKBarRo0btGUWzduqcrUqFOL/oMGUr9xw7fuwz+hVCpZfXA/A1q1oWmVqng6OfNd3wGkpqezJ/C0xnrVynnRpHIV3B0dcbG1o0fTZng6O3Mx+M47bH2OEzsOUrVJHao1rYutswNt+nXGzNqSwH1H8y3v6O5KxXrVsXN1xNLOmkoNauJZ0Yf7N4JVZS4dO0PDTi0pV8UXK3sbarZsgGclH47vOPhO+nRm12EqNa5F5Sa1sXG2p0Wf9zCztuDCgeMF1tu9aD3l61TF2TNv5knp8p541aiIjbM9lvY21GjVEDtXRx7cvFdU3VBz/eAZPOtWoly9ylg42lCzSwuMLcy4eeR8gfUMTY0xNDdRPbS0ck9lI+49wq6MCx41fDGxtsC5vAfu1csTdT+sqLvzxk6FXmPB6b85fPdicTfljR3auofazRtSp0UjHFydeP+jXljaWHF8d/7/Dy4epanWsDaOpZyxtrelRuO6eFepQPD1m++45bmUSiWrt/3NgC5daVqnLp6lSzPx8xGkpqWx+8gRjfV8y5bl8/4D8G/QED2d/C/+Ld20EXsbGyYM/xy/cuVwsrenZqVKuDg6FlV3ALi87yRe9avi3aAalo621O3WGhNLc64fPltgPUNTY4zMTVWPF/+nnLzcca/ig6WjLWZ2VlRoVhsrZ3vC74QWaV8KolQqWb1zBwPee5+mtWrhWaoUE4d+lvPdHTumsd4Pwz+ni78/Xm7uuDk7879PPkGpVHLmytV32Ho4u/soFRrWoGLjWlg72dO0VwdMrSy4eOBUgfX2LtmET50qOOaTnXvlSCCpicl0Gt4P53JumNtY4lLOHbtSmjPGC9PpXYeo3LgWVZ4do1o+O0adf8Uxatei9fjWqYqzp1ue10qX98T7hWNUzVaNnh2j7hZRL0oopbJkPv6fkuCZKHRr167Fy8sLLy8vevfuzeLFi1G+wT9Zt27d+PLLL/H19SUsLIywsDC6deuGUqmkU6dOREdHc/jwYfbt20dwcDDdunVTqx8cHMyWLVvYvn0727dv5/Dhw/z444+q17/66is2btzI0qVLOX/+PJ6envj7+xMdHa22nu+++44///yTEydO8ODBA7p27cqMGTNYtWoVO3bsYN++ffzxxx8a+5Gens6kSZO4dOkSW7Zs4d69e/Tv37/Avvfv35+QkBAOHjzIhg0bmD17tlrATalU0rZtW8LDw9m5cyfnzp2jatWqNGvWLE/7NcnOzsbFxYV169Zx/fp1vv32W8aOHcu6deveqs6CBQsYN24ckydPJigoiClTpjB+/HiWLlUfEvD1118zfPhwgoKC8Pf3Z+bMmUyfPp1p06Zx+fJl/P396dChA7dvqweDxo0bx6hRo7h48SLlypWjR48eqsBWamoq1apVY/v27Vy9epVBgwbRp08fTp/O+bHfpUsXnj59SkBAgGp9MTEx7Nmzh169egGwZ88eevfuzfDhw7l+/Trz5s1jyZIlTJ48+bU+138iMyOTR8H3KVdZPTOnXCVf7t8M1lBL3aO7oYTcDMbDV32Y57712zA2M6Vm8waF1t78ZGRkcPvmLarWVB/qVbVmdYKuXsu3TtDVa3nKV6tVnds3bmoMVu7ZvotGzZtg8OyKclZmFtlZ2ejp6amV09PX59rltzsRLqo++VaqwO2bt7h5PQiAsEePCTx5mpp1a71VewvTo6inRMXHU9sndziinq4uVcuW4/Ld1zthVSqVnLkRxP0nT6j6jofDZGZmEnbvAWUqqg9T8azoQ+it1wsKhd17wINbd3Ern9v2zIxMdF7KktXV0yX0xuv9n76NrMxMwu89xN1P/X/c3c+Lh7dDNNa7fPgMMU+iaPB+/kO6X6RUKgm5eovo8EhKvcZQ0LeVlZlFVGgYTj7q7+Xk407k3YcF1t02dSHrvpnJnpkrCbsZovaafRkXokLDiQx5DEDC0xgeXQ3Gxa/4MmT+azIzMnkQHIJXZT+15V6V/bh34/WC5Q/v3ufejdt4+hVf9sijJ0+IiomhduXKqmV6urpU9fPl8o2gt1r3kTNn8PH05Ouff6RFvz70/OJzNu/d85YtLlhWZiZPQx/jUl59mJxL+TI8CX5QYN1Nk+axfNQ0tv+6lMc3NO8nlUolj4LuEvckSuNQ0HfhUUQEUbGx1K6YO3RUT1eXquXLc/nW6wdkU9PSyczMxNzEpCiama+szEyehDzC7aX9uZtfWR7fCdFY78qRQGIjoqjbqXm+rwdfuI6TZ2kOLNvM7GHfs3jsdE5tO5jnonFRyMrMJOzeQ9z91Idte7ziGHXp8Glinjyl4fuvHr6tVCq5pzpGvXooqBBFRYZtikK3cOFCevfuDUCrVq1ITEzkwIEDNG+e/w7/ZYaGhpiYmKCjo4ODg4Nq+b59+7h8+TL37t3D9dmcBsuXL8fX15fAwEBq1MgZA5+dnc2SJUswNTUFoE+fPhw4cIDJkyeTlJTEnDlzWLJkCa1btwZygj779u1j4cKFjB6dO9b+hx9+oF69egAMHDiQMWPGEBwcjIdHzsl+586dCQgI4Ouvv863Hx9++KHqbw8PD37//Xdq1qxJYmIiJvkcqG/dusWuXbs4deoUtWrVUn2WPj65PwIDAgK4cuUKERERqpsSTJs2jS1btrBhwwYGDRr0ys9XV1eXiRMnqp67u7tz4sQJ1q1bR9euXf9xnUmTJjF9+nTef/99VZnnQagXs7tGjBihKvO8/V9//bVqrriffvqJgIAAZsyYwaxZs1TlRo0aRdu2bQGYOHEivr6+3LlzB29vb5ydnRk1Kncun2HDhrF7927Wr19PrVq1sLKyolWrVqxatYpmzZoBsH79eqysrFTPJ0+ezDfffKNqq4eHB5MmTeKrr75iwoQJeT6TtLS0PHdVfdMbRSQlJJKdnY2puZnachMLMxJi4wqs+8PHo0mMTyA7O4sWXTtQq3luptK9G7cJPHCML6Z/+0bt+SfiY+Ny5qKwUk/Lt7S0JDoq/4BuTHQMlpYvlbeyJCsri7jYOKxt1IdP3LweRMjde3wxJvc7NjI2wsevPKuWLKdU6VJYWFlyaP9Bbl4PwsnFuUT2qXHzpsTFxPLlp5+jVCrJysqi3Xsd6Nan51u1tzBFxcUDYGWqvk1amZoRHh1VYN3ElGTajP2a9IwMtLW0+Lp7T2r5vLs5wQCS43P+p0zMTdWWG5ubkhgbX2DdaUP+R1J8ItlZWTTp3IZqTeuqXvOs6MOJnQdx8/HE0t6Gu1dvcuPsZbLfwaS5yQlJKLOzMc6nT0mxCfnWiQ6PJGDtdnqP/wwtbW2N605NTuHPYRPJysxEoaWFf/8PcK+Q/7xVhSktMRllthJDU/VhbAZmxqTEJ+Vbx9DchDo9W2NdypGszEzunr7K3t9X4T+iNw5lczIx3Kv7kvp/7N11dBRX+8Dxb4y4u4cQBYK7hECB4BSneJGW4qUt0PalQGmpUFpeatAWL+7u7u4EC4RgAeKuu78/AptsshugJCRvf8/nnD0nO3tn9t7dyczsM8+9NymV7TMW5d4cVyjwb1yDoNAGGrcpXl1KUhIKhQILK/VjhLmlBUlxRZ+3vhj0IckJueet1j3epn6LJkWWL0kx8XEA2BboCWFracWjp09fa9sPHkexZvs2enfoyLtdu3Hl5k1++OtPDAwMaNe02Ys38A+kP/+fslD/nzK2MCU1MVnjOiaW5jTu2x57D2dysnO4efwCm39aSPuPBuDs56Uql5mazt/jZ5CTlYOurg4Ne7UtFKR7k2Li4wGwtVTv3m1racmjp9EvvZ1fli7B3saGOkFBxVm9IqU9O56bWKr/DjCxNC/Utf65uKinHFq1jZ6ff6D1eJ7wNJbIsHAC61en89iBxD+OZvei9Shycmjwdsl2WX9+jnqV8+7zc1TfiSNfeI6aNXKy6hzVakBXvN/AOUoIbSR4JorV9evXOXnypGr8L319fXr06MG8efNeOnimTVhYGO7u7qrAGUDFihWxsrIiLCxMFTzz8vJSBc4AnJ2dVdlb4eHhZGVlqYJikBsYqlOnDmFh6ncaq1TJGxjc0dERExMTVeDs+bKTJ09qre+5c+eYPHky58+fJzY2VnX3JzIykooVC/+gDAsLQ19fn1q18rJXAgIC1Lq4njlzhuTkZGxt1YMLaWlphIe/fAbE7Nmz+euvv7h79y5paWlkZmZSLd/d11dd5+nTp9y7d49BgwYxZMgQ1TrZ2dlYFri4yd++xMREHj58qPZ9ADRs2LBQV9T834fzs64PT548ISAggJycHL799ltWrFjBgwcPVIEt03zji/Tu3Zv33nuP3377DUNDQ5YsWULPnj3Re3bSPnPmDKdOnVLLNMvJySE9PZ3U1FRMTNTHP/nmm2/UAooAkyZNokbXt4r8HDV61mVZRakEdDQWfW7YV+PISM8g8sZttv69BjsnB6o3rkt6WjrL/juXrh/0w9TCvMhtFKsCbVCiVHXFfqnyyueLC6+zffM2vLzL419RPZvok4mf8tM30+n9dnd09XTx8fMlpMVbhN+4WWgb/0gxt+nC2fMsX7SE4R+NJqBSIA/vP2D2f3/Fev5ier/bt3jq/Iq2nTzBN8v+Vj3/6YMRz+qsXk6JsvDCAkwMjVjy6URSMzI4dT2Mn9aswtXOnpp+pXChW6iqL/jugEGTx5CZnsG9mxHsWrYBGyd7qjTMPV61GdCVDX8sY9bYqejo6GDtaEf1kHqc2190N5tipWF/1PSdKBQKNvz6N427tMLW2aHITRoaGTLw64/Iysgk4spN9izZgJW9LZ4V31CmVsHqF3Hos3S0xdIx79zn4O1GSlwiV3afUAXPom7c5eKOo9Tt2Qp7LxcSn8ZxatUuLmw9TNU2b3bijX8/DV9e0f9ijJ72GRlp6UTcCGfT4lXYOTtSM7heidUwv20H9jPt97yhJGb+J/fmkg4F/69eeKh7IYVSScUKPgzv2w+AAO8K3I6MZM32bSUWPHuuUHuKiO9bOdlhlW+yDccK7iTHJXJh51G14JmBUTm6TBxKVkYmD8PucHzVDizsrXF5iQHui8O2Q4eY9ucc1fOZEz4FCl8vKF98mlJZuGEDO44cZs6kKRgWyGB/Ewqdj5Saz1EKhYLNs5fRoFMLbJy0D8GhVCgxMTej5btd0NXVxam8G8nxiZzaeqDEg2cqhc5Rmq/pFAoF639d/NLnqMFff0xmRiYRV26we8l6rN/kOaosUJZ89qB4eRI8E8Vq7ty5ZGdn4+qal/mhVCoxMDAgLi43M0NXV7dQN86srKwXblup5cRScHnBAeh1dHRUgavn71v4hFt42/m3o6OjU+R2C0pJSaFly5a0bNmSv//+G3t7eyIjIwkNDSUzM1Nr+zTVLT+FQoGzszP79+8v9FrBceS0WblyJR9++CEzZsygfv36mJubM336dFUXx3+yzvPP4c8//1RlzT2nV+COkqmGAXP/yfeR/31nzJjBTz/9xMyZMwkKCsLU1JQxY8aofdbt27dHoVCwZcsWateuzaFDh/jxxx9VrysUCqZMmaKWFfeckYZB0j/99FPGjh2rtszQ0JAdN7UHVAsyNc8dr6dglllyQhLmBe7qF/R8HDNnTzeSEhLZtXIj1RvXJSbqCXFPopn/TV6X4uf71vhu7/HJz19h51T0xcqrsLCyRFdPl7gCGVnxcfGFMrees7axJi62YPk49PT0sCiQhZeens6B3fvoN3hAoe24uLky/deZpKelkZKSiq2dLdMmfomjs1OhsmWhTYv+nE+z0Ba07pCbQVm+gjfp6enM+u5H3unfW22cmTcluEpVKnvl/QDKfNbFNCYxETtLK9XyuKQkbM2L3id1dXVxd8jdt/zd3YmIimLBjm1vNHhmYpH7P5VcICMrJSG5UOZWQdYOuT8iHT1cSU5IYt/qrargmamFOb0+fo+szCzSklMwt7Zk19INWDmU/CDTJuam6OjqklLgDn5qQjKmloWzmDPTMoi6c4/Hdx+wc2HujSzlszFKvu33MT3Hv49XpdwuqTq6uqofZI6ersQ8eMyxTXtK/IeJoZkJOro6hbLM0pNSCmWjFcW+vCu3T+Z10z636QAV6lTGr2E1AKxdHcjOyOLY0q1UadUQHd3XjIoITM1zx8RKLHDeSkpIwtxK+0QPALbPzlsuXu4kxSeyffn6NxY8C65Th8p+eV3lMp9NSBMdH4edTd5EIrEJ8di85PWUNnbW1pQvMOtjeTc39h47+lrbLYrRs/+pgllm6UkpmFi8fLdEh/Ju3DpxUW2Zjq4uls+OdXbuzsRFPeX8tsNvLHgWXKsWlX3zjkl53108dvkyvmMTE7DJd97SZvGmjcxfv5bf/vMFvp5vtvupsep4rn6OSk1M1vg9ZaZl8PjOfZ7cfciexRuAvOP5jHcn0O2TwXhU9MHUyhxdPT216wgbZwdSEpLIyc5GT7/kfvI/P0cVzDJLTUjSeN7NTMvg0Z17RN19wI4C56hp/T6i1/ihGs9RTp6uRD94zNFNu/9/Bc9EmSLBM1FssrOzWbRoETNmzKBlS/UxVrp06cKSJUsYMWIE9vb2JCUlkZKSogqknD9/Xq18uXLlyMnJUVtWsWJFIiMjuXfvnir77OrVqyQkJKh1bSyKj48P5cqV4/Dhw/TqldtVKisri9OnT6sN9v+6rl27RnR0NN9++62qrqdPFz1ga2BgINnZ2Zw+fZo6deoAuZl88c/S0wFq1KhBVFQU+vr6eHl5/aO6HTp0iAYNGjBs2DDVshdlrb1oHUdHR1xdXbl9+7ZqDLGXYWFhgYuLC4cPHyY4OK/b4dGjR1Wfwcs4dOgQHTt2VHUXVigU3Lx5U22/MDY2pnPnzixZsoRbt27h5+dHzZo1Va/XqFGD69ev4/OS08gbGhq+cjfNgvQN9HGt4MnNC1cJqltDtfzGxatUql3tpbejVCrJfnYx6eDqzEc/qWfEbV+6joz0dDoOfAcr28KzDb4OAwMDfP39OHfqDA2b5I2vdu7UGeo10txVKrByJU4cOaa27OzJ0/gG+KNf4ALv4J79ZGVl0ixUe+aqkbExRsbGJCUmcebkKQYNe/81WlRybcrISC8UIHt+M+FVxoUsTqZGRmozaCqVSmwtLDgRdhV/99xsnqzsbM7evMHItwsHlouiVCpVwbg3RV9fH+fy7oRfukbFOnnj4YRfukZArVfomqNUkpNVuO4G5QwwsLEiJzuHqyfPU6leDQ0rFy89fX2cyrtx5/IN/GvnZeDeuXwDv5qFZzI1NDZk8DefqC07s/sId6/eovOo/ljaaz8GKNHc7uKmp6+HrYczj8Lu4FktL7j68Nod3Kv4FbGmutj7URjnCyBmZ2YXuvHyPGCmRFkoK0e8On0DfdwreHH9/BWq1ss7h14/f4WgutVffkNKJdkvceO0uJgam6jNoKlUKrG1tubE+fMEeOd2QczKyuLs5SuMzDfUxD9RNSCQuw8eqC27+/AhzvbFd+OqID19few8XHhwNZzy1fOufe6HheNV9eXHlou596hQl8JClLnjXL0ppsbGajNoKpVKbK2sOHHxIgHlcwN4WdlZnL16lZG9+hS5rUUbNzB37Rp++ew/VKzw5rue6unr4+jlSsSVm/jWyhs3MOLKTXyqaz6e9/9a/Ubt+T3HuBd2i/Yj+qqO566+XoQdP49SoUDn2XVG3ONoTK3MSzRwBrltcn52jgoodI6qXKi8obEhQ74Zp7Ys9xx1k86jBmBVxDkKUF3vClEaJHgmis3mzZuJi4tj0KBBhbrqde3alblz5zJixAjq1q2LiYkJn332GSNHjuTkyZOFZuT08vLizp07nD9/Hjc3N8zNzWnevDlVqlShd+/ezJw5k+zsbIYNG0aTJk3UugIWxdTUlA8++IBPPvkEGxsbPDw8+P7770lNTWXQoEHF9VHg4eFBuXLl+Pnnnxk6dCiXL19m6tSpRa7j7+9Pq1atGDJkCH/88Qf6+vqMGTMG43wXDM2bN6d+/fq8/fbbfPfdd/j7+/Pw4UO2bt3K22+//VKfg4+PD4sWLWLHjh2UL1+exYsXc+rUKcqX134H8WXWmTx5MqNGjcLCwoLWrVuTkZHB6dOniYuLK5Shld8nn3zCpEmTqFChAtWqVWP+/PmcP39e40yjRdVvzZo1HD16FGtra3788UeioqIKBVV79+5N+/btuXLliirQ9twXX3xBu3btcHd3p1u3bujq6nLx4kUuXbrEV1999dJ1eVXB7VuwfNZc3Cp44envzYldB4mPjqV+yxAAtv69hoTYeN4Zlbt/Htm2F2s7G+xdc7uuRly7ycGNO2nYOrcriEE5A5w81Mf8MjLN/cFQcHlx6dyjG9OnfoNvgD+BlSuybcNmnjx+TNtO7QGY9/ufxERH88nE3K4Wbd9uz8Y165kz6zdad2hL2OWr7Ni8jQmT/1No2zs2b6NB40ZYWBbOaDh94hQolbh5uPPw/gP++nUObh7utGzbqky2qW7D+qxbvpoKfj4EVMzttrnoz/nUa9RAlaGZlprGw/t5P7qiHj4i/MYtzC3McXByfO12vYiOjg7vNGvO/B3bcHdwxN3BgQXbt2FUrhyhtfOySictmIe9lRUjngXU5m/fRkVPT1zt7cnOzubIlctsOXGMCe+8fDC9uDRo24y1vy7C1dsDd7/ynN59hIToWGo/mzxj17INJMYm0GV4bpeqEzsOYGlng71L7ud793o4RzbvoW6rvPGY7t2MICkuHidPNxJj49m3eitKpZJGHV5vOIKXVad1Ezb9vhRnb3dcfbw4v+8YiTFxVH8rN5i7f8VmkuISaT+0Fzq6uti7q8/qZ2phhr6Bvtryoxt341zeHStHOxTZ2YSfD+Py4dOEDuj6RtpUsVkdDi/ciK2nM/blXblx5BwpcYn4N84NSJ5Zv4/U+CQaD+gA5M7OaWZjiZWLPTnZOdw+eZm7564TMiQvqOse5MPVvSexcXfEzsuVpKdxnN98EPcg31LJ7CyKsYEhbpZ53bBcLOzwtXMjMT2Fx8lxpVizFwvpGMrfM//Aw8cLL38fju7cT1x0DA1DmwKwafEqEmLi6DMmdxzWQ1t3Y21ni4Nb7v53O+wmezdsJ7jtm/n/0URHR4d32ndg/urVeLi44O7swvzVqzAyNKRVvpt5X8z8CQdbG0b0zQ2oZWVlcfte7gD8WdnZPI2N5frt25gYG+HunDuzYa8OHRk4YRzzVq2kRaNGXLlxk3U7d/D5sOEl2qYqLeqzb95a7DxdcKzgTtjBMyTHJhDYJPfa8OTa3aTEJ9J0YO7/zKXdxzC3tcLaxYGcnBxuHb/InbNhtBiaN/7tuW2HsPd0wcLeGkV2DpGXb3Lj2AUa925bom0pio6ODu+0acv89WvxcHbC3cmZ+evX5n53jfK6Z3/xy8842NgwolfueWjhhg3MXrmcr0aNxtnBnuhn496ZGBlhYmSs8b1KQq1Wjdk6ZwVO5d1w8fHg4r4TJMXEU7VZbhbmwZXbSI5LoM37PXOP527q2fQmFqboGeirLa/arD5ndx9h75KNVG/RkLioaE5s2kuNFurDopSUuq1D2PD7Epy93XHz8eLcvqMkxMRR49k5at+KzSTFJdBhaG90dHVx0HCO0jPQV1t+5Nk5ytrRlpzsHMLPh3Hp8ClaDej2RtpUZryBsVXFy5PgmSg2c+fOpXnz5oUCZ5CbeTZt2jTOnj1LjRo1+Pvvv/nkk0/4448/aN68OZMnT1Yb7L5Lly6sXbuWpk2bEh8fz/z58xkwYADr169n5MiRBAcHo6urS6tWrYqc8VKTb7/9FoVCQd++fUlKSqJWrVrs2LGj0GDfr8Pe3p4FCxbw2WefMWvWLGrUqMEPP/xAhw4dilxv/vz5DB48mCZNmuDo6MhXX33FxIkTVa/r6OiwdetWPv/8cwYOHMjTp09xcnIiODgYR8eX+2E9dOhQzp8/T48ePXIvQN55h2HDhrFt27bXWmfw4MGYmJgwffp0xo0bh6mpKUFBQS/M6Bs1ahSJiYl89NFHPHnyhIoVK7Jx40Z8fV9+lr6JEydy584dQkNDMTEx4b333uPtt98mIUG9W0mzZs2wsbHh+vXrqszD50JDQ9m8eTNffvkl33//PQYGBgQEBDB48OCXrsc/Ua1hHVKTUti9ahOJcQk4ebgw6LPRWD/rIpEYl0B8dN5A7Uqlkq1L1hL7JBo9PT1sHe1p3bsz9VqW3sDLTZo3JTExkSXzFxEXE4untxdTf/gGx2cTfsTGxPLkcd6ssU4uzkz94RvmzPqVzWs3YGNnywdjRtCoabDadu9H3uPKxUtM++l7je+bmpzC/Nl/Ev00GjMLcxo1acyA9wcVyl4rK23q1b8vOjo6LPxjHjFPo7G0tqJuw/oMeC8vcH/j2nXGj8wLNv/x8+8ANG8dysf/0Tw5SXHr1yKUjMxMvlu+hKTUVCp5lefnkWPUMtSi4mLVusClZ2bw3fKlPImPw9DAAE9HJ74cMIiWtWq/kTrnF9SgJmnJKexfs42k+EQc3J3pM2GY6m52UlwiCdF5XWyVSiW7l20k7mkMurq62Dja0eKdjtRqnvejIzsriz0rNhP3JJpyRob4VqtEl+H9MDY1KfT+JaFiveqkJaVyZN1OkuMTsXdzpvsnQ7C0y21TcnwSidGvFnDJyshkx4I1JMXGo1/OAFsXR9p/0JuK9V4he+g1lK9VkYyUNC5sPUxaYjJWzva8NawHZra51xBpicmkxOV1A1Jk53B63V5S45PQM9DHytmOt4Z1V5tJs0rrRqCjw7lNB0mNT8LIzAS3IB9qdAh5I216FQH2nvzSKe9/fVSj3B+EW8OO8fXehdpWKxNqNKpLSmIyO1ZsICEuAWcPV96fOBabZ12fE2PjiXua77ylULLp79XEPn6Krp4edk4OtO/bjQahIaXUglz9O3UmIyODb+fMJik5mcp+fvwyeYpahlrU06fo5stmfBobS++xY1TPF69fx+L166hRqTJ/fD0NgEq+vvww4TN+WbyIv1auwMXRkY8GDaZ1k5ASbU+F2pVJT0nl7JYDpCYkY+PiQOuRvTG3tQJyu9Elx+ZdF+Vk53B89U5S4pPQN9DH2sWBViN74RGUl/2ZnZHJ4aVbSIlLRN9AHysnO5oN6kyF2oUzit6k/h06kpGZybdz/yIpJYXKPj788tl/1DLUomKi0c13nlq9awdZ2dmM/3GG2raGdO3G+900T5hVEgLqViMtOZVjG3KDmXauTnQeOxBLu9zfISkJiSTGxr/SNi1srej2yRD2Ld3Ewv/8hJmVBTVaNqJO25Dib4AGFetVJzUphcPrdqjOUT0/eS/fOSqRhH9wjtq+YDVJsQnPzlEOdPygzxs7RwmhiY6ytPqKCCHEv9DGy4dKuwrFqkPlxtyJfvDigv9jytu5/uvaVd7OlcQ9B0q7GsXK4q0mrDi3q7SrUax6VG/BglNbSrsaxWpA7bZM21O2Az6v6rO3+tPw16GlXY1idWT4bLaHHXtxwf8xrQLrkxR2vbSrUazMA/2ZcWBZaVejWH3U5B2Szl98ccH/IebVqvDn8Q2lXY1iNaReRxad2lra1ShW/Wq3Ke0q/GNJ98vmtar5a85s/79KMs+EEEIIIYQQQgghyhLJcypTytYAEEIIIYQQQgghhBBClCESPBNCCCGEEEIIIYQQQgvptimEEEIIIYQQQghRlki3zTJFMs+EEEIIIYQQQgghhNBCgmdCCCGEEEIIIYQQQmgh3TaFEEIIIYQQQgghyhKForRrIPKRzDMhhBBCCCGEEEIIIbSQ4JkQQgghhBBCCCGEEFpIt00hhBBCCCGEEEKIskRm2yxTJPNMCCGEEEIIIYQQQggtJHgmhBBCCCGEEEIIIYQW0m1TCCGEEEIIIYQQoixRSLfNskQyz4QQQgghhBBCCCGE0EKCZ0IIIYQQQgghhBBCaCHdNoUQQgghhBBCCCHKEplts0yRzDMhhBBCCCGEEEIIIbSQ4JkQQgghhBBCCCGEEFpIt00hhBBCCCGEEEKIskSpKO0aiHwk80wIIYQQQgghhBBCCC0keCaEEEIIIYQQQgghhBbSbVMIIYQQQgghhBCiLFHIbJtliY5SKfOfCiGEEEIIIYQQQpQVSddvlnYVNDL39y3tKpQKyTwTQohi1Hvp5NKuQrFa0msySXciSrsaxc68vBcfrJle2tUoVr93+YT4fkNLuxrFymrRbPbfOlPa1ShWIT41+XjTz6VdjWL1Q/uRJEU9Lu1qFCtzJ0e2hx0r7WoUq1aB9Wn467/rGAFwZPhsfti/pLSrUaw+DunN0rM7SrsaxapXjVBmHVpZ2tUoVqMad2fkuh9LuxrF6udOY5l5cEVpV6NYjQnuUdpVEP8SEjwTQgghhBBCCCGEKEukk2CZIhMGCCGEEEIIIYQQQgihhQTPhBBCCCGEEEIIIYTQQrptCiGEEEIIIYQQQpQl0m2zTJHMMyGEEEIIIYQQQgghtJDgmRBCCCGEEEIIIYQQWki3TSGEEEIIIYQQQoiyRKEo7RqIfCTzTAghhBBCCCGEEEIILSR4JoQQQgghhBBCCCGEFtJtUwghhBBCCCGEEKIskdk2yxTJPBNCCCGEEEIIIYQQQgsJngkhhBBCCCGEEEIIoYV02xRCCCGEEEIIIYQoS6TbZpkimWdCCCGEEEIIIYQQQmghwTMhhBBCCCGEEEIIIbSQbptCCCGEEEIIIYQQZYlCum2WJZJ5JoQQQgghhBBCCCGEFhI8E+Jf5vHjx3z55ZfExcWVdlWEEEIIIYQQQoj/eRI8E/+TvLy8mDlz5hvdno6ODuvXr3+t95k8eTLVqlV7rW0URalU0rdvXwwNDbG2tn6ldYv7M31VwcHBLF26tNTeP7+PP/6YUaNGlXY1hBBCCCGEEP9fKRVl8/H/lATPxBvVvn17mjdvrvG1Y8eOoaOjw9mzZ99wreDUqVO89957b/x9i9s333yDt7c348ePf+V1S/Mz2Lx5M1FRUfTs2VO1rGAwT6lU8tFHH2Fubs7evXsBCAkJYcyYMYW2t3TpUvT09Bg6dKjG95szZw5Vq1bF1NQUKysrqlevznfffad6fdy4ccyfP587d+4UTwOFEEIIIYQQQvzPkgkDxBs1aNAgOnfuzN27d/H09FR7bd68eVSrVo0aNWq88XrZ29u/8fcsCZ999tk/Xrc0P4NZs2bx7rvvoqurOZ6fk5PDkCFD2LRpE3v37qV27dpFbm/evHmMGzeO33//nR9//BETExPVa3PnzmXs2LHMmjWLJk2akJGRwcWLF7l69aqqjIODAy1btmT27NlqQbWS0ty3Nm0DG2BlbM6DhCcsPrOd608jtZZv4BVEu8CGOJnbkpqVzsWHt1h6bifJmWkANK1Qg0blq+Ju5QDAndhHrLiwh9sxD0q8LfkplUr++Ptv1m3bSlJyMpX8Axg/fDgVvLy0rhMeEcHsxYu4dvMWj548Zuz779OrU2e1MmcvXWLx6lWE3bxJdGwsP3wxiZAGDUq4NbmCvavRwq82lkZmPEqMZtWFvdzS8rn2q9ma+l6VCy1/mBjN1F3zCy2v5RbAoLrtOf/wJnOOrS/uqhfJqFM7yoU0QsfUhJzwCFIXLUPx4FGR6xiGNqNcs2B0bW1QJiWTeeoc6avWQVa2qoyOtRXG3TuhX7USOgblUEQ9JnXuYnIitO/fxWH/5l3sXLuZhNh4XDxc6f5eP3wrB2gse/bISQ5u3c2923fJzsrG2dOV9r26UKlmVbVyqckprF+0knNHT5GanIKdoz1dB/cmqHb1Em3Lcw08gwjxqY65oSmPk2LZcOUQd2Ifai2vp6tLC7861HT1x9zQlPj0ZPbcPMWpe2GqMo3LV6W+VxDWxuakZKZx8dEttoYdI1uRUyJtUCqV/LFgPus2bSIpKYlKFSsyfsyHVChfvsj19hzYz+y5c7n/8CFuLi4MGzyEpsHBamWePH3Kz3Nmc/TECdIzMvB0d2fiuPEE+vuTnZ3Nb3/9yZHjx3nw6BFmpqbUqVmLke+/j72dXYm0Nb9DW/ewd/02EuPicXJ3pfOgXlSo5K+xbPjVG2xatJLHDx6RlZGJtb0tDUKb0rRDaInX83VVdfahV/WWBDh4YGdqxYStv3PozoXSrpZWV/ef4sLOY6QlJGHt4kC97i1x9vXUWPbh9Qi2/Lio0PJuU4Zh5VR4Hwo/dZm9f63Fs6o/LYf1KPa6v6xTOw9xdPMekuITcXBzIrRfFzwDKrxwvcjrt1nw5Swc3J0Z+u2r35AtTpf2neDcjsOkxidj4+JAo56tcfHz0lj2wbU7rP9hXqHlvaaOwto59zo35sFjTm7Yy9O7D0mKiadRj9ZUbfFmriGea1y+Km/51sLCyJRHiTGsvbSfcC3XEn1qhFLXs1Kh5Y8So5m2J3efdDK3pW1gA9ytHLA1tWTNxX3sDz9Xkk0o5PK+k5zfcZjUhGSsXexp2KOI7+n6HTb+UPg6qOeXI1Xf09WDp7l+7DyxD58AYO/pQt1OzXEs71ZibRDiRSTzTLxR7dq1w8HBgQULFqgtT01NZcWKFQwaNAiAo0ePEhwcjLGxMe7u7owaNYqUlBSt242MjKRjx46YmZlhYWFB9+7defz4sVqZjRs3UqtWLYyMjLCzs6Nz57wf5AWznG7evElwcDBGRkZUrFiRXbt2FXrP8ePH4+fnh4mJCd7e3kycOJGsrCy1Mt9++y2Ojo6Ym5szaNAg0tPTi/x89u/fj46ODjt27KB69eoYGxvTrFkznjx5wrZt2wgMDMTCwoJ33nmH1NRU1Xrbt2+nUaNGWFlZYWtrS7t27QgPD1e9vmjRIszMzLh586Zq2ciRI/Hz81N9rgU/Ax0dHebMmUO7du0wMTEhMDCQY8eOcevWLUJCQjA1NaV+/fpq7xMeHk7Hjh1xdHTEzMyM2rVrs3v37iLbHB0dze7du+nQoYPG1zMyMujWrRu7du3i4MGDLwycRUREcPToUSZMmEBAQACrV69We33Tpk10796dQYMG4ePjQ6VKlXjnnXeYOnWqWrkOHTqwbNmyIt+rONTzqETfGq3YcOUQn2+bzbUnkYwL6YOtiaXG8n72HnxQrxMHws8xfsuvzDq8Cm9bVwbXzfv8Ah29OHb3Ml/vXsiknXOJTklgQtO+WBubl3h78lu4aiVL161l3LDhLJz1M7Y21gz/7FNS8u27BaVnZODm5MyIgQOxtbbRWCYtPR3f8t6MGza8pKquUU03f7pVbcb2a8eZtmcht6LvM7xRV62f68oLexi/+TfV49Otv5OckcbZ+9cLlbUxsaBzUAg3n94r6WYUYti2JYat3iJt8XKSJn2LIiEBs3GjwchQ6zoG9etg1K0T6eu3kDRhCqlzF1Oubk2MunVSldExMcH8P5+gzMkh5YdfSPp0CmnLVqMs4vsvDqcOHmPln4to0+Nt/jNrGj6VA/h50nfEPonWWP7mlWsEVg9i5JRxfPbfr/CvUpFfv/yByPAIVZnsrGxm/ucbYh4/5f3PRvPlHz/Qd9RgrGw176PFraqLLx0qN2b3zdP8dHA5t2MfMrhue6yMzbSu07dma3zt3Fl5YS/f7VvMkrM7eJKcNxZmdVc/2gQ2YNeNk3y/729WXthDVRdf2gTWL7F2LFy2lKUrVzJuzBgWzvkDWxsbhn80tshjwsXLl/lsyhTatAxl2dx5tGkZyoTJk7ic74ZHYlISg0YMR19Pn/9+/z2rFi5izLDhmJvlfj7p6elcu3GTwf368/effzF96ldE3r/H2M8+LbG2Pnf28AnWzVtKy27t+eTHL6lQ0Y/ZU38k9mmMxvKGRoY0btOcUV9/xqc/T6Nltw5sXbKGozv2l3hdX5exgSG3Yu7z48HlpV2VFwo/dYVjK3dQvU0jOv3nPZx8PNj+81KSYxOKXK/bl8Pp/f1Y1cPCofAxICkmnhOrd+Hk41FS1X8pl4+dZfuitTR+uyXvfzMOD/8KLPn2dxKiY4tcLz01jfW/Lca7st8bqql2N09e4vDybdRq04TuX3yAs58nm/67mKSY+CLX6/3VaAbMGKd6WDraql7LzszCwt6a+l1aYGKp/RhaUmq4+tG5Sgg7rp/gu31/Ex7zgA8adNJ6LbH64j4+2zpb9Zi47Q9SMtM49yDvmr6cnj7RqQlsvHKYhPTkN9UUlVunLnFkxTZqtG1Cty8+wNnXky2z/n7h9/TO1FH0/+ET1SP/9/TwegS+darQ8aN36TxhCOY2lmz+aRHJcYkl3JoyRqksm4//pyR4Jt4ofX19+vXrx4IFC1Dm+8dbtWoVmZmZ9O7dm0uXLhEaGkrnzp25ePEiK1as4PDhw4wYMULjNpVKJW+//TaxsbEcOHCAXbt2ER4eTo8eeXf6tmzZQufOnWnbti3nzp1jz5491KpVS+P2FAoFnTt3Rk9Pj+PHjzN79myN3SDNzc1ZsGABV69e5b///S9//vknP/30k+r1lStXMmnSJL7++mtOnz6Ns7Mzv/3220t9TpMnT+aXX37h6NGj3Lt3j+7duzNz5kyWLl3Kli1b2LVrFz///LOqfFJSEh9++CGnTp1i9+7dKJVKOnXqhEKR2ye9X79+tGnTht69e5Odnc327duZM2cOS5YswdTUVGs9pk6dSr9+/Th//jwBAQH06tWL999/n08//ZTTp08DqH0vycnJtGnTht27d3Pu3DlCQ0Np3749kZHas0wOHz6sCs4VlJycTNu2bbly5QpHjhzRWKagefPm0bZtWywtLenTpw9z585Ve93JyYnjx49z9+7dIrdTp04d7t2798Jyr6t1QH323z7L/vCzPEyM5u+z24lJTaC5r+b908fWjacp8ey4cYKnKfHceBrJ3lun8bZxUZX57ehadt88xd34KB4lRvPXyY3o6uhQycm7RNuSn1KpZNm69bzbsyfNGjXCx8uLKR99THpGBtv37dO6XiV/f0YPGUJoSAjlDAw0lmlYuzbDBgygWaNGJVV9jd7yrcXRiEscibhEVFIsqy7uIy41iWDvahrLp2dnkpiRonp4WjthUs6IY3cvq5XTQYd3a7dlc9gRolOK/uFWEgxD3yJ94zayTp9H8eAhqX8sRKdcOcrVr6N1HX0fb7JvhpN17BSK6BiyL4eRefwU+uXzfiwatmuJIjaWtL8WkXM7Irfc1esotASxisvudVtp2DKERqFNcfZwpcd7/bC2s+XAVs2B/B7v9SO0a3u8/Crg6OpMp/49cXBx4uKJvCEEjuzaT0pSMsMmjsWnoj+2Dvb4VArA3Vtzlkpxa+JdjZORVzkZeZUnyXFsvHKI+LRk6nsGaSzvb+9BBVtX/jqxkZvR94hLS+Je/GPuxkWpynhZOxMR+4hzD24Ql5bEjaf3OP/gJm6WjiXSBqVSybJVq3i3b1+aBTfBx9ubKZ9+lntM2F34BtVzy1avom7NWrzbpw9enp6826cPdWrWZOmqVaoyC5cuwdHegUmffkrlwIq4ODtTp2ZN3FxdATAzM+O3H3+kRbNmeHl4EFSpEp+MGk3Y9etEFbjRVtz2b9hBvebB1G/RBCd3FzoP7o21nQ1Htu/VWN7N25OawfVw9nDF1tGe2iENCKgeRPjVwkH3suZ45BX+PLGRA7fPl3ZVXujS7mP4N6xOQKMaWDvbU79HKGbWllw9cLrI9YzNTTGxNFM9CmbMKxQK9s1dR432IZjbv9rYs8Xt+JZ9VG9ajxrNGmDv6kSr/l2wtLXm1K7DRa63+a8VVG5YCzdfrzdT0SKc33WUwEY1qBhcCxsXBxr3bIO5tQWX958scj1jC1NMLc1Vj/zfk2N5Nxp2a4VvnSro6b/5TlhNfWpyLOIyx+5e5nFSLGsv7ScuLYlG5atqLJ+enUlSRqrq4WHtiLGBEcfzXUtExj9mw+WDnH1wneyckskcLsqFXUcJaFSDio1rYu1sT6OebTCztuDKgVNFrmdsYYqJpbnqkf97aj6kK5Wb1sHOwxlrZ3ua9OuIUqnkQdjtkm6OEFpJ8Ey8cQMHDiQiIoL9+/erls2bN4/OnTtjbW3N9OnT6dWrF2PGjMHX15cGDRowa9YsFi1apDFza/fu3Vy8eJGlS5dSs2ZN6taty+LFizlw4ACnTuUetL/++mt69uzJlClTCAwMpGrVqlq7OO7evZuwsDAWL15MtWrVCA4OZtq0aYXK/ec//6FBgwZ4eXnRvn17PvroI1auXKl6febMmQwcOJDBgwfj7+/PV199RcWKFV/qM/rqq69o2LAh1atXZ9CgQRw4cIDff/+d6tWr07hxY7p27cq+fAGIbt260aVLF3x9falevTrz5s3j0qVLal0R58yZw6NHjxg1ahQDBgxg0qRJL8zievfdd+nevTt+fn6MHz+eiIgIevfuTWhoKIGBgYwePVrte6xatSrvv/8+QUFB+Pr68tVXX+Ht7c3GjRu1vkdERASOjo4au2xOnTqV8+fPc+jQITw8XnwHV6FQsGDBAvr06QNAz549Vdlyz02aNAkrKyu8vLzw9/dnwIABrFy5UhVofM712Y+uiIiIF77vP6Wnq0d5GxcuPQpXW34pKhxfO3eN69yMvoeNiQVVXXwBsDAypY57Rc4/vKmxPIChngF6OrqkZKQVX+Vf4EFUFDFxsdSrUVO1rFy5ctQICuJi2NUi1iyb9HR08bBy4urjCLXlYU8i8LZ1faltNPAK4tqTu8Smqt81bRvYgOSMNI5GXCqu6r40XXs7dK0syb6c15WP7Gyyr99E31d7sDX7xi30vTzQ8/ZSbcegamWyLuRdzBtUr0r2nUhMRgzB4pfvMZv6GeVCSjbgmZ2VTeStO1SsXkVtecUaQYSH3XipbSgUCtLT0jE1z7uxcPHEGbwDfFn623w+7j2UKcPGsXXFehQ5JT9orp6OLq6WDtwo0JX7xtNIvGycNa5Tyak89+Kf0LRCTSY2f5fxTfvQrmJD9HX1VGXuxD7EzcoBd6vcYJmNiQUBDp6EPYkokXY8ePSImNhY6tXKO++UK1eOGlWrcvHyZa3rXbxyhboFzlX1atfh4pW8dQ4eOUJggD/jv/iCFh070GvQINZt2lRkfZJTUtDR0cHMrOQyT7KzsrkXHoF/NfXu2/7VKnPn2i0ta6m7f/sud67dxEdLt2Px6nKyc4iOfIRrRfXui64VvXkcXnT279qv/uDvT35ky4+LeHi98Lio5zYfxMjchIBGb6Y7tzY52dk8vHOPClXU9xvvKgHcv6F9PNdz+48T9ziakC6tSrqKL5STnc3Tuw/xqOSjtty9kg9RL/ieVnz5G/M/+o71P8zn/rWyE2zR09HF3cqRa0/Ub8xee3yX8rYuWtZSV8+zMtef3CUuLakkqvjKcr+nR7gX+H/K/Z6KHqJh1Ze/s/Dj79k4Yz4PXvA9ZWdmocjJwdDU+LXrLMQ/JWOeiTcuICCABg0aMG/ePJo2bUp4eDiHDh1i586dAJw5c4Zbt26xZMkS1TpKpRKFQsGdO3cKZR+FhYXh7u6Ou3tesKFixYpYWVkRFhZG7dq1OX/+PEOGDHmp+oWFheHh4YGbW16f+vr1C3djWb16NTNnzuTWrVskJyeTnZ2NhYWF2nYKDlhfv359taCXNlWq5P3wc3R0VHUNzb/s5Mm8u26RkZFMnTqVEydOEB0drQoERUZGUrly7kW7tbU1c+fOJTQ0lAYNGjBhwoRXrgdAUFCQ2rL09HQSExOxsLAgJSWFKVOmsHnzZh4+fEh2djZpaWlFZp6lpaVhZGSk8bWWLVuye/dupk2b9lIzge7cuZOUlBRat24NgJ2dHS1btmTevHmqAKizszPHjh3j8uXLHDhwgKNHj9K/f3/++usvtm/frgriGRvnnpxTtXQnysjIICMjQ22ZoaH2bm6amBuaoKerS0K6epfkhLQULJ01/6C7GX2P346uZWTDrhjo6aOvq8eZ+9dYeHqr1vfpWa05sWlJXI56cxeQMXG53UJsC8z6amttzaPHT95YPYqLmaExerq6JBX4rpLSU7B01J69+ZyFkSmVHL2Zd3Kz2nJvW1caeAXx9Z6FxVrfl6VjmXvMUiSoB/QUCYno2mnvkph14jRpFmaY/edjQAcdfT0y9hwgY/MOVRldezsMmwWTsX03KZu2o+fthXGf7iizssg6cqJE2pOcmIRCocDCSr3bs7mVJYlxL5fVt2vdFjLTM6jZuJ5q2dOoJ8Q8vkrdkIaMnDyOJw+jWPb7AhQ5Ctr16lzE1l6fabln+16G+rEoKSMNc0MTjevYmFhS3saZbEU2C05vxbScEZ2DQjAxMGLlhT0AnH94E9Nyxgxv2AUdcoP5RyMusu/WmRJpR0xsbjdFWxv1/crW2oZHj6M0rfJsvViNx5GY2LyuZw8ePWLNhg307tadd/v04cq1MH6Y9V8MDAxo16pwECAjI4Nf/phDq+bNMSsi+/p1pSQ93x8t1JabW1qQ9IL98YtBH5KckIRCkUPrHm9Tv0WTEqvn/zfpyakoFUpMLNS/e2NzU9ISNQ8RYmJpRuM+7bDzdCYnK5ubJy6x5afFtBvbH2e/3AzUqFuRXD9yjs4T3y/xNrxIamIKSoUCM0v1roBmluaEJ2gOusQ8esKeZZt4d/JodPX0NJZ5k3K/JwXGFurXQ8YWZqRqaYOJlRkh/Tri4OlCTnY2149dYMOMBXT6ZKDW8bfeJNPn1xIZBa4lMlKx0HI8z8/C0JSKjuWLvOZ7055/TyYFvydzU1ITNHchNbE0p0nfDtg/+55uHL/Axh8X0vHjd7V+T8fX7MLUygK3im+uF0VZoFT8/+0iWRZJ8EyUikGDBjFixAh+/fVX5s+fj6enJ2+99RaQe9f//fffZ9SoUYXW05R9pFQq0dHRKXL580DIy1Bq6MddcPvHjx9XZbKFhoZiaWnJ8uXLmTFjxku/T1EM8nVX09HRUXv+fFn+TKl27dpRvnx5/vzzT1xcXFAoFHh5eZGZmam23sGDB9HT0+Phw4ekpKSoBfteph7alj2vyyeffMKOHTv44Ycf8PHxwdjYmK5duxaqR352dnbExcVpfO2tt95i1KhRdOzYkZycHLWuqprMmzeP2NhYtQkCFAoF586dY+rUqejluxisXLkylStXZvjw4Rw+fJjGjRtz4MABmjZtCkDssx9m2iZS+Oabb5gyZYraskmTJsE/GCJEifo+p6MDaDlXulrY069ma9ZdPsDFR+FYGZvRq1pLBtZpx58nCmf4tQtsSH3PIL7as4AsRbaGLRaPbXv3Mm3Wf1XPZ36ZO4Zcwf/M3P/LEqtGidNwdCj0/WlS37MyaVnpXMiXIWiob8C7tduw5OwOUjLfTFagQf06mLzbS/U8ecavuX8UPO7p6GjdBwH0A/wwat+atIXLyA6/g56jA8Z9uqOITyBjw7OLel0dcu7cJX31BgBy7t5Dz9UZw7ealFjwLK/+BZ4rNSzT4OT+o2xespZhE8eqBeCUCiXmVhb0GTkYXT1dPH29iY+NY+eaLSUePNOmqP+j58fmpWd3kp6de/zdeOUw/Wq1Zu2l/WQrcqhg68pbvrVYe2k/kXGPsTO1pGPlYJqnp7L7ZtFdbV7Gtl07mZbvnDjz2+801lvbObxAg9TXQX0dhUJBRX9/hj+bMTrAz4/bdyJYs2FDoeBZdnY2n305BYVCwfgPx75qs/4hDTvkC5o8etpnZKSlE3EjnE2LV2Hn7EjN4HpFryRKjJWTndrEAI4V3EmJTeDirmM4+3mSmZ7Bvnnrady3HUZmLw6CvDkF/neUmvc9hULB2l8WEdK1NbbODm+obi+n0OFBqdR6ALR2ssfaKe+6zamCB8mxCZzbcbhMBM+e+6fhkLqeFUnLyuDiw5fLXH2jNHwl2o7t1k52WOf7f3r+PZ3feUTj93Ru+yFunbxEx0/eRV/LkB5CvAkSPBOlonv37owePZqlS5eycOFChgwZojrA1qhRgytXruDj4/OCreSqWLEikZGR3Lt3T5V9dvXqVRISElRZalWqVGHPnj28++67L729hw8f4uKSm0J97NgxtTJHjhzB09OTzz//XLWs4NhYgYGBHD9+nH79+qmWHT9+/KXa9CpiYmK4dOkSv/32G3Xr1gXgwIEDhcodPXqU77//nk2bNjFhwgRGjhzJwoXFm+1y6NAhBgwYQKdOuYOGJycnv7DbY/Xq1YmKiiIuLg7rAtkFAC1atGDz5s20b98ehULBL7/8ovFkHBMTw4YNG1i+fDmVKuXNSqRQKGjcuDHbtm2jXbt2GuvwvDtt/kkpLl++jIGBgdq28vv0008ZO1b9h5ehoSED13xTZHvzS8pIJUehwMpI/W6dhZGp1gFfO1RqxI3oSLaEHQXgXvxj5mdvYVKLgay6sJf4fOu1CWhAh0qN+WbvIu7Fl+y4PsH16lE5IG/2uMzM3MkzouPisLPNGwA2Nj4eGw3fc1mXnJFGjkKBhZF6poK5kQmJ6S8eAL+BVxAnIq+So8wLetubWmNnasUHDfKCL8/37V86fcTknXOJTokvngY8k3XuAknh+brsGOReBuhaWZKTL/tM18IcZaL2QXmNurQn8+gJMg8cAUBx/yEYlsPk3T5kbNwGSiXK+ARyCszYmfMwCoNaJTejsplF7pgpBbPMkhISCmWjFXTq4DEWzfqD9yeMJrC6+lhiljZW6OnpoauX173c2d2VxLh4srOy0TcoucuplMzcfa9glplZOeNC2WjPJaWnkJCerAqcATxJjkVXRwcrYzOiUxII9a/H2fvXORmZ2406KimGcnoGdK3alD03T/3jH3fPBTdsROXAvKEKMp9NqBMdE4udbd6Pptj4uCKPCbY2NmpZZgCxcerHETtbW8oXmMW3vKcnew+qnwuzs7OZMGkSDx894vefZpZo1hmAqfmz/TG+4P6YhPkL9kdbx9wAgIuXO0nxiWxfvl6CZ8XEyMwEHV0dUgtkmaUlpWBs8fL7hIO3G7dO5Ha3T3oaR3JMPDt+zZss4fnN2L8+mEr3L4djYf9mJhgBMLEwRUdXl+QCWcUpicmYWRQemD4zLZ2HtyN5FHGfrQtyJ1pSPhsY/MveY+j76TDKv+EJBHK/J91C2UtpSSmFspyK4ujtxo3jZWPW15Tn1xKGBa4lDE1I1HI8z6+eZ2VO3VO/lihtRX1Pr/L/5OjtrvF7Or/jMGe3HqL92P7Yujm9dn2FeB0SPBOlwszMjB49evDZZ5+RkJDAgAEDVK+NHz+eevXqMXz4cIYMGYKpqSlhYWGFBsl/rnnz5lSpUoXevXszc+ZMsrOzGTZsGE2aNFFNCjBp0iTeeustKlSoQM+ePcnOzmbbtm2MGzdO4/b8/f3p168fM2bMIDExUS1IBuDj40NkZCTLly+ndu3abNmyhXXr1qmVGT16NP3796dWrVo0atSIJUuWcOXKFbXul8XBysoKGxsbZs+ejZOTE3fv3i00wUFSUhJ9+/Zl5MiRtG7dGg8PD2rVqkW7du3o1q1bsdXFx8eHtWvX0r59e3R0dJg4cWKhscQKql69Ovb29hw5ckRrcKtZs2Zs2bKFdu3aoVQq+fXXXwsF0BYvXoytrS3dunUrNH5au3btmDt3Lu3ateODDz7AxcWFZs2a4ebmxqNHj/jqq6+wt7dX65576NAhGjdurDVr0dDQ8JW7aRaUo8jhTuxDKjtV4PT9a6rlQU4VOJPveX7l9AxQFLhoUj3P95m0DWzA25WC+W7f39yJffha9XwZpiYmmObL+FMqldha23Di3FkCngXCs7KyOHvpEiMHDirx+hS3HKWCyPgoAh081bLHcp8XfQfY184dBzPrQmOaRSXFMHWX+lTt7Ss1wki/HKsu7CUuVXvw6h9Lz0CR/lRtkSI+Af1KgeTcfTaGjJ4e+v6+pK1cp2EDz5QrBwW7EigUanees2+Go+esPvi8rpMjihjNswwWB30DfTx8yhN27hLVG+SNkxV27jJV69XUut7J/UdZ9N85DB43gqA6hccqqlDRj1P7j6JQKFTHl8cPHmFpY1WigTPI3fceJDzBz95dreu1n72H1q7Yd2IfUcXFh3J6BmTm5Aat7E2tUCgVxKfl/sApp6dfKGtSoVSggw65X+Trhc80HhNsbDhx+jQBfrk/wrOysjh74QIj39feza1KpUqcOH2K3t27q5adOHWKKpXyxhGrWjmIu5HqYyDdvX8PZ8e8/e954CzywX3mzPwvVpZFB6+Kg76BPu4VvLh+/ora/nf9/BWC6r7CmFhKJdkFZvMW/5yevh52Hs48CLtN+ep5Y4I9CLuNZ1X/ItZUF3MvCuNnszVaOtnR5Qv1oTpOb9hHVnoG9Xu0wtS65Pe3/PT09XEp787ti9cJrJ03EP3tS9fwr1l4ohFDYyM++F59OI9TOw9z5+oNuo8ZiJW9baF1Spqevj72ni7cuxqOd428QPy9q+GUr/byYwBG33uEidWbnW1cmxylgnvxjwlw8ODio7xrB38Hz0Lj3xbkY+eGg5k1f0VoHyOyNOR+T87cD1P/nu5fDcfrVb6nyEeYFOhmfG7HYc5uOUDb0f1w8Hq58WX/df4fz2xZFsmEAaLUDBo0iLi4OJo3b67WHbNKlSocOHCAmzdv0rhxY6pXr87EiRNxdtY8MLKOjg7r16/H2tqa4OBgmjdvjre3NytWrFCVCQkJYdWqVWzcuJFq1arRrFkzTpzQ3G1IV1eXdevWkZGRQZ06dRg8eDBff/21WpmOHTvy4YcfMmLECKpVq8bRo0eZOHGiWpkePXrwxRdfMH78eGrWrMndu3f54IMP/unHpZWenh4rVqzg7NmzVK5cmbFjxxbqPjp69GhMTU1V435VqlSJ7777jqFDh/LgwYNiq8tPP/2EtbU1DRo0oH379oSGhlKjRtFZJnp6egwcOFBtjDtNQkJC2Lp1K4sXL+aDDz4o1L123rx5dOrUSePEA126dGHz5s08fvyY5s2bc/z4cbp164afnx9dunTByMiIPXv2YJsvQ2rZsmUvPU7e69h27RhNK9SgiXd1XCzs6FMjFFsTS/bczJ3xq0fVtxhav5Oq/LkHN6jlHshbPrWwN7XGz86dfjVbcyv6PvHPBo9tF9iQblWa8ceJDTxNicfSyAxLIzMM9cuVeHue09HR4Z1ObzN/+XL2HTnCrYgIJs/4ASNDQ1o96xoL8MX07/ll3jzV86ysLK6Hh3M9PJys7CyeRsdwPTycew/z9tPUtDRVGcidnOB6eDhRT0p2LLU9N0/TsHwV6ntWxsnchq5VmmJtYsGhO7l3SjtWakz/Wm0KrdfQK4g7MQ95mKg+y2S2IoeHidFqj7TMDNKzM3mYGP3G7ixn7NiDUftWGNSshq6rCybv9UeZmUnmsbxxFU3eG4BRt7fz6n7+EoZvBWNQtxa6drboVwrEqEsHss5dVF3oZWzfg14Fbwzbt0LXwR6D+rUxbNqIjN2FM2OLU/NObTi8cx9Hdu7nUeQDVv6xmNin0QS3yR0aYN2C5cyfkTfz8cn9R5n/4+90HdSH8v6+JMTGkxAbT1pKXhZAkzYtSE5KZsWcRTx+8IhLJ8+xbeUGQtq2LNG2PHfg9nnqeFSitnsgDmbWdKjUCCtjM9Vsa60D6tOzWgtV+XMPbpCamU6Pam/haGaNt40L7So25GRkGNmK3JnYrj6+Q33PIKq5+GJjbIGvnTutAupxJerOS3VFflU6Ojq8060b85f8zb6DB7l1+zaTv/km95jQPK/uX3z9Nb/8MUf1vGfXrpw4fZoFS5cQcfcuC5Yu4cSZ0/TKd+OnV7duXLp6hXmLF3Pv/n2279rFuk2b6PYsCzo7O5txX0wk7Po1vvrPRHJycoiOiSE6JoasEg5KhXQM5fjuAxzffZCoew9ZO3cpcdExNAzNPQ5uWryKv2f+oSp/aOtuLp88x5OHUTx5GMXxPYfYu2E7tUIalGg9i4OxgSG+dm742uWOGetiYYevnRuOZmUv2zioeX2uHz7L9SPniHv0lGMrd5Acm0BgcG6Q8+S6Peybv15V/tLu40Scv0bC4xhiHz7h5Lo93DkbRqWQ3CC9voE+Nq4Oao9yJkYYGBli4+qAnv6bH0OsXtumnN13jHP7jvH0QRTbF60lITqOWs1zJ27ZvWwj635bDICOri4O7i5qD1NLM/QNDHBwd6Gc0evdLPynqrVowNVDZ7h6+AyxD59wePlWkmITqBSSOxv0sTU72T13tar8hV1HuX3uKvGPY4h58Jhja3YSfuYqQU3rqsrkZGfzNPIRTyMfkZOdQ3J8Ik8jHxH/uORu7OS379YZ6nsFUc+zEo7mNnQOaoKNiTmHn11LtK/YiL41C4/VWN+zMndiH/EoqXA9cyeWscfV0h59XT0sjc1xtbTHztSqpJsDQNUWDQg7dJaww2eJe/SUIyu25X5PTXL/P46v3cWeuWtU5S/sPsqdc2HEP44h9sETjq/dxe2zVwlqlvc9ndt+iJPr9xDS/20s7KxITUgiNSGJrPSMQu8vxJsimWei1NSvX1/j+GIAtWvXVk0goEnBroAeHh5s2LChyPfr3LkznTtrHpum4Pb8/Pw4dOiQ2rKCdf3+++/5/vvv1ZaNGTNG7flnn31WaFbP7777TmsdQ0JCCr3PgAED1DLzACZPnszkyZNVz5s3b642s2bB+s7LF5x4btSoUWrjyhX8DArWw8vLq9CygvX18vJi7969amWGDx9e6L0LGjNmDJUqVeLu3bt4enpqrA9AcHAwSUl5g8Tmn+nz4sWLWrffuXNn1Q+kLl260KVLlyLrs2XLFvT09OjatesL6/66jkdewczQhE6Vm2BlbMb9hCdM37+E6NTcbj5WxubYmuTdsT545zxGBuVo6VeH3jVCSc1M58rjOyw/v0tVprlvbQz09BnTuIfae625tJ+1l/aXeJue69+tOxkZmXz7yy8kJSdROSCAX6Z9o5aNEvXkKbo6eQHPpzEx9B4+TPV88ZrVLF6zmhpBVfhj+nQArt64wdDxeVmjPz37od2ueQsmf/xxibXnzP3rmJYzpm1gAyyMTHmUGM2vR9aoZs+0NDLDxkT9rqmRfjmqu/qx8sJeTZssEzK27ESnXDmM+7+DjokJObfvkPz9LMh3gapra6N29zN9w1aUSiVGXTuga22FMimZrHMXVeObAeTcuUvKrNkYd3sbo45tUURHk7ZkFVn5gnIloXZwfVISk9mybC0JsfG4eLoxYso4bB1yu8ElxMYT+zTvx8eh7XtQ5OSw7Pf5LPs9LxOw/lvBDBibm0liY2/L6KkTWPXn33w5fAJWttY069CKVl07lGhbnrvw8CamBka08KuDhaEpUUkxzD2xSTXbmoWRKdbGed2YMnOymHN8A50qBzM6uAepmelceHiLbdfyhiDY/axrZquAelgamZGcmcbVqDtqZYpb/3d6kZGRwbc//UhScjKVAwP55YcZBY4Jj9HVzUthrFo5iK+/mMTvc/9i9ty5uLm48M3kyVTON3t1pcBAfvgqN+j216KFuDg58dGIkbRukRvcfPL0KQeP5HYx7jVooFqdZs/8L7Wql9zMiDUa1SUlMZkdKzaQEJeAs4cr708ci41DbtfVxNh44vLtj0qFkk1/ryb28VN09fSwc3Kgfd9uNAgNKbE6FpcAe09+6ZQ3nMGoRrkBzq1hx/h6b+lMiqJNhdqVyEhJ5eyWg6QmJGPj4kCrEb0wt7UCIDUhmZTYvO62ipwcTqzeRUp8EvoG+li52BM64h08gnxLqQUvVrl+DdKSUjiwdgfJ8Qk4uDvTe/xQrJ51H02OTyQhWvOYs2WFb50g0lNSOb1pPykJSdi6ONJ+dF8s8n1PSTF531NOdg5HVu4gJT4RfQMDbFwdaDuqL15V8rqcpsQnsfLLvBso53cc4fyO3LG2Oo0r+cz4sw9uYFrOmFb+9Z5dS8Tw+9F1quO5pZEp1saFryWqufiy5tJ+jdu0NDZjQrO+qufNfWvR3LcWN5/eY9bhVSXVFBWf2kGkJ6dxZnPu92Tj4kDbUX3y/p/ik0jO//+UncPRVXnfk7WLPW1G9cEzKO97urL/FIrsHHbOXqH2XrXah1C7Q7MSb5MQmugotUUvhBDiDdqwYQM2NjY0bty4tKvCypUr8fT0VI0h9yp6L51c/BUqRUt6TSbpTkRpV6PYmZf34oM100u7GsXq9y6fEN9v6IsL/g+xWjSb/SU0+2NpCfGpycebip785H/ND+1HkhRVsuMqvmnmTo5sDyu5QGJpaBVYn4a//ruOEQBHhs/mh/1FZ6//r/k4pDdLz+54ccH/Ib1qhDLr0MrSrkaxGtW4OyPX/Vja1ShWP3cay8yDK15c8H/ImOAeLy5URiWeKpvXQBa1tQ+F8W8mmWdCiDKhY8eOpV0Fle75xtYRQgghhBBCCPH/m4x5JoQQQgghhBBCCCGEFpJ5JoQQQgghhBBCCFGWFJzZXJQqyTwTQgghhBBCCCGEEEILCZ4JIYQQQgghhBBCiFIVFxdH3759sbS0xNLSkr59+xIfH6+1fFZWFuPHjycoKAhTU1NcXFzo168fDx8+VCsXEhKCjo6O2qNnz56vVDcJngkhhBBCCCGEEEKUJUpl2XyUoF69enH+/Hm2b9/O9u3bOX/+PH379tVaPjU1lbNnzzJx4kTOnj3L2rVruXHjBh06dChUdsiQITx69Ej1mDNnzivVTcY8E0IIIYQQQgghhBClJiwsjO3bt3P8+HHq1q0LwJ9//kn9+vW5fv06/v7+hdaxtLRk165dast+/vln6tSpQ2RkJB4eHqrlJiYmODk5/eP6SeaZEEIIIYQQQgghhHihjIwMEhMT1R4ZGRmvvd1jx45haWmpCpwB1KtXD0tLS44ePfrS20lISEBHRwcrKyu15UuWLMHOzo5KlSrx8ccfk5SU9Er1k+CZEEIIIYQQQgghRFmiUJbJxzfffKMak+z545tvvnnt5kZFReHg4FBouYODA1FRUS+1jfT0dCZMmECvXr2wsLBQLe/duzfLli1j//79TJw4kTVr1tC5c+dXqp902xRCCCGEEEIIIYQQL/Tpp58yduxYtWWGhoZay0+ePJkpU6YUuc1Tp04BoKOjU+g1pVKpcXlBWVlZ9OzZE4VCwW+//ab22pAhQ1R/V65cGV9fX2rVqsXZs2epUaPGC7cNEjwTQgghhBBCCCGEEC/B0NCwyGBZQSNGjHjhzJZeXl5cvHiRx48fF3rt6dOnODo6Frl+VlYW3bt3586dO+zdu1ct60yTGjVqYGBgwM2bNyV4JoQQQgghhBBCCPE/Sako7RoUCzs7O+zs7F5Yrn79+iQkJHDy5Enq1KkDwIkTJ0hISKBBgwZa13seOLt58yb79u3D1tb2he915coVsrKycHZ2ful2yJhnQgghhBBCCCGEEKLUBAYG0qpVK4YMGcLx48c5fvw4Q4YMoV27dmozbQYEBLBu3ToAsrOz6dq1K6dPn2bJkiXk5OQQFRVFVFQUmZmZAISHh/Pll19y+vRpIiIi2Lp1K926daN69eo0bNjwpesnwTMhhBBCCCGEEEIIUaqWLFlCUFAQLVu2pGXLllSpUoXFixerlbl+/ToJCQkA3L9/n40bN3L//n2qVauGs7Oz6vF8hs5y5cqxZ88eQkND8ff3Z9SoUbRs2ZLdu3ejp6f30nWTbptCCCGEEEIIIYQQZYlSWdo1eONsbGz4+++/iyyjzPe5eHl5qT3XxN3dnQMHDrx23STzTAghhBBCCCGEEEIILSR4JoQQQgghhBBCCCGEFtJtUwghhBBCCCGEEKIsUfz/67ZZlknmmRBCCCGEEEIIIYQQWkjwTAghhBBCCCGEEEIILaTbphBCCCGEEEIIIURZolSUdg1EPpJ5JoQQQgghhBBCCCGEFjpKpVJGoRNCCCGEEEIIIYQoIxIPHC7tKmhk0aRRaVehVEi3TSGEKEb7b50p7SoUqxCfmiQ9fFja1Sh25i4uLDq1tbSrUaz61W5T2lUoEWfuhZV2FYpVTfdAZhxYVtrVKFYfNXmntKtQIpLCrpd2FYqVeaA/P+xfUtrVKHYfh/Sm4a9DS7saxerI8NksPbujtKtRrHrVCC3tKpSIidv/KO0qFKuprd5j85WyGbD5p9pV+h8O9EieU5ki3TaFEEIIIYQQQgghhNBCgmdCCCGEEEIIIYQQQmgh3TaFEEIIIYQQQgghyhKFdNssSyTzTAghhBBCCCGEEEIILSR4JoQQQgghhBBCCCGEFtJtUwghhBBCCCGEEKIskdk2yxTJPBNCCCGEEEIIIYQQQgsJngkhhBBCCCGEEEIIoYV02xRCCCGEEEIIIYQoS5SK0q6ByEcyz4QQQgghhBBCCCGE0EKCZ0IIIYQQQgghhBBCaCHdNoUQQgghhBBCCCHKEoXMtlmWSOaZEEIIIYQQQgghhBBaSPBMCCGEEEIIIYQQQggtpNumEEIIIYQQQgghRFmilG6bZYlkngkhhBBCCCGEEEIIoYUEz4QQQgghhBBCCCGE0EK6bQohhBBCCCGEEEKUJdJts0yRzDMh/octWLCAbdu2lXY1hBBCCCGEEEKIfy0JnokS4+XlxcyZM9/o9nR0dFi/fv1rvc/kyZOpVq3aK62zf/9+dHR0iI+Pf633fhVr167l+++/p169ev9o/QEDBvD222+rnoeEhDBmzJgi1ymu73Tv3r0EBASgUChee1sl4cmTJ9jb2/PgwYPSrooQQgghhBBCiFIm3TZFIe3btyctLY3du3cXeu3YsWM0aNCAM2fOUKNGjTdar1OnTmFqavpG37Osun37Nv/5z3/Ytm0b1tbWxbLNtWvXYmBgUCzbepFx48bx+eefo6ubG79fsGABY8aMUQs+hoWF0aJFC+rUqcOyZcswNDRk3759TJ8+nRMnTpCWloaXlxetW7dm7NixuLq6qr2Hv78/d+7c4c6dO4Veu337Np9//jkHDhwgNjYWOzs7atasyfTp0/Hz88PBwYG+ffsyadIk/vrrrxL/PPZv3sXOtZtJiI3HxcOV7u/1w7dygMayZ4+c5ODW3dy7fZfsrGycPV1p36sLlWpWVSuXmpzC+kUrOXf0FKnJKdg52tN1cG+CalcvkTYolUr+WLiQdZs3k5SURKXAQMaPHk2F8uWLXG/PgQPMnj+f+w8f4ubiwrBBg2jauLHq9fY9e/Lo8eNC63Xr2JHxz4K9ew8eZO2mTYTduEFCYiJL/vwTfx+fYm3fc6d3Heb41n0kxydi7+pEiz5v4xFQ4YXr3btxm8Vf/Yq9mxNDpn2iWn5u3zEuHTrF0/tRADiVdyOke1tcK3iWSP3/v9i1YSubV60nPiYOVy93+g0bREBQJY1l42JiWTJ7PnduhhP14BGhndrSb9jgQuW2rdnI7k3biX4SjbmlOXUbN6DH4L6UK1eupJsDwJX9J7m44yipCUlYuzhQv0crnH017ycPr99h84yFhZZ3nzIcK2f7QstvnbzE3r/W4FnVn9Dh7xR73f+/USqV/LF8Get27iQpJZlKvn6Mf38oFTw8tK4THhnJ7KVLuBYezqOnTxg7cBC9OnQsVO5JTAw/L1rA0bNnSc/IwNPFlYkjRhJYQsc8gKv7T3Fh5zHSnu179bq3LGLfi2DLj4sKLe82ZRhWTnaFloefuszev9biWdWflsN6FHvdX1dVZx96VW9JgIMHdqZWTNj6O4fuXCjtar20UzsPcXTzHpLiE3FwcyK0Xxc8X+KcFXn9Ngu+nIWDuzNDvx3/Bmr6/0sd94o0Kl8FM0MTniTHse3aMe7GRWktr6ejS1OfmlR18cHM0ITE9BQOhJ/j7IPrAFR09CLYuzo2Jhbo6egSk5rAkYhLXHh48001iSPb9rJ/ww4S4+Jxcnel48CeeFf001j2dthNtixazZMHj8jMzMTa3pb6LZvQpH1LVZnjuw5wev8xoiJzb2S7VfCkTe/OePh6v5H2lBllNNHg/ysJnolCBg0aROfOnbl79y6enuoXR/PmzaNatWpvPHAGYG9f+IL//ytvb2+uXr36UmWzsrJeKihmY2PzutV6KUePHuXmzZt069ZNa5lTp07RunVrOnbsyB9//IGenh5z5sxh2LBh9O/fnzVr1uDl5UVkZCSLFi1ixowZ/Pjjj6r1Dx8+THp6Ot26dWPBggV8/vnnqtcyMzNp0aIFAQEBrF27FmdnZ+7fv8/WrVtJSEhQlXv33XepU6cO06dPL7YApca2HjzGyj8X0WvYQCoE+nFw+x5+nvQdk3+fjo1D4R8aN69cI7B6EG/374GxqQlHdx/g1y9/YMKPU/Go4AVAdlY2M//zDeaWFrz/2Wis7WyIexqDobFxibVj4fLlLF21iknjx+Ph7s7cxYsZ/sknrFm0CFMTE43rXLxyhc++/JKhAwfStHFj9h06xIQpU5g7axaVK1YEYNHs2eTku3AIv3OH4R9/zFshIaplaenpVK1cmeYhIXz1ww8l1sarx8+x6+/1tBrQFXe/8pzde5Tl0//g/e8mYGmnfR9JT01j4+yllK/kS3JCktprd8NuUbF+Ddz8yqNvoM+xzXtZ9t1s3vt2PBY2ViXWln+zY/sOs+j3eQwc9T5+lQLYs2UH3306lelzf8bOsfB5JDsrC3MrSzr26sa2NRs1bvPwngMs/2sx7308Ar9KATy6/5DZ02cB0HfYoBJtD+QGGI6t2E6jXm1x9PEg7OBpts36m+6Th2Nma6V1ve5TR1DOyFD13Mi88A2opJh4TqzeiZOv9sCOeDUL161l6cYNTBo1Gg8XV+auWsnwSV+w5rffMDXWfDxMz8jAzcmJ5g0b8uO8uRrLJCYnM2jCeGoFBfHfiZOwsbTkflQU5iV4YzH81BWOrdxBw15tcKzgzrWDZ9n+81K6TR6GmY2l1vW6fTm8wL5XuN25+94unHzK7r5nbGDIrZj7bL12lGmth5Z2dV7J5WNn2b5oLW0HdsPd35szu4+w5NvfGf7DZ1jaab/mS09NY/1vi/Gu7FfonCVeX2Unb1oH1mfz1cNExj2mlnsgfWu25ufDK0lIT9G4To9qzTEzNGbd5YPEpiZgWs4YXZ28DmSpWRkcCD9HdEo82Yoc/B086VS5CSmZadyKvl/ibTp3+CQb5i+n85A+lA/04diOA/z51UzG/Xcq1va2hcqXMyxHwzbNcPF0o5yRIXfCbrJ69iLKGRpSv2UTAG5dvk71RnXwCvBB38CAfeu3MWfKj4z771QsbUvuulyIoki3TVFIu3btcHBwYMGCBWrLU1NTWbFiBYMG5f5QOHr0KMHBwRgbG+Pu7s6oUaNISdF80AeIjIykY8eOmJmZYWFhQffu3XlcIKNk48aN1KpVCyMjI+zs7OjcubPqtYJdBm/evElwcDBGRkZUrFiRXbt2FXrP8ePH4+fnh4mJCd7e3kycOJGsrCy1Mt9++y2Ojo6Ym5szaNAg0tPTX/gZbd26FT8/P4yNjWnatCkRERGFyrzq5/O8u+icOXNwd3fHxMSEbt26FeoKOn/+fAIDAzEyMiIgIIDffvtN9VpERAQ6OjqsXLmSkJAQjIyM+Pvvv8nJyWHs2LFYWVlha2vLuHHjUBYYgLJgt80nT57Qvn17jI2NKV++PEuWLClU5x9//JGgoCBMTU1xd3dn2LBhJCcnF/nZLV++nJYtW2JkZKTx9b1799KsWTPeffdd5s6di56eHvfv32fUqFGMGjWKefPmERISgpeXF8HBwfz111988cUXatuYO3cuvXr1om/fvsybN0+trVevXuX27dv89ttv1KtXD09PTxo2bMjXX39N7dq1VeWCgoJwcnJi3bp1Rbbnde1et5WGLUNoFNoUZw9XerzXD2s7Ww5sLZz5CdDjvX6Edm2Pl18FHF2d6dS/Jw4uTlw8cVZV5siu/aQkJTNs4lh8Kvpj62CPT6UA3L1LJptJqVSybPVq3u3Th2bBwfiUL8+UCRNIT09nu4YM1ueWrV5N3Vq1eLd3b7w8PHi3d2/q1KjB0jVrVGWsrayws7FRPQ4fO4abiws1q+Zl2rVt2ZIh/ftTp2bNEmnfcye27adaSF2qN62HnasjLft2wsLWirN7jhS53rZ5q6hUvwauPl6FXnt7WF9qtWiEk6crdi6OtB3cA6VCScSVN3e3+N9m65oNhLRqTtM2LXD1dKffsMHYOtixe9N2jeXtnRzpP3wwwS2bYmKqObBx8+p1/CoH0PCtJtg7OVKlVnUaNG3M7Ru3SrIpKhd3HcO/UQ0CGtfE2tmeBj1aY2ZtydUDp4tcz9jcFBNLc9XjebbvcwqFgr1/raFmh6ZYFBEAFi9PqVSybNNG3u3WnWb1G+Dj6cmU0WNIz8hg+8GDWter5OvL6AHvEto4mHL6mm94LVy7Bkc7OyaNGk1lPz9cHB2pU7Uqbs7OJdUcLu0+hn/D6gQ0qoG1sz31e4S+wr5npnpo2vf2zV1HjfYhmNuX3X3veOQV/jyxkQO3z5d2VV7Z8S37qN60HjWaNcDe1YlW/btgaWvNqV2Hi1xv818rqNywFm6+Xm+mov/PNPCqwtn71zlz/zpPU+LZdu0YienJ1PGoqLG8j50bXjbOLD6zndsxD4hPS+ZBwlPuxef9hoqIfUTYkwiepsQTl5bE8buXeZwUi6eV0xtp08FNO6nzVmPqtQjG0c2Ftwe9g5WtDUd37NdY3s3bkxqN6+Lk4YqNgx01m9THv1pl7oTdUJXp8+F7NGzdDNfyHji6OdP9gwEolUpuXgx7I20SQhMJnolC9PX16devHwsWLFALOqxatYrMzEx69+7NpUuXCA0NpXPnzly8eJEVK1Zw+PBhRowYoXGbSqWSt99+m9jYWA4cOMCuXbsIDw+nR4+8FP0tW7bQuXNn2rZty7lz59izZw+1atXSuD2FQkHnzp3R09Pj+PHjzJ49m/HjC6eVm5ubs2DBAq5evcp///tf/vzzT3766SfV6ytXrmTSpEl8/fXXnD59GmdnZ7VglCb37t2jc+fOtGnThvPnzzN48GAmTJigVuZVP5/nbt26xcqVK9m0aRPbt2/n/PnzDB8+XPX6n3/+yeeff87XX39NWFgY06ZNY+LEiSxcqN49Z/z48YwaNYqwsDBCQ0OZMWMG8+bNY+7cuRw+fJjY2NgXBoUGDBhAREQEe/fuZfXq1fz22288efJErYyuri6zZs3i8uXLLFy4kL179zJu3Lgit3vw4EGt3+u6deto27Ytn3/+OdOnT1ctf77vadu2lZWV6u+kpCRWrVpFnz59aNGiBSkpKezfv1/1ur29Pbq6uqxevZqcnJwi61qnTh0OHTpUZJnXkZ2VTeStO1SsXkVtecUaQYTnu4AoikKhID0tHdN8GSUXT5zBO8CXpb/N5+PeQ5kybBxbV6xHkVMyqd8PHj0iJjaWevm+13LlylGjalUuXrmidb2LV69St8C+UK92ba3rZGVlsXXXLjq0bo2Ojk7xVP4l5WRn8+jOfcpX9ldb7l3Zn/s3I7Sud+HACeIeRxPcOfSl3icrIxNFjgJjM81BHFG07Kws7twIp0qtamrLg2pW48bVa/94u/6VA7lzI5xb13L/Lx8/jOL8ybNUr6v5WFaccrKziY58iFtF9a5WbhUr8Dj8XpHrrp06h8Uf/8DmHxfy8NqdQq+f3XwAY3NTAhq9+Wzyf6sHjx8TExdHvXxjp5YzMKBG5UpcvPZ6P/oOnjxJoI8P47//lhb9+9Lrw9Gs27njNWusXU52DtGRj3AtsO+5VvR+8b731R/8/cmPbPlxEQ+vF973zm0+iJG5CQGNSmYogf/vcrKzeXjnHhWqqA8B4V0lgPs3Cn8fz53bf5y4x9GEdGlV0lX8f0lPRxcXC7tC2WC3ou/jbuWocZ0AB08eJjylUfmqfBLSm9GNuxPqXxd9XT2t7+Nt44KdqSURcY+Ktf6aZGdlcz/8Lv5V1YdG8K9WkYhrL3eD6f7tu0Rcv4V3RX+tZTIzM8jJycFEQwb1v5pSWTYf/09Jt02h0cCBA5k+fTr79++nadOmQG6Xzc6dO2Ntbc3o0aPp1auXKlPJ19eXWbNm0aRJE37//fdCWUW7d+/m4sWL3LlzB3d3dwAWL15MpUqVOHXqFLVr1+brr7+mZ8+eTJkyRbVe1XzZJQW3FxYWRkREBG5ubgBMmzaN1q1bq5X7z3/+o/rby8uLjz76iBUrVqiCMDNnzmTgwIEMHpw7vs1XX33F7t27i8w++/333/H29uann35CR0cHf39/Ll26xHfffacqM3369Ff6fJ5LT09n4cKFqjb9/PPPtG3blhkzZuDk5MTUqVOZMWOGKiOvfPnyXL16lTlz5tC/f3/VdsaMGaOWtTdz5kw+/fRTunTpAsDs2bPZsUP7BfeNGzfYtm0bx48fp27dukBuNldgYKBaufyZauXLl2fq1Kl88MEHRQYgIyIicHFxKbQ8OTmZbt268dlnnxUKRt68eRMLCwucX+Lu+vLly/H19aVSpdyTeM+ePZk7d65qP3Z1dWXWrFmMGzeOKVOmUKtWLZo2bUrv3r3x9lYfR8HV1ZVz585pfJ+MjAwyMjLUlhkaGmosq01yYhIKhQILK/WuL+ZWliTGJWhZS92udVvITM+gZuO8iSOeRj0h5vFV6oY0ZOTkcTx5GMWy3xegyFHQrlfnIrb2z8TExgJgW6B7q621tcbxyvKvp2md59sraP/hwyQnJ9O+1Zu/qE9NSkGpUGBmaa623NTSnOT4RI3rxEY9Zd+KzfSdOBJdPe0XufntW7EZc2tLylfSPE6IKFpSQu7/lKW1ldpyS2tLEmLj/vF2GzRtTFJ8AlPGfAZKJTk5OTRv34oO73R5zRq/WHpyKkqFEmML9R8MxhampCZqzvQ1sTSncd/22Hs4k5Odw83jF9j800LafzQAZz8vAKJuRXL98Fm6TPzf6opW1sXE5+5ntvlu6gDYWlrx6OnT19r2g8dRrNm+jd4dOvJu125cuXmTH/76EwMDA9o1bfZa29bk+b5nUnDfMzclLVFzJr2JpRmN+7TDztOZnKxsbp64xJafFtNubH+c/XKzn6NuRXL9yDk6T3y/2OsscqUmaj5nmVmaE66lK2bMoyfsWbaJdyePfulzlng1JuWM0NPVJTkzTW15cmYa5oaab5rZGFvgYe1EtiKHped2YmJgRPtKjTA2MGL95QOqcob6BnwS0gd9XT0USgWbrx4hPKbkJ75KSco975pZWagtN7O0JCn+cpHrfjn442fXwjmEdu9IvRbBWstuWbwGSxtrfKtoztAT4k2Q4JnQKCAggAYNGjBv3jyaNm1KeHg4hw4dYufOnQCcOXOGW7duqXXlUyqVKBQK7ty5UyjIEhYWhru7uypwBlCxYkWsrKwICwujdu3anD9/niFDhrxU/cLCwvDw8FAFmQDq169fqNzq1auZOXMmt27dIjk5mezsbCwsLNS2M3So+g+H+vXrs2/fviLfu169emqZLwXf+1U/n+c0tUmhUHD9+nX09PS4d+8egwYNUvucsrOzsbRUD77kz+xKSEjg0aNHanXU19enVq1ahbpu5m/j8zLPBQQEqGV4Aezbt49p06Zx9epVEhMTyc7OJj09nZSUFK2TO6SlpWkMHhobG9OoUSP+/PNP3nnnHbXPSKlUvnSm0dy5c+nTp4/qeZ8+fQgODiY+Pl5V/+HDh9OvXz/27dvHiRMnWLVqFdOmTWPjxo20aNFCrU6pqaka3+ebb75RC/QCTJo0iZA+7V+qnmoKNk2pYZkGJ/cfZfOStQybOFYtAKdUKDG3sqDPyMHo6uni6etNfGwcO9dsKZbg2bZdu5iWb4y5md98k9uMAt+RUsOyQgquo1RqbfqGrVtpULcu9naFx4J7Y16yjQqFgvW/LqZxl1bYOju81KaPbd7DlWPn6PP5cPTLvZnJO/61NP5P/fNsxavnL7F+6WoGjnqfCgG+PH4YxaJf/2Kt7Qo693kzg5zrUPB/RXtZKyc7tcHZHSu4kxyXyIWdR3H28yIzPYN9c9fSuG8HjeOgiZe37cB+pv2ed8No5n9yhxEo9H3xWrsgAAqlkooVfBjetx8AAd4VuB0ZyZrt20okePZPaNr3UmITuLjrGM5+nrn73rz1NO7bDiPJsH0DCp9jNZ1kFQoFa39ZREjX1i99zhKvQ/0AroOO1mP682uMVRf3kpGdO/TM9mvH6FGtBZuvHiZbkduLIjM7i9+OrqGcngHeti60CqhHbFoiEbEln32WW8+CS5QvPOgN/3o8mekZ3L0RzpbFa7B1dqBG47qFyu1dt41zh08w7MtxGMj1kShFEjwTWg0aNIgRI0bw66+/Mn/+fDw9PXnrrbeA3JPs+++/z6hRowqt56FhNiltwY/8y41fYTBzTUGfgts/fvy4KpMtNDQUS0tLli9fzowZM176fV72vQt61c9Hm+dt0tHRQfFs0PQ///xTlQ32nF6BO4SvOyvp8zYWFfi4e/cubdq0YejQoUydOhUbGxsOHz7MoEGDCo0rl5+dnR1xcYUzQPT09Fi/fj1dunShadOm7N27l4rPBo338/NTBQGLyj67evUqJ06c4NSpU2rdeHNycli2bBkffPCBapm5uTkdOnSgQ4cOfPXVV4SGhvLVV1+pBc9iY2O1TlTx6aefMnbsWLVlhoaGHLtX9F22/MwscscgKphllpSQUCgbraBTB4+xaNYfvD9hNIHVg9Res7SxQk9PD129vJ75zu6uJMbFk52Vjb7B6x36gxs2VA3oD7mTMABEx8ZiZ5s3MGxsXBw2RUy2YGtjUyjLLDY+XuPkFY+iojh59izfFwhYvikm5qbo6OoWyjJLTUjCtMCdfYDMtAwe3blH1N0H7Fi4Fnj2f6VUMq3fR/QaPxSvSr6q8se37OPIxt30mvABjh6FMzPFyzF/Nq5XQmy82vKE+IRC2WivYtWCpTRqHkLTNrnHBw9vLzLS0/nrp994u1e3QuM5FScjMxN0dHUKZZmlJ6VgYmH20ttxKO/GrRMXAUh8GktSTDw7fl2qev35cf/PoVPo8eVILBzezCQy/+uC69Shsl9epmhmVjYA0fFx2OU7lsUmxGNT4AbUq7KztqZ8vpuQAOXd3Nh77OhrbVebvH1PPcssLSmlUCZkURy83bh14hIASU/jSI6JZ8evy1WvP9/3/vpgKt2/HI6Fvex7r8vE4tk5K0H9nJWSmIyZhaZzVjoPb0fyKOI+WxesBvLOWV/2HkPfT4dRvrJkRL+u1Mx0chQKzMqpB45NyxmRnKn5Zm1SRiqJ6SmqwBnA0+R4dHV0sDAyJTY19ztWgurvqKQY7E2tCfauVuLBM1Pz3PNuUpz6vpackIi5pYWWtXLZPpvEx9nTjaT4RHau2FAoeLZv/Xb2rNnC0Mkf4+Llrmkz/27/j7tIlkUSPBNade/endGjR7N06VIWLlzIkCFDVMGUGjVqcOXKFXxecmr0ihUrEhkZyb1791TZZ1evXiUhIUGVYVSlShX27NnDu++++9Lbe/jwoaoL4LFjx9TKHDlyBE9PT7WZFu/evatWJjAwkOPHj9OvXz/VsuPHj7/wvdevX6+2rOA6r/r5PKepTbq6uvj5+eHo6Iirqyu3b9+md+/eL71NS0tLnJ2dOX78OMHBuenQ2dnZnDlzRuusqYGBgWRnZ3P69Gnq1KkDwPXr19UmLzh9+jTZ2dnMmDFD9cNx5cqVL6xP9erVtc4UamhoyNq1a+natStNmzZlz549VK5cma5duzJhwgS+//57tTHrnnueVTZ37lyCg4P59ddf1V5fvHgxc+fOVQue5aejo0NAQABHj6r/ALl8+TIh+WZ1LFjXV+2mWZC+gT4ePuUJO3eJ6g3yJisIO3eZqvW0D35/cv9RFv13DoPHjSCoTuHxYipU9OPU/qMoFArVd/P4wSMsbaxeO3AGYGpiojaDplKpxNbGhhOnTxPgmxsQysrK4uyFC4x87z2t26lSsSInzpyhd76ZV0+cPk2VSpUKld24fTvWVlY00pBh+ibo6evjXN6NO5dvEFA7b4y6O5dv4FezcqHyhsaGDPlGfYy+M7uPcPfqTTqPGoBVvh+Hxzbv5ciGXbwz/n1cvMvurHP/C/QNDCjvV4FLZ85Tu1FeV+bLZ85Ts0Hhu9kvKyMjA11d9ZsJurq6ude0JXxhq6evj52HCw+uhlO+el5G7v2wcLyqBhSxprqYe48wscwNtlk52dF1kvrx8NT6vWRlZNKgRytMbYr+wSPymBqbqM2gqVQqsbW25sT58wR4544VlpWVxdnLVxiZb3iFf6JqQCB3H6h3w7r78CHO9iWTKaSnr4edhzMPwm5TvnrevvYg7DaeVbWPTVRQzL0ojJ/te5ZOdnT5Qj3j//SGfWSlZ1C/RytMrYu+cSRejp6+Pi7l3bl98TqBtfOGQLl96Rr+NYMKlTc0NuKD79WHzDi18zB3rt6g+5iBWGmYMVG8uhylgoeJ0VSwcyXsSYRqeQU7N67le55fZFwUlZy8KaenT2ZObnDe1tQShVJBopbZOSE36auocdGKi76BPm4VPLlx4QpB9fJ+V9y4cJVKGq5RtVLmjp+W377129m9ejPvTfwQdw2TLgnxpknwTGhlZmZGjx49+Oyzz0hISGDAgAGq18aPH0+9evUYPnw4Q4YMwdTUlLCwMHbt2sXPP/9caFvNmzenSpUq9O7dm5kzZ5Kdnc2wYcNo0qSJqmvgpEmTeOutt6hQoQI9e/YkOzubbdu2aRwkvnnz5vj7+9OvXz9mzJhBYmKiWpAMwMfHh8jISJYvX07t2rXZsmVLoUHyR48eTf/+/alVqxaNGjViyZIlXLlypdDYV/kNHTqUGTNmMHbsWN5//33OnDlTaGbSV/18njMyMqJ///788MMPJCYmMmrUKLp3746TU+5sOZMnT2bUqFFYWFjQunVrMjIyOH36NHFxcYWyoAq289tvv8XX15fAwEB+/PHHQrN45ufv70+rVq0YMmQIf/zxB/r6+owZM0YtO7BChQpkZ2fz888/0759e44cOcLs2bO1bvO50NDQQhMc5FeuXDnWrFlD9+7dadasGXv27CEoKIiffvqJESNGkJiYSL9+/fDy8uL+/fssWrQIMzMzvv32WxYvXsyXX35J5crqwYzBgwfz/fffc+HCBZRKJZMmTaJv375UrFiRcuXKceDAAebNm6eWrZaamsqZM2eYNm3aC9v0Opp3asP8Gb/h6euNd4Avh7bvJfZpNMFtcrM81y1YTnxMLO9+NAzIDZzN//F3erzXj/L+vqoMm3KG5TB+NlNgkzYt2LdpJyvmLKJZh1CePIhi28oNNGtfMmOF6ejo8E7XrsxfsgQPNzfc3dyY//ffGBkZ0ap5c1W5L6ZNw8HenhHPuh337NKF90aPZsGyZYQ0bMj+I0c4ceYMc2fNUtu+QqFg0/bttAsNRV/DOCwJiYlEPXnC0+hoAO5GRgK5mW12GrLY/qm6rUPY8PsSnL3dcfPx4ty+oyTExFHjrQZA7nhlSXEJdBjaGx1dXRzc1bMkTS3M0DPQV1t+bPMeDqzextvD+mJpZ6PKbCtnZEg5o9cLzv5/1aZLR377bibefj74VvRn75adRD+J5q32uZM2LP9rMbHRMQybMEa1TsSt20DuuJOJ8YlE3LqNvoEBbp65N3tq1KvNtjUb8fTxxifAj8cPH7FqwVJq1q/9RsYGqtKiPvvmrcXO0wXHCu6EHTxDcmwCgU1yz58n1+4mJT6RpgNzu2Vf2n0Mc1srrF0cyMnJ4dbxi9w5G0aLod2B3CCjjav64NSGJrnd6QsuF69GR0eHd9p3YP7q1Xi4uODu7ML81aswMjSkVXDeeD5fzPwJB1sbRvTNDahlZWVx+17uIPxZ2dk8jY3l+u3bmBgb4e6ce0OtV4eODJwwjnmrVtKiUSOu3LjJup07+HzY8MIVKSZBzeuzf/467D2dcfB249qhs7n7XnDuDZ6T6/aQEp9E03ffBuDS7uOY21lh7Wyfu++duMSds2E0fz/3Jom+gT42rurBvnKqfa/sdRc0NjDEzTIvA93Fwg5fOzcS01N4nPzPx1F8E+q1bcq6Xxfj4u2Om195zuw5SkJ0HLWaNwJg97KNJMUl0GlY32fnLPWsZ1NLM/QNDAotF6/naMRFulRpysOEaO7FP6aWeyCWRmacjMydUKSFX20sDE1Zc2k/ABcf3SKkQg06BYWw9+ZpTMoZEepfl7P3r6u6bAZ7V+NBwlNiU5gnmD4AAQAASURBVBPR09XFz96Dai5+bLpacpNe5RfcviXLZv2Fm48XXv4VOL7zIHHRsdRv2QSALX+vISEmjl6jc8eYPrxtL9Z2Nji45l4P3Qm7yf6NO2jUJq/7+d5129i+bD19PhyCtYOdqpeGoZEhhsaax44WoqRJ8EwUadCgQcydO5eWLVuqdTesUqUKBw4c4PPPP6dx48YolUoqVKigNntmfjo6Oqxfv56RI0cSHByMrq4urVq1UgskhYSEsGrVKqZOncq3336LhYWFKlOqIF1dXdatW8egQYOoU6cOXl5ezJo1i1b5BhLv2LEjH374ISNGjCAjI4O2bdsyceJEJk+erCrTo0cPwsPDGT9+POnp6XTp0oUPPvigyMH0PTw8WLNmDR9++CG//fYbderUYdq0aQwcOPAffz7P+fj4qGbyjI2NpU2bNmqD7w8ePBgTExOmT5/OuHHjMDU1JSgoSG3gfk0++ugjHj16xIABA9DV1WXgwIF06tSJhATtg9LPnz+fwYMH06RJExwdHfnqq6+YOHGi6vVq1arx448/8t133/Hpp58SHBzMN998o5bFp0mfPn0YP348169fx99f851rAwMDVq5cyTvvvKMKoA0bNgw/Pz9++OEHOnXqRFpaGl5eXrRr146xY8eyceNGYmJi6NSpU6Ht+fr6EhQUxNy5c/niiy/w8vJiypQpREREoKOjo3r+4YcfqtbZsGEDHh4eNG7cuMj2vK7awfVJSUxmy7K1JMTG4+Lpxogp47B1yL1YT4iNJ/ZpjKr8oe17UOTksOz3+Sz7fb5qef23ghkwNvduvo29LaOnTmDVn3/z5fAJWNla06xDK1p17VBi7ejfsycZGRl8O3MmSUlJVA4M5Jfp09Uy1KKePFHr3la1cmW+/uILfp87l9nz5uHm4sI3X3yh1iUU4OSZM0Q9fkyHAhOCPHfw6FGm5Juw47OpUwEY0r8/7+cL+r+uivWqk5qUwuF1O0iOT8TezZmen7yHpV1ugC45PpGE6Ff7MXVm9xFysnNYM2uB2vLGnUIJltnO/pH6TRuRnJjI2r9XEB8bh5uXB+OmTcTeMfeHeXxsLDFP1Adu/2xo3s2HOzfCObr3IHaO9sxa8icAnfp0R0dHh1XzlxAbHYuFpQU16tem+8CXzwJ+HRVqVyY9JZWzWw6QmpCMjYsDrUf2xtzWCsjtPpwcm3c8z8nO4fjqnaTEJ6FvoI+1iwOtRvbCI0i6Xb0J/Tt1zj0ezplNUnIylf38+GXyFLUMtainT9HNNzTC09hYeo8do3q+eP06Fq9fR41Klfnj69ybOJV8fflhwmf8sngRf61cgYujIx8NGkzrJiEl1pYKtSuRkZLK2S0HVfteqxG98u17yaTk2/cUOTmcWL1Lte9ZudgTOuIdPIJ8tbxD2RZg78kvnfKOD6Ma5QYBt4Yd4+u92m8ElgWV69cgLSmFA2t3kByfgIO7M73HD1VlPv+Tc5Z4fZejbmNiYESITw3MDU14nBTL4jPbSEjP7ZpvZmiCpXFel/zMnGwWnN5C28CGDG3QmbTMdC5H3Wb3zVOqMgZ6+rSv2AgLI1OycrKJToln9cW9XI66/UbaVL1RHVKTktm1chOJcQk4e7gy+PPR2Djkjn+YGBdPfHTeMB1KhZKtf68h9kk0unp62Dra07ZPF+o9C7YBHN2+j5zsbBZO/13tvVp270Boz45vpF1lgkK6bZYlOsqXGcBJCFHiJk+ezPr16zl//nxpV6XEjRs3joSEBObMmVPaVdGqTp06jBkzhl69er3SevtvnSmhGpWOEJ+aJD18WNrVKHbmLi4sOrW1tKtRrPrVblPaVSgRZ+6FlXYVilVN90BmHFhW2tUoVh81eae0q1AiksKul3YVipV5oD8/7F/y4oL/Yz4O6U3DX/9ds8YeGT6bpWe138j9X9SrRmhpV6FETNz+R2lXoVhNbfUem68cLu1qFKt2lRqVdhX+scSN20q7ChpZdNB8Q/vfruRGuBVCCC0+//xzPD09ycnJKe2qaPTkyRO6du3KO+/8O38QCiGEEEIIIYR4edJtUwjxxllaWvLZZ5+VdjW0cnBw0DjWnhBCCCGEEEK8EUpFaddA5COZZ0KUEZMnT/5/0WVTCCGEEEIIIYT4XyLBMyGEEEIIIYQQQgghtJBum0IIIYQQQgghhBBlicy2WaZI5pkQQgghhBBCCCGEEFpI8EwIIYQQQgghhBBCCC2k26YQQgghhBBCCCFEWaKUbptliWSeCSGEEEIIIYQQQgihhQTPhBBCCCGEEEIIIYTQQrptCiGEEEIIIYQQQpQl0m2zTJHMMyGEEEIIIYQQQgghtJDgmRBCCCGEEEIIIYQQWki3TSGEEEIIIYQQQoiyRKEo7RqIfCTzTAghhBBCCCGEEEIILSR4JoQQQgghhBBCCCGEFtJtUwghhBBCCCGEEKIskdk2yxTJPBNCCCGEEEIIIYQQQgsJngkhhBBCCCGEEEIIoYV02xRCCCGEEEIIIYQoS6TbZpkimWdCCCGEEEIIIYQQQmghwTMhhBBCCCGEEEIIIbTQUSolF1AIIYQQQgghhBCirEhctqa0q6CRxTtdSrsKpULGPBNCiGI059j60q5CsXq//tvci4sq7WoUO3drJ3r8PbG0q1GsVvSZSuLWnaVdjWJl0aYlH278b2lXo1j91GE0/ZdPLe1qFKuFPSeSFHG3tKtRrMy9PJlxYFlpV6NYfdTkHZae3VHa1Sh2vWqE/uva1atGKA1/HVra1ShWR4bPpuP88aVdjWK14d3v+GDN9NKuRrH6vcsntPrzw9KuRrHaPuSn0q6C+JeQbptCCCGEEEIIIYQQQmghmWdCCCGEEEIIIYQQZYhSqSjtKoh8JPNMCCGEEEIIIYQQQggtJHgmhBBCCCGEEEIIIYQW0m1TCCGEEEIIIYQQoixRKku7BiIfyTwTQgghhBBCCCGEEEILCZ4JIYQQQgghhBBCCKGFdNsUQgghhBBCCCGEKEsU0m2zLJHMMyGEEEIIIYQQQgghtJDgmRBCCCGEEEIIIYQQWki3TSGEEEIIIYQQQoiyRGbbLFMk80wIIYQQQgghhBBCCC0keCaEEEIIIYQQQgghhBbSbVMIIYQQQgghhBCiLFEqSrsGIh/JPBNCCCGEEEIIIYQQQgsJngkhhBBCCCGEEEIIoYV02xRCCCGEEEIIIYQoSxQy22ZZIplnQgghhBBCCCGEEEJoIcEzIYQQQgghhBBCCCG0kOCZEOJ/VkhICGPGjCntagghhBBCCCFE8VIqy+bj/ykZ80wIIUrZ+T3HOL3tACnxSdi6OhLSqz1u/uVfuN6DmxGs/GYOdq6O9J06RrV85TdzuH/9dqHy5asE0Gnsu8VZdZUNq9exaslyYmJi8SrvxbAPRxBUrarW8hfOnmf2f38l4k4Etna29OjzDu07d1S9vmPzNqZ/9W2h9bYe2Ek5Q0MAer/dg8dRUYXKdOjyNqM++bAYWlVYS786tK/YCCtjM+7HP2Hh6W1ce3pXa/lGXlXoUKkxTuY2pGZlcOHhTRaf2U5yZlqhsg08gxjduDun7oXxw4GlJVJ/TZRKJX/u2Ma6Y0dISkujkocn47p0p4Kzs9Z19l48z4JdO7kXHU22Igd3O3v6hDSjTe06qjJ/bN/Knzu2qa1nY27Oji+nlVhbnmvoVYWmFWpgYWRKVFIM6y8f5HbsQ63l9XT1CPWrQ023ACwMTYhPT2bXjVOcvHcVAF0dXZr71qK2eyCWRmY8SY5j89UjRX73xa2ZT03aBNTH0tichwlPWXJuBzee3tNYdnDdDjQuX/j/70HCUz7bNhsAPR1d2lVsSKPyVbAytiAqMYaVF/ZwKSq8RNuRn1Kp5I+/F7Nu61aSkpOpFBDA+OEjqODlpXWd8IgIZi9axLVbN3n0+DFj3x9Kr86d1cqcvXSRxatWEXbzJtGxsfwwaRIhDRqWcGtyXdl/kos7jpKakIS1iwP1e7TC2ddTY9mH1++wecbCQsu7TxmOlbM9AHfOXuXctkMkPolFkaPA0sGGoBYN8Kuv/fj6JpzaeYijm/eQFJ+Ig5sTof264BlQ4YXrRV6/zYIvZ+Hg7szQb8e/gZq+vH9jmzSp6uxDr+otCXDwwM7Uiglbf+fQnQulXS2tWgfUo1PlJlgbmxMZ/5i5Jzdx9XGE1vJNvKvRKagJLhZ2pGSmc+7BDeaf2kJSRiqQe+zrWqUpTX1qYmtiwYPEpyw8vY1zD268oRZBsHc1WvjVxtLIjEeJ0ay6sJdbMQ80lu1XszX1vSoXWv4wMZqpu+YDUM3Fl1YB9bA3tUJPV5cnyfHsvnmKk5FXS7Qd+bULbEjXqk2xMbbgblwUs4+v50pU4WvR59pXbEj7io1xNLfmaXI8y87vYs/N06rXW/nXo7lfbTytnQC4FX2f+ae2cONpZIm3RQhtJHgmhCgxmZmZlCtXrrSrUaSsrCwMDAxK7f2vn7jA/qWbeKvf27j4enJx3wnW/TiP/tPGYmFrrXW9jNQ0tv+xAo+KFUhNSFZ7rf3Iviiyc1TP01JSWDzxv/jVDiqRNuzbtZffZ/7CqE8+pFKVymxZv4lPPxzP3GULcXRyLFT+0cNHfD52PG06tmPC5M+5cvEys6b/hKWVFcHNmqjKmZiasmDlYrV1nwfOAH6dPweFIq+dd8LvMH7URwQ3Cyn+RgL1PSvTv2Zr5p7azPUnkTT3rcWnzfoydtPPxKQmFCrvb+/B8AZdWHhmG2fuX8PGxIIhdTvwfr23mXFwmVpZO1NL+tQIJayIHwQlZdHe3Szdv48vevXGw96Bebt2MGL2L6z+dCKmRkYa17E0MeXdFqF4OTpioKfHoStX+HL5EqzNzakfEKgq5+3kzK8fjFA919PVKfH2VHPx5e3Kway+uI87sQ9p4BnEe/U68u2+v4lPS9K4Tv+arTE3NGHF+d08TYnH3NAEXZ285Pw2AfWp6RbAygt7eJIci7+DJ+/WacesQyt5kPi0xNtUx70ivauHsujMVm5E36dphRp8FNyLT7f9TmxqYqHyS87uYNWFParnujq6fNXqPVUwEKBLlaY08KzMvFNbeJQYTZBzBUY16sbU3QuIjC8clC4JC1euZOnatUz66GM83FyZu3Qpwz+dwJq58zA1MdG4TnpGBm7OTjQPbsyPc+ZoLJOWno6vtzftW4YybuqXJdkENeGnLnNsxXYa9WqLo48HYQdPs23W33SfPBwzWyut63WfOoJyRnnHNiNzU9XfhqbGVG8TjJWTHXp6ety9dIMDC9djbGGKeyWfkmyOVpePnWX7orW0HdgNd39vzuw+wpJvf2f4D59haWejdb301DTW/7YY78p+JCdo/l8sLf/GNmljbGDIrZj7bL12lGmth5Z2dYrUqHwVBtVpz5xj6wl7cpdQ/7p80WIgI9b9SHRKfKHygQ5ejG7cg3knN3HyXhi2JpZ80KATIxp24Zu9udcTvWuGEuJdnV+PruF+/FOqu/rxabN+jN/yG3eKuMlSXGq6+dOtajOWn9tFeMwDGpevyvBGXfly5zziNJyjVl7Yw/rLB1XPdXV1+PytAZy9f121LCUznW3XjvM4KYZshYIgZ2/61WxNUkbqG7muCPauxvv13+bXI6u58vgObQIa8FWr93hv1bc81fA9tQ1swIDa7fjvoRXceHoPf3sPRjfuTnJGGicirwBQxcWH/bfOcvXxHTJzsulWtRnTWg/l/dXfabzmEv8ecXFxjBo1io0bNwLQoUMHfv75Z6ysrLSuM2DAABYuVL8ZVbduXY4fP656npGRwccff8yyZctIS0vjrbfe4rfffsPNze2l6ybdNoX4l1AqlXz//fd4e3tjbGxM1apVWb16NQD79+9HR0eHPXv2UKtWLUxMTGjQoAHXr+eeeK9fv46Ojg7Xrl1T2+aPP/6Il5cXymfpuVevXqVNmzaYmZnh6OhI3759iY6OVpUPCQlhxIgRjB07Fjs7O1q0aAHAxo0b8fX1xdjYmKZNm7Jw4UJ0dHSIj48HICYmhnfeeQc3NzdMTEwICgpi2TL14EJKSgr9+vXDzMwMZ2dnZsyYUegz0NHRYf369WrLrKysWLBgAQARERHo6OiwcuVKQkJCMDIy4u+//36p9y8pZ3YconJwbYKa1MHWxZGmvTtgbmPJhb3Hi1xv94K1BNSrhnOFwhkNxmYmmFqZqx6Rl29iUM4AvzpVSqQNa5atpFX7NrTp2A7P8l4M+3AkDg72bFq7QWP5zWs34ODowLAPR+JZ3os2HdvRqn0bVi1drlZOR0cHG1tbtUd+VtZWaq+dOHIMFzdXqtaoViLtbBvYgL3hZ9l760zuneoz24hJTaSlXx2N5X3t3HmSEs/268d5mhLP9aeR7L55Cm9b10LtHNmwG6su7uVxcmyJ1F0bpVLJsgP7ebdFS5pVqYaPswuTe/UhPTOLHWdPa12vpo8vTatUpbyjE2529rzTJAQfZxfO31bPWtLT1cXOwkL1sDYzL+kmEVKhBicir3Ai8gpPkuNYf+Ug8WnJNPTSHDwOsPfEx86NP09s4Eb0PeLSkoiMf0xE3CNVmVruAey+eYqwJxHEpCZyNOIS15/cJcSnRom3B6BVQD0O3j7HgdvneZQYzdJzO4lNTeQtn1oay6dlZZCQnqJ6lLdxwaScMYdu52WXNPAKYtPVI1x8dIunKfHsvXWG/2PvvsOjKtoGDv92N733RkiBQEIIvSX03jsIAqIIigVsqK/9tX32gl2xgCgCUqQ3QXoPJdQkJIGQ3ntPdvf7I7Bhk11ATeHV576uvXRnZ87OcE7OOTvnmZlzaZcZERTaKG3SarWsWL+O+++exsDevQnw8+f1Z56lrLyc7Xt2Gy3XNjCQJx6cy7D+AzAz8uCjV7fuPDrrfgb27t1Q1Tfo7M4jBPbuTFCfLjh6utJz6ghsHO25uM/43xKApa01Vva2updSWXN77hXoj3+nNjh6umLn5kS7QaE4NXMnLbbpoi+ObtlDpwGhdB7YE9dmHgy/bxL2zo6E7zx403Kbv/+VkF5d8W7l1zgV/RP+iW0y5mjCBb47tpF9lyOauiq3NK5tH3bFhLMzJpyk/Ax+OL6JrOJ8o+epQDef6sjgyMNkFOUSmRHPjuhjBLjU/Dge0LIza87u4WRSNOlFOWyPPsrp5EuMD+nTKG0a1Korh+PPcSj+HGmFOaw+u4fckkL6tuhoMH9ZVQUF5cW6l6+jB1ZmFhy5el6XJyYrkTMpMaQV5pBVnMee2FMk52cSUOt+o6FMbNefHdHH2B59jMS8DBYdXU9mUR6jgw1H/A5q1ZVtkYfZfzmCtMJs9l0+zY7oY0zpMFCX5/09y9gceYjLOSkk5Wfw6YFfUSgUdGzWqlHadMfQaO/MVwOaPn06ERERbN++ne3btxMREcHMmTNvWW748OGkpqbqXlu3btX7/Mknn2TdunWsXLmSgwcPUlRUxOjRo1Gr1Ua2WJd0ngnxD/Hyyy+zZMkSvv76ay5cuMBTTz3FPffcw759+3R5XnrpJT766CNOnDiBiYkJs2fPBiAwMJAuXbrwyy+/6G1z+fLlTJ8+HYVCQWpqKv369aNjx46cOHGC7du3k56ezpQpU/TKLF26FBMTEw4dOsSiRYuIj49n8uTJjB8/noiICB566CFeeuklvTJlZWV06dKFzZs3c/78eebOncvMmTM5duyYLs+zzz7Lnj17WLduHb///jt79+7l5MmTf+nf6rnnnuPxxx8nMjKSYcOG3db3NwR1VRXp8cn4hujfCPiGtCYl1vhwsPMHwsnLzCFs/ODb+p5zB04Q2KMDpub1HwVYWVnJpehLdO3RTS+9S49uXDx33mCZi+cv0KVW/q49unEpMpqqqipdWmlpKdPHT+HuMZN56enniYk2PqSisrKSXdt3Mnz0CBSK+o9uUilVtHDy4mxqrF76mdRYWrs2N1jmUmYCzlZ2dPSq3r/2Ftb08GnL6eRovXyT2w2goKyYPXGn6r3et5KcnU12YQGhgUG6NDMTUzoHBHD2ypXb2oZWq+X4pWiuZmbQuaV+JExiViYjXn2JcW++yos/LSHphs72hqBSKPG2dyM6Q79jITrzKn6OhoehtvVoQWJeOgMDuvLqkDm8MPBexgb3xlSp0uUxUaqo0ujfXFWqq2jh5FX/jahFpVTi5+jJ+VrDX86nxen9ILyZvi06cjH9st7TelOlikpNlV6+SnUlrYwcz/UtOS2N7JwcQrt00aWZmZnRuV17zl5svKFG9UVdVUVWQgrewfrD/LyDW5IeZ3h47XW/vbmIn5/5kM0fLyUlyvjfnVarJTnyMvnp2UaHgjY0dVUVKVcSadk+SC+9Rfsgki4Zr/vpvUfJTc+i/6ThDV3FP+2f2KZ/AhOlipbOzYhIjtFLj0i5RJCb4eM/KuMqLtb2dPEOBMDewoaefu04kVjzcNhEqaJCrX/uq1BX0sbNr34bYIBKocTHwaPOsNPIjPg6D9aM6enXjqiMqwajjq8LdPXB3daRmKykv1Pd22KiVNHKxZtTte5tTiVH08bdz2AZU6WJwX3Q2tUHlcJw94S5iRkmSqVu+K34Z4qMjGT79u18//33hIWFERYWxnfffcfmzZt1QR/GmJub4+HhoXs5OdVEDefn5/PDDz/w0UcfMXjwYDp16sSyZcs4d+4cu3btuu36ybBNIf4BiouL+fjjj9m9ezdhYWEAtGjRgoMHD7Jo0SLmzp0LwFtvvUW/ftXD4p5//nlGjRpFWVkZFhYWzJgxgy+++II333wTgEuXLnHy5El++uknAL7++ms6d+7M22/XzFe0ePFimjdvzqVLl2jdujUAAQEBvP/++7o8zz//PIGBgXzwwQdAdUfd+fPneeutt3R5mjVrxjPPPKN7/9hjj7F9+3ZWr15Njx49KCoq4ocffuCnn37SRbMtXbr0T4XZ3ujJJ59kYq05cm72/YaUl5dTXl6ul2Z+w5DC21FaWIJWo8HazkYv3crOhhIjwz9y07I4uHo7U198GKVKZTDPjVIvJ5KdlMbQ2ZP/VN1uV35ePhq1Gkcn/WEtjk6O5GQbjqLKyc7B0cmxVn4n1Go1+Xn5OLs409zPh/+8/Dz+AS0oKS7mt1/X8uTc+Sz6eTHePnX3+6F9BygqKmLoqBH117gb2JlboVKqyC/VHyKbX1qEg5fhaKpLWYl8fmgNT/aZiqnKBBOlivDESJaEb9HlCXT1YUDLzjy39asGqfetZBdW33w72drppTvZ2JKWe/MouKLSUka+9jIVVVWolEqemzyFHjd0wrX19eX16TPxcXUju7CAxTt3MOezj/n1uZdwsLa+yZb/OmszS1QGbq4Ly0uxszD8nc7Wdvg7eVGpVrMkfDPWZpZMbj8AKzMLVkZU31BFZSTQv0Un4rKTyS7Oo5WrDyEeLVA2QEdtbbZmVqiUSvLLivXS88uLsbewMVKqhr2FDe09A/jmyDq99HNplxkeGEp0RgIZRTkEu/vTqVlgo7QJIDun+vhydtQ/Fzg7OpCakdEodahPZUUlaDVaLO30jzNLO2tKCooMlrGyt6XPzDG4+niirlITc/QMmxcuZczTs/Bs7afLV1FSxrLnPkJdqUapVNBr+qg6nXSNpaSgGK1Gg429/nnPxt6WOCPXrezUDP5YsYn7X3vitq5bje2f2KZ/guvX3bwy/b+fvNIiHC0NX3ejMq7y8b6VPNt/hu66eyzhAt8erYmEP518iXFt+3Ah/TJpBTm09wqgh0+w3lD9hmJjfu0aVet8XlhWjL37ra+LdhbWtHVvweLjm+t8ZmFixjujHsFUqUKj1bLi9E6iMhp+Xk47C2tUShW5Jfp/K7mlhThZ2hksczIpiuFBoRy+eo7YrCRauTRnaOsemKpMsLewIae0bsfg7G6jyS7Ob9S56YRxxn4H/dnfQrUdOXIEe3t7vd9foaGh2Nvbc/jwYQIDA42W3bt3L25ubjg4ONCvXz/eeust3NzcADh58iSVlZUMHTpUl9/Ly4uQkBAOHz7MsGHDbqt+0nkmxD/AxYsXKSsr03UsXVdRUUGnTp1079u3rxm253ltMvCMjAx8fHy4++67efbZZzl69CihoaH88ssvdOzYkeDgYKD6pLNnzx5sbOr+WIuLi9N1nnXtqj+MKDo6mm7d9KOMunfXH+amVqt59913+fXXX0lOTtadkK2v/cCOi4ujoqJC1zEI4OTkdNMT6M3UruOtvt+Qd955h9dff10v7dVXX8VzWMc/X6HaP1a1AHV/wGo0GrYuWkHY+CE4erje1qbP7z+Os7cHni0aNpqkThO03DwCrNZn14cGX08ODmlLcEhb3edt27fjkfseZP3qtcx/+ok6m9u2aSvdQ7vj4ury1xpwm2oHqisUCl3da2tm78qsriNZe24PZ1JicbS0ZUbnYTzQYyyLjq7HwsSM+b0m8+2xDY32JHXbyXDeWVUzPHbhg9Xz3dTeU1q0dXdqLVbm5vzyzPOUVJQTfimahevX0czZhS4B1ZF2vdrU7L8AvGjv58/4t15nS/gxZvQfaGyz9UJbZ09hdD8pUaAFlp3aTllVBQDrL+xnVtdRrD27h0qNmnXn9zG1wyBeGDgTrRayS/I5nniR7s2DG7IZ+vWv1SYFCoPtrK2PfwdKKss4maw/LP+XUzu4v9to3h35CFogoyiXA1ci6OPfsR5rXWPb7j94+9NPde8/efP/AAPHnra6bf+ratf9ZouSOXi44OBRc85yb9mcotwCzvx+WK/zzNTCjEmvPExleQUpkVc4unoHdq6OeN3GwjINx8A53MBu02g0/PbFT/SfPAJnT7dGqttf9U9s0/++2uduhYG065rbu/Fg6Fh+jdjFqeRLOFnaMavbSB7pOZEvDlVPZfL9sU3M6zWJLyc8A2hJK8zhj5gTDGpleBh8Q6hb+9s7n4f5hlBaWcaZlJg6n5VXVfD2rqWYm5gR6ObD5PYDyCrOJybr5pGv9cfAfjLSpuWnd+JoZccn455EQXVH286Y40zpMAi1VlMn/+T2A+nfshP/2fIllbUi1v7xDPx73AmM/Q567bXX/tZ209LSdB1eN3JzcyPNwCJh140YMYK77roLX19frly5wiuvvMLAgQM5efIk5ubmpKWlYWZmhmOtB3bu7u433W5t0nkmxD+ARlN9Yt2yZQvNmumHfZubmxMXVz0P0Y0T41/v2Lhe1tPTkwEDBrB8+XJCQ0NZsWIFDz30kN53jBkzhvfee6/O93vesCpf7Q4nrVZbpxOl9k3PRx99xMKFC/nkk09o164d1tbWPPnkk1RUVBjMb4yhjozKyso6+WrX8Vbfb8gLL7zAggUL9NLMzc358dQ2IyXqsrS1QqFUUlzryXZJYRFW9nU7KStKy0m/kkTG1RR2L6t+iqq9tmT0wtkvMOmZOfgE1wybqyyvIPrYGXpOGFpnW/XF3sEepUpVJ8osLze3TnTZdU7OTuQayK9SqbCztzdYRqlU0rpNIMmJdYcgpKemcTr8JK++++ZfbMWtFZSXoNaocbDU3y92FtbklxmOKhnfti+XMhPYdPEQAAl56ZQdr+CNYQ/y65ld2FvY4GbjyH/6z9CVuf63snz6azy18VPSi3LrtR1927Yj5Bk/3fuKa8NkswsLcLnh3z63qAjnW8xPplQqae5a3Ykb2Myb+PR0ftz1u67zrDZLc3MCPL1IzGy4CfaLK0pRazTYmev/jduaWxrtoCwoLya/rEjXcQaQXpiDUqHA3tKWrOI8iitKWRy+GROlCmszC/LLihndptdNh83Ul8KKEtQaDQ61oszszK0oqBW9YEifFh04HH8WtUb/BrywvITPDq7CVKnCxtyK3NJCpnQYZHAS7vrQNzSMkBsiEyuunZuzcnNxuWE+w5y8PJwcHRqkDg3JwsYKhVJRJ8qsrLAYK7tbRwhe5+bvTeyxs3ppCqUSe7fqfyOX5p7kpmUSse1gk3SeWdlZo1AqKcrXP/aLC4qwsat7zqgoLSPlcgKp8Uls/bG68+L6deuNGU8y84VH8Q9p3Sh1N+af2KZ/guvX3dpRZvaWNnWi0a6b1H4AkenxrLs2wf7V3DTKjlTw7qhH+OXUDnJLCykoL+ad3T9hqjLB1tyKnJIC7u06gvTC+r3eGlJUfu0aVSsS2tbCioKyWz9E6+nXjmMJFw12MGlBNzl/Un4GnrbODA/qQczBhu08Kygrrt5PVvpRZg6WtgYXQIDqIZoL96/kswOrcLSyJaekgBFBYRRXlNW5rk1q15+7Ow7mha1fcyUn1eD2ROMz9jvImNdee61OZ1tt4eHhgOGH74Z+T95o6tSpuv8PCQmha9eu+Pr6smXLljqjjf7MdmuTzjMh/gGCg4MxNzcnISFBNyzzRtc7z25lxowZPPfcc0ybNo24uDjuvvtu3WedO3dm7dq1+Pn5YWJy+6eOoKCgOhM2njihP3nygQMHGDduHPfccw9Q3VEXExNDmzbVq/YFBARgamrK0aNH8fHxAapXYrl06ZJee11dXUlNrbmwxsTEUFJy65uRW32/IfURmqwyMcHdrxkJF2Jo1aVmGfKrF2Jo2aluRIu5pTn3/t9Temlndh8h4WIcY+bfg72r/tDJS8fPoq5U06ZnJxqKqakprQNbc/L4CXr376tLP3n8BD37Gp6sOzikLUcOHtZLO3EsnNZtAo0eW1qtlriYWPxbtqjz2fbN23BwdCC0Z8NNdK7WqLmck0J7j5aEJ0bq0tt7tOREUpTBMuYmpnU6LDTXI+xQkJKfxTObPtf7fGrHwViYmLH0xFayGqBjxtrCQm8FTa1Wi7OtHceiown0ro5OrKyq4lRsLI+NGfuntq1Fq+uMM6SiqpL49HQ6tmi44WZqrYak/Axau/pwLq3mvNfa1afOnGHXXclJpYNnK8xUplSoqzt03Gwc0Wg15Ne68a/SqMkvK0apUNLeK6DOXDwNQa3REJ+bSluPFpy8YU6Zth4tbjl8JcjNFw9bZz67vNponkqNmtzSQlQKJV29g/RW5KxP1lZWeitoarVanJ2cOHbqFEEB1Z3+lZWVnDp3lsfmzGmQOjQklYkJLj5eJF+Mw79TzbUjKTIOvw5BNympLzsx1eDDEz3a6nm6moLKxAQv/+ZcPhtNm24ddOmXz0UR2KXuohzmlhY88v7zemnhvx/kysVLTHlyNg6uznXKNLZ/Ypv+Cao0auKyk+ng1Yqj11ZghOoVlY8lGD5PmZuYotHWvu5Wv6/987hSXUVOSQEqhZKeviEcvHKWhqbWakjIS6ONm69e9Fj1+9iblKxehMjNxpHD8edu+/tMlA0/pLhKoyYmK4lOzVrr1a1Ts9YcvWp47tvr1FoNWcXVc3H2a9mJ4wkX9KLVJrcfwLROQ3hp26JGjKATt+PP/g6aP3++3u9KQ/z8/Dh79izp6el1PsvMzMTd3f22v8/T0xNfX19iYqr/zjw8PKioqCA3N1cv+iwjI4OePXve9nal80yIfwBbW1ueeeYZnnrqKTQaDb1796agoIDDhw9jY2ODr+/tTSw8ceJEHnnkER555BEGDBigF8U2b948vvvuO6ZNm8azzz6Li4sLsbGxrFy5ku+++w6VkTk/HnroIT7++GOee+455syZQ0REhG71y+s9/QEBAaxdu5bDhw/j6OjIxx9/TFpamq7zysbGhjlz5vDss8/i7OyMu7s7L730kt6KZAADBw7kiy++IDQ0FI1Gw3PPPacXbWfMrb6/IXUZ1odt3/6Ku583ngE+nNt7nMLsPDoMqO4IOrB6G0W5BYyYOxWFUomLt4deeUtbG0xMTeqkQ/XCAgGdg7G0aZj5pa6bNG0K773+Fq3bBBIc0pYtGzaTkZ7BmAnVnS/ff/UtWZmZPP9q9UIRoyeOY8OadXz9yReMHDeai+cvsH3TVl5847+6bf70/Y+0CQmmWXNvSoqLWbdqLXGXYnn8Gf3OQ41Gw44t2xgycjiqP9Gp+1dsiTzM/J6TiMtJISYzkUGtuuJibc/OmOMATOs4BCcrO748vBaAk0nRzA0dx5BW3TiTWj1s876uI4i5tqIjQGK+/txOxRWlBtMbikKhYFq//izZ9TvNXV1p7urKj7t+x8LMlGGda4awvPrLT7jaOzB/dPU+XbLrd4Kb+9DM2YUqdRWHIi+yJfw4z99V8+Tvkw3r6NM2BA9HR3KLivjh9x0Ul5UxupvheQTry964U8zoPIzEaytm9vRth6Olre6mflSbnthb2LD89O9A9X4a0ro70zoNYXvUUazNLBgT3JtjCRepvLZIgI+DO/aWNqTkZ2JvYcOwwFCUKNgde/NVFOvL9qijPBQ6nis5KcRmJzOgZSecrezZHVu9aMpd7QfiaGnLt8f0V7jt26IjsVlJJOfXjfZr4eSFo5UdCblpOFrZMj6kHwqFgq2Rh+vkbQgKhYJp4yewZOUKfJp50bxZM5asWImFuTnDB9QM6/3v++/j5uLM/NnVHWqVlZVcTkjQ/X9mdhbRcXFYWVjQ/No1q6S0lMSUFN02ktPSiI6Lw97WFg8Dw0HqS/shYexZ/Bsuvl64t2xO5P6TFOXk06Zf9d/S8d92UZxXwIDZ1U/Bz+06gq2zA45ebqjVamKPnuXKqUiGPFyzEM/pbQdw9fXCztURTZWahPMxXDpyhj4zRjVYO24ldNQA1n35M14tmuPd2p+TfxwmPyuXroOrH5jsWrGRwtx8Jjw6E4VSiVtz/YU1rO1tMDE1rZPelP6JbTLG0tQcb/uaqR+87Fxo5eJNQVlxvUc7/10bLhzgyT5Tic1OIjojgWGB3XGxdmB7VPWK5DO7DMfZyo5PDqwCIDwxknm9JjE8MJTTyZdwtLLlge5juJSZQM61625rl+Y4WdtxJTsVZ2s77u44BIVCwbrz+4zWoz79EXOCWd1GcTU3jSs5KfT274CjlR0HrlSvhjyubR8cLG1ZekL/wXMvv3ZcyU4hpaDuwjvDAntwNTeNrOI8VEoVIR4tCPVty4rTOxulTb+d28uz/WcQk5lIZEY8I4J64mbjyJZr15P7u43C2dqeD/cuB6qntQh09SEq4yo25lZMbNcPP0dPPrr2OVQP1by36wje2/0z6YU5ugjE0spyvUjxfzzNnTls889ycXHBxeXWU6uEhYWRn5/P8ePHddP8HDt2jPz8/D/VyZWdnU1iYqJudFSXLl0wNTVl586dusXuUlNTOX/+vN5c3bcinWdC/EO8+eabuLm58c4773D58mUcHBzo3LkzL774om5o5q3Y2dkxZswYVq9ezeLFi/U+8/Ly4tChQzz33HMMGzaM8vJyfH19GT58eJ1OrBv5+/uzZs0ann76aT799FPCwsJ46aWXeOSRR3RPLF555RWuXLnCsGHDsLKyYu7cuYwfP578/JqV4T744AOKiooYO3Ystra2PP3003qfQ/Xwy/vvv5++ffvi5eXFp59+elsrct7O9zeUwB4dKC0q4eiGPyjOL8C5mQcTFtyPnUv1U5HivEIKs/P+9HZz0zJJvhTPpGcaPoJjwJCBFOTns+yHn8jJzsavhT9vf/we7p7VHXo5WdlkpNV0Bnl6efLWx+/x9SdfsHHtepxdnJm34HH6DqyJIiwqKmLhux+Sm52DtY01LVu3YuE3nxHUVr9D81T4STLS0hkxZmSDt/PI1fPYmlsxqV1/HC1tScxL5909P+uemjpY2uBsXTP0cd/l01iamjEsMJSZXYZTXFHGhfQr/HJqR4PX9c+4d+BgyisreW/NKgpLS2jr68fnD8/Ti1BLy83VC2svq6jgvTWryMjPw9zUFF83d964516GdqpZOTEjP4+Xf/6RvOJiHG1sCPH1Y/GTC/CstbhEfYtIicHazJJhgT2wM7citTCbb49u0HVY2plb6w0DqlBX8s2RdUxs158Ffe+muLKMiJQYtt3QiWSqMmFkUBjOVvaUV1USmRHPL6d2NNoN/PHEi9iYWzIupC8OFjYk52fy8f4VutUz7S1tcLLWHzJjaWpOV+82Ro83U5UJk9r1x9XGkfKqCs6mxPLtkfWUVJYbzN8Q7psyhfKKct794gsKCwsJCQrii3fe0YtQS8vMQKmsOfYys7OZ8egjuvc/r1nDz2vW0Ll9e7794EMALl66xMP/eVaXZ+GiRQCMHjKE156pSa9vLbuFUFZcwqkt+yjJL8LJy40Rj83A1tkBgJL8Qopyaq4r6io1R9f8TnFeISamJjh6uTH8sen4tKsZ8ldVXsHB5Vsozi3AxNQEBw8XBs6ZSMtuIbW/vtGEhHWmtLCYfb/toCgvH7fmnsx47mEcrkU/F+UVkJ91Z3XC3Mo/sU3GBLn68sWEmuFWj/e+C4CtkUd4a/fSpqqWQQevnMXW3IqpHQbhZGXH1dw03ti5RDc80dHSFhdrB13+3bEnsTQ1Z1SbnszuPoriijLOpsay9ETNlBqmKhPu6TwMdxsnyqoqOJkUxSf7V1JcUdYobTqZFI21mSWj2vTEzsKa1IIsvjy0VjcNgL2FDU5W+kNVLUzM6NSsNavO7Da4TXOVKdM6DcHB0oZKdRVphTksCd/CyaSbr05YX/ZfjsDO3JoZnYfhaGXH1ZxUXtn+LRnXOmOdrOxws66J9lEqFExs1x9vBzfUGjVnUmJZUGuqijHBvTBTmfDKkPv1vmvZye0su8Puo0T9adOmDcOHD+fBBx9k0bVr99y5cxk9erTeXNdBQUG88847TJgwgaKiIl577TUmTZqEp6cn8fHxvPjii7i4uDBhwgQA7O3tmTNnDk8//TTOzs44OTnxzDPP0K5dOwYPHnzb9VNob3cyISGEqCdvvfUW33zzDYmJ/7wQ7EVH1jd1FerVQ2HjScy9/Yk0/1c0d/Rg6rJXmroa9erXe96kYOvvTV2NemU3cihPbfz01hn/hywc+wT3rWy4+fmawtK7X6EwvuFXdWtMtn6+fLRvRVNXo1493W8ay/+BPzqndx72j2vX9M7D6PXlw01djXp1aN43jFvyXFNXo15tuP89Hln7QVNXo159PelZhn/31K0z/g/Z/uDCpq7CX5a/aElTV8Eg+4fuv3WmvygnJ4fHH3+cjRs3AjB27Fi++OILHBwcdHkUCgVLlixh1qxZlJaWMn78eE6fPk1eXp5uHu8333yT5s1rFkwrKyvj2WefZfny5ZSWljJo0CC++uorvTy3IpFnQogG99VXX9GtWzecnZ05dOgQH3zwAfPnz2/qagkhhBBCCCHEnelfGOfk5OTEsmXLbprnxvgvS0tLduy49QMUCwsLPv/8cz7//PNb5jVGOs+EEA0uJiaG//u//yMnJwcfHx+efvppXnjhhaaulhBCCCGEEEIIcUvSeSaEaHALFy5k4cL/3ZBpIYQQQgghhBD/XtJ5JoQQQgghhBBCCHEn0fz7hm3eyYwvkSeEEEIIIYQQQgghxL+cdJ4JIYQQQgghhBBCCGGEDNsUQgghhBBCCCGEuJP8C1fbvJNJ5JkQQgghhBBCCCGEEEZI55kQQgghhBBCCCGEEEbIsE0hhBBCCCGEEEKIO4lW09Q1EDeQyDMhhBBCCCGEEEIIIYyQzjMhhBBCCCGEEEIIIYyQYZtCCCGEEEIIIYQQdxKNrLZ5J5HIMyGEEEIIIYQQQgghjJDOMyGEEEIIIYQQQgghjJBhm0IIIYQQQgghhBB3Eq0M27yTSOSZEEIIIYQQQgghhBBGSOeZEEIIIYQQQgghhBBGyLBNIYQQQgghhBBCiDuJDNu8o0jkmRBCCCGEEEIIIYQQRkjnmRBCCCGEEEIIIYQQRsiwTSGEEEIIIYQQQog7iUbT1DUQN1BotTKQVgghhBBCCCGEEOJOkf/xl01dBYPsF8xr6io0CYk8E0KIejTl55ebugr1atXM/yOjILupq1Hv3Oyc6fvVo01djXq1/9GvyHvqxaauRr1yWPg2vb58uKmrUa8OzfuGyT+91NTVqFdr7n2LvCeeb+pq1CuHT9+lMOJsU1ejXtl2bM9nB1Y1dTXq3eN9pjR1FRrEuCXPNXUV6tWG+9/7R57PZ696u6mrUa8WT3mRfl//szpG9j1yZ3ZAif890nkmhBBCCCGEEEIIcSeRQYJ3FFkwQAghhBBCCCGEEEIII6TzTAghhBBCCCGEEEIII2TYphBCCCGEEEIIIcSdRIZt3lEk8kwIIYQQQgghhBBCCCOk80wIIYQQQgghhBBCCCNk2KYQQgghhBBCCCHEnUQjwzbvJBJ5JoQQQgghhBBCCCGEEdJ5JoQQQgghhBBCCCGEETJsUwghhBBCCCGEEOJOotU0dQ3EDSTyTAghhBBCCCGEEEIII6TzTAghhBBCCCGEEEIII2TYphBCCCGEEEIIIcSdRCurbd5JJPJMCCGEEEIIIYQQQggjpPNMCCGEEEIIIYQQQggjZNimEEIIIYQQQgghxJ1EI8M27yQSeSaEEEIIIYQQQgghhBHSeSaEEEIIIYQQQgghhBEybFMIIYQQQgghhBDiTiKrbd5RJPJMCPGvs3fvXhQKBXl5eU1dFSGEEEIIIYQQdziJPBNCNIq0tDTeeusttmzZQnJyMm5ubnTs2JEnn3ySQYMGNXX1mtTQ1t0Z27YPDpY2JOVl8OOJrURlXDWav7d/B8YG98bTzpmSinIiUmL4+eQ2iipKAejXohPzek2qU27GL69RqalqkDasW72WFcuWk52VjV8Lfx5f8AQdOnU0mv/0ydN88clnxF++grOLC9PvncH4SRN0n1+Ju8wPi74nOiqKtNQ0HnvqCaZMn6q3jbvGTiQtNa3OtidMnsiC556pt7bdaHzbvkzrNBgnK3vic1L5/NBqzqbGGc0/IaQvE9v1x8PWifTCXH4+tZ0d0cf08tzVfgDj2vbF3daR/LJi9sad4tujG6hQN8y+MsRi2CDMwrqhsLREnZBIydqNaNIyjOa3mfcAJgEt6qRXXoyi+Luf6qSbD+qH5ehhlO87ROn6LfVad0MmhPRjeqchOFvZcyUnhc8OruZMaqzR/BND+jGpXX887ZxJL8xh6cltbL9hP6mUSu7tPJwRQWG4WDuQkJfO10d+41jCxQZvy3XDAnswNrg3jla2JOZl8GP4FiJvcp7o49+BcW376M4Tp1Mu8dPJbRSVl+ryWJlaML3TEHr4tMXa3IKMwlyWntzG6eRLjdEkACyGD8asZ/fqY+9qIiVr1t/82Js/F5NWBo69C1EUf/ujbpsWIwbrfa4pKKTglbfqte63S6vV8u2a1az7YxeFRUW0bdWK52Y/QMvmzY2WWffHLrbs30dcYiIAbfxb8Oi0aYQEtGqsauuc23OM0zsOUpJXhJOXG73vHoFXaz+DeZOjrrD+w8V10qe/+TiOnq4AZCenc3zDbjKvplCYnUfvqSPoMKRnQzbhX2NEUCgTQvrhaGlLQl46PxzfxMX0eKP5+7XoyIR2/fCyc6G4oozTyZdYEr6FwvISAFQKJZPbD2BAQBecrexILshk6YnGPUfcrg6eAUzvNJQgNx9crB14fuvXHLhypqmrZdCAlp0ZHhiKg6UNyfmZrIjYRUxWosG8s7uNprd/+zrpyfmZvLLjO937Ia26MaBlZ5ys7CiqKOVEUhRrzu6hSqNusHbcaHzbPtzd8dr9UW4qXxxac9P7o/Ft+zKxXb/q+6OiXJad3M6OS8d1n38y9gk6NWtdp9yRq+d5fuvXDdIGIW5FOs+EEA0uPj6eXr164eDgwPvvv0/79u2prKxkx44dzJs3j6ioqAb53oqKCszMzBpk2/UlzDeEWV1H8v3xTURnJDC4dTdeHHgvT238jOyS/Dr5A119md9zEktPbuVEUjROlnY8GDqWh8Mm8OG+5bp8JRVlPLHhE72yDdVx9sfvu/js409Z8NwztOvQno2/refZJ57m51W/4O7hUSd/SnIK/3nyacaMH8srb7zKuTNn+fi9D3FwdKD/wAEAlJWV4dnMi/6DB/D5x58Z/N5vl/6ARq3Rvb8Sd5mn5j/BgMEDG6SdAwO68FjvyXy8fyXn0y4zNrg374+ex70r3iSjKLdO/nFt+zA3dBwf7F1OZEY8bdz8+E//GRSWlXD46jmg+mZ3buh43tvzM+fTLtPcwZ0XBs4E4ItDaxukHbWZD+yLef9elCxfizozC4shA7B5eDYF73wM5RUGyxQv+QVUKt17hbUVts88RmXE+Tp5Vc2bYRbWDXVyaoO14UaDArrwRO+7+GjfCs6mxTG+bR8+HDOfe5a/TrqB/TS+bV8eDhvPe3uWEZlxlTZufjw/4B4Ky0s4FF+9n+b2GMew1j14b+8yruam0b15MO+MeJiH1n5g9AdPferp1676PHFsE1GZVxnSqhsvDrqPpzZ+SlZx3fNEkJsv83tNZumJrZxIisLJyo65PcbxSNhEPtj7CwAmShX/HXI/+WXFfLhvOdklBbhY21NaWd7g7bnOfFA/zAf0puSX1dXH3tCB2Dz6AAVvfWj82Fv8c91j7z9PUBlxTi+fOjWNoi+/r0lowhXLlm7cwPItm3n1kXn4eHryw29rmffWm6xd+CnWlpYGy5y8cIFhPXvTPrA15qZmLN24gflv/R+rPvoYNyfnRqt7zPFzHFy5jX4zRuMR4MOF/SfY9OnPTH/jMWydHYyWm/F/T2Bqaa57b2lrrfv/qopK7FwdCejaloO/bmvI6v+r9PZvz5zuY1h0ZD2RGVcZFtiD/w6Zzfx1H5NVnFcnfxs3P57oM5XFxzdxPDESZyt7Huk5gfm9JvHO7p8BmNFlGP1bdOLLw2tJysukU7PWvDDwXp7b8hVXclIauYU3Z2lqTmx2ElujDvP2iIebujpGdWvehmkdh/Dzqe3EZiXRv2UnnuozlZd3fEtOSUGd/CsidrLm3B7de5VCyetD53AiqebeOdSnLZPbD2Bx+GZis5LxsHViTvfRAKyM2NXgbRrQsjPze01m4YFfOZ8ax5i2vXlv1DzuW3mz+6OxfLB3OVEZV2nj7sez/aZTWF7C4avV9xGv7PgOU2VNV4WdhTU/THmBvXGnG7w9dxSN5tZ5RKORYZtCiAb36KOPolAoOH78OJMnT6Z169a0bduWBQsWcPToUeLj41EoFEREROjK5OXloVAo2Lt3ry7t4sWLjBw5EhsbG9zd3Zk5cyZZWVm6z/v378/8+fNZsGABLi4uDBkyBICtW7fSunVrLC0tGTBgAPHx8Xr1y87OZtq0aXh7e2NlZUW7du1YsWJFQ/6T6IwO7sXu2JPsjj157YnuVrJK8hka2N1g/tau3mQU57Et6iiZRblEZ15l16VwWjg308unRUt+WZHeq6H8unwlo8aNYcz4sfj5+/H400/i5u7GujXrDObf8Ns63D3cefzpJ/Hz92PM+LGMGjualctqOv/atA1m3hPzGTx0CGZmpga34+joiLOLs+51+OAhmnk3o2PnTg3SzikdBrIl8jBbIg9zNTeNzw+tIbMoj/EhfQ3mHxbYg40XDrI79iSpBdnsjj3JlsjDTO88RJenrYc/59Pi2BVzgrTCHMITI/kj5gSBrr4N0gZDzPv1pGznXirPXUCTlk7J8tUozEwx69zRaBltSSnawiLdy7R1AFRWUnFGvwMDMzOs7plK6ap1aEtLDW+snk3tOJjNkYfYFHmIq7lpfHpwNRmFuUwI6Wcw//DAHmy4cIA/Yk+SUpDFH7En2Bx5iBmdhunl+enkNo5cPU9KQRbrL+znWMJFpnUcbHCb9W1Mm+rzxB+xJ0jOz+THE1vJLs5naOseBvO3dmlOZnEuW6OOkFGUS1TGVXbGHKels5cuz8CALtiYW/L+nmVEZyaQVZxHVMZVrubWjeZsKOb9elH2+x4qz15Ak5pOybJVKExNMevS0WiZOsdeYKvqYy/irH5GtUYvn7a4uGEbY6y+Wi0rtm7h/gkTGdijBwE+Prw+bz5l5eVsP3jQaLn/e/wJ7ho2jEA/f/yaNePlhx5Cq9Vy/FzdDuqGFLHzMG16dya4b1ecvNzoc/dIbB3tOL/3+E3LWdpZY21vq3splTU/Odz9vel113BadW+PykSe49eXcW37sCsmnJ0x4STlZ/DD8U1kFeczIijUYP5ANx8yinLZHHmYjKJcIjPi2RF9jAAXb12eAS07s+bsHk4mRZNelMP26KOcTr7E+JA+jdWs23Y04QLfHdvIvssRTV2VmxrWujsHrpzhwJUzpBZmsyJiFzmlBQxo2dlg/tLKcgrKinUvP0dPrMwsOXhDVF1L52bEZCVxLOEi2SX5XEi/wrGEi/g5ejZKm6Z0GMTWqCPV90d56XxxaC2ZRbmMa2v4OBnaujsbLx5iT9wpUguv3R9FHWZap6G6PIXlJeSUFuheXZsHUV5Vwd64U43SJiEMkc4zIUSDysnJYfv27cybNw9ra+s6nzs4ONzWdlJTU+nXrx8dO3bkxIkTbN++nfT0dKZMmaKXb+nSpZiYmHDo0CEWLVpEYmIiEydOZOTIkURERPDAAw/w/PPP65UpKyujS5cubN68mfPnzzN37lxmzpzJsWP6w+vqm0qpooWTV53hZGdTYgl09TFYJjozAWcrOzp5VYey21tYE+rbltPJ0Xr5LEzM+HLCM3w98VmeG3BPg91AVVZWcikqmu499Dv7uvXozvmz5wyWuXDuPN1q5e8e2oOoi1FUVf216LjKykp+37aDkWNHo1Ao/tI2bsZEqaK1qw/hiZF66eGJkYS41x1CBmCqNKFCXamXVq6upI2bH6prPyTPpsbR2tWHNm7VnWWeds6E+oZw9Grj/EBWOjuitLOjKjqmJlGtpir2Cib+ho9BQ8x6dKXi9Fmo0G+v1eSxVEZGUXXJ+NCN+mSiVBHo6sPxBP39dDwxkhAPI/tJZUJFVa39VFVJsHvNfjJVGdiXVZW09wyox9obZqJU0cLZizMp+ueJM6m3Ok/Y64a82FtYE+oTwqmkmqFWXb2DuJSZyAM9xvL9XS/w8ZjHmRjSD2UD/P0YonR2QmlvR1VUrWMv7gom/rffeWwW2o2KU2fqHHtKVxfs3ngR2//+B6v7pqF0dqqvqv8pyRkZZOflEdq+gy7NzNSUzsHBnL0UfZOS+srKK6iqqsLexqYhqmmQuqqKzKsp+LTVP86btw0gLe7mEZe/vvEVS55+j/UfLiEp6nJDVlNQfZ5o6dyMiOQYvfSIlEsEuRn+e4rKuIqLtT1dvAMBsLewoadfO04k1kQ0mShVdaYQqLh2HRN/nkqpxNfRkwvp+n8TF9KuEODsbaSUvj4tOnAx/QrZN0SpxWQl4efogb9T9b2eq7UD7TxbcvYm0xXUl+r7o+aG74/+5HW3jZuv7rpb26igMHbHnqSsynBUshCNQR73CCEaVGxsLFqtlqCgoL+1na+//prOnTvz9ttv69IWL15M8+bNuXTpEq1bV/9IDAgI4P3339flefHFF2nRogULFy5EoVAQGBjIuXPneO+993R5mjVrxjPP1MyR9dhjj7F9+3ZWr15Njx6GIzvKy8spL9cf3mRubm4wrzF25laolKo6UWH5ZcU4WBj+gXQpM5HPDq7myb5TMVWZYKJUEZ4YyeLjm3V5Ugoy+erwbyTkpWNpas7IoDDeHP4gz27+krTC7D9Vx1vJz8tDrVbj6KT/w9TR2Ymc7ByDZbKzc+he64eso5MTarWavLw8XFxc/nQ9DuzdT1FRESNHj/zTZW+HvYUNJkoVuaWFeuk5JQU4NbczWOZ44kVGt+nFgStnuJSZSKCrDyODwjBVmeBgYUN2SQG7Y0/iYGnLFxOeRoECE5WKdef388vp3xukHbUpbG0B0BTqH4OaoiKUjg63tQ2VjzcqLw9Kfv1NL920U3tUzbwoWfhVvdT1djhc2085pfpDX3JLC3C2usl+Cu7N/itniM5MIMjVh1Fteurtp2MJF7m742AiUmJJzs+kq3cQffw7oFQ2fEeTrbHzRGkRDl6GzxPRmQl8emAVC/rerXee+OH4Jl0ed1snQmwcOHD5DG//sRRPO2ce6DEWpVLJmrN7DG63Pilsq+uuKdT/m9IUFqJ0dLytbeiOvRVr9NKrriag/mUV6oxMlLa21cNBn3yEwncWoi0pqZ8G3KbsawvTONvb66U729uTmplloIRhXyz/BVcnJ7q3a1ef1bupsqIStBoNlnb6x5mlnQ0l+YUGy1g52ND/3nG4+Xqhrqoi+sgZNnz0IxOenW10njTx912/n8irdZ7IKy3C0dLWYJmojKt8vG8lz/afoTtPHEu4wLdHN+jynE6+xLi2fbiQfpm0ghzaewXQwycYpULiL/4KWzMrVEol+WX6kbAF5cXYW9R9wFybvYU17Txa6u0jqL6O2Zpb8cKAe0FR3aG1O/YkW6OO1Gv9Ddfp2nW3pPZ1txAnI9fd8MRIRrfpycErZ7iUpX9/ZG9hU2dbQW6+tHBuxnvXph34V5HVNu8o0nkmhGhQ2msn/b8bDXTy5En27NmDjYGn7nFxcbrOs65du+p9FhkZSWhoqN73h4WF6eVRq9W8++67/PrrryQnJ+s6xgxFyl33zjvv8Prrr+ulvfrqq9DyTzfN4HXR2KWymb0r93cbxZqzeziTEoOjpS33dBnOg6Hj+OZI9TDJmKwkYrKSdGWiMxJ4b9SjjAgKZUl4w0zWXmf3arV1027Mj/6H2mstrp1+uzZv3ESPsFBcXF3/Uvnbpa21sxQKha7utS09sQ0nKzu+mfgfUEBuSSHbo44yvfNQ1NrqOSw6erViZpdhfLx/JZHp8TSzd+Xx3neR3WUEP52s/7mATDt3wGrKeN37IgOT+1dTGD8IazHr0RV1ShrqhJpjTuFgj+WE0RR9sxj+YjTh31F7P4HCaHOWhG/FycqObyc9p9tPW6OOcE/nYaivbefTA6t4bsA9LJ/+Glq0pORnsiXqMKOCGm+S8zptuskfmLe9K7O7j2b12d2cSY7BwcqWe7uMYG7oOL6+dp5QKBTklxWz6Oh6NFotl3NScLS0Y1zbPg3SeWbapSNWU2sWBSla9KORnApu9+AzC+1W59gDqIqsibDTpKZTFH8Vu1f+g1n3zpTvNT5Usj5sO3CAt79bpHv/yfMvAHWvgVrtTXehnqUbNrDj0EEWvfo65k0wj6eh87uxyjt6uOLoUXMe9mjpQ1FOPqd3HJTOs0ZQ5xplIO265vZuPBg6ll8jdnEq+RJOlnbM6jaSR3pO5ItD1R3S3x/bxLxek/hywjOAlrTCHP6IOcGgVl0NblP8Nbd71uvl156SyjJOpehHrQa6+jC6TU9+PrWdyzkpuNs4Mq3jEPKDi9h08VCD1Pl2GDv2lp7YhpOlHV9PfLbm/ij6KNM7DUWjrTvH16ignlzOTr7pYlpCNAbpPBNCNKhWrVqhUCiIjIxk/PjxBvNcnwvlxotsZaV+OLdGo2HMmDF6EWPXeXrWDEms3eFl7MJ9o48++oiFCxfyySef0K5dO6ytrXnyySepqDAeGv7CCy+wYMECvTRzc3Nmrnrzlt93XUF5CWqNGgdL/Q5Bewtro3OUTQjpR3RmApsuVv8ATMhLp+zYJt4c/iArI3aSV1q3nBYtcdnJeNjW/yTT9g4OqFSqOlFmuTm5daLRrnN2diInWz8CLi8nF5VKhb2DvcEyN5OWmsrJ4yf4v/ffvnXmvyi/rIgqjbrOU1RHS1tySwxHYFSoK3lvzzI+3LccJ0s7skvyGRPcm+KKUvJLq586z+k+ht+jj7Ml8jAAl3NSsDA159l+0/n55HajHXN/VeWFSAo/vGG41bX5hpS2NqgLatqhtLFGW3Qb8+SZmmLWqT2l2/UnJDbx9kJpa4Ptgnm6NIVKhaqFH2a9Q8l/9r8N8jQ179p+crbSP44cLW0NTsQM1fvpnd0/8/7eX3T7aWxwn2v7qUi33Re2fYOZygQ7CxuyivN4JGwCqYW3Hzn0VxXqzhP60SP2FtYG/97h2nki4yobL1SfJ67mpfNd1Ub+b/hcVkTsIq+0kNySQtRaNZob9kNyfiaOVraYKFX1vkJb5fmLFF698dirnvRfaWurf+zZ2qAtvM1jr3MHSrftvHXeikrUqWkoXf98VOuf1bdrV0Ja1QxzrKis7jzOysvD5YaIupyCfJzsHW65vZ83bWTJ+t/46uX/0sq38eZCBLCwsUKhVFKSr78/SguLsbK7/eGj7i28uXT0zlz18J/i+v1E7Sgze0ubOtFo101qP4DI9HjWnd8PwNXcNMqOVPDuqEf45dQOcksLKSgv5p3dP2GqMsHW3IqckgLu7TqC9MK6k8CLWyusKEGt0dSJMrM1t6ag7NbzMvbx78CRq+dR15pEfkJIPw5fPa9bXTQ5PxMzlSn3dR3J5ouH6vlOQt9N749Kb3J/tHcZH+43fn90nbmJKQMDurA4fLPBbQnRmCTmVgjRoJycnBg2bBhffvklxQYmbM7Ly8P1WrRQamrNanw3Lh4A0LlzZy5cuICfnx8BAQF6r5tFiAUHB3P06FG9tNrvDxw4wLhx47jnnnvo0KEDLVq0ICZGf96Q2szNzbGzs9N7/dlhm2qNmss5KXXmTWrvGUB0ZoLh71WZ1ukQvP6U7mZRW76OHuQZuYn5O0xNTWkdFEj4Mf3Jo8OPhxPS3vDworbtQgg/Hq6XdvzYcYKCgzD5C5NHb920BQdHR8J6NVwUUJVGzaXMBLo2b6OX3tU7iPPpN5/PR63RkFmch0arZVBAVw7Hn9d1ilmYmNXdnxoNCsXtR6X8KeUVaLJyal5pGWgKCjAJvOEYVKkwCfCn6orhY/BGZh3bgYmKyhP6q19VxsRR8N6nFH74he5VlZBE5akzFH74RYMNQ6jSqInOTKBbrf3UrXkbzqfd/n4a3Korh+LP1em8rFBXkVWch0qppH/LTrofKg2pSqPmcnYK7b3+xHnCxBQNdY8rQHeWiM68ioets955w9POmZySgnrvOAOuHXvZNa+0DDT5Bo69lv5UXbl1dIFZp/bVx174bay8plKhcndDU1D/58DarC0tae7hqXu18PbG2cGBY2drFjSorKrk1MWLtG8deNNt/bRxA9+vXcPnL7xEcMu/ENb8N6lMTHD19SLxov6chYkX4/Bo2fy2t5OVmIqVg+Ghg6J+VGnUxGUn08GrlV56R69WRqN1zE1M65zjau4n9FWqq8gpKUClUNLTN4RjCRfqre7/JmqNhqu5qQS7++ult3X3JzY7yUipaoGuPrjbOnHgct3rjpnKpM6+1Gq11fuxgeexrL4/SqSrt/70LF29g/7UdXdgQBeOXD1fpx0DWnbBVGXCzkvhRrbyD6fV3pmvfynpPBNCNLivvvoKtVpN9+7dWbt2LTExMURGRvLZZ58RFhaGpaUloaGhvPvuu1y8eJH9+/fz8ssv621j3rx55OTkMG3aNI4fP87ly5f5/fffmT17Nmq18R96Dz/8MHFxcSxYsIDo6GiWL1/Ojz/+qJcnICCAnTt3cvjwYSIjI3nooYdIS2ucFec2XzzEoIAuDGjZmWZ2rtzXdQQu1va6m4RpnYYwr+ckXf4TSVF09wlmSOvuuNk4Eujqw/3dRhGTlah7wje5/QA6eAbgZuOIr6MHj4RNwM/Jk98v3Xx1tL9q6vS72bxhE1s2bib+SjyfffwpGWnpjJ80HoBvvvia/3v1DV3+cRMnkJ6axucLPyX+SjxbNm5my4ZN3H3PdF2eyspKYqIvERN9icrKKjIzM4mJvkRSov7NpUajYeumLYwYNeIvdbz9GavO7GZ0m56MDArD19GD+b0m4WbryIbzBwCYGzqOFwfdp8vvbe/GkNbd8bZ3pY2bL68OmY2/syffHauZq+Tw1XOMC+nDwIAueNo609U7iDk9RnMo/pxeRFBDKt93GIvB/TFtF4zSwx2raZPRVlRScSpCl8dq+mQsRg2tU9YstCuV5yLRltRaSbO8Ak1aut6Ligq0xSXV/9+Afo3YxZjgXoxq0xNfRw8e73UX7raOrLtQHV3xcOh4Xh40S5e/ub0bQ1t3x9vejTZufrw+dA4tnL1YdMOcMsHufvRr0REvOxc6eAbw8ZjHUaDgl1ONMzfdpsjq88TAgC40s3dlVteRuFjb6/6mp3caymO9Juvyn0iKoodPW4becJ6Y3X00MZk154kd0cexNbfi/u6j8LR1pnOzQCa268/26IZdKOVG5fsOYTFkAKbt26L0dMdqxl1oKyupOBmhy2M1YwoWo4fVKVt97F00OIeZxbiRqFr6o3RyROXbHOvZ96CwMKfi+MmGbI5BCoWCaSNHsWT9b+w5fozYhARe++pLLMzNGd67ty7ff7/4nC+W18zns3TDBr7+dSX/feRRPN1cycrLJSsvl5Kyxlm19rqOQ3py8cBJLh48SU5KBgdXbqUwJ5+2/asXfTmy9nd2/VAz59yZnYe5fPoieenZZCenc2Tt78SdvEi7ATXzh6qrqshMSCUzIRV1lZqivAIyE1LJS6/fOTn/bTZcOMCQ1t0Y1Kor3vZuzOk+GhdrB7ZHVT8wnNllOE/2qVlkKTwxklDfEIYHhuJu40SQmy8P9hjLpcwEcq6dJ1q7NCfUty3uNk4Eu/vx6tA5KBQK1p3f1yRtvBlLU3NauXjT6tpqoV52LrRy8cbd5vbmUGwsOy4dp69/R3r7t8fT1pm7Ow7GycpOt4rkpHb9eaD7mDrl+vh3IC47meSCzDqfnUmNZUDLznRvHoyLtT3B7n6MD+lLRErMbY3A+LtWnfmDUdfvjxzcmddzEm62Trro5wd7jOXFgffq8nvbuzGkVTea2bsS5ObLfwffj7+TJ98d21hn26PahHHwyhkKyptmxWQhbiTDNoUQDc7f359Tp07x1ltv8fTTT5OamoqrqytdunTh66+/Bqon/589ezZdu3YlMDCQ999/n6FDa36se3l5cejQIZ577jmGDRtGeXk5vr6+DB8+XDfs0xAfHx/Wrl3LU089xVdffUX37t15++23mT17ti7PK6+8wpUrVxg2bBhWVlbMnTuX8ePHk5+f33D/KNccuXoeW3MrJrUfgKOlLYl56byz+2eyivOA6rB3F2sHXf59l09jaWrO8MAe3NtlOMUVZVxIu8yyUzt0eazNLJgbOh4HSxtKKsu4kpPKqzu+Jy47uUHaMGjoYAry8/nx+8VkZ2Xj37IF73/yIR7XhtNmZ2WTfkOHiVczL97/5CM+X/gp61b/hourC0888xT9Bw7Q5cnKzGL2PbN071cuW87KZcvp2LkTny/6Upd+4ng46WnpjBw7ukHadqPdsSexM7fmvq4jcba240p2Ks9t/or0ouohq85Wdno36SqlkqkdBuHj4E6VRs3p5Es8+tuHpBXWDHH96cQ2tFotD/QYg6u1A3mlRRyOP2fwBrKhlO/ej8LUFMvJY1FYWqK+mkTRN0ugvGbYstLRoc6TRqWrMyYt/Cj6enGj1fV2/BF7EjsLG+7vOgpnazsuZ6fwzKYvSC+8vp/scbetGVKsVCqZ1nEwPg4eVGnUnEqO5uG1H+gtrmGmMuXBHuPwsnOhtLKcI1fP8+bOJRRVNE5HxuH4c9iaWzH52nkiIS+dt//4qdZ5omao6t646vPEiKBQ7us6guKKMs6nXWbZyZrzRHZJPm/uXMKsbiP5aOxj5JQUsDXyMOuvdTI2hvI/9l079sahsLJEfTWRoq9/uI1jzwWTlv4UffW9we0qHeyxvm8aCmsrtEXFVF1NpPDjr9Dm5jVga4y7b+w4yisqePeH7yksLiYkIIAvXnwZa0tLXZ607Cy9BSjW7NxBZVUVz338kd62Hpx8Fw/dpb/KdENq1b0dZcUlnNi0l+L8Qpy93BnzxEzsnB0AKMkvojC75lqprlJzaNUOivMKMDE1xamZG6Men4lf+9a6PMV5hax6o2YhkYgdh4jYcQiv1n5M+M+cxmraP87BK2exNbdiaodBOFnZcTU3jTd2LiHTyP3E7tiTWJqaM6pNT2Z3H0VxRRlnU2NZeqJmvk1TlQn3dB6Gu40TZVUVnEyK4pP9KymuKGvk1t1akKsvX0yomU7j8d53AbA18ghv7V7aVNWqIzwxEhszS8YG98bewobk/Ew+OfCrbvVMewubOkMgLU3N6eIdxIoIw8PUN108iFarZUJIXxwtbSksL+FMaixrz+1t4NZU2xN3CnsLa+7tMqL6/ignlee23Hh/ZI/bjfdHCgVTOwyi+fX7o5RLzFv3kd79EVR3srX3DODpTZ83SjuEuBWFtjG6o4UQ4l9iys8v3zrT/5BVM/+PjIJ/XjSAm50zfb96tKmrUa/2P/oVeU+92NTVqFcOC9+m15cPN3U16tWhed8w+aeXmroa9WrNvW+R98TzTV2NeuXw6bsURpy9dcb/IbYd2/PZgVVNXY1693ifxutMbEzjljzX1FWoVxvuf+8feT6fvarh5lttCounvEi/r+fdOuP/kH2PfHnrTHeo/FffaeoqGGT/+gtNXYUmIcM2hRBCCCGEEEIIIYQwQjrPhBBCCCGEEEIIIYQwQuY8E0IIIYQQQgghhLiDyAxbdxaJPBNCCCGEEEIIIYQQwgjpPBNCCCGEEEIIIYQQwggZtimEEEIIIYQQQghxJ5Fhm3cUiTwTQgghhBBCCCGEEMII6TwTQgghhBBCCCGEEMIIGbYphBBCCCGEEEIIcSfRyLDNO4lEngkhhBBCCCGEEEIIYYR0ngkhhBBCCCGEEEIIYYQM2xRCCCGEEEIIIYS4k2g1TV0DcQOJPBNCCCGEEEIIIYQQwgjpPBNCCCGEEEIIIYQQwggZtimEEEIIIYQQQghxJ9HKapt3Eok8E0IIIYQQQgghhBDCCOk8E0IIIYQQQgghhBDCCBm2KYQQQgghhBBCCHEn0ciwzTuJRJ4JIYQQQgghhBBCCGGEdJ4JIYQQQgghhBBCCGGEDNsUQgghhBBCCCGEuJPIapt3FIk8E0IIIYQQQgghhBDCCIVWK92ZQgghhBBCCCGEEHeKvP+82tRVMMjh/debugpNQoZtCiFEPXpywydNXYV69cm4J7mcldTU1ah3LVy8Gb342aauRr3aPPsD8u6e09TVqFcOK3/gjZ2Lm7oa9eq/Q2Yz/sfnm7oa9Wr9rHf5YM+ypq5GvXp2wD18d3RDU1ejXj0YOo7H1n3c1NWod59PWMAr279t6mrUqzeHz+WRtR80dTXq1deTnmX2qrebuhr1avGUF+n15cNNXY16dWjeN//I/fQ/S6tp6hqIG8iwTSGEEEIIIYQQQgghjJDOMyGEEEIIIYQQQgghjJBhm0IIIYQQQgghhBB3Eo1MT38nkcgzIYQQQgghhBBCCCGMkM4zIYQQQgghhBBCCCGMkGGbQgghhBBCCCGEEHcSrQzbvJNI5JkQQgghhBBCCCGEEEZI55kQQgghhBBCCCGEEEZI55kQQgghhBBCCCHEnUSrvTNfDSg3N5eZM2dib2+Pvb09M2fOJC8v76ZlFAqFwdcHH3ygy9O/f/86n999991/qm4y55kQQgghhBBCCCGEaFLTp08nKSmJ7du3AzB37lxmzpzJpk2bjJZJTU3Ve79t2zbmzJnDpEmT9NIffPBB3njjDd17S0vLP1U36TwTQgghhBBCCCGEELdUXl5OeXm5Xpq5uTnm5uZ/a7uRkZFs376do0eP0qNHDwC+++47wsLCiI6OJjAw0GA5Dw8PvfcbNmxgwIABtGjRQi/dysqqTt4/Q4ZtCiGEEEIIIYQQQtxJNJo78vXOO+/ohlVef73zzjt/u7lHjhzB3t5e13EGEBoair29PYcPH76tbaSnp7NlyxbmzJlT57NffvkFFxcX2rZtyzPPPENhYeGfqp9EngkhhBBCCCGEEEKIW3rhhRdYsGCBXtrfjToDSEtLw83NrU66m5sbaWlpt7WNpUuXYmtry8SJE/XSZ8yYgb+/Px4eHpw/f54XXniBM2fOsHPnztuun3SeCSGEEEIIIYQQQohb+rNDNF977TVef/31m+YJDw8Hqif/r02r1RpMN2Tx4sXMmDEDCwsLvfQHH3xQ9/8hISG0atWKrl27curUKTp37nxb25bOMyGEEEIIIYQQQog7SQOvbNlY5s+ff8uVLf38/Dh79izp6el1PsvMzMTd3f2W33PgwAGio6P59ddfb5m3c+fOmJqaEhMTI51nQgghhBBCCCGEEKLpuLi44OLicst8YWFh5Ofnc/z4cbp37w7AsWPHyM/Pp2fPnrcs/8MPP9ClSxc6dOhwy7wXLlygsrIST0/PWzfgGlkwQAghhBBCCCGEEEI0mTZt2jB8+HAefPBBjh49ytGjR3nwwQcZPXq03kqbQUFBrFu3Tq9sQUEBq1ev5oEHHqiz3bi4ON544w1OnDhBfHw8W7du5a677qJTp0706tXrtusnnWdCCCGEEEIIIYQQdxKN9s58NaBffvmFdu3aMXToUIYOHUr79u35+eef9fJER0eTn5+vl7Zy5Uq0Wi3Tpk2rs00zMzP++OMPhg0bRmBgII8//jhDhw5l165dqFSq266bDNsUQgghhBBCCCGEEE3KycmJZcuW3TSP1sBccHPnzmXu3LkG8zdv3px9+/b97bpJ5JkQ4n+KQqFg/fr1/5jvEUIIIYQQQghxZ5PIMyGEuAP18mvPwIAu2FlYk1aYzbpz+7ick2Iw7/ROQ+nuE1wnPbUgm/f2/GygRP3b/NsG1ixfRU52Nr7+fjz0+KOEdGxvNP/Z02f47vOvuXolHmcXFyZPn8qoCWP08qz7dS1b1m0kMz0DOwd7evfvy/0PP4CZuRkA902aTkZa3RV5Rk8cy7ynn6jfBhoxMiiMie3642RpS0JeOt8d28iF9CtG849q05PRbXriZuNEZnEuq87sZnfsyUap681YTB6L2cB+KGysUMdepmTxL2iSDB9v15mPGIzZkAEoXZzQFhZRcewEZSvWQmXVtY1aYDllPKbdOqOwt0Udn0DpjytQX45v+AbVcmn/KS7+cZzS/CIcPF3oMmkQbgHNjeZXV1ZxbtthroRfoKywGCsHW0KGhdEyzPgx3dBGBIYyPqQvjla2JOam88PxzVzMiDeav2+LjkwI6YeXnTPFFWWcTr7Ejye2UlheosszJrgXwwNDcbF2oLC8mMPx5/n51HYq1VWN0CK4uPcEZ3ceoTS/EAcvV8LuGoZHKx+DeVOi49m6sO75bPJrj+DgUT0J8aXDZ9j/08Y6eWZ9/gImpo1zy3v6j8OEb91HcX4hLl7uDJgxFu9A/1uWS74Uz8p3vsHF25373nxK77Oy4lIOrt1OzInzlJWUYu/iRP9po2jRoU1DNUNPH/8ODGrVFTsLa1ILsvnt3F7ispMN5r2n8zB6+Latk55akMXbf/wEgIetM6Pa9KS5gxvO1vasPbuHvXGnG7IJBnVvHkxv//bYmFuRUZTLtqgjXM1NM5pfpVAyIKALHbwCsDG3oqCsmH1xpzmVHA1AsLsffVt0wsnKDpVCSXZJPofiz3EmJaaxmkTfFh0Z0rob9hY2pBZksfrMbmKN7Kt7u4wgzC+kTnpKQRZv7lwCQEevVgwPCsXV2gGVUklGUR67YsI5nnCxQdtxowEtOzM8MBQHSxuS8zNZEbGLmKxEg3lndxtNb/+65+nk/Exe2fGd7v2QVt0Y0LIzTlZ2FFWUciIpijVn91ClUTdYO/6KDp4BTO80lCA3H1ysHXh+69ccuHKmqatl0L95PzWof8hqm/8U0nkmhBB3mE5erZnQrh9rzuzmSk4KPf3a81DYeN7Z/TN5pYV18v92bi+bLh7UvVcqlPxnwIxGu2Hft2sPiz79inlPP05w+xC2rt/MK8+8wKJli3HzqLusdFpKKv995kWGjxnJs/99gYtnz/PlR59h72BP7wF9Adi9YxdLvvmOp154luB2bUlKSOLjt94H4KEnHgXg0++/QqPR6LZ79fIVXnzyP/QZ0K8RWl39g/LBHmP5+sg6LqbHMyIolNeGzuHR3z4kszivTv4RQWHc12UEnx9aw6WsRAJdmjO/92SKyks4nhjZKHU2xHzsCMxHDqXk68WoU9OxmDgamxefpmDBS1BWZrCMaa8eWEybTMmiJagvxaL09MDq4dkAlP1UvTy41UP3ofJuRvGX36PNzcOsTyg2Lz9NwdOvoM3Na6zmEX8ykpNr/6Db1KG4tmhGzMEI9ny1mtEvP4C1k53BMgcXb6C0sJjQGSOwdXWkrLAY7Q3HWmPr5dee2d1Hs+joBqIy4hkW2INXhtzPY+s/Jqs4v07+Nm6+PNF7CovDNxOeGImzlR0Ph01gXs9JvHutQ71vi47M7DKcLw6uISozAS87Fx7vfRcAi8M3N3ib4k5c4OjqHfScNhL3lt5EHTjF9i+WM/nVR7Bxsjda7q7XH8XUwlz33sLWSu9zUwtz7nr9Ub20xuo4izoWwZ5fNjH43vE0a+3HmT3HWPvRD9z/ztPYOTsaLVdeUsrWb1fiGxxAcYH+OV5dVcXqD77Dys6GsfNnYuNkT2FOHmY3/Bs0pM7NWjOxfX9WRfzB5ZwUevm155GeE3hr11JyDVyP1pzdw4YLB3TvVQolzw+ayenkmuuRmcqErJJ8TidfYmL7xjlf1xbi0YIRbcLYfPEgCbnpdG3ehpldRvD5wVXklxUbLDO142BszC1Zd34/OSX5WJtZolTUDOIpqSxnX9xpsorzqNKoCXTzZUJIP4orSonNSmrwNnXxDuSuDgNZeXoncdnJ9PHvwLzek3nj98UG99WqM3+w/vx+3XulUsFLg2ZxKilal1ZcUca2qKOkF2ZTpdHQzrMF93YZQWF5CZHp8Q3epm7N2zCt4xB+PrWd2Kwk+rfsxFN9pvLyjm/JKSmok39FxE7WnNuje69SKHl96BxOJEXp0kJ92jK5/QAWh28mNisZD1sn5nQfDcDKiF0N3qY/w9LUnNjsJLZGHebtEQ83dXWM+rfvJ/HvIcM2hRD1pn///syfP5/58+fj4OCAs7MzL7/8sm5c+rJly+jatSu2trZ4eHgwffp0MjIygOqx6wEBAXz44Yd62zx//jxKpZK4uDiD33nu3DkGDhyIpaUlzs7OzJ07l6KiIt3n4eHhDBkyBBcXF+zt7enXrx+nTp3S20ZMTAx9+/bFwsKC4OBgdu7cWZ//LH9a/4DOHLt6gaMJF0gvymXd+X3klRbR289w1EtZVQWF5SW6l4+DO5amFhxLuNAo9V336xqGjh7B8LGj8PHz5eEn5+Hq5saWdZsM5t+yfhNu7m48/OQ8fPx8GT52FENHDWftilW6PFHnLxLcLoQBQwfh7ulBlx5d6T9kADFRNTf1Do4OODk76V7HDh3Fs5kX7Trdennq+jA+pC87L4Xz+6XjJOVn8N2xjWQV5zEyKMxg/oEtO7Mt+igHrpwhvTCH/VfOsPNSOJPaD2iU+hpjPmIwZeu3UBl+Ck1SMiVf/YDC3AyzXj2MljFp3ZKqS7FUHjqGJjObqrMXqDh8DJMWftUZTE0x7d6F0uVrUEddQpOeQdmajWgysjAf0rjtjdodTsuw9gT07IC9hwtdJw/GytGWSwcMR7qkXLxMemwiAx65C88gP2yc7XHx88K1hXej1vtG49r2ZlfMCXbFhJOUn8kPxzeTVZzP8MBQg/lbu/qQWZTLlsjDZBTlEplxld+jjxPg0kyXJ9DVh6j0q+y/coaMolwiUmI4cPmMXp6GdH7XUVr36kRQ7044eroSNmUY1o52RO47cdNyFrbWWNnb6F5Kpf6trEKB3udW9jYN2Qw9J7YfoF3fbrTv3wNnL3cGzhiLrZMDEX8cvWm533/8jTZhnfAMqBt1d25/OGVFJYx//D6atfbD3sUR79b+uPl4NVQz9AwI6MKR+PMcuXqe9MIcfju3l9zSQnr7Gz7P1rkeOVZfj45ePa/Lk5CXzobz+zmVHE2VumkiSHr6tedUUjQnk6LJLM5jW9QRCsqKDEZxAwS4eOPn5MnPJ7dzOTuZvNIikvMzScyriX6Oz0klMiOezOI8cksLOXrt38zXwaNR2jSoVVcOx5/jUPw50gpzWH12D7klhfRt0dFg/rKqCgrKi3UvX0cPrMwsOHLDvorJSuRMSgxphTlkFeexJ/YUyfmZBDg3znliWOvuHLhyhgNXzpBamM2KiF3klBYwoGVng/lLK8spKCvWvfwcPbEys+TgDdFaLZ2bEZOVxLGEi2SX5HMh/QrHEi7i5+jZKG36M44mXOC7YxvZdzmiqatyU//2/ST+PaTzTAhRr5YuXYqJiQnHjh3js88+Y+HChXz//fcAVFRU8Oabb3LmzBnWr1/PlStXmDVrFlA9x9js2bNZsmSJ3vYWL15Mnz59aNmyZZ3vKikpYfjw4Tg6OhIeHs7q1avZtWsX8+fP1+UpLCzkvvvu48CBAxw9epRWrVoxcuRICgurn8JqNBomTpyISqXi6NGjfPPNNzz33HMN9K9zayqFEm97N6Iyr+qlR2Vcxc/p9m4Yevi25VJmgsEnzfWtsrKSmOhLdO7eVS+9c/cuXDxvuPMu6vxFOnfvop+/Rzdioi5RVVU9XCy4Qwix0ZeIvlj9FDI1OYXwI8fp3tNwZ0FlZSV7ft/F0FHDUSgUf7dZt2SiVBHg3IzTKZf00k8nXyLIzddgGVOVSZ3hcBVVlbR2aY5K0TSXY6WbC0pHB6rO3rCvqqqoiozGpHXdvzldlqhYTPx9UbX0123HtFM7Kk+drc6gUqFQqaCyUq+ctqISk6CAem+HMeoqNTmJaXi20R8259nGn6wrhocyJZ2LxdnHg4u7jvHbS1+y8fVvOfXbbqoqKg3mb2gmShUtnZsRUSuSNCIlxuixFpVxFWdre7o0q17W3d7ChjC/EL2n+pEZ8bR0aUYrl+pOQXcbJzp7B+rlaSjqKjVZCal4t2mhl+7dpiXpl28eobPure/45T8L2brwZ1Ki4+t8XllewcoXP2P585+w48uVZCWk1mfVjVJXVZEen4xfSGu9dL+QVqTE1q3ndef2h5OXkU3P8YMNfh53+iJeAb788dM6vnrsDZa8+BFHN+3Wi7ptKCqFkuYO7kRl1LoepV/F3/n2Ou9CfUOIzrjaKNej26VSKPGyc6kTDRablURzh7rR0gBBbr6k5GfS278Dz/afwRN9pjAssAcmSuOrtLVw8sLF2p743IY/BlUKJT4OHlysFQ0WmRFPi9vs6Orp146ojKsGI4WuC3T1wd3WkZhGiKRTKZX4OnpyIf2yXvqFtCsEON/ew4w+LTpwMf0K2Te0KSYrCT9HD/yv3VO5WjvQzrMlZ1Nj66/y/yKynxqYVnNnvv6lZNimEKJeNW/enIULF6JQKAgMDOTcuXMsXLiQBx98kNmzZ+vytWjRgs8++4zu3btTVFSEjY0N999/P//97385fvw43bt3p7KykmXLlvHBBx8Y/K5ffvmF0tJSfvrpJ6ytrQH44osvGDNmDO+99x7u7u4MHDhQr8yiRYtwdHRk3759jB49ml27dhEZGUl8fDze3tUX+bfffpsRI0bctJ3l5eWUl5frpZmb//1hNNbmlqiUSgrLSvTSC8tLsLOwMlKqhp25FW3c/Pj55La/XZfbUZCXj0atwdFJf0iSg6Mjudk5Bsvk5uTg4Kif39HJEbVaTUFePk4uzvQfPJD83HyeeeQJtFotarWaURPGMmVm3eWnAY7sP0RRURFDRg6rn4bdgp25NSqlqs4PwtzSIjpb2Roscyo5mqGtu3Pk6nnispMJcPZmcOtumKpMsLOwbpIflwqH6uFxmnz9H0ua/AKULs5Gy1UeOU6pnQ02rz9fvR0TE8p/30P5xmvHXVkZVZdisZg4muLkVLR5+Zj26oEqwB9NWkbDNMaA8qIStBptnaF9FrbWlBYYHppVlJVHRlwSShMT+j44gfLiUsJ//Z3ykjLC7hnZGNXWY2tuhUqpqjNkO7+0EEfL1gbLRGcm8PH+lTzTfzqmKhNMlCqOJVzku6M184EdvHIWe3Mb3h7xMAqFAhOlim1RR/jt3N9fjepWyq7tF0s7a710SztrSguKDJaxsreh94xRuPh6oq5SE3v0LFs/+ZlRC+7Fs1V1J6KDhzN97xuLUzM3KksrOL/7GJs++JGJL8/F3t348VwfSq8N7a0d6WZlb0txvuG/7dy0TA6s3sbdLz2CUmW4EyY/M4eEyDjahHVi4oLZ5KVnseun9WjUanqOH1Lv7biR7npUrv+3Ulhegp357VyPrAl292fpia0NVcW/xMrMApVSSVFFqV56UUUptkba5WRph4+jB1UaNctP/46VqQVj2vbG0tSC9edr/mbMTUx5tv89mChVaLQaNl88ZHR+uPpko7t3qLWvyoqxd7c2UqqGnYU1bd1bsPh43SHbFiZmvDPqEUyVKjRaLStO76zTodoQbM2sUCmVdYbRFpQXY29x6zbZW1jTzqMl3x7doJd+PPEituZWvDDgXlBUP6DYHXuSrVFH6rX+/xayn8S/iXSeCSHqVWhoqF7kT1hYGB999BFqtZqzZ8/y2muvERERQU5Oju7JeUJCAsHBwXh6ejJq1CgWL15M9+7d2bx5M2VlZdx1110GvysyMpIOHTroOs4AevXqhUajITo6Gnd3dzIyMvjvf//L7t27SU9PR61WU1JSQkJCgm4bPj4+uo6z63W+lXfeeYfXX39dL+3VV1+FTg63/W/1ZygUtzdnaHeftpRWlnMu1fAw14ZSO9hLCzeNAKv9mW7J6WvpZ09F8OtPvzDv6ccJbNuGlKQUFn36JcuXODH9/pl1trdj8za6hnbH2dXlb7XjT6u1T262n1ZG7MLR0paPxjyGAsgrLeKPmBNMbj8ATSM9xTPt1QOrB+/VvS9679Pq/6lT6ZsfcCbBgVhMGE3pD8uoir2MysMNy/umockbTflv1T++Sr78HquH7sf+64/QqtWor1yl8tAxVP6Go6UaVu0DVFvnmK35SItCoaDXrDGYWVZ3iKsnDuTAD+vpNmUIJmamDVzX26RQoK19AF7jbe/Ggz3G8mvEH5xOuYSjpS2zuo7kkbAJfHF4LVA959PkDgNYdHQDMZkJeNi58ED3MeS2L2TV2d2N1oYbVZ8HDO8YBw8X3cIAAO4tvCnKLeDcziO6zjO3Ft643TC81r1lc9a9/R0X9obTc+rw+q+/AXXOe9eOp9o0Gg2bv1lBzwlDcPJwNbo9rUaLla0NQ++fhFKpxMPfm6K8AsK37mvwzjNdHf5iuR6+wZRWlnM25U6NFNFvmQKF0dPe9X24+uxuyquqo1C3Rx1haschbL54UDeBeUVVJV8dXouZypQWzl4MDwolp7SA+JzGiYCsW33j54kbhfmGUFpZZnCu1PKqCt7etRRzEzMC3XyY3H4AWcX5RieDb2gKbu+Y7OXXnpLKMk6lROulB7r6MLpNT34+tZ3LOSm42zgyreMQ8oOL2HTxUIPU+d9I9pP4J5LOMyFEoygrK2Po0KEMHTqUZcuW4erqSkJCAsOGDaOiokKX74EHHmDmzJksXLiQJUuWMHXqVKysDD8J1hr5UQI1N7qzZs0iMzOTTz75BF9fX8zNzQkLC9N9p9bAnfLtDPt74YUXWLBggV6aubk5z23/+pZlb6a4vBS1RoNtrSgzGzMrvVXyjOnhE8yJpEjUjdQZY+dgj1KlJCc7Vy89PzcXh1rRaNc5OjmRm6MflZaXm4dKpcLOvnoC95++W8LAYUMYPnYUAP4tW1BeVspn7y3k7vtm6M1zlJ6WTsSJU7z89mv12LKbKygvRq1R41gryszBwsbgog4AFeoqPj24mi8OrcXB0pbc0gKGBYZSUlFGQdmt9219qDx5hsLYGzp9r02krnSwR51XM/G80t4Wbb7xoTsWU8ZTceAIFXuqJwbXJCaDuTlWD95L+botoNWiSc+k6I33wdwMhaUl2rx8rJ54CE1GVsM0zgBzGysUSgVlhfpPxMuKSrCwNfxE3NLOBkt7G13HGYC9hzNooSSvEDs3pwatc22F5SWoNWocLPWPNXsLG/JKDUdpTW7fn8iMeNZfqJ4M/GpuGouOruedkY/wy+nfyS0tZHqnIeyNO8WumPDqPHnpWJiY8mjPiaw+u+e2fnD/VRbX9ktpvn79ywpL6kSj3YybfzNij58z+rlCqcDV14uCDMNRsPXJ0tYahVJJcZ7+339JQRFWdnXnXasoLSf9ShIZV1P44+fqiAutVgtaLR/d/zx3PfsAPsEBWDvYolSp9M55Tp5uFOcXoq6qQmXScLfy169Hdub6+8TW3IqC27gehfqGEJ54sdGuR7erpKIMtUaDjZn+ddbazIKiCsPtKiwvoaCsWNdxBpBZlIdSocDOwlo31FELuv9PK8zG1dqRvi06NnjnWdH1fVUr0sfWwuq2ri89/dpxLMHwvtKCbhGcpPwMPG2dGR7Ug5iDDdt5VlhRglqjqRO9ZGtuTYGRRR1u1Me/A0eunkdda4jzhJB+HL56XrdqZXJ+JmYqU+7rOpLNFw814Jnvn0n2UwPT/Gta+j9B5jwTQtSro0eP1nnfqlUroqKiyMrK4t1336VPnz4EBQXpFgu40ciRI7G2tubrr79m27ZtekM9awsODiYiIoLi4pqL86FDh1AqlbRuXT2c6cCBAzz++OOMHDmStm3bYm5uTlZWlt42EhISSElJ0aUdOXLrkHBzc3Ps7Oz0XvUxbFOt1ZCUn0Ggq/7E0YFuPre8+Q5w9sbVxpGjVxtnoQAAU1NTWgW25nT4Sb30U+EnCQ5pa7BMUEgwp2rnP36CVkGtMbn2Q7C8vByFUr8TU6lUodVq63R47tyyHXtHB7qHGZ4PrSFUadTEZifT0auVXnpHr9a3HM6i1mrILslHo9XS178DxxMjG7SjQk9ZGZr0jJpXUgqa3DxM2t0wSbZKhUmbQKou3SR60cysbmSaRlM3BBGgvAJtXj4KaytM24dQedLwRP0NQWWiwqm5B6lR8XrpqVHxuPgbngfItUUzSvOLqCyv6dQvyMhFoVBg5WB4SG5DqtKoictOpqOX/lxxHb0CjB5r5iqzOn8nmlrvzVWmRvIojEbl1ReViQoXH0+SI/XnyEmOvIz7n1iYITsxDSt74/tEq9WSnZTWKIsGqExMcPdrRvwF/cid+AsxeAX41clvbmnOfW8t4N43n9S9OgwIxcnTlXvffBKPltXXgGat/MjLyNZb7TU3PQtrB9sG7TiD6nNVYl46QW61r0e+XMlOMVKqWoCLN242jhyJP3/TfE1BrdWQUpBFy1qLY7R08dZbAOBGCblp2FpYY6aq+Td3trZHo9XctINAcW24WUNTazUk5KXRptY8iG3cfLl8i2GjrVya42bjyOF44x3RtTVKmzQaruamEuyuP2dlW3d/YrNvPuda9dxsThy4fKbOZ2YqkzrXXK1WWx3z2ghzpv7TyH4S/ybSeSaEqFeJiYksWLCA6OhoVqxYweeff84TTzyBj48PZmZmfP7551y+fJmNGzfy5ptv1imvUqmYNWsWL7zwAgEBATcdQjljxgwsLCy47777OH/+PHv27OGxxx5j5syZuLtXT/obEBDAzz//TGRkJMeOHWPGjBlYWlrqtjF48GACAwO59957OXPmDAcOHOCll16q/3+YP2Fv7ClCfUPo4ROMu40j40P64mhpy6H46snYR7fpxYzOQ+uU6+HblvicVNIKsxu1vhOmTmbHpq3s2LyNhPirLPr0KzLTMxg5YQwAS77+ng/ffFeXf9T4MWSkZfDtZ1+REH+VHZu38fvmbUyaNqWmLb3C2LJuE3t37SYtJZVTx0/w03dLCO3dE9UNcwNpNBp2btnO4BFDUZk0/M38jdaf38/Q1t0Z0qob3vZuPNB9DK42Drr5OO7rMoIFfe/W5feyc6F/y8542bnQ2qU5/+k/A19HD35qpPnpjCnftguL8aMw7dYJpXczrB6djba8gopDx3R5rB6dg8XdE3Xvq06dwXxwf0zDuqN0dcGkXTAWU8ZTeTJC16lm0r4tJh1CdJ/bvPIs6tQ0KvY27nCLoIHdiDt8hrgjZ8lPy+Lk2j8oySmgVZ+OAJzesI/DP9XM8+PXLRhza0uOLttKfmoW6bGJnF63hxZh7ZpsyOaGCwcZ3KobgwK64m3vyuxuo3GxdmBHdPU+uqfzMJ7oXfP3E54USahvCMMDe+Bu40SQmy8P9Bijt5BIeFIUwwND6e3fHjcbRzp4BjC90xDCEy/W6WhrCCGDQ4k+dJroQxHkpmZydNXvFOXmE9S3ejGR8HV/sHfJel3+838cIz4iivz0bHJTMghf9wfxp6MI7l+zWMmpzftIuhBHQWYu2YlpHPh5E9mJ6QT16VL76xtE1+F9OLfvOOf2h5Odks6eXzZSmJ1Hh4HVHfv7V21j66KVACiUSly9PfReVnbWqExNcPX2wMzcDIAOA8MoLSpm9y8byUnLJC4ikmObdtNpUM9GadOe2JOE+bUj1Lct7rZOTGzXDycrW92qeGOCezOzS90hsWG+IVzJSSXVwPVIpVDSzN6VZvaumChV2Fva0szeFRdrh4Zujs7h+LN08Q6ic7NAXK0dGBEUhr2FDccTIgEY0robk9r11+U/mxpLaUUZE9r1x9XaAV9HD4YF9uBUUrRuyGbfFh1p6dwMR0tbXKzt6enXjo5erQ0OhWwIf8ScoJd/e8J8Q/CwdWJy+wE4WtnpInfGte3DfV3rztvYy68dV7JTSCmoGxU8LLAHQW6+uFjb427rxKBWXQn1bcvxhIsN3h6AHZeO09e/I7392+Np68zdHQfjZGXH3rjqVdMntevPA93H1CnXx78DcdnJJBdk1vnsTGosA1p2pnvzYFys7Ql292N8SF8iUmIMjkZoSpam5rRy8dYt7OJl50IrF2/cbQxH9jeVf/t+Ev8eMmxTCFGv7r33XkpLS+nevTsqlYrHHnuMuXPnolAo+PHHH3nxxRf57LPP6Ny5Mx9++CFjx46ts405c+bw9ttv3zTqDMDKyoodO3bwxBNP0K1bN6ysrJg0aRIff/yxLs/ixYuZO3cunTp1wsfHh7fffptnnnlG97lSqWTdunXMmTOH7t274+fnx2effcbw4Y0zP44hp1MuYWVmwbDAUOzMrUgtzGbR0Q26H712FtY4WtrplbEwMaODZwC/3TBxcWPpN3gAhQUFLF/yMznZOfi18OOND9/B3aO6AzMnO5uM9JooQw8vT9748G2+/ewrNv22EWcXZx5+cj69B/TV5Zl23z0oFAp++nYJ2ZlZ2Ds60KNXKPfNnaP33afDT5GRnsHQUY2/vw5cOYOtuZXuJvFqbhqv/f6DbniLo5Udrjf8GFQqlEwI6Usze1fUGjVnU+N4dvOXZBTlGv6CRlK+cRsKM1MsZ9+Dwtoadexlit7+GMrKdHmULk56kWZlv21GqwWLqeNROjmiLSik8uQZyn79TZdHYWWJxbRJ1Z8XFVN5/CSlK9eBWt2o7fPr0oaK4lLObTtEaUExDp4u9H/0LmycqhdLKCsoojinZoiqqbkZA+dP5cTqnWx7fynm1pb4dA6iw+g+jVrvGx2KP4uduRVTOw7C0dKWhNw03tz1o+5Yc7Kyw9XGQZd/d+xJLE3MGRnUk/u7jaK4ooyzqXF6HbWrzuxGq9Uyo9NQnKzsKSgrJjwxkl9O72iUNrXs2pbyolJOb9lPSUERjl6uDJs/DVvn6naU5BdRdMN+UVepOb52F8V5hZiYmuDg5cqweXfTvF1N9GdFSTkHf9lCSUERZpbmODf3YPQz9+FmJMqwvgX16EhpUQlHNuyiOK8Al2YeTFwwG3uX6h+6xfkFFOTk/alt2jk7cNezD7Jn+SaWvrwQGwc7Og/tTfdR/eu/AQacSr6EtZklwwNDsbOwJrUgm68Pr9Ndj+wtrHGsNaTYwsSMjl6tWHtur8Ft2lva8PzAmrkrB7fqyuBWXYnJTOSzg6sbqil6zqddxsrUgv4BnbE1tyK9MIefT24jv6x6KLGNuRX2ljURixXqKn48sYVRbXrxcM+JlFaUcT7tsm7YM1SvqDwmuDd2FtZUqqvIKs5jzdndnE+7XOf7G8LJpGiszSwZ1abntX2VxZeH1uqGkdpb2OBkVXdfdWrWmlVnDM9zaK4yZVqnIThY2lCpriKtMIcl4Vs4mRRtMH99C0+MxMbMkrHBvbG3sCE5P5NPDvyqW5Wxuk3690OWpuZ08Q5iRcROg9vcdPEgWq2WCdceTBaWl3AmNdbo8dqUglx9+WJCzRQhj/eungN4a+QR3tq9tKmqVce/fT81KOkovKMotNJ1K4SoJ/3796djx4588sknf2s7hw4don///iQlJekiyP5XPLnhk6auQr36ZNyTXG6EJekbWwsXb0Yvfrapq1GvNs/+gLy759w64/8Qh5U/8MbOxU1djXr13yGzGf/j801djXq1fta7fLBnWVNXo149O+Aevqu1+tv/ugdDx/HYuo9vnfF/zOcTFvDK9m+buhr16s3hc3lkreGVxv9XfT3pWWaverupq1GvFk95kV5fPtzU1ahXh+Z984/cT/+r8h56qqmrYJDDooVNXYUmIZFnQog7Rnl5OYmJibzyyitMmTLlf67jTAghhBBCCCHEP4/MeSaEuGOsWLGCwMBA8vPzef/995u6OkIIIYQQQgjRNK6twnzHvf6lJPJMCFFv9u7d+7fKz5o1i1mzZtVLXYQQQgghhBBCiPogkWdCCCGEEEIIIYQQQhghkWdCCCGEEEIIIYQQdxKNpqlrIG4gkWdCCCGEEEIIIYQQQhghnWdCCCGEEEIIIYQQQhghwzaFEEIIIYQQQggh7iT/4pUt70QSeSaEEEIIIYQQQgghhBHSeSaEEEIIIYQQQgghhBEybFMIIYQQQgghhBDiTiLDNu8oEnkmhBBCCCGEEEIIIYQR0nkmhBBCCCGEEEIIIYQRMmxTCCGEEEIIIYQQ4k6ikWGbdxKJPBNCCCGEEEIIIYQQwgjpPBNCCCGEEEIIIYQQwggZtimEEEIIIYQQQghxJ9FqmroG4gYSeSaEEEIIIYQQQgghhBHSeSaEEEIIIYQQQgghhBHSeSaEEEIIIYQQQgghhBEy55kQQgghhBBCCCHEnUSrbeoaiBtI5JkQQgghhBBCCCGEEEYotFrpzhRCCCGEEEIIIYS4U+Td+3BTV8Egh5++aeoqNAkZtimEEPXop/CtTV2FenVvt5EUxsQ2dTXqnW2rAIZ8+0RTV6Ne7Zz7Kf/d/l1TV6NevTH8Qcb/+HxTV6NerZ/1LoMWPd7U1ahXfzz0GYVp6U1djXpl6+H+jzyff7L/16auRr17su9UNl842NTVqFej2/Zm+HdPNXU16tX2BxfS7+t5TV2NerXvkS+Zvertpq5GvVo85UV6fXlndtj8VYfm/Q939GgkzulOIsM2hRBCCCGEEEIIIYQwQjrPhBBCCCGEEEIIIYQwQoZtCiGEEEIIIYQQQtxJZHr6O4pEngkhhBBCCCGEEEIIYYR0ngkhhBBCCCGEEEIIYYQM2xRCCCGEEEIIIYS4k2g0TV0DcQOJPBNCCCGEEEIIIYQQwgjpPBNCCCGEEEIIIYQQwggZtimEEEIIIYQQQghxJ5HVNu8oEnkmhBBCCCGEEEIIIYQR0nkmhBBCCCGEEEIIIYQRMmxTCCGEEEIIIYQQ4k4iwzbvKBJ5JoQQQgghhBBCCCGEEdJ5JoQQQgghhBBCCCGEETJsUwghhBBCCCGEEOIOotXIsM07iUSeCSGEEEIIIYQQQghhhHSeCSGEEEIIIYQQQghhhAzbFEIIIYQQQgghhLiTaDVNXQNxA4k8E0IIIYQQQgghhBDCCOk8E0L8T5s1axbjx4//U2X8/Pz45JNPGqQ+QgghhBBCCCH+WWTYphD/Q2bNmsXSpUvrpA8bNozt27c3+Hfn5eWxfv36Bv2eP+vTTz9Fq63flWji4+Px9/fn9OnTdOzYsV63bciJnQc5unUPRXkFuDbzYMg94/EJannLcomXLvPz/32Jq7cHD779rC49KvwshzbuJDc9C41ag6O7C6Ej+9Oud7eGbEYdWq2Wb5cvZ92O7RQWFdG2dSDPPfIILX19jZaJu3qVb35ZRlRsLKkZGSx48EGmjxtvNP+SVav48qelTBs7jqfnzm2AVugbE9ybu9oPxNnKjvjcNL4+8hvn0y4bzT82uDfj2vbB3daJjKJclp/eya6YcIN5+7fsxEuDZnEo/iyv/f5DQzWhjm7N29DbvwM25pZkFuWyLeooV3PTjOZXKZT0D+hMB68AbMytKCgrZl/caU4nXwKgi3cgHb1a42brCEBKfha7YsJJzs9slPYAjAgMZXxIXxytbEnMTeeH45u5mBFvNH/fFh2ZENIPLztniivKOJ18iR9PbKWwvESXZ0xwL4YHhuJi7UBheTGH48/z86ntVKqrGqFF1cfSlA6DdMfeV4fXcu4mx964tn0Y17YPHteOvV9O/c7OG469Ya27858B99QpN/z7BQ3WJq1Wy7c/LmHdpk0UFhbSNjiY5558ipb+/jct98e+vXzzww8kpaTg7eXFow88yIC+fXWfL1qymO9+/FGvjLOTEzvWrde9z87J4fNF33A0PJzCoiI6d+jAs088gY938/ps4j/yfH5+z3EidhykJL8IRy9Xek0dgVdrP4N5k6OvsPHDJXXS737jMRw9XQG4uP8E0UciyEnJAMDV14seEwbj7u/dYG0w5NC23ezdsIOC3Dw8mjdj3Oy7aRHc2mDey5ExbPlpDRnJqVRUVODo6kzY0H70GzNUl+fozn2c2HuEtIRkALxb+jJyxkR8WrVolPYAjG7Ti8kdBuBkacfV3DS+ObqeCzc5T4wJ7sWY4D642zqSWZTHioid/BFzQvf58MBQBrfuhq+jBwCxWUksCd/CpcyEBm/LdePb9uHujoNxsrInPjeVLw6t4Wxq3E3y92Viu3542DqRXpTLspPb2XHpuO7zT8Y+QadmdffzkavneX7r1w3ShtoGtOzM8MBQHCxtSM7PZEXELmKyEg3mnd1tNL3929dJT87P5JUd3+neD2nVjQEtO+NkZUdRRSknkqJYc3YPVRp1g7Xjr+jgGcD0TkMJcvPBxdqB57d+zYErZ5q6Wneuev6NI/4e6TwT4n/M8OHDWbJE/8bU3Ny8iWrT9Ozt7Zu6Cn/LxaOn2blsPcNnTaZ5a39O7T7Myg++5aH3nsfexdFoubKSUjZ+sxz/tq0oyi/U+8zS2opeY4fg4uWOykRFzOkLbPp2JVZ2trRsH9TQTdJZunYNy9ev49WnnsLHqxk//Por8155mbXfLMLayspgmbLycrw9PBjcqzcff/+dwTzXXbh0iXU7ttPK7+Y/vOtLvxadeCRsAp8fXM2F9CuMatOTt0c8zJxV75BZnFsn/+g2vZjdfQwL968kOjOBIDcfnupzN0XlJRxNuKCX183Gkbk9xnM2NbZR2nJdiEcLRrQJY/PFQyTkptOteRD3dBnOFwdXk19WbLDMlI6DsDG3ZP35/eSUFGBtZolSodB97ufkxdnUWBIj06nSqOnt34F7u47gi4Nr9DqjGkovv/bM7j6aRUc3EJURz7DAHrwy5H4eW/8xWcX5dfK3cfPlid5TWBy+mfDESJyt7Hg4bALzek7i3T0/A9WdazO7DOeLg2uIykzAy86Fx3vfBcDi8M0N3qb+LTvxaM+JfHZwNefTLjM6uBfvjHyE2aveJqOo7rE3Jrg3c7qP4eP9K4jOSCDIzZcFfe+mqKKUI1fP6/IVlZcy69f/0yvbkJ2BS1csZ/mqVbz6wgv4eDfnh59/Yt7TC1i77Bej54Sz58/z4uuv8/DsOQzo04c9Bw7w/Guv8sMXXxISHKzL18Lfn68++lj3XqVS6f5fq9XyzEsvYWKi4qO33sba2ppfVv3KowsWsHrpT1haWtZL+/6J5/PY8HMc+nUbfWaMxjPAhwv7wtny2TLufn0+ts4ORstNe/NxzCxr7k0sbK11/58SHU+r7u3xaNkclakJETsOsnnhT0x9fT42jnYN2Ryd0wePs2HJSiY+eA/+bQI4smMf3/3fJ/zn0zdxdHWuk9/M3IxeIwfi5euNmYU5VyJjWPPNT5iZmxM2tB8Aseej6dS7O35BAZiYmrJn/TYWvf4x//n0Teydje//+tK3RUceChvPl4fWcCH9CiODevJ/w+cyd/W7ZBbn1ck/qk1PZnUbzacHfuVSZiKBrj480WcKReWlHLt2jWrvFcDe2FNcTL9ChbqKuzoM5O0RD/PQmvfILql7Pq1vA1p2Zn6vySw88CvnU+MY07Y3742ax30r3zR47hvXtg9zQ8fywd7lRGVcpY27H8/2m05heQmHr537XtnxHabKmp/AdhbW/DDlBfbGnW7w9kD1A6tpHYfw86ntxGYl0b9lJ57qM5WXd3xLTklBnfwrInay5twe3XuVQsnrQ+dwIilKlxbq05bJ7QewOHwzsVnJeNg6Maf7aABWRuxq+Eb9CZam5sRmJ7E16jBvj3i4qasjxJ8iwzaF+B9jbm6Oh4eH3svRseamTKFQsGjRIkaPHo2VlRVt2rThyJEjxMbG0r9/f6ytrQkLCyMuruap3WuvvUbHjh1ZtGgRzZs3x8rKirvuuou8vDzd50uXLmXDhg0oFAoUCgV79+5l4MCBzJ8/X69+2dnZmJubs3v37jp1z8/PR6VScfLkSaD6B42TkxPdutU8QV+xYgWenp6698nJyUydOhVHR0ecnZ0ZN24c8fHxus9rD9ssLCxkxowZWFtb4+npycKFC+nfvz9PPvmkXl1KSkqYPXs2tra2+Pj48O233+o+878WBdGpUycUCgX9+/e/+U75G45t20vH/j3oNCAUl2buDJ05ATtnB079ceim5bYtXk3bsM40C/Cr85lvcABB3drj0swdR3cXug/vh1tzTxKjjT99rm9arZYVGzZw/9SpDOzZiwA/P15fsICy8nK279tntFzb1q15YvYchvXrh5mpqdF8JaWlvPLhB7z02GPY2tg0RBPqmNS+P9ujj7It+igJeel8fWQdmUW5jAnuZTD/4Fbd2BJ5iH2XT5NWmM3euNNsjz7K1I6D9fIpFQpeGHgvP53cRlpBdmM0RaenXztOJUVzKimarOI8tkUdpaCsiG4+wQbzB7h44+fkybKTO7icnUJeaRHJ+Zkk5mXo8qw9u4fwxEjSCnPIKs5nw/kDKBQKWjg3a5Q2jWvbm10xJ9gVE05SfiY/HN9MVnE+wwNDDeZv7epDZlEuWyIPk1GUS2TGVX6PPk6AS019A119iEq/yv4rZ8goyiUiJYYDl8/o5WlIk9sNYFvUUbZGHSEhL52vDv9GRlEuY4J7G8w/pFU3NkceYm/caVILs9kTd4pt0UeZ2mFQrZxacksL9V4NRavVsmL1au6fOZOBffsR0KIFr7/wYvU5YddOo+VWrFlNjy5duf+ee/Dz9eX+e+6he5cuLF+9Wi+fiUqFi7Oz7uXo4KD7LCEpiXMXL/D8gqdp26YNfj4+PP/UAkpLS9nxxx/11sZ/4vn8zM7DBPXuTHCfLjh6utL77pHYONpxYZ/hCNrrLO2ssbK31b2UypqfHIMfnEzIgO64+Hji6OlKv3vHodVqSY5svGvU/k2/031QH0KH9MXd24vxc6bh4OzE4R17Deb3buFL5z498PBphpObC136hRHYMYQrkZd0ee55ai69Rgykmb8P7t6eTHlkFlqtlpizkY3Spont+rMj+hjbo4+RmJfBoqPrySzKY7SRa9SgVl3ZFnmY/ZcjSCvMZt/l0+yIPsaUDgN1ed7fs4zNkYe4nJNCUn4Gnx74FYVCQcdmrRqlTVM6DGJr1BG2RB7mal46XxxaS2ZRLuPa9jGYf2jr7my8eIg9cadILcxmd+xJtkQdZlqnmgjBwvISckoLdK+uzYMor6pgb9ypRmnTsNbdOXDlDAeunCG1MJsVEbvIKS1gQMvOBvOXVpZTUFase/k5emJlZsnBG6K1Wjo3IyYriWMJF8kuyedC+hWOJVzEz9HT4Dab0tGEC3x3bCP7Lkc0dVWE+NOk80yIf6A333yTe++9l4iICIKCgpg+fToPPfQQL7zwAidOVIfj1+70io2NZdWqVWzatInt27cTERHBvHnzAHjmmWeYMmUKw4cPJzU1ldTUVHr27MkDDzzA8uXLKS8v123nl19+wcvLiwEDBtSpl729PR07dmTv3r0AnD17VvffgoLqp2179+6lX7/qp7glJSUMGDAAGxsb9u/fz8GDB7GxsWH48OFUVFQYbPuCBQs4dOgQGzduZOfOnRw4cIBTp+reEH300Ud07dqV06dP8+ijj/LII48QFVX9FO/48erw/l27dpGamspvv/12e//wf5K6qorUK0n4hwTqpbcICSQpJt5ouTP7jpGbnkXficNu+R1arZYr5y+Rk5Z5W0OH6ktyehrZubmEdqq5GTQzNaVzSAhnI//+D4n3vv6aXt260aNjp7+9rdtholTR2qU5J5Oi9dJPJkXT1t1w5JupyoSKWlE85VWVBLr6oFLUXH7v6TycvNIitkcfrf+K34RKocTTzoW4rGS99NisZHwc3A2WCXLzJSU/i97+7Xmm/3Qe7zOFYYE9MFGqDOaH6n8HlUJJaWW50Tz1xUSpoqVzMyJSYvTSI1JiCHIzPFw4KuMqztb2dGlW/Xdob2FDmF+I3lP9yIx4Wro0o5VL9bAydxsnOnsH6uVpKCZKFa1dm9f5rpNJUTc/9qoq9dLKqyoJcvNFdUMnhqWpOcunv8bKGW/w1vC5BDg33LC55NRUsnNyCO1a87DEzMyMzh06cPb8eaPlzl64QI9u+kMUQ7t15+wF/TIJSUkMnziBsVOn8MLrr5GUkqL7rPLa9cLczEyXplKpMDExIeLc2b/Vruv+iedzdVUVmVdTaR6s/13N2waQFnfzYXur3/iapc+8z8aPlpAcdfNOsaqKSjRqNebW9RMBeCtVlVUkxV0lsENbvfTAjsHER91e9G/S5avER8fSIjjQaJ6KinLUajVWN0TdNRQTpYpWLt6cSta/Rp1KjqaNu5/BMqbKuteoCnUlrWtdo25kbmKGiVLZKFHE18994Yn69wzhiZGEeBgeCmvs3Nem1rnvRqOCwtgde5KyKsP3lfVJpVTi6+jJhXT9v4kLaVdu+/zbp0UHLqZfIfuGKLWYrCT8HD3wd6ruLHO1dqCdZ8tGj2YXDUCjvTNf/1IybFOI/zGbN2/GplakzXPPPccrr7yie3///fczZcoU3WdhYWG88sorDBtWfXP+xBNPcP/99+tto6ysjKVLl+LtXX3x/vzzzxk1ahQfffQRHh4eWFpaUl5ejoeHh67MpEmTeOyxx9iwYYPu+5YsWcKsWbNQ3DCM60b9+/dn7969PP300+zdu5dBgwZx+fJlDh48yMiRI9m7dy9PPfUUACtXrkSpVPL999/rtrdkyRIcHBzYu3cvQ4cO1dt2YWEhS5cuZfny5QwaNEiX38vLq049Ro4cyaOPPqr7N1q4cCF79+4lKCgIV9fqeVmcnZ312nuj8vJyvU5D+PPDZ0sKi9FqNNjY2+qlW9vbUpRXN3QfICctkz2/bmbmK4+hVBnvsCgrKeWzx15DXVWFQqlk+KzJtGhn/Ca/vmXnVg+ncL4h+uP6+9SMvzf31Y59+4iKi+WnhZ/8re38GfYW1qiUKnJL9fdLbmkhjla2BsucTIpiRFAoh+PPEpOVRGuX5gwPDMVUZYK9hQ05pQW0dfdneGAoD699vzGaocfKzAKVUklRhf6PoOKKUmzMDf+IdbS0xcfRnSqNmhWn/5+9+46K4mrDAP4svTcBCyJVEBQVBLsCKmrQ2EsUBYm9gdjzGUvsxt6xYW+IxqixK6AoNqoCAlLEAoogvbPz/UHYuIIKCzLM+v7O4Rz2zhCfG5bZ2bv3vvcGFKTl0L9lF8hLy+L8szuV/oyDiQ2yCnIRn/am0uO1SVlWAZISksj4bAZVZn421OUrr2UUnZqETXdOYa7daEhLSkFKQhIPkyKx78EFwTkBCeFQlVXC6p+mgMfjQUpCEleeB+Lc0y/Poqwt/z33hPv0MT8bGl947j15HQXHFp1wL/EpYj+8gommLn4y7fDfcy8vC0kZ7/Gn33HEp72FoowchljYYevAWZjksw5vsmq/Pl1aetmsygYaGkLtDdQ1kPzuyzX20tLT0UBdeMlbA3V1pKWnCx63MjPHH//7H/Sa6iLt40ccOHoE46dPw+lDh6Gmqgp9PT00btQIO/buxf/mzoW8nByOe59GWno6PqTVzmxPcbyeF+TkgeHzoaAifM8hr6yIvMycSn9GQVUZtmMHQEuvCUpLShDzIAwXNh3GwLmuX6yT9uDsDSiqqaCped3UBsvNzgafz4eSmvASUSVVVWRnfHkgFwCWT5iLnKxs8Pml6DNiIDo6dP/iuf8cPQtVDXU0b135TN7apFJ+ncir5DohX/lS2KDXz9G3RUfcf/kULz68RnNNXfQ26SD0GvW5X236Iy03U1Dj8ntSlVOClIRkhaWMZde+yvv0+FUU+pt1RkBCGGI+lC1FdWzRSeja96kW2nowbKCDdX7Hv1s/PqUsowBJCYkKZRGyCnOhKvftQVZVOUVYNDLC3gd/C7U/ehUJZVkF/GbvDPDKBh5vvwjC5eeBtZqfkB8dDZ4RwjH29vbYvVu4oKnGZ29GWrf+r7Bow4ZlM0gsLCyE2goKCpCVlQUVlbIbkGbNmgkGzgCgU6dO4PP5iI6O/uIAkqysLMaMGQMvLy+MGDECoaGhCAsL++qmAnZ2djhw4AD4fD78/f3Rs2dPNGvWDP7+/rCyskJMTIxg5llQUBBevHgBZWXhNyMFBQVCy07LxcfHo7i4GO3btxe0qaqqwtS04puMT/8f8Xg8NGrUCO/fv69w3pesWbMGf/zxh1Db0qVLYdiv/Rd+4is+G2hk/s30OT6fj/M7j6Lb0L5o0Fj7q/9JWTlZTFg1F0WFRUiMiMHN4+ehrtUAeubG1c9XBVd8fbF65w7B4y1LlwGo2A+GqdDdaklJTcXGfXuxY/kKoVkkdeXzuq08XtnvqzLHgq9BXUEZ2wbNBg9lN/zXYx5iZNte4DN8yEvLYoH9WGy+ewpZhZXXF2PLl+rTlv8+fcJvo/DfT/evPi9binop8l6FwsRdDVrDorERDj76h92ixTwemC/8ppqqamNihwE4HXoLIW9joC6vjHHWjpjaaTB23D8LoKw23LA29tjz4G/EpiahkYomJrT/GR9bZ8M7vOIS9e/j8/y8L/6ejgZdg7q8CnYMmg0er+y5dy3mEX5p2wt8Ph9A2Wy6qE82UXiWkgDPofMwqFV37Py33zVx5cZ1rN64UfB4y9p1Zak/+/tnGOaLH7YIVLhGCv9Ml47/Lck1BtC6ZUsMGj0Kl65exZiRIyElJYU/l6/Aij/XoUf/fpCUlET7du3QuUMH0TpXrazcu55XUMmv50u/M/VGmlBvpCl43MioGXLSMxF6/V6lg2chV+/ixaOnGDjPFVJfWar/PVTswrdfoKavWoCigkK8jInDP0fPokFjbVh1q/g8uv3XFYQEPMS05fMhLVOX/RK+KPCAL177ToTcgLqCCrYMnCV4jboR+wgj2vREKcOvcP6w1j1gZ2SJ+f/srLONUr7kSxtFHX5yBRryKtg9ZB7AAz7mZeNq9AOMtuwNfiV96teiM+LT3uD5+5ffO/JXlf2evq2LfmvkFRcg+K3wDENTrWbob9YZR4OvIj79LRoqqWNUWwdkmufgYuTXl40TQqqOBs8I4RhFRUUYG3/9hln6kxvQ8hvcytrK30RVpvycb72pmTBhAtq2bYvXr1/Dy8sLPXv2hN5XdlPs3r07srOzERwcjLt372LFihXQ1dXF6tWr0bZtW2hra8PMzEyQr127djh+vOInguWzwz5VfjNVccCm4i2J9Gc36Twe76v/Pz7322+/Yfbs2UJtsrKyOB1e9fo5CsqK4ElIVJiVkJeZDUXVijNKivILkZzwCikv3+Da4bKlpAzDAAyD1c5zMHrBFOi3LKtDwpOQgEajsv9HjfR08OHNO9y/ePO7vdnq3qEDWn0ySFlUXDaw8uHjR2h+MribnpkBDTXRCyc/f/EC6RkZGDvLXdBWyucjJOIZvC9dxP2/zgsVCq8tmQW5KOWXVvi0W01OGRl5ldeJKiotxkb/k9hy5zTUFZSRnpcFxxadkVtUgMyCXBg2aILGKg2wos9Ewc+UP3evTtgE19OrkJz9/Wqg5RUVoJTPh5KMcKF2RRl55BblV/oz2YV5yCrIFQycAUBqTgYkeDyoyCkKfarfRd8C3Qzb4vDjy3iXk17Zf67WZRfmoZRfCjV54b8fVTklZORXPlNmWGs7RL1PxPmIsplzLz+mYM+D81jjOBXHQ67jY342Rls6wC8uWLBT6suMd5CTksa0zkNwJtz3i29Oa0P5c0/9s9kj6vJKX6xRVlRajA3+J7D57imoy6sgPS8T/cy6CJ57lWHAIDo1CU1VK15bRdG9S1e0Mvtvxo3gmpCWDs0G/w2upGd8hIb6l68JDTQ0hGaZAUD6x4yv/oy8vDyMDAzx6vVrQZuZqSlOHPBCTk4OiktKoK6mBpcpk2FeyYcrohCn63k5OSUF8CQkKswyy8/OhbxK1ZciNjTURcyDirvphV4LQPDlu/h5tgsaNK38Q7rvQVG5rAZb9kfh31VOZhaUVb++YUGDhmW/h8Z6TZGdkYXrp/+uMHjme/4qbp39B1OWzUUT/drdzfVLssqvE5+/Rskrf/U6sfnOKWy76y14jfqpRSfkFhUg67PrxFALO/zSthd+u7wbCenJ360fn8osyEFJJa+76t/o0zq/Y9hw5wQ05FWQlpeJn827IrcoH5n5wn2SlZJGD+N2dbLpS7nsojyU8vkVZpkpyypW+H9emW4GbRD48hlKP7tfHdzKFvdfPhPsWvkmMxUyktJwsXbEpch73/EVinx3lQz6EvbQ4BkhBACQlJSEt2/fCpY4BgYGQkJCAiYmZUudZGRkUFpaceaIhYUFrK2tsW/fPpw4cQLbt2//6r9TXvdsx44d4PF4MDc3R5MmTRASEoJLly4JZp0BgJWVFU6fPg1tbW3BDLmvMTIygrS0NB49egRd3bIb1qysLMTGxgr9d79F5t8ZTZX1t5ysrGyNdzmVlJJCY4OmSHgWgxY2/82ES3gWA5N2rSr+m/KymLhmvlBb0M17eBkZiyFu46CmpVHhZz5VUvz9PilWVFAQ2i2PYRg0UFfHw5AQtDAqq5dTXFyM4GfPMHOc65f+M99k06YNTu3YKdS2fOsW6DVtCpehw77LwBkAlPBLEfPhFax0THEv8b8aSVZNTXE/8elXf7aU4Qt2ebQ3ssLDpAgwYJCU8Q4Tz6wVOnecjSMUpOWw6/65SndHq02lDB/JWR9gpKkjNAPJSFPni5/CJ318h5aNDCHzST03TUVV8Bm+0I1/F/3WsDWyxJEnV/A268N37cenSviliEt7g7ZNjAW7xQH493FkpT8jKymDUkb4b53/2YC7rKR0hUH4snN4ZbMPv+M7kxJ+KWJSX6FdU+HnXrumLXDvW889Ph8f/n0e2RtZ4cHLZ18d6DNqoFNrb4wrvSZoaODhkydo8e/rSnFxMYLDwjBz8uQv/ndat2yJh08ew+nf0gAA8PDxY7RuWfEaWa6oqAiJSS9h+ckM43LlZQ+SXr9CVHQ0po4fX+2+VUacruflJKWkoKXXGK+j4mBo9d9A6OvIOOi3rfpOnx+SkqHw2QBiyLUABP/jj37uztDWr5uNN8pJSUuhqZEeYsIiYNHxv7qcMWGRaNm+GnU0mYq/B9/zV3HT5xImLfaAbiUbQHwvJfxSxH54DUsdE6HXJEsdEzx4+fWlqJ++RtkaWeLRv69R5Ya1tscoSwcsurIHsR9efZ8OVKL82mfdtIVgUAgArJu2QEDi12sVlvL5gtfQHsbtEFjJtc/eqB2kJaVwI+brm1/UplI+Hy8/JsO8oQGCP1n62rKhAULefn0prKlWMzRU1sDdexUHomUkpSr0j2GYskmj3/tFipAfCA2eEcIxhYWFSEkRrg8jJSUFTU3NL/xE1cjJycHFxQUbNmxAVlYW3NzcMGLECMGSTX19fVy7dg3R0dFo0KABVFVVBbO3JkyYgBkzZkBBQQGDBw/+5r9lZ2eHrVu3YvDgweDxeFBXV4e5uTlOnz6Nbdu2Cc5zcnLC+vXrMXDgQCxfvhxNmzZFUlISzp07h3nz5gktMwUAZWVluLi4YN68edDQ0IC2tjaWLl0KCQmJby8L+oS2tjbk5eVx9epVNG3aFHJyclBVVa3yz1dHh5/s8Pfu42hsqIumxvoI8b2PzLSPsOrZGQDge/oSsj9mYsAUJ/AkJKCtK7xzkqKKEiSlpYTa7124icYGulBv2AClJaWIC43C04DH6Dtu+HfpQ2V4PB5GDRyIg2e80axJE+g2aYKDZ7whJyuLvp8MZC7ZuBHaDRpgxrhxAMreTMe/KitEXVxSgtS0NETHx0FBTh66TZpAUUEBxvr6Qv+WnKwc1JRVKrTXtrPhflhgPwYxH5IQ9S4Rjmadoa2kjktRZUsifrXpD01FVfz5b+0UHVUttNDSw/P3L6EkK4+hre2hr9FYcLy4tASJH4UHKnILy2Z8fd7+vdxPfIohre0EO2Za67aAqpwSHieVFWjuZWIDFVlFnHvqBwB4mvwCdkaWGGRhC9/YICjIyKG3aQcEv44RLMvsatAaPZpbwyfsNjLys6EkU1Y/rai0uEJx6u/h74gAzOo2Ai8+vEF06kv0NukATUU1XIt+CAAYY9UHDRRUsTXAGwDw+HUUpnUegr6mHRDyJhbqCsoY374/YlKTBLMbHr9+jgHmXRGf/hYxqa/QWLkBRls64PGryAoDbd+Dz1NfLLQfi5jUV4h8l4B+/z73LkYGAADGt/8ZmoqqWOd7DADQVFULLbT1EPWu7Lk3vLU9DDQaC44DwNh2fRH1LhFvMlOhICOHwa1sYdygKbYFnKk0Q03xeDyMGj4cB48fQ7OmTaHbtCkOHjtWdk3o5SA4b8mqVdDW0sSMSWUDar8MG4ZJbm44dOI47Lp0hd+9ADwMeoIDnwyib9m1E906d0Gjhtr4+DEDB44cQW5uLvr37Ss456avL9TU1NCoYUO8iI/Dxu3bYdu1KzraiLDU/gvE8XrexqEzbh04By09HTQy0kXknSfITs9ES9uyTRwenLuB3I9Z6Dl+KAAg7OZ9qDRQh3oTbfBLShHzMAzxwZHoM/UXwX8z5OpdPPr7NnpNGAYVTTXkZZb9nUnLykBarmYfSlVV95974+S2/WhqrA99UyM8uH4HHz+ko1Pvstenf46dRWbaR4x2nwAACLhyG+qaGtDWKfvdJETFwu/CNXR1/G9nytt/XcHVk+cxxmMi1LU1kfWxbEBKVk4WsvJy371P5576YZ6dE2JTXyHqfSJ+alF2nfgn6j4AwNWmHxooqmKD3wkAZa9RplrN/n2NUsAQC1voqzfGxn+PA2VLNZ2tf8K620fxLjsd6v/O6s0vLqyTAvveYbewqKcLolOTEJESj/7mXaGtrIELEWXXvokdBkBLUQ2rbx8BULYM30xbD5HvE6Esq4ARrXvAQKMx1vx7/FP9zDohICGszssmXIt5hIntByDxYzLiPryBrZElNBRUBLt9DrWwg7q8MvY/uij0c90M2iAu7U2lNSnDkl+gt0l7JH18h/j0N9BWUsegVt0R+jb2i0tc2SIvLSs0w7mJiiaaazZFVkEu3uV8ZDEZId9Gg2eEcMzVq1fRuLHwDbepqalgp0hRGRsbY8iQIXB0dER6ejocHR2xa9cuwfGJEyfCz88P1tbWyMnJga+vL+zs7AAAo0aNwqxZszB69GjIyX37BtHe3h6bNm0S/DwA2NraIjQ0VGiGmIKCAu7cuYMFCxZgyJAhyM7Oho6ODnr27PnFmWibNm3ClClT0L9/f6ioqGD+/Pl49epVlXKVk5KSwrZt27B8+XIsWbIE3bp1E+wQWtvMO1oiLzsXAX9dQ05GFrSaNsYv8yZBVbNs1kFORhYyP1TvZqK4sAhXD/kgOz0TUjLSaNBEGwOnjoF5x7rZmbKcy9BhKCwswtrdu5Cdk4NWpqbYsXyF0GyUlNRUSEj8N7CZmp4OJzc3weOj587h6LlzsGplgb1rhWdp1TX/+BCoyClijFUfaCioIjE9GYuu7MH7f2/2GiioQFvpv6VkkjwJDGttj6Zq2ijllyL0bSzc/95SZ0sYq+JZSjzkpWVhZ2wFZVkFvM9Ox7Ggq8gsKFumpSyrAFX5/5aXFJWW4PCTy+hn1hmTOw9GflEBnqXE41bsE8E5Ns3MISUhiV8sHYT+Ld8XQfB9UXHn29p2LzEcKrIKGNm2J9TllZH0MQUrbh4SzELQUFCBlpKa4PzbL4IgLyULxxad4WrTD7lFBQhPjsORoCuCc7zDboNhGDhZ9oaGgiqyCnLx+FUUjodc++79AQC/uBCoyCpibLv/nnu/XfH84nNPgieBYa17QFdVGyX8UoS9jcXM85uFnntKMvKY3f0XqCuoILcoHy8+vIbHxa2ITv36Loo14TJqNAoLC7F286aya4KZGXZs2Ch8TXj/Tuia0KaVBVYtWYrdB/bD88ABNG3SBGuWLUMr8/9mQr1LTcWi5X8gIzMT6mpqaGVujoO7PdH4k3qdH9LSsHnnDqR9/AjNBg3Qr08fTHB2qdX+ieP13NjGAgU5+Qi65IfczGxoNNFGP7cxUG6gBgDIy8hGTnqm4Hx+SSnun7mG3IwsSElLQ72JFhzdxkDP4r8NOyL8HoNfUorrnqeF/i3rn+1gM6AH6oJl1/bIy87BDe+LyPqYicbNdDBhkTs0tMs+iMz6mIGMD//9vTB8BpePnUX6+w+QkJREg4Za6DdmKDr2/u+e5f5VX5SWlODweuG6tL1HDECfXwZ+9z7diQ+FiqwinKz6QF1BBS/Tk7H46l7BdUJDQQXaip9eJ3gYYmEneI0Ke/sCsy9sFRrA+Nm8C2QkpbDYQXjG+LGgqzgW/P2vf75xwVCVU4Rzu5/QQFEFCenJWPDPLsG1rIGC6mevuzyMbNMTumplG9uEvI3B9L82IiVb+HW3qao2Wjc2xpyLX18t8T08fhUFJRl5DDDvClU5JbzJTMWWu6cFu2eqyilVWKoqLy2Ldk1b4GTojUr/mxcjA8AwDAa36g51eWVkF+YhLPkFzj71+869qb4WWnrYMfi/siduXcs+CLgcFYhVtw+zFav+qmeDnz86HlPfhqMJIXVu2bJlOH/+PEJDQ0X6+VevXkFfXx+PHz+GlZXVt3+gDuXm5kJHRwcbN27E+FpanvM1Rx5f/u7/Rl1ytnFEdqz4bXWu3NwYDnvdv30ih9yYtBVLru5jO0atWt53IgYdWsh2jFp1ftxa9Nzj9u0TOeTW5G3ITnnHdoxapdyooVhez7fcOf3tEzlmVveRuPTvTCRx0b9lV/Td58F2jFp1deJm2O6eznaMWuU/dSd+9V7Ndoxa5TXif+iycwrbMWrVvemebEcQ2ceBo9mOUCn1v098+yQxRDPPCCEiKy4uRnJyMhYuXIiOHTvWi4GzkJAQPH/+HO3bt0dmZiaWL18OABg48Pt/6ksIIYQQQgghRPzQ4BkhRGT37t2Dvb09TExM4OPjw3YcgQ0bNiA6OhoyMjJo164d7t69W+OacIQQQgghhBBSZ/i0SLA+ocEzQgiWLVuGZcuWVfvn7Ozs6l0hUktLSwQFBbEdgxBCCCGEEEKImJBgOwAhhBBCCCGEEEIIIfUVzTwjhBBCCCGEEEIIqU/q2QqfHx3NPCOEEEIIIYQQQggh5Ato8IwQQgghhBBCCCGEkC+gZZuEEEIIIYQQQggh9QnDZzsB+QTNPCOEEEIIIYQQQggh5Ato8IwQQgghhBBCCCGEkC+gZZuEEEIIIYQQQggh9QmfdtusT2jmGSGEEEIIIYQQQgghX0CDZ4QQQgghhBBCCCGEfAEt2ySEEEIIIYQQQgipTxhatlmf0MwzQgghhBBCCCGEEEK+gAbPCCGEEEIIIYQQQgj5Alq2SQghhBBCCCGEEFKf0LLNeoVmnhFCCCGEEEIIIYQQ8gU0eEYIIYQQQgghhBBCWLVq1Sp07twZCgoKUFNTq9LPMAyDZcuWoUmTJpCXl4ednR0iIiKEziksLMTMmTOhqakJRUVFDBgwAK9fv65WNho8I4QQQgghhBBCCKlP+Pz6+fUdFRUVYfjw4Zg6dWqVf+bPP//Epk2bsGPHDjx+/BiNGjWCg4MDsrOzBefMmjULf/31F06dOoWAgADk5OSgf//+KC0trfK/QzXPCCGEEEIIIYQQQgir/vjjDwDAoUOHqnQ+wzDYsmULFi1ahCFDhgAADh8+jIYNG+LEiROYPHkyMjMzceDAARw9ehS9evUCABw7dgy6urq4efMm+vTpU6V/i2aeEUIIIYQQQgghhJBvKiwsRFZWltBXYWEhK1kSEhKQkpKC3r17C9pkZWVha2uL+/fvAwCCgoJQXFwsdE6TJk3QqlUrwTlVwhBCCOGUgoICZunSpUxBQQHbUWoN9YkbqE/cIY79oj5xA/WJG6hP3CGO/RLHPv1Ili5dygAQ+lq6dGmt/hsHDx5kVFVVv3nevXv3GADMmzdvhNonTpzI9O7dm2EYhjl+/DgjIyNT4WcdHByYSZMmVTkTj2Fo/1NCCOGSrKwsqKqqIjMzEyoqKmzHqRXUJ26gPnGHOPaL+sQN1CduoD5xhzj2Sxz79CMpLCysMNNMVlYWsrKylZ6/bNkywXLML3n8+DGsra0Fjw8dOoRZs2YhIyPjqz93//59dOnSBW/fvkXjxo0F7RMnTsSrV69w9epVnDhxAq6urhUyOzg4wMjICJ6enl/9N8pRzTNCCCGEEEIIIYQQ8k1fGyirzIwZM/DLL7989Rx9fX2RsjRq1AgAkJKSIjR49v79ezRs2FBwTlFRET5+/Ah1dXWhczp37lzlf4sGzwghhBBCCCGEEEJIrdPU1ISmpuZ3+W8bGBigUaNGuHHjBiwtLQGU7djp7++PdevWAQDatWsHaWlp3LhxAyNGjAAAJCcn49mzZ/jzzz+r/G/R4BkhhBBCCCGEEEIIYVVSUhLS09ORlJSE0tJShIaGAgCMjY2hpKQEAGjRogXWrFmDwYMHg8fjYdasWVi9ejWaN2+O5s2bY/Xq1VBQUMDo0aMBAKqqqhg/fjzmzJmDBg0aQENDA3PnzoWFhYVg982qoMEzQgjhGFlZWSxdurRa06XrO+oTN1CfuEMc+0V94gbqEzdQn7hDHPsljn0itWPJkiU4fPiw4HH5bDJfX1/Y2dkBAKKjo5GZmSk4Z/78+cjPz8e0adPw8eNHdOjQAdevX4eysrLgnM2bN0NKSgojRoxAfn4+evbsiUOHDkFSUrLK2WjDAEIIIYQQQgghhBBCvkCC7QCEEEIIIYQQQgghhNRXNHhGCCGEEEIIIYQQQsgX0OAZIYQQQgghhBBCCCFfQINnhBBCCCGEEEIIIYR8AQ2eEUIIYRXDMKC9a0hde/HiBa5du4b8/HwAoOdgPVdQUMB2hFonjn0ihJDK9OjRAxkZGRXas7Ky0KNHj7oPRIgIaLdNQgip5woLC/Ho0SMkJiYiLy8PWlpasLS0hIGBAdvRauTIkSNYv349YmNjAQAmJiaYN28exo4dy3Iy0ZSWlmLz5s3w9vZGUlISioqKhI6np6ezlKzmXr9+jQsXLlTar02bNrGUSjRpaWkYOXIkbt++DR6Ph9jYWBgaGmL8+PFQU1PDxo0b2Y4oslevXgldJ1q2bAlZWVm2Y4mMz+dj1apV8PT0xLt37xATEwNDQ0MsXrwY+vr6GD9+PNsRq00c+wSI13MvMzMTf/31F+7evVvhdbdPnz7o3Lkz2xEJ4RwJCQmkpKRAW1tbqP39+/fQ0dFBcXExS8kIqToptgMQQgip3P3797F9+3acP38eRUVFUFNTg7y8PNLT01FYWAhDQ0NMmjQJU6ZMgbKyMttxq2XTpk1YvHgxZsyYgS5duoBhGNy7dw9TpkzBhw8f4OHhwXbEavvjjz+wf/9+zJ49G4sXL8aiRYuQmJiI8+fPY8mSJWzHE9mtW7cwYMAAGBgYIDo6Gq1atUJiYiIYhoGVlRXb8arNw8MDUlJSSEpKgpmZmaB95MiR8PDw4Nzg2cuXL+Hp6YmTJ0/i1atXQjPoZGRk0K1bN0yaNAlDhw6FhAS3FhysXLkShw8fxp9//omJEycK2i0sLLB582ZODjSJU5/E7bmXnJyMJUuW4Pjx42jUqBHat2+Ptm3bCl53fX19sWHDBujp6WHp0qUYOXIk25FF8vjxY5w5c6bSD0POnTvHUqqaEcc+iYvw8HDB95GRkUhJSRE8Li0txdWrV6Gjo8NGNEKqjyGEEFLvDBgwgGncuDEzZ84cxt/fn8nNzRU6HhcXxxw6dIjp06cP06hRI+b69essJRWNvr4+c/jw4Qrthw4dYvT19VlIVHOGhobMpUuXGIZhGCUlJebFixcMwzDM1q1bmVGjRrEZrUZsbGyYxYsXMwxT1q+4uDgmOzubGTBgALNr1y6W01Vfw4YNmdDQUIZh/usPwzBMfHw8o6ioyGa0anNzc2OUlZWZoUOHMocPH2aioqKYrKwspri4mHn37h1z69YtZtmyZYypqSnTsmVL5tGjR2xHrhYjIyPm5s2bDMMI/66ioqIYNTU1NqOJTFz6JI7PPS0tLWbOnDnM06dPv3hOXl4ec+LECaZ9+/bM+vXr6zBd7Th58iQjLS3N9OvXj5GRkWH69+/PmJqaMqqqqsy4cePYjicScerT33//XeUvruDxeIyEhAQjISHB8Hi8Cl8KCgrMgQMH2I5JSJXQzDNCCKmHevfujTNnzkBGRqbS44aGhjA0NISLiwsiIiLw9u3bOk5YM8nJyZUufencuTOSk5NZSFRzKSkpsLCwAAAoKSkhMzMTANC/f38sXryYzWg1EhUVhZMnTwIApKSkkJ+fDyUlJSxfvhwDBw7E1KlTWU5YPbm5uVBQUKjQ/uHDB84tM5ORkUFcXBy0tLQqHNPW1kaPHj3Qo0cPLF26FJcvX8bLly9hY2PDQlLRvHnzBsbGxhXa+Xw+Z5f4iEufxPG5FxERUWl/PiUvL49Ro0Zh1KhRSE1NraNktWf16tXYvHkzpk+fDmVlZWzduhUGBgaYPHkyGjduzHY8kYhTnwYNGiT0mMfjCc3o5PF4gu9LS0vrKlaNJCQkgGEYGBoa4tGjR0J/YzIyMtDW1oakpCSLCQmpuvo/h5oQQn5A06dP/+LA2edatmwJBweH75yodhkbG8Pb27tC++nTp9G8eXMWEtVc06ZNBQN/xsbGuH79OoCy5SRcG5T5lKKiIgoLCwEATZo0QVxcnODYhw8f2Iolsu7du+PIkSOCxzweD3w+H+vXr4e9vT2Lyapv/fr133yzX87R0RHDhg37zolqV8uWLXH37t0K7WfOnIGlpSULiWpOXPokjs+9qvZH1PPrg7i4OPTr1w8AICsri9zcXPB4PHh4eGDv3r0spxONOPWJz+cLvq5fv462bdviypUryMjIQGZmJi5fvgwrKytcvXqV7ahVpqenB319ffD5fFhbW0NPT0/w1bhxYxo4I5xCM88IIYRDCgoKcPr0aeTm5sLBwYGzA01//PEHRo4ciTt37qBLly7g8XgICAjArVu3Kh1U44LBgwfj1q1b6NChA9zd3TFq1CgcOHAASUlJnKzhVq5jx464d+8ezM3N0a9fP8yZMwdPnz7FuXPn0LFjR7bjVdv69ethZ2eHJ0+eoKioCPPnz0dERATS09Nx7949tuNV2++//44ePXqgc+fOkJOTYztOrVq6dCnGjh2LN2/egM/n49y5c4iOjsaRI0dw6dIltuOJRBz79Dl/f3/k5uaiU6dOUFdXZztOtbx48QKZmZlo166doO3WrVtYuXIlcnNzMWjQIPzvf/9jMWHNaGhoIDs7GwCgo6ODZ8+ewcLCAhkZGcjLy2M5nWjEsU8AMGvWLHh6eqJr166Ctj59+kBBQQGTJk1CVFQUi+lEExMTAz8/P7x//x58Pl/oGJdrw5IfB+22SQgh9dS8efNQVFSErVu3AgCKiorQoUMHREREQEFBASUlJbhx4wY6derEclLRBAUFYfPmzYiKigLDMDA3N8ecOXM4Nfviax48eID79+/D2NgYAwYMYDuOyOLj45GTk4PWrVsjLy8Pc+fORUBAAIyNjbF582bo6emxHbHaUlJSsHv3bgQFBYHP58PKygrTp0/n3BIfADAyMkJCQgJkZGTQoUMH2Nvbo0ePHujYsWOVZ6/WZ9euXcPq1auFfldLlixB79692Y4mMnHp0/r165GTk4M//vgDAMAwDH766SfBrFttbW3cunULLVu2ZDNmtQwePBitWrXCihUrAJQtOWvZsiW6deuGFi1awMvLCytWrMCsWbPYDSqi0aNHw9raGrNnz8aqVauwdetWDBw4EDdu3ICVlRUni+uLY5+AsiXCjx49EpSDKBceHo4OHTogPz+fpWSi2bdvH6ZOnQpNTU00atRIaAkqj8dDcHAwi+kIqRoaPCOEkHqqVatWWL16tWDg5eDBg5gzZw5CQkLQrFkz/Prrr3j//j3++ecflpMSQtj05s0b3L59G35+fvDz80NCQgLk5eXRqVMn2Nvbw97evtIag4TUhJWVFRYsWCDYdfLMmTNwcXHBjRs3YGZmBmdnZygoKHBqNrGuri68vb0FH0qtXLkSPj4+CA0NBQAcOHAA27dvFzzmmvT0dBQUFKBJkybg8/nYsGGD4MOQxYsXc26mICCefQLKSgxIS0vj2LFjgg92UlJSMHbsWBQVFcHf35/lhNWjp6eHadOmYcGCBWxHIURkNHhGCCH1lIqKCoKDgwXFpUeNGgVlZWVBDY/Q0FA4OjpybrOAT71//77S6futW7dmKVH1XLhwocrncnn2GdeFh4ejVatWkJCQQHh4+FfPVVJSgq6uLqSlpesoXe179eoVfH194efnh7NnzyI3NxclJSVsx6o2V1dXjBkzBj169BCapcBl4tQndXV13L9/H2ZmZgDK+lZSUoKjR48CKJt9O3z4cLx69YrNmNUiLy+PmJgY6OrqAgB69uyJzp07C2aixcXFoV27dsjIyGAxpWhKSkpw/Phx9OnTB40aNWI7DvmGFy9eYPDgwYiOjkazZs0AAElJSTAxMcH58+cr3XikPlNRUUFoaCgMDQ3ZjkKIyKjmGSGE1FMSEhJCuyw9ePBAaNdGNTU1fPz4kY1oNRYUFAQXFxfBks1P8Xg8zuwi9fnOWF/CpT4BZW+Kq/rGPj09/Tunqbm2bdsiJSUF2traaNu2bYUdzD6nqqoKT09PwYwaLomLi4Ofn59gJlppaSnnNkIol5aWhn79+qFBgwb45ZdfMGbMGM4v6xanPhUXFwtthhIYGAh3d3fB4yZNmnBuUxENDQ0kJydDV1cXfD4fT548EapZWVRU9NVrR30mJSWFqVOncrJW1ueysrKqfK6Kisp3TPL9GBsbIzw8HDdu3MDz588F5S169erFyYH34cOH4/r165gyZQrbUQgRGQ2eEUJIPdWiRQtcvHgRs2fPRkREBJKSkoTeBL98+RINGzZkMaHoXF1dYWJiggMHDqBhw4acvBEEUGHGnLjYsmUL2xFqVUJCgmBnvISEhK+eW1hYiDNnzggtR6vPEhIS4OvrK5hplpmZiS5dusDW1hYzZsyAjY0NpKS4ebt34cIFZGRkwNvbGydOnMCWLVtgamqKMWPGYPTo0dDX12c7YrWJU5+MjY1x584dGBoaIikpCTExMbC1tRUcf/36NRo0aMBiwuqztbXFihUrsGvXLpw5cwZ8Pl/odTcyMpJTv6PPdejQASEhIZysVfkpNTW1Kt83cOmDq8/xeDz07t2bc/UQy23btk3wffky2gcPHsDCwqLC7G43N7e6jkdItdGyTUIIqafOnj2LUaNGoVu3boiIiICNjQ0uXrwoOL5gwQIkJCRwqp5MOWVlZYSEhHBu2QH5MXz8+BHjx4/nRKFpCQkJNGvWDNOmTYO9vT2srKwgKSnJdqzv4vXr1zh58iS8vLwQGxvLyaWon+Nyn/bs2YM5c+Zg5MiRePDgAdTU1IR2rF25ciUePnwo9LpV3yUkJMDBwQEJCQmQkJDAtm3bMHXqVMHxQYMGwcDAAJs3b2YxpejOnDmDhQsXwsPDA+3atYOioqLQca6UTPi03ldiYiIWLlyIcePGCWrVBQYG4vDhw1izZg1cXFzYilljubm58Pf3R1JSEoqKioSOcWGwycDAoErn8Xg8xMfHf+c0hNQcDZ4RQkg9dvPmTfzzzz9o1KgRZs6cCQUFBcGxP/74A3Z2dkKf9HPFoEGDMHbsWAwdOpTtKLWK6ze635Kfn4/i4mKhNq4uicnLy6v098SVN4/lRo4ciTt37qCgoADdunWDra0t7O3tYWlpydkZnZUpLi7GP//8g2PHjuGff/6BhoYG3rx5w3asGhGHPh04cACXLl1Co0aNsHTpUqFaWtOmTUOvXr0wZMgQFhNWX3FxMSIjI6GlpYUmTZoIHQsLC4Ouri40NDRYSlczEhISFdrKl7FzrbxAuZ49e2LChAkYNWqUUPuJEyewd+9e+Pn5sROshkJCQuDo6Ii8vDzk5uZCQ0MDHz58gIKCArS1tWmwiRAW0OAZIYRwWGhoKNq2bct2jGr78OEDXFxc0L59e7Rq1arC9H0uFtcX1xvd3NxcLFiwAN7e3khLS6twnGtvtlJTU+Hq6oorV65Uepxr/Sn3/PlzwdJNf39/FBQUoGvXrrC1tYWdnR1sbGzYjigSX19fnDhxAmfPnkVpaSmGDBkCJycn9OjRo9KBAC4Qxz59SWpqqmDJtDh4+vQpDhw4wNml7S9fvvzqcS4u51RQUEBYWBiaN28u1B4TE4O2bdsiLy+PpWQ1Y2dnBxMTE+zevRtqamoICwuDtLQ0xowZA3d3d84NShMiDmjwjBBCOCYzMxPHjx/HgQMHEBoaysk3+xcuXMDYsWORnZ1d4RhXP/0W1xvd6dOnw9fXF8uXL4ezszN27tyJN2/eYM+ePVi7di2cnJzYjlgtTk5OSExMxJYtW2Bvb4+//voL7969w8qVK7Fx40b069eP7Yi1IjIyEidOnMD27ds5u9tm06ZNkZaWhj59+sDJyQk///wz5OTk2I5VI+LYp88xDIMrV64IZqUVFhayHalGsrKycPLkSRw4cABPnjxB69atERoaynYs8i9TU1P0798fGzduFGqfM2cOLl26hOjoaJaS1YyamhoePnwIU1NTqKmpITAwEGZmZnj48CFcXFzw/PlztiNWy+zZsytt5/F4kJOTg7GxMQYOHMjZWZ3kx0CDZ4QQwhG3b9+Gl5cXzp07Bz09PQwdOhRDhw7l5E5t+vr66N+/PxYvXszZTQ8+J243uuWaNWuGI0eOwM7ODioqKggODoaxsTGOHj2KkydP4vLly2xHrJbGjRvj77//Rvv27aGiooInT57AxMQEFy5cwJ9//omAgAC2I4rs3bt38PPzg5+fH3x9fRETEwNZWVl07NgRvr6+bMertr1792L48OFQV1dnO0qtEcc+lYuPj4eXlxcOHz6MnJwc9OvXD0OHDsXgwYPZjiYSf39/HDhwAGfPnkVBQQHmzZuHCRMmcLpW55EjR7563NnZuY6S1J7Lly9j6NChMDIyQseOHQGU7U4eFxeHs2fPwtHRkeWEotHS0sK9e/dgYmICU1NTbNu2DX369MHz589hZWXFuRl19vb2CA4ORmlpKUxNTcEwDGJjYyEpKYkWLVogOjoaPB4PAQEBMDc3ZzsuIZXi5vZLhBDyg3j9+jUOHToELy8v5ObmYsSIESguLsbZs2c5fXORlpYGDw8PsRk4AwBpaWlBjamGDRsiKSkJZmZmUFVVRVJSEsvpRJeeni4o+quiooL09HQAQNeuXYUKaXNFbm4utLW1AQAaGhpITU2FiYkJLCwsEBwczHK66jtz5oxguWZ0dDSkpKTQvn17jBgxAvb29ujcuTNkZWXZjimSSZMmsR2h1olbnwoKCuDj44P9+/fjwYMHcHBwQHJyMkJDQ9GqVSu241VbcnIyDh48KHjNHTVqFPz9/dGpUyc4OztzeuAMANzd3YUeFxcXIy8vDzIyMlBQUODk4JmjoyNiY2Oxe/duREVFgWEYDBw4EFOmTIGuri7b8URmaWkp+HDH3t4eS5YswYcPH3D06FFYWFiwHa/aymeVHTx4UFArNSsrC+PHj0fXrl0xceJEjB49Gh4eHrh27RrLaQmpHA2eEUJIPeXo6IiAgAD0798f27dvR9++fSEpKQlPT0+2o9XYkCFD4OvrCyMjI7aj1Bpxu9EtZ2hoiMTEROjp6cHc3Bze3t5o3749Ll68CDU1NbbjVZupqSmio6Ohr6+Ptm3bYs+ePdDX14enpycaN27Mdrxqc3JygrW1NQYPHgx7e3t06dIF8vLybMeqNY8fP8aZM2cq3dyBC7uhVkZc+jRt2jScOnUKpqamGDNmDM6ePYsGDRpAWlqas7XbDAwMMHz4cOzcuRMODg6c7ceXfPz4sUJbbGwspk6dinnz5rGQqGaKi4vRu3dv7NmzB6tWrWI7Tq1avXq1oLTFihUr4OLigqlTp8LY2BgHDx5kOV31rV+/Hjdu3BDaZEhFRQXLli1D79694e7ujiVLlqB3794spiTk62jwjBBC6qnr16/Dzc0NU6dOrVAIl+tMTEzw22+/ISAgABYWFhU2DODizpTidqNbztXVFWFhYbC1tcVvv/2Gfv36Yfv27SgpKcGmTZvYjldts2bNQnJyMgBg6dKl6NOnD44fPw4ZGRkcOnSI3XAi+PjxIxQVFdmO8V2cOnUKzs7O6N27N27cuIHevXsjNjYWKSkpnF0KKE592rt3LxYsWICFCxdCWVmZ7Ti1Qk9PDwEBAWjWrBn09PTQokULtiN9d82bN8fatWsxZswYzpUXkJaWxrNnz8RqZ+Fy1tbWgu+1tLQ4VyLhc5mZmXj//n2FVROpqanIysoCUFb+4vMPFAipT6jmGSGE1FOBgYHw8vKCt7c3WrRogbFjx2LkyJFo0qQJwsLCOL1ss3wZYGV4PB5nd6b8ESQlJeHJkycwMjJCmzZt2I5TY3l5eXj+/DmaNWsGTU1NtuOI7M2bNzh79ixiYmLA4/HQvHlzDB06FDo6OmxHE1nr1q0xefJkTJ8+HcrKyggLC4OBgQEmT56Mxo0b448//mA7YrWJU59OnDiBgwcPIjAwEP369cPYsWPRt29fyMvLc/o16t69ezhw4ADOnDkDExMTjBkzBvPnz0d4eDjMzMzYjvddhISEwNbWVjCIwSVz5syBtLQ01q5dy3aU7yI1NVVQD8zU1JSzr1NOTk4IDAzExo0bYWNjAx6Ph0ePHmHu3Lno3Lkzjh49ilOnTmHDhg148uQJ23EJqRQNnhFCSD2Xl5eHU6dOwcvLC48ePUJpaSk2bdqEX3/9VWw+7SeEiG7Xrl2YPXs2ioqKoKqqCoZhkJWVBRkZGWzatAnTpk1jO6JIFBUVERERAX19fWhqasLX1xcWFhaIiopCjx49BDMIuUQc+5SYmIiDBw/i0KFDyMvLQ3p6Ok6fPo1hw4axHa1GcnJycPLkSXh5eeHhw4ewtbXF6NGjMWjQIGhpabEdTyQXLlwQeswwDJKTk7Fjxw7o6uriypUrLCUT3cyZM3HkyBEYGxvD2tq6wkxcLs6QBsrqc86cORNHjx4V7EAuKSkJZ2dnbN++HQoKCiwnrJ6cnBx4eHjgyJEjgt2fpaSk4OLigs2bN0NRUVGwi23btm3ZC0rIV9DgGSGE1FNJSUnQ1dUVWo4QHR2NAwcO4OjRo8jIyICDg0OFm2FCiLDZs2dX+VyuvdH6559/MHDgQMyaNQtz5swR1G1LTk7G+vXrsX37dvz999+c3HFOV1cXly9fhoWFBdq0aYOFCxdi1KhRCAwMRN++fZGZmcl2xGoTxz6VYxgG165dg5eXFy5cuABNTU0MGTIE27ZtYztajUVFRQlee9PT01FcXMx2JJF8XsONx+NBS0sLPXr0wMaNGzlZ99He3v6Lx3g8Hm7fvl2HaWrP5MmTcfPmTezYsQNdunQBAAQEBMDNzQ0ODg7YvXs3ywlFk5OTg/j4eDAMAyMjIygpKbEdiZAqo8EzQgippyQlJZGcnCzYGfBTpaWluHjxouBNChfMnj0bK1asgKKi4jcHM7g2gEHqt6+9ufoUF99o2draolu3bli5cmWlx3///XfcvXsX/v7+dZys5kaPHg1ra2vMnj0bq1atwtatWzFw4EDcuHEDVlZWnCquX04c+1SZ9PR0HDlyBAcPHkRYWBjbcWpNSUkJLly4gCFDhrAdhYg5TU1N+Pj4wM7OTqjd19cXI0aMQGpqKjvBCPmB0eAZIYTUUxISEkhJSal08IyL7O3t8ddff0FNTU1sPykmpK6pqKjg8ePHMDU1rfR4dHQ0rK2tBZtZcEl6ejoKCgrQpEkT8Pl8bNiwAQEBATA2NsbixYuhrq7OdsRqE8c+iZO3b99i06ZNWLJkidCugEBZwfOVK1di7ty5aNiwIUsJyZe8ePECcXFx6N69O+Tl5cEwDKc3ElBQUEBQUFCFOnsRERFo3749cnNzWUpWdUOGDMGhQ4egoqLyzQFncfnggIg3GjwjhJB6StwGzwipT8TljZaSkhLCw8NhaGhY6fH4+Hi0bt0aOTk5dZyMiLuqLIfm8XjYuHFjHaSpHXPnzkVWVhb27t1b6fEpU6ZAVVUV69atq+NkohPnZesAkJaWhhEjRsDX1xc8Hg+xsbEwNDTE+PHjoaamxqnn36d69uyJBg0a4MiRI5CTkwMA5Ofnw8XFBenp6bh58ybLCb/N1dUV27Ztg7KyMlxdXb96Lpd3JSc/Dim2AxBCCPmy/fv3f7MehJubWx2lqT3v3r374if34eHhaN26dR0nEk11avlw6fdUnR3XPp+dUd996Y3WhAkTOPlGq2XLlvj777/h4eFR6fHz58+jZcuWdZyq9vD5fLx48QLv378Hn88XOta9e3eWUtWMuPQpJCSE7Qi17urVq/D09PzicWdnZ0ycOJFTg2ef/56CgoJQWloqmK0aExMDSUlJtGvXjo14Nebh4QFpaWkkJSUJzdIaOXIkPDw8OHdNL7d161b07dsXTZs2RZs2bcDj8RAaGgo5OTlcu3aN7XhV8umAGA2OEXFAM88IIaSekpCQQNOmTSEpKfnFc3g8HuLj4+swVe3Q1tbG/v37MWDAAKH2DRs2YPHixcjPz2cpWfUYGBhU6Tyu/Z4kJCS+OQurfKZW+S5gXOHs7Iz3799j//79MDMzQ1hYGAwNDXH9+nV4eHggIiKC7YjVcvjwYUydOhUbNmzApEmTICVV9rloSUkJ9uzZg3nz5mHXrl0YN24cu0FF8ODBA4wePRovX77E57erXHzuAeLZJ3GiqKiIqKgoNGvWrNLj5QM0XFgyV5lNmzbBz88Phw8fFiwR/vjxI1xdXdGtWzfMmTOH5YTV16hRI1y7dg1t2rSBsrKy4JqekJAACwsLTs+6zc/Px7Fjx/D8+XMwDANzc3M4OTlBXl6e7WgiKSkpgZ+fH+Li4jB69GgoKyvj7du3UFFRoY0DCCfQzDNCCKnHnjx5IpbLNhcsWICRI0cKtihPT0/H2LFjERERgdOnT7Mdr8oSEhLYjvBd+Pr6sh3hu7l+/TquXbuGpk2bCrU3b94cL1++ZCmV6FxcXPD06VPMmDEDv/32G4yMjAAAcXFxyMnJgZubGycHzoCyJXLW1tb4559/0LhxY04uq/2cOPXJz8+vQjHzz02bNg27du2qm0C1QF5eHomJiV8cPEtMTOTswAUAbNy4EdevXxeqraeuro6VK1eid+/enBw8y83NhYKCQoX2Dx8+QFZWloVEtUdeXh4TJ05kO0atePnyJfr27YukpCQUFhbCwcEBysrK+PPPP1FQUPDVGZ+E1Bc0eEYIIaTOzZkzB7169cKYMWPQunVrpKeno2PHjggPD+d8IeaioiIkJCTAyMhIMAuIa2xtbdmO8N2I4xutDRs2YNiwYTh58iRiY2MBlC3/++WXX9CxY0eW04kuNjYWPj4+MDY2ZjtKrRGnPg0cOBC+vr6wsrKq9Pj06dNx/PhxTg2edejQAUePHv3i8tkjR46gffv2dZyq9mRlZeHdu3cVlnK/f/+ek5uKAGXXuiNHjmDFihUAymZw8vl8rF+/vso7LdcX1dk9/fOZ+/Wdu7s7rK2tERYWhgYNGgjaBw8ejAkTJrCYjJCq4+ZdPSGEEADA8ePH4eTkxHYMkRgaGqJly5Y4e/YsAGDEiBGcHjjLy8vDzJkzcfjwYQBldWQMDQ3h5uaGJk2aYOHChSwnFN3du3exZ88exMfH48yZM9DR0cHRo0dhYGCArl27sh2vWsTpjRYALF++HHPnzkXHjh05PVBWmQ4dOuDFixdiMdBUTpz6NGHCBPz000+4e/cuTExMhI7NmDEDR44cweXLl1lKJ5q5c+fCwcEBqqqqmDdvnuA16d27d/jzzz9x6NAhXL9+neWUohs8eDBcXV2xceNGwfXiwYMHmDdv3jd3Q6yv1q9fDzs7Ozx58gRFRUWYP38+IiIikJ6ejnv37rEdr1oGDRpUpfO4uMQ7ICAA9+7dg4yMjFC7np4e3rx5w1IqQqqJIYQQUi+1aNGCefv27RePHz9+nJGWlq7DRLUnICCA0dfXZ9q1a8dERkYy+/btY5SVlZnhw4cz6enpbMcTiZubG9OuXTvm7t27jKKiIhMXF8cwDMP8/fffTNu2bVlOJzofHx9GXl6emTBhAiMrKyvo186dO5mffvqJ5XTVFxERwWhpaTF9+/ZlZGRkmGHDhjFmZmZMw4YNmRcvXrAdr9okJCSYd+/esR3juzh37hxjbm7OHDx4kHny5AkTFhYm9MVF4tYnV1dXplmzZszr168FbTNnzmQUFRUZPz8/FpOJztPTk5GVlWUkJCQYNTU1Rl1dnZGQkGBkZWWZXbt2sR2vRnJzc5mpU6cK+ichIcHIyMgwU6dOZXJyctiOJ7Lk5GRmyZIlTL9+/ZiffvqJWbRo0Vfvn0jdU1dXZyIiIhiGYRglJSXBvcTdu3cZbW1tNqMRUmW0YQAhhNRTdnZ2yM/Px+3bt6GoqCh07NSpU3B2dsa6deu+uMtefSYrKwsPDw+sWLEC0tLSAMpqNI0dOxZJSUl4/fo1ywmrT09PD6dPn0bHjh2Fiha/ePECVlZW1drBsj6xtLSEh4cHnJ2dhfoVGhqKvn37IiUlhe2I1ZaSkoLdu3cjKCgIfD4fVlZWmD59Oho3bsx2tGqTkJBASkqKWNZGlJCQqNDG4/E4u1kFIH594vP5GDZsGKKionD37l2sWrUKe/fuxaVLlzg5k7O4uBjS0tJ48+YNvL298eLFCzAMAxMTEwwbNgxNmzbFs2fP0KpVK7aj1khubi7i4uLAMAyMjY0r3GMQ9iUkJFR5UyIuGDlyJFRVVbF3714oKysjPDwcWlpaGDhwIJo1a0a7cRJOoMEzQgipp3JycmBnZwc1NTVcuXJFMMjk7e2NMWPGYPXq1Zg7dy7LKUXj7+9faV0tPp+PVatWYfHixSykqhkFBQU8e/YMhoaGQoNMYWFh6N69OzIzM9mOKBIFBQVERkZCX19fqF/x8fEwNzdHQUEB2xF/aBISEnj37h20tLTYjlLrvrWBg56eXh0lqT3i2KeioiL069cPYWFhyM3NxYULF9CzZ0+2Y4lk2LBhOHPmzBc3cnj27Bl69uyJd+/e1XEy8iV37tz56vEv1a+r7yQlJdG9e3eMHz8ew4YNg5ycHNuRauTt27ewt7eHpKQkYmNjYW1tjdjYWGhqauLOnTti+QEQET80eEYIIfVYamoqunfvDnNzc/j4+MDHxwdOTk5YsWIFFixYwHY88glbW1sMGzYMM2fOFHyqamBggBkzZuDFixe4evUq2xFFYmRkhD179qBXr15Cg2dHjhzB2rVrERkZyXbEarl69SqUlJQEtdp27tyJffv2wdzcHDt37hTahY4LJCQk0KpVq29uThEcHFxHiciPYtu2bYLvs7OzsWLFCvTp06fCwJmbm1tdRxOZrq4ufvrpJ+zdu7fCsYiICPTo0QPdu3fHmTNnWEhXOx4/fowzZ84gKSkJRUVFQsfOnTvHUirRfWk2ZzmuzeYs9+zZM3h5eeH48eMoLCzEyJEjMX78eE5vWJGfn4+TJ08iODhYMOvbycmJ0zvYkh8LDZ4RQkg99+rVK3Tt2hXGxsYICAjAkiVLsGjRIrZj1Vhubi78/f0rvYHn0putcvfv30ffvn3h5OSEQ4cOYfLkyYiIiEBgYCD8/f3Rrl07tiOK5M8//8Thw4fh5eUFBwcHXL58GS9fvoSHhweWLFmCGTNmsB2xWiwsLLBu3To4Ojri6dOnsLa2xpw5c3D79m2YmZlxbumIhIQE5syZAyUlpa+et3Tp0jpKVLvi4uKwZcsWREVFgcfjwczMDO7u7jAyMmI7msjEpU9VWVLG4/EQHx9fB2lqR1RUlGC2z9q1a4Xa7e3t0blzZ5w5cwaSkpIsphRdecmH3r1748aNG+jduzdiY2ORkpKCwYMHc+76B6DCrO7i4mKEhIRg8eLFWLVqFWdnQZYrKSnBxYsXcejQIVy5cgXNmzfH+PHjMXbsWLGccUxIfUaDZ4QQUk+Fh4cLvn/+/DmcnZ0xaNAg/O9//xM6r3Xr1nUdrdpevXoFXV1dweOQkBA4OjoiNzcX+fn5UFdXx4cPH6CgoABtbW1Ovdn61NOnT7FhwwahWloLFiyAhYUF29FqZNGiRdi8ebNgiaasrCzmzp0r2LGSS5SUlPDs2TPo6+tj2bJlePbsGXx8fBAcHAxHR0fO1XAT55pn165dw4ABA9C2bVt06dIFDMPg/v37CAsLw8WLF+Hg4MB2xGoTxz6Jm8ePH6Nnz55YvHgx5s2bh+fPn8Pe3h7t27fHuXPnODtwBpTdL0yePBnTp08XzCQ2MDDA5MmT0bhxY/zxxx9sR6w1d+7cgYeHB4KCgtiOUisKCwuxa9cu/PbbbygqKoK0tDRGjhyJdevWcaJeZ5MmTWBnZyf4+nyHXkK4gAbPCCGknpKQkBAqJF1+uf78ey4sSVi5ciWSk5OxY8cO8Hg82NnZwcjICHv37kXTpk0RFhaG/Px8jBkzBh4eHhgyZAjbkcln8vLyEBkZCT6fD3Nz82/OdKqvNDQ0EBAQAHNzc3Tt2hXOzs6YNGkSEhMTYW5ujry8PLYjVoukpCSSk5PFcvDM0tISffr0EZoBBAALFy7E9evXObkUVRz7JI5u376N/v37Y/78+di3bx+srKxw7tw5Qe1RrlJUVERERAT09fWhqakJX19fWFhYICoqCj169EBycjLbEWtNVFQUbGxskJOTw3aUGnny5Am8vLxw6tQpKCoqwsXFBePHj8fbt2+xZMkSZGdn49GjR2zH/KaTJ0/C398ffn5+iImJQcOGDWFraws7OzvY2trCzMyM7YiEfBMNnhFCSD31rcLS5bhQYDo/Px8zZsxAamoqLly4ADU1NTx48AAtWrRA06ZNERgYCF1dXTx48ADjxo3D8+fP2Y5cJdXZQVNFReU7JiFVNWDAABQVFaFLly5YsWIFEhISoKOjg+vXr2PGjBmIiYlhO2K1iPPMMzk5OTx9+hTNmzcXao+JiUHr1q05uVmFuPTp1KlT+OWXX6p07qtXr5CUlIQuXbp851S16/z58xg+fDh69+6N8+fPc37gDCir6Xb58mVYWFigTZs2WLhwIUaNGoXAwED07duXkxvbfDpLHwAYhkFycjLWrl2L4uJi3Lt3j6VkNbNp0yYcPHgQ0dHRcHR0xIQJE+Do6ChU4+3Fixdo0aIFSkpKWExafe/evYOvry8uXbqE06dPg8/nc+KDYEK+Xl2WEEIIa7gwKFZV8vLyOHDgAE6dOgUAkJaWFtwAamtr4+XLl9DV1YWamhqSkpLYjFotampqX9yV7XNcujGszsw/rhWY3rFjB6ZNmwYfHx/s3r0bOjo6AIArV66gb9++LKervoSEBLGte6OlpYXQ0NAKA02hoaGcHSwUlz7t3r0by5Ytg6urKwYMGFBh1khmZibu3buHY8eO4ebNmzhw4ABLSatHXV29wjX97t27aNiwoVBbenp6XcaqNd26dcONGzdgYWGBESNGwN3dHbdv38aNGzc4Wxusbdu2QjPyy3Xs2BFeXl4spaq53bt349dff4WrqysaNWpU6TnNmjXjzN8WULaLfEBAgGAGWkhICCwsLCrdfZ2Q+ogGzwghpB5KSkpCs2bNqnz+mzdvBIMA9Vn5TAVLS0s8fvwYJiYmsLW1xW+//YapU6fi8OHDnKoP5uvrK/g+MTERCxcuxLhx49CpUycAQGBgIA4fPow1a9awFVEkqqqqgu8ZhsFff/0FVVVVWFtbAwCCgoKQkZHByeW1zZo1w6VLlyq0b968mYU0NXf48OEqnbdkyZLvnKT2TZw4EZMmTUJ8fDw6d+4MHo+HgIAArFu3DnPmzGE7nkjEpU/+/v64dOkStm/fjv/9739QVFREw4YNIScnh48fPyIlJQVaWlpwdXXFs2fPODMwuGXLFrYjfFc7duwQzG787bffIC0tjYCAAAwZMgSLFy9mOZ1oEhIShB5LSEhAS0sLcnJyLCWqHbGxsd88R0ZGBi4uLnWQpuY6dOiA8PBwtGrVCnZ2dvjf//6Hbt26QU1Nje1ohFQZLdskhJB6qGHDhhgwYAAmTpz4xW3JMzMz4e3tja1bt2Ly5MmYOXNmHacU3ZMnT5CVlYUePXogPT0d48aNg5+fH4yMjHDo0CG0adOG7YjV1rNnT0yYMAGjRo0Saj9x4gT27t0LPz8/doLV0IIFC5Ceng5PT09BoezS0lJMmzYNKioqWL9+PcsJq+dbMxurM2hdH0hISKBJkybQ1tauMPOiHI/H42QtLYZhsGXLFmzcuBFv374FUFZ0et68eXBzc6vyrM/6RBz7lJaWhoCAACQmJiI/Px+ampqwtLSEpaWl0BIzQmrT7du3MWPGDDx48KBCWYTMzEx07twZnp6e6NatG0sJa0deXl6lu5JzYbOoT2loaIDH46FXr16CTQOozhnhGho8I4SQeig9PR2rV6+Gl5cXpKWlYW1tjSZNmgg+1Y+MjERERASsra3x+++/46effmI78g9PQUEBYWFhldYyatu2LecK0ZfT0tJCQEAATE1Nhdqjo6PRuXNnpKWlsZRMNOUbcXwJl5bXAoCjoyN8fX3Rp08f/Prrr+jXrx+ndwMsV1JSguPHj6NPnz5o1KgRsrOzAQDKysosJxOdOPbpR1a+mQ+XfKlOJ4/Hg6ysLGRkZOo4kegGDBgAe3t7eHh4VHp827Zt8PX1xV9//VXHyWpHamoqxo0bh6tXr1Z6nGuvVUBZfTo/Pz/4+/vj7t27kJCQgK2tLezt7TFlyhS24xHyTfRxECGE1EMaGhrYsGED3r59i927d8PExAQfPnwQTON3cnJCUFAQ7t27RwNn9YSuri48PT0rtO/Zswe6urosJKodJSUliIqKqtAeFRUFPp/PQqKaCQkJQXBwsODr4cOH8PT0hImJCc6cOcN2vGq7fPky4uPj0aFDB8ybNw9NmzbFggULEB0dzXa0GpGSksLUqVNRWFgIoGyAieuDTOLYJ3FiZmaGEydOVJjh87nY2FhMnToV69atq6NktUdNTQ3q6uoVvtTU1CAvLw89PT0sXbqUE9f2sLCwr9ap7N27N4KCguowUe2aNWsWMjIy8ODBA8jLy+Pq1as4fPgwmjdvjgsXLrAdTyStW7eGm5sbzp49iytXruCnn37CuXPnMH36dLajEVIlVPOMEELqMTk5OQwZMoSTtaW+Ji0tDUuWLIGvry/ev39f4Uadi8WYN2/ejKFDh+LatWvo2LEjAODBgweIi4vD2bNnWU4nOldXV/z666948eKFUL/Wrl0LV1dXltNVX2VLgstndq5fv56Tf2uNGzfGb7/9ht9++w137tzBwYMHYWNjAwsLC9y8eRPy8vJsRxRJhw4dEBISIlabp4hjn8TFzp07sWDBAkyfPh29e/eudMZ3QEAAIiMjMWPGDEybNo3tyNV26NAhLFq0COPGjUP79u3BMAweP36Mw4cP4/fff0dqaio2bNgAWVlZ/O9//2M77le9e/fuqzugSklJITU1tQ4T1a7bt2/j77//ho2NDSQkJKCnpwcHBweoqKhgzZo16NevH9sRqyUkJAR+fn7w8/PD3bt3kZ2djTZt2sDd3R329vZsxyOkSmjwjBBCSJ0bM2YM4uLiMH78eDRs2JBzS18q4+joiNjYWOzevRtRUVFgGAYDBw7ElClTOD3zbMOGDWjUqBE2b96M5ORkAGWDNfPnz+dUgfNvMTExwePHj9mOUWM2NjZITExEZGQkQkJCUFxczNnBs2nTpmHOnDl4/fo12rVrB0VFRaHjXKv5A4hnn8RFjx498PjxY9y/fx+nT5/GiRMnKtRxc3Z2xpgxYzhb5Pzw4cPYuHEjRowYIWgbMGAALCwssGfPHty6dQvNmjXDqlWr6v3gmY6ODp4+fQpjY+NKj4eHh6Nx48Z1nKr25ObmCjba0NDQQGpqKkxMTGBhYcHJGpY2NjawtLSEra0tJk6ciO7du1eoVUdIfUc1zwghhNQ5ZWVlBAQEcHJjgB9Zeb0cLt/wfl7zh2EYJCcnY9myZXj+/DlCQ0PZCVZDgYGB8PLygre3N0xMTODq6orRo0dz9k0+gEqLzfN4PEGtKS7W/BHHPhHu+FJtztjYWLRp0wZ5eXlISEhAy5Yt632dzpkzZ8LPzw+PHz+usLNmfn4+2rdvD3t7e2zbto2lhDVjY2ODlStXok+fPhg0aJBgxtm2bdvg4+ODuLg4tiNWS1ZWFqfvHQgBaOYZIYQQFrRo0QL5+flsxyDVJA43vmpqahVmOjIMA11dXZw6dYqlVKL7888/cfDgQaSlpcHJyQkBAQGwsLBgO1atSEhIYDtCrRPHPhHuaNq0KQ4cOIC1a9cKtR84cEAwQzotLQ3q6upsxKuW33//HefOnYOJiQlmzJgBU1NT8Hg8REVFYefOnSgtLcWiRYvYjimyWbNmCWZ7L126FH369MGxY8cgIyODw4cPs5yu+sTh/oEQmnlGCCGkzj1+/BgLFy7EkiVL0KpVqwp1S+gmi3wv/v7+Qo8lJCSgpaUFY2NjSElx7zNFCQkJNGvWDP379//qTnmbNm2qw1TkR/P69WtcuHABSUlJFQru03Ov/rhw4QKGDx+OFi1awMbGBjweD48fP8bz58/h4+OD/v37Y/fu3YiNjeXE7+3ly5eYOnUqrl27hvK3tDweD3369MGuXbugr6/PbsBawjAM8vPz8fz5czRr1gyamppsRyLkh0SDZ4QQQupcbGwsRo0ahZCQEKF2WrpEvgcrKyvcunUL6urqWL58OebOnQsFBQW2Y9UKOzu7b9YM5PF4uH37dh0lql1Hjx6Fp6cnEhISEBgYCD09PWzZsgUGBgYYOHAg2/FEIm59unXrFgYMGAADAwNER0ejVatWSExMBMMwsLKy4uxzT1y9fPkSnp6eiI6OBsMwaNGiBSZPnszpgaaPHz/ixYsXYBgGzZs358TMuao4cOAANm/eLNhpvXnz5pg1axYmTJjAcjJCfkw0eEYIIRwQFxeHLVu2ICoqCjweD2ZmZnB3d4eRkRHb0UTSvn17SElJwd3dvdINA2xtbVlKRsSRvLw8YmNj0bRpU0hKSiIlJQVaWlpsxyLfsHv3bixZsgSzZs3CqlWr8OzZMxgaGuLQoUM4fPgwfH192Y5YbeLYp/bt26Nv375Yvnw5lJWVERYWBm1tbTg5OaFv376YOnUq2xEJgOLiYvTu3Rt79uyBiYkJ23HINyxevBibN2/GzJkz0alTJwBltS137NgBd3d3rFy5kuWEhPx4aPCMEELquWvXrmHAgAFo27YtunTpAoZhcP/+fYSFheHixYtwcHBgO2K1KSgoICQkBKampmxHIT+ATp06QUlJCV27dsUff/yBuXPnQklJqdJzlyxZUsfpyJeYm5tj9erVGDRokGBQxtDQEM+ePYOdnR0+fPjAdsRqE8c+KSsrIzQ0FEZGRlBXV0dAQABatmyJsLAwDBw4EImJiWxHJP/S0tLC/fv3K2wYQOofTU1NbN++HaNGjRJqP3nyJGbOnMm5a4Wfnx/s7OzYjkFIjXCvuAchhPxgFi5cCA8PjwoFfhcuXIgFCxZwcvDM2toar169ErvBMx8fH3h7e1da94eLW8uX8/f3x4YNG4RmPs6bNw/dunVjO1qVHDp0CEuXLsWlS5fA4/Fw5cqVSuub8Xg8Tg2erV27FjNnzoSiouI3z3348CE+fPiAfv361UGy2pGQkABLS8sK7bKyssjNzWUhUc2JY58UFRVRWFgIAGjSpAni4uLQsmVLAODcG/zK5Ofno7i4WKiNq3U5nZ2dK90wgNQ/paWlsLa2rtDerl07lJSUsJCoZvr27QsdHR24urrCxcVFsEEFIVxCg2eEEFLPRUVFwdvbu0L7r7/+ii1bttR9oFowc+ZMuLu7Y968ebCwsKiwYUDr1q1ZSia6bdu2YdGiRXBxccHff/8NV1dXxMXF4fHjx5g+fTrb8UR27NgxuLq6YsiQIXBzcxPMfOzZsycOHTqE0aNHsx3xm0xNTQU7aUpISODWrVvQ1tZmOVXNRUZGQk9PD8OHD8eAAQNgbW0tWI5aUlKCyMhIBAQE4NixY0hOTsaRI0dYTlw9BgYGCA0NhZ6enlD7lStXYG5uzlKqmhHHPnXs2BH37t2Dubk5+vXrhzlz5uDp06c4d+4cOnbsyHY8keTl5WH+/Pnw9vZGWlpaheNcrctZVFSE/fv348aNG7C2tq4w8M6FTQJ+FGPGjMHu3bsr/E727t0LJycnllKJ7u3btzh27BgOHTqEZcuWoWfPnhg/fjwGDRr01c1uCKlPaNkmIYTUc7q6uti0aROGDx8u1O7t7Y25c+ciKSmJpWSik5CQqNDG4/E4vWFAixYtsHTpUowaNUpoOdaSJUuQnp6OHTt2sB1RJGZmZpg0aRI8PDyE2jdt2oR9+/YhKiqKpWQEAMLDw7Fz506cOXMGmZmZkJSUhKysLPLy8gAAlpaWmDRpElxcXCArK8ty2uo5ePAgFi9ejI0bN2L8+PHYv38/4uLisGbNGuzfvx+//PIL2xGrTRz7FB8fj5ycHLRu3Rp5eXmYO3cuAgICYGxsjM2bN1cYKOSC6dOnw9fXF8uXL4ezszN27tyJN2/eYM+ePVi7di0nBy8AwN7e/ovHuLyxiDiaOXMmjhw5Al1dXcEg9IMHD/Dq1Ss4OzsLfejItUHP0NBQeHl54eTJk+Dz+XBycsL48ePRpk0btqMR8lU0eEYIIfXc8uXLsXnzZixcuBCdO3cGj8dDQEAA1q1bhzlz5uD3339nO2K1vXz58qvHufhmS0FBAVFRUdDT04O2tjZu3LiBNm3aIDY2Fh07dqx09gIXyMrKIiIiAsbGxkLtL168QKtWrVBQUMBSMvIphmEQHh6OxMRE5OfnQ1NTE23btoWmpibb0Wpk3759WLlyJV69egUA0NHRwbJlyzB+/HiWk4lOHPskbpo1a4YjR47Azs4OKioqCA4OhrGxMY4ePYqTJ0/i8uXLbEckYu5rA52f4uqg59u3b7F3716sXbsWUlJSKCgoQKdOneDp6SlY9k1IfUODZ4QQUs8xDIMtW7Zg48aNePv2LYCyujLz5s2Dm5tbhZ0qCTsMDQ3h4+MDKysr2NjYYMKECZg8eTKuX7+OX375Benp6WxHFImxsTHmzZuHyZMnC7Xv2bMHGzZsQGxsLEvJyI/kw4cP4PP5YrHctpw49iknJwd8Pl+ojYv1wZSUlBAREQE9PT00bdoU586dQ/v27ZGQkAALCwvk5OSwHbFGXrx4gbi4OHTv3h3y8vKCWd+EfE/FxcX4+++/4eXlJVg6PH78eIwaNQrp6elYsGABQkNDERkZyXZUQipFNc8IIaQeKykpwfHjxzFq1Ch4eHggOzsbQNnuZqR+6dGjBy5evAgrKyuMHz8eHh4e8PHxwZMnTzBkyBC244lszpw5cHNzQ2hoqNDMx0OHDmHr1q1sxyM/CK7PoKuMuPQpISEBM2bMgJ+fn9BMVC4vwzc0NERiYiL09PRgbm4Ob29vtG/fHhcvXoSamhrb8USWlpaGESNGwNfXFzweD7GxsTA0NMSECROgpqaGjRs3sh2RiKmZM2fi5MmTAMrquf35559o1aqV4LiioiLWrl0LfX19lhIS8m0084wQQuq5T5cDkvqLz+eDz+cLdnH09vYW1P2ZMmUKpwvi/vXXX9i4caOgvln5bpsDBw5kORkRd+K4g6249alz584AAHd3dzRs2LDCDCZbW1s2YtXI5s2bISkpCTc3N/j6+qJfv34oLS1FSUkJNm3aBHd3d7YjisTZ2Rnv37/H/v37YWZmJqjNef36dXh4eCAiIoLtiERM9ezZExMmTMDQoUO/eD9UUlKCe/fucfKaQX4MNHhGCCH1nL29Pdzd3TFo0CC2oxBCSJ35dAfbffv2VdjBdtWqVWxHrDZx7JOSkhKCgoJgamrKdpTvJikpCU+ePIGRkRGni5o3atQI165dQ5s2bYQ2thGX5aik/rpz5w46d+4s+ICxXElJCe7fv4/u3buzlIyQqqNlm4QQUs9NmzYNc+bMwevXr9GuXbsKW8u3bt2apWTkc3fv3sWePXsQFxcHHx8f6Ojo4OjRozAwMEDXrl3ZjicSQ0NDPH78GA0aNBBqz8jIgJWVFeLj41lKVnXq6upVrufD1dp04mjXrl3Yu3cvRo0ahcOHD2P+/PlCO9hykTj2ycbGBq9evRLrwbNmzZqhWbNmbMeosdzcXCgoKFRo//DhA+d24yXcYm9vj+Tk5Ao1HjMzM2Fvb8/J5d3kx0ODZ4QQUs+NHDkSAODm5iZo4/F4nK4nUy4oKAhRUVHg8XgwMzODlZUV25FEdvbsWYwdOxZOTk4ICQlBYWEhACA7OxurV6/m7O5siYmJlT7HCgsL8ebNGxYSVd+WLVvYjlBnxKkQeFJSkmBJoLy8vKDm49ixY9GxY0fs2LGDzXgiEcc+7d+/H1OmTMGbN2/QqlUrSEtLCx3nygc827Ztq/K5n74ec0n37t1x5MgRrFixAkDZvQSfz8f69eurvLsjIaL40mtRWlpahQ+FCamvaPCMEELquYSEBLYj1Lr379/jl19+gZ+fH9TU1MAwjODTx1OnTkFLS4vtiNW2cuVKeHp6wtnZGadOnRK0d+7cGcuXL2cxmWguXLgg+P7atWtQVVUVPC4tLcWtW7c4U9jXxcWF7QjfXVpaGkaOHInbt2+LTSHwRo0aIS0tDXp6etDT08ODBw/Qpk0bJCQkgKtVR8SxT6mpqYiLi4Orq6ugjYsf8GzevFnocWpqKvLy8gQbBGRkZEBBQQHa2tqcHTxbv3497Ozs8OTJExQVFWH+/PmIiIhAeno67t27x3Y8IobKN0zi8XgYN26c0AzH0tJShIeHCz5QIKS+o8EzQgip58Rxo4CZM2ciKysLERERMDMzAwBERkbCxcUFbm5ugh2ZuCQ6OrrSmh0qKirIyMio+0A1VF5jj8fjVRh8kpaWhr6+PicHZAAgLi4OBw8eRFxcHLZu3QptbW1cvXoVurq6aNmyJdvxROLh4QEpKSkkJSUJ/qaAspmrHh4enPxdieMOtuLYp19//RWWlpY4efJkpRsGcMWnH1SdOHECu3btwoEDBwTLUaOjozFx4kRMnjyZrYg1Zm5ujvDwcOzevRuSkpLIzc3FkCFDMH36dDRu3JjteEQMlX/wxjAMlJWVIS8vLzgmIyODjh07YuLEiWzFI6RaaMMAQgjhgKNHj8LT0xMJCQkIDAyEnp4etmzZAgMDA07ueKiqqoqbN2/CxsZGqP3Ro0fo3bs3JwebjIyMsGfPHvTq1UuoEPORI0ewdu1aREZGsh1RJAYGBnj8+DE0NTXZjlIr/P398dNPP6FLly64c+cOoqKiYGhoiD///BOPHj2Cj48P2xFFIo6FwMVxB1tx7JOioiLCwsJgbGzMdpRaY2RkBB8fH1haWgq1BwUFYdiwYWI5I5yQ7+mPP/7A3LlzaYkm4TQJtgMQQgj5ut27d2P27NlwdHRERkaGYAmMmpoaZ2s58fn8CnVxgLIZTXw+n4VENTd58mS4u7vj4cOH4PF4ePv2LY4fP465c+di2rRpbMcTWUJCgtgMnAHAwoULsXLlSty4cUNooMLe3h6BgYEsJqsZcSwELiEhIbQz24gRI7Bt2za4ublxcpAJEM8+9ejRA2FhYWzHqFXJyckoLi6u0F5aWop3796xkKh2GBgYYPHixYiOjmY7CvnBLF26FIqKinj//j3u3r2LgIAAvH//nu1YhFQLDZ4RQkg9t337duzbtw+LFi2CpKSkoN3a2hpPnz5lMZnoevToAXd3d7x9+1bQ9ubNG3h4eKBnz54sJhPd/PnzMWjQINjb2yMnJwfdu3fHhAkTMHnyZMyYMYPteCJzc3OrtJD2jh07MGvWrLoPVENPnz7F4MGDK7RraWkhLS2NhUS1o7wQeDkqBE7qys8//wwPDw8sW7YMZ8+exYULF4S+uKhnz56YOHEinjx5IqhF9+TJE0yePBm9evViOZ3oZs6ciatXr8LMzAzt2rXDli1bkJyczHYs8gPIysrC2LFjoaOjA1tbW3Tv3h06OjoYM2YMMjMz2Y5HSJXQsk1CCKnn5OXl8fz5c+jp6Qktx4qNjUXr1q2Rn5/PdsRqe/XqFQYOHIhnz55BV1cXPB4PSUlJsLCwwN9//42mTZuyHbFaSktLERAQAAsLC8jJySEyMhJ8Ph/m5uZQUlJiO16N6Ojo4MKFC2jXrp1Qe3BwMAYMGIDXr1+zlEw0TZs2hbe3Nzp37iz09/TXX39h7ty5iIuLYzuiSCIjI2FnZ4d27drh9u3bGDBggFAhcCMjI7YjEjElIfHlz+K5tGHAp1JTU+Hi4oKrV68KZkmXlJSgT58+OHToELS1tVlOWDMxMTE4fvw4Tp06hfj4eNjb22PMmDFwdnZmOxoRUyNGjEBoaCi2b9+OTp06gcfj4f79+3B3d0fr1q3h7e3NdkRCvokGzwghpJ4zNzfHmjVrMHDgQKE3+9u2bcPhw4cRFBTEdkSR3bhxA8+fPwfDMDA3N+f0J/pycnKIioqCgYEB21FqlZycHJ49e1ahntGLFy/QqlUrFBQUsJRMNPPnz0dgYCDOnDkDExMTBAcH4927d3B2doazszOWLl3KdkSRpaSkYPfu3QgKCgKfz4eVlRUVAiekBmJiYgSvUWZmZjAxMWE7Uq178OABpk6divDwcE4OdBJuUFRUxLVr19C1a1eh9rt376Jv377Izc1lKRkhVUe7bRJCSD03b948TJ8+HQUFBWAYBo8ePcLJkyexZs0a7N+/n+14NeLg4AAHBwe2Y9QKCwsLxMfHi93gmbGxMa5evVph6emVK1dgaGjIUirRrVq1CuPGjYOOjo5g0La0tBSjR4/G77//zna8GmnUqBH++OMPtmMQIjZMTEzEcsAMKNug58SJEzh9+jQyMzMxbNgwtiMRMdagQQPBzpufUlVVhbq6OguJCKk+mnlGCCEcsG/fPqxcuRKvXr0CULaUbtmyZRg/fjzLyapu27ZtmDRpEuTk5CqtofUpNze3OkpVe65fv44FCxZgxYoVaNeuXYUdpVRUVFhKVjNeXl6YMWMG5s2bhx49egAAbt26hY0bN2LLli2c3WI+Li4OISEh4PP5sLS0RPPmzdmOVCN37tz56vHu3bvXURLyIxDH6/ns2bOxYsUKKCoqYvbs2V89d9OmTXWUqnaVL9c8ceIEEhMTYW9vDycnJwwZMgTKyspsxyNibO/evThz5gyOHDkimA2dkpICFxcXDBkyBJMnT2Y5ISHfRoNnhBDCIR8+fACfz+dkvRUDAwM8efIEDRo0+OrsLB6Ph/j4+DpMVjs+rfvD4/EE3zMMw9m6P+V2796NVatWCTZ40NfXx7Jly6g+Tj1SWd2pT5+HXH3++fj4wNvbG0lJSSgqKhI6FhwczFKqmhGHPonj9dze3h5//fUX1NTUvrrJBo/Hw+3bt+swWe2RkJCAtbU1Ro8ejV9++QWNGjViOxL5QVhaWuLFixcoLCxEs2bNAABJSUmQlZWt8OEVV66D5MdDyzYJIaSeS0hIQElJCZo3bw5NTU1Be2xsLKSlpaGvr89euGpISEio9Htx4evry3aE72bq1KmYOnUqUlNTIS8vz7lNEL41i+RTXJ1R8vHjR6HHxcXFCAkJweLFi7Fq1SqWUtXMtm3bsGjRIri4uODvv/+Gq6sr4uLi8PjxY0yfPp3teCIRlz6J4/X802u4uF7Pnz9/LrbLUEn9NmjQILYjEFJjNPOMEELqOVtbW/z6669wcXERaj927Bj2798PPz8/doKRKgsNDUXbtm3ZjiGykpIS+Pn5IS4uDqNHj4aysjLevn0LFRUVTgykfT6LJCgoCKWlpTA1NQVQtpRJUlJSsFOlOLlz5w48PDw4ubFIixYtsHTpUowaNUpos5QlS5YgPT0dO3bsYDtitYljn/Lz8yEvL1/pseTkZNqwoh4qKirC+/fvwefzhdrLZwQRQgipiAbPCCGknlNRUUFwcHClux1aW1sjIyODnWA1MGzYMFhbW2PhwoVC7evXr8ejR49w5swZlpLVnszMTBw/fhz79+9HWFgYZ5fNvXz5En379kVSUhIKCwsRExMDQ0NDzJo1CwUFBfD09GQ7YrVs2rQJfn5+OHz4sKBI8cePH+Hq6opu3bphzpw5LCesXVFRUbCxsUFOTg7bUapNQUEBUVFR0NPTg7a2Nm7cuIE2bdogNjYWHTt2RFpaGtsRq00c+9SiRQucOHECVlZWQu0+Pj6CGatcMGTIkCqfe+7cue+Y5PuJjY3Fr7/+ivv37wu1i0N5AcIdOTk5FQZuuVoXlvxYKhbIIIQQUq/weDxkZ2dXaM/MzOTsja6/vz/69etXob1v377fLHxe392+fRtjxoxB48aNsX37djg6OuLJkydsxxKZu7s7rK2t8fHjR6HZJYMHD8atW7dYTCaajRs3Ys2aNUK7e6mrq2PlypXYuHEji8lqJjw8XOgrLCwMV69exdSpU9GmTRu244mkUaNGgsEkPT09PHjwAEDZMkGufvYrjn1ycHBA586dsXbtWjAMg5ycHIwbNw4uLi5YsmQJ2/GqTFVVVfCloqKCW7duCV27g4KCcOvWrUp3DOSKcePGQUJCApcuXUJQUBCCg4MRHByMkJAQqjNFvquEhAT069cPioqKgh021dXVoaamRrttEs6gmmeEEFLPdevWDWvWrMHJkychKSkJoKz495o1a9C1a1eW04kmJycHMjIyFdqlpaWRlZXFQqKqS01NhZaWllDb69evcejQIXh5eSE3NxcjRoxAcXExzp49C3Nzc5aS1o6AgADcu3evwu9LT08Pb968YSmV6LKysvDu3Tu0bNnVrS96AABheElEQVRSqP39+/eVDlJzRdu2bcHj8SoMwHTs2BFeXl4spaqZHj164OLFi7CyssL48ePh4eEBHx8fPHnypFqzhOoTcezT9u3b0a9fP7i6uuKff/4RLOl+/Pgxp65/Bw8eFHy/YMECjBgxAp6enkKvu9OmTeP0DJnQ0FAEBQWhRYsWbEchPxgnJycAZTt4N2zYUGhDG0K4ggbPCCGknvvzzz/RvXt3mJqaolu3bgCAu3fvIisri7P1mVq1aoXTp09XmJVw6tSpev9ma+/evXj9+jW2bdsGaWlpODo6IiAgAP3798f27dvRt29fSEpKcm4545fw+fxKZzi+fv0aysrKLCSqmcGDB8PV1RUbN25Ex44dAQAPHjzAvHnzODt4AVQs2i4hIQEtLS3IycmxlKjm9u7dK1jaM2XKFGhoaCAgIAA///wzpkyZwnI60YhjnwCgd+/eGDJkCHbv3g0pKSlcvHix3l/Lv8bLywsBAQGCgTMAkJSUxOzZs9G5c2esX7+exXSiMzc3x4cPH9iOQX5A4eHhCAoKEtQaJYSLqOYZIYRwwNu3b7Fjxw6EhYVBXl4erVu3xowZM6ChocF2NJFcuHABQ4cOxejRo9GjRw8AwK1bt3Dy5EmcOXOmXu/KVFhYiMWLF0NTUxPz58+HlJQU3NzcMHXqVKHt1qWlpREWFsbpN5AAMHLkSKiqqmLv3r1QVlZGeHg4tLS0MHDgQDRr1kxotgYX5OXlYe7cufDy8kJxcTEAQEpKCuPHj8f69euhqKjIckJCuKV8I5GUlBTs378f/v7+2LBhA9zc3LBq1SpIS0uzHbHa1NXVcfDgwQqvRefPn4erq2uF3W254vbt2/j999+xevVqWFhYVPjdcHlWHanf7O3tsWjRIvTq1YvtKISIjAbPCCGEsOKff/7B6tWrERoaKhgQXLp0KWxtbdmOViVFRUWQkZFBYGAgvLy84O3tjRYtWmDs2LEYOXIkmjRpIhaDZ2/fvoW9vT0kJSURGxsLa2trxMbGQlNTE3fu3IG2tjbbEUWSm5uLuLg4MAwDY2Njzg+abdu2rdJ2Ho8HOTk5GBsbo3v37kIzaeqj8PDwKp/bunXr75ik9ohjnz6lrKyMfv36wdPTE2pqagCA+/fvw9nZGcrKyggJCWE3oAhmz56NQ4cO4X//+5/QDNW1a9fC2dkZmzZtYjmhaCQk/it3/emyOdowgHxvcXFxmDJlCsaMGYNWrVpVGLjl4rWP/Hho8IwQQuq5bxXQ7969ex0lIV+Tl5eHU6dOwcvLC48ePUJpaSk2bdqEX3/9lZPLGz+Vn5+PkydPIjg4GHw+H1ZWVnBychLaQICwy8DAAKmpqcjLy4O6ujoYhkFGRgYUFBSgpKSE9+/fw9DQEL6+vtDV1WU77hdJSEhUWrvtc1x6oy+OffrU0aNHMXbs2Art2dnZmDVrFg4cOMBCqprh8/nYsGEDtm7diuTkZABA48aN4e7ujjlz5tT7Qegv8ff3/+pxrnx4RbjnwYMHGD16NBITEwVt5ddFrl77yI+HBs8IIaSe+/ST4nKffmJMNxz1T3R0NA4cOICjR48iIyMDDg4OuHDhAtuxiBg7efIk9u7di/3798PIyAgA8OLFC0yePBmTJk1Cly5d8Msvv6BRo0bw8fFhOe2XvXz5ssrn6unpfccktUcc+yTOSkpKcPz4cfTp0weNGjUSbGIjLksa7969iz179iAuLg4+Pj7Q0dHB0aNHYWBgwNlNiEj9Z25uDjMzM8yfP7/SDQPo2ke4gAbPCCGknsvMzBR6XFxcjJCQECxevBirVq1Cz549WUpWPRoaGoiJiYGmpibU1dW/utNSenp6HSb7fkpLS3Hx4kV4eXlxevAsOjoa27dvR1RUFHg8Hlq0aIEZM2bQjm31iJGREc6ePYu2bdsKtYeEhGDo0KGIj4/H/fv3MXToUMFMGkJqU2RkJJKSklBUVCRo4/F4+Pnnn1lMJRoFBQVERUWJ3Rv6s2fPYuzYsXBycsLRo0cRGRkJQ0ND7Nq1C5cuXcLly5fZjkjElKKiIsLCwmBsbMx2FEJERrttEkJIPaeqqlqhzcHBAbKysvDw8EBQUBALqapv8+bNguWLmzdv/iG2KZeUlMSgQYPq9QYI3+Lj44NRo0bB2toanTp1AlC2/MLCwgInTpzA8OHDWU5IACA5ORklJSUV2ktKSpCSkgIAaNKkCbKzs+s6GhEzmZmZQq9L8fHxGDJkCMLDw4WWp5Zf47k4O7pDhw4ICQkRu8GzlStXwtPTE87Ozjh16pSgvXPnzli+fDmLyYi469GjBw2eEc6jwTNCCOEoLS0tREdHsx2jylxcXATfjxs3jr0gpFrmz5+P3377rcIbq6VLl2LBggU0eFZP2NvbY/Lkydi/fz8sLS0BlM06mzp1qmBH26dPn8LAwIDNmEQMbNu2DXJycpg3bx4AwN3dHbq6urh27Rratm2Lhw8fIiEhAXPnzuVsYf1p06Zhzpw5eP36Ndq1a1dhQxGuFjePjo6utE6qiooKMjIy6j4Q+WH8/PPP8PDwwNOnTyvd6XXAgAEsJSOk6mjZJiGE1HOf79TGMAySk5Oxdu1aFBcX4969eywlE529vT3GjBmDYcOGVTqzjtQfCgoKCA8Pr/BpcWxsLNq0aYO8vDyWklVddZbMcvUGPiUlBWPHjsWtW7cEb0pKSkrQs2dPHD16FA0bNoSvry+Ki4vRu3dvltMSLnv//j3Gjh0LY2Nj7Ny5E5qamrh16xbatGkDHR0dPHr0CDo6Orh16xbmzp3Lyd02v1RrlOvFzY2MjLBnzx706tULysrKCAsLg6GhIY4cOYK1a9ciMjKS7YhETFX2N1WOy39T5MdCM88IIaSea9u2baU7tXXs2BFeXl4spaoZCwsL/P7775gxYwYcHR0xduxYODo6QkZGhu1o5DN2dna4e/duhcGzgIAAdOvWjaVU1fP5stnP/57EYQOORo0a4caNG3j+/DliYmLAMAxatGgBU1NTwTn29vYsJiTiQltbG9euXcOaNWsAlP3NlC/J19TUxNu3b6GjowN9fX1OzY7+VEJCAtsRvovJkyfD3d0dXl5e4PF4ePv2LQIDAzF37lwsWbKE7XhEjPH5fLYjEFJjNPOMEELquc93apOQkICWlhbk5ORYSlQ7+Hw+bt68iRMnTuCvv/6CpKQkhg0bBicnJ9ja2rIdj/zL09MTS5YswYgRI9CxY0cAZTXPzpw5gz/++ANNmjQRnMuFWVs3b97EggULsHr1anTq1Ak8Hg/379/H77//jtWrV8PBwYHtiDVSVFSEhIQEGBkZQUqK+5+RZmRkwMfHB3FxcZg3bx40NDQQHByMhg0bQkdHh+14IhG3PnXr1g2zZ8/G4MGDMWnSJKSmpmLevHnw9PREcHAwnj17xnZE8olFixZh8+bNKCgoAADIyspi7ty5WLFiBcvJCCGkfqPBM0IIqcfKl1jt2bMHJiYmbMf5bgoKCnDx4kWsWrUKT58+5ezsH3H0taUWn+LKsotWrVrB09MTXbt2FWq/e/cuJk2ahKioKJaS1UxeXh5mzpyJw4cPAwBiYmJgaGgINzc3NGnSBAsXLmQ5YfWFh4ejV69eUFVVRWJiIqKjo2FoaIjFixfj5cuXOHLkCNsRq00c+3Tt2jVkZ2dj2LBhePXqFfr164dnz55BQ0MD3t7egpp7XHP06FF4enoiISEBgYGB0NPTw5YtW2BgYICBAweyHa9G8vLyEBkZCT6fD3NzcygpKbEdiYgpR0dHnDx5UlCiY9WqVZg+fTrU1NQAAGlpaejWrRstGSacULU7YkIIIayQlpbGs2fPxHpnypSUFHh6emLdunUIDw+HtbU125HIJ/h8fpW+uDBwBgBxcXGV1tkrH8zgqt9++w1hYWHw8/MTmpXaq1cvnD59msVkops9ezbGjRuH2NhYoT799NNPuHPnDovJRCeOferTpw+GDRsGANDV1UV4eDg+fPiA9+/fc2bg7Nq1a8jMzBQ83r17N2bPng1HR0dkZGQIrm9qamrYsmULSylrj4KCAqytrdG+fXsaOCPf1bVr11BYWCh4vG7dOqSnpwsel5SUcHZ5N/nx0OAZIYTUc87Ozjhw4ADbMWpVVlYWDh48CAcHB+jq6mL37t34+eefERMTg4cPH7Idj3wDl3dls7GxwaxZs5CcnCxoS0lJwZw5c9C+fXsWk9XM+fPnsWPHDnTt2lVosN3c3BxxcXEsJhPd48ePMXny5ArtOjo6SElJYSFRzYljnyqjoaFR5Vmr9UFKSgq6dOmC169fAwC2b9+Offv2YdGiRZCUlBScZ21tjadPn7IVkxDO+XyRGy16I1zG/WIYhBAi5oqKirB//37cuHED1tbWUFRUFDq+adMmlpKJrmHDhlBXV8eIESOwevVq2NjYsB2JfMG6deugr6+PkSNHAgCGDx+Os2fPonHjxrh8+TLatGnDcsLq8fLywuDBg6Gnp4dmzZoBAJKSkmBiYoLz58+zG64GUlNToa2tXaE9NzeXszNX5eTkkJWVVaE9OjoaWlpaLCSqOXHsU0FBAbZv3w5fX1+8f/++QmHw4OBglpJVnYuLC5SVldG3b188e/YMCQkJsLS0rHCerKwscnNzWUhICCGEbTR4Rggh9dyzZ89gZWUFoKyO0ae4+KaYYRhs3boVY8aMgYKCAttxyDfs2bMHx44dAwDcuHEDN2/exNWrV+Ht7Y158+bh+vXrLCesHmNjY4SHhwt2pmQYBubm5ujVqxcn/57K2djY4J9//sHMmTMB/Hdt2LdvHzp16sRmNJENHDgQy5cvh7e3N4CyPiUlJWHhwoUYOnQoy+lEI459+vXXX3Hjxg0MGzYM7du35+zf0ZAhQwQDZgYGBggNDYWenp7QOVeuXIG5uTkb8QjhJB6PV+GawNVrBCG0YQAhhJA6xefzIScnh4iICDRv3pztOOQb5OXlERMTA11dXbi7u6OgoAB79uxBTEwMOnTogI8fP7IdkQC4f/8++vbtCycnJxw6dAiTJ09GREQEAgMD4e/vj3bt2rEdsdqysrLg6OiIiIgIZGdno0mTJkhJSUGnTp1w+fLlCrNwuUAc+6SqqorLly+jS5cubEepNQcPHsTixYuxceNGjB8/Hvv370dcXBzWrFmD/fv345dffmE7IiGcICEhgZ9++gmysrIAgIsXL6JHjx6Ca11hYSGuXr3Kmbqp5MdGM88IIaSey8zMRGlpKTQ0NITa09PTISUlBRUVFZaSiUZCQgLNmzdHWloaDZ5xgLq6Ol69egVdXV1cvXoVK1euBFA2g5CLN7vLly//6vElS5bUUZLa1blzZ9y7dw8bNmyAkZERrl+/DisrKwQGBsLCwoLteCJRUVFBQEAAbt++jeDgYPD5fFhZWaFXr15sRxOZOPZJR0cHysrKbMeoVa6urigpKcH8+fORl5eH0aNHQ0dHB1u3bqWBM0KqwcXFRejxmDFjKpzj7OxcV3EIqRGaeUYIIfXcTz/9hJ9//hnTpk0Tavf09MSFCxdw+fJllpKJ7p9//sHatWuxe/dutGrViu045CtmzJiBS5cuoXnz5ggJCUFiYiKUlJRw+vRprFu3jhP1jD71eR2j4uJiJCQkQEpKCkZGRpzrDyFsu3LlCrZt2wZPT88KyxzFwYcPH8Dn8yutKUgIIeTHQYNnhBBSz2loaODevXswMzMTan/+/Dm6dOmCtLQ0lpKJTl1dHXl5eSgpKYGMjAzk5eWFjn+6jTlhV3FxMbZu3YpXr15h3LhxgsGnLVu2QElJCRMmTGA5Yc1lZWVh3LhxGDx4MMaOHct2HJGVlpbir7/+QlRUFHg8HszMzDBw4EBISXFnocG2bduqfK6bm9t3TFJ7xLFPn0pNTcWIESNw584dKCgoQFpaWug4l6/n79+/R3R0NHg8HkxNTTm7qQMhhJCao8EzQgip5xQVFfHgwYMKS6+ePn2KDh06IC8vj6Vkojt8+PBXj38+zZ+Q7+3Zs2fo378/EhMT2Y4ikmfPnmHgwIFISUmBqakpgLINRrS0tHDhwgXOLN00MDAQepyamoq8vDyoqakBADIyMqCgoABtbW3Ex8ezkLD6xLFPn+rVqxeSkpIwfvx4NGzYsEIxcC5ez7OysjB9+nScPHlSsHuopKQkRo4ciZ07d0JVVZXlhIQQQuoaDZ4RQkg9Z2dnBwsLC2zfvl2offr06QgPD8fdu3dZSkZ+JJGRkUhKSkJRUZFQ+4ABA1hKVLsCAgLw888/c3YDhI4dO0JbWxuHDx+Guro6AODjx48YN24c3r9/j8DAQJYTVt+JEyewa9cuHDhwQDAgGB0djYkTJ2Ly5MlwcnJiOWH1iWOfFBQUEBgYiDZt2rAdpdaMGDECoaGh2L59Ozp16gQej4f79+/D3d0drVu3FuyWSggh5MdBg2eEEFLP3bt3D7169YKNjQ169uwJALh16xYeP36M69evo1u3biwnFE1cXBwOHjyIuLg4bN26Fdra2rh69Sp0dXXRsmVLtuORf8XHx2Pw4MF4+vQpeDweym8bymeXcG3TgM+X0DEMg+TkZBw9ehTdu3fHyZMnWUpWM/Ly8njy5EmFv51nz57BxsYG+fn5LCUTnZGREXx8fCrUqQsKCsKwYcOQkJDAUjLRiWOfrKyssGvXLnTs2JHtKLVGUVER165dQ9euXYXa7969i759+yI3N5elZIQQQtgiwXYAQgghX9elSxcEBgZCV1cX3t7euHjxIoyNjREeHs7ZgTN/f39YWFjg4cOHOHfuHHJycgAA4eHhWLp0KcvpyKfc3d1hYGCAd+/eQUFBAREREbhz5w6sra3h5+fHdrxq27x5s9DXtm3b4OfnBxcXF+zdu5fteCIzNTXFu3fvKrS/f/8exsbGLCSqueTkZBQXF1doLy0trbSvXCCOfVq7di3mzJkDPz8/pKWlISsrS+iLixo0aFDp0kxVVVXBzE5CCCE/Fpp5RgghpM516tQJw4cPx+zZs6GsrIywsDAYGhri8ePHGDRoEN68ecN2RPIvTU1N3L59G61bt4aqqioePXoEU1NT3L59G3PmzEFISAjbEQmAy5cvY/78+Vi2bJlgBtCDBw+wfPlyrF27VmgGjYqKClsxq+Xnn39GUlISDhw4gHbt2oHH4+HJkyeYOHEidHV1ceHCBbYjVps49klCouyz+M9rnTEMAx6Px7nZqQCwd+9enDlzBkeOHEHjxo0BACkpKXBxccGQIUMwefJklhMSQgipazR4RgghpM4pKSnh6dOnMDAwEBo8S0xMRIsWLVBQUMB2RPIvdXV1BAUFwdDQEEZGRti/fz/s7e0RFxcHCwsLzm1Y8euvv2Lr1q1QVlYWas/NzcXMmTPh5eXFUrKaKR/AAP4bxPh8iS3XBjNSU1Ph4uKCq1evCnZwLCkpQZ8+fXDo0CFoa2uznLD6xLFP/v7+Xz1ua2tbR0lqj6WlJV68eIHCwkI0a9YMAJCUlARZWVk0b95c6Nzg4GA2IhJCCKlj3Nm7nBBCiNhQU1NDcnJyhV3oQkJCoKOjw1IqUplWrVohPDwchoaG6NChA/7880/IyMhg7969MDQ0ZDtetR0+fBhr166tMHiWn5+PI0eOcHbwzNfXl+0ItU5LSwuXL19GbGwsoqKiwDAMzMzMYGJiwnY0kYljn7g4OPYtgwYNYjsCIYSQeoZmnhFCCKlz8+fPR2BgIM6cOQMTExMEBwfj3bt3cHZ2hrOzM9U9q0euXbuG3NxcDBkyBPHx8ejfvz+eP3+OBg0a4PTp0+jRowfbEaskKysLDMNAXV0dsbGx0NLSEhwrLS3FxYsXsXDhQrx9+5bFlIQQQgghpD6iwTNCCCF1rri4GOPGjcOpU6fAMAykpKRQWlqK0aNH49ChQ5CUlGQ7IvmK9PR0qKurV6hxVJ9JSEh8NS+Px8Mff/yBRYsW1WGq2nP16lUoKSkJapvt3LkT+/btg7m5OXbu3ElFzgkhhBBCaoAGzwghhLAmPj4ewcHB4PP5sLS0rFBLhpDa4u/vD4Zh0KNHD5w9exYaGhqCYzIyMtDT00OTJk1YTFgzFhYWWLduHRwdHfH06VNYW1tjzpw5uH37NszMzHDw4EG2IxJCCCGEcBYNnhFCSD00ZMiQKp977ty575ikbpSWluLp06fQ09OjGTLku3r58iWaNWvGqVlzVaGkpIRnz55BX18fy5Ytw7Nnz+Dj44Pg4GA4OjoiJSWF7YiEEEIIIZxFGwYQQkg9pKqqynaE72rWrFmwsLDA+PHjUVpaCltbW9y/fx8KCgq4dOkS7Ozs2I5IxEh4eDhatWoFCQkJZGZm4unTp188t3Xr1nWYrPbIyMgIdj69efMmnJ2dAQAaGhrIyspiMxohhBBCCOfRzDNCCCF1rmnTpjh//jysra1x/vx5TJs2DX5+fjhy5Ah8fX1x7949tiMSMSIhIYGUlBRoa2sLap9VdvvD4/FQWlrKQsKaGzBgAIqKitClSxesWLECCQkJ0NHRwfXr1zFjxgzExMSwHVEkd+/exZ49exAXFwcfHx/o6Ojg6NGjMDAwENR346K8vDwkJSWhqKhIqJ0rg7eWlpZVnr0ZHBz8ndN8P0VFRUhISICRkRGkpGjOASGE/Mgk2A5ACCHk20pKSnDz5k3s2bMH2dnZAIC3b98iJyeH5WSi+fDhAxo1agQAuHz5MkaMGAETExOMHz/+q7OCCBFFQkKCYHfNhIQExMfHIyEhocJXfHw8y0lFt2PHDkhJScHHxwe7d++Gjo4OAODKlSvo27cvy+lEc/bsWfTp0wfy8vIICQlBYWEhACA7OxurV69mOZ1oUlNT0b9/fygrK6Nly5awtLQU+uKKQYMGYeDAgRg4cCD69OmDuLg4yMrKws7ODnZ2dpCTk0NcXBz69OnDdlSR5OXlYfz48VBQUEDLli2RlJQEAHBzc8PatWtZTkcIIYQNNPOMEELquZcvX6Jv375ISkpCYWEhYmJiYGhoiFmzZqGgoACenp5sR6w2PT097Nu3Dz179oSBgQF27dqF/v37IyIiAl27dsXHjx/Zjkg+cfToUXh6eiIhIQGBgYHQ09PDli1bYGBggIEDB7Idj4gpS0tLeHh4wNnZGcrKyggLC4OhoSFCQ0PRt29fTtZxc3JyQmJiIrZs2QJ7e3v89ddfePfuHVauXImNGzeiX79+bEestgkTJqBx48ZYsWKFUPvSpUvx6tUreHl5sZRMdO7u7rh37x62bNmCvn37Ijw8HIaGhrhw4QKWLl2KkJAQtiMSQgipYzT/mBBC6jl3d3dYW1sjLCwMDRo0ELQPHjwYEyZMYDGZ6FxdXTFixAg0btwYPB4PDg4OAICHDx+iRYsWLKcjn9q9ezeWLFmCWbNmYdWqVYJljWpqatiyZQvnBs8uXLhQaTuPx4OcnByMjY1hYGBQx6lqR1xcHA4ePIi4uDhs3boV2trauHr1KnR1ddGyZUu241VbdHQ0unfvXqFdRUUFGRkZdR+oFty+fRt///03bGxsICEhAT09PTg4OEBFRQVr1qzh5ODZmTNn8OTJkwrtY8aMgbW1NScHz86fP4/Tp0+jY8eOQstTzc3NERcXx2IyQgghbKHBM0IIqecCAgJw7949yMjICLXr6enhzZs3LKWqmWXLlqFVq1Z49eoVhg8fDllZWQCApKQkFi5cyHI68qnt27dj3759GDRokNByJWtra8ydO5fFZKIZNGhQpTXPytt4PB66du2K8+fPc2rnV39/f/z000/o0qUL7ty5g1WrVkFbWxvh4eHYv38/fHx82I5YbY0bN8aLFy+gr68v1B4QEABDQ0N2QtVQbm4utLW1AZRt5pCamgoTExNYWFhwtjaYvLw8AgIC0Lx5c6H2gIAAyMnJsZSqZlJTUwW/p0/l5uaK3U69hBBCqoZqnhFCSD3H5/MrLWL++vVrKCsrs5CodgwbNgweHh5o2rSpoM3FxYVzM5nEXUJCQqW1mGRlZZGbm8tCopq5ceMGbGxscOPGDWRmZiIzMxM3btxA+/btcenSJdy5cwdpaWmcGxhcuHAhVq5ciRs3bggNtNvb2yMwMJDFZKKbPHky3N3d8fDhQ/B4PLx9+xbHjx/H3LlzMW3aNLbjicTU1BTR0dEAgLZt22LPnj148+YNPD090bhxY5bTiWbWrFmYOnUqZsyYgWPHjuHYsWOYMWMGpk+fDg8PD7bjicTGxgb//POP4HH5gNm+ffvQqVMntmIRQghhEc08I4SQes7BwQFbtmzB3r17AZTdxOfk5GDp0qVwdHRkOR0RdwYGBggNDYWenp5Q+5UrV2Bubs5SKtG5u7tj79696Ny5s6CtZ8+ekJOTw6RJkxAREYEtW7bg119/ZTFl9T19+hQnTpyo0K6lpYW0tDQWEtXc/PnzkZmZCXt7exQUFKB79+6QlZXF3LlzMWPGDLbjiWTWrFlITk4GUFYTrE+fPjh+/DhkZGRw6NAhdsOJaOHChTA0NMTWrVsFz0EzMzMcOnQII0aMYDmdaNasWYO+ffsiMjISJSUl2Lp1KyIiIhAYGAh/f3+24xFCCGEBDZ4RQkg9t3nzZtjb28Pc3BwFBQUYPXo0YmNjoampiZMnT7Idj4i5efPmYfr06SgoKADDMHj06BFOnjyJNWvWYP/+/WzHq7a4uDioqKhUaFdRURHsttm8eXN8+PChrqPViJqaGpKTkyvUawsJCRHsvMlFq1atwqJFixAZGQk+nw9zc3MoKSmxHavKiouLIS0tLXjs5OQk+N7S0hKJiYl4/vw5mjVrBk1NTTYi1ooRI0ZwdqCsMp07d8a9e/ewYcMGGBkZ4fr167CyskJgYCAsLCzYjkcIIYQFtNsmIYRwQH5+Pk6dOoWgoCDw+XxYWVnByckJ8vLybEcjP4B9+/Zh5cqVePXqFQBAR0cHy5Ytw/jx41lOVn1du3aFsrIyjhw5Ai0tLQBl9Y2cnZ2Rm5uLO3fu4ObNm5g2bRpiYmJYTlt18+fPR2BgIM6cOQMTExMEBwfj3bt3cHZ2hrOzM5YuXcp2xGrLzMxEaWkpNDQ0hNrT09MhJSVV6SBofbN27VoYGhqK1cASIYQQ8iOiwTNCCCGEVMmHDx/A5/MrLaTNFdHR0Rg4cCASEhKgq6sLHo+HpKQkGBr+v707j6q62v8//jogChjiXM6C5EChgpqpZU44ZDlfKk1N9NpwFXMqu6Vm5fDNGb1KpjjmgJpmV0scSRJSERxREy3MOQ1UkFQ4vz+68ZPQUlP2OYfnYy3Xgn3OH0/XNb3nzf7s7a0vvvhCVatW1erVq3X58mV1797ddO4du379ul555RUtXbpUVqtVBQoUUGZmprp27ap58+bJ2dnZdOJda9OmjZ5//vlc55uFhYVpzZo1WrdunaGyO3fkyBF17NhRffv21YABAzRo0KA/ff+kSZPyqOz+yczM1OTJkxUREaHk5GRdu3Ytx+sXL140VHZ3Ll26dMfvtYfBLQDg/mJ4BgAwIikpSXPnzlVSUpKmTp2q0qVL6+uvv1aFChX02GOPmc6DA7NarVq/fr2OHDkiq9Wq6tWrKzAwUE5O9n+PUlJSkuLj45WVlSV/f/9cNyDak+LFi+vbb79VjRo1cqwfOnRIjRo1spuz3C5fvqxXXnlFK1euVNOmTW/7PovFos2bN+dh2f0xYsQIzZ49W4MGDdLw4cP17rvv6ocfftDq1as1YsQIhYSEmE68I05OTnd8k+atLvEBADg2hmcAgDwXFRWlNm3aqFGjRvrmm2+UmJgob29vffzxx9qxY4dWrFhhOjFf8/f3v+MPkbt3737ANcivChcurNjY2FxnTO3bt0/169dXenq6oTLcrEqVKgoNDVXbtm3l4eGhhISE7LXY2NhbXmRhi26+COCHH37QsGHD9Morr2TfrhkTE6P58+dr7Nix6tmzp6lMAIAhDM8AAHmuQYMG+sc//qFBgwbJw8NDe/bskbe3t3bu3KkOHTro5MmTphPztVGjRmV/nZGRoRkzZsjX1zf7Q2RsbKwOHDigN954Q2PHjjWVec82bdqkTZs26dy5c8rKysrxWnh4uKGqu/dXjwDezB4fB2zSpIn8/Pw0bdq0HOv/+te/tHfvXm3bts1QGW5WuHBhJSYmqmLFiipTpozWrl2rgIAAHTt2TP7+/kpNTTWdeNeaN2+uPn366KWXXsqxvnjxYs2aNUtbt241EwYAMIbbNgEAeW7fvn233I1QqlQpu3kUy5HdfLh8nz59FBISog8//DDXe36/QMCejBo1Sh988IHq1q2rMmXK3PEOO1sUHx+f4/u4uDhlZmaqWrVqkn47b8vZ2Vl16tQxkfe3jR49Wi1atNCePXvUvHlzSb8NPnfu3KnIyEjDdfcmIyND06ZN05YtW245vLXHnZzly5fX6dOnVbFiRfn4+GTfTLlz504VKlTIdN49iYmJUVhYWK71unXrqk+fPgaKAACmMTwDAOS5okWL6vTp0/Ly8sqxHh8fr3Llyhmqwq0sX75cu3btyrX+8ssvq27duna1U0v67bD5efPm2dVlALezZcuW7K8nTZokDw8PzZ8/X8WKFZMk/fLLL+rVq5eefvppU4l/S6NGjRQTE6Px48crIiJCbm5uqlmzpubMmWO3Z7kFBwdrw4YN6tKli5544gm7Ht7+rmPHjtq0aZPq16+vAQMG6KWXXtKcOXOUnJysgQMHms67JxUqVFBYWJgmTpyYY/2TTz5RhQoVDFUBAEzisU0AsHGOcpPZzd566y3FxMRo+fLlqlq1qnbv3q2zZ8+qR48e6tGjR46dTzDrkUce0dixY9WrV68c63PnztWwYcN09uxZQ2X3pkSJEtqxY4eqVKliOuW+KleunCIjI3NdtrF//361bNlSp06dMlSGm3l6emrdunVq1KiR6ZQHJjY2Vtu3b5ePj4/atWtnOueerFu3Tp07d1aVKlX05JNPSvrt95WUlKSVK1fq2WefNVwIAMhr7DwDABs3atSoP73JzB6NHj1ar7zyisqVKyer1SpfX19lZmaqa9eueu+990zn4SZvvvmmXn/9dcXFxeX4EBkeHm6Xf/769OmjxYsXa/jw4aZT7qtLly7p7NmzuYZn586d0+XLlw1V/X1ZWVk6evToLR9xbNy4saGqe1euXDl5eHiYznignnzyyey/K+zVs88+q++//14zZ85UYmKirFar2rdvr9dee42dZwCQT7HzDABsnKPcZHYrSUlJio+PV1ZWlvz9/e32USxHFxERoalTpyoxMVGSVKNGDQ0YMEBBQUGGy+7egAEDtGDBAtWsWVM1a9aUi4tLjtft8WB9SerRo4eioqI0ceLEHEPOoUOHqnHjxpo/f77hwrsXGxurrl276scff9Qf/++qxWJRZmamobJ799VXXyk0NFRhYWGqVKmS6Zx7tmbNmjt+r73uPgMA4GYMzwDAxjniTWaAKU2bNr3taxaLRZs3b87DmvsnPT1dQ4YMUXh4uK5fvy5JKlCggHr37q3x48ercOHChgvvXu3atVW1alWNGjXqlpc7eHp6Giq7d+fPn1dQUJC++eYbubu75xre2stj+E5OTjm+t1gstxxwSrLLIScAAH/EY5sAYOMc5SazQYMG3fF77XX3D2zfzYfsOxJ3d3fNmDFD48ePV1JSkqxWq3x8fOxyaPa777//XitWrJCPj4/plPvmpZde0smTJzVmzBg9/PDDdnthwM2P0G7cuFFvv/22xowZowYNGshisWj79u167733NGbMGIOVAADcPwzPAMDGOcpNZvHx8Tm+j4uLU2ZmpqpVqyZJOnLkiJydnVWnTh0TeciHfvrpJ1ksFoe64bVw4cKqWbOm6Yz7on79+jp69KhDDc+2b9+umJgY1apVy3TKffPmm28qLCxMTz31VPZaq1at5O7urr59+2Y/7g0AgD1jeAYANm7cuHHZX3fp0kUVKlTQt99+a3c3md2842fSpEny8PDQ/PnzVaxYMUnSL7/8ol69eunpp582lYh8ICsrSx999JEmTpyoK1euSJI8PDw0ePBgvfvuu7keR4M5/fv31+DBg3XmzBn5+fnlesTRHoeE1atX19WrV01n3FdJSUm3fITW09NTP/zwQ94HAQDwAHDmGQAgz5UrV06RkZG5bgbcv3+/WrZsqVOnThkqg6N75513NGfOHI0aNUqNGjWS1WrVt99+q/fff1///Oc/NXr0aNOJ+J9bDTJ/P1vLXi8MiIyM1KhRozR69OhbDgSLFCliqOzeNW7cWC4uLlq0aJHKlCkjSTpz5oy6d++ua9euKSoqynAhAAB/H8MzALBxzs7Oaty4sVauXKnixYtnr589e1Zly5a1yw+QHh4e+uKLL9SsWbMc65s3b1b79u11+fJlQ2VwdGXLllVYWFiuXZtffPGF3njjDZ08edJQGf7oxx9//NPX7fG2yt8Hgn8868yeB4JHjx5Vx44ddfjwYVWsWFGSlJycrKpVq2r16tV289itv7//HZ9Bt3v37gdcAwCwNTy2CQA2zmq16tdff1XdunW1Zs0aPf744zles0cdO3ZUr169NHHiRD355JOSpNjYWA0dOlSdOnUyXIebZWZmat68edq0aZPOnTuX46BwSXZ3O+XFixdVvXr1XOvVq1e3m5sO8wt7HI79FUe8sMLHx0d79+7Vhg0bdOjQIVmtVvn6+qpFixZ2dSFChw4dsr/OyMjQjBkz5OvrqwYNGkj67d+oAwcO6I033jBUCAAwiZ1nAGDjnJ2d9dNPP2ncuHGaO3euFi5cqPbt29v1zrP09HQNGTJE4eHhun79uiSpQIEC6t27t8aPH2/XNwQ6mn79+mnevHlq27atypQpk+vD8OTJkw2V3Zv69eurfv36Cg0NzbHev39/7dy5U7GxsYbK7t6aNWvu+L32dD7iHx08eFDJycm6du1ajnV7/j3BtvXp00dlypTRhx9+mGN95MiROnHihMLDww2VAQBMYXgGADbOyclJZ86cUenSpTVr1iyFhITovffeU58+fVSuXDm7HJ79Li0tTUlJSbJarfLx8WFoZoNKliypBQsW6NlnnzWdcl9ERUWpbdu2qlixoho0aCCLxaLt27frxIkTWrdunV1dWHGnlxvY6+OAx44dU8eOHbVv377ss86k///Ioz3+niQpJSVFc+bMUWJioiwWi3x9fRUcHHzLQ/dtVWhoqPr27StXV9dcg+g/CgkJyaOq+8fT01O7du3So48+mmP9+++/V926dZWammqoDABgCsMzALBxNw/PJGnr1q3q0qWL/P39tXnzZrv9AAn7ULZsWW3dulVVq1Y1nXLfnDp1Sv/5z39yPGL2xhtvqGzZsqbTcJPnn39ezs7O+vTTT+Xt7a0dO3bowoULGjx4sCZMmGBXg87f7dq1S61atZKbm5ueeOIJWa1W7dq1S1evXlVkZKQCAgJMJ94RLy8v7dq1SyVKlJCXl9dt32exWHTs2LE8LLs/HnnkEY0dO1a9evXKsT537lwNGzZMZ8+eNVQGADCF4RkA2LibP6T87ujRo3r++ed15MgRhmd4oCZOnKhjx45p+vTpdnV+0d06ceKERo4cyeNYNqRkyZLavHmzatasKU9PT+3YsUPVqlXT5s2bNXjwYMXHx5tOvGtPP/20fHx89Omnn6pAgd+OHr5x44b69OmjY8eO6ZtvvjFcCEkaN26c3n//ffXp0yfHuZzh4eEaMWKEhg0bZrgQAJDXGJ4BgJ3KyMjQ2bNnHfJQbdiOjh07asuWLSpevLgee+wxubi45Hj9888/N1R2f+3Zs0cBAQF2PYxOS0tTVFTULc8Hs8dH54oVK6a4uDh5e3urSpUqmj17tpo2baqkpCT5+fkpPT3ddOJdc3NzU3x8fK5LKw4ePKi6deva5e/JUUVERGjq1KlKTEyUJNWoUUMDBgxQUFCQ4TIAgAnctgkAdsrV1ZXBGR64okWLqmPHjqYz8Bfi4+P17LPPKj09XWlpaSpevLh+/vlnubu7q3Tp0nY5PHv88ce1d+9eeXt7q379+vr4449VsGBBzZo1S97e3qbz7kmRIkWUnJyca3h24sQJeXh4GKr6e7p06aK6devm2o01fvx47dixQ8uXLzdU9vcEBQUxKAMAZGPnGQDYoOLFi+vIkSMqWbKkihUr9qePy128eDEPywDHZO87z5o0aaKqVatq5syZKlq0qPbs2SMXFxe9/PLLGjBggDp16mQ68a6tX79eaWlp6tSpk44dO6bnnntOhw4dUokSJbRs2TI1a9bMdOJdCwkJ0apVqzRhwgQ1bNhQFotF0dHRGjp0qDp37qwpU6aYTrxrpUqV0ubNm+Xn55djfd++fWrRogXngwEAHAI7zwDABk2ePDl7F4I9fpi6E0lJSZoyZUr2jXO/PxJTpUoV02m4hfPnz+vw4cOyWCyqWrWqSpUqZToJN0lISNAnn3wiZ2dnOTs769dff5W3t7c+/vhj9ezZ0y6HZ61atcr+2tvbWwcPHtTFixf/8gcKtmzChAmyWCzq0aOHbty4IUlycXHR66+/rnHjxhmuuzdXrlxRwYIFc627uLjo0qVLBor+vszMTE2ePFkRERG3fAyaH1oBQP7D8AwAbFDPnj1v+bWjWL9+vdq1a6fatWurUaNGslqt2r59ux577DF9+eWXCgwMNJ2I/0lLS1P//v21YMECZWVlSZKcnZ3Vo0cPTZs2Te7u7oYL78xfDY9SUlLyJuQBcXFxyR4oPfzww0pOTlaNGjXk6emp5ORkw3X3JjU1VZmZmSpevHj2WvHixXXx4kUVKFBARYoUMVh3bwoWLKipU6dq7NixSkpKktVqlY+Pj938d3Qrjz/+uJYtW6YRI0bkWF+6dKl8fX0NVf09o0aN0uzZszVo0CANHz5c7777rn744QetXr061+8TAJA/8NgmANigu/lpvT1+gPT391erVq1y7bQYNmyYIiMjtXv3bkNlmDJlivz8/NS8eXNJ0quvvqqNGzdq+vTpatSokSQpOjpaISEhCgwM1MyZM03m3rFevXrd0fvmzp37gEsejJYtW+qVV15R165d9dprryk+Pl4hISFauHChfvnlF3333XemE+9amzZt9Pzzz+uNN97IsR4WFqY1a9Zo3bp1hspwszVr1qhz587q2rVr9qO0mzZt0pIlS7R8+XJ16NDBbOA9qFKlikJDQ9W2bVt5eHgoISEhey02NlaLFy82nQgAyGMMzwDABjk5Of3lY0lWq1UWi8Uuz2hydXXVvn379Oijj+ZYP3LkiGrWrKmMjAxDZYiLi1NQUJDef/99de/eXSVLltSKFSvUpEmTHO/bsmWLgoKCdP78eTOhyGHXrl26fPmymjZtqvPnz6tnz56Kjo6Wj4+P5s6dq1q1aplOvGvFixfXt99+qxo1auRYP3TokBo1aqQLFy4YKrt3aWlpGjdunDZt2qRz585l7+b83bFjxwyV/T1r167VmDFjlJCQIDc3N9WsWVMjR47UM888YzrtnhQuXFiJiYmqWLGiypQpo7Vr1yogIEDHjh2Tv7+/UlNTTScCAPIYj20CgA3asmWL6YQHqlSpUkpISMg1PEtISFDp0qUNVUGS6tSpo++++049e/ZU9+7dlZ6erocffjjX+0qXLq309HQDhbiVunXrZn9dqlQph9iV9euvv2afC3az69ev6+rVqwaK/r4+ffooKipK3bt3V5kyZez27LY/atu2rdq2bWs6474pX768Tp8+rYoVK8rHx0eRkZEKCAjQzp07VahQIdN5AAADGJ4BgA2y15/W36l//vOf6tu3r44dO5bjxrn/+7//0+DBg03n5XslS5bU2rVrJUkNGjTQyJEjtWDBArm6ukqSrl69qlGjRqlBgwYmM+Hg6tWrp1mzZmnatGk51sPCwlSnTh1DVX/PV199pbVr12Y/Au1I4uLisi+A8fX1lb+/v+mke9axY0dt2rRJ9evX14ABA/TSSy9pzpw5Sk5O1sCBA03nAQAM4LFNALAT6enpt7z1q2bNmoaK7p3VatWUKVM0ceJEnTp1SpJUtmxZDR06VCEhIQ6zG8MR7N+/X61bt1ZGRoZq1aoli8WihIQEubq6av369XrsscdMJ8JBffvtt2rRooXq1auXfQbfpk2btHPnTkVGRurpp582XHj3vLy8tG7dulyPotqzc+fO6cUXX9TWrVtVtGhRWa1WpaamqmnTplq6dKlD3MwbGxur7du3y8fHR+3atTOdAwAwgOEZANi48+fPq1evXvrqq69u+bo9nnl2s8uXL0uSPDw8DJfgdq5evapFixbp0KFDslqt8vX1Vbdu3eTm5mY6DQ4uISFB48ePz3GW1jvvvJPrkW97sWjRIn3xxReaP3++Xd+webMXXnhBSUlJWrhwYfZQ8ODBg+rZs6d8fHy0ZMkSw4UAAPx9DM8AwMZ169ZNP/zwg6ZMmaKmTZtq1apVOnv2rD766CNNnDjRoc6ZAQBH5u/vr6SkJFmtVlWuXFkuLi45XrfHm4Y9PT21ceNG1atXL8f6jh071LJlS6WkpJgJu0tr1qy54/ey+wwA8h/OPAMAG7d582Z98cUXqlevnpycnFSpUiUFBgaqSJEiGjt2rF0Oz86ePashQ4Zk3zj3x5/j2PtuOnu3Zs0atWnTRi4uLn/5gZIPkbifLl26dMfvLVKkyAMseTA6dOhgOuG+y8rKyjUElCQXF5dct4nasj/+b2OxWHL92/T7kQL8GwUA+Q87zwDAxhUpUkR79+5V5cqVVblyZX322Wdq1KiRjh8/rscee8wubzxs06aNkpOT1a9fv1veONe+fXtDZZAkJycnnTlzRqVLl5aTk9Nt32exWPgQaUOioqI0YcKE7EPba9SooaFDh9rV2WBOTk5/eeah1Wrlz54Nad++vVJSUrRkyRKVLVtWknTy5El169ZNxYoV06pVqwwX3r2NGzfq7bff1pgxY9SgQQNZLBZt375d7733nsaMGaPAwEDTiQCAPMbOMwCwcdWqVdPhw4dVuXJl1a5dW5988okqV66ssLAwlSlTxnTePYmOjta2bdtUu3Zt0ym4hZt3i9jTzpH8bNGiRerVq5c6deqkkJAQWa1Wbd++Xc2bN9e8efPUtWtX04l3ZMuWLaYT8oQj3Uw5ffp0tW/fXpUrV1aFChVksViUnJwsPz8/LVq0yHTePXnzzTcVFhamp556KnutVatWcnd3V9++fZWYmGiwDgBgAjvPAMDGffbZZ7p+/bpeeeUVxcfHq1WrVrpw4YIKFiyoefPm6YUXXjCdeNd8fX312Wef2fUHRsCW1KhRQ3379tXAgQNzrE+aNEmffvqpw33YT0hIsMvhuyPfTLlhw4Ycl4q0aNHCdNI9c3Nz044dO+Tn55djfe/evapfv76uXr1qqAwAYArDMwCwM+np6Tp06JAqVqyokiVLms65J5GRkZo4cWL2LjrYrpCQEPn4+CgkJCTH+vTp03X06FFNmTLFTBhyKFSokA4cOCAfH58c60ePHtXjjz+ujIwMQ2X3T2pqqj777DPNnj1be/bsscvHNrmZ0j40btxYLi4uWrRoUfYO7zNnzqh79+66du2aoqKiDBcCAPIawzMAsBPXrl3T8ePHVaVKFRUoYH9P3RcrVizHWUZpaWm6ceOG3N3dcx02ffHixbzOw22UK1dOa9asUZ06dXKs7969W+3atdNPP/1kqAw38/Hx0dChQ/Xqq6/mWP/kk080YcIEff/994bK/r7NmzcrPDxcn3/+uSpVqqTOnTurc+fOdrlz1VFupgwNDb3j9/5x8G4Pjh49qo4dO+rw4cOqWLGiJCk5OVlVq1bV6tWrcw2pAQCOz/4+fQFAPpOenq7+/ftr/vz5kqQjR47I29tbISEhKlu2rIYNG2a48M6wQ8k+XbhwQZ6enrnWixQpop9//tlAEW5l8ODBCgkJUUJCgho2bCiLxaLo6GjNmzdPU6dONZ1313766SfNmzdP4eHhSktLU1BQkK5fv66VK1fK19fXdN49c5SbKSdPnnxH77NYLHY5PPPx8dHevXtv+SjqX11oAQBwTOw8AwAbN2DAAH377beaMmWKWrdurb1798rb21tr1qzRyJEjFR8fbzoRDuzxxx/Xa6+9pn79+uVYnzZtmmbOnKmDBw8aKsMfrVq1ShMnTsw+3+z32zbt7fbaZ599VtHR0XruuefUrVs3tW7dWs7OznJxcdGePXvsenjmiDdTAgCQH7DzDABs3OrVq7Vs2TI9+eSTOX7i7evrq6SkJINl927dunVydnZWq1atcqxHRkYqMzNTbdq0MVSGPxo0aJD69eun8+fPq1mzZpKkTZs2aeLEiewmtDEdO3ZUx44dTWf8bZGRkQoJCdHrr7+uRx991HTOfeWIN1P+zt6PFggNDVXfvn3l6ur6l4+l2uNuOgDA32N//7IBQD5z/vx5lS5dOtd6Wlqa3T4+MmzYMI0bNy7XelZWloYNG8bwzIYEBwfr119/1ejRo/Xhhx9KkipXrqyZM2eqR48ehutwK1euXMn1CGCRIkUM1dy9bdu2KTw8XHXr1lX16tXVvXt3u7xV+FYqVKig3bt3O9TNlI5ytMDkyZPVrVs3ubq6/uljqfb6KCoA4O/hsU0AsHHPPPOMunTpov79+8vDw0N79+6Vl5eX+vXrp6NHj+rrr782nXjX3NzclJiYmOumzR9++EGPPfaY0tLSzIThT50/f15ubm566KGHTKfgD44fP65+/fpp69atOW7WtFqtslgsdnkzZXp6upYuXarw8HDt2LFDmZmZmjRpkoKDg+Xh4WE6754sWLBAL7zwggoVKpRj/dq1a1q6dKldDqQ5WgAAkB8wPAMAG7d9+3a1bt1a3bp107x58/Tqq6/qwIEDiomJUVRUVK5bEO3BI488osWLF2c/Bvi7jRs3qmvXrjp37pyhMsA+NWzYUNJvg4yHH344167UZ555xkTWfXP48GHNmTNHCxcuVEpKigIDA7VmzRrTWXfN2dlZp0+fzrWb+MKFCypdurRdDjkrVaqUfbSAh4eH9uzZI29vbx09elQBAQG6dOmS6UQAAP42HtsEABvXsGFDbd++XePHj1eVKlUUGRmpgIAAxcTEyM/Pz3TePWnXrp3efPNNrVq1SlWqVJEkHT16VIMHD1a7du0M1+GPVqxYoYiICCUnJ+vatWs5Xtu9e7ehKtxs7969iouLU7Vq1UynPBDVqlXTxx9/rLFjx+rLL79UeHi46aR78vtOwD/66aefbnmrrT1wxKMFunTporp16+Z65HT8+PHasWOHli9fbqgMAGCKk+kAAMDtXb9+Xb169ZK7u7vmz5+v/fv36+DBg1q0aJHdDs6k3z6AFC5cWNWrV5eXl5e8vLxUo0YNlShRQhMmTDCdh5uEhoaqV69eKl26tOLj4/XEE0+oRIkSOnbsGGfT2ZB69erpxIkTpjMeOGdnZ3Xo0MHudp35+/srICBAFotFzZs3V0BAQPavWrVq6emnn7bbc8/q1auntWvXZn//+8Ds008/VYMGDUxl/S1RUVFq27ZtrvXWrVvrm2++MVAEADCNnWcAYMNcXFy0atUqDR8+3HTKfeXp6ant27drw4YN2rNnj9zc3FSzZk01btzYdBr+YMaMGZo1a5ZeeuklzZ8/X2+99Za8vb01YsQIXbx40XQe/mf27Nl67bXXdPLkST3++ONycXHJ8XrNmjUNlUGSOnToIElKSEhQq1atcpwbWLBgQVWuXFmdO3c2VPf3jB07Vq1bt9bBgwd148YNTZ06NcfRAvboypUrKliwYK51FxcXHkMFgHyKM88AwMb16tVLfn5+GjRokOmUByIjI0OFChWy28d7HJ27u7sSExNVqVIllS5dWhs2bFCtWrX0/fff68knn9SFCxdMJ0JSbGysunbtqh9++CF7zWKx2PWFAY5o/vz5euGFF+Tq6mo65W9LSEhQ7dq1JUn79u3ThAkTFBcXp6ysLAUEBOjtt9+22x3S9erV0/PPP68RI0bkWH///ff15ZdfKi4uzlAZAMAUdp4BgI3z8fHRhx9+qO3bt6tOnToqXLhwjtdDQkIMld27rKwsjR49WmFhYTp79qyOHDkib29vDR8+XJUrV1bv3r1NJ+J/HnnkEV24cEGVKlVSpUqVFBsbq1q1aun48ePi52+2Izg4WP7+/lqyZMktLwyAbejZs6fphPsmICBA/v7+6tOnj7p27ar58+ebTrpvhg8frs6dOyspKSn7YptNmzZpyZIlnHcGAPkUO88AwMZ5eXnd9jWLxaJjx47lYc398cEHH2j+/Pn64IMP9M9//lP79++Xt7e3IiIiNHnyZMXExJhOxP/06dNHFSpU0MiRIxUWFqZBgwapUaNG2rVrlzp16qQ5c+aYToSkwoULa8+ePfLx8TGdgj/h5OT0p4NNe9ohGBMTo/DwcEVEROj69evq3LmzgoOD1bRpU9Np98XatWs1ZswYJSQkZB8tMHLkSLu/uRYAcG8YngEA8pyPj48++eQTNW/eXB4eHtqzZ4+8vb116NAhNWjQQL/88ovpRPxPVlaWsrKyVKDAb5vVIyIiFB0dLR8fH7322mu3PBcIee/555/XK6+8YrfnZuUXq1evzjE8u379uuLj4zV//nyNGjXKLnfdXr16VREREZo7d662bdumypUrKzg4WD179lT58uVN5wEAcF8wPAMA5Dk3NzcdOnRIlSpVyjE8O3jwoJ544glduXLFdCJgV2bNmqWPPvpIwcHB8vPzy3VhQLt27QyV4U4sXrxYy5Yt0xdffGE65W9JSkrS3LlztWDBAp0+fVqBgYFat26d6ax7FhcXp8TERFksFvn6+srf3990EgDAEIZnAIA8V7duXb355pt6+eWXcwzPRo0apY0bN2rbtm2mE/O1vXv33vF7ucXRNjg5Od32NS4MsH1JSUmqWbOm0tLSTKf8bVeuXNFnn32mf//730pJSbHLP3vnzp3Tiy++qK1bt6po0aKyWq1KTU1V06ZNtXTpUpUqVcp0IgAgj3FhAAAgzwQHB2vq1KkaOXKkunfvrpMnTyorK0uff/65Dh8+rAULFui///2v6cx8r3bt2tk3Nf4ZhjK2Iysry3QC7tHVq1c1bdo0u3/EMSoqSuHh4Vq5cqWcnZ0VFBRkl4+hSlL//v116dIlHThwQDVq1JAkHTx4UD179lRISIiWLFliuBAAkNfYeQYAyDPOzs46ffq0SpcurfXr12vMmDGKi4tTVlaWAgICNGLECLVs2dJ0Zr73448/3vF7K1Wq9ABLAMdSrFixHGeeWa1WXb58We7u7lq0aJHdPV574sQJzZs3T/PmzdPx48fVsGFD9e7dW0FBQbluhrYnnp6e2rhxo+rVq5djfceOHWrZsqVSUlLMhAEAjGHnGQAgz9z885pWrVqpVatWBmtwOwzE7ENoaKj69u0rV1dXhYaG/ul7Q0JC8qgKf2bKlCk5vndyclKpUqVUv359FStWzEzUPQoMDNSWLVtUqlQp9ejRQ8HBwapWrZrprPsiKysr17mBkuTi4sIuTwDIp9h5BgB2YNu2bfrkk0+UlJSkFStWqFy5clq4cKG8vLz01FNPmc67Y05OTjp79iznxdiZhQsXKiwsTMePH1dMTIwqVaqkKVOmyMvLS+3btzedl295eXlp165dKlGihLy8vG77PovFomPHjuVhGe5FQkKCateubTrjjrVr1069e/fWc889J2dnZ9M591X79u2VkpKiJUuWqGzZspKkkydPqlu3bipWrJhWrVpluBAAkNduf7osAMAmrFy5Uq1atZKbm5vi4+P166+/SpIuX76sMWPGGK67e1WrVlXx4sX/9Bdsx8yZMzVo0CA9++yzOQ7/Llq0aK5dNMhbx48fV4kSJbK/vt0vBme2KzU1VTNmzFBAQIDq1KljOueurFmzRu3bt3e4wZkkTZ8+XZcvX1blypVVpUoV+fj4yMvLS5cvX9a0adNM5wEADGDnGQDYOH9/fw0cOFA9evTIcTNlQkKCWrdurTNnzphOvGNOTk6aMmWKPD09//R9PXv2zKMi/BVfX1+NGTNGHTp0yPHnb//+/WrSpIl+/vln04nQb4fOu7m53fK106dPq0yZMnlchD+zefNmhYeH6/PPP1elSpXUuXNnde7cWf7+/qbTcJMNGzbo0KFDslqt8vX1VYsWLUwnAQAM4cwzALBxhw8fVuPGjXOtFylSxC4PLX7xxRdVunRp0xm4Q8ePH7/lB/pChQopLS3NQBFuxd/fX4sXL1ZAQECO9RUrVuj111/X+fPnDZXhdz/99JPmzZun8PBwpaWlKSgoSNevX9fKlSvl6+trOg+3EBgYqMDAQNMZAAAbwPAMAGxcmTJldPToUVWuXDnHenR0tLy9vc1E3aObb5mDffDy8lJCQkKuSwS++uorPvDbkMDAQDVs2FDvv/++3n77baWlpalfv35avny5xo0bZzov33v22WcVHR2t5557TtOmTVPr1q3l7OyssLAw02n4n7+6dONmXMABAPkPwzMAsHGvvvqqBgwYoPDwcFksFp06dUoxMTEaMmSIRowYYTrvrnBSgP0ZOnSo/vWvfykjI0NWq1U7duzQkiVLNHbsWM2ePdt0Hv5n2rRpatu2rXr16qW1a9fq1KlTKlKkiHbu3MmQ0wZERkYqJCREr7/+uh599FHTObiFyZMn39H7LBYLwzMAyIcYngGAjXvrrbeUmpqqpk2bKiMjQ40bN1ahQoU0ZMgQ9evXz3TeXcnKyjKdgLvUq1cv3bhxQ2+99ZbS09PVtWtXlStXTlOnTtWLL75oOg83admypTp16qSZM2eqQIEC+vLLLxmc2Yht27YpPDxcdevWVfXq1dW9e3e98MILprNwk+PHj5tOAADYMC4MAAA7kZ6eroMHDyorK0u+vr566KGHTCchn/n555+VlZWVfWbdyZMnVa5cOcNVkKSkpCR17dpVZ86c0ezZsxUVFaUJEyYoJCREo0ePlouLi+lE6Le/x5cuXarw8HDt2LFDmZmZmjRpkoKDg+Xh4WE6D39w7do1HT9+XFWqVFGBAuw5AID8jOEZANiZS5cuafPmzapWrZpq1KhhOgf50JkzZzR69GjNnj1bV69eNZ0DSR4eHmrbtq3CwsJUtGhRSdL27duzb+mNj483G4hcDh8+rDlz5mjhwoVKSUlRYGCg1qxZYzoL+m3I2b9/f82fP1+SdOTIEXl7eyskJERly5bVsGHDDBcCAPKak+kAAMCfCwoK0vTp0yVJV69eVb169RQUFKSaNWtq5cqVhuvgqFJSUtStWzeVKlVKZcuWVWhoqLKysjRixAh5e3srNjZW4eHhpjPxPzNmzNDSpUuzB2eS1LBhQ8XHx+e6gRO2oVq1avr444/1008/acmSJaZzcJN33nlHe/bs0datW+Xq6pq93qJFCy1btsxgGQDAFHaeAYCNe+SRR7R+/XrVqlVLixcv1siRI7Vnzx7Nnz9fs2bNYkcJHog33nhDX375pV544QV9/fXXSkxMVKtWrZSRkaGRI0fqmWeeMZ0IAA9EpUqVtGzZMj355JPy8PDQnj175O3traNHjyogIECXLl0ynQgAyGM8vA8ANi41NVXFixeXJH399dfq3Lmz3N3d1bZtWw0dOtRwHRzV2rVrNXfuXLVo0UJvvPGGfHx8VLVqVU2ZMsV0Gv7EwYMHlZycrGvXrmWvWSwWPf/88warAPty/vz57LMdb5aWliaLxWKgCABgGsMzALBxFSpUUExMjIoXL66vv/5aS5culST98ssvOR4nAe6nU6dOZd/U6O3tLVdXV/Xp08dwFX6XmpoqT0/P7O+PHTumTp06ae/evbJYLPr9wYLfP+hnZmYa6QTsUb169bR27Vr1799f0v//7+jTTz9VgwYNTKYBAAxheAYANu7NN99Ut27d9NBDD6lSpUpq0qSJJOmbb76Rn5+f2Tg4rKysrBw3NDo7O6tw4cIGi3Cz0NBQubq6Zu8+HTBggCpUqKD169erdu3a+u6773T8+HENGTJEkyZNMlwL2JexY8eqdevWOnjwoG7cuKGpU6fqwIEDiomJUVRUlOk8AIABnHkGAHYgLi5OycnJCgwM1EMPPSTpt8fqihYtqkaNGhmugyNycnJSmzZtVKhQIUnSl19+qWbNmuUaoH3++ecm8vK9c+fOqXv37vLx8dF//vMflSxZUps2bVKtWrVUrlw57dixQ+XKldOmTZs0ZMgQzkYE7kBCQoJq164tSdq3b58mTJiguLg4ZWVlKSAgQG+//TY/tAKAfIrhGQAAyKVXr1539L65c+c+4BL8mbFjx+qdd95RsWLFFBcXJ29vb9WqVUuzZ89WvXr1lJSUJD8/P6Wnp5tOBWyek5OT/P391adPH3Xt2jXHo9EAgPyN4RkAAICde/rppzVo0CB17NhRffv21fnz5zV06FCFhYVp9+7d2r9/v+lEwObFxMQoPDxcERERun79ujp37qzg4GA1bdrUdBoAwDCGZwAAAHZu/fr1unz5srp06aITJ06obdu22r9/v4oXL66IiAg1a9bMdCJgN65evaqIiAjNnTtX27ZtU+XKlRUcHKyePXuqfPnypvMAAAYwPAMAAHBAFy9eVNGiReXk5GQ6BbBbSUlJmjt3rhYsWKDTp08rMDBQ69atM50FAMhjDM8AAAAA4DauXLmizz77TP/+97+VkpKizMxM00kAgDxWwHQAAOCvbdu2TZ988omSkpK0YsUKlStXTgsXLpSXl5eeeuop03kADMvIyNC0adO0ZcsWnTt3TllZWTle3717t6EywH5FRUUpPDxcK1eulLOzs4KCgtS7d2/TWQAAAxieAYCNW7lypbp3765u3bopPj5ev/76qyTp8uXLGjNmDI+PAFBwcLA2bNigLl266IknnpDFYjGdBNilEydOaN68eZo3b56OHz+uhg0batq0aQoKClLhwoVN5wEADOGxTQCwcf7+/ho4cKB69OghDw8P7dmzR97e3kpISFDr1q115swZ04kADPP09NS6devUqFEj0ymA3QoMDNSWLVtUqlQp9ejRQ8HBwapWrZrpLACADWDnGQDYuMOHD6tx48a51osUKaKUlJS8DwJgc8qVKycPDw/TGYBdc3Nz08qVK/Xcc8/J2dnZdA4AwIZw/RIA2LgyZcro6NGjudajo6Pl7e1toAiArZk4caLefvtt/fjjj6ZTALu1Zs0atW/fnsEZACAXdp4BgI179dVXNWDAAIWHh8tisejUqVOKiYnRkCFDNGLECNN5AGxA3bp1lZGRIW9vb7m7u8vFxSXH6xcvXjRUBgAAYP8YngGAjXvrrbeUmpqqpk2bKiMjQ40bN1ahQoU0ZMgQ9evXz3QeABvw0ksv6eTJkxozZowefvhhLgwAAAC4j7gwAADsRHp6ug4ePKisrCz5+vrqoYceMp0EwEa4u7srJiZGtWrVMp0CAADgcNh5BgB2wt3dXXXr1jWdAcAGVa9eXVevXjWdAQAA4JDYeQYANq5p06Z/+gjW5s2b87AGgC2KjIzUqFGjNHr0aPn5+eU686xIkSKGygAAAOwfO88AwMbVrl07x/fXr19XQkKC9u/fr549e5qJAmBTWrduLUlq3rx5jnWr1SqLxaLMzEwTWQAAAA6B4RkA2LjJkyffcv3999/XlStX8rgGgC3asmWL6QQAAACHxWObAGCnjh49qieeeEIXL140nQI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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "NameError", + "evalue": "name 'df_filtrado' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32m~\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\main.py:126\u001b[0m\n\u001b[0;32m 117\u001b[0m tabla_completa \u001b[38;5;241m=\u001b[39m calcular_correlaciones_y_generar_heatmap_con_resumen(df_procesado, parametros_seleccionados)\n\u001b[0;32m 119\u001b[0m \u001b[38;5;66;03m# Paso 10: Ajustar rango e imputar valores faltantes\u001b[39;00m\n\u001b[0;32m 120\u001b[0m \u001b[38;5;66;03m#print(\"\\n=== Paso 8: Imputación con ajuste de rango ===\")\u001b[39;00m\n\u001b[0;32m 121\u001b[0m \u001b[38;5;66;03m#imputacion_similitud_con_rango(df_filtrado, df_procesado)\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 124\u001b[0m \n\u001b[0;32m 125\u001b[0m \u001b[38;5;66;03m# Paso 10: Llamar a la función principal\u001b[39;00m\n\u001b[1;32m--> 126\u001b[0m df_procesado_actualizado, resumen_imputaciones \u001b[38;5;241m=\u001b[39m bucle_imputacion_similitud_correlacion(df_procesado, parametros_preseleccionados, tabla_completa, reduccion_confianza\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.05\u001b[39m, max_iteraciones\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m7\u001b[39m)\n\u001b[0;32m 128\u001b[0m \u001b[38;5;66;03m# Paso 11: Exportar resultados a Excel\u001b[39;00m\n\u001b[0;32m 129\u001b[0m archivo_destino \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIngrese la ruta donde desea guardar el archivo con las imputaciones (incluya .xlsx): \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32m~\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\config_and_loading.py:1022\u001b[0m, in \u001b[0;36mbucle_imputacion_similitud_correlacion\u001b[1;34m(df_procesado, parametros_preseleccionados, tabla_completa, reduccion_confianza, max_iteraciones)\u001b[0m\n\u001b[0;32m 1011\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1012\u001b[0m \u001b[38;5;124;03mRealiza un bucle alternando imputaciones por similitud y correlación, consolidando los resultados.\u001b[39;00m\n\u001b[0;32m 1013\u001b[0m \u001b[38;5;124;03mAhora se evita actualizar los DataFrames inmediatamente, y se eligen las imputaciones finales\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1018\u001b[0m \u001b[38;5;124;03m df_resumen (pd.DataFrame): Detalle consolidado de imputaciones realizadas.\u001b[39;00m\n\u001b[0;32m 1019\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1021\u001b[0m df_procesado_base \u001b[38;5;241m=\u001b[39m df_procesado\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;66;03m# Copia base del DataFrame original\u001b[39;00m\n\u001b[1;32m-> 1022\u001b[0m df_filtrado_base \u001b[38;5;241m=\u001b[39m df_filtrado\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;66;03m# Copia base del DataFrame original\u001b[39;00m\n\u001b[0;32m 1024\u001b[0m convertir_a_html(df_procesado_base, titulo\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdf_procesado_base\u001b[39m\u001b[38;5;124m\"\u001b[39m, ancho\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m100\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m, alto\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m400px\u001b[39m\u001b[38;5;124m\"\u001b[39m, mostrar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 1025\u001b[0m convertir_a_html(df_filtrado_base, titulo\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdf_filtrado_base\u001b[39m\u001b[38;5;124m\"\u001b[39m, ancho\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m100\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m, alto\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m400px\u001b[39m\u001b[38;5;124m\"\u001b[39m, mostrar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'df_filtrado' is not defined" + ] + } + ], + "source": [ + "%run main.py" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (adrpy)", + "language": "python", + "name": "adrpy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ADRpy/analisis/main.py b/ADRpy/analisis/main.py new file mode 100644 index 00000000..970231f1 --- /dev/null +++ b/ADRpy/analisis/main.py @@ -0,0 +1,171 @@ +# ===================== # +# IMPORTACIONES # +# ===================== # + +# Librerías estándar +import os + +# Librerías externas +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +from openpyxl import load_workbook +from openpyxl.styles import Font, PatternFill +from openpyxl.utils.dataframe import dataframe_to_rows +from openpyxl.comments import Comment +from tkinter import simpledialog, messagebox +import tkinter as tk +from sklearn.linear_model import LinearRegression +from sklearn.metrics import r2_score +from IPython.display import display, HTML + +# ===================== # +# IMPORTAR MÓDULOS # +# ===================== # + +from ADRpy.analisis.Modulos.config_and_loading import configurar_entorno, cargar_datos +from ADRpy.analisis.Modulos.data_processing import procesar_datos_y_manejar_duplicados +from ADRpy.analisis.Modulos.user_interaction import seleccionar_parametros_por_indices, solicitar_umbral +from ADRpy.analisis.Modulos.correlation_analysis import calcular_correlaciones_y_generar_heatmap_con_resumen +from ADRpy.analisis.Modulos.similarity_imputation import imputacion_similitud_con_rango, imprimir_detalles_imputacion +from ADRpy.analisis.Modulos.correlation_imputation import Imputacion_por_correlacion +from ADRpy.analisis.Modulos.imputation_loop import bucle_imputacion_similitud_correlacion +from ADRpy.analisis.Modulos.excel_export import exportar_excel_con_imputaciones +from ADRpy.analisis.Modulos.html_utils import convertir_a_html + + +# Solicitar la ruta del archivo al usuario +archivo_origen = input("Ingrese la ruta del archivo Excel original: ") + +# Paso 1: Configurar entorno +configurar_entorno(max_rows=20, max_columns=10) + +# Paso 2: Cargar datos +try: + df_inicial, ruta_archivo = cargar_datos(archivo_origen) # Aquí se valida la entrada + print(f"Datos cargados correctamente desde: {ruta_archivo}") +except ValueError as e: + print(f"Error al cargar datos: {e}") + exit(1) # Detiene el programa si hay un error + +# Validar que los datos se hayan cargado correctamente +print("\n=== Validando datos cargados ===") +if df_inicial.empty: + print("El archivo cargado no contiene datos. Verifica el archivo y vuelve a intentarlo.") + exit(1) + +# Continuar con el siguiente paso solo si los datos son válidos +print("\n=== Continuando con el procesamiento de datos ===") + +# Validar encabezados iniciales +print("\nEncabezados iniciales cargados:") +print(df_inicial.columns.tolist()) + +# Paso 3: Mostrar datos iniciales en HTML +print("\n=== Mostrando datos iniciales en formato HTML ===") +convertir_a_html(df_inicial, titulo="Datos Iniciales", mostrar=True) + +# Paso 4: Procesar datos +print("\n=== Procesando los datos ===") +df_procesado = procesar_datos_y_manejar_duplicados(df_inicial) + +# Validar encabezados después del procesamiento +#print("\nEncabezados después del procesamiento:") +#print(df_procesado.columns.tolist()) + +# Comparar encabezados antes y después del procesamiento +if df_inicial.columns.tolist() == df_procesado.columns.tolist(): + print("\n✅ Los encabezados se preservaron correctamente.") +else: + print("\n❌ Los encabezados fueron modificados durante el procesamiento.") + +# Paso 5: Mostr en HTML +print("\n=== Mostrando datos procesados en formato HTML ===") +convertir_a_html(df_procesado, titulo="Datos Procesados", mostrar=True) + +# Paso 6: Selección de parámetros + +# Parámetros disponibles en el índice del DataFrame +parametros_disponibles = df_procesado.index.tolist() +print("Parámetros disponibles en df_procesado antes de seleccionar:") +print(parametros_disponibles) + +# Parámetros preseleccionados de interés +parametros_preseleccionados = [ + "Velocidad a la que se realiza el crucero (KTAS)", + "Techo de servicio máximo", + "Área del ala", + "Relación de aspecto del ala", + "Longitud del fuselaje", + "Peso máximo al despegue (MTOW)", + "Alcance de la aeronave", + "Autonomía de la aeronave", + "Velocidad máxima (KIAS)", + "Velocidad de pérdida (KCAS)", + "envergadura", + "Cuerda", + "payload", + "Empty weight" +] +# Filtrar preseleccionados para mantener solo los parámetros válidos +parametros_preseleccionados = [p for p in parametros_preseleccionados if p in parametros_disponibles] + +# Imprimir parámetros preseleccionados válidos +#print("Parámetros preseleccionados válidos:") +#print(parametros_preseleccionados) + +parametros_seleccionados = seleccionar_parametros_por_indices(parametros_disponibles, parametros_preseleccionados) +# Imprimir parámetros seleccionados después de filtrar +print("Parámetros seleccionados después de filtrar:") +print(parametros_seleccionados) + +# Filtrar el DataFrame por los parámetros seleccionados +try: + df_filtrado = df_procesado.loc[parametros_seleccionados] +except KeyError as e: + print(f"Error al filtrar df_procesado: {e}") + print(f"Parámetros seleccionados inválidos: {set(parametros_seleccionados) - set(df_procesado.index.tolist())}") + raise + +# Mostrar la tabla en formato HTML con 3 cifras significativas (sin notación científica) +convertir_a_html(df_filtrado, titulo="Datos Filtrados por Parámetros (df_filtrado)", mostrar=True) + +# Paso 7: Mostrar celdas faltantes con selección de columna + +# Analizar celdas faltantes en la columna seleccionada +df_celdas_faltantes = mostrar_celdas_faltantes_con_seleccion(df_filtrado) + +# Verificar si hay celdas faltantes +if df_celdas_faltantes.empty: + print("No se encontraron valores faltantes en la columna seleccionada.") +else: + # Mostrar resultados en formato HTML + convertir_a_html(df_celdas_faltantes, titulo="Celdas Faltantes Identificadas en df_filtrado (df_celdas_faltantes)", mostrar=True) + +# Paso 8: Resumen de valores faltantes por columna +print("\n=== Generando resumen de valores faltantes por columna ===") +resumen_faltantes = generar_resumen_faltantes(df_filtrado, titulo="Resumen de Valores Faltantes de df_filtrado") + +# Paso 9: Calculando correlaciones y generando heatmap +print("\n=== Calculando correlaciones y generando heatmap ===") +tabla_completa = calcular_correlaciones_y_generar_heatmap_con_resumen(df_procesado, parametros_seleccionados) + +# Paso 10: Ajustar rango e imputar valores faltantes +#print("\n=== Paso 8: Imputación con ajuste de rango ===") +#imputacion_similitud_con_rango(df_filtrado, df_procesado) + #Paso 11: Ajustar rango e imputar valores faltantes por correlación +#Imputacion_por_correlacion(df_procesado, parametros_preseleccionados, tabla_completa, umbral_correlacion=0.7, min_datos_validos=5, max_lineas_consola=250) + +# Paso 10: Llamar a la función principal +df_procesado_actualizado, resumen_imputaciones = bucle_imputacion_similitud_correlacion(df_procesado, parametros_preseleccionados, tabla_completa, reduccion_confianza=0.05, max_iteraciones=7) + +# Paso 11: Exportar resultados a Excel +archivo_destino = input("Ingrese la ruta donde desea guardar el archivo con las imputaciones (incluya .xlsx): ") +exportar_excel_con_imputaciones( + archivo_origen=ruta_archivo, + df_procesado=df_procesado_actualizado, + resumen_imputaciones=resumen_imputaciones +) +print("\n=== Flujo completado. 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from Small Unmanned Fixed-wing Aircraft Design (Keane et al.).ipynb b/docs/ADRpy/notebooks/Example from Small Unmanned Fixed-wing Aircraft Design (Keane et al.).ipynb index c5e6b077..6532cd65 100644 --- a/docs/ADRpy/notebooks/Example from Small Unmanned Fixed-wing Aircraft Design (Keane et al.).ipynb +++ b/docs/ADRpy/notebooks/Example from Small Unmanned Fixed-wing Aircraft Design (Keane et al.).ipynb @@ -218,7 +218,7 @@ "outputs": [ { "data": { - "image/png": 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DBw9m27Zt9OzZM0edX331FQB//fUXx44dIyQkhBMnTrB27VoGDRpkGwWQUuLl5cXYsWNL4JMriqIorsz1euA4d9JRVFQUPXr0oFun9qxfvZzDhw/byt577z2SkpKYPXs2QgjCwsIICwujXbt2tG/fnmPHjnHy5EmHOnfs2MFTTz0FQIsWLfD19eXEiRNl9pkURVEU1+NSPfDJD7SCOD3o3aBmY6e0YdSoUaxevZoWrVrz3y9mc2T/n7ayoKAg9u3bR2JiIjVq1EBKyRtvvOHQY/7qq6+YM2cOAL/88gtllVBGURRFqThcrwcuym4Wel6Sk5OpV68emM38sno5Frvg279/f15//XUGDhxIcnIyoaGhfPfdd6SkpABw8eJF4uLieOGFF4iMjCQyMpL69evTs2dPFi1aBMCJEyeIiYmhefPmTvl8iqIoimtwqR44YJ2F7rwA/t5779G5c2d8fX1p3LQ5mek5J7E99thjJCcnM3jwYH755RdGjBhBly5dAPDy8mLhwoXUrp1zFbnnn3+ecePGERAQgMFgYN68ebi7qyQpiqIoSv7KLB94UeSVD/zo0aPcc889f7+RcEpbE72W83uoJ+OSMeh0+PtULtPjqqVUi6ao58nhd8xJVD7w8k+dp6JT56poymU+8FLj5B64PTe9jkxT+WiLoiiKcmdxvQBO2S3kUpjs5VTL0yiGoiiKcmdwvQDu5IVc7LnpdVikxGxRAVxRFEUpWy4YwMvPELrRoJ0+ldREURRFKWsqgN8GN722qEymWfXAFUVRlFuTFHvltvZ3zcfIkNp9cCengjTqrT1wNZFNURRFKSJpsXBgw3q2L553W/W4aABH64ULfakeKiEhgb59+wJaZjC9Xk+tWrUAiIiIwGg0ohOCTDWEriiKohTBtSuXCJv1BReORt12XS4cwEt/2LqgdKLZsmei2zObzej1pXtxoSiKorgOabFw4Ld1bF88H1NmRonU6Zr3wMFp98FHjRrFihUrbK/bN6lHltlCeHg4vXv3ZsSIEQQEBNgWMXj00Udp0aIFTzzxhHrcTFEU5Q6UeOkiS6a8zpYf5pRY8AZX64H/+jpc2g+mdDBW/juY3466ATBgWvH3F5Bp0gJzREQEUVFR+Pv7Ex4ezoEDBzh8+DD169enW7du7Ny5k+7du99+mxVFUZRyz2Ixs3/9GnYuXYgpK9Oh3C+wAyxbX+z6Xa8HbksnWn56syaLBYtF0qlTJ/z9/W3vd+rUiYYNG6LT6QgMDCQ6Otp5jVQURVHKTMLF8yx55zW2LvzOIXi7V6pM6PhJPPz6lNs6hmv1wAdMg/QbkHgaat4N7l5l3gSDwYDFog3fSynJytR+MCaLhcqVc66Jbp+QRK/XYzKZyq6hiqIoSpmzWMzs+3k1O5ctxJyV5VDeuH0Q/Ua/QJUaPrd9LNcK4OD0e+B+fn7s27ePoUOHsmbNGrKsPyCTWo1NURTljpZwIYbfZk7nyqkTDmXulSvTZ9RY7unRG1FCj0CrAH6LRo8ezYMPPkinTp3o27evrddtUo+SKYqi3JEsZjN71v3EruWLMOcx0tqkY2f6PfcCXtVrlOhxVQAvoilTpti+//PPP23f/+c//yHq4g06de3J4AEhtvd79eqVI5XejBkzyqKZiqIoShmKj4nmt5mfE3vmpEOZh1cV+jwzlhbdgkus121PBfDbJITAaBAqraiiKModxGwysWftSnatWIzF7NjrbhrUhX7PPU/latVLrQ0qgJcAN71OrcamKIpyh7h67iy/fT2duOjTDmUeVarS99lxNO/So1R63fZUAC8BbgYdN9LUDHNFUZSKzGwyEbF6OX/+tDTPXneze7vT99lxVPKuVibtccEAbr2iKUermrkZdJgsFswWiV7n3AQriqIoSsmLiz7Db1//j6vnzjqUeVb1pu+z42nepWwX6nLNAF6OUoqCNoQOkGm24KlTa6AriqJUFGZTFn/+tIyI1cuwmM0O5c279qTPM2OpVNW7zNvmegEcyl8AN1gDuMmCp1EFcEVRlIog9swpfps5nfiYaIeySt7V6PeP57m7c9eyb5iVCy6lSpkH8CtXrjB8+HCaNGlCy5Ytuf/++zlx4gStW7cG4NCB/Ux751+3NBPdz8+P+Pj40mqyoiiKUkymrCx2LFnAorf+mWfwbtEtmFGffu3U4A0u2wMXZRbApZQMGTKEkSNHsmTJEgAiIyOJjY21bdO5UxBvNWzukFZUURRFcS1XTp/kt6//R8KFGIeyytWq0++5F2gadK8TWubIRQN42fXAt2zZgtFoZNy4cbb3cicm2bp1K+9+MI3vF69kypQpnD17lsuXL3PixAk+++wz/vzzT3799VcaNGjAunXrMBqNAHz88cds2bIFgB9//JGmTZuWyWdSFEVRcjJlZrJrxY/sWfcT0uIYX1r27EOvkaPx9KrihNblzaUC+EcRH3Es8RhkpWlvGD1vu84WNVrwr07/yrc8KiqKDh06FFqPTggyrEPop0+fZsuWLRw5coQuXbqwcuVK/vvf/zJkyBDWr1/PQw89BEDVqlWJiIhg/vz5TJo0iZ9//vm2P4+iKIpyay6fPM5vM6eTePG8Q5lX9Rr0G/0iTTp0ckLLCuZSATyn8vMYGYBOQJbZgpSSAQMGYDQaCQgIwGw2079/fwACAgJy9Nwff/xx278vv/yyM5qtKIpyx8rKzOCPZYvY9/NqZB6juq2C+9Hr6efw8Cr7zJdF4VIB3NZTTjwDpkyo3aLUj9mqVStWrFhR6HZCCCxSYpF/pxHV6XQYjUbbajw6nS5HSlH7VXpKe8UeRVEU5W+XThzlt5mfc+3SBYcyrxo1CRnzEv7tOjqhZUXnmrPQKbt74H369CEjI4M5c+bY3tuzZw/nzp3L2SLrAi7mW0grunTpUtu/Xbp0KYHWKoqiKAXJykgnfP63LH7ntTyDd+veIYz69OtyH7zBxXrgNrqyC+BCCFatWsWkSZOYNm0aHh4e+Pn5MX369JxNsnagbyWAZ2Rk0LlzZywWC4sXLy7JZiuKoii5XDh2mLBZn3Pt8iWHsio1axEy5kX8Aguf81ReuGYAL+PnwOvXr8+yZcsc3o+KigK01KE9g4OJunidf77+FnWqeti2SUlJsX1vn5I0+1745MmTS6fRiqIoCgBZ6ensWDKf/b+ty3MZ7jZ9+9PzyWdxr1TJCa0rPhXAS4hOCNz0OttMdEVRFMX5LhyJYsOsz0mKvexQVrVWbULGTMC3TaATWnb7XDeAI7UgLsrPbXw3g07lBVcURSkHMtPT2P7jD0RuyPvx3Lb33U/PJ0bh5ulavW57LhzAAYsF9OUngLsbdCSlZSGlVLPKFUVRnCQm6hBhsz/nelysQ5l37TqEjJ3AXa3bOqFlJcu1A3g5G0Z3M+gxWzIxWyQGvQrgiqIoZSkzLZVti+Zx8Pdf8iwPDB1EjxEjcfO4/UXAygPXDODZKTulY2o3Z3K3ZiXLMFkwlKORAUVRlIru3F+RhM3+ghtX4xzKvOvUJXTcRBq1DHBCy0qPawbwctoDtw/gld2d3BhFUZQ7QEZqKtsWfcehjb85FgpB+/4P0H340xg9PBzLXZxrdhNFdg+8bAJ4fulEczMadAiEw0S2d955h40bN5ZJWxVFUe4U0Qf388MrL+QZvKvVrcewyR/Se9SYChm8wdV74HlkjClpBaUTbdasGQBmsxm9Xq89SmYQZJhyDu1PnTq11NupKIpyp8hIvUn4/LlEbQlzLBSCDvc/SLdhT2J0r5iBO5uL9sCzh9BL/x54fulEzWYzvXv3ZsSIEbYkJa1bt8bNoCfTZOGTTz6xLdwyatQo23rqr7/+Oi1btqRNmza88sorAFy9epVHHnmEoKAggoKC2LlzZ6l/LkVRFFd09sBe5r3yQp7Bu3q9Bgx/97/0evq5Ch+8wcV64Ff+8x8yjh4DJGTeBIM76Iy3Vaf7PS2o++ab+ZYXlE40IiKCqKgo/P39bSuruRt03MwwIfNY7ScxMZFVq1Zx7NgxhBAkJSUBMHHiRF5++WW6d+9OTEwMoaGhHD169LY+l6LcKr1eT0DA35N8Vq9ejZ+f323Xu3fvXubPn88XX3zBlClT8PLysl28ZouOjmbQoEG21Q3LwjvvvEPPnj3p169fmR1TKb70mymEz/+Ww+GOtyOF0NFh0EN0HfoERrc7ZwKSSwVwB07OKNqpUyf8/f1zvOdu0NmykuVWtWpVPDw8eO655xg4cCCDBg0CYOPGjRw5csS23Y0bN0hOTqZKlfKTOF6p+Dw9PYmMjCzxejt27EjHjqWXGEJKiZQSne7WBhTVrS3XcWb/Hn7/5ktSriU6lNWo35DQ8ZOo36z0s1OWNy4VwG09ZSnhciRUqQtV6pXqMQtKJ1q5cmXb9waDAYvFgpt1JvrN1DSH+xMGg4GIiAg2bdrEkiVLmDFjBps3b8ZisbBr1y48PSvGs4lKxbFv3z7++c9/kpKSgo+PD/PmzaNevXrMmTOHb775hszMTJo2bcqCBQuoVKkSy5cv591330Wv1+Pt7c22bdsIDw/nk08+4eeftRWxDh48SJ8+fTh//jyvvfYao0ePznFMs9nM66+/Tnh4OBkZGbzwwguMHTs2xzbR0dEMGDCA3r17s2vXLlavXs2yZctYtmwZGRkZDBkyhHfffReA9957j0WLFtGoUSN8fHzo0KEDr7zyCqNGjWLQoEE8+uijbNq0iVdeeQWTyURQUBAzZ87E3d0dPz8/Ro4cybp168jKyuLVV18tmxOvAJCWkkz4vG84sn2LQ5kQOjoOfpiuj47A4ObmhNY5n4veAxeArkwmseWXTnTr1q05tqtTpw5xcXGkXL9GZkYGv/yy3qGulJQUrl+/zv3338/06dNtvZ2QkBBmzJhh2640ekGKUpi0tDQCAwMJDAxkyJAhZGVl8dJLL7FixQr27dvHs88+y1tvvQXAww8/zJ49ezh48CD33HMPc+fOBbRe7YYNGzh48CBr167N8ziHDh1i/fr17Nq1i6lTp3LpUs7MUHPnzsXb25s9e/awZ88e5syZw9mzZx3qOX78OE8//TQHDhzg+PHjnDx5koiICCIjI9m3bx/btm1j7969rFy5kgMHDvDTTz+xd+9eh3rS09MZNWoUS5cu5a+//sJkMjFz5kxbuY+PD/v372f8+PG2FMBK6Tu1dzc//N/zeQbvmg3v4vH3P6bniFF3bPCGMuiBCyH0wF7gopRyUIlVXEYpRfNLJ/rQQw/l2M5oNPLOO+/Qo1tXatVvRJOmzRzqSk5O5sEHHyQ9PR0pJf/73/8A+OKLL3jhhRdo06YNJpOJnj17MmvWrFL/bIpiL/cQelRUFFFRUdx3332A1jOuV6+erezf//43SUlJpKSkEBoaCkC3bt0YNWoUQ4cO5eGHH87zOA8++CCenp54enrSu3dvIiIiCAz8O5lEWFgYhw4dso18Xb9+nZMnTzrcrvL19eXee++17RMWFka7du0A7WL55MmTtv9z2aNbDzzwgEN7jh8/jr+/v+2pkpEjR/LVV18xadIkANvn6NChg+1CRSk9ack32DLvG47uCHcoEzodnR58jHsfGY7BeHvznyqCshhCnwgcBaqWaK1CV2YrseWXTjT30N+ECROYMGECJ2KTcdPr8PPRhtjnzZtn2yYiIsKhHh8fH3Vlr5Q7UkpatWrFrl27HMpGjRrF6tWradu2LfPmzSM8PByAWbNmsXv3btavX09gYGCeo0m58wTkfi2l5Msvv7RdFOTH/haWlJI33njDYag9+yK5IHlNOLXn7q5NitLr9ZjN5Wv1x4rmZMQfbPz2a1KvJzmU+TTypf/zL1OncVMntKx8KtUhdCFEQ2Ag8G3JV17+Uopm8zDoSDep/+iKa2vevDlXr161BfCsrCwOHz4MaKNJ9erVIysri0WLFtn2OX36NJ07d2bq1Kn4+Phw/vx5h3rXrFlDeno6CQkJhIeHExQUlKM8NDSUmTNnkpWVBcCJEye4efNmgW0NDQ3lu+++IyUlBYCLFy8SFxdH9+7dWbduHenp6aSkpLB+veOtrRYtWhAdHc2pU6cAWLBgAcHBwUU9TUoJSL1xnZ+nf8TaT//jELx1ej33PjKcJ6dNV8E7l4QUzK4AACAASURBVNLugU8HXgNKfjq10JfJPfDicDfqSUrLwmKR6HQqqYnimtzc3FixYgUTJkzg+vXrmEwmJk2aRKtWrXjvvffo3Lkzvr6+BAQEkJycDMCrr77KyZMnkVLSt29f2rZt6zBfpFOnTgwcOJCYmBjefvtt6tevb3sME+C5554jOjqa9u3bI6WkVq1arF69usC2hoSEcPToUbp06QKAl5cXCxcuJCgoiMGDB9O2bVt8fX3p2LEj3t7eOfb18PDg+++/57HHHrNNYrNf90EpXSf+3MHGuTNJu3HdoayWrz+h4ydRx7+JE1pW/onCho+KXbEQg4D7pZTPCyF6Aa/kdQ9cCDEGGANQq1atDrmHqr29vWna1PGqyzP1IkhJWuWGpdH823IzSxKXaqG+lw73UshKlr3ym1Kwop6nU6dOcf264x+PstZgtzYgdrFz2V+YpqSk4OXlVebHLQtpaWl4enqSnp7OxIkT+b//+z/b/e5bVZHPU0kr7Fxlpd7k/I5NXDvtuCw1Oh312t9L3fad0VXwv3W9e/feJ6Us1nOWpdkD7wYMFkLcD3gAVYUQC6WUT9pvJKX8BvgGoHnz5rJXr145Kjl69Gjez0NnuYEpo1w+K23MMhOXmozBzYMqlUp+hqR6RrxoinqePDw8bJOfnCnu+CEA7u7VpsyPHR4eTu7/exXFiBEjOHLkiG22+ZgxY4pdV0U+TyUtv3MlpeT4ru1sXriItOQbDuW1/ZoQOn4itf0al0ErXVupBXAp5RvAGwB2PfAnC9zpVgh9ub0H7mbQIYD0rPLZPkW5k/z444/OboJidTPpGhu//ZpTexwnRur0Bro8MpygBx9Fb3CpJUqcxnXPUjmexKYlNdE7JDVRFEW5E0kpObp9C1vmfUP6zRSH8jqNmxI6fhK17vIr+8a5sDJZyEVKGV6iz4CD9hy4pWwC5AcffECrVq1o06YNgYGB7N69u9B93A06MgrpgTsjzejatWuZNm0aAFOmTOGTTz5xWlsURan4khPjWf3fqfz61WcOwVtvMNB9+NOMeP9TFbyLwYV74HpAasuqitKb6b1r1y5+/vln9u/fj7u7O/Hx8WRmZha6n7tRR3KGiaysLIz5LDjgjLWYBw8ezODBg8tFWxRFqbiklPy1JYyt8+eSker4GGDdps0IHTcRn0a+TmhdxeCaS6lCmaUUvXz5Mj4+PrbFHHx8fKhfvz6grRMdHBxMhw4dCA0N5fLlywD06tWLj9+fwjOP3M/U997Hz88Pi/WRt9TUVBo1akRWVlaONKN79uyha9eutG3blk6dOpGcnIzZbObVV18lKCiINm3aMHv27DzbOH/+fNq0aUPbtm156qmngPxTlM6bN48XX3zRoQ77tvj5+TF58mTat29PQEAAx44ds9V533330b59e8aOHYuvry/x8fElcZoVRalAbsTHcWr9SsJmfeEQvPVGIz2feIbHp36sgvdtcqke+PZlJ4g/bx2CsWSBKQOMkX8H82LwaeRFj6H5P1ISEhLC1KlTadasGf369WPYsGEEBwfb1oles2YNtWrVYunSpbz11lt89913AKTcuM53K9bjW7Myhw5GsnXrVnr37s26desIDQ3N0SvPzMxk2LBhLF26lKCgIG7cuIGnp2eONaEzMjLo1q0bISEh+Pj42PY9fPgwH3zwATt37sTHx4fERC1bz+2mKM1e//nrr7/mk08+4dtvv+Xdd9+lT58+vPHGG/z222988803t3q6FUWpwKSUHNr4G9sWfUdmWppDef1m9xA6fiI16pe/x39dkUsF8JzKZoEULy8v9u3bx/bt29myZQvDhg1j2rRpdOzYMd91ogGGDx8OQEaW2Race/fuzZIlS3j++edzHOP48ePUq1fPtiJV1araqrP5rQltH8A3b97Mo48+anuvRo0aQP4pSovKfv3nn376CYAdO3awatUqAPr370/16tWLXJ+iKBXb9bgrhM3+gpioQw5lBjd3ejz+NIH9B6HTVeznusuSSwXwHD3l9BuQeBp8moFb5fx3KgF6vZ5evXrRq1cvAgIC+OGHH+jQoUO+60QDVK3ihVGvI8NkYfDgwbzxxhskJiayb98++vTpk2NbKaXDetDZ7+e1JrR9IM5v39tNUWq//rPJZLIdS1EUxZ60WDiwYT3bF8/DlJHhUN6wZWtCx06kWt3STf18J3Lde+DZV3GlPBM9O01htsjISHx9fQtcJzqbh1FPepYZLy8vOnXqxMSJExk0aJDD6mAtWrTg0qVL7NmzB9ACtMlkKtKa0H379mXZsmUkJCQA2IbQSyNFaffu3W1JXcLCwrh27dpt16koiuu6dvkiS999gy3zZjsEb53BSN9nxzP07f+o4F1KXKoHnkMZTWJLSUnhpZdeIikpCYPBQNOmTfnmm28KXCc6m4dRR3yGCSklw4YN47HHHrNlbbLn5ubG0qVLeemll2zLPm7cuDHfNaF1ur+vu1q1asVbb71FcHAwer2edu3aMW/evFJJUTp58mQef/xxli5dSnBwMPXq1VMrwinKHchiMbP/l7XsXLIAU5bjUzl3BQRStU0QgaEDndC6O0eprYVeHM2bN5fHjx/P8d7Ro0e55557HDc2ZULcYfBuBJV9HMvLgWupmZxPTKVZnSp4GEvuvo+zllLNyMhAr9djMBjYtWsX48ePL5GefWkp6nnK93esjMXN1u4d1h6rllItr9R5goQL59kwazqXTx53KHPzrETwU/8goE8IW7duvePPVVEIIcrlWuilK3sIvYxygheHh0FrY3qWuUQDuLPExMQwdOhQLBYLbm5uzJkzx9lNUhSljFjMZvas+4ldK37EbL2tZ88/sAP9Rr9IVZ9aTmjdncl1A3j2EHo5TSkK2mIuAkF6Vvm9yLgVd999NwcOHHB2MxRFKWPxMdH8NvNzYs+cdChzr1yZ3iPH0LJnnzwn1Cqlx4UDuLAmNCm/wVEnBO5GnUpqoiiKSzKbTESsWc6fK5diMZscypt07Ey/fzyPV42aTmid4roBHLReeBmth15cHkY9qRmOv/iKoijlWezZ02yY9TlXo884lHlUqUqfZ8bSomtP1et2ItcO4Lry3QMHbSZ6UqoFk8WCQee6T+0pinJnMGVlsfunJUSsWYHF7Pj3tVnnbvR5dhyVq6mFnJzNtQO40LtEDxwgI8uCwV0FcEVRyq8rp07w28zpJFyIcSjzrOpNv3+Mp9m93Z3QMiUvrh1RdPoyyQl+5coVhg8fTpMmTWjZsiX3338/J06cKNK+9jPR7dknD7G3d+9eJkyYcPuNJv/EJYqiKPZMmZls+3EeP/77lTyDd4tuwYz69GsVvMsZF++Bl/49cCklQ4YMYeTIkSxZsgTQVjWLjY2lWbP8k6BkM+oFep0grYgz0Tt27EjHjsV6JFBRFOWWXTpxlA0zPyfx0gWHssrVqtPvuRdoGnSvE1qmFMZlAvinwwaVav3/t/TnPN/fsmULRqORcePG2d4LDAxESsmrr77Kr7/+ihCCf//73wwbNozw8HAmT55MnTp1iIyM5OGHH6a279388M1MLKYMVq9eTZMmTQAt4cjnn39ObGwsn332GYMGDSI8PJxPPvmEn3/+mSlTphATE8OZM2eIiYlh0qRJtt75woUL+eKLL8jMzKRz5858/fXX6PV6vv/+ez788EPq1atHs2bNbGuaK4qi2MvKSGfn0oXs+2UN5LGgV6vgvvR6ejQeXl5OaJ1SFC4TwJ0lKiqKDh06OLz/008/ERkZycGDB4mPjycoKIiePXsCcPDgQY4ePUqNGjVo3Lgxw54cyY8/b+L35d/z5ZdfMn36dACio6PZunUrp0+fpnfv3pw6dcrhOMeOHWPLli0kJyfTvHlzxo8fz/Hjx1m6dCk7d+7EaDTy/PPPs2jRIu677z4mT57Mvn378Pb2pnfv3rRr1650T5CiKC7nwpEoNsz+nKQrlx3KvGr6EDL6RfzbqZHA8k4F8GLasWMHjz/+OHq9njp16hAcHMyePXuoWrUqQUFBttSiTZo04b777sMsJS1atcqxFvrQoUPR6XTcfffdNG7cmGPHjjkcZ+DAgbi7u+Pu7k7t2rWJjY0lPDycffv22dKPpqWlUbt2bXbv3k2vXr2oVUtbCWnYsGFFvlevKErFl5mexvYffyByQ94jjgF9Qwl+8lncK5VuhkelZKgAXohWrVrlOdmsoDXk7YetdTodVSprKT1NZmypOQGH5yfzep7Svq7s1J5SSkaOHMmHH36YY9vVq1erZzIVRcnTub8iCZv9JTeuxjqUVa1Vm5AxE/BtE+iElinF5TIBPM971KkJkBQDtVuCoXTu9fbp04c333yTOXPmMHr0aAD27NlD9erVWbp0KSNHjiQxMZFt27bx8ccf59mLdjPoEUKQYco5Y3758uWMHDmSs2fPcubMGZo3b86ff/5ZaJt69erFiBEjePnll6lduzaJiYkkJyfTuXNnJk6cSEJCAlWrVmX58uW0bdu2ZE6EoiguKSM1lW0Lv+PQpt/yLG8bMpCeI0bi5lmpjFum3C6XCeB5EqWfE1wIwapVq5g0aRLTpk3Dw8MDPz8/pk+fTkpKCm3btkUIwX//+1/q1q2bZwDXCYGHQecQwJs3b05wcDCxsbHMmjULDw+PIrWpRYsWvP/++4SEhGCxWDAajXz11Vfce++9TJkyhS5dulCvXj3at2+POY+FGBRFuTNER+4j7JsZJCdcdSjzrlOX0LETaNSq7LPfKSXDddOJAmQkQ8IpqNkU3Mt3XuoL11K5npZFy3pVb3uY21npRF2NSidadCpNZtG4ynlKv5lC+PxvORy+0bFQCNr3f4Duw5/GWMROQ3G4yrlytjsznSiUSQ+8pHga9STezCTLbMHN4PqpRRVFKZ9O74tg45wZpFxLdCirXq8BoeMm0qBFSye0TClprh3AXSAneDZPN62taVlmFcAVRSlxack32PLDHI5u3+JQJoSODoMeouvQJzC6qbUhKoqKEcBdoAfuYdAjEKRlWvD2dHZrFEWpSE7u/oONc78m9XqSQ1nNhncROm4i9e5u7oSWKaXJtQO4bQi9/Kfr1Om03OBFXVJVURSlMKk3rrPpu1mc2LXdoUzodHR68FHufeRxDEajE1qnlDYXD+DCJTKSZfM06klO157jVs9rK4pSXFJKju/azubvZpGWfMOhvNZdfoSOn0Sdxk2d0DqlrLh2AAeXyAmezdNNz7XUTLLMEjeDCuCKoty6m0nX2Pjt15zas8uhTKfX03nIMDoPeQy9QfW6KzrXTicKoDOU+hB6fulEo6Ojad26dZHr8TT+PZHtdhw6dIhffvml0O3Cw8MZNEhLAnOrqUUjIyPzPcbvv/9Ohw4dCAgIoEOHDmzevNlWlpmZyZgxY2jWrBktWrRg5cqV+R4jJiYGLy8vPvnkE9t7fn5+xMfHA7Bv3z78/f05cOAAKSkpjB07liZNmtCqVSt69uzJ7t27bfutWrUKIUSO5/AtFgsTJkygdevWBAQEEBQUxNmzZ4t8DhSlPJFScmTbZub9c3yewbu2fxOe/HA6XR8boYL3HcJleuBJ6UnEpsbSpFoTDDq7ZpfyEHpB6UQbNWp0S3V5GrMnspnw9iz+f7C//vqLqKgo7r///mLXUZjIyEj27t2b5zF8fHxYt24d9evXJyoqitDQUC5evAjABx98QO3atTlx4gQWi4XERMdHWbK9/PLLDBgwIM+yQ4cO8eijj7J06VLatWvH8OHD8ff35+TJk+h0Os6cOcPRo0dt2y9evJju3buzZMkSpkyZAsDKlSu5dOkShw4dQqfTceHCBSpXVms8K64nOTGejXO+4sz+PQ5leoOBLo+OoOMDD6M3uMyfdKUEuEwPXCIxWUxYZM7VzNCVbgDPL51ojx49cmxnNpt59dVXCQoKok2bNsyePRuAlJQU+vbtS/v27Wnbtg07Nv1KaqaZ6Oho7rnnHkaPHk2rVq0ICQkhLS3N4fjLly+ndevWtG3blp49e5KZmckHH3zA0qVLCQwMZOnSpURERNC1a1fatWtH165dyb0YTkHy2jczM5N33nknxzHstWvXjvr16wPaWvHp6elkZGQA8N133/HGG28A2jrwPj4+eR539erVNG7cmFatWjmUHT16lIceeogFCxbQqVMnTp8+ze7du3n//ffR6bRf2caNGzNw4EDbOd65cydz5861XWQBxMbGUq9ePds+DRs2pHr16kU+N4ribFJK/toSxg//90Kewbte0+Y89dEXdB4yVAXvO5DL/MT1Qo/H5pskJh5GJ+yeozala0PoboeKVa9b/cpUe6BJvuX5pRPNbe7cuXh7e7Nnzx4yMjLo1q0bISEhNGrUiFWrVlG1alXi4+Pp2Kkz3fv2x0tKTp48yeLFi5kzZw5Dhw5l5cqVPPnkkznqnTp1Khs2bKBBgwYkJSXh5ubGW2+9RVRUFDNmzADgxo0bbNu2DYPBwMaNG3nzzTcLHLq216JFizz3nTp1Knv37rUdIz8rV66kXbt2uLu7k5SkPcLy9ttvEx4eTpMmTZgxYwZ16tTJsc/Nmzf56KOP+P3333MMn2d78MEHWbhwId27dwfg8OHDBAYGotfn/fz86tWr6d+/P82aNaNGjRrs37+f9u3bM2TIEPr378/27dvp27cvTz75pEqvqriMG/FxhM3+knOHDjiUGYxudB32JB0GPohOp9aVuFO5TgDP75dUCEBav5w3MSwsLIxDhw7ZMpddv36dkydP0rBhQ9588022bduGTqcj9vIlrsbFYqxiwN/fn8BALftPhw4diI6Odqi3W7dujBo1iqFDh/Lwww/neezr168zcuRITp48iRCCrKysIrf7dvY9fPgw//rXvwgLCwO0TGsXLlygW7dufPbZZ3z22We88sorLFiwIMd+kydP5uWXX8bLyyvPevv168e3335LaGhovkHb3uLFi5k0aRIAw4cPZ/HixbRv354GDRpw/PhxNm/ezObNm+nbty/Lly+nb9++Rf6MilLWpMXCoU2/sXXh92SlO47K1W/ektBxE6lRv4ETWqeUJy4TwHVCR3qfytSq0oiq7lX/LkiJhRuXoG7rvxd2KUH5pRPNTUrJl19+SWhoaI73582bx9WrV9m3bx9GoxFfPz8yMjJI93BMFZrXEPqsWbPYvXs369evJzAwkMjISIdt3n77bXr37s2qVauIjo6+pfWHi7vvhQsXGDJkCPPnz6dJE20Eo2bNmlSqVIkhQ4YA8NhjjzF37lyHfXfv3s2KFSt47bXXSEpKQqfT4eHhYZtkN2PGDMaNG8fzzz/P7NmzadWqFQcPHsRisdiGw7MlJCSwefNmoqKiEEJgNpttyWVAO8cDBgxgwIAB1KlTh9WrV6sArpRbSbFXCJv9BecPO44oGtzc6fH40wT2H6R63QrgQvfA9dZhc3PuR8ZKeT30Pn36kJGRwZw5c2zv7dmzh61bt+bYLjQ0lJkzZ9p6sCdOnODmzZtcv36d2rVrYzQa2bJlCzHnzqHXQVpmrnv5+Th9+jSdO3dm6tSp+Pj4cP78eby8vEhOTrZtc/36dRo00K7G582bd0ufL799q1SpkuMY9pKSkhg4cCAffvgh3bp1s70vhOCBBx4gPDwcgE2bNtGypeOay9u3byc6Opro6GgmTZrEm2++mWOGvE6nY/HixRw/fpx33nmHJk2a0LFjRyZPnmzLw37y5EnWrFnDihUrePrppzl37hzR0dGcP38ef39/duzYQWRkJJcuXQK0GemHDh3C19f3ls6PopQFabGw/9d1/PDqC3kG74YtWzPy4xm0v18NmVcUJouJPy79cVt1uFwAd5zEZh1EkKXzKFl2OtHff//d9gjTlClTbJO4sj333HO0bNmS9u3b07p1a8aOHYvJZOKJJ55g7969dOzYkUWLFtGiRQs8jYYiP0r26quvEhAQQOvWrenZsydt27alR48eHDlyxDbB7LXXXuONN96gW7dut5w+NL99e/funeMY9mbMmMGpU6d47733CAwMJDAwkLi4OAA++ugjpkyZQps2bViwYAGffvopAGvXruWdd94pcrvc3d1Zs2YNa9eu5auvvuLbb7/lypUrNG3alICAAEaPHk39+vVZvHixrcef7ZFHHuHHH3/k6tWrPPDAA7Ru3Zo2bdpgMBhu6VE6RSkL1y5fZOm7b7Bl3mxM1smg2YzuHvR9djxD3/4P1erWc1ILlZKSHbSn/DGF3st6M/b3sbdVn8ukE5VSciThCLUq1aJ2pdp/b+BCKUWzxd5IJ/ZGOq3qV0Wvu/VrKJVOtGhUOtGiU6kfi6Ykz5PFYmb/+jXsXLoQU1amQ7lvm3bcN/pFvGvXyWPv8k/9TmlMFhN7ruwh7FwYm85t4lrGNSoZKhHcKJhQ31D6+fWr+OlEhRDohK7Mh9BLQ6XszGSZZrw8XGYQRFGUEpJw4TwbZk3n8knHRz7dPCsR/NQ/COgTopZcdlF5BW1Pgye9GvUi1DeUbg264WG4/VzsLhPAQRtGt1jyGUJ3gYQm2bJXZEvNNOPloVZMUpQ7hcVsZs+6n9i14kfMeTzx4d+uI/eNfpEqNfNeP0Epv0wWE3tj9xIWHcbGcxv/DtoNexHiF0L3Bt1LJGjbc6kArtPl0QN3oZzg2Qx6He4GPamZrtNmRVFuz9WYaDbMnE7smVMOZe6VK9N75Bha9uyjet0uxGQxsS92HxuiN7ApZhOJ6Yl4GjwJbhhMqJ/W0/Y0lF7+aJcI4NnZu/RCn8cQug4QLjWEDlDZTc/19CyVmczJytMcEKViMptMRKxezp8/LcVidhwpbNLxXvo99zxe1Ws4oXXKrcoO2mHRYWyM2ZgjaGf3tEszaNsr9wHcw8ODhIQEatasiV7oybLkGnYSokwSmpS0Su4GElMzyTBZ8DCqx0KcQUpJQkICHh4lO6ylKNliz55mw8zpXD3nmETHo0pV+jwzlhZde6qL+HLObDHbetr2Qbtnw56E+oWWadC2V+4DeMOGDblw4QJXr14lKT2JDEsGmZVyzdhMvgq6a1D5pnMaWQwms4XYGxlkxBvxcr+1H0N6eroKOkVQlPPk4eFBw4YNy6hFyp3ClJXF7p+WELFmBZY8Hu1sdm93+jwzlsrV1Nr85VV20A47F8bv537PEbRDfEPo0bCHU4K2vXIfwI1GI/7+/gBMi5jG2lNr+WNErofff3gNTJnwjw1OaGHxSCl57sNNdPavyRePB9zSvuHh4WpN7yJQ50lxhiunTvDbzOkkXIhxKKvkXY2+/xhPs87d8thTcTazxcz+uP1aT/vcRhLSE/DQe2hB2y+EHg16UMlYydnNtCkwgAshPIBBQA+gPpAGRAHrpZSHS795OVVxq0JKVgoWaUEn7B6/qlQTrvxV1s25LUIIgvxqEHE2Ud0HV5QKwJSZyR/LF7F33Spk7gWngBbdguk9agyVqno7oXVKfvIL2j0a9iDUL7TcBW17+QZwIcQUYDCwBdgNxAEeQDNgmjW4/5+UsnhpwIqhirEKEklyZjLe7nb/CSrVhNSEsmpGiensX4OfD13mwrU0GtUon78giqIU7uLxo2yY9TnXLl1wKKtcvQb9nnuBph07O6FlSl4KCtohfiH0bNCz3AZtewX1wPdIKafkU/aZEKI2cFfJNyl/2UlMbmTeyBXAfSDtGphNoC/3dwVsgvy1WacRZxNVAFcUF5SVkc7OpQvY98tayOOJhlbBfen19Gg88sm8p5Sd7KCdPXs8Pi3eJYO2vXyjnZRyvRCiFuALnJJSJuUqj0PrlZeZ6u7ahI+k9CQaVWn0d0Glmtq/adfAq1ZZNum2NKtdBW9PI3uiE3mkg5pIpSiu5MKRKDbM/pykK5cdyrxq+hAy+kX82xVrhUylhJgtZg7EHbBNRItPi8dd706PBtrweM+Grhe07RU0hP4c8B/gNOAvhBgjpVxbZi3LQzWPagAkZSTlLKhsDeCp8S4VwHU6QUff6kScTXR2UxRFKaLMtFS2/fgDB8PW51ke0DeU4Cefxb1S5TJumQI5g/bGcxu5mnbVFrRD/EIIbhjs0kHbXkHjzZOAVlLKq0KIxsAiwKkB3NYDzx3As3vgLngfvJN/DTYdi+Nqcga1qrgXvoOiKE5z43w085b/QHL8VYeyqrVqEzJmAr5tAp3QsjubRVq0oB2t9bSzg3b3Bt1tPe3Kxop3QVVQAM+UUl4FkFKeEUI4Pbpk3/e+ln4tZ0El67rBN+PLuEW3z/4++MA2Kl2gopRH6SkphC/4lpPhG/MsDwwdSI/HR+LmWTF6dq7AIi1ExkXaJqLFpcXhpnPT7mn7hhDcKLhCBm17BQXwhkKIL/J7LaWcUHrNylsVtyrohb5C9cDbNPDGy93AH6fjVQBXlHLo1J4/2Tj3a25ec7zVVa1OPULGTaBRy1tby0EpnryGx910brae9p0QtO0VFMBfzfV6X2k2pCh0Qoe3u3eFCuAGvY7O/jXYecr1Rg8UpSJLvXGdzd/P5vgf2xzKhNDRfuCDdBv6BEZ3tSpiabJ/5GtTzCbi0+JtQTv7nraX2505y7+gWeg/lGVDiqqaezXHAG5wA/eqLhnAAbo19WHTsTguXEulYXU1BKcoziSl5Pgf29j8/WzSkm84lNdo0IjQcROp36yFE1p3ZzBZTOyP3W/raSekJ+SYiFZR72nfqoJmoa8D8k3VJKUcXCotKkQ192qO98BB64W74D1w0AI4wB+nEhgapAK4ojhLSmICG+fO5PTePx3KhE5HnXadGPbyvzAYjU5oXcVmn087OzWnqz+nXdoKGkL/xPqvAOYAz5V+c/KXFJtK7NnrVDfW4NzNaMcNXHQ1NoBmdbzw8XJn5+l4hgY1KnwHRVFKlJSSw+EbCZ//LRmpjkmRavk1JnTcRI6eO6+CdwkyWUxEXIkgLDqMzTGbuZZxzZYw5D7f+8r1MqblQUFD6FuzvxdCwfRxggAAIABJREFUpNi/LgrrUqvbAHfrcVZIKScXt6EXjiWydfEJqj/mw8GMSMcNKvvA9YvFrd6phBB0a1qTnacS1LroilLGblyNI+ybLzl36IBDmd5goMujI+j4wMPoDQaOnjvvhBZWLFmWLCIuRxB2TgvaSRlJTsun7eqKuu5ovkPpBcgA+kgpU4QQRmCHEOJXKaXj2FQRGD20pnqL6iSlJzkGOq/acMnxP6Cr6NbEhzWRlzgRm0LzulWc3RxFqfCkxcLB339l24/zyEpPcyivd3dzQsdNpGbDMl0xukLKMmex+8purad9fjPXM65TyVCJ4EbBhPqG0q1BNzwMajLgrSroHngNu5d6IUR1tOF0AKSUBS4fJqWUQIr1pdH6VZwLAa0Cdz0AVUU1TNJESlYKVdzsAp1XHbh5FSxm0OmLexin6Xa3dh9856l4FcAVpZRdu3yRsNlfcuFolEOZwc2d7sOfot2AB9C54N+S8sIkTWy7sI2w6DC2nN/CjcwbVDZWplejXoT4htC1flcVtG9TQT3wfWgBNzto77crk0DjwioXQuit9TQFvpJS7s5jmzHAGIBatWoRHh6eZ10pV7TYnxCjzUD/betv1DL+vWxq/cvXaSYt7Ny4jiy3aoU1rVyqU0mwevdxGpvOFbhdSkpKvudJ+ZurnacGSVqK3CNOaLOrnavikhYLcYf2cTFiJ9Jscij3qt8I314hJFeuxrZt2x3K75TzVFxZMovjacc5kHqAQ6mHSI9Jx0N4EFApgHbe7Wjh2QKj2Qhn4M8zxRqMVewUdA/c/3Yrl1KagUAhRDVglRCitZQyKtc23wDfADRv3lz26tUrz7piz97gXPheWt3VGmKgadumtKvd7u8NjlyHk9/QrU0TqOuaiyoMuHGYJXtiuLdbDzyM+V/5h4eHk995Uv7maucp7riWmffuXm3K/Niudq6KI/78OTbM+pwrp044lLl5etLziWdo07c/QqfLt4474TzdqgxzBn9c/IOwc2GEnw/XRkeNVWhTqQ1Pd36aLvW74KZ3c3YzK6SChtD9pJTRBZQLoIGU0jEBbi5SyiQhRDjQH3AcsyoCo4cW0CqjDS/Hp+V6ZMyrrvZvSizgmgE8uHkt5v0Rze6ziQQ3c52kLIpSnplNJvasWcGulUuw5NHr9gvswH2jX6CqT20ntM41ZZgz2Hlxpy1o38y6SRW3KvS9qy8hfiF0qdeFndt3Etwo2NlNrdAKGkL/WAihA9agDYNfBTzQhsN7A32ByUCeAdyaijTLGrw9gX7AR8VtqJs1gHtI7ZECxwBu/c+XUqYZTktUl8Y1cTfo2HIsTgVwRSkBsWdOsWHW51w9d9ahzL1yZXqPHEPLnn3Ukx9FkG5KZ+fFnWw4t4Gt57eSakrF292bEN8QQvxC6Fy3M0a9esSuLBU0hP6YEKIl8ATwLFAPSAWOAr8AH0gp0wuoux7wg/U+uA5YJqX8ubgNzZ6FbjS7oxO6AgJ4bHEP4XQeRj1dmtRk6wnHTEeKohSdKTOTXSsXs2ftSqTF4lDeNKgLff8xHq/qNfLYW8mWZkpjx8UdhEWHsfXCVtJMaVRzr8YA/wGE+IYQ9P/snXdYVGf6v+8zBWYGZqgzQ2+CClgQNfaumE0xbZNsujExdZNs8s1u9rdpm92U3U12U0w1ieltUza9iBpbNHbE3qnSe2fK+f1xEERARwWGgfe+Li5k3nPOPIPDfM7zvE8JHYtWJUTbXZy0jEyW5d3Ag2dyYVmWM4FRpzzQRbxastDtjU4CdYGUNZzQtMXLB7yMHu2BA8wYYuHRr3dxpLSO2GDRKlAgOF2O7t/DT688T/nRjsFBvcmPWQtuZ/D4ScLr7oJ6Wz1r8teQnp3O6rzVNNgbCPAO4LzY80iLSWNsiBDtvoKrdeBuR1JJaHVqmhscBOuDO3rgoHjhHuyBA0wfooTOV+4rJjb4rPMIBYIBg62pkV8+eY8t338NcseK1aGTpjFj/i0YTH5usK5vU2+rZ3W+UvK1Nn8tDfYGAnWBXBB3AWkxaYyxjkGj8hi5GDB41P+It15DU6OdoMCgLgTc6vEeeHSQD3HBPqzcV8KNk4SACwSukLMzk6WLX6CqqLDDmm9AILMX3smg0ePcYFnfpd5Wz6q8Va2i3ehoJEgXxLxB80iLTmO0dTRqUQffp/EoAffSa2husBOsC+ZgxcGOB/haoHh37xvWzUwfYuH9Ddk0NDvQe4k/IIGgK5rq61nz4VtsT/+h0/VhM9KYdt0CdD4Dc9zkidTZ6liZu5L07HTW5q+lydFEsD6Yi+MvJi0mjVRLqhBtD+JkZWSpJztRluWtJ1vvCbx0ioCbDWbKGstwyk5U0nE1m75WOPxzb5vV7cwYambJL0dYe7CUOUlWd5sjEPRJjmzbTPrrL1FT1jHp02S2MOeWu4gZ0W1pOB5LbXMtP+f+THp2Or/k/0Kzsxmz3sxlCZcxJ3oOoyyjhGh7KCfzwP/d8l0HjAG2o3RlGwFsACb3rGkd8dJraKxtJlgfjN1pp6qpigBdQNsBvhZorAJbI2g9t0XfuNggjDoNP+0qFAIuEJxAQ20Nq959g12rlne6PurcC5l81fV46QbuQIzq5mpW5Srh8V+O/oLNacNisHD5kMtJi04jxZLS3vkReCQnKyObASBJ0sfALbIs72j5eRhwf++Y1x5vvZqqEjvB+iAAShpKThDwFrGrLYSAmN43sJvw0qiYNdTC8j1F2B1ONGrxhyYQABzYuI5lb7xMfVVlh7WA0DDSbr2biMRhbrDM/VQ3V/Nzzs8szV7KuqPrsDvtWA1WrhxyJXNj5jLCPEKIdj/DlT3wocfEG0CW5Z2SJKX0oE1dom3ZAzfrlUztkvoSBgcMbjvAFKZ8ry7waAEHSEsO4cuMo2zKqmDCoCB3myMQuJX6qkqWL3mV/b+u7bAmSSrGXHgJEy6/Gq2Xtxuscx9VTVWsyFlBenY66wvWY3faCfUJ5eqhV5MWk8bw4OFCtPsxrgj4HkmS3gDeRxlici1KM5dex1unobnBQYiP0ja1sO6EjFNTuPK92jPngh/PtMFmvDQqftpVKARcMGCRZZm9v6xixduLaayp7rAeHBnN3NvuISR+cCdn908qGytZkbuCpdlL2XB0A3bZTphPGNcmXsuc6DkMDx4uatwHCK4I+I3A7cA9LT+vBl7pMYtOgpdeg8PuJEgbjIREYf2JAn7MAz/a+8Z1Mz7eGqYmBJO+u4hHL0wSf5CCAUdNeSnLXn+Jw1s3dVhTqdWMu+QKxl1yBWpN/28qUtFYwYocRbQ3FmzELtsJ9w3nuqTrSItJIzkoWXxGDEBOKeCyLDdKkvQq8L0sy/t6waYu8dIr5srNEma9uaMHrjMp3dj6gYCDEkZftqeYXUerGRYumk8IBgayLLNjxVJWvfcmzQ31HdatcfHMve0ezNH9u09CeWM5y3OWszRrKZsKN+GQHUT4RnB98vWkxaSRFChu7Ac6pxRwSZLmAU8DXkBsy/7332RZntfTxp2It14pdWhqsBPiE9JRwEHxwvtBCB1gdqIVlQQ/7SoUAi4YEFQVF7J08Yvk7MjosKbWapl4+TWMueASVOr+WfZU2lDK8uzlpOeks7lwMw7ZQZQxihuH3UhadBpDA4cK0Ra04koI/VHgHGAlgCzLGZIkxfScSV1zzANvbrBj9bFyoOJAx4NMYf3GAw/08eKc2EB+2lXI/6UNcbc5AkGPITudbPvpO9Z+9A62po4zksIGJzL39nsIDItwg3U9S2FdYaunva14GzIyMaYYFgxbQFpMGkMChgjRFnSKKwJul2W5qi+8gY4X8BCfENbmr0WW5fZvblM4HFrhJgu7n98MC+XRr3exv6iGwVaju80RCLqd8qP5LH3tefL3duyiqPH2ZspVN5Ay93xU/ajZSH5tPsuyl7E0eymZJZkAxPvHc/vI25kdPZt4/3gh2oJT4oqA75Qk6WpALUlSAnA3sK5nzeqcNgF3EGIIocHeQFVTFf46/7aDTGFKHbjDDmqP6hTbKecND+Wxb3bxdcZR7p8rvHBB/8HpcLD52/+x7tMPcNhsHdajho1gzi13428NcYN13U9WVRbLcpaRnp3O7jLlZiUxMJG7R93N7OjZxPr17z19QffjisLdhTJStAn4EPgJeLwnjeoK7xYBb2qwExoYCkBhfWFHAZedylQyv3B3mNmtmI3eTBwUzNfbj/J/aYPFXbmgX1CSk8VPrzxP0eGO22BeegPTrlvA8JlzPf79fqjyEEuzl5Kend665TcieAT3jb6P2dGziTRGutlCgSdzUgGXJEkNfC3L8mzOcC54d9IuhG5oqwUfGji07aDWWvCj/ULAAeaNDONPn2eSmVfFyEj/U58gEPRRHHYbG/73KRv+91+cDnuH9bjUscy++U6MQcFusO7skWWZfRX7SM9OJz07nSNVR5CQGGUZxQNjH2B29OzWPhYCwdlyUgGXZdkhSVK9JEl+sixX9ZZRXeGla8tCj/NVPPD82hMyzltrwfOBsb1oXc8xd1gID325k6+3HxUCLvBYCg8d4KdXn6c0J6vDms7XyMz5tzB08nSP87plWWZX2S6WZi9lWfYycmtyUUkqxljHcPXQq5kVNQuzwexuMwX9EFdC6I3ADkmS0oG6Yw/Ksnx3j1nVBSq1Ci+dmqZ6G0G6IPQaPbk1ue0P6kfNXI7hp9cybYiZbzOP8pfzEt1tjkBwWtiaGln36Yds+fZLZNnZYX3wuEnMXHAbPv4BnZzdN3HKTjJLMltFu6CuAI2kYVzoOBYMW8DMqJkE6gLdbaagn+OKgH/X8tUn0PlqaayzIUkSUcaojgKuDwCtAary3GNgDzFvZBjpu4vYeKTc3aYIBC6TuyuTpYsXUVlY0GHN4OfPrJtuZ/C4SW6w7PRxOB0caDzAug3rWJ69nOKGYrQqLRPDJnJnyp1Mj5yOn7fo1yDoPVzpxPZObxjiKt4GLU11yt5ZpDGSQ1WH2h8gSeAfBZXZbrCu55iVaEGvVfP19qPMFTf2gj5OU30dqz94i8xlP3a6njR1JtNvWIjet2+XRtqcNjYVbiI9O50VOSsobyzHu9SbyeGTmRM9h2kR0/D18nW3mYIBSpcCLknSf2VZvkKSpB0oQ0zaIcvyiB61rAt0Phoa65SSk0hjJKvyVuFwOtoPpPeP7ncCbvDSkJZs5fsdBcyc0v97Pws8l0NbNrDsjZepLS/rsGYMMjN74R3Ejeq7+SnNjmZ+LfiV9Ox0fs79maqmKvQaPdMiphFWG8atabdi0BrcbaZAcFIP/Njwkgt6wxBX0floqS5TOjVFGCOwOW0U1xcT2pLUBigeeM6vbrKw57g0NYKvMo6yrVjFHHcbIxCcQH11FT+/vZi9v6zqdH1k2vlMueoGvA19T/wa7Y2sO7qO9Ox0VuaupNZWi6/Wl+mR05kTPYeJYRPRaXSsXLlSiLegz9ClgMuyXNDyvU+5st4+7UPoALk1ue0FPCAamqqgoULZE+8nTI4PJtRPx9o8G39ytzECQQunGvkZEBpO2q13EZE4zA3WdU29rZ41+WtYlr2MVXmraLA34Oftx+zo2cyJnsP40PF4qb3cbaZA0CWuDDOpoS2E7gVogTpZlk09aVhX6Hy0NNXbkJ0yUaYoQBHwc0LPaTvIX3mcypx+JeBqlcRlqRG89PNBCqsaCfHTudskwQCnurSE5W++3OnIT0mlYuy8y5hw2VVovPqGENY217IqbxXp2en8kv8LjY5GAnWBXBB3AbOjZzM2ZCxaldiiEngGriSxtcsykSTpYpThJm7B26BBllsmkhlC0Kg05NTktD/IP1r5XpENoSN738ge5LejI3jx54N8sS2PO6bHu9scwQBFdjrJXP4jqz94i+aGhg7r5pg45t52D9bYQW6wrj1VTVWszF1JenY6646uw+a0YdabuSThEuZEzyHVkto+h0Yg8BBOu1m4LMtfSpL0554wxhV0vsrdcVO9DZ2PgRhTDIerDrc/KKBFwPtZIhtATLAPgwNUfLY5j9unDfK4phcCz6f8aD7pixeRt2dnhzW1VsuEy65izIWXota4bxZBeWM5K3JWsCx7GRsKNmCX7YT6hPK7ob8jLTqNEeYRqCSV2+wTCLoDV0Lolx73owoYQydZ6b2FzqAIeGOdHT8zxPnFsbd87wkH+YO3SQmh90OmhGt4c2cdW7IrGBMjasoEvcOx4SPrP/0Qu625w3rYkCTSbr2LoHD39PcuqS9hec5ylmUvY1PRJpyyk0hjJNcnX8+c6DkkByWLG15Bv8KVW+QLj/u3HcgCLuoRa1zA2+eYgCulZIP8B7EsZxmN9kZ0mpY9YUlSwugV/c8DBxgbouHj/Q4+2pgrBFzQKxRnHeanV5+n+MihDmtanZ6pV89n5JzfIKl616vNq8lrFe3tJduRkYn1i+Xm4TeTFp3G4AAxAEjQf3FlD/zG3jDEVXQ+LRPJWgQ8zj8Op+wkuzqbIYHHjdv0j4Lyjh82/QGdRuKS1HA+3ZzHQ+cnEuDTNxKEBP0Pe3Mzv37xCZu+/gynw9FhPSZlNHMW3okp2NIr9siyzOGqwyzLXsbynOXsKd8DKGM570y5k1lRs4gPELkhgoGBKyH0F0623ts90XU+bSF0gEF+SpLMocpD7QU8IBoO/wyyrHjk/Yxrx0fz/q85fLYlj4VT49xtjqAfkr93N0tfe4Hyox3bEut8jcyYfwuJvTB8RJZldpftZlnOMpZlLyOrOguAFHMK94+5n5lRM8VYTsGAxJUQug5IAj5p+flyYAuQ0VNGnQxvQ4sHXq944NGmaNSSumNL1cA4sNVDTSGYQk+8jMczNMTEmOgAPtiQzU2TY1Gp+t9NisA9NDc2sPajd9n207fKDfAJDJ4whVk33orBr+cm4zmcDjJKMlo97YK6AtSSmrEhY7k28VpmRM3AYugdr18g6Ku4IuAJwAxZlm0AkiS9CiyVZfneHrWsC1RqFV56DQ01ioB7qb2UnuiVJwh4UEv5StnBfingANdNiOaejzNYe7CUqYPFuELB2ZOVsYWlr79ITWlJhzWfgEBm3XQ7CWMn9Mhz2xwtfcdz2vqOe6m8mBg2kTtS7mB6xHT8dWKcrkBwDFcEPAwwAsfGYPm2POY29EYtDbVtWbAJAQnsK9/X/qCgln2wsoMQO6UXres9zh0WQpCPF+//mi0EXHBW2Bsb+PHlZ9m1anmn68NnzWXqNTei8+newR0N9gbWHVWme63MW0lNcw0GjYGpEVOZFT2LKeFT8NH6dOtzCgT9BVcE/B/ANkmSfm75eRrw1x6zyAUMRi8aatoEPDEwkfTsdGqaazB6tfSdMUWA2rvfJrIBeGvUXDE2ktdWHSK/soFwf727TRJ4GLIsc2DDL+z6+C3sDfUd1v2sIaTdchdRw7qvIVJNcw2r81azPGc5a/PXtrYwnRk5k9nRs5kQNgFvtXe3PZ9A0F9xJQv9LUmSfgDGtTz0Z1mWC3vWrJOjN3lRUdj2YZMYlAjA3vK9jA1pmXKkUin74GX9V8ABrhkXxeLVh3lnXRZ/OS/R3eYIPIjainKWv/kKBzet77AmSSpSz7+ISVdcg9b77Fv2ljeWszJ3Jcuyl/Frwa+t3dDmDZrH7OjZjLaOFi1MBYLTxJUsdAmYDcTJsvw3SZKiJEk6R5bljT1vXufojV4cPVDZ+nNioCJce8r2tAk4KPvgpft727xeJSLAwG+GhfDRhhzunpWAr7f7ul8JPANZltm5Mp1V775JU31dh/XgyGjSbrub0PghnZztOoV1hSzPWc7ynOVsKdqCU3YS7hvO1UOvZnb0bNENTSA4S1z5tH8ZcAIzgb8BNcDngNsG+uqNWhrrbDgdTlRqFUH6IKwGK7vLd7c/MCge9v8ETgf0417HN0+J49vMAj7ZlMtNk2PdbY6gD1NZVEj64kXk7NzeYU2l1jDukisYd8nlqDVn5g3nVOewLGcZy7OXk1maCUC8fzwLhy9kdvRshgQMEY1VBIJuwhUBHyfLcqokSdsAZFmukCTJrZ1DDEYvkKGh1oaPn7JXlhiUyJ6yPe0PDIoHp01pqRrYf4UtJdKfsTEBLFl7hBsmRKNRC69G0B6n08G2H75l7SfvYm9q6rDuYwnlt396iODI6NO6rizL7K/Yr3RDy1nGgYoDACQHJXNP6j3MippFrF///dsTCNyJKwJukyRJTUv/c0mSzCgeudvQG5X7h4aaNgFPCkxiVe4q6mx1bVmrraVkh/q1gIPihd/63hZ+3FXIBSPcWiQg6GOU5maz9NUXKDi4r8OaxtubyVdeT7Xe6LJ4O2UnO0p3KOHx7OXk1OQgIZFqTeWBsQ8wM2omYb7iPSgQ9DSuCPgLwP8AiyRJTwC/BR7qUatOgcGkhPeOz0QfaR6JjExmSSYTwlrqVI8vJUuY3dtm9iqzE63EBBl4fc0Rzh8eKsKUAhx2Gxu//Ixfv/gEp8PeYT1q2Ejm3HIX/tYQVq5cedJr2Rw2NhZuZHnOcn7O/ZnShlI0koZxoeOYP2w+MyJnEKwP7qFXIhAIOuOkAi5Jkgo4AvwJmAVIwMWyLO852Xk9zTEPvL66TcCPJcRkFGe0CbiPGXR+UNrR8+hvqFUSN02J4+Evd7LhSDnj44LcbZLAjRQc3MfSV1+gNLfjQB9vgw/Trr+JYdPnnPRGr85Wx5r8NazIXsGa/DXU2mrRa/RMDp/MzKiZTI2YisnL1JMvQyAQnISTCrgsy05Jkv4ty/IEYO/Jju1N2kLobQLu6+VLgn8CW4u3th0oSWBJguI+Y3qPcvnoCF5YfoBFKw4IAR+g2Joa+eWT99n6/dfIcsedrvix45m14HZ8Azt/f5Q2lLIydyUrcla0lnsF6gJJi0ljZuRMxoeNFzXaAkEfwZUQ+lJJki4DvpDlThojuwFvgwaVSmptp3qMFEsK3xz6BrvTjkbV8tLMQ2HX//rtUJPj0WnV3Do1jse/28OW7HJGR4tRowOJnJ2ZLF38AlVFHds0GPz8mbXgNhLGTergdedU57C8ajlLflhCRnEGMjLhvuFcNfQqZkbNJMWcgrofV3EIBJ6KKwJ+H+AD2CVJakQJo8uyLLstdiZJEnqjlvrjPHCAVEsqn+z7hAMVB1qbu2BJhC1vQW0RGEPcYG3vcvW4KF5ZeYgXlh/knQXnuNscQS/QWFfL6g/eYsfynzpdT542i2nX3YTeqPzJyrLM7vLdrMhZwYqcFRysPAjA0MCh3J5yOzMjZ4o52gKBB+BKJzZjbxhyuuhNXtRXnSDg1lQANhZubC/gAMW7B4SAG7w03Dwljn/+uJeM3EpSIsXwh/7MgY3rWL7kVeoqyjusGYPNpC38PTEpo7E77WwsUJLQVuSuoLCuEJWkItWiZI7rj+q5bPZlbngFAoHgTPHYtl2+/t7UlLevZw3xCSHWL5b1Beu5IfkG5UHzMQHfC4Nm9rKV7uG6CdG8tvoQi5Yf4M35buu3I+hB6iorWL7kFQ5sWNdxUZJISTufMZdfweaKbby+9kFW5a2iqqkKb7U3E8ImcGfKnUyLmEaALgCAlcUre/cFCASCs8ZjBdzH35vCI9UdHp8YNpHP939Ok6NJSbbxNYMhSPHABwi+3hpumhTLv9P3szO/imHhfu42SdBNyE4nO1ctY9V7b9JU17ENql9oKL4XjuEraRd//moujY5GTF4mpkVMY2bUTCaGTcSgNbjBcoFA0N14tIA31tpw2JyotW2dxyaGTeSDPR+wrXgb40PHKw9akqBkYGSiH+OGSTG8+csR/vXTPt4Ve+H9grw9O1n13psUHjrQcVElUTJczwehm7Dl/YrVYOWShEuYGTVTDAoRCPoprtSBZ8qyPKyX7HEZH3+llKWuqglTcNsYzTHWMWhUGtYdXdcm4OahsP3jAZGJfgyTTsud0+N54vs9rDtYysR40WTDU6koyGf1B293OjUMoNSviV+GlxEUFc38yAXMippFUlCSSEITCPo5rtSBb5ckKUqW5ZzeMsoVfFsEvLayvYAbtAZSLamszl3NfaPvUx60JkFzDVRmQ0CMG6x1D9dNiOatX47wjx/38tWdHcuHBH2X5oZ6srZv5eDmDexbtxqnw9HhGLvKSWGqgeFzL+G2mNlEm06vj7lAIPBsXAmhhwK7JEnaCLRuusmyPK/HrHKBVg+8suNghplRM/nHxn9wuOowcX5xEDpSWSjIHFACrtOquXfOYP74WSbf7yjk/BGh7jZJcBIctmYaamtY8+Qj5O7KxGHv2P70GPqkKM694U7iYpJ70UKBQNCXcEXAH+txK86Akwn4rKhZ/GPjP1iRs4K44XFgSQZJDQXbIcmt9x29zqWpEby+5jDPLN1HWrIVrZhU1meQnU6Kjhzi0JYNHNq8gWFNypZPVuHWLs+xDIpnxvULiRgqhFsgGOi4Uge+SpKkaCBBluVlkiQZALe3ZfI2aFBrVZ0KeIhPCMODh7Msexk3D78ZtDqlHryg4wzk/o5aJfGnuUO5+d3NfLIpl2vHizCrO7E1N5G7M5NDmzdwaOvG9vXbIeO7PM8UbGHy1TcwdMIUJJW4CRMIBC4IuCRJC4FbgEBgEBAOvIoy3MRtSJKEj793pwIOihf+3NbnyKvJI8IYoYTRDywdUIlsx5iVaGFsTADPLdvPvJQwTDqRkdyb1FVWcHjbJg5t3kj2jm2dzuPuDF+VmoQpM0iYPovwIUmo1G6/bxYIBH0IV0LodwLnABsAZFk+IEmSpUetchFff29quxDw82LP4/mtz/PN4W+4feTtioBnfAA1hWAaWHvBkiTxyAXJzHtpLYuWH+DB85PcbVK/RpZlyvJyFC97ywYKDu5XbhxdQON0klhRT/INC4i86mpUwtsWCARd4IqAN8my3Hwsg1mSJA3QJ4aa+Ph7U3SkqtO1UN9Qzgk9h68OfsWtI25F1ZrItn3ACTjA8Ag/rhwTyVu/ZHGJ/MbTAAAgAElEQVTl2CjiLb7uNqlf4bDbyd+7qzU03tlAkc7QeuuIiIolIHMXAXWNaIOCSPrkczQBAT1ssUAg8HRcEfBVkiT9BdBLkjQHuAP45lQnSZIUCbwLhABOYLEsy8+fjbEnYgzScWhLMU6njErVMSx+0aCL+Mvav7C1aCtjrMMASRHwIed2pxkew/1zh/DdjgL+/u1u3r5xrCgrO0saaqrJytjCoa2byMrYQlN9x85oneEbFMyg1HOITR6O9/fp1Hz6KdrwcHSDB6P28xPiLRAIXMIVAf8zcBOwA7gV+B54w4Xz7MD/ybK8VZIkI7BFkqR0WZa7raepKUiH0ylTV9mEMVDXYX129Gye2PAEXxz4gjFTnoTgBCjI6K6n9ziCfb25Z1YCj3+3h+V7ipmdZHW3SR6FLMuUZB/h8NZNHN62icID+zudud0Z1rh4Bo0eR9zoc7DExFG7fDmFDzxETWkpgfPnY777LkrfO9jDr0AgEPQnXBHw6cAHsiy/fjoXlmW5ACho+XeNJEl7UBLgulHAlQYuNWWNnQq4XqPn4viL+WTfJ9w7+l7M4aPh4LIBmch2jBsmxvDJplwe/XoXE+ODMHh5bDfdXqG5sYHsHRkc2bqJI9s2U9vJ1K/OUGu1RA0b2SLaYzEGKp3wbMXF5N/zB2qWLsV7yBAiXnoR/fDhPfkSBAJBP8WVT+/5wKuSJJUBa1q+1sqyXOHqk0iSFAOMoiURrrswBimiXVPWAAmdj828eujVfLjnQz7Z9wm/jxgL2z+CiiwIjO1OUzwGrVrFk5cO5/JX1/PcsgP85bxEd5vU56goyOfw1s0cydhM3u4dJ22ocjx6kx9xqWMZNGYc0cNT8NK1dQiUZZnKzz6j+F9PIzc1Yb73XoIW3IikFRUBAoHgzJBkF7NjJUkKA34L3A+EybLskusmSZIvsAp4QpblLzpZvwWlTA2z2Tz6v//9r4umg9Mhs+dTGfMwCcuwrj3qxcWLOdJ0hKf9b2Tylj+yO/Feiq3TXX6evkZtbS2+vmeXhPbWzibW5Nt5dIKOaFP/LE9y9ffkdNipPZpHVc4RqrIP01Tl8r0p+iAzftFx+EXH4WMJ7bRGW11UjOmDD/Dav5/mhASqr70Gh7Xj9kX4BuXc/HGuheW7k+54Tw0ExO/JdcTvyjVmzJixRZblMWdyrit14NcCU4DhQCnwIooXfkokSdICn6OE4DuIN4Asy4uBxQBDhgyRp0+f3um16tavp/KL/xH6t8dQ6ds8m5ylawkyBTF9eteepLHIyPwf53Mo1ovJO3xJ8q0lqYvn8QRWrlxJV78nVxl1jo1Z/1nJFznefHHHJNSdJAF6Oif7PdWUl3Jk2xaObNtE9o7t2BobXLqm1ltH1PAU4kaNIXbUGIxBXQ+JkW02yt56m9IXX0Ty9sby97/hf9llXTZiKd6XCUDC9BEu2dKddMd7aiAgfk+uI35XPY8rXvRzwCGU5i0/y7Kc5cqFJSXF+U1gjyzL/zljC1uw5edT/c03WO79QzsBNwbplRD6SRhtHc2E0Am8uXMJl4enYMjbeLbmeDx+Bi0PX5DEPR9n8Pa6LG6a3L+3FJwOBwUH93Nk22YOb9tESdZhl8/1t4YSmzqGuFFjiUgajsaFsHfDjp0UPPwwTXv3YkxLw/rQg2gtfaJ9gkAg6Ce40ko1WJKkZGAq8IQkSQnAPlmWrzvFqZOA64AdkiQdS/3+iyzL35+JoeqW0hp7RQXasLDWx41Bui5rwY/n96N+zzXfX8MHpnAWZq2H5jrw8jkTU/oN80aG8VXGUZ7+aS8zhpiJM/evcFdzbTWZy38ie/tWsndm0FTnWpmXSq0hIjGZuNSxxI4aS2BYuMvP6ayvp2TRi5S/8w6a4GAiXlyEcfbsM30JAoFA0CWuhNBNQBQQDcQAfih13SdFluW1QLfFZdX+SpKao7z9/qTpWC24w4nqJIM6RphHMCNyBm/kr+MiFViOboOYyd1lnkciSRL/uHQ4c55dzf2fbufT2yZ6dCjd1txE/u6dZGVu5UjGVsrzc9nh4rk+AYHEpowhLnWMkoCmN5z289euXk3hY3/Dlp+P/5VXYrn//1Abjad9HYFAIHAFV0Loa4/7elGW5byeNalz1IGBADjKy9o9bgrW43TK1Fa0nwveGX8c+0cuzr+YZwL9+Vf2ugEv4AAWk46/XZTMPR9n8Pqaw9w2bZC7TXIZWZYpz88la/tWjmRsIX/PLuy2ZtdOliRC4wcTO0oJjVti4s54SIi9tJSiJ5+i+vvv8YqLI/r99zCMOaOcFIFAIHAZV0LoIwBamrG4rYWqJlhJFrKXta/D9bcqol1ZXH9KAY80RnLT8Jt4ZfsrXHzkJyZO+1PPGOthzBsZxg87CvnP0v3MHGphsLXveo2NtbXk7MzgSMZWsjK3UltW6vK5OqOJ6JYEtJiU0RhMfmdli+x0KqVhz/wbuaGB4Lt+T9DChai8vM7qugKBQOAKroTQhwHvoUwjkyRJKgFukGV5Z08bdzwqX18krRb7CR/YfhYl1FlZ1ECUCzM6FgxbwI873+PhpgK+qCvGz0ckFkmSxOOXDGPus6u5+6NtfHnnJHTavlFa5rDbKDiwj5yd28nK3HZa3c+QJMIGJxIzchSxI0djiRuEStU9r6vp0CEKHnmUhi1bMIwdS8hjj+Ed178TAQUCQd/ClRD6YuA+WZZ/BpAkaXrLYxN70K4OSJKEOjgYxwkeuMHkhZdOTWVRvUvX0Wl0PJV4I9fueIHHV/2Jf/3mLdETHKXN6tOXj2DB25t56vs9PHbRMLfYITudlORkkbMjg5yd28nbswtbU6PL5xuDzcSMTCV25GiyK6uZPbd7+947m5ooe20xpa+/jspgIPSJx/G79FLxHhIIBL2OKwLuc0y8AWRZXilJklvStzWBgdjL2u+BS5KEv9VAZbFrAg6QnHQFd6x5nBekLaTs/ZBrEq/pblM9kplDrdw0OZY31x5hUnwwackhvfK8lUWF5OzIIHvndnJ3bqehptrlczVe3kQkDSN2ZCrRI1MJDItoFdP8lSu71c66DRspfPRRmrOyMF14IdY/P4AmKKhbn0MgEAhcxRUBPyxJ0sMoYXSAa4EjPWdS16iDg3CUdNzz9LMYKDx86lKyVvT+3KSPYafcyNObnibOL44JYRO60VLP5U/nDmHDkTL++Fkmw8L9CPM/eV7BmVBXWUHOrkxydmwnZ+d2qkuKTuv8oIgoYlJGEzMylYihyWh6eM/ZUVlJ0dNPU/X5F2gjIoh84w18J0/q0ecUCASCU+GKgC8AHgOOdVJbDdzYYxadBE1gEE1793V43N9q4MDmIuw2BxoX925VsVN5cuNirh02gftW3scbc98gOSi5u032OLw1ahZdlcoFL6zhno+38eHC8WhPUp7nCvXVVeTt3kHu7h3k7tpBWV7OaZ3v4x9A1LCRRA0bSfSIUSftftadyLJM9bffUfTUUziqqghaeDPBd9zRrpGQQCAQuIsuBVySJB1wGxCPMkr0/2RZtvWWYZ2hCQ7CXl6OLMvt9hz9rXqQoaqkgaAwF5uRxE3HZ90iXom/lvn73uTW9FtZMncJgwMG94zxHkRssA9PXjqcez7O4Knv9/LIhS5kB3bB//75GIe3bjqtc7z0BiKThxM1LIXo4SMJDI/s9T3m5txcCv/6GHW//IJu5Aii3lqCbsiQXrVBIBAITsbJPPB3ABtK3/PfAInAH3rDqK5QBwaBzYazuhq1X1sJkH9rJnq96wIePRk0ekJyNvF62uvM/3E+N/54I4tmLiLVmtoT5nsUF6WEsy2nkiW/HGFkpB8XpbjejQygrKGMLUVb2N+cfcowj1qrJXxIIlHDUogaNhJrXDwqtXuy4GWbjbK336b0pZeR1GqsDz1EwFW/Q3KTPQKBQNAVJ/tsTZJleTiAJElvAm5vIK4JVhKG7GXl7QQ8IMQHJCg/WsegUS5eTKuDuGlw4Ccif/NP3jn3HW5fdjsLly7k75P+znlx5/XAK/AsHjw/kd0F1TzweSbxFl+Sw7qumy6pL2FL0RY2FW5ic9FmDlcpvcaHav0ZT/vzJEmFNW5QS1g8hbChiWi9vHv0tbhCQ2YmBQ8/QtO+fRjnzMb64INoQ3onkU8gEAhOl5MJeGu4XJZle18okzmW8WsvLWlXc6v1VmMK1lOW71qv61YS5sD+H6HsEBHB8bz3m/e45+d7eGDNA2wp2sIfx/4RnUbXnS/Bo9CqVbx0dSoXLlrLbe9v4ZvfT8bf4IXdaWd/xX4yijPIKMkgsyST/Np8AAwaA6nWVOYNmseYkDFEYuGtu27BEhNHZPIIopJHED40CW9D3+lD76itpeTZ56j48EM0FovoXy4QCDyCkwn4SEmSjtXzSIC+5WcJkGVZNvW4dSegaZmhbC8q7rAWFOZD+dHa07tg/Bzl+4GlEByPv86fN+a+waJti3hr51tsKdrCIxMeGdAhdbPRm39dOYhbPvmcSz/+kaiwEnaX7aLBrkyAs+gtjLSM5KqhVzHaOpqhgUPRqNq/rX6/5OM+JdjHU7NsGYV/fxx7cTEB11yD+Q/3oBYzjAUCQQ9itzkozaulOMv1ktnO6FLAZVnuc5t+GqsSzrQXdyw7Cgr3JWtH2WllohMQDeahioBPuAMArUrLfaPvY1zIOB5b/xg3/HgD8wbN465RdxHi0//DqfW2evaU72F32W52l+1mZ+lOsqqz0IZDsayiqSSaSwZfwijLKEaaRxLiE3LKBLO+KN62wkIKH3+c2mXL8R46lIhFL6Af0ftzuAUCQf9GdspUFtdTdKSaoqxqirOqKc2rxek4+87krpSR9RnUvj6ofHywFXYu4LJTpqKgHnPUafTyTpgDv74KDZWg9299eFL4JL686EsWZy7mnd3v8MORH7g04VLmJ88nwhjRHS/H7dTb6tlXsY9dpbvYXbabXWW7OFJ1BLml5b3FYCEpKImL4y9mpHkkKzO9eXFFDsbwwZwbm+Bm688M2eGg4sOPKHnuOWSHA8v9/0fgDTcguTDjWyAQCE5FXWUTRVltYl2cVU1zowMArU6NJdpEyuworDEmLDEmfv/amT+XRwk4gCYkBHthYYfHg8IVL6/saO3pCXjiPFi3CPb/BCOvbLdk0Br4w+g/cMWQK3hjxxt8fuBz/rvvv0wMn8jlCZczJWIKXuq+P7jC5rSRXZXNwcqDHKg8wIGKAxysPEheTV6rWJv1ZpKDkjk35lySg5NJCkoiWN++3nq0VeZohYNnlu4nMtBw2pnp7qZx714KHnmUxsxMfCZPJuSvj+IV0T9uxgQCQe/T3GinOLuG4uMEu7aiCQCVSiIowpfB54RgiTFhjTEREGJA6saRzR4n4FqrFVtRRw/cz6xHrVGdfiJb+BgwhsGerzsI+DHCfMN4ZMIj3DLiFr448AWfH/icP6z8AwaNgcnhk5keOZ2xIWPdGmKXZZnKpkqyq7PJrcklpyaH7GpFtI9UHcHutAOgltREm6JJDEzkwkEXkhiYSFJQEhbDqYe6SJLEU5cNJ7+ygT9+monVpGN8XN9vJeqsq6Nk0YuUv/ceaj8/wp55BtP554n+5QKBwGUcDifl+XXtvOvygrrWGZ1+Zj2h8f5YY0xYY00ER/q6vp17hnicgGtCQmhau7bD4yq1ioBQA2V5Nad3QZUKkubB5regqQa8u/beQ3xCuCPlDm4ZcQvrj65nRe4Kfs75maXZS1vXR5lHER8QT4wphli/WMJ9wzFoDadnUyc02BsoqS+hqL6IzXWbydqZRVF9EcX1xeTV5pFbnUuNre21qyQVoT6hDPIfxJTwKSQEJJDgn0CsX+xZRQ28NWpeu240v311PTe/s5kPF45jRIT/qU90B7KsJKk9/gT2wkL8r7wSy333titBFAgEghORZZnq0gZFqI/UUJRVRUluLQ6bMglR56vFGmsifrRF8a6jTeh8e38bzuMEXBtixV5aimy3I2nam2+JNnFoa3GHTm2nJHEebHhVSWYbdtkpD9eoNEyJmMKUiCk8PP5h9pTvIaM4g23F29hWso0fsn5od7xeoydQF0iQPghfrS9eai90ah3eam80Kg0O2YHdacfutOOQHdTb66lprmn31eRoam9EqXJdq8FKuG84I+JGEG2KJsoURaQxknDf8B4L7/sbvHj/pnH89tV13LBkI/+9dQIJfWyGuC0/H/9XXiEvcwfeQ4YQ/ux/MIxytUmAQCAYSDTUNLd61cc87KY6JWqp0aowRxsZNi1c8a5jTBiDdH0igudxAq6xhoDTib20tEOTDUu0kd1rj1Jd2oCf+TS83qjx4GOB3V+5JODHo5JUJAclkxyU3DrVrN5WT1Z1FllVipdc2lBKWWMZZQ1l1NpqaWpoosnRRKOjEbvTjkalQS2p0aq0qCU1eo0eo5eREEMIRi8jJi8TJm8TZr0Zi8FC9q5sLph2AT5aH7e9iUL8dHxw8zh+++p6rn1zA5/dNpHIwLOPNJwtss1G+TvvUPLSy2idTiwPPEDgddd2uNkTCAQDE1uzg9Kcmnah8OpSZWSxJEFgmA9xKebWUHhgqA+qs5wH0VN43KeaNkSpBbcVFHQi4EppenFWzekJuEoNyZfAlrc7ZKOfCQatgaSgJJKCzryH+Mlo2t+Er5f7a5Wjg3x4/6ZxXLl4Pde8sYFPb5uA1eS+xjf1W7ZQ+Ne/0nTgIMY5szk8YwbJl17qNnsEAoF7cTplKgra71uX5dchO5WNa99Ab6wxJpKnhhMSayI40oiXrpdk0emE8kNndQnPE/CwMABs+UfhhJBoYLgPaq2KouxqEsZaT+/CI38HG1+DXf+DMW4ZtuaRDAkx8vaN53DN679y1eJf+XDheEL8elfE7RUVFD/zDFWff4EmLJSIl1/GOHMGB7t5HrhAIOi7yLJMbUUTRUfaQuHFOTXYm5QSLi+9BmuMkdS5bSVcPn691MJZlqH8MBRkwNFtcDRD+Wo+zZytE/A8AW8p+7Hl5XZYU6tVBEf4UpJ9Br+UsFEQPAS2fywE/DRJifTnnQXnMP+tTVzx2no+XDiOiICeD6fLskzV/76k+F//wlFbq4z7vP12VAb3h/IFAkHP0lhnoyS7fSi8vroZAJVGIjjCSOKEUKwxRqyxfviZ9d1awtUlsgyVOS1C3fJVkAGNVcq62htChitOY1gKPHbdGT+Vxwm4Sq9HYzbTnNtRwEEJo+9ZX4DTKaM6nf8sSYKUq2DZX6HsEAQN6h6DBwhjYgJ576ZzuH7JRq587Vc+WjieqKCeE9Kmgwcp/Otj1G/ejD41lZBHH0U3RIyCFQj6I/ZmByW5tW2edXY1VcUNrev+VgORiYFYYxXPOjjcF7W2F/atZRmq89uL9dFt0FChrKu0EDJMya0KTVEcRUsiqI/PWB9AAg6gjYzElpvX6Zo11sSOlXmU5ddijjzNzOjhV8CyxxQvfOaD3WDpwGJUVAAfLRzPtW9u4MrF6/lw4Xhig7u3jaqzoYHSV16lbMkS1D4+hD7+d/wuvRRJ1TeTTAQCwenhdDgpL6ijOKumVayP37f28fPCEmNi6IRQrNEmLDFGvA29VMJVXdDeqz66DepKlDWVRhHnxAsVoQ4bBZYk0PRcmN4jBdwrMoK6jZs6XQtLUBLQCg5Wnr6A+4VD/CzY9h5M+9MJd0kCVxgW7seHNysifsVr63nnxnNICuueuTc1K1dS9PfHseXn43fJJVj+eD+awMBuubZAIOh9ZFmmqqSB4myl3ro4u5qSnBrsLfXW3gYNlmgjqWlRrd3MfPx7ad+6trhlr/o4z7q2pQuopAJzIiTMVcLgYalgTVbGVPciHing2ohI7F9/g7O5GZVX+1pnY6AOY6COowcqGTEj8vQvPvZm+Oh3sPc7SL64myweWCSFmfjklvFcv2QjV7y2nteuG82k+OBTn9gFtsJCip54kpr0dLwGDSLq3XfwOeecbrRYIBD0BnVVTceFwZUWpE31LV0itSrMkUaSpoQpSWbRJvws+t4pla0rVcS6YFubaFfntyxKYB4Cg2YoXnVoirKH7eX+XBvPFPDICJBlbPn5eMfGdlgPTfAjd0/F6Td0AUhIA78o2PSGEPCzIMFq5Is7JjJ/ySbmv7WRp387kotHnV7vdNlmo/z9DyhdtAjZ4cB8770E3Tgfyavv958XCAY6jmaZ3L3lLQM9FO/6WJ9wSSURGObDoFFmLC0Z4YFhPqh7o976mGddkAEF25V/Vx+3JRuUANGTWjzrURAyArzdX7bbGR4p4F5RUQDYcnM7FfCweH/2byiiqrgBf+tp3iWp1EoW+vLHoHgvWIZ2h8kDklA/Pf+9bQK3vreZP3ySQUFVI7dNi3Pppqpu40aK/v53mg4cxGfaVEIeegivyDOIqAgEgh7HbnNQmlvbumddnFVDZZHMXjKA9n3CLdFGgqOMaL16YWJ1dYEi0gUZbaJdU9CyKEFQvNLIKyxF8axDR4DOc1ote6aAx8QA0HT4ML5Tp3ZYP7YPfvRA5ekLOEDq9bDyKdj0Opz/77MxdcDjp9fyzoJz+L//buefP+7laGUDj1yYhLaLO21bcTHFTz9D9TffoA0LI+KlF/GdObNPtC0UCATtm6MUt4TCy/JqcbYkmRn8vLBEm/CyNDBuxggs0SZ0Pj2cTyTLUH20vVddkAG1xwZfSRA8GGKnQujINrE+yewLT8AjBVwTGIg6IIDmQ4c7Xfe3GjD4eZG7t5ykyWGn/wQ+wTDiCtj2Pkz7M/iaz9LigY23Rs0LvxtFqJ+O19cc4WBxLS9dk0qgT1soXLbbqfjgA0peWITc3EzQ7bcRfMstqPR6N1ouEAxslKEejcq+dbYi2CU5NdiblSQzL72SZJaSFtWSEW7CN0BJMlu5ciVRST0wrVCWoSqvvVddsL0tG1xSKT09Bs1sEeqRyp51Hw2Dnw0eKeAAXoPiaDrUeRs6SZKISgrkyPbS068HP8akP8C2D5QhJ7MePktrBSqVxIPnJzHYauTBL3cy78W1LL5uDElhJuo3b6bwb3+naf9+ZU73Qw+2RlkEAkHvUVfV1Jpcdsy7bqyzAaDWqDBH+ZI0Kaw1I7zHm6PIMlRmtwj19jaxri9T1iW1UrqVkNZSZ50C1mF9IsGsN/BYAfceFE/1jz92magWlRTE3vWFFGdXExJ7BnsawQlKPd/G12HSPaDrnlKogc7lYyJJsBq57b0tLHj2B56v/AXTmnQ0YaGEL3oB4+zZIlwuEPQCTQ12SrIVkT4WDm9NMpMgMMyX2JRgLNGKWAeG93CSmSxDxZE2r/qYaDdWKuvH6qyHnNe2Z21NBu3AjdJ5sIAPwllVhaOsDE1wxxKlyMRAkCBnV/mZCTjA5Hthz9eweQlM/sNZWiw4xshQXz4xZ1P+8Yuobc3sn3MZ5z31/9D6dm/TF4FAoGC3OSjNq1WywVsSzSqK6kHZtsZk1hM6yK81I9wcaUTr3YNJZk5nW2/wVrHOhKaWdqMqLViTIOmiNrG2JPV6nXVfx2MF3GtQHABNBw91KuA6Xy3WGBM5u8o454KOmeouEZ4KcTNg3Qsw9iaPT3joC9Rv3UrhY3+jad8+AiZM4MMJV7D4sIOPP9zBc79Lces0M4GgP3Asyaw4u5qiFsEuy6/F6VDUWm/ywhpjImGstbXeWufbg0lmTieUHWzvVRdmQlO1sq72Vjzp4Ze1JZhZkkAjykVPhccKuHd8PABNBw7gM35cp8dEJQWy6fss6qubMZjO8M0w62F4fSasfwmm//lMzR3w2EtLKX7m31R9+SWakBDCn38eY9oc/iJJJGzO5ZGvdnHe82v4z5UpTBsskgYFAldoTTLLbtuzbjeBS6fGHG0iZXak4l1HK0lmPbZN5XRA6QEoyCD+wLdw+B9QuAOaa5V1jU7Zox5xRVuCWYfe4AJX8VgB11gsqAMDadyzp8tj4kaZ2fRdFke2l5A85fSaiLQSPhoS58G6RUqXNp8z7yg2EJHtdio+/oSS55/H2dhI0MKFBN9+W7uJYZePiSQl0p/ff7iNG5Zs5LZpg7hvzmC8NKK/uUBwjGPjMkuyayjKrm7dv27tZKZRERzpS+LEUKzRRiwxJvwthp5LMnPYoXRf+wSzwh1gqwcgVOWlhL9Trm5LMAseAmqPlZ0+h8f+JiVJQpeUROPu3V0eExTui59Zz6GtxWcu4AAzH4a938LqZ+A3/zjz6www6jdvpvCJJ2naswfDhPGEPPww3nFxnR6bYDXy5Z2T+Nu3u3h11SHWHCjhuStTSLCKbQvBwKSuShHr4mxlrnVxVjUNNUpGuEolERjuw6BUC5ZoI5bolk5mPXXTa2uE4l3KPnVhpiLYRbvA3qisa32UuurU61vFes2uo0yfMatn7BEAHizgALrkZMrefBNnUxMq744N7iVJYlCqmYz0XBrrbGfeTMA8GEZdq7RXHbNA+VnQJbaCAoqffprq739AExpK+LP/wXjuuacM2+m91Dx16QimD7Hw/77YwfmL1vLnc4cyf2LMmZUCCgQeQmOtjeKctpajJTk17TLCA0J9iB4WhCXahDnaSHC4L5qe6mTWWKV40seLdck+kJWwPN5+iliPuUkJgYelKB3NVCfYs7uo47UF3YpnC3hSEtjtNO3fj3748E6PiRtlYetPORzZXkLixDNo6nKMmY/A7q/ghz/CdV8qf1WCdjgbGylbsoSyxa+DLBN8xx0ELbz5tJuxzE0OITUqgD9/nsnfvt3Nsj1F/POyEUQGDozaTkH/pqnBTklOi1C3eNjVpY2t6/5WA6Hx/opnHWMiOMIXL10PfVTXFLWJ9LHvFVlt675WRaSHnKeIdsgICIgRn399BM8W8ORkABp37e5SwC3RRkzBOvZvLDo7Afc1w4yHFAHf/ZUYdHIcsixTk55O8T//hS0/H2NaGpY//QmviDPftjAbvXnjhjF8vCmXJ77bQ9qzq7l/7hDmTz+VQUgAACAASURBVIxBLbxxgYdga3JQmlujJJe17FlXFtW3rhuDdFiiTSRPCccSbcQc1UOzrWVZEebCzPaede1xXnJArCLWo65r6V42AozW7rdF0G14tIBrw8NQ+/nRsCOTgN9d2ekxkiQxZFwIm77Poqa8EWPgWZQpjVkAW9+Fn/6itOkTzV1o3L+foiefov7XX/FOSCDq7bfwGT++W64tSRJXnRPFtMFmHvpyJ3//djffbD/Kv347gsFib1zQx3DYnEqt9XF71hUFdcgttdY+/t5Yoo0MGReiiHW0Eb1vD5RKOexQuv8EsT6uxlpSg3mo8hkWMqLFsx7uUUM8BAoeLeCSJKFPTaVh85aTHjd0Qiibvsti368FjDnvDGvCQcmevOBZWJIG6Q/Dhc+f+bU8HEdVFSWLXqTio49Q+fpifeghAn53JZKm+99SYf563rxhDF9vP8pj3+zm/BfWsHBKHL+fGY/By6PfwgIPxeFwUn60TindyqmhJLumfa21UYsl2kTcKDOWaGUCl49fxzyds8bWAEW7oXB7m1gfn1ym0bfVWB8Ta0vSgO5e1p/w+E8/w5jR1P78M/aSEjTmzuuHTcF6wgf7s2d9IaN/E3N2NZCRY2HiXfDL80p5WfzAyrKUHQ4qP/2Mkueew1Fdjf+VV2C++240AQE9+rySJHFRSjiT44N54vs9vLzyEF9lHOXhC5KYm2wV7VcFPYbTKVNRWEdJdg0FW5x8tmEzpbm1OOzKQA9vgwZzlJGUOVGtGeE9UmvdUKkklx3vWR+fXKbzU0R67M1tYh2UIMq2+jEe/z9rGD0agPotWzGdO7fL4xInhrLs7T3k7asgcmjg2T3p9L/Avh/h67vg9nWg9z+763kI9Zs2UfjkU0pZ2JgxWB96EN3Q3p2XHuTrzX+uSOGqc6J4+Mud3Pb+FqYNNvPXecnEBotWrIKzQ3bKVBbXtySZtSSa5da2NkZRaSAkVsXw6eGKZx1jxBSs716xlmVlb7ogs71nfXxymTFUEemh57eJtX+0SC4bYHi8gOuSkpB0Ouq3bDmpgA8abeGXzw+SuSLv7AVcq4NLXoE30+CrO+HK9/v1H05zVhbF//43NenLTqssrCcZGxPIt3dN5r1fs/nP0v2kPbuK68bHcPesePwNogWj4NQ4nTJVxfUUZ9dQktPylVuDrVERa7VWhTnSl6SJoS171ia2793IjBmp3WeEww5lB1o865avop1tozEBAuOU2urU6yFkpCLWvpbus0HgsXi8gEteXuhTUqjfsOGkx2m0apKnhLP5hyyqSurxM59lSVL4aJj9GCx9EH59BSbccXbX64M4KispfeUVyj/8CEmrxXzP3QTOn99nZnRr1CpunBTL+SNCeTZ9P2+vO8JnW3K5e1YC102Idrd5gj6E0ylTWVhPSU7LnnVODaW5tdia2sQ6OMKXoeNCMLdkgweG+qA6YfqWtO8sblobq6BwpyLQhZnKv4v3gEOp90btDZahMHguWIcriWUhw0WyrKBLPF7AAXynTKb46WewFRaiDQnp8rhhU8PZ+mM2mT/nMeWKbmjGMuFOyF6nJLRFjFX2x/sBcnMzFR9/TMlLL+OsqcH/skuVfe4ucgzcjcWo46lLR3DDxBie/H4vj3+3h3fXZ3NBpIOpZzoPXuCxOB1OKgpbwuAtCWaleTXYm5U9a42XiuAII0MnhmKONGKJNhIQYugg1mfMsRnWhTvbPOrCTKjMaTvGEKyI87hblBC4dZgywlj0BBecBv1CwH2mTIGnn6F29WoCrrii6+P8vUkYa2X32qOM+U0MeuNZhlolCS5+GV6bCp9cCwuXg1/E2V3TjciyTO3y5RQ9/TS27Bx8Jk7A8sAD6IYMcbdpLjE0xMS7C85h1f4SnvxuDy9vr+fn4rX835zBzEq0iES3fojT4aS8oL4tBJ5TTWluLXZbm1ibI40kTQpr9awDQny676bO1ggle1rC38cEe9dxJVsqpUtZ+BgYfWObV+1r7dfbboLeoV8IuHdCApqQEOrWrDmpgAOM/k00+zYWsi09h4mXxp/9k+v94er/wptz4MMrYcGPHjl2tGHXLor/8U/qN23Ca9AgIl97FZ+pUz1S9KYNNjM5Pph/frycn/Lt3PzuZlIi/bk/bQiT4oM88jUJlNItZUxm2551aV4tjhax1nqrCY70JXlKeKtY+1sN3SfWtcUElG+DX7a3CXbp/rYscC9fpWRrxOWKRx0yQpm05SU6CAp6hn4h4JIk4Tt1KtXffttlX/RjBIT4kDDGyo5V+YyaE3X2Xjgo+1ZXvAPv/xY+vRGu+shjQmG2wkJKnn2Oqq+/Ru3vT8ijj+B/+eU9Us/dm6hVEhPDNPzxyql8tiWPF5Yf4No3NzAuNpDfz4xncnywEPI+jMOu1Fm3hcGrKcuvay3d0urUmCON/P/27jtOruq++/jnTO9lZ7YXlVVBHSRZIEAgYUyRwRRjDLHBToyJ2ys2xDF5HuexnYTHBB7bSYzjuOECwRQbIjumgy2EBUJIoqhXVHa1vc3OzM5OO88fd7apjqTd1c7u7/16zevembkz9+py2O+cc889Z+6llf3N4MM281YmDe17h3Yqa9wM0SYW9G3jrzZCeta1UDbXWA9OAZPMoCdGT2H/lR7E+6EP0fnkk8Reew3v5ZefcNsPfHgyuzc0sfG5A1x88/ThOYDay+Ca78H/fBmeugM++tCYvv8yE4nQ9tOf0f7II5DJELrjM4TuvBOzt/BaD07EajZx65IabjivksfWH+RHr+7ltofWs6DKzxdWTONDs0rlGvlZlkllaTscHdQM3k1rfZRs2hgUxeYwU1zjZd5yo2ZdUuPDX+wcnrCONhsB3bTNaPpu3grNOwY6lpmsxg/0aZdD6Vzeacxw7pWfANcZ3skixDAYuwlzitwXnI85GCTy7LMnDfBgmZtZF5azeXUdcy6pIFg2TPcPL/o0JGPGUKtmK9zw46Nn6DnLsokEHY8+SutPfko2EsF3zTUUf/lvsFUV7rX7fDisZv7yoin8xfk1PL2pnv9cvZe/fmQj00s8fGFFLdfOr8AyXJ2YxHH1hfXgZvDBI5jZXRbC1V4WrKjubwb3h4chrFM9Ro/v5lxQ9z3irQPbeMqMJvDz74SSOca16vAMsAy00nWuXi3hLcaMcRPgymrFe+UVdP3u92TjcUyuE193uuC6WvZsbGbtU3u45osLTrjtKVn6Rcgk4eVvGcsbfwqWERhC8RTpdJquVato+cF/kG5sxH3JMkruvnvUB2I52+wWM7cuqeFji6p4ZnMDP/zTXu564l3+3/M7+dSFk7nlAzX4R2IyiQko2ZOmta6bloNRWg9103IoSkdDjGx2IKyLa7yce3k14Vwz+BkPipLNGj3Am7bmwnqLsd6+D7TR/I7VZYwFPvNqo+m7dLYR2O7QMPyrhRg94ybAAfwf/jCdjz9B5IUXCdxw4tnCXD4bi1dO5o2n97J/cyuT54WH70Auvstoenvx6xBvh1sePWsTBWit6X75ZVr+7d9J7t2LY8F8Kh64H/eSJWfleMYKi9nEdedWcu38Cv64o5mH/vw+9z23g39/ZTc3Lari0xdOZmqx52wfZsGIdfXSeihKy6Hu/rCOtPT0v+/02Siu9jBpXqj/mrU35DizsI6350I6F9R966lYbgMFRVOMWvXcm4xl6RxjOswx1jImxOkYVwHuXLwY29SpdDz+2EkDHGDBimp2rmtk9aM7ufWbAezOYTwdF37JGC1p1efhFyvhll9DcHQHF4mtX0/Ld79Hz7vvYps6lcoHv4/38sul89YgJpPi8tmlXD67lK2Hu/jF2v08vv4Qj6w7wGUzS/jUhZO5eFpYrpPnaK2JtPYMqVW3HuomHkn2b+MLOyiu9jJraTnhag/FNWc4kUcqYfT2btkxtPm7+/DANs6gUZteeJsxWUfpXOPatU2G1xXj17gKcKUUwVs+TtO37yOxbRuO2bNPuL3ZauKy22fx1P0beP23u1lx26zhPaD5N4MrZPRM/8lyuOnnULtiePdxDPFNb9P6gweJvf4GltJSyu/9Z/zXX1/wPctH2pwKP9/52AK+dtVMHl13kP9ad4BXfr6emiIXtyyp5mOLqin2nv3LIaPFuG0rngtqY+Sy1kPdJHNDjSqToqjcRfXsIoqrvYSrPYSrvaf/QzidNHp/N28zOpI1bzNCe3Dzt8lqNH9PWTZQoy6ZA94yua9aTDgj9hddKfVz4BqgWWs9d6T2cyT/ddfR/L1/pf1XD1Nx/7+cdPvSyT7Ou6KGTS8cZNK8MFPPHebRxqZ9EO78Ezz+CfivG+GD34ALvzwit5v0vPMOLQ/+gNjatZhDIUruuYfgrbdgcpzBHOgTUInXwV0fmsEXVtTywtYmHl13gAee38m/vrSLK+aU8Ynza1g6dXzdT55Naxr3deWGGDVq1u2HB27bslhNhKo8zFhS1l+rLqpwY7GeRlN0Jg0d7+c6lW03BkJp3g5teyCbNrZRJiiqNe6jnvtRI7RLZkOotmBu0RRipI1kleyXwA+Ah0dwH0cx+/0EP34z7f/1KOEvfRFbdfVJP7Pkmqkc2t7BHx/eTrjKgy88zGN9h2rhjpeNiU9e/hbseQVu+NGwjdrW8957tPzgB8TWvIY5GKTk775K8NZbT9qRT5yY3WLmIwsq+MiCCvY0d/PrNw/x1KY6nnmvgSlhNx9dWMkNC6uoDIyNseHzlYimaDmiVt3RpNmuNwIDPcHnLa8kXO2luNpLoOw0BkTp61DWH9I7jPXWXQO3aaGMS0sls2HmSmNZco4xDaZVfngKcSIjFuBa6zVKqckj9f0nUvRXn6Hjscdp/fGPqbj33pNub7aauOrOuTzxf9/i+Z9s4cavLsRiG+ZOLnYPfOyXsOlheP5/wQ8vhKvvhwW3nHbTX8+WrbQ++CDRV1/FHAhQ/Ld3U/QXf4HJLdf9htu0Ei/fuHY2X7tqJs+818ATGw7xnRd38d2XdrF0aogbF1Zx9dwy3Paxc5mib7at1roorXVR2nLLWGdv/zaeoJ1wtRdLKM7iZfMIV3vwFp1i57JM2pjqsnWnMT91624jsFt2Qio+sJ2/2qhJ1y43grr4HCieKdephThNY+evzTCylpYQuPlmOh57jKLbbscx8+QTl/jCTi7/9Cye/dFmXvrFNq787Nzh77ikFCz6lHH97r8/B6s+B+88Ch/+rvGHLA9aa6y7d3Pw178mtuY1TH4/xV/5CsFPfhKzR/4QjjSH1cxHF1Xx0UVVHGqP8/Smep5+u46v/uZdvvG7LVw1t4wbz6tiaW0I8yh2fOuNp2irj9JaF6OtzhhitP1wrH9McJNJEShzUTkjQKjSY1yzrvHg9Bj3OK9evfrkl4+SMSOcW3flOpXtNJZteyGbGtjOW26U50WfHmj6Lp4ps2oJMcyU1nrkvtyogf/hRNfAlVJ3AncCFBcXL3ryySeHZ9/RKOFvfot0ZSUdd30l71pu605N09uaohlQvnAEB/bQWcobXmTqvocxZ3qpq/oIB2s+Stp6nFuXsllsW7bgfv4FbPv2kfV6iF12GT3Ll6PHyPSeY000GsXjGflbwbTW7OnM8uf6NOsb0/SkwWeDxWUWlpRZmBE0Ycqj/FW+aZS3+vOzJ9xXMgq9nZDo1CQ6IdExtKJrtoEjAPYAOAIKRxDsPjCZj38Mg8+VNdmFK16XexzCHTPWHb0Dc1RrTPQ4y4i7qoi7qoi5q4m7Kom7qshYxu8PydEqU+OBnKv8rFixYqPWevHpfPasB/hgM2fO1Dt37hy2/Xc89hiN//hPlH/72wRuvCHvz7325C7e+2Mdi1dOZsm1U0a2s1K0BV76Brz7mFFDufguWPLX/RMg6FSKyHPP0fbTn9G7ezfWigraLlnGB+65Z8zMyz1WrV69muXLl4/qPhOpDH/c0cwz7zXwyo4mEqksJV47K+eVc838chbWBI/bstP84/cAKPnr+QAkE2na6gdq1G31UdrqY/1zWCsFgVIXoSoP4SoPoUoP4Sov7oDt5GU2m4Wug7nm7p0c3ryaCmvUqFX3tA9sZ3Ea01wWzzRGJQvPMNaLpo6JAYpG29koU4VKzlV+lFKnHeDjsgm9T+Dmm4k88yxN996L6wOL8+rQBnDxTdNJJTJseHY/Oqs5/7qpIxfinmK44T9h6RfglX8yOrmt+xGZ+Z+h830PHU88RerwYezTp1HxwP34rr6aurVrJbzHKIfVzMp55aycV048meaV7UaYP7b+IL98fT/lfgdXzinjQ7NLWTKlCKvZRDaTpaulh0Q0Sao3w8Yfb6a1buhAKDanhXCVh3MuLCecC+yicveJ+2pobQx20rbHuD2rbW9uuQda90B64PvDVh+U5ybnKJ4J4ZlGcPurZYIOIcaokbyN7DFgORBWStUB39RaPzRS+zvmMZjNVNz/L+y7/gbqv3IXkx55OK+e2cqkWPHJc1BmxcbnD9AbT7Ps49MxjeRY2WXz4BO/oXf1k7T/6Lt0/eIhdMaEqzZM6Xf+Gc/KG1Hyh7SguGwWrl1QwbULKoj2pnl5WyMvvHWY1/9cx7Y/1lGGmUkWK46eLGQ0F3mMMG5LZCmu9jBraRmhSg+hqpN0LOvpzAXzvoGA7gvrRNfAdsoMgRrjrojJl0BxrkYdnsnrb22W2pIQBWYke6HfOlLffSqslZVUPHA/dV/8EofvuYfKf/s3lPnkPcyVSbH81pnYnRbefvEgkdYervjs3OEdrS1Hp9NE16yh/ZFHiL+xDmW347vyMoqmtONofxE2fhYiv4WFt8P0K4Z9/2L49USTtNfHaDscpe1wjPb6GO2Ho8xNZJiLcR9z2m6iMZXmsCVNh0NzrsuNz2Nj+WfnURU84odmb9QY0KQ/oPcN1KoHT8iBMm5PDNUaw4eGao37qUPTjPC2DMP0uUKIMWFcN6H38a5YQenf30PTt+/j8NfuoeJf7kNZTz4YhDIpLrxxGoESF6/+eie/ue8trrxjLsU1wzPlZnL/fjqf/m+6Vq0i3dyMpayM4rvvJvCxm7AEg8ZGHfth46+M3uq7ngNPGbWBJVDrgsrF0rx5liUTadobYrTnQrovsHsGDS1qd1kIVXqYcb5Roy6qcFNU7sbhtpLJat4+2MHLWw+TeqOJtvYIv/rOt5jv7mSBp4vJphZ8iXpUtHHojj1lRiifs3IgoEO1xpzUcv+0EBPChAhwgKLbb0cnkzR/57tkot1UPvAAZn9+E4zMvriCQKmTFx/axm8f2MDS62uZf1n1ad1mlonG6H7pJbqeeor4hg1gMuFZtgz/P3wd74oVR/+wCE6Gy78JK74Ou1+Etx+hctez8NDvwVcJs6+DGVdBzQUTslPRaEn2pGlvjNHZGKejMUZ7Q5y2+ijdbYn+bSw2E0XlbibNDRGqcFNU4SZU6cHly3Uq6+kwfpB1HIC390PnAcwdB1jcsZ/FXYdozvwjKFhu/THZpKKxrYgduph6ZpAJXkVR9WymzVpAzbQ5KPv4mrddCHHqJkyAA4TuuAOT10fjvffy/k0fo+K+b+NanF/nv4rpQW75hyW88vB21v52D7s3NLP8EzMprj75H9JMNEr0T38i8vwLxF57DZ1MYp1UQ/Fdd+G//nqspSUnPwCzxahtnbOStS8/w7Libti6Ct76Gaz7oTFF4uRlxtCtU5cbI1lJ7fyUaK2JR5J0NMbpaIgZy8YYHQ0xYl0DNWqTWREodVE6xcfsiyqMoC534rN1orrrIbINuurhQD28VwedB43Q7u0aukNnEAKTjP4Ps66Bd2vB4oDbNmHyV1GkLex+v50tO1tYs7uFPRuisKGNcv96Lp4WZmltiKW1Icr90qFRiIloQgU4QPDjN+OYOYP6u/+WA5+8Df8NNxD+/Oew1dSc9LMOj5WVn5/HrvVNrP3tbn5z3wbmXlrJ4qsn4/INvbaYPHSI2Nq1RNe8RuzPf0Ynk1hKjAFmfFdfhXPhwtPu2Z6xuGHBh41R3Hq74f3XYO8rsPePsPuF3MEGoOoDUH0+VH8AKhbKQBo52aymu62Hjob4QEg3GoHdG0/3b2d1mAmWuqg6J0gwBEFvjCJ7Cz5Vh6m7DiL10FAHO+qhuwF0ZuiObF7wVxo9uavPN4YMDU42Qjs46egpZvcZt5ERqgXAAVw6o5hLZxgDrNR39vDarhZe3dXCi9ua+M3GOgAmhVxcMMUI8wumhijzSxO6EBPBhAtwAOe55zL1D/9D6w9/SPuvHqbrd7/D+8EP4rv2GjyXXHLCyT+UUsw8v4xJc0OsW7WXLavr2L72MPMWeqh1HiK77V1ib7xB6uBBACwV5QRu+Ti+q67Cee65w9+T3O7tr5kDRken/Wuhbj0cWg97XhrY1l8DpbONCSJK5hjLoqn995yPJ1pr0glNw94uuprjdDbF6WzuyS3jZFIDg6U43YqiQIrpNTGCzjaClnqCaj/u5F5UrBkONsOBI8LZbAdfhdFhbMoy43KGvxJ8VcZr/sphnwO+MuDkliU13LKkhmxWs6Oxmzf2tbFuXxvPbTGGdwWYHHJxwVQjzM+fWiQ1dCHGqQkZ4AAml4uSr36V4O230/7LX9G1ahXdL70EVivO2bNxzJuHtaoSa1k5JqcDZbOhs1my3d1kurtJNzQy/eBBQp1d7MjM4u11C3g346Ci1cyM2nMpvf123BddiG3y5NGdtapoqvFYeJvxvKcT6jZAwzu5aRq3w56XB2Z9AvCUGjXDvtqhv8p4zVNiTNPoLh6zM0AlYim6mmJ0Hu6gq7GLzqY4XS29dLZnSSYVOzEm6FAqi88eIWBrodpbR5B9BNlN0FKHwxSDDNAJdJmMf6+n1Pi3l88zOox5SsFXngvqanCHz+r0lSaTYnaFj9kVPj5z8RQyWc32hgjrcoH+zOYGHn/LCPQKv4OFk4Isyj1mlfuwjuQtkUKIUTFhA7yPtaSE0q/9HSV330XszTeJv/EG8U1v0/X002Tj8eN/UCms5eX4JtWwbEqUnilRdneVsXf7xdRlNKX1PmYesDItnMLpPYu37jgDMP1y49EnnYS23UaYt78PnfuNa7QHXofNvxmYe3kwVwicRUYzvN1nLB1+Y93uM25PMtsHLe3GmJ5mmzE1JNoYWOSYyyykeyGdMJapnv7n2VSCWFTRHTETiVmJdNuIxJ109fjo7C0ikRncByGL19xCwNzATMth/PYG/JbDBBxdeH0ZzO6g8W9whcBVBe7zBoLaU2o83GEwDfNENqPAbFLMrfQzt9LPHcumkslqth2O8Nb+djYe7GDTgQ7+8F4DAA6riflVASPQa4IsnBQ8y0cvhDgdEz7A+yiLBc9FF+G56CLAaILNdnWRampG9ybQvb1gMmHyeDF7PZjDYUy2ocE8BYh19bJzXSO71jey5vFdvPbkbspr/UyaF2LS3BBF5e6zP4+0xQalc4zHkdJJiDZBtBmijQPr3Y0YA293QSJiXP9NRIzng0b0OlVaQ0/WTyRTSiRTQiRTSnemJLdeTTQTJjukmGo8tih+V5SpRS0E/A34g5pAkRl/2I7Z7QPnInBdwevv7mTBB6+ZkL3zzSbFvCo/86r8/BVTADjc2cOmgx1sPGAE+k/X7OM/s8ZQyqUuxUVN7zCvys/8Kj+zy/04h3tGPiHEsJIAPw6lFOZAAHMgcEqfc/vtLLxyEguvnERrXZTdG5o4sLmNN57eyxtP78Xtt1FWG6C81k/ZVD/hGg/msdScabFBoNp45CuTNuZ3ziSNHwCZXkgn0ekEie4E0UiWWEQTjWpikSzR7twykqW7M0M6PfTrnB4L3lInJWEn00JOfGEHvpATb9iBN+jAbM3vfCV3tE7I8D6eioCTioCTa+ZXANCTzLC5vouNBzp4cdNu1uxu5em36wEwKZhe4jV+BFQaPwRml/twWCXUhRgrJMBHUN+Y1UuvryXakeDAljbqd3XSuLeLvZuaATBZFMHSvnuG3QTL3PjCTrwhx4iM+jYcUr0ZerqTxLuT9HSn6OlO5h4p4pEksc5eoh0JYp1JMumhzfFKgctvxx1wEqyyU7PAMSSgfSEnVruExGhw2swsmVLEkilFzOIQl156KU2RXjbXd7G5rpPN9V2s3tnMb3O93c0mxfQSD/Or/MyrCjC3wsfMMi8u29gsp0KMd/J/3ijxBB3MWVbJnGWVAEQ7emnc10XzgQjtDTEa93ax+62mIZ+xuyx4ihx4g3YcHisOtxWHx0pHvWafvwWr3YzFZsJiM2O2moz1I2pIg1vrs1lNJpUlncoOWmb6nycTGZI9aXp70kOXcWOZiBphnU4de7pLq92M02vFE3RQOsWPJ2DHHbQPWbp8tpEdU16cNqUUZX4HZX4HH5pdChiXkhq6ErlQ72JzfRcvb2/myQ11uc/ApCIX55T5OKfcyzllPmaVe6kOuk5roCMhRP4kwM8ST9DOtEUlTFs0MIhLsidNR2OcSFsP3e0Jom0JIu0Jop29tNZFSURT/eF5+K3NI3p8VrsZm9OC3WXB5rDg9NooKnfj9Fpxem25h3XI0irXTMcdpVR/0/uVc8oAI9TrO3vYejjCzsZudjRG2NHQzQvbGumbndhlMzOzbCDQzykzaut+59i8m0GIQiQBPobYnBZKp/gonXL8AVdSyQyrX1rDufMXk+rNkElmSSUzuRp1hnTy2LVjMMZ2t1hNmK0mzBZT/7rFasZsVdgcFmxOCzaHWWrJ4riUUlQFXVQFXf2hDsY19V1NRqBvbzCWz20xplLtU+F3ML3US22xh2klHmqL3Uwr8VDkzmMOcyHEEBLgBcZqM2N1q7yGcBViNDltZhZUB1hQPdDxU2tNc3cv2xsi7GjsZkdDhD0tUda/305PamBwnIDLyrRiz0Cwl7iZVuylMujELE3xQhyTBLgQYsQopSj1OSj1OVg+c+ByUTaraYgk2NMcZU9zlL0txvKVHU39I8oB2C0mpoTdudq6kuT/rgAADEpJREFUh6nFbiaF3EwqchFwWaXWLiY0CXAhxKgzmRSVASeVAWf/WO99OmJJ9rYMhPrelhjv1XXxzOaG/mvsAD6HhclhNzVFLiaH3NSEjOXkkItir13CXYx7EuBCiDEl6Lax2F3E4slFQ15PpDIcao+zvy3OgbYYB9ri7G+Lsbm+i+e2NJLJDqS702pmUshlhHvYzaSQi0lFxrIiIM3yYnyQABdCFASH1cz0Ui/TS4/u/5HKZDnc2cP+tjgH22L9If9+a4zVu1pIDhqPwGxSlPkcVAadVAWcVAaNloC+ZUXAKQPWiIIgAS6EKHhWs8m4Nh5yA0Ob5LNZTVN3gv2tRqjXdfRQ39lDfUcPb77fTsM7PQyqvAMQ9tiPDviAk4buLJFECp9DbocTZ58EuBBiXDOZFOV+J+V+J0trQ0e9n8pkaexK9If64OW2hggvbW8aUoP/P2tfxOuwUBlwUhV0UupzUOZzUOp39K+X+Rz4nBa5Di9GlAS4EGJCs5pNVBe5qC5yHfP9bFbTGuulvqOHl9ZuxF8xpT/k6zp62Higg4546qjPOawmynwOSvpCfVDAl/rs/b3zbRYZc0GcHglwIYQ4AZNJUeJ1UOJ10LXPwvJLa4/aJpHK0BzppTGSoCn3aOxK9D9/51AnjVsTQ2ryfUJuWy7M7UNCvthrJ+yxE/baCXts2C1yXV4MJQEuhBBnyGE1UxNyURM6di0ejEFtOuOpI0LeCP3miBH2m+u7aI0mj/l5r8NCsacv1G2EPXZC7oH1sMduvO+1yQQzE4T8VxZCiFGglCLothF025hVfvzhkpPpLM3dCVq6e2mLJmmN9uYeSVqivbR297KzsZu10Ta6eo5uugdjLHoj1G0DtXi3LVebtw+857Xjtcu1+kIlAS6EEGOIzWLqH2v+ZJLpLG2xXlq7jaBv6Qv77qTxerSXA21xNh7ooD2eHDIQTh+rWRFw2Shy2Qi4rARdxo+M4JHrbpvx3GXF57DKbHNjgAS4EEIUKJvF1N/D/mTSmSzt8WR/2Pc92mMpOuNJ2mNJOuMp9rZE6TiQpCOeGjI4zmAmBYFc4BvBb6PIbQR+33pdUxr3/naCLquxrdOKRSZJGlYS4EIIMQFYzKb+znj50FrT3ZumI2aEubEcut4ZT9EeS1LXEWdLfYr2eHJIR70H335jyHf6HJb+mnzAZcXvNGrzPqcFnyP3/BiveR0WCf9jkAAXQghxFKWUEaQOK5OOvn3+mLTW9KQytMeSvLzmDWpnz++v2XfEkwM/BuJJ2qJJ3m+NEelJEUmkj1vb7+O2mfvD3Qh6Sy7orfgcFmN5nB8EXrtlXDb5S4ALIYQYFkopXDYLLpuFyX4zy6YXn/xDGMEfT2bo6kkRSaSI9KRzwZ4yXutJ514feP9wZ4IdiW4iPSm6e9PHvL4/cFzgsVuOE/4DtXyPw4LHbjzc/UszXrsVt9085loBJMCFEEKcVUop3LnQrODk1/OPlM1qosk0XfFBPwBygd+Vq+EPDv9IIsXB9nh/7T/am85rP3aLCa/DOE63bSDw+8LeYzcPWh/0uiO3fd+63Tws9/VLgAshhChoJtNAc//pSGeydOeCPJZME+tN051IE+vNGOu9xmtHrSfSNHcniLVmctun6Ull8tqn1azw2M8sgiXAhRBCTGgWs6n/Hv0zlc5kiSUzxw38WG+aWHIg8N85k+M+46MVQgghBGD8GPA7Tfid+bUG3HsG+xpbV+SFEEIIkRcJcCGEEKIASYALIYQQBUgCXAghhChAEuBCCCFEAZIAF0IIIQqQBLgQQghRgCTAhRBCiAIkAS6EEEIUIAlwIYQQogBJgAshhBAFSAJcCCGEKEAS4EIIIUQBkgAXQgghCpAEuBBCCFGAJMCFEEKIAiQBLoQQQhQgCXAhhBCiAEmACyGEEAVIAlwIIYQoQBLgQgghRAGSABdCCCEKkAS4EEIIUYBGNMCVUlcppXYqpfYopf5+JPclhBBCTCQjFuBKKTPwH8DVwGzgVqXU7JHanxBCCDGRjGQNfAmwR2u9T2udBB4HrhvB/QkhhBATxkgGeCVwaNDzutxrQgghhDhDlhH8bnWM1/RRGyl1J3Bn7mmvUmrLCB7TeBEGWs/2QRSAwjxPnzsrey3MczX65DzlT85Vfmae7gdHMsDrgOpBz6uAw0dupLX+CfATAKXUBq314hE8pnFBzlN+5DzlT85VfuQ85U/OVX6UUhtO97Mj2YT+FjBdKTVFKWUDbgF+P4L7E0IIISaMEauBa63TSqkvAS8AZuDnWuutI7U/IYQQYiIZySZ0tNbPAs+ewkd+MlLHMs7IecqPnKf8ybnKj5yn/Mm5ys9pnyel9VH9yoQQQggxxslQqkIIIUQBGvUAP9nwqsrw/dz77ymlFo72MY4VeZyr5UqpLqXUO7nHN87GcZ5NSqmfK6Waj3f7oZSnAXmcqwlfngCUUtVKqT8ppbYrpbYqpb58jG0mfLnK8zxJmQKUUg6l1Hql1Lu5c/WPx9jm1MuU1nrUHhid2fYCUwEb8C4w+4htVgLPYdxHfgHw5mge41h55HmulgN/ONvHepbP0yXAQmDLcd6X8pT/uZrw5Sl3HsqBhbl1L7BL/k6d9nmSMmWcBwV4cutW4E3ggiO2OeUyNdo18HyGV70OeFgb1gEBpVT5KB/nWCBD0eZBa70GaD/BJlKecvI4VwLQWjdorTfl1ruB7Rw9iuSEL1d5nicB5MpJNPfUmnsc2QHtlMvUaAd4PsOryhCshnzPw9Jcs8xzSqk5o3NoBUXK06mR8jSIUmoycB5GjWkwKVeDnOA8gZQpwJjgSyn1DtAMvKS1PuMyNaK3kR1DPsOr5jUE6wSQz3nYBEzSWkeVUiuBVcD0ET+ywiLlKX9SngZRSnmAp4CvaK0jR759jI9MyHJ1kvMkZSpHa50BzlVKBYD/VkrN1VoP7o9yymVqtGvg+QyvmtcQrBPASc+D1jrS1yyjjXvurUqp8OgdYkGQ8pQnKU8DlFJWjFB6VGv99DE2kXLFyc+TlKmjaa07gdXAVUe8dcplarQDPJ/hVX8P3J7rkXcB0KW1bhjl4xwLTnqulFJlSimVW1+C8d+zbdSPdGyT8pQnKU+G3Dl4CNiutf7ecTab8OUqn/MkZcqglCrO1bxRSjmBy4EdR2x2ymVqVJvQ9XGGV1VKfS73/o8wRm5bCewB4sBfjuYxjhV5nqubgM8rpdJAD3CLznVnnCiUUo9h9HQNK6XqgG9idBCR8nSEPM7VhC9PORcBtwGbc9csAf43UANSrgbJ5zxJmTKUA79SSpkxfsQ8qbX+w5lmn4zEJoQQQhQgGYlNCCGEKEAS4EIIIUQBkgAXQgghCpAEuBBCCFGAJMCFEEKIAiQBLoQQQhQgCXAhhBCiAEmACyFOSCl1vVLqp0qp3ymlrjjbxyOEMEiACzFOKKW+rpTaqpR6Tyn1jlLq/GNs41RKvZobESovWutVWuvPAp8GPp77HptSao1SarQnRBJC5Mj/fEKMA0qppcA1wEKtdW9uwgjbMTb9K+Dp3MxIp+ofgP8A0FonlVKvYAT6o6d52EKIMyA1cCHGh3KgVWvdC6C1btVaH2smo08AvwNjDmel1A6l1M+UUluUUo8qpS5XSq1VSu3OTT5BbnKF+4HntNabBn3Xqtz3CSHOAhkLXYhxIDcn858BF/Ay8ITW+tUjtrEBB7XWZbnnkzEmTjgP2IoxA967wGeAjwB/qbW+Xin1N8Cncu+/k5t4gVwzfKPWunjE/4FCiKNIE7oQ44DWOqqUWgQsA1YATyil/l5r/ctBm4WBziM++r7WejOAUmor8IrWWiulNgOTc9/9feD7x9hnRimVVEp5tdbdw/6PEkKckAS4EONE7rr2amB1LoA/Bfxy0CY9gOOIj/UOWs8Oep4lv78PdiBxGocrhDhDcg1ciHFAKTVTKTV90EvnAgcGb6O17gDMSqkjQ/x09xkCWrTWqeH4PiHEqZEauBDjgwd4UCkVANIY17bvPMZ2LwIXY1wnP1MrgGeH4XuEEKdBOrEJMYEopc4D7tZa3zYM3/U08L+01jvP/MiEEKdKmtCFmEC01m8DfzqVgVyOJdejfZWEtxBnj9TAhRBCiAIkNXAhhBCiAEmACyGEEAVIAlwIIYQoQBLgQgghRAGSABdCCCEKkAS4EEIIUYAkwIUQQogCJAEuhBBCFKD/D1+wBTcqP88QAAAAAElFTkSuQmCC\n", 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", 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" ] @@ -299,7 +299,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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yde68804mT55MYmIiJpOJ+fPnY7VaTzq21WjFrd043I56z6ZnMVsodpbgdpZgNFvq1YYQQoimQZJ9gM2cOdP/9R133HHS/vnz5/u/vvnmm7n55psBuOeee7jnnnuqnU7WZrOVq18qPj6ebdt+XyzwyVlPsvv4bvJK8uqd7G3hERRnZ2I/cYKQVq3r1YYQQoimQYbxWyCTwUSwOZg8R/3fSrCEh6MAR2H9Z+QTQgjRNEiyb6HCLeE43A4crvo9qKcMBixGI06PW2bTE0KIZk6SfQsVbgkHIK+k/r17a0goGnDknAhQVEIIIRqDJPsWymw0E2QOOqVkb4uKRgH2Cg8XCiGEaF4k2bdg4ZZw7C47Je6SmgtXwmAyYVIGSlzOer/GJ4QQovFJsm/BAjKUHxSEB3D65gAQQgjR/MirdwGQnZ3N6NGjAe8KdEajkbi4OADWrl2LxdI476lbjBZsJht5JXnEBsXWqw1bVDQFRYXYc3OwVPFKoBBCiKZNkn0A1HeJW7fbjdFobNDYwi3hZBRl4HQ7MRvNda5vstkwASUOe+CDE0IIcVrIMH4DmTRpEosWLfJ/Dg0NBSA1NZVRo0Zx3XXXkZiYSGpqKiNHjuSqq66iR48e/OUvfwno/fHSofz8kvo/ZGex2nABLrskfCGEaI5aXs/+fx+Eoz8Hts02iXDR7IA1t3btWrZt20ZCQgKpqals2rSJ7du3065dOwYPHsyqVasYNmxYQI5lNVmxGC3kleQRHRRdrzZsEREUZdix55wgtE3bgMQlhBDi9JGefSMYOHAgCQkJ5T536NABg8FAUlISaWlpAT1euCWcQmdhvZe9NYeGYQAcsuStEEI0Sy2vZx/AHvipMJlMeHwzz2mtKSn5/fW3kJCQcmXLLmZjMBhwuVwBjSXCGkFWcRa5jtx6PainlMJiMuNwOXE7nRjNdb/3L4QQovFIz76BxMfHs2HDBgCWLVuG01m/XnUg2Ew2gkxB5Dpy691GUEQkGrAfzw5cYEIIIU4LSfYN5NZbb+W7775j4MCBrFmz5qTe/OkWaY3E7rJT7KrfGvWWiAgMgL1Q3rcXQojmpuUN4zeyskvc/vTTT/6vn376aQBGjhzJyJEj/dsrfv7nP/9Z5RK3pyLcGs7RoqPkOHIIMgXVub5SCmvpGvclDowWa82VhBBCNAnSsz9DmAwmwixh5Dpy8ej6rWIXFBkFQHG2DOULIURzIsn+DBJpjcTtcVNQUr+heHNYGEbAXixP5QshRHMiyf4MEmoOxWQwkePIqVd9pRRWixWX1jLBjhBCNCOS7M8gSikirBEUlBTg8tTv9T5blG8oX57KF0KIZkOS/Rkm0hqJRtf7NTxLqHco31Fcv6f6hRBCnH6S7M8wNpMNm8lW76F8AKvVhguNSxK+EEI0C5LsA+jo0aNMnDiRLl260LNnTy6++GL27NlD7969AVi/fj1TpkypU5vx8fFkZWUFNM7Sd+7trvrddw+K8s6xL0P5QgjRPMh79gGiteaKK67gpptu4oMPPgBg8+bNHDt2zF9mwIABDBgwoLFC9IuwRnCs6Bg5jhzamNrUub45JMT7VL69GFnhXgghmj7p2QfIihUrMJvNTJ482b8tOTmZjh07+j+npqYyfvx4wDv5zk033cSYMWOIj49n8eLFPPDAAwwePJhx48aVm1732WefZeDAgQwcOJB9+/adcqwmg4lQc+gpvXNvswXhBpyFhaccjxBCiIbV4nr2z6x9hl3HdwW0zR7RPfj7wL9XW2bbtm3079+/Tu3u37+fFStWsGPHDoYMGcLHH3/Mo48+yo033sjnn3/OH/7wBwDCw8NZu3Ytb7/9NlOnTuWzzz6r97mUirRGkl+ST6GzkDBL3fvnQdExFB5Op/jEccyNPBWwEEKI6knPvhFddNFFmM1mEhMTcbvdjBs3DoDExMRyy9xee+21/r9Xr14dkGOHWkIxGoycsJ+oV31TUBAmFA6HvG8vhBBNXYvr2dfUA28ovXr1YtGiRXWqU7q0rcFgwGw2o5Tyfy67zG3p9opfnwqDMhBljSKrOIsSdwkWo6XObViDgigsLqKkIB9LqNy9F0KIpkp69gFy/vnn43A4mDdvnn/bunXr+O2330657YULF/r/HjJkyCm3VyrK5p0g57j9eL3qB0XHAFB8vH71hRBCnB4trmffWJRSLFmyhKlTpzJ79mxsNhvx8fE8//zzp9y2w+Fg0KBBeDweFixYEIBovSxGC+HWcHIcObQKboVB1e3az2SzYVYGHM4StMeDMsi1oxBCNEWS7AOoXbt2fPjhhydt37ZtG1B+OduyS+ECFBT8vjhN2X2l9+5nzJgR0FhLRduiyXPkkevI9ff06yIoLIy8vFzsx7MJio1rgAhFU2I0GklMTPR/Xrp0KfHx8afc7vr163n77bd54YUXmDlzJqGhodx3333lyqSlpTF+/Hj/v6fTYfr06QwfPpwLLrjgtB1TiIYgyf4MF2wKxmqykm3PJtIaWednAoKiY8jPy6U4P0+S/RkgKCiIzZs3B7zdhp6DQmuN1hpDHUefZs2a1UARCXF6ybjrGU4pRYwtBofLQZGr7kvXKqMRm9lCiceD2+FogAhFU7dhwwZGjBhB//79GTt2LEeOHAFg3rx5pKSk0KdPH6688kqKiry/Xx999BG9e/emT58+DB8+HCg/BwXAli1bOP/88+natWu552BKud1u7r//flJSUkhKSuLVV189qUxaWhrnnHMOd955J/369ePgwYM8++yz/jplR8sef/xxbrzxRi688EKuvfZannvuOQAmTZrkf/D2m2++oW/fviQmJnLzzTfj8P2+x8fHM2PGDPr160diYiK7dgX21V8hAkGSvSDCGoFRGev/oJ5v+tyirMxAhiWaoOLiYpKTk0lOTuaKK67A6XRy9913s2jRIjZs2MDNN9/Mww8/DMAf//hH1q1bx5YtWzjnnHN44403AG9v+csvv2TLli188sknlR5n69atfP7556xevZpZs2Zx+PDhcvvfeOMNIiIiWLduHevWrWPevHn8+uuvJ7Wze/dubrzxRjZt2sTu3bvZu3cva9euZfPmzWzYsIGVK1eyfv16Pv74Y+bNm8fixYtZv379Se3Y7XYmTZrEwoUL+fnnn3G5XLz88sv+/bGxsWzcuJE77rjDf6EgRFPSoMP4SqlxwH8AI/C61np2hf0jgWVA6b/SxVprGTc7zQzKQKQtkuzibEqC6/4aniUsDFPGUez2YkK1DtjrgaLpqTiMv23bNrZt28aFF14IeHvcbdu29e975JFHyMnJoaCggLFjxwIwdOhQJk2axIQJE/jjH/9Y6XEuv/xygoKCCAoKYtSoUaxdu5bk5GT//uXLl7N161Z/rzs3N5e9e/eSkJBQrp1OnToxePBgf53ly5fTt29fwPuczN69e8nPz+fyyy/HarUSFhbGpZdeelI8u3fvJiEhgW7dugFw0003MXfuXKZOnQrgP4/+/fuzePHiunxLhTgtGizZK6WMwFzgQiAdWKeU+kRrvaNC0e+11uNPakCcVtG2aLKLszlhP0HrkNZ1rm8LDqGgqJCSvDysERENEKFoirTW9OrVq9LJniZNmsTSpUvp06cP8+fPJzU1FYBXXnmFNWvW8Pnnn5OcnFzpMwAVLxgrftZa8+KLL/ovIKoSUmZ2R601Dz30ELfffnu5Mv/+97+rbaO0bnVK58wwGo3l5sgQoqloyGH8gcA+rfUvWusS4APg8gY8njgFFqOFMEsYJxwn6jVfflBsHAoozpF37s8k3bt3JzMz05/snU4n27dvByA/P5+2bdvidDp57733/HX279/PoEGDmDVrFrGxsRw8ePCkdpctW4bdbic7O5vU1FRSUlLK7R87diwvv/yyfw2JPXv2UFjDOg1jx47lzTff9L/5cujQITIyMhg2bBiffvopJSUlFBQU8Pnnn59Ut0ePHqSlpfnXpnjnnXcYMWJEbb9NQjS6hhzGbw+U/VecDgyqpNwQpdQW4DBwn9Z6e8UCSqnbgNsA4uLi/D2EUhEREeTn5wco7Po7duwYf//739m4cSNWq5WzzjqL2bNn07Vr11rVf+KJJxgyZAijR49u4EgrF6SDyPfkcyz3GKHG0DrXNymFw+UiLzcHZTBWWsbtdjeJn1Vd2e32k37vKlNQUFCrcs1RQUEBbrf7pPN78MEHmTx5sn//VVddxfjx47nhhhvo06cPrVu3pnPnzhw9epTU1FSmT59Oeno6Wmv69evHiRMn2LJliz+xp6Wl0aFDB84991wyMjKYOHEie/bs4ejRoxQWFpKamsrZZ59NSEgI3bt3R2tNZGQkjz/+OKGhv//eli0PYLFY/A/ngfeWxLRp02jfvj1JSUncfPPNtG3blo4dO/pjPXr0KNu3byc2NpZ77rmHiy66CLfbTffu3TnnnHNITU3FbrezatUqIiIi2L17Nzk5OY3yO9A+x9t321HFsVvy7ya0/PM7ZaWvpAT6D3A13vv0pZ9vAF6sUCYcCPV9fTGwt6Z2u3XrpivasWPHSdtON4/HowcPHqxffvll/7ZNmzbplStX+j+7XK4a28nLy2uQ+GrD4/HovSf26n0n9mmPx1Pn+sXZ2frIvj264OiRKss05vmditr+jq1YsaJhA2lELfnc8vPz9YoVK3RhYaHu37+/3rBhQ2OHVGfHXtmij72ypcr9Lfnnp3XLPz9gvT6FnNyQw/jpQMcynzvg7b2XvdDI01oX+L7+AjArpWIbMKYGU9USt263m1GjRnHdddf5F7jp3bu3v8xzzz3nn0Sn9B4neHtLPXv2JCkpyT+5SGZmJldeeSUpKSmkpKSwatWqgJ6DUopoWzR2l71er+FZo6IwAMWFBTWWFaIpue2227jlllvo168fV155Jf369WvskIQIqIYcxl8HdFVKJQCHgInAdWULKKXaAMe01lopNRDvMwTZp3LQo089hWNnYN9ztZ7TgzbTplVbprolbteuXcu2bdtISEgot5pdVY4fP86SJUvYtWsXSilycnIAuOeee7j33nsZNmwYBw4cYOzYsezcubPO51OdSGskmUWZZBZlEhJRt6VrlVLYrDaKHHacRYWYg2XpW9E8vP/++6SmpvpnuBSipWmwZK+1diml/gp8iffVuze11tuVUpN9+18BrgLuUEq5gGJgom+4okUZOHDgSa8EVSc8PBybzcYtt9zCJZdc4p9s5Ouvv2bHjt9fZsjLyyM/P5+wsMCtOGdQBmKCYjhWeIwiZxHB5uA61Q+OiaXocDrF2dmS7IUQoolo0PfsfUPzX1TY9kqZr+cAcwJ5zJp64A2luiVuy77+YzKZ8Hh+f9rdbj95PXiTycTatWv55ptv+OCDD5gzZw7ffvstHo+H1atXExQUFPgTKKN06dus4izOMp9Vp7qmoCDMSmEvcRAmi+MIIUSTIP8TB0hVS9x+99135cq1bt2ajIwMsrOzcTgcfPbZZye1VVBQQG5uLhdffDHPP/+8/z3kMWPGMGfO79dGDTFHOYDRYCTGFkN+ST7FruI61w8KC8cDFGdnBT44IYQQdSbJPkBKl7j96quv6NKlC7169WLmzJm0a9euXDmz2cz06dMZNGgQ48ePp0ePHie1lZ+fz/jx40lKSmLEiBH+ST9eeOEF1q9fT1JSEj179uSVV145qW6gRNuiMSgDWcV1T9hBMbEYgKL8vMAHJoQQos5k1bsAqmqJ21tvvbXc5ylTpjBlypSTys2fP99/D37t2rUn7Y+NjWXhwoWBC7gaRoORaFs0WcVZ2IPs2Ey2WtdVBgM2WxBF9mJK8vOxBPCZAiGEEHUnPXtRpZigmHr37kPivDPqFR2XoXwhhGhskuxFlUwGE1G2KHIduZS4S+pU12ixYjGacLhcuEvqVlcIIURgSbIX1YoJikEpRWZx3ZevDYmOQQNFmRmBD0wIIUStSbIX1TIbzERZo8i11713bwkPx4Si2F6M9tR9cR0hhBCBIcle1CgmKAYU9bp3HxwWhgewHz+liRGFEEKcAkn2okYWo4VIayQ5jpw69+6DYuO8r+Hl5TZMcEIIIWokyT5AnnzySXr16kVSUhLJycmsWbOmzm3YC524HOVnC54+fTpff/11oNHX1eYAACAASURBVMKslU8++YTZs2cDMHPmTJ577jniguKY8/QcPv784zq1pQwGbFYbTq1xFsgCOUII0RjkPfsAWL16NZ999pl/HfusrCxK6vAEusvlwmQyYS9w4nKW3zdr1qwAR1uzyy67jMsuu6zcNrPRzMxZM8ksyqTYWUyQufZT9obEtaI4/QCF2VkYo2MCHa4QQogaSM8+AI4cOUJsbCxWqxXwTn5TOnPehg0bGDFiBP3792fs2LEcOXIEgJEjRzJt2jRGjBjBk08+SXx8PEaLQrshP6+Ajh074nQ6mTRpkn/O/XXr1nHuuefSp08fBg4cSH5+Pm63m/vvv5+UlBSSkpJ49dVXK43x7bffJikpiT59+nDDDTcAVS+ZO3/+fP7617+e1Mb9d9zP1599zdGio8THxzNjxgz69etHYmIiu3bt8rd54YUX0q9fP26//XY6derEifx8LEYjDpcT7XYH8DsvhBCiNlpcz/77D/eQdTCww8WxHUM5b0K3KvePGTOGWbNm0a1bNy644AKuueYaRowYgdPp5O6772bZsmXExcWxcOFCHn74Yd58800AcnJy/HPnb9y4kZ/WrqJPj4EsXbKMsWPHYjab/ccoKSnhmmuuYeHChaSkpJCXl0dQUBBvvPEGERERrFu3DofDwdChQxkzZky5Vfa2b9/Ok08+yapVq4iNjeX48eNA3ZfMVUoRbgmnyFmERhMbG8vGjRt56aWXeO6553j99dd57LHHOP/883nooYf4v//7P1577TUAgqNicGRl4M7LhcjI+v8whBBC1FmLS/aNITQ0lA0bNvD999+zYsUKrrnmGmbPns2AAQPYtm0bF154IQBut5u2bdv6611zzTXlvl60+CP6PDyQDz9cyJSpd5c7xu7du2nbti0pKSmAdxlcgOXLl7N161Z/7z83N5e9e/eWS/bffvstV111FbGxsQBER0cDVS+ZW50QcwgWowW3x80VV1wBQP/+/Vm8eDEAP/zwA0uWLAFg3LhxREVFAWCNiMCUlYmzxIGW1fCEEOK0anHJvroeeEMyGo2MHDmSkSNHkpiYyH//+1/69+9Pr169WL16daV1yi59e9lll/HQQw9x35SH2LR5E+eff365slprlFIntaG15sUXX2Ts2LFVxlZV3fosmWtQBloHt0ajseNdntdoNOJyufzHqkpIRAS5uTkUZWYQ0rpNrY8phBDi1Ej3KgB2797N3r17/Z83b95Mp06d6N69O5mZmf5k73Q62b59e6VthIaGMnDgQB6Z+SAXnj8WPOWTc48ePTh8+DDr1q0DvCvjuVwuxo4dy8svv4zT6X2yb8+ePRQWFparO3r0aD788EOys73vupcO49d3ydwwSxgKRVZxFm5P+Xvww4YN8y8GtHz5ck6cOOHfZ4uJxaihqCC/2osCIYQQgdXievaNoaCggLvvvpucnBxMJhNnn302r732GhaLhUWLFjFlyhRyc3NxuVxMnTqVXr16VdrONddcw9VXX82SDz6nxOEiyGzx77NYLCxcuJC7776b4uJigoKC+Prrr7nllltIS0ujX79+aK2Ji4tj6dKl5drt1asXDz/8MCNGjMBoNNK3b1/mz5/PCy+8wF133UVSUhIul4vhw4fXatlcpRRGgxGXx8Vx+/Fy+2bMmMG1117LwoULGTFiBG3btiXMt+qdUgqzzYbdYac4K4vguLi6fquFEELUg2puPazu3bvr3bt3l9u2c+dOzjnnnEaKKLDy8vIoyVVYgkyEx9Z+eL0xHMg7QKGzkK5RXTEZvNeNDocDo9GIyWRi9erV3HHHHeVGDPLy8rBnHsMAxHQ+u9LbC01RbX/HUlNTGTlyZMMH1Aha8rlB8z+/jFe3AtDq9qRK9zf386tJSz8/pdQGrfWA+taXnn0T4+39miixu6q8195UtA5uzf6c/RwrOkb70PYAHDhwgAkTJuDxeLBYLMybN69cHaUUwcEhFBQV4jhxApvvYUEhhBANR5J9E2SxGXEUOXG7PJjMxsYOp0pWk5XooGiyi7OJskYRbA6ma9eubNq0qdp6wa1aU5T2C4U5xyXZCyHEaSAP6DVBZpv3GqzE3vQnoIkLisNsMHO48HCtH7ozGI3YbEE4tcYhc+YLIUSDk2TfBBlNCoPRgNPuauxQamQ0GGkT0gaHy0G2vfYr24W0ao0CCrPrvpKeEEKIupFk3wQppbDYjJTY3c3iFbUwSxihllAyizJxup01VwCMZjM2i4USjwdnoSyQI4QQDUmSfRNlsZnQHo2rxNPYodRIKUXbkLZoNEeLjta6XmnvviAzs+GCE0IIIck+kI4ePcrEiRPp0qULPXv25OKLL2bPnj21b0Br0N7kbrZ5H8z7882/L4RT1vr165kyZUpA4q5q4Zu6sBgtxAXFkefII7+k+il3S5msNqwmMw63C1dx8SkdXwghRNUk2QeI1porrriCkSNHsn//fnbs2MFTTz3FsWPHat9I9j6Cir09Y6PJgNFswOOqfBh/wIABvPDCC4EIPWBigmKwGC0cKTyCR9duRCK0VSsUkH+s9iMCQggh6kaSfYCsWLECs9nM5MmT/duSk5MZNmwY999/P7179yYxMZGFCxcC3gkgRowYwYQJE+jWrRsPPvgg7y3+X84ddyWJiYns378fi9WEx6P56quvOO+88+jWrRufffaZv/748eMBmDlzJjfffDMjR46kc+fO5S4C3n33XQYOHEhycjK33347bt8Ss2+99RbdunVjxIgR/qVtT5VBGWgb0han20lWce0evDMFBft7984K0/wKIYQIjBb3nv2K+a+R8dsvAW2zVafOjJp0W7Vltm3bRv/+/U/avnjxYjZv3syWLVvIysoiJSWF4cOHA7BlyxZ27txJdHQ0nTt35pab/sTaz9/hPwu+4sUXX2T2k8+Chl9/TeO7775j//79jBo1in379p10nF27drFixQry8/Pp3r07d9xxB/v27WPhwoWsWrUKs9nMnXfeyXvvvceFF17IjBkz2LBhAxEREYwaNYq+ffsG5HsVagklwhpBVnEW4ZZwbCZbzXVat8Fx6CD5GceITugckDiEEEL8rsUl+6bmhx9+4Nprr8VoNNK6dWtGjBjBunXrCA8PJyUlxb/kbZcuXRhz0cVoILFbAiu+/xGL7779FZf9EYPBQNeuXencuTO7du066TiXXHIJVqsVq9VKq1atOHbsGN988w0bNmzwL4tbXFxMq1atWLNmDSNHjiTONzf9NddcU7dnC2rQJqQNBc4CDhUcIiEiAYOqfgDJZLNhM1sodpZQkp+PxTeXvhBCiMBoccm+ph54Q+nVq1elD9JV9+qc1Wr1f20wGLAGheAxWDG47bhcLgxGA8qocFe4b1/ZFLpl2ypdclZrzU033cTTTz9druzSpUsbdBpek8FEu5B2HMw/SGZxJq2DW9dYJ7RNG+wHD1CQlUG0JHshhAgouWcfIOeffz4Oh6PcXPDr1q0jKiqKhQsX4na7yczMZOXKlQwcOLDKdlymYHDZvU/mAwaDYumyxbhdbvbv388vv/xC9+7daxXT6NGjWbRoERkZGYB3advffvuNQYMGkZqaSnZ2Nk6nk48++ugUzrxy4dZwIq2RZBVlUeQsqrG80WIlyGKlxOORWfWEECLAWlzPvrEopViyZAlTp05l9uzZ2Gw24uPjef755ykoKKBPnz4opfjHP/5BmzZtKh2KB3AbfSvdae+DdAajokvnrowYMYKMzAxeeeUVbLaa74MD9OzZkyeeeIIxY8bg8Xgwm83MnTuXwYMHM3PmTIYMGULbtm3p16+f/8G9QCo7nN8lskuNw/khbdpSfCCNgqxMrOERAY9HCCHOVLLEbROTn5dLWEEahMRARAc8Hk1WegFBoWbComuX5JuSgpICfsv7jZigGNqEtCE/P9+/vn1l8g+lU2gvJjImFltk1GmMtHqyxG3LPjdo/ucnS9y27PM71SVuZRi/qVEGsIaAwzsxjcGgfKvguZrF1LkVhVpCibJFkV2cTaGz5lfrQlq3wQAUZGc3y/MVQoimSJJ9U2QN8963d5d4Pwab8Lg9uJxNf+rcyrQObo3FaOFQwaEaJ9sxmEwEBQXjQmM/fvw0RSiEEC2bJPumyOIb5nZ4F4ixBPmWvC1q+qvgVcZoMNI+tD1Ot5MTrhM1lvf37nOOS+9eCCECQJJ9U2QOAmX0D+UbjQbMViOO4uaZ7AGCzcHEBcdR6CnkhL36hG8wGgkJDccNFMk0ukIIccok2TdFSnmH8h35/lfwrMEmXCVu3M10KB8gLigOq8HKkcIj2F32assGt2qFCSgsLMDjar4XOUII0RRIsm+qrGHgcYLLAfw+lN+ce/dKKWJNsRiVkYP5B3F7qn7dTylFWEwcHiD/yOHTF6QQQrRAkuwDpKrlbdPS0ujdu3fdG7SW3rf3DuWbzEaMZgOOYmetqm/evJkvvviixnJlF9Sp61K31R3jq6++on///iQmJtK/f3++/fZbAIzKSJwljmn3TKNrt6706NGDjz/+uNI2rJGRZBw5Rrse5/CPp57yb4+Pjycry7vQzoYNG0hISGDTpk0UFBRw++2306VLF3r16sXw4cNZs2aNv96SJUtQSpWb48Dj8TBlyhT/QkUpKSn8+uuvtf4eCCFEcyCT6gRA6fK2N910Ex988AHgTYTHjh2jY8eO9WvUZAWjxZvsQ71z2FuDTBTlleBxezAYq79O27x5M+vXr+fiiy+u3/FrobpjxMbG8umnn9KuXTu2bdvG2LFjOXToEAD/efY/dGjbgZk/zfROpVvNBHszn3uO84cPx16Qf9K+rVu3ctVVV7Fw4UL69u3LxIkTSUhIYO/evRgMBn755Rd27tzpL79gwQKGDRvGBx98wMyZMwFYuHAhhw8fZuvWrRgMBtLT0wkJCTm1b4wQQjQx0rMPgKqWtz3vvPPKlXO73dx///2kpKSQlJTEq6++CkBBQQGjR4+mX79+DB48mGXLlgGQdiyHcwZfwK233EKvXr244upLKbYXnzSU/9FHH9G7d2/69OnD8OHDKSkpYfr06SxcuJDk5GQWLlzI2rVrOffcc+nbty/nnnsuFScmqk5ldSs7Rll9+/alXbt2gHfdALvdjsPhvSXx5ptv8vijjxNiDiGjOIPQyNBKj7t06VK6dO1K7x49cGuNIyfHv2/nzp384Q9/4J133mHgwIHs37+fNWvW8MQTT2AweH+tO3fuzCWXXOL/Hq9atYo33njDf0EGcOTIEdq2beuv06FDB6Kims5kPkIIEQgtrmef8+l+Sg4Hdl10S7sQIi/tUuX+qpa3reiNN94gIiKCdevW4XA4GDp0KGPGjKFjx44sWbKE8PBw0tLSuOCCC7jsssvAEsreXw+w4P2/MO/115kwYQJffPkp1117HUGhFn+7s2bN4ssvv6R9+/bk5ORgsViYNWsW69evZ86cOQDk5eWxcuVKTCYTX3/9NdOmTaty+LyiHj16VFq34jGq8vHHH9O3b1+sVqt/nv7p06ezInUFrTu2ZsY/ZjCw60CMBqO/TmFhIc888wxfffUVz/7jHyhnCfnZmVgivNPoXn755bz77rsMGzYMgO3bt5OcnIzRaDw5ALwXDuPGjaNbt25ER0ezceNG+vXrx4QJExg2bBjff/89o0eP5k9/+lPAlvsVQoimQnr2p9Hy5ct5++23SU5OZtCgQWRnZ7N371601kybNo2kpCQuu+wyDh06xLFjx8AcQkLHdiT3SACgf//+HD6aTkmxG+35/f3zoUOHMmnSJObNm1flHPe5ublcffXV9O7dm3vvvZft27fXOu5Tqbt9+3b+/ve/+0cx3G436enpDB06lE0bNzFy2EiefvRp0gvSy71TP2PGDO69915CQ0NRBgMWixUXUJRxDIALLriA119/vdZz+i9YsICJEycCMHHiRBYsWAB4e/K7d+/m6aefxmAwMHr0aL755ptan58QQjQHLa5nX10PvKFUtbxtRVprXnzxRcaOHVtu+/z588nMzGTDhg3Y7XYSExOx2+1gNGG1Wb337cPaYDQa0cqD1poSuwtrsBmAV155hTVr1vD555+TnJzM5s2bTzr2o48+yqhRo1iyZAlpaWl1mkO6vnXT09O54oorePvtt+nSxftziY6OJjg4mCuuuAKA6ydez/y35lNQUsDRoqO0DWkLwJo1a1i0aBEPPPAAOTk5GAwGDGhuvvEGAObMmcPkyZO58847efXVV+nVqxdbtmzB4/H4h+RLZWdn8+2337Jt2zaUUrjdbv+iREoprFYrF110ERdddBGtW7dm6dKljB49utbfHyGEaOoatGevlBqnlNqtlNqnlHqwmnIpSim3UuqqhoynoVS1vO13331XrtzYsWN5+eWXcTq9T9Tv2bOHwsJCcnNzadWqFWazmZUrV/Lbb7/9XkkZoaQQfK+pGU0KpVS5+/b79+9n0KBBzJo1i9jYWA4ePEhYWBj5+b8/1Jabm0v79u0B78VFXVRVt+IxysrJyeGSSy7h6aefZujQob+fjlJceumlpKamAvDNN9+Q2CuR6KBojhcf57jdO0Xu999/T1paGmlpaUydOpVp06Yx9d578QAetwuDwcCCBQvYvXs306dPp0uXLgwYMIAZM2b4Rwj27t3LsmXLWLRoETfeeCO//fYbaWlpHDx4kISEBH744Qc2btzI4cPeV/s8Hg9bt26lU6dOdfr+CCFEU9dgyV4pZQTmAhcBPYFrlVI9qyj3DPBlQ8XS0EqXt/3qq6/8r33NnDnT/4BaqVtuuYWePXvSr18/evfuze23347L5eL6669n/fr1DBgwgA8//JAePXqUadwAaCgp8B/LEmSipMzCOPfffz+JiYn07t2b4cOH06dPH0aNGsWOHTv8D8898MADPPTQQwwdOrTOy9lWVbfiMcqaM2cO+/bt4/HHHyc5OZnk5GT//fpnnnmGmTNnkpSUxDvvvMM///lP2gS3YfXXq3n4kYerXDDHFhmF1WhEaygpKMBqtbJs2TI++eQT5s6dy+uvv87Ro0c5++yzSUxM5NZbb6Vdu3YsWLDAP5JQ6sorr+T9998nIyODSy+9lN69e5OUlITJZKrT64dCCNEcNNgSt0qpIcBMrfVY3+eHALTWT1coNxVwAinAZ1rrasfDW/wStxWXgPV44NjPEBQFkWcBYC90kpdVTGTrYCy25nUnprolbt0eN7/k/oJbu+kc0RmL0XJymRIHWQcPYEQR07kLSqmGDhmQJW6hZZ8bNP/zkyVuW/b5neoStw2ZKdoDB8t8TgcGlS2glGoPXAGcjzfZV0opdRtwG0BcXJx/CLhURERElcPJzY3b7T7pXGzGYIzFORQaIkEpf4++ILcIs/P0JLtAqez8yooxxnDMeYy0nDRam1tjUCcPPlktVuwlDnLTD2I8TWve2+32k37vKlNQUFCrcs1RSz43aP7n1z7H+29lRxXn0NzPryYt/fxOVUMm+8qyUMVhhOeBv2ut3dX10LTWrwGvgbdnX/HqbefOnVX2FpubSnu+JhecSCPMqvwz63mKi3C7PISGhpy23m0gVNezL2UpsXAg7wA55NAxtONJCV+HhuL6ZT+OEgexFgtGq7UhQwbAZrPV6pW8lty7aMnnBs3//DJ2e3v2XUdKz16crCEf0EsHyk4f1wGoOMn5AOADpVQacBXwklLqDw0YU/NkDQcU2HN/3xRiwu3y4Cqp2/335iDUEkrb0LYUlBRwuODwScvcKqUIb90GDeQdOdQ4QQohRDPSkMl+HdBVKZWglLIAE4FPyhbQWidoreO11vHAIuBOrfXS+hysRa97bjB6E35xTplV8MwopbAXNN+FcaoTZYuiVXArch25HC06etLP1xIaSpDFisPtxn48u0FjadG/W0KIM0KDJXuttQv4K96n7HcCH2qttyulJiulJldfu25sNhvZ2dkt+z/loAjvKnhO70TyBoP3qXx7kbPcBDstSWxQLDFBMRwvPk5WcdZJ+8PatcMA5J84jqeObxjUltaa7OxsbDZbg7QvhBCnQ4M+yq21/gL4osK2V6ooO6m+x+nQoQPp6elkZmbWt4kmw263V55YPB7Iy4RjdrBFAuByuinOc3LkuBmzpfJpYpuaKs+vChpNrj2Xo66jRFojCTYHl9vvLCyguKiIg8cyCIqKDnS4gPdiskOHDg3SthBCnA7N672tKpjNZhISEho7jIBITU2t+kGwt6dBbjr8dT0ohcej+e9Dq2jVKZxL7qzHMrqNoNrzq4LT42TKt1P48fCP/HPEP7mg0wXl9i/683UcKMxlwl330WHEqECGK4QQLYLMjd+cnHMpZO+DTO88AwaDovvANhzYlk1RXkkjB9dwzAYz/xzxT3rH9ubvK//Oj4d/LLd/7KzZmD2a/5vzL1xFgV0ESQghWgJJ9s1Jj/GAgp2f+jd1H9wGj0ezd92xxovrNAg2B/PS6JeIj4hnyrdTWH14tX9fWMezGHnRH8g1aFZMn9aIUQohRNMkyb45CWsDHQfCzt9faohpH0rcWWHsXnO0EQM7PSKsEbw+5nXOCj+Lu7+9u1zCT/zLbcSHRPDzgX389lWznXlZCCEahCT75uacS+HoVjiR5t/UfVAbMg/kk32ooPHiOk2ibFG8MeaNShP+uCeexerRfPnai5QUynC+EEKUkmTf3PQY7/1752f+TV1TWmMwKHb/1PJ793Bywv/pyE8AhLRrx6g/XEO+Ab59+P5GjlIIIZoOSfbNTXQCtEksd98+ONzCWb1j2L32KJ4W+s59RVG2KP+Q/l+/+as/4ff80010CY9h++Hf+PXzT2toRQghzgyS7Jujcy6Dg2sg//eefPdBbSjKLSF91/FGDOz0irZF+xP+XV/fxbcHvgVgzFPPEuTRLH/rFRw5OY0cpRBCND5J9s3ROZcCGnZ97t+UkBSLNdjErtVnxlB+qWhbNG+OeZMe0T24N/VeluxdQnBcKy6YcAMFRsWXD/5PY4cohBCNTpJ9cxTXA2LOLvdUvtFs4OwBrfl1cyYlxS1zvvyqRNoimTdmHoPbDmb6j9N5a9tbdJtwLefEtWfviQy2zHu5sUMUQohGJcm+OVIKel4Ov64sN5TfY3AbXE4P+zZmNGJwjSPYHMyc8+cwLn4c/9rwL/614V9c+Mw/idSK7778lOPbf27sEIUQotFIsm+u+lwH2gNbPvBvap0QTlTbEH5OTW/ZiwJVwWw0M/u82VzT/Rre2vYWj2/+Bxff/whawSezHsHtcDR2iEII0Sgk2TdXsWfDWUNg07v+ZW+VUiSN6kDWwQKO7s9t5AAbh9Fg5OFBDzO5z2SW7FvCrNz5DBo6kmzcfDPtvsYOTwghGkWNyV4p1UopdYVS6i6l1M1KqYFKKblIaAqSr4fsvZC+zr+p+6A2WINNbPk2vREDa1xKKe5KvotHBz/K6sOreSZhNWeFhPPzwV/Y89HCxg5PCCFOuyqTtlJqlFLqS+Bz4CKgLdATeAT4WSn1mFIq/PSEKSrV6w9gDvb27n3MViPnDG3HL5szyT9ub8TgGt+E7hN4+YKXOVZ4jDfOSyfEo1m+8L/k/ZbW2KEJIcRpVV0P/WLgVq11itb6Nq31I1rr+7TWlwF9gE3AhaclSlE5axj0ugK2LYaS36eHTRzRHrRm28pDjRhc0zCk3RDevfhdVHgIPyRl4lSKT6f9DbfT2dihCSHEaVNlstda36+1PlDFPpfWeqnW+uOGC03USvL1UJJfbka98NggEvrEseP7w7hK3I0YXNPQObIz71/yPsaUnhyPyOaoy8GSqXfgdp1ZrygKIc5c1Q3j/6m6e/NKqS5KqWENE5aotU7nQlRCuaF8gKRRHbAXOtnTwpe+ra0oWxTzxszDc9P5WDzZ/JZ1lMWP/E0SvhDijFDdMH4MsEkp9abv4bwJSqkblVKzlFLfAf8AJJM0NqWg7/WQ9j0c/9W/uV23SGLah7L12zPzNbzKWIwWnhj2JDEP307rvCwO/LqfBY/dh9slQ/pCiJatumH8/wD9gAVAHDDa9/kQcIPW+kqt9d7TEqWoXp9rAQVbFvg3KaVIOr8D2YcKOLxH5ocvpZTiuuRJ9Hr6MbpkZHFszz5enzkFl9zDF0K0YNW+Qqe1dmutv9Jaz9Ra3661nqq1frWqe/mikUR0gC6jYPP74PH4N3dLaY0txMzWFWfua3hV6Zs8lpTpM+iZnknB3oP8+5E/U1xcWHNFIYRohuR9+ZYi+XrIPQi/fuffZLIY6XleO37dkkleVnEjBtc0tR91Ef0n3UziwQxU2gmeve86dv6yqbHDEkKIgJNk31L0GA+2CNj8XrnNvYe3B6XY9p28hleZuFtuo2fKufT7LQPbcSfLHn2YBYufb+ywhBAioCTZtxRmGyRe7X0Fr/j3e/Rh0Ta69I1jx6rDlNjlyfOKlFK0e+pJ4uPacEFaNp5QI4cXfs1TM27gRF5WY4cnhBABUd2rdf9T3Z/TGaSopeTrwWWHnz8qv/mCs3AUuaR3XwVDSAgdXn6FUIOZq36xYxnQCfOu48y59yZ+3PBlY4cnhBCnrLqefZjvzwDgDqC9789kvNPmiqamXV9omwxrXyv3oF7rhHDO6hXDpuUHpHdfBUuH9nR8aS6ejEwu3ZRDvztuRLlh1bMv8MLc/8FeIs88CCGar+pevXtMa/0YEAv001r/TWv9N6A/0OF0BSjqQCkY8lfI2gP7vi63K2V8PPZCp/TuqxHUpw/tnplN8caN9PhsPbc+9yqOsyNwrtzDP+6ZwIZtKxs7RCGEqJfa3LM/Cygp87kEiG+QaMSp6/UHCGsHq+eU29wmIYKzekWz6Svp3VcnfNw44u69l7zPPsPw/hIeeeJ9Ot0wHlOhm2+eeIY5rzxAidPR2GEKIUSd1CbZvwOsVUrNVErNANYAbzdsWKLejGYYdLv3FbyjP5fblXJJAvYC6d3XJOa2W4m43d6J6QAAIABJREFU8o9kvfQSucuWcdX4yUx67iVKOofjWLGD2fdczcbt3zd2mEIIUWs1Jnut9ZPAn4ETQA7wZ631Uw0dmDgF/W8Ccwisnltuc5vOEZzVU3r3NVFK0XbGDIIHDeLwI49S+NNPtGsVzyNPLaDDdWMx5bv4+vHZ/Ps/Uygszm/scIUQoka1ffUuGMjzTaGbrpRKaMCYxKkKioJ+N8DPiyDvSLldKeN9vXtZ/rZaymKhwwv/wRofT/qdd1H88zYArrn8bm58bi7OblF4fvyFf/11Iv+74v1Gjvb/2bvvsKiu9IHj3zvDDDPMMMMMvQkDIgqCdOy9xthi1Nhikk1Mb5u2Kb/NbnrbJLuJrumaakzRxMQSTYLGSrGjYgMUEJHepcz9/TGE1YiIUarn8zw8zMy9c+dcQd57z3nPewRBEJp30WDf0HX/GPB4w0sq4NMLv0PoEOLvAGsdJL13zsseAUZ8Q8zsWnec2jNi+dvmKI1GfN9/H6XJxIn58zlzzLbQkI+7hSef+ZSQO2YBsH/R5zz/9znk5GW0Y2sFQRAurCV39lOAiUAFgCzLOdim5AkdmdkCva6F5A+h5tya77HjLVSVibH7llC5u9Htg/dBoeD4rX+hNje3cdu4YbN48K3PUfQPQHWoiCV/vYutKd9QW1fTzBEFQRAurq7eyt6sEj7YlM7tnyRf9vFaEuxrZNsaqTKAJEm6y/5UoW30uweqis5ZDQ/AM9B2d79zXaa4u28Btb8/3d57F2tpGcdvvZW6oqLGbXoHIw/e/x9G/f0Jap3tUSem8+I9U1m/6et2bLEgCJ1NdW09iemFLPj1CDd+mEjEM+uY8PYmnv1hP/tPll728VsS7JdJkvQO4CRJ0m3AeuC9i7xH6Ah848E7GrYuPKfIDvzv7n7vBrEiXktoQkLwWbiA2uMnOHHHHVgrzu0tiQwZwFNvfAODQ1FUW9n91mKee2ImRzL3tVOLBUHoyMrP1LHx0GleW5vG9EVbCf/nT0x/Zyuvrk0jt6SKyZFe/GdmJNseH8Fvjw6/7M+za26jJEkS8CXQEygFgoG/y7K87rI/WWh9kgT97oavb4HDayF4XOMmz0DbvPsdazLp1d8TrV7djg3tHHRxcXi//i+y7rufrHvvxee//0Vhb9+4XaFQEB06jqibHmfxh/+kbsshvvnbY2j6B3HTTX/H6Ghux9YLgtCeCitqSMooJDG9kKSMQlJzSqm3yigVEr29DMzr50esv5lYfzMm3ZX/e9xssJdlWZYkaYUsy9GACPCdUa9JYPS1TcM7K9gD9J/anS+fSyJpZTqDZwa3UwM7F8eRI/F8/nlOPvEEWffci8+Ct1Goz/2PadA5cd+9b3Bk4j6WvvM8dpuO8HbSHDxH92fOjEdQq+wvcHRBELqKnOKqxuCemF7I4bxyANR2CiJ9nbhraCBxFjNR3Uzo7JsNxVdESz5hmyRJsbIsJ7V6a4QrT2lny8z/6Uk4vg269W3c5Oylp/cgL/b9lkPoEG+cvfTt2NDOw2nKZKiv4+RT/0f2vffh/dZ/zgv4AN39evPUC1+wYfv3bPj0Q4pWbuPlhKmEXDeBKWNvQ6EQi04KQlcgyzLp+RW2wN4Q4LOKbOtp6O3tiPYzMTnSm3iLmTAfI/Z2yjZvY0uC/TDgdkmSMrFl5EvYbvrDW7VlwpUTczNs/jf88hzc9MM5m2InWDiUdIrNXx1mwn0R2EZuhItxuv565HoruU8/TfYDD+Lz5htITQR8gCHxExkUey0r1rzPvuXfk7FkJc+tXs2AmTcyov91bdxyQRAuV71V5mBuKUmNwb2I/HJbGW1nnZpYfzO3DLAQZzHTy9OAUtH+f1dbEuzHXXwXoUNT62DQQ7DmMTi2AQKGNG7S6tXEjrew6avDZO4rwD/MpR0b2rmYZkwHaz25/3yG7Icewvv11y+4r0Kh4Lpr5nPtqHl89tW/qF67iV3//pDNX33G0Jk3MThuQhu2XBCES1FTZ2VvdklDl3wByZlFlDVUIfV20jIoyIU4i228PdBV1yFvmi4Y7CVJMsiyXAqIeqBdQfRNsOU/8MuzYBlsS95r0HuoN/s2ZrP56yP4hphRKkX3ckuZZs5Erqvn1PPPk/3QwzBpYrP7q1X23DzrCUonFfPZF69wZuMukv71Dgm+Sxg5+zb6R45po5YLgnAhlTV17DxezPb0QpLSC9l5oojqWtuMpkBXHdeGexFnMRHrb8bH5NDOrW2Z5u7sPweuBVKwzbE/+1JFBgJasV3ClabSwJBHYeX9cPgn6PG/oKJUKhhwfXd+XLCHfQnZ9Bnh244N7XzMc+eAtZ5TL76E8dQprIMGNTmGfzaDzok7b32BkhmFfPLZS9Rs2sfWl97iZ/8PGDH9L/SPFkFfENpK3ukiNm1OIk12ZntONfuyS6izyigkCPEyMDOuG/EWMzH+Zlz0nTPB9oLBXpblaxu+izr4XUXEbNj0hm3svvsoOCtBzK+3M74hZpJ+TKdHvLuYineJzPPmgdKOU889R9Ydd+Lz9lsoHC5+xW90NHPPHa9QODOPzz59mZqtB9j6ylus936XftfdwIj+U0UinyBcYenHT7J1cxIZqXupyT6CvvI0EpDgMRZDryjmDw4gzmIm2s+Eo0bV3s29Ii46Zi9J0uCmXpdleeOVb47QqpQqGPo4LL8dDnwPoZMbN0mSxIDrxVS8y2GeM5tDx4/Dp59y/Nbb8H1nEUrHllWWNhvduPfuf1E4J4+lX75O9aY97HlrCVuWfkrkpMlcO2KeCPqC8CdYrVYOHM4gcWsyWQdSsZ48iv5MMQAKSYVs9sUuJIZekRHc3i8Sva5zdMtfqpYk6D1y1mMNEIeta//yS/oIbS9sGvz2Ovz6AvSaAIr/TQE5eypeyCBvXHzEVLxLVd2/HyFRUWQ/8gjH592E7wfvY2cytfj9ZqMbd81/ifI5JSz9+k2qfk3k8Pvf8NzX3+I/YhDTJt6NViMqVgvChdTX1bNjTxo7ElPIPbQfxaljONTZKl4qlRrqXfzQdB9KWHQk8TFh2NtfHb2YFw32siyfkyYsSZIv8EpLDi5J0ljg34ASeF+W5Zf+sH0S8CxgBeqAB2RZ3tSypgt/ikIJw56Ar+bB3q+gzw3nbI6bEMCRHXn8+skBpj4ajUIk610yw9gxKLQasu67n8y5c+n2wYeo3N0u6Rh6ByO33vg01TdU8dWKt0lfv4HT32zk9R8TMPYNZdr0B3B39m6lMxCEzqP6TA3bk/eS8Mt2Er5ZibogE/v6agAUKj2yRwD6HiHExEcT0TsYZTvMce8I/kzZniyg98V2kiRJCSwARjW8J0mSpO9lWd5/1m4/A983VOoLB5ZhK80rtKZeE8EjDBJehN5Tbd37DTR6FYNm9OCn91PZ/XMWkaO7tWNDOy/9kCH4vvsuWXfeSebs2fi+9y72lktPf9Gotcyd/gjW6x9iTcLnJP+wnDMJB1j823zo7cG4qbcRHtz34gcShC6ivKKSTVt2cmDnLorT09AWnUAl1+IIlNubsPqG4hzSm/i+MQQH+YnhrwYtGbN/i4YV77AtnBMB7G7BseOAI7IsH2s4zlJgEtAY7GVZLj9rf91ZnyO0JoUChj0FX8yAnZ/aiu6cpXu0G4eTTrF95TEsES44uXXNMazWpouPo9uSxZyYfzuZM2fhu+i/aCMi/tSxFAoF1wyfwzXD55C8L4Gfvv4Qu725rNv9HCu87Akfcw3jR85FZXd1dEkKV4/8wmI2bU7m8O49lGceQleagxKrbYqYgytyUAxeoWGo1HD9dc1Pfb2ateTO/uyFdOuAL2RZ3tyC93kDJ856ngXE/3EnSZKmAC8CbsD4FhxXuBJ6jLGtivfr89D7OtAYGzdJksSQmcF8/s/t/PrJQSY/GInUASpAdUbasDD8l37B8dvmk3nTzXi//i8ch19euktM76HE9B7KidyjrPhmIVVJBzn60XJeXvYt5r5hXHfd3Xi4iOmTQud0IiePLZsSObZvL2eyjqCryEOBjIwCHD2g92ACwsMZ0D8WN9f/5cMkJCS0X6M7Acm2VH0rHFiSpgFjZFm+teH5XCBOluV7L7D/YGwr6o1sYtt8YD6Aq6tr9LJly1qlzR1BeXk5en3bJMbpy44QnfIw2d7XciTo1vO2Fx2VyUmS8YyRMHe/MsG+Lc+vPVzo/KTSUkwLFmJ3/DhlM2dSNXjQFfvMuroa9hz6heJ9qRgLZOoVMmU+GrxDY+nRLe6KdWNerT+7zsJ7u+3nnB1vbXJ7Rz2/3NPFpB89TtnJbFSF2RhqbJnytZIdZXp3lK7euPr60t3ijVZ74Z6rjnp+V8qwYcNSZFmO+bPvv2iwlyRpL013rzdbI1+SpH7AP2RZHtPw/HFsb3ixmc9KB2JlWc6/0D7BwcFyWlpas23uzBISEhg6dGjbfeAPD0LKErjjN3APPWeTLMt89+Yu8jJLmfV0PHqT5rI/rs3Pr401d37WykqyHnyQig0bcb7jdlzvv/+Kl9VM2vMrv/zwKXLqSVR1CioM4Na3D5Mn3XHZd/tX88+uM8h7Zw8Abrc3vWxJRzg/q9XK7n2HSN6ewsm0VMhNR1drK9Jao1Bzxtkfc/eehEVH0jc2HK2m5QVsOsL5tSZJki4r2LekG391w/dPGr7PBiqBJRd5XxIQJEmSBcgGbgBmnb2DJEndgaMNCXpRgBooaGHbhSth+P9B6nJY9ahtkZyzgo8kSQyb05Olz2wn4fM0xt8V3iFrPncWCgcHfBcsIPef/6Rg0TvUZufg+dyzKOyvXEWu2PBhxIYPo6SskO9Wv0/5xs1U/LSbj9ffQY3FQNiQUYwZNhONWnvFPlMQLqSmtpaklFR2JaVw+sgBVKcz0dTbVoNT2DlQ72ZB1yOEyNgooiN6YWfX+ku9Xq1a8i87QJblAWc9/5skSZtlWX6muTfJslwnSdI9wFpsU+8+lGU5VZKkOxq2LwKmAjdKklQLVAEz5NYaVxCa5mCGEX+33eHv+wbCrj9ns9FVS/ykADZ/fYTDSafoEefRTg3tGiQ7OzyeeQaVtzen3/w3tceP4/P2W9i5ul7RzzE6mrlx+qMwHZL3JfDLD5+hSM3myIffkvrZ19j18mbg6OvpGzlKZCsLV0xFZTVbtu9k/45dFB47iKbwOGprLQAKtRGrT09MPXsT2zea0J4B4nevDbUk2OskSRr4+/x3SZL6Y8ucvyhZllcBq/7w2qKzHr8MvNzy5gqtImoepCyGn56CHmPB/txxr/DhvhxJyWPj0kN4dnfC0Xz53flXM0mScLnjDtSWAHL+9jfSp8/Ad8HbaEJCWuXzfk/oq66pYu2vX7B3wzrYk8PWXW+x3vAW5ugQxo67ke5+F51RKwjnKCwuY/OWZNJ27aYs4xAOpdnYyfUASFoXrAFReISG0a9/LAH+oi5Ee2pJsP8L8KEkSUZsY/clwC2t2iqhbSmUcM1r8MEo2PgqjPrnuZsVEiNvDmHZ80ms+zCVyQ9GimI7V4BhzGjUvj6cuOtuMmbPwevllzCMHt1qn6dRa5k05hYmjbmF00Un+WH1R5RvS+TMr/tZ8etjVHqq8e/bl2vH3YzZeGlFgISrQ05uPps3J3F07x6qThxGX36qIVNeAr0H9BqAX1gYAwbE4enu3N7NFc7Skgp6KUAfSZIM2BL6Slq/WUKb842DiDmwdQFEzgGXoHM2O7k5MGRWMOs/2k/SjxnETxSLHl4JmpAQ/Jd9Sda995J93/3UPHA/zrff3uq5Ea4mT26e9QTMgoPHdvHT6o+p2pHG6eW/8d53G6m1GAiK78/Y4bMxOppbtS1Cx3UkPYttW5LI3L+P2uyjOFbZcqclSQlGbxQRIwiOiGBg/2icjC1bB0JoHy0pquMOvAB4ybI8TpKkEKCfLMsftHrrhLY18mk4sBJWPQJzl5+TrAcQHO9B1sFCkldn4B1swie45TXfhQtTubnh9/HHnHzyKU6/+W+qU1PxfPFFlG00jahnQAQ9747AarWyKflHtq5fgXTwJFlH17Jo6RpquukJjIvH4CAu8Loyq9XK3v1HSN6eQs7BVOTcdHQ1tns7hUKFbO6GXVg8vaMi6Nc3EgetGM7rTFrSjb8Y+Ah4suH5IeBLQAT7rkbvZqubv+axJpP1AAbN6EHusVLWfZjKDU/FoXUUFduuBIW9PV6vvoImNJS8117jzPXT8HnrP9gHBV38zVeqDQoFg+MmMDhuArV1NWzY9j07fluLlJbDyWW/kKX4mZ2/fIZfbCxjRswWtfk7udq6OpJ37mdX0g7yDh9AmZeOtq4SAIVSS52rP9qgkUTERhIb3RuVyJTv1Fry03ORZXnZWfPk6yRJqm/ldgntJfZW2Pc1/PgQ+A+0Vaw6i1pjx+hbQ/n65WR+XnKA8XeL6XhXiiRJON98E5rQELIf/CvpM27A67lnMVxzTZu3RWWnZuTA6xk58Hrq6+vYlLyKX374ElVWMae/2cjH326gyluDb3Q0o0bMxMf90uv+C22rpraOXzYmsi9lJwVHD6IpyERtrQFAoXLE6hmEsWco0XHR9OkdJDLlu5iWBPsKSZKcaSisI0lSX2xJekJXpLSDyf+FRQNh5f0wc+l53fmuvo4MmBrEb18eYvfPJ4gYKRbLuZJ0cXFYvv2G7PsfIPuvD1G1ew9uDz+EpFJd/M2tQKm0Y0j8ROQqA4MHD2bbznVs3fA9UmoGhd9tYel3m6l0scMpJJDY/mOJ7zNSBIoOoKS0nM1bd3Bg5y5KM9LQFmejkusAUGjM1PtH4BoSSny/GIK7+7Vza4XW1pJg/1fgeyBQkqTNgCtwfv+u0HW4BMHIf8Cav8Guz2wJe38QNtSbrIOFbF1+FM9AJ9wthjZvZlemcnfH7+MlnHr5FQqXLKFq3z68X3sVladnu7ZLoVDQP3oM/aPHYLVaSdm3ga2/raRy/1FqNqaxZeMhftH8G0WgKz1i+jFy0DSR4NdGamtrKSkp46NnXqXy+GF0ZbkosWJFQtK5UeYVSp9+/ek3IBZfLzHb4mrTbLBvWKZ2SMNXMLYSuWmyLNe2QduE9hR3Oxz4AdY8DpYh4HRuqVVJkhh+Yy+WPZ/E6kV7mPZELDrjlasEJ4CkVuPxf0+hjYgg9+mnOTZ5Cl4vvnDZC+lcKQqForFiH0DWqXR+3fA1mbt2IKXlkZG6knc++Z5qDw0eYSEMGjyFXt2j2rnVXUf68ZNs3ZxERupearKPMNFgW1ZEyv0NDF5I4UPp3qcPAwfE4GwydvlyskLzmg32sizXS5I0SZblN4DUNmqT0BEoFDB5ASzsD9/fA3NXnNedr9GpuOaucL55NYVV/93LlIcisVMp26nBXZdxwrVow3qT9de/knXX3ZjmzsXtkYdRqDtWcqSPu4W50x+B6VBdU8Vv239gz/ZfIO0EZWt3smrtTr52BHWAO937xDGo3wTczF7t3exOwWq1cuBwBolbk8k6kIr15FH0Z2wLxigkFbLZF8nRjINez10vLEWvE8tSC+dqSTf+ZkmS3saWgV/x+4uyLO9otVYJHYPJH8Y8Zyulm/yBLXnvD1x89Iy6KYTV7+wl4dM0RtzUSyTstQK1vz/+S5eS9+prFH3yCZUpyfi8/jpqf//2blqTNGotowZNY9SgaQAcOLaD3zZ+R0XqfuR9J8nYvZJjH39PlYsSffduhEYNZGDceHRaMVcboL6unh170tiRmEzuoQMoTh3Doc7251ep1FDv4oem+1DCoiOJjwnD3l7duBCOCPRCU1oS7Ps3fD+7Fr4MdIy+RKF1Rd9sm3v/0/9B4HAwnz/XOiDSlbgJFhJXpuPsrSdytEjYaw0KtRqPJ59A168vOY8/Qfp1U3F/8kmM103p8BdYvQKi6BVg68KvPlPJlpS17E3ZQNWhDOq3p7NvWwa7Fn3CGQ8NLr2CiIwdTlz4cJTKq2O6V/WZGrYn72Vv8g7yjxxEXZCJfX01AAqVHtkjAH2PEGLio4noHYzSTvSgCZemJRX0hrVFQ4QOSpJg4tuwsB8svwNu+hGU52eFx1zjT0F2BVuWH8Hk6YB/mEs7NPbq4Dh8OAErlpPz6GOcfPJJyn75Bc9n/omdc+coT6qxd2B4/ykM7z8FgKLSfH7btpK0nVvhaA5VP+9jy8/7SFC9SZ2XHpegQMIiBhHTZ1iXWa2vvKKSTVt2cmDnLorTD6ItykLVkAol2Zuw+obiHNKb+L4xBAf5idkNwmW7Oi6bhctj9IYJb8DXt8D6f8CY58/bRZIkRszrRcnpStZ9kMrUx2Iwe7ZovSThT1B5etJt8UcULl7C6Tff5NjESXg++yyOwzvftbnJ4MLE0TfD6JsByMw5zOatK8nctxtOFFC5fi/b1+9ls3IBZ9ztcQr0p1effvSLHo3ewdjOrW+Z/MJiNm1O5vDuPZRnHkJXmoMSKzIgObgiB8Xg1Tucfv1i8O/WvjMuhK5JBHuhZXpPhePbYOvb4BsPIRPP20Vlr+SaO8P56sUkfly4h6mPRONg6FhJZF2JpFTi/Jdb0A0cSM6jj5J11104TZuG+98eQ6HrvBdafl5B+E39q20BbODEySNsTVpDRupuyMyl5rc09vx2iJ3SYqpc7NAH+BDUO4a4qJF4uPg2f/A2ciInjy2bEjm2by/VWUfQV+Q1LBijsBWq6j0YS1g4AwfE4uYqyk4LrU8Ee6HlRj8H2Snw3d3gHgrOgeft4mjWcM2d4Xz3xk5+eHs3kx+MRK0Vv2atSRPcA/+vlpH/n/9Q8MGHVGzdiudzz6Lr27e9m3ZF+Hp2x3fiPdBwfXm66CRbk9ZweF8yHMuiPjGDw9szOfzBN1Q6gtLLCbfuQYSG9SMydFCrd/3LskzakeNs35rEiQOp1OccRV9dCIBCsgMnH5TBo+kV0Yf+/aIwGtpmzQNBONsF/wpLkrQb2ARsATbLspzRVo0SOig7e5i2BN4ZBF/OhVvXg/r8zF+PACNjbw9j1cI9rFq0h2vv6SOm5LUyhVqN28MPox86lJwnn+T4TTfjNGMGbo883GYL6rQVV5PnOd3+pRXFbN+xjkP7k6k8loGcUUhJWhJbfkxio+LfVLuq0Hfzwq9nONERQ7H49Lysz7darezed4jk7SmcTEuF3HR0tWUAKBT21Dn7oYocSFh0JH1jw9FqRP0Jof01d8s1G1sm/ijgaUmSdNgC/xZgiyzL29ugfUJH4+QL170Pn11vq58/eeF58+8B/Ho7M3xeL9Z/tJ91H+5nzG29USg6dsZ4V+AQE0PAihWc/s9bFC5ZQvmGDXg+80/0gwe3d9NajUHndM40P6vVytHjqezYlcDxtL1w4hT1KZlkJh0n85MfqNLKyO56nPx8sQSHExk+BG/XC5eLramtJSkllV1JKZw+cgDV6Uw09VUAKOwcqHezoOsRQmRcNDF9eolMeaFDumCwl2V5H7APeBdAkiQX4AbgAeA1QPxGX62CRsKQR2HDy9CtL0TPa3K34HgPqstr2fTVYTZ8dpChcy7vjkpoGYVWi/tjj2IYM5qcJ5/ixPzbMU6ejPvfHkPp5NTezWt1CoWCIP8wgvzDGl+rqq4geW8CB/Ztp/roEeTcUmoyDpK24SBpLKNSZ6VWr2LnmsU4KB2g3p6KaivlVWdQlZ5CbbVlyivURqw+PTH17E1s32hCewaITHmhU2iuG18JRGK7ux8ABALZwPvA1jZpndBxDXkMTiTCqkfAIwy8my6D2meEL1XlNaSszkTjqIauH2s6DG1EBJbl35L/3/9S8O57lG/caLsImDixw8/Lv9K0Gh2DYsczKHZ842sFRadI2buBQ/t3UHIsg7riEk5VZ2NVyMiAjBJJ0oCfCefuPRnUbzj9QmJQSCK4C51Pc934pcABYAHwN1mW09umSUKnoFDC1Pfh3aHwxQ1w68/n1c//XfzEAKrLa9mxJhP3PhIMbdOWXtUUajVu99+PYcwYcp/+BzmP/Y3ib5fj8fTT2AdcncvSFlbUkJRRSGJ6IUkZXqTm6KlXDELhDKHe9gT4lOBoOEWVlMnhkoNklO7EKqfwWfJnaHdpCXIKooe5Bz1MPQg2BRNkCsJRLSr/CR1bc8H+VqBfw/ebJUlKwnZHv1WW5ey2aJzQwelcYPZX8MFo+Hw63LIGNOfPe5YkicEzg6mpquNwch4pazKIHuvf9u29iml69sTvi88pXraMvH+9TvqkSTjfdhvOt89HYd+1E8hyiqtIyihke3ohSemFHM4rB0BtpyDS14m7hgYSZzFTkbmPsSPPr1NQXVfN0ZKjHCo8RFpRGoeKDvFTxk98fejrxn28dF70MPcgyCmIQKdAujt1x9/oj72ya//bCp1Hc2P2XwBfAEiS5ADEYevOf1GSJLUsy2IBZAHcesGMT+DTqbBsni34N1FhT6GQGHlzCHmn89i24hjWepnY8VfnnWV7kRQKTDfcgOOIEZx6+RXyFy6k5McfcH/8cRy7yGposiyTnl9BYnohiQ1371lFtmQ6vb0d0X4mJkd6E28xE+ZjxP6sZLqE7KaHNjR2GkKdQwl1Dj3nc05VnuJQ0SEOFR0irTCNtKI0fsv6jXq5HgCFpMDX0ZdAYyCBTv/7shgt4iJAaHMXW+JWB8Tzv3H7WOAEsLn1myZ0GgFDYcK/bfPvf/wrTPhPkxn6CqUC73gJD093ElemY7XKxF1ruerGj9ubnasr3q+9inHKZE49+xxZd9yJfsgQ3J94HLVf57qGr7fKHMwtJakxuBeRX34GAGedmlh/M7cMsBBnMdPL04DyCs0IkSQJD50HHjoPBvv8b6ZDTX0NGaUZHCs+xpHiIxwrsX3fkLXhvIsAi9GCv8Eff4M/fgY//I3+OGucxf8HoVU0l6C3E+gGJGObbvcvYJssy+Vt1DahM4mcA0UZsPFVMFlg0F+b3E1SSAy/sRcKhUTyjxnI9TLxkwLEH7h2oB8wAN3331H4yafkL1wkp8nBAAAgAElEQVTIsWsnYL7pJlzuuL3DVuCrqbOyN7uYxPQiEtMLSM4soqy6DgBvJy2DglyIs5iJ9TcT6Kpr898rtVJND5NtPP9stfW1ZJRmcLTkKEeLbV/pJelsyd5CjbWmcT+9So+fwc8W/A3++Bv9G5/rVB3zZyJ0Ds3d2d8P/CbLstxWjRE6uWFP2gL+z/8Ep24Qdn2TuykUEsPm9ERSSKSsycRaL9PvukAR8NuBpFbj/JdbMEy4ltOvv0HBe+9R8t13uD30VwwTJiC187Syypo6dh4vZnt6IYnpBew6UUx1rRWAQFcd14Z7EWcxEetvxsfUcZd2VSlVBJmCCDIFnfN6vbWe3MpcMksySS9NJ7M0k8zSTHbl7WJ1+mps8wJsXLWujcH/996Abo7d8NJ7obHTtPUpCZ1Mc8F+FvCmJEmHgDXAGlmWc9umWUKnJEkwaQGUZMOKO0HrBN1HNr2rQmLorGAUSomd645TXVnb8FxMa2oPKjc3vF56EdMNM8h97nlyHvsbhR9/gttjj6KLi2uzdhRX1pCcUdQ43r4vu4Q6q4xCghAvAzPjuhFvMRPjb8ZF3/nHvZUKJd56b7z13vT37n/Otuq6ak6UnSCjNIPM0kwySjLIKM1gfeZ6is8Un7Ovm9aNpwvnY6+05+tdm/Bx9MFH74O33htXB1cxXVBoNtjfI8tynSRJPYFxwGJJkozAr9iC/2ZZbhiEEoTf2dnDzM9hyQRYOtuWsGdpunqbpJAYfEMPNDoVyasyqCypYcxtvVHZi3pN7UUbEYH/si8p/fFH8l5/g+M3zkM/fDhuDz/cKp93qrS6YQqcLbgfzLWVnVUrFYT7GJk/OIA4i5loPxOOmvMTP7syjZ2myd4AgOLqYjJKM8gqzyKrzPYlHZUoqylj0e5F5/QIqBVqvB290dZo2bxts+1CoOFiwMfRRwwPXCWaC/aJQJQsyweBg8AbkiRpgWHANOB1IKb1myh0OloTzP0OFo+Hz2fA3OW2SntNkCSJ+IkB6Jzs2fhFGite38H4u/uI1fLakaRQYJwwAcdRoyhc8jEF777LsYkTcRwwgLrevbFzcflTx5VlmeOFlbZM+YaEusyCSgAc1Eqi/UyMD/Mk1mImwtcJjVhP4YKcNE5EaCKIcItofC0vdQ8AyXOSySnPIas8i+yy7MYLgoO5B/nh2A+U156bdmVQG/DUeeKp88RD54Gn3vOc565aV5QK8bPo7JoL9ucNoMqyXAWsavgShAvTOcO87+GjcfDp9XDjd+ATfcHdew/2RmdU89P7qXz7agoT7uuD0bXjjsFeDRQaDS63z8fp+qmcfvtt5C+XcWT0GJxvmof55ptROjZfSMZqlTmUV0ZSesMc94xCTpXaMuWdHFTE+puZ29ePWH8zoV4G7MQQzhWhVqrxN9qS+86WkJDAkCFDKK0ptfUGNFwEnKw4SW5FLjkVOaTkpVBWU3bO+5SSEncH9yYvBH7/clQ5ipybDq65YO8qSVLTKdWALMuvt0J7hK5E7wbzVjYE/Ckw74dmd7f0cWXSg5H8uGAP37ySwvi7+uBuMbRRY4ULsXN2xvPppzkSHIxl23byF/6Xos+/wHn+fEyzZzUW5amtt5KaU0piekFD13wRJVW2mvIeBg3xFmdiLWbiLWa6u+rFwkjtQJIkjPZGjPZGQl1Cm9ynoraC3IpcTlactH2Vn2x8vitvF2sr1lIn153zHq2dFjcHN9wc3HDVuuLu4I6rg2vja7+/rlaKHrv20lywVwJ6mrjDF4QWM3jZAv6H4+DjSehC/9Hs7h4BRqY+Gs3Kt3ax/PUdDJvTk+B4j7Zpq9Cseg8PfN58g6p9t3L6zTfJe+UVcj9czIFR0/jeI5KkrDKqam1pPBYXHWNC3YmzOBNvMeNj0oo7v05Cp9I1FgBqSr21noLqgsYegdyKXPIq8xq/9pzeQ15l3jlTCn9nsjc1eRHg5uCGi9YFZ40zzlpncVHQCpoL9idlWX6mzVoidF1O3Wxd+ovHE7HrSYgIBZ8Lp3s4uTtw/WMxrHl3H+s/2k/+iTL6TQkUmfrtrLJW5teDeSRmKUmMvhmrIoy5e1cR+sUCPBydGTF6Gi7TryO2uytuBjEVrKtSKpSNgbqPa58m95FlmdKaUk5VnuJ05elzLgbyKvPIq8rjYOFBCqoKzkkm/J1BbcBZ64yL1gUXjQvOWufG586ahte1Lpg0JuwUzdaGExpc0pi9IPxpzoFw82rq3h2LaslEuOEzCDy/DvnvtI5qJj4QweavjrBr/QkKsssZfWtvNLqrKyO7PZ0uO9OYJZ+YXsiBk5XIJGGnkAjzMRI3ZRTGB6Zjzt6P5t1FOH+zCNX2H1DfeQfyxIlIKvGzulqdPVzwxwJDZ6uz1pFflU9+VT4FVQWNj/Or8imoLqCgqoDUglQKqguoqK04/3OQMGlMOGudkaokftzwIyaNCZO9yfZdY8KsMeNk74RJY8LJ3umqvTho7qxHtFkrhKuD2cLOyBfpf/Q128I5U9+HkEkX3F2pVDD4hh64+OrZ8EUaX72YxDV3huPsrW/DRl8dZFkmq6jqnOB+LN/2x1WjUhDVzcSk7iqmD40iopsTDuqz/nSEeCCPHEb5hg3kv72Ak08+Rf6id3CefxtOkyYhqUWXrNA0O4VdY5LfxVTWVjZeADReGFTbLhJOV50mszKTA4UHKKwuPC/J8GwGtQGzxnzeRcE5j896rrXTXslTbjfNLYRT2JYNEa4ONfZmuPlH+Gw6fHWTraZ+1I3NvidkgBdmTx2rF+3l61dSGDYnmB6xYhz/csiyzJG88sbiNUnpheSUVANg0NgR629meqwvcRYzvb2MqO0UJCQk0L9709PuJEnCcehQ9EOGUJ6QQP6CheT+39/JX7AQ51tuxmnaNBTarvFHU2gfDioHHFQO+Do2vZR2QkICQxsWdKq11lJcXUzRmSKKqhu+Gh4XVhdSfKaYouoijpcdZ0/+Hoqri89LOvydRqnBYG+w9VSobb0VBrWhseeiycdqIzpV25drbs7V2Z8htC+tCW5cAV/Ohe/vhaoiGHB/s2/xCDAy7fFY1r63j3Uf7CfrYBGDZvRApRbzf1uirt7KgZNlbE8vICnDlilfWGFLoHJ1tCfO38ztFjNxFjPB7o5/OlNekiQchw1DP3QoFZu3ULBoEadeeJH8Re9gnjcP06yZF52yJwiXS6VQ4ergiquDa4v2/z3H4PeLgMLqwsYLhOLqYkprSik5U0JJTQmZpZmUnimlpKaEM/VnLnhMpaRsvAAw2BsaLxR+vxgw2BtwVDuiV+lxVDue81iv0l/x2gYi2AvtQ62DmUth+e2w7u9QfBzGvtTk8ri/05vsmfxQJEkr00lZm0nusVLG3BoquvWbUF1bz56sksZ13FMyCqmosWXK+5q1DAt2I95iJtZixt/Z4YrfgUiShH7gAPQDB1CZkkL+onc4/cYbFLz7Lk7TpmGeOweVt/cV/UxB+LPOzjHwM7R85cfquur/XQg0XAyUnik993GNbVt+VT7HSo5RcqbkvMJGTXGwc2i8CHBUX/4Fsgj2QvuxU9vG7Z18YfO/oeAITFtsu/O/AKVSQd/JgXj3MLFu8X6+eimZQdODCBno1aG6zNpa+Zk6UjKLbEu9pheyK6uYmjrbgjE93PVMifIm1t925+5pbNvudIfoaLq99y5VqakUfvgRhZ98QuEnn+A4ehTON92Etk/TGd2C0NFp7DRo7DS4Obhd0vtqrbWU1ZRRXlNOWW3Z/x7XNDyuPfdxec3lLzYrgr3QvhRKGPUMuATDyvvh/ZEwa5kte78ZviFmZjwZy8+L95PwWRpZB4sYMjMYjf7qyAAvrKg5J5kuNacEqwxKhURvLwPz+tkq08X6mzHpOkaCnDY0FO9/vYbbww9R+OmnFC/7irLVa9BGRWG+aR6OI0YgKcWwjND1qRQqzBozZo25xe/5gA8u6zNFsBc6hsjZYA6AL2fDe8Nh+scQMKTZt+iM9ky4N4IdP2WSuDKdnMPFDJ3TE0v4n6vd3pHlFFc1dsknpRdyOM92pa+2UxDp68Tdw7oTZzET1c2Ezr5j/7dWeXri/sgjuNx5FyXffkvhxx+Tfd/9qHx8MN94I8brrkOpF4uzCMKV1LH/KghXF79+cNsv8PkN8MkUGPcyxN5qWzr3AiSFRPRYf/x6O7P+owOsWriHnn09GDg9CHuHznmXL8sy6fkVjYvFJKYXklVUBYDe3o5oPxOTI72Jt5gJ8zFib9c574aVeh3mG+dimj2LsvU/U7h4MadeeIHTb72F0/RpmOfMQeXp2d7NFIQuQQR7oWMx+cNffoJvboVVD0PmFpj4H7BvPkHFxceRaY/HkLwqg5Q1mWSlFTFsTk+6hTq3TbsvQ71V5mBu6VlLvRaRX27L8nXWqYn1N3PLAAtxFjO9PA0ou1hNeUmpxDBmNIYxo6natYuCJUso/GgxhYuXoB82FNOMG9AN6I+kEBUUBeHPEsFe6Hg0Blum/uY34JfnIHcPTFsCHr2bfZvSTkH8xAD8w134efF+Vr61m579Pel/XSBafccYtwaoqbOyN7uYxPQiEtMLSM4soqzaNsfX20nLoCAX4iy28fZA1441V7e1aSMi8ImIoCYrm+KlX1D87XLK1/+MytcXp+nTcLruOuycO/4FnCB0NCLYCx2TQgGDHgLfePj6Fnh/BFzzKkTObbZbH8Dd38D0J2NJ+iGdXetOkLE7n/5Tu9Ozn0e7BM7Kmjp2ZBY3dMkXsOtEMdW1tkz5QFcd14Z7EWcxEetvxscklvUFUPt44/bww7jcdx9l69ZRvPRLTv/rdU7/5y0Mo0bhdMMMHGJjr6oLIUG4HCLYCx2b/0C4Y5OtW//7e23d+te8BvbNz623UynpN6U7PeI8SPgsjV8+PsDBrScZOjsYk0frJn8VV9aQnFHUON6+L7uEOquMQoIQLwMz47oRbzET42/GRW/fqm3p7BRqNcbx4zGOH8+Zo0cp+vJLSlZ8R+mqVagDAzHNmIFx0kSURmN7N1UQOjQR7IWOT+8Gc5fDxlch4SU4vhUmL7Il9F2Es7ee6x6OYv/mHLYuP8rSZxOJGuNH1Bg/VPZXJrHtVGl143j7L3sryVqzDgC1UkG4j5H5gwOIs5iJ9jPhqOmcSYMdgX1gIB5PPIHbgw9SumYtxUuXcuqFF8h7/XUM48bhdN0UtDEx4m5fEJrQqsFekqSxwL8BJfC+LMsv/WH7bOCxhqflwJ2yLO9uzTYJnZRCCUP/BpbBsPwO+GgcDLgPhj0Jds3fHUsKidBB3lj6uLL5m8Mkr8rgwJaT9JsSSI9Yd6RLSHiTZZnjhZWNU+ASMwrJLKgEwEGtxOIoMaNfELEWMxG+TmhUnTNTviNTaLU4TZmM05TJVB84QNGXX1L6/UpKli9H5eODcdIkjJMnofZtuoa6IFyNWi3YS5KkBBYAo4AsIEmSpO9lWd5/1m7pwBBZloskSRoHvAvEt1abhC7Arz/cuRnWPmmrund4PVz3DniEXfStDgY1o24OpfcgbzZ9dZj1H+1nb0IWA6cF4RHQdDew1SpzKK+MpHTbHPfE9ELyymyZ8k4OKmL9zcztaytgE+plYNNvGxk6NOiKnrJwYZpevfD8xz9wf/RRytavp2TFCvIXLiR/wQIcYmIwTpmM45gxKPWipLJwdWvNO/s44Igsy8cAJElaCkwCGoO9LMtbztp/G+DTiu0Rugp7R9t0vJ7jbeP47w6DoY/BgAeara3/O8/uTlz/WAxp23PZuuIo37ySQlCsO/2mBKIxqtmXXdJYnS4po4iSqloAPAwa+gY4E2sxE28x091V/6cXjBGuLIWDA8aJEzFOnEjtyZOUfPc9JStWcPLJp8h99jkcR43COHkSur59RZU+4aokybLcOgeWpOuBsbIs39rwfC4QL8vyPRfY/2Gg5+/7/2HbfGA+gKura/SyZctapc0dQXl5OfoufBdypc/PrraUHocW4XZ6M+U6Pw71uJNSY68Wv7/6jJWju2SsGSDLsEdTz2Z1LVUKcHeQ6GFSEmxW0MOkxFUrXXQ8uCv//DrduckyqvR0NNu2oUlORlFZRb2TE9Xx8VTHxVLn5XXOzI5Od35/4L3dVocgO97a5PbOfn4X09XPb9iwYSmyLMf82fe3ZrCfBoz5Q7CPk2X53ib2HQYsBAbKslzQ3HGDg4PltLS01mhyh3D2msxdUaud38FVsOoRKM2C6Jth5NNNLqhTWl1LSkaRbcw9o5A9WcXU1ssYZIlxaPEtkVGoFPQa4sWA8QGotZfW+dWVf36d+dysZ85Q/uuvFC9fTsWmzVBfj7p7IIZx4zCMuwb7AEunPj+AvHf2AOB2e3iT2zv7+V1MVz8/SZIuK9i3Zjd+FnB2howPkPPHnSRJCgfeB8ZdLNALwgX1vMaWvJfwImxbCAd/hLEvctrvWpIyixoXjDmQW4osg51CIszHyC0DLcT5m4nxM2N0UFF4soLE74+xf30Wx7aeInqcH6GDvVGpRddvZ6awt8cwdiyGsWOpy8+n9KefKFu1mvy3F5D/1tvY9+qFQ3AwNd27o/YRo4lC19OawT4JCJIkyQJkAzcAs87eQZKkbsC3wFxZlg+1YluEq4Cs1pEV+ySH7IbQI/n/8P3mLxyuD+Gturmk21mI6mbi/hFBxPmbiejmhIP6/F9/s6eOsbeHcSqjlG0rjrL56yPsWJtJ5Cg/Qgd7odaI2aqdnZ2LC+ZZszDPmkXtqVOUrVlD6arVOK5YwdEVK9CEhzfc8Y9F5eHR3s0VhCui1f5yybJcJ0nSPcBabFPvPpRlOVWSpDsati8C/g44AwsbxkLrLqebQri6yLLMkbzyxuI1SemF5JRUA+Ck+QcPmzdzfenHrLJ7EmvEXJQj/g/0ri06tru/gUkPRJJzuIikHzPY8q0t6PcZ6Uv4UJ9L7t4XOiaVuzvmefMwz5vHxq+/IbS4iNJVq8l7+WXyXn4ZbVQUhrFj0A8fgdrHu72bKwh/Wqv+xZJleRWw6g+vLTrr8a3AeQl5gtCUunorB06WsT29gKQMW6Z8YUUNAK6O9sT5m7ndYibOYibY3RGFYhxUPQQbXkGZ+C6kfgtDHoH4Oy46N/93XkEmJj1gIvdYCUk/ZrD9u2PsWnecsKE+hA31wcHQcWruC5fH6uKM8/VTcb71VmoyMihdvZrSVas59cKLnHrhRex79cJx5AgcR47EvkcPUbxH6FTE7YnQYVXX1rMnq4TE9AISM4pIySikoqYeAF+zlmHBbsRbzMRazPg7OzT9x1drgrEvQswt8NNTsO7vkPyhrRhP76m2Yj0t4BFgZMK9fcjLLCV5VQbJqzLYue44Pft6EDGyG07uoqZ9V6L298flzjtxufNOajIzKVv/M2U//9w4xq/y8cFxxAgcR41EGxkppvMJHZ4I9kKHUX6mjpTMIltluvRCdmUVU1Nnm0bUw13PlChvYv1td+6eRu2lHdwlCGZ9CUd+hvVPw7e3waY3bEG/5/iLLq7zOzc/A9fcGU5RbgW71p/g4NZcUjflYAl3IXJUN1prdovQftR+fjj/5Rac/3ILdfn5lP36K2Xr11P0+ecULlmC0mxGP2wojiNHouvXD4VG095NFoTziGAvtJvCipqz1nAvJDWnBKsMSoVEby8D8/rZKtPF+psx6a5Qd3n3ERAwDPavgF+fhy9ng3c0DP8/CBzW4sOYPHQMm9OT+IkB7E3IYu+GLNJ356MxgYf9SYJi3LATGfxdjp2LC6Zp0zBNm0Z9eQUVm36z3fWv/YmSb75F0mjQxcejGzIY/eAhYpxf6DBEsBfaTE5xFVtz6vhp+V6S0gs5nFcOgNpOQaSvE3cP606cxUxUNxM6+1b81VQooPd10Gsi7P7CtrjOJ5OhW38Y/BAEjmjxnb6DQU38xACixviRtj2XbT/aVtjb8s0RQgZ6EjrIG4PLJfZCCJ2CUq9rnM4n19RQkZhE+YYNjV+neBZ190D0g4egHzwYh6hIJLXI8RDahwj2QquQZZn0/IrG+e2JGYVkFVUBoLfPIdrPxORIb+ItZsJ8jNjbtcNdsNIOouZC+HRIWWyrtf/pVPCKhEEPQfB424VBC6jslfQe7M3p+kP08Ipgb0IWO9edYOdPx/ELcyFkoBd+oWYUypYdT+hcJLUa/cAB6AcOQH7icWoyMqjYuJHyDRsp+uQTCj/8EIVOh65/f/RDBqMbNBiVu1t7N1u4iohgL1wR9VaZg7ml53TL55fbMuXNOjVx/mZuGWBBUXCMuROGo+xINeXt7CH+doi+CXYvtY3lfzkHXHvBoL9C6JQW1dwHkCQJ72AT3sEmygqrSd2Yzf4tJ8nYk4/OyZ5e/T3p1d9T3O13YZIkYW+xYG+xYJ43D2tFBRXbt1OesIHyjRspW2dbAtm+Z090/fuj69cPh5hoFFrxOyG0HhHshT+lps7K3uxiEtOLSEwvIDmziLLqOgC8nbQMCnJtTKYLdNU1ZsonJGR2rEB/Njt7iJ4HEbMhdTn89i9bIt/6f0DcfNu2JkrwXoijWUPfyYHETrCQuaeA1E05JK/OIHl1Br49TfTq74Wlj4sY2+/iFDodjsOH4zh8OLIsc+bQYco3bKBi8+bGu35JpUIbFWUL/v37ownpJTL8hStKBHuhRSpr6tiRWdxQwKaAnceLOdOQKR/oquPacC/iLCZi/c34mDr5NDSlHYRPs03NO/wTbFtgy+Df8LLtQqDvneAc2PLDKRUERLoSEOlKWWE1B7ac5MCWHH76IBW1RklgtBs9+3rgGeiE1FEvhIQrQpIkNME90AT3wGX+bVgrK6lM2UHFli1UbN3K6Tfe4PQbb6A0GnHo27ch+PdD7et78YMLQjNEsBeaVFxZQ3JGUWN1un3ZJdRZZRQShHgZmBXfjXiLmRh/My76lhWo6XQUCggea/vK3Qvb/gs7lkDS+xA0CmL+Yvvewrn6YLvbj7vWQuw1/mQfLiZt20mOJOdxYPNJHJ01BMd7EBTjjtlL14onJnQUCgcH9IMGoh80EIC6/Hwqtm23Bf8tWyhbuxYAlY8PDnFxOMTFoouNReUtsvyFSyOCvQDAqdLqxmS6pIxCDuaWAaBWKgj3MTJ/cABxFjPRfiYcNS0bv+5SPMJg8kIY8TQkfwApS+CLGWD0hah5tkQ/x5bXUZcUEj7BJnyCTQy+oZ5ju06Ttj2XlNW2gj3O3jq6x7gTFOOG0bWT95QILWbn4oLx2vEYrx2PLMvUpGdQsXULldu2Uf7LL5R8+y0AKm9vW/CPjcUhLk5M8RMuSgT7q5AsyxwvrLQt89qQKZ9ZUAmAg1pJtJ+J8WGexFrMRPg6oVGJscNGju4w7AkY/AikrbZV4/v1OdjwEgRfg9kuAuoH2oYCWkhlryQ43oPgeA8qSs5wdEceR5Lz2P7dMbZ/dww3P0cCo9wIiHAVlfquIpIkYR9gwT7Agnn2bGSrlTOHj1CZmEhlUhLlCQmULF8OgMrLC03svSgMjtScOIHKx0eU8xXOIYL9VcBqlTmUV0ZSeiHbG+7e88rOAODkoCLW38yceD/iLGZCvAyoxPSwi1OqIGSi7avgqG3q3s5PCa/6HtLfhT4zoM8scOt5SYfVGe0JH+ZL+DBfygqrOZKcx5GUU2xdfpSty49i8nDA0scVSx8X3P0NYoz/KiIpFI3j/ea5c2zB/8gRKpOSqExMor6kmLqCfI6Ougc7V1e0UVE4REWijYpC0/PSfg+FrkcE+y6ott7KvuySxilwSRlFlFTVAuBh0NA3wJlYi5l4i5nurnoUImBcHudAGP0sDP8/9i3/F73r9sCWt23z9r2joc9M2/Q9ncslHdbRrCFydDciR3ejrLCa9N35pO8+za51x9mxNhMHgxr/Pi4E9HHFJ9iEUiUu0q4mkkKBpkcPND16YJ49m7x3diNXVWMc8Xcqd+ykaseOxjF/SavF5OtL3q5dOERFoY2IQGkwtPMZCG1JBPsuoLq2np3HixuK1xSwI7OYqlrbgjEWFx1jQt2JszgT52/G16wV3XutxU5Nvms/GPo4lOfBnmWw6zNY9TCsfgwChkLY9bZa/BrjJR3a0awhfJgP4cN8qK6oJXNfAem78zmceIr9v+WgslfSLdSZgAgXuoU6o9FdhXkVVz3JFtRnzsQ0cyYAtadOUbVjB5U7dpK3YQMF771PQX09SBL2QUFooyLR9olA2ycctb8/UguLSAmdjwj2nVBpdS0pGUW2MfeMQvZkFVNbLyNJEOzuyPQYH+IszsT6m3AziEU52oXeDfrfA/3uhlOpsO8b2Pc1rLgTlPa2LP7QKRA0GjSXdoel0akax/jrauvJOlhE+p580nfnc3RHHpIEbv4GuoWY8Q1xxt3fUVTuu0qp3N1RjRuHYdw4Dg7oz+DYWKr27qUyJYWqHTspXfkDxUu/BEDh6Ig2LAxNn3C04eFo+/TBzmxu5zMQ/r+9Ow+u7CzzO/597i7dq721WN1S76sxXgDbYDxpxgPYBDAJyQQySUjIxKECNZAJNWFChYJkktQkTKhJZRLjIU4INQwDNQNjwNhkgDaJ1zamu9373q3e1a1Wa7m6m+6bP94j6Uot9aLF6nv0+1S9dc55z7nnvm8f3X7es73vfFGwrwK9g/nxS/KvHOtj37kBnINYxLhjRQMff+dq7l3VzFtXNtNQqzO6W4oZdLzJp4e+AKde9YF/z1/A/h9ANOEH5tn8Adj4Pki33NTuY/Eoq+5Yxqo7lrH1o47zxwc4sfsSPfv6ePXp42z/4XESqSgrNjXTtaWZrs3NNLSqp7alKpJOk77/ftL33w+AGx2lcPQoI7t2MbJzFyO7dnHpq09A2fehEV+xIgj8PvgnN23SqH5VSsH+FuOco6cvO6nb2aMXhwFIxSPc093Epx9az72rmrmru5HahA5h1TCDrrf59N5/B6e2w77vw96n4NCzYBFY+SKWRG4AABqxSURBVABsfAQ2PHxTHfeAf52vY00DHWsauO+Da8gNFzm1/zI9+/o4ufcSR3f0AlDfWkP3Zh/8V2xsIlGjv6GlyqJRkuvXk1y/nsYPfxiAcjZLbs+e8QZA9rXXGHj6af+BaJTkunWktmwhdfvtfrp5k7r6rQL6ld9C/s339/K9X4zQ9+zPAKhLxbh3VTO//rYu7l3dzJs6G0jEdDk2FCJR6L7fp/f8Hpzd6QP//h/Cs//Kp5Z1PuhveC90v/2G++cfk0rHWfeWNta9pQ3nHP3ns/Ts66Nnbx/7Xz7H7p+fxgyWddXRub7Rp3WNpDK6OrSURWpr/fv7b3vbeF7x/AVGdu0kt2cPub17Gfr5z8df+yMSIbl2zZQGwGYiaXUMdStRsL+FjJbLrG2M8Ol7N/G2Vc1s7Ki7dfuRl/ljBp13+fTQv4bLJ3w3vQefgVeegBf/KyQy/qx/zVaf2jbf8DC8/iuMpo40TR1p3vyuLkZLZc4dvcKp/Zc5c6if3c+dZudPegBo7kxPBP/1jaQbQtpDotyweHsb8Xe/m/p3vxvwVyBL58+T27uX3G7fABh+4UWu/OVT/gNmJFavntwA2LKZaF3dItZiaVOwv4V86dE3sW3bRba+Y9ViF0UWU9NKuPef+JQfgmPPwZGfwtFt/nI/QLptIvCv+WvQsOKmviIai7B8QxPLN/iBfUaLZc6fGODMoX7OHurnwEvn2P3caQAaWmvoXN/IlZLj8qZhGttq9X7/EmdmxDs6iHd0UPervzqeX7xwwTcA9uwht3cf2VdfZeAHPxhfH1/ZTWrjJpIbN5DcsIHUxo2+AyC9BbDgFOxFbmXJjH9Vb9Nf98v9PT74H90GR38Gr3/b57esnwj+qx64qdH5AKLxCJ3r/GV8HoHyaJmLp4Y4c6ifM4f6Obqzl/yw45uvvEyyNkbbqnraV9XTvtqnmkxi/uosVSve1ka8rY26rVvH80qXLgUNgL3k9u4lf+CAH+bXOQCstpbk+nWkNmwkuXEjqaAhEG24uddT5doU7EWqSWMX3P33fHIOLuwNAv822PFN2P7HfrvWzdB9H3QFqXnNTV32j0QjtK2sp21lPXf9Wjeu7Hj2+9voXraR88cHOH9sgF/86PjY/9fUt9ZMCv6tK+rUyY8AEGtpIfPgg2QefHA8r5zNkj9yhPyBA+QOHCR/8CCDP/4x/d/5zsTnOjpIbvSdBiU3bPRXA1avxuJ6pmQ2FOxFqpUZtN/u09s/CaWCf8L/xAvQ8xLs/q7vxhcg3ToR+Lvu888HxG78XrxFjFSDseWBTrY80AlAIVei9+Qg548NcP64vwVwaPt5ACIxo6Uzw7KuDK1ddSzrqmPZigzxpMZZEP8QYM0dd1Bzxx3jec45Shd6yR88QP7gQXIHDpA/cJBLL7wIRd8DKPE4yTVrSK5dQ2LtWpJBSqxcuUg1qR4K9iJhEUv4S/irHvDL5TL07veBv+cVOPmSf7cffMc+nXf71wBvu8vPN632w/reoEQqNum+P8DQ5dx48O89OcixHRfZ9/xZv9Kgqb3WB/6gEdDaVaen/wUIngNobyPe3jbpKoArFskfO0b+wMGgIXCIkdd3M/CjZ8ZvBRCN0rJsGT13vInk2nW+MbBmLck1q4nUavAoULAXCa9IBNq3+PTWj/u8oQvQ87JPJ1+Gl78KowW/LlkPHW/2Z/233ekbAS3rbqoBkGlKkWlKsfaeNsCfrQ1dznOxZ5Dek4P09gxx9vDEFQCATHOSZcszNHdmaO5M07I8TVN7WrcBBACLx8fHAID3j+eXR0YoHD9O/vAR8kePcPqllygcPcbQtuegVBrfLt7ZSWLdWpJr1pJct9Y3AtauWXLPBCjYiywlmTbfW9/mD/jl0SJc2Adnd/h3/c/sgO1fg1LOr09koOMOuO0u2q8k4GwTLNsA8RvrRMXMqGtOUdecYvWdreP5I0MFLvYM0dszyMWTg1w6M8zJPX2Uy8FDWxGjsa2G5s40zZ0ZWjrTNHemaWir1cBNAkCkpobU5s2kNm8GYO+dd3L31q24QoHCyZPkjxwlf+QwhSNHyR85QvblV3D5/Pjno42NJFatmpxWryLR3R3KToIU7EWWsmgcbnuzT2NGS3DxgA/8Y42A177O5mIW9v+h7+mveY1/1791s5+2bfE9/t1gxz81mQRdm333vRNfW6b/Qpa+M8P0nRnm0ukhLvYMceSXvTB2tTYWobG9lsb2Wpo6Jk8TKf13JmCJBMl160iuWwe8ZzzfjY5SPH2a/OEjFI4fH0/DL7zAle99b9I+Yh0dQQNg5URDYOVKEitWVO0Dgvp1iMhk0djEg393/4bPK4/yyo++yb2rMv5KwFja/0Nwvh91InF/1t+2Gdo2+fmWdb5hcANXAqKxCC2dGVo6M5Pyi4VRLp+daABcPp+lt2eQo7+8MH7LFiDdmLy6EdBWS6Y5pasBgkWjJLq7SXR3X7WuPDxM4eTJSY2A/PHjDPzoGcpXrkxsGI2SWLGCxKpVxLu7SXR1Ee9aQaK7m/iKFUSSt24HVAr2InJ9kSjZdBfcvtWP1jemmIOLB4Pgv9dPT73iR/ir1NDlz/yb1/oGQMs6v9y40jcuriGeiI6/BlhptFimvzdL//ksl8/5af/5LAdfOU9hZOKebSRq1C+roX5ZDQ1tNTQsq6Gh1c/Xt9To2QAhkk5PuiVQqXT5ctAAOOGnJ05QOHaM7PbtlLPZSdvG2tuDBkAXie4u4iuCaVcX0aamRR1eXMFeRGYvnrr6NgD4nv/6jsClw3BpbHrYNwJyFWdKkTg0rQrSSh/8K6epxhn7B4jGp78S4JxjZLDI5XPDXLkwwpXeEa70ZrnSO8LZI/0Uc6MTGxtkmpI0tNYwXCzzavaYf8agJUWmOUWmManhgZe4WFMTsaYmau++e1K+c47Rvj6KPT0UglQ82UPhVA/Dzz/Ple9emLR9JJ32jYCuLuLdwXT5cuKdncQ7Oxf8OQEFexGZf8lM8ET/nZPznYNs30TwH0uXj/srApUNAfBvCExtADSsgPrlPqWXXdUYMDNq6xPU1icmvRbov96RGyoGDYARrlzIcuXiCAO9IwyehpePHpu8r4iRbkxQ15yivqWGupbU+AOHvkGQJBZX3wFLkZkRa2kh1tJCzV13XbW+nMtRPHXKNwJ6eij0nKJ48iT5o0cZeu45XKEwaftoS4sP/GMNgOWdFcvL51xeBXsReeOYQbrFp+77rl4/0g/9J6D/pB8QqP+En1467McHKE6+bEo0AfWdE8F/bL4hmK/r9A2CSDT4eqOmLkFNXYKONZNfvdq2bRvvfMeDDPblfLqUmzR/+uBlhvvzk54TAKipT1AfNAIyTUnSjRMp05gk3ZDUrYIlKJJKVTwoOJkrlyn19lI8fYbi6dM+nfHz+f37GfrpT69qDMyVgr2I3DpqGn2aekUA/FWB4V64cgoGzsDA6SCdgSunfd8BA2egXJz8OYtA7TKoa4dMO2Q6/CuImfaKvHaipSyxeGR8dMDpjI6WGb6cn9wgCBoFvScHObbrIqPF8lWfS6XjFY2AxERDIGgMpBuT1GTiGmBoibBIhHh7O/H2drjn7qvWu3KZ0UuXxhsBhdOn4bHH5vSdCvYiUh3MgiDdBsvvmX6bchmyF30j4MppGDzrOxIaOj+Rzu+F4QtQLk366IMALyagtmXGFK1tpr62hfq6Fmhvgdrb/HMLAecc+WyJ4f48w/15hoLp8JXCeN7FnkGyg4Xx1wnHRKJGbUOC2vqkvw1RF6cmuB1RUzd5mqyNLerDXrKwLBIh1tpKrLV14haBgr2ISCASmWgQdF59xjSuXIaRyzB0LmgEXODIzhdYe1sTZC/55wqyl+Dc677xMHJ55n3F074xkG7BUo2kUg2kUg20pOoh1QCtjbAimE81QKqJ0Xg92UINw9lYRUPAT7ODBQb7clw4PsDIUBFXdld9ZSRqUxoAcVZeHCEaNQ68fI6aTJzUWErHNSaBKNiLyBIUiUw8O9B+OwA9lztYWzE06ySjJcj1Bw2BKWl4bP6if8Bw4LSf5gagNDLt7qJAHVBnEf8QYqoBUvX+7YNlGehMQyKNi6fJuQay5QaypTpGirWMFFJk80myuRIjI45sf56+U2VaRgEcz//PvVd/XyyCxcuc+7+vjDcAaioaA2MNg5qx5XSceCqqqwchomAvInI90Zh/0C+97OY+VypAfiAI/v2+AZC74lO+Yr4yf+A0FIahMIwVs9QUhqhxZVqu9T01cCH/H3BE+LsdT5CzZnLWQo5GRsoN5Fw9A7kE5ZFmcoO19JVqGCmmyBcTOKYP6GaOZNKRSEIqBcmUkayJkKyJ+lQbJVkb9ykdJ5lOkswkSWZSJGtTWDzpH4xUg2Huylc/B3KzFOxFRBZKLAGxWTQSKjnnxyooDENhaLwh4OezE/PbbsPKZZruesRvX8r5To9Kp6F0hP7eczRmkkGeT66QI1+IkivGGRnNkCvXkSvXk3N15MsZ8i5NPp8mP+LnB8sZ8i5DvpymfM3wUSZhIz5FxlKeeDRPIponEcmRiBaC+TyJWCFYLhCPl/x8rEg8NkokEvEPWUaifjppPjo+f/vFS3D+a9co0zUaHddrkJRH/XFwZXCjwTRIk9ZNt748efl668slv89yyT9sWi5N9FI5Bwr2IiK3MjPf3XC85tqNhtd2+el73z/t6h3btrF1ym0KA1JAyjkaR4v+tkMp7xsDo0WfykU/MuJoyU/LRVypSDGXJZ8tUsiVyWdHyeXK5Ecc+Rzk845C3igUjEIxRaFQS64YZbAYpZCPUSjFKI7eWB/z8UiBRCxPIuIbB/FonnikQNwKxCJ54pE8cSvgSq2c64NYpODXR4p+WysSj+SIRYpBfoGIVQbPq5+JmMS5oEERXKWY2vCY1OiIz7B+SrrW+kjUdzYViUIk5sebiMTgS797Q/9eM1GwFxFZ6syCqxCJG9scSARptsplRzFXopAbpTASTHMlCiMlihXzhZFgvmLbkcIoxXyQCmVK+dHrf2GFSMyIJ6PEE1E/TUaJJcZShFjcp2giOj4/sS5KNB4Zn69cF40HecE+ovHIPD73oGAvIiJVJhKx8Xv+c+XKjp/9dBv33/sApcqGQJAq8ybmyxTzJYr58nhebqhAqVimVCwzWhj184Uyo6XZX0YfC/qRWIRozIjGIpNTfJq8YLtIfCJvrhTsRUSkqlnEiMR8N8kLwZUdpVKZ0UKZUnGU0qSpbyyMFifmK6ejhbL/bKlMueinoyUXTH0q5opX5Y1tVy6WKU/z+uXNUrAXERG5BouYv+SfiAJv/Hj2ruz41Ffntg912CwiInILm49ulBXsRUREQm5Bg72ZPWxmB8zssJl9bpr1m8zsRTPLm9lnF7IsIiIiS9WC3bM3syjwR8C7gVPAdjN7yjlX2ZdjH/BbwIcWqhwiIiJL3UKe2d8LHHbOHXXOFYBvAY9WbuCcu+Cc2w4Up9uBiIiIzJ05N/dH+qfdsdnfAh52zv1msPz3gfucc5+aZtsvAkPOuS/PsK/HgMcAWltb3/Ltb397Qcp8KxgaGiKTySx2MRaM6le9wlw3qP76LX/Zn7udvm/6d8KrvX7XE/b6vetd7/qFc+6ts/38Qr56N93jg7NqWTjnngCeANi4caOb2uVjmGybpkvLMFH9qleY6wbVX78LB3x3ueu3vnna9dVev+sJe/3maiEv458CuiqWVwBnFvD7REREZBoLGey3A+vNbLWZJYCPAE8t4PeJiIjINBbsMr5zrmRmnwKeBaLAk865PWb2iWD942bWAbwK1ANlM/sMsMU5N7BQ5RIREVlqFrS7XOfc08DTU/Ier5g/h7+8LyIiIgtEPeiJiIiEnIK9iIhIyCnYi4iIhJyCvYiISMgp2IuIiIScgr2IiEjIKdiLiIiEnIK9iIhIyCnYi4iIhJyCvYiISMgp2IuIiIScgr2IiEjIKdiLiIiEnIK9iIhIyCnYi4iIhJyCvYiISMgp2IuIiIScgr2IiEjIKdiLiIiEnIK9iIhIyCnYi4iIhJyCvYiISMgp2IuIiIScgr2IiEjIKdiLiIiEnIK9iIhIyCnYi4iIhJyCvYiISMgp2IuIiIScgr2IiEjIKdiLiIiEnIK9iIhIyCnYi4iIhJyCvYiISMgp2IuIiIScgr2IiEjIKdiLiIiEnIK9iIhIyCnYi4iIhJyCvYiISMgp2IuIiIScgr2IiEjIKdiLiIiEnIK9iIhIyCnYi4iIhNyCBnsze9jMDpjZYTP73DTrzcz+S7B+l5nds5DlERERWYoWLNibWRT4I+ARYAvwUTPbMmWzR4D1QXoM+O8LVR4REZGlaiHP7O8FDjvnjjrnCsC3gEenbPMo8L+d9xLQaGa3LWCZRERElpyFDPbLgZ6K5VNB3s1uIyIiInMQW8B92zR5bhbbYGaP4S/zA+TNbPccy3YrWwZcXOxCLCDVr3qFuW4Qlvp9YsY14ajfzMJev41z+fBCBvtTQFfF8grgzCy2wTn3BPAEgJm96px76/wW9dah+lW3MNcvzHUD1a/aLYX6zeXzC3kZfzuw3sxWm1kC+Ajw1JRtngL+QfBU/v3AFefc2QUsk4iIyJKzYGf2zrmSmX0KeBaIAk865/aY2SeC9Y8DTwPvAw4DWeAfLVR5RERElqqFvIyPc+5pfECvzHu8Yt4Bn7zJ3T4xD0W7lal+1S3M9Qtz3UD1q3aq3zWYj7ciIiISVuouV0REJOSqKthfr/vdamJmXWb2MzPbZ2Z7zOzTQf4Xzey0me0I0vsWu6yzZWbHzez1oB6vBnnNZvZ/zOxQMG1a7HLOhpltrDhGO8xswMw+U83Hz8yeNLMLla+2Xut4mdnvBr/FA2b23sUp9Y2boX7/ycz2B911f9fMGoP8VWY2UnEcH595z7eGGeo3499jNR2/Ger2ZxX1Om5mO4L8ajx2M8WD+fv9OeeqIuEf8jsCrAESwE5gy2KXaw71uQ24J5ivAw7iuxX+IvDZxS7fPNXxOLBsSt5/BD4XzH8O+P3FLuc81DMKnANWVvPxA34FuAfYfb3jFfyt7gSSwOrgtxld7DrMon7vAWLB/O9X1G9V5XbVkGao37R/j9V2/Kar25T1fwB8oYqP3UzxYN5+f9V0Zn8j3e9WDefcWefca8H8ILCPpdF74KPA14P5rwMfWsSyzJeHgCPOuROLXZC5cM79HOibkj3T8XoU+JZzLu+cO4Z/o+beN6SgszRd/ZxzP3bOlYLFl/B9fVSlGY7fTKrq+F2rbmZmwK8Df/qGFmoeXSMezNvvr5qCfWi71jWzVcDdwMtB1qeCy4pPVutl7oADfmxmvwh6QQRod0FfCsG0bdFKN38+wuT/aMJy/GDm4xXG3+PHgR9VLK82s1+a2XNm9uBiFWoeTPf3GKbj9yBw3jl3qCKvao/dlHgwb7+/agr2N9S1brUxswzw58BnnHMD+JH/1gJ3AWfxl6eq1QPOuXvwoxt+0sx+ZbELNN/Mdxj1QeA7QVaYjt+1hOr3aGafB0rAnwRZZ4Fu59zdwG8D3zSz+sUq3xzM9PcYpuP3USY3tqv22E0TD2bcdJq8ax6/agr2N9S1bjUxszj+wP6Jc+4vAJxz551zo865MvDH3MKX1q7HOXcmmF4Avouvy3kLRjYMphcWr4Tz4hHgNefceQjX8QvMdLxC83s0s48B7wd+wwU3RIPLo5eC+V/g74luWLxSzs41/h5DcfzMLAb8TeDPxvKq9dhNFw+Yx99fNQX7G+l+t2oE95n+B7DPOfefK/Irh/j9G0BVDvpjZmkzqxubxz8ItRt/zD4WbPYx4C8Xp4TzZtJZRViOX4WZjtdTwEfMLGlmq4H1wCuLUL45MbOHgX8JfNA5l63IbzWzaDC/Bl+/o4tTytm7xt9jKI4f8GvAfufcqbGMajx2M8UD5vP3t9hPId7kE4vvwz+leAT4/GKXZ451eSf+sssuYEeQ3gd8A3g9yH8KuG2xyzrL+q3BPy26E9gzdryAFuAnwKFg2rzYZZ1DHWuBS0BDRV7VHj98o+UsUMSfOfzjax0v4PPBb/EA8Mhil3+W9TuMv/c59ht8PNj2w8Hf7U7gNeADi13+WdZvxr/Hajp+09UtyP9fwCembFuNx26meDBvvz/1oCciIhJy1XQZX0RERGZBwV5ERCTkFOxFRERCTsFeREQk5BTsRUREQk7BXkREJOQU7EVCyMy+YmafqVh+1sy+VrH8B2b22xXLz5jZcjN7f9Cn+E4z22tm/3SG/X/IzL4QzFcOo7rbzD54nbL9VQjGDBCpKgr2IuH0AvAOADOLAMuA2yvWvwN4PlhfAzTju+J8At8JyZ34wTi2zbD/3wH+W8XyV5xzdwF/G3gy+M6ZfAP4ZzdZHxGZAwV7kXB6niDY44P8bmDQzJrMLAlsBn4ZrN+KD+p1QAzfKyDO9zF+YOqOzWwDkHfOXZy6zjm3Dz+gzDIz+14w4uGeilEPwffk9tG5V1FEblRssQsgIvPPOXfGzEpm1o0P+i/ih8B8O3AF2OWcKwSbPwJ8zznXZ2ZPASfM7CfAD4A/dX4QlUoP4LshvYqZ3QeUgV7g48E+a4DtZvbnzrlLzrnLQZ/eLS4YsEREFpbO7EXCa+zsfizYv1ix/ELFdg8A/w/AOfebwEP4QTU+Czw5zX5vwwfzSv/czHYAXwb+jvP9cP+Wme0EXsKP0LW+YvsLQOdcKiciN05n9iLhNXbf/g78Zfwe4F8AAwRBPBgVrKfiLB/n3OvA62b2DeAY8A+n7HcEaJiS9xXn3JfHFsxsK35Esrc757Jmtg1IVWyfCvYjIm8AndmLhNfz+HHa+5wf07wPaMRfyn8x2OYR4BkAM8sEQXrMXcCJafa7D1h3ne9uAC4HgX4TcP/YimA4zw7g+M1WSERmR8FeJLxexz+F/9KUvCsVD9c9TBDsAQN+x8wOBJfkv8TVZ/UAPwfuDoL2TJ4BYma2C/i3U8rwFuAl51zpJusjIrOkIW5FlqjgqfznnXNvncVn/xD4vnPur2b52aeccz+52c+KyOzozF5kiQperbvpQB/490DtLD+7W4Fe5I2lM3sREZGQ05m9iIhIyCnYi4iIhJyCvYiISMgp2IuIiIScgr2IiEjI/X8MtN8sxbJ9hAAAAABJRU5ErkJggg==", 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\n", 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", 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" ] @@ -400,9 +400,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python (adrpy)", "language": "python", - "name": "python3" + "name": "adrpy" }, "language_info": { "codemirror_mode": { @@ -414,7 +414,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.12.3" } }, "nbformat": 4, From 0ac02b11d059c39f943b244470aa9dedb6cfa488 Mon Sep 17 00:00:00 2001 From: Delpoo Date: Tue, 8 Apr 2025 19:30:46 -0300 Subject: [PATCH 2/9] Second commit --- ADRpy/analisis/Modulos/config_and_loading.py | 41 +++-- .../analisis/Modulos/correlation_analysis.py | 70 +++++--- .../Modulos/correlation_imputation.py | 6 +- ADRpy/analisis/Modulos/data_processing.py | 168 +++++++++++------- ADRpy/analisis/Modulos/imputation_loop.py | 151 +++++++++++----- .../analisis/Modulos/similarity_imputation.py | 3 +- 6 files changed, 281 insertions(+), 158 deletions(-) diff --git a/ADRpy/analisis/Modulos/config_and_loading.py b/ADRpy/analisis/Modulos/config_and_loading.py index de1dced2..0cf27d97 100644 --- a/ADRpy/analisis/Modulos/config_and_loading.py +++ b/ADRpy/analisis/Modulos/config_and_loading.py @@ -4,32 +4,35 @@ from openpyxl import load_workbook - def configurar_entorno(max_rows=20, max_columns=10): """ Configura el entorno para mostrar más datos en la consola. :param max_rows: Número máximo de filas para mostrar en consola. :param max_columns: Número máximo de columnas para mostrar en consola. """ - pd.set_option('display.max_rows', max_rows) - pd.set_option('display.max_columns', max_columns) + pd.set_option("display.max_rows", max_rows) + pd.set_option("display.max_columns", max_columns) -def cargar_datos(ruta_archivo='Datos_aeronaves.xlsx'): +def cargar_datos(ruta_archivo=None): """ Carga los datos desde un archivo Excel y realiza validaciones. Devuelve el DataFrame cargado y la ruta utilizada. """ # Solicitar al usuario la ruta del archivo si no se proporciona if ruta_archivo is None: - ruta_archivo = input("Ingrese la ruta del archivo Excel original (o presione Enter para usar 'C:/Users/delpi/OneDrive/Tesis/ADRpy-VTOL/ADRpy/analisis/data/Datos_aeronaves.xlsx'): ").strip() + ruta_archivo = input( + r"Ingrese la ruta del archivo Excel original (o presione Enter para usar 'C:\Users\delpi\OneDrive\Tesis\ADRpy-VTOL\ADRpy\analisis\data\Datos_aeronaves.xlsx'): " + ).strip() if not ruta_archivo: - ruta_archivo = "C:/Users/delpi/OneDrive/Tesis/ADRpy-VTOL/ADRpy/analisis/data/Datos_aeronaves.xlsxC:/Users/delpi/OneDrive/Tesis/ADRpy-VTOL/ADRpy/analisis/data/Datos_aeronaves.xlsx" # Asignar valor predeterminado - + ruta_archivo = r"C:\Users\delpi\OneDrive\Tesis\ADRpy-VTOL\ADRpy\analisis\data\Datos_aeronaves.xlsx" # Asignar valor predeterminado + # Validar el formato del archivo - if not ruta_archivo.endswith(('.xlsx', '.xlsm')): - raise ValueError("El archivo debe estar en formato .xlsx o .xlsm compatible con openpyxl.") - + if not ruta_archivo.endswith((".xlsx", ".xlsm")): + raise ValueError( + "El archivo debe estar en formato .xlsx o .xlsm compatible con openpyxl." + ) + # Mostrar mensaje de carga print(f"=== Cargando datos desde el archivo: {ruta_archivo} ===") @@ -39,24 +42,28 @@ def cargar_datos(ruta_archivo='Datos_aeronaves.xlsx'): # Validaciones adicionales if df.empty: - raise ValueError("El archivo cargado está vacío. Verifica el archivo de origen.") - + raise ValueError("El archivo cargado está vacío.") + # Manejar índices nulos if df.index.isnull().any(): - print("Advertencia: El índice contiene valores nulos. Se reemplazarán por 'indice_desconocido'.") + print( + "Advertencia: Índices nulos encontrados. Reemplazando por 'indice_desconocido'." + ) df.index = df.index.fillna("indice_desconocido") # Manejar columnas nulas if df.columns.isnull().any(): - print("Advertencia: Algunas columnas contienen valores nulos. Se reemplazarán por 'columna_desconocida'.") + print( + "Advertencia: Columnas nulas encontradas. Reemplazando por 'columna_desconocida'." + ) df.columns = df.columns.fillna("columna_desconocida") # Mostrar información básica del DataFrame cargado print("\n=== Resumen inicial del DataFrame cargado ===") print(df.info()) - #print("\n=== Vista previa de índices y columnas ===") - #print(f"Primeros índices: {df.index.tolist()[:10]}") - #print(f"Primeras columnas: {df.columns.tolist()[:10]}") + # print("\n=== Vista previa de índices y columnas ===") + # print(f"Primeros índices: {df.index.tolist()[:10]}") + # print(f"Primeras columnas: {df.columns.tolist()[:10]}") return df, ruta_archivo except FileNotFoundError: diff --git a/ADRpy/analisis/Modulos/correlation_analysis.py b/ADRpy/analisis/Modulos/correlation_analysis.py index 093a34da..89ebd64b 100644 --- a/ADRpy/analisis/Modulos/correlation_analysis.py +++ b/ADRpy/analisis/Modulos/correlation_analysis.py @@ -1,16 +1,18 @@ import pandas as pd import seaborn as sns import matplotlib.pyplot as plt -from sklearn.metrics import r2_score -from sklearn.linear_model import LinearRegression import numpy as np from .html_utils import convertir_a_html from .user_interaction import solicitar_umbral - - -def calcular_correlaciones_y_generar_heatmap_con_resumen(df_procesado, parametros_seleccionados, valor_por_defecto=0.7): +def calcular_correlaciones_y_generar_heatmap_con_resumen( + df_procesado, + parametros_seleccionados, + umbral=None, + valor_por_defecto=0.7, + devolver_tabla=False, +): """ Calcula las correlaciones completas y filtradas entre variables seleccionadas, genera tablas en HTML con un resumen agregado, y crea un heatmap. @@ -20,6 +22,7 @@ def calcular_correlaciones_y_generar_heatmap_con_resumen(df_procesado, parametro :param devolver_tabla: Si True, retorna la tabla completa de correlaciones. :return: Tabla completa de correlaciones (opcional). """ + def agregar_resumen_a_tabla(tabla, titulo): """ Agrega un resumen al final de una tabla HTML indicando: @@ -30,35 +33,45 @@ def agregar_resumen_a_tabla(tabla, titulo): total_valores = tabla.size valores_numericos = tabla.count().sum() valores_nan = total_valores - valores_numericos - - resumen = pd.DataFrame({ - "Resumen": ["Total de valores", "Valores numéricos", "Valores NaN"], - "Cantidad": [total_valores, valores_numericos, valores_nan] - }) - + + resumen = pd.DataFrame( + { + "Resumen": ["Total de valores", "Valores numéricos", "Valores NaN"], + "Cantidad": [total_valores, valores_numericos, valores_nan], + } + ) + convertir_a_html(tabla, titulo=titulo, mostrar=True) convertir_a_html(resumen, titulo="Resumen de la Tabla", mostrar=True) try: - # === Paso 1: Solicitar umbral al usuario === - umbral = solicitar_umbral(valor_por_defecto) + # === Paso 1: Obtener umbral desde el argumento o pedirlo al usuario === + if umbral is None: + umbral = solicitar_umbral(valor_por_defecto) print(f"\nUmbral seleccionado para correlaciones significativas: {umbral}") # === Validación de parámetros seleccionados === - parametros_no_encontrados = [v for v in parametros_seleccionados if v not in df_procesado.index] + parametros_no_encontrados = [ + v for v in parametros_seleccionados if v not in df_procesado.index + ] if parametros_no_encontrados: - raise ValueError(f"Los siguientes parámetros no se encontraron en los datos procesados: {', '.join(parametros_no_encontrados)}") + raise ValueError( + f"Los siguientes parámetros no se encontraron en los datos procesados: {', '.join(parametros_no_encontrados)}" + ) # === Tabla completa (sin filtrar) === print("\n=== Cálculo de tabla completa ===") tabla_completa = df_procesado.transpose().corr() - agregar_resumen_a_tabla(tabla_completa.round(3), "Tabla de Correlaciones con todos los parametros(tabla_completa)") + agregar_resumen_a_tabla( + tabla_completa.round(3), + "Tabla de Correlaciones con todos los parametros(tabla_completa)", + ) # Filtrar correlaciones por el umbral tabla_completa_significativa = tabla_completa[ (tabla_completa.abs() >= umbral) & (tabla_completa != 1) ] - #agregar_resumen_a_tabla(tabla_completa_significativa.round(3), f"Tabla de Correlaciones Significativas (Umbral >= {umbral})") + # agregar_resumen_a_tabla(tabla_completa_significativa.round(3), f"Tabla de Correlaciones Significativas (Umbral >= {umbral})") # === Filtrar datos seleccionados === print("\n=== Filtrando datos seleccionados ===") @@ -68,7 +81,10 @@ def agregar_resumen_a_tabla(tabla, titulo): # Tabla filtrada print("\n=== Cálculo de correlaciones filtradas ===") tabla_filtrada = datos_filtrados.corr() - agregar_resumen_a_tabla(tabla_filtrada.round(3), "Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)") + agregar_resumen_a_tabla( + tabla_filtrada.round(3), + "Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)", + ) # Filtrar correlaciones por el umbral para la tabla filtrada tabla_filtrada_significativa = tabla_filtrada[ @@ -76,12 +92,14 @@ def agregar_resumen_a_tabla(tabla, titulo): ] agregar_resumen_a_tabla( tabla_filtrada_significativa.round(3), - f"Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= {umbral})" + f"Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= {umbral})", ) # Preparar datos para el heatmap print("\n=== Preparando datos para el heatmap ===") - heatmap_data = datos_filtrados.dropna(thresh=2) # Excluir variables con menos de 2 valores válidos + heatmap_data = datos_filtrados.dropna( + thresh=2 + ) # Excluir variables con menos de 2 valores válidos heatmap_correlaciones = heatmap_data.corr() # Generar heatmap @@ -95,14 +113,18 @@ def agregar_resumen_a_tabla(tabla, titulo): center=0, linewidths=0.5, vmin=-1, - vmax=1 + vmax=1, + ) + plt.title( + f"Heatmap de Correlaciones de Variables Seleccionadas (Umbral >= {umbral})" ) - plt.title(f"Heatmap de Correlaciones de Variables Seleccionadas (Umbral >= {umbral})") plt.show() except ValueError as e: print(f"Error: {e}. Por favor verifica los parámetros seleccionados.") except KeyError as e: - print(f"Error: {e}. Asegúrate de que las variables seleccionadas existen en los datos.") + print( + f"Error: {e}. Asegúrate de que las variables seleccionadas existen en los datos." + ) - return tabla_completa \ No newline at end of file + return tabla_completa diff --git a/ADRpy/analisis/Modulos/correlation_imputation.py b/ADRpy/analisis/Modulos/correlation_imputation.py index 05ce634f..252e460a 100644 --- a/ADRpy/analisis/Modulos/correlation_imputation.py +++ b/ADRpy/analisis/Modulos/correlation_imputation.py @@ -2,6 +2,7 @@ import numpy as np from sklearn.linear_model import LinearRegression from .html_utils import convertir_a_html +from .correlation_analysis import calcular_correlaciones_y_generar_heatmap_con_resumen @@ -32,7 +33,10 @@ def Imputacion_por_correlacion( """ # Cargar datos simulados df = df_correlacion.copy() - + + if tabla_completa is None or tabla_completa.empty: + raise ValueError("La tabla de correlaciones completa no fue proporcionada o está vacía.") + # Mostrar df en formato HTML print("\n=== DataFrame inicial ===") convertir_a_html(df, titulo="DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())", mostrar=True) diff --git a/ADRpy/analisis/Modulos/data_processing.py b/ADRpy/analisis/Modulos/data_processing.py index eed26b63..560cd863 100644 --- a/ADRpy/analisis/Modulos/data_processing.py +++ b/ADRpy/analisis/Modulos/data_processing.py @@ -2,29 +2,26 @@ import numpy as np import tkinter as tk from tkinter import simpledialog +from Modulos.html_utils import convertir_a_html - -def procesar_datos_y_manejar_duplicados(df): +def procesar_datos_y_manejar_duplicados(df, respuesta_global=None): """ Limpia un DataFrame preservando la estructura original y maneja duplicados en índices y columnas. Incluye interacción para gestionar duplicados según las elecciones del usuario. :param df: DataFrame a procesar. :return: DataFrame limpio y procesado. """ - import tkinter as tk - from tkinter import simpledialog - try: print("=== Inicio del procesamiento de datos ===") - + # Paso 1: Limpieza inicial de encabezados - df.columns = df.columns.str.strip().str.replace('\xa0', ' ', regex=True) - df.index = df.index.astype(str).str.strip().str.replace('\xa0', ' ', regex=True) + df.columns = df.columns.str.strip().str.replace("\xa0", " ", regex=True) + df.index = df.index.astype(str).str.strip().str.replace("\xa0", " ", regex=True) # Paso 2: Eliminar filas y columnas completamente vacías - df.dropna(how='all', inplace=True) # Filas vacías - df.dropna(how='all', axis=1, inplace=True) # Columnas vacías + df.dropna(how="all", inplace=True) # Filas vacías + df.dropna(how="all", axis=1, inplace=True) # Columnas vacías # Paso 3: Manejo de duplicados print("\n=== Comprobación de duplicados ===") @@ -38,41 +35,40 @@ def procesar_datos_y_manejar_duplicados(df): print(f"Columnas duplicadas: {duplicados_columna}") # Crear ventana emergente para interacción - root = tk.Tk() - root.withdraw() - - # Preguntar manejo global de duplicados - respuesta_global = simpledialog.askstring( - "Manejo global de duplicados", - "Se encontraron duplicados. ¿Deseas aplicar una acción global a todos?\n" - "[1] Eliminar todos los duplicados\n" - "[2] Conservar el primero en todos\n" - "[3] Conservar el último en todos\n" - "[4] Procesar duplicados uno por uno" - ) + if respuesta_global is None: + root = tk.Tk() + root.withdraw() + respuesta_global = simpledialog.askstring( + "Manejo global de duplicados", + "Se encontraron duplicados. ¿Deseas aplicar una acción global a todos?\n" + "[1] Eliminar todos los duplicados\n" + "[2] Conservar el primero en todos\n" + "[3] Conservar el último en todos\n" + "[4] Procesar duplicados uno por uno", + ) # Aplicar acción global si corresponde - if respuesta_global in ['1', '2', '3']: - if respuesta_global == '1': + if respuesta_global in ["1", "2", "3"]: + if respuesta_global == "1": print("Eliminando todos los duplicados...") if duplicados_fila: df = df.loc[~df.index.duplicated(keep=False)] if duplicados_columna: df = df.loc[:, ~df.columns.duplicated(keep=False)] - elif respuesta_global == '2': + elif respuesta_global == "2": print("Conservando el primero en todos los duplicados...") if duplicados_fila: - df = df.loc[~df.index.duplicated(keep='first')] + df = df.loc[~df.index.duplicated(keep="first")] if duplicados_columna: - df = df.loc[:, ~df.columns.duplicated(keep='first')] + df = df.loc[:, ~df.columns.duplicated(keep="first")] - elif respuesta_global == '3': + elif respuesta_global == "3": print("Conservando el último en todos los duplicados...") if duplicados_fila: - df = df.loc[~df.index.duplicated(keep='last')] + df = df.loc[~df.index.duplicated(keep="last")] if duplicados_columna: - df = df.loc[:, ~df.columns.duplicated(keep='last')] + df = df.loc[:, ~df.columns.duplicated(keep="last")] else: # Procesar duplicados uno por uno si respuesta_global es '4' for duplicado in duplicados_fila + duplicados_columna: @@ -82,39 +78,41 @@ def procesar_datos_y_manejar_duplicados(df): f"{tipo} duplicado '{duplicado}' encontrado. Opciones:\n" "[1] Eliminar\n" "[2] Conservar el primero\n" - "[3] Conservar el último" + "[3] Conservar el último", ) # Realizar la acción según la elección del usuario - if respuesta == '1': + if respuesta == "1": if tipo == "Índice": df = df[df.index != duplicado] else: df = df.loc[:, df.columns != duplicado] - elif respuesta == '2': + elif respuesta == "2": if tipo == "Índice": - df = df.loc[~df.index.duplicated(keep='first')] + df = df.loc[~df.index.duplicated(keep="first")] else: - df = df.loc[:, ~df.columns.duplicated(keep='first')] - elif respuesta == '3': + df = df.loc[:, ~df.columns.duplicated(keep="first")] + elif respuesta == "3": if tipo == "Índice": - df = df.loc[~df.index.duplicated(keep='last')] + df = df.loc[~df.index.duplicated(keep="last")] else: - df = df.loc[:, ~df.columns.duplicated(keep='last')] + df = df.loc[:, ~df.columns.duplicated(keep="last")] # Paso 4: Convertir valores internos a numéricos print("\n=== Convirtiendo valores a numéricos donde sea posible ===") for col in df.columns: try: - df.loc[:, col] = pd.to_numeric(df[col], errors='coerce') + df.loc[:, col] = pd.to_numeric(df[col], errors="coerce") except Exception as e: - print(f"Advertencia: No se pudo convertir la columna '{col}' a numérico. Error: {e}") + print( + f"Advertencia: No se pudo convertir la columna '{col}' a numérico. Error: {e}" + ) print("=== Procesamiento completado ===") return df except Exception as e: raise ValueError(f"Error durante el procesamiento y manejo de duplicados: {e}") - + def mostrar_celdas_faltantes_con_seleccion(df): """ @@ -124,31 +122,43 @@ def mostrar_celdas_faltantes_con_seleccion(df): :param df: DataFrame procesado. :return: DataFrame con los detalles de las celdas faltantes (si las hay). """ - def seleccionar_columna(df): + + def seleccionar_columna(df, columna_numero=None): """ Permite al usuario seleccionar una columna específica para validar. - Si no selecciona ninguna, retorna la primera columna como predeterminada. + Si no se selecciona ninguna, retorna la primera columna como predeterminada. + :param df: DataFrame a analizar + :param columna_numero: (opcional) número de columna a seleccionar automáticamente (modo debug) """ - # Crear un diccionario para asociar números con las columnas columnas_dict = {i + 1: col for i, col in enumerate(df.columns)} - opciones_texto = "\n".join([f"{num}: {col}" for num, col in columnas_dict.items()]) + + if columna_numero is not None: + if columna_numero not in columnas_dict: + raise ValueError("Número de columna predefinido fuera de rango.") + return columnas_dict[columna_numero] + + opciones_texto = "\n".join( + [f"{num}: {col}" for num, col in columnas_dict.items()] + ) try: - # Solicitar al usuario seleccionar una columna - columna_numero = simpledialog.askstring( + seleccion_usuario = simpledialog.askstring( "Selección de columna", - f"Selecciona el número correspondiente a la columna que deseas validar:\n\n{opciones_texto}" + f"Selecciona el número correspondiente a la columna que deseas validar:\n\n{opciones_texto}", ) - if not columna_numero: # Si no se selecciona nada, usar la primera columna - print("No se seleccionó ninguna columna. Usando la primera columna como predeterminada.") + + if not seleccion_usuario: + print( + "No se seleccionó ninguna columna. Usando la primera columna como predeterminada." + ) return df.columns[0] - columna_numero = int(columna_numero) + seleccion_usuario = int(seleccion_usuario) - if columna_numero not in columnas_dict: + if seleccion_usuario not in columnas_dict: raise ValueError("Número ingresado fuera del rango válido.") - return columnas_dict[columna_numero] + return columnas_dict[seleccion_usuario] except ValueError as e: print(f"Error: {e}. Finalizando la ejecución.") @@ -162,7 +172,7 @@ def indice_a_columna_excel(indice): """ etiqueta = "" while indice >= 0: - etiqueta = chr(indice % 26 + ord('A')) + etiqueta + etiqueta = chr(indice % 26 + ord("A")) + etiqueta indice = indice // 26 - 1 return etiqueta @@ -171,26 +181,34 @@ def indice_a_columna_excel(indice): columna_prueba = seleccionar_columna(df) # Identificar celdas faltantes en la columna seleccionada - print(f"\n=== Analizando celdas faltantes en la columna: '{columna_prueba}' ===") + print( + f"\n=== Analizando celdas faltantes en la columna: '{columna_prueba}' ===" + ) missing_indices = df[df[columna_prueba].isna()].index.tolist() if not missing_indices: - print(f"No se encontraron valores faltantes en la columna '{columna_prueba}'.") + print( + f"No se encontraron valores faltantes en la columna '{columna_prueba}'." + ) return pd.DataFrame() # Devuelve un DataFrame vacío si no hay faltantes # Crear un DataFrame para almacenar los resultados resultados = [] for idx in missing_indices: - fila_excel = df.index.get_loc(idx) + 2 # +2 para ajustarse al formato Excel (encabezado en fila 1) + fila_excel = ( + df.index.get_loc(idx) + 2 + ) # +2 para ajustarse al formato Excel (encabezado en fila 1) columna_excel = indice_a_columna_excel(df.columns.get_loc(columna_prueba)) celda_excel = f"{columna_excel}{fila_excel}" - resultados.append({ - "Índice": idx, - "Celda": celda_excel, - "Columna": columna_prueba, - "Valor Actual": "NaN" - }) + resultados.append( + { + "Índice": idx, + "Celda": celda_excel, + "Columna": columna_prueba, + "Valor Actual": "NaN", + } + ) # Convertir resultados a DataFrame df_resultados = pd.DataFrame(resultados) @@ -202,11 +220,13 @@ def indice_a_columna_excel(indice): raise -def generar_resumen_faltantes(df, titulo="Resumen de Valores Faltantes por Columna", ancho="50%", alto="300px"): +def generar_resumen_faltantes( + df, titulo="Resumen de Valores Faltantes por Columna", ancho="50%", alto="300px" +): """ Genera un resumen de los valores faltantes por columna en un DataFrame. También genera una tabla HTML con la sumatoria total de los valores faltantes de todas las columnas. - + :param df: DataFrame a analizar. :param titulo: Título opcional para mostrar en la tabla HTML. :param ancho: Ancho del contenedor HTML. @@ -222,13 +242,23 @@ def generar_resumen_faltantes(df, titulo="Resumen de Valores Faltantes por Colum # Calcular la sumatoria total de los valores faltantes total_faltantes = faltantes_por_columna.sum() - resumen_total = pd.DataFrame({"Resumen": ["Total de Valores Faltantes"], "Cantidad": [total_faltantes]}) + resumen_total = pd.DataFrame( + {"Resumen": ["Total de Valores Faltantes"], "Cantidad": [total_faltantes]} + ) # Mostrar el resumen por columna como una tabla HTML - convertir_a_html(resumen_faltantes, titulo=titulo, ancho=ancho, alto=alto, mostrar=True) + convertir_a_html( + resumen_faltantes, titulo=titulo, ancho=ancho, alto=alto, mostrar=True + ) # Mostrar la sumatoria total como una tabla HTML - convertir_a_html(resumen_total, titulo="Sumatoria Total de Valores Faltantes", ancho=ancho, alto="100px", mostrar=True) + convertir_a_html( + resumen_total, + titulo="Sumatoria Total de Valores Faltantes", + ancho=ancho, + alto="100px", + mostrar=True, + ) # Retornar ambos DataFrames para su posible uso posterior - return resumen_faltantes, resumen_total \ No newline at end of file + return resumen_faltantes, resumen_total diff --git a/ADRpy/analisis/Modulos/imputation_loop.py b/ADRpy/analisis/Modulos/imputation_loop.py index 77349906..be466cee 100644 --- a/ADRpy/analisis/Modulos/imputation_loop.py +++ b/ADRpy/analisis/Modulos/imputation_loop.py @@ -5,56 +5,105 @@ from .data_processing import generar_resumen_faltantes - - -def bucle_imputacion_similitud_correlacion(df_procesado, parametros_preseleccionados, tabla_completa, reduccion_confianza=0.05, max_iteraciones=7): +def bucle_imputacion_similitud_correlacion( + df_procesado, + parametros_preseleccionados, + tabla_completa, + reduccion_confianza=0.05, + max_iteraciones=7, +): """ Realiza un bucle alternando imputaciones por similitud y correlación, consolidando los resultados. Ahora se evita actualizar los DataFrames inmediatamente, y se eligen las imputaciones finales al final de cada iteración. - + Retorna: df_procesado_base (pd.DataFrame): DataFrame con imputaciones realizadas. df_resumen (pd.DataFrame): Detalle consolidado de imputaciones realizadas. """ df_procesado_base = df_procesado.copy() # Copia base del DataFrame original - df_filtrado_base = df_filtrado.copy() # Copia base del DataFrame original + df_filtrado_base = df_filtrado.copy() # Copia base del DataFrame original - convertir_a_html(df_procesado_base, titulo="df_procesado_base", ancho="100%", alto="400px", mostrar=True) - convertir_a_html(df_filtrado_base, titulo="df_filtrado_base", ancho="100%", alto="400px", mostrar=True) - resumen_imputaciones = [] # Lista para consolidar detalles de todas las imputaciones finales + convertir_a_html( + df_procesado_base, + titulo="df_procesado_base", + ancho="100%", + alto="400px", + mostrar=True, + ) + convertir_a_html( + df_filtrado_base, + titulo="df_filtrado_base", + ancho="100%", + alto="400px", + mostrar=True, + ) + resumen_imputaciones = ( + [] + ) # Lista para consolidar detalles de todas las imputaciones finales # Configuración inicial para imputaciones por similitud print("\n=== Configuración Inicial ===") try: - rango_min = float(input("Ingrese el rango mínimo de MTOW (1-200, predeterminado 85): ") or 85) / 100 - rango_max = float(input("Ingrese el rango máximo de MTOW (1-200, predeterminado 115): ") or 115) / 100 - nivel_confianza_min = float(input("Ingrese el nivel mínimo de confianza (0-1, predeterminado 0.5): ") or 0.5) + rango_min = ( + float( + input("Ingrese el rango mínimo de MTOW (1-200, predeterminado 85): ") + or 85 + ) + / 100 + ) + rango_max = ( + float( + input("Ingrese el rango máximo de MTOW (1-200, predeterminado 115): ") + or 115 + ) + / 100 + ) + nivel_confianza_min = float( + input("Ingrese el nivel mínimo de confianza (0-1, predeterminado 0.5): ") + or 0.5 + ) if not (0.01 <= rango_min <= 2.00 and 0.01 <= rango_max <= 2.00): raise ValueError("Los rangos deben estar entre 1% y 200%.") if rango_min >= rango_max: - raise ValueError("El rango mínimo no puede ser mayor o igual al rango máximo.") + raise ValueError( + "El rango mínimo no puede ser mayor o igual al rango máximo." + ) if not (0 <= nivel_confianza_min <= 1): raise ValueError("El nivel de confianza debe estar entre 0 y 1.") except ValueError as e: - print(f"Error: {e}. Usando valores predeterminados (85% mínimo, 115% máximo, 0.5 confianza mínima).") + print( + f"Error: {e}. Usando valores predeterminados (85% mínimo, 115% máximo, 0.5 confianza mínima)." + ) rango_min, rango_max, nivel_confianza_min = 0.85, 1.15, 0.5 - print(f"\nValores configurados: Rango MTOW [{rango_min*100:.0f}% - {rango_max*100:.0f}%], Confianza Mínima: {nivel_confianza_min:.2f}") + print( + f"\nValores configurados: Rango MTOW [{rango_min*100:.0f}% - {rango_max*100:.0f}%], Confianza Mínima: {nivel_confianza_min:.2f}" + ) # Configuración inicial para imputaciones por correlación try: - umbral_correlacion = float(input("Ingrese el umbral mínimo de correlación (0-1, predeterminado 0.7): ") or 0.7) - nivel_confianza_min_correlacion = float(input("Ingrese el nivel mínimo de confianza para correlación (0-1, predeterminado 0.5): ") or 0.5) + umbral_correlacion = float( + input("Ingrese el umbral mínimo de correlación (0-1, predeterminado 0.7): ") + or 0.7 + ) + nivel_confianza_min_correlacion = float( + input( + "Ingrese el nivel mínimo de confianza para correlación (0-1, predeterminado 0.5): " + ) + or 0.5 + ) if not (0 <= umbral_correlacion <= 1): raise ValueError("El umbral de correlación debe estar entre 0 y 1.") if not (0 <= nivel_confianza_min_correlacion <= 1): raise ValueError("El nivel de confianza debe estar entre 0 y 1.") except ValueError as e: - print(f"Error: {e}. Usando valores predeterminados (umbral = 0.7, confianza mínima = 0.5).") + print( + f"Error: {e}. Usando valores predeterminados (umbral = 0.7, confianza mínima = 0.5)." + ) umbral_correlacion, nivel_confianza_min_correlacion = 0.7, 0.5 # Definir valores predeterminados para correlación @@ -65,13 +114,14 @@ def bucle_imputacion_similitud_correlacion(df_procesado, parametros_preseleccion max_lineas_consola = 40000000 for iteracion in range(1, max_iteraciones + 1): - print("\n" + "="*80) + print("\n" + "=" * 80) print(f"\033[1m=== INICIO DE ITERACIÓN {iteracion} ===\033[0m") - print("="*80) + print("=" * 80) print(f"\n=== Iteración {iteracion}: Resumen antes de imputaciones ===") resumen_antes, total_faltantes_antes = generar_resumen_faltantes( - df_procesado_base, titulo=f"Resumen de Valores Faltantes Antes de Iteración {iteracion}" + df_procesado_base, + titulo=f"Resumen de Valores Faltantes Antes de Iteración {iteracion}", ) # Crear copias independientes para cada método @@ -79,15 +129,15 @@ def bucle_imputacion_similitud_correlacion(df_procesado, parametros_preseleccion df_correlacion = df_procesado_base.copy() # Imputación por similitud (no actualiza todavía) - print("\n" + "-"*80) + print("\n" + "-" * 80) print(f"\033[1m*** IMPUTACIÓN POR SIMILITUD - ITERACIÓN {iteracion} ***\033[0m") - print("-"*80) + print("-" * 80) df_resultado_final, reporte_similitud = imputacion_similitud_con_rango( df_filtrado=df_similitud, df_procesado=df_procesado_base, rango_min=rango_min, rango_max=rango_max, - nivel_confianza_min=nivel_confianza_min + nivel_confianza_min=nivel_confianza_min, ) if reporte_similitud and len(reporte_similitud) > 0: @@ -97,14 +147,20 @@ def bucle_imputacion_similitud_correlacion(df_procesado, parametros_preseleccion for registro in reporte_similitud: registro["Iteración"] = iteracion registro["Método"] = "Similitud" - registro["Nivel de Confianza"] *= (1 - reduccion_confianza) ** (iteracion - 1) + registro["Nivel de Confianza"] *= (1 - reduccion_confianza) ** ( + iteracion - 1 + ) else: - print("\033[1mNo se realizaron imputaciones por similitud en esta iteración.\033[0m") + print( + "\033[1mNo se realizaron imputaciones por similitud en esta iteración.\033[0m" + ) # Imputación por correlación (no actualiza todavía) - print("\n" + "-"*80) - print(f"\033[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN {iteracion} ***\033[0m") - print("-"*80) + print("\n" + "-" * 80) + print( + f"\033[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN {iteracion} ***\033[0m" + ) + print("-" * 80) df_correlacion_final, reporte_correlacion = Imputacion_por_correlacion( df_correlacion, parametros_preseleccionados, @@ -113,7 +169,7 @@ def bucle_imputacion_similitud_correlacion(df_procesado, parametros_preseleccion max_lineas_consola=max_lineas_consola, umbral_correlacion=umbral_correlacion, nivel_confianza_min_correlacion=nivel_confianza_min_correlacion, - reduccion_confianza=reduccion_confianza + reduccion_confianza=reduccion_confianza, ) if reporte_correlacion and len(reporte_correlacion) > 0: @@ -121,9 +177,13 @@ def bucle_imputacion_similitud_correlacion(df_procesado, parametros_preseleccion for registro in reporte_correlacion: registro["Iteración"] = iteracion registro["Método"] = "Correlación" - registro["Nivel de Confianza"] *= (1 - reduccion_confianza) ** (iteracion - 1) + registro["Nivel de Confianza"] *= (1 - reduccion_confianza) ** ( + iteracion - 1 + ) else: - print("\033[1mNo se realizaron imputaciones por correlación en esta iteración.\033[0m") + print( + "\033[1mNo se realizaron imputaciones por correlación en esta iteración.\033[0m" + ) # Combinar las imputaciones de similitud y correlación imputaciones_candidatas = {} @@ -162,41 +222,40 @@ def registrar_imputacion(regs): df_procesado_base.loc[parametro, aeronave] = valor df_filtrado_base.loc[parametro, aeronave] = valor resumen_imputaciones.append(imp) - print(f"Imputación final aplicada: {parametro} - {aeronave} = {valor} ({metodo})") + print( + f"Imputación final aplicada: {parametro} - {aeronave} = {valor} ({metodo})" + ) print(f"\n=== Iteración {iteracion}: Resumen después de imputaciones ===") resumen_despues, total_faltantes_despues = generar_resumen_faltantes( - df_filtrado_base, titulo=f"Resumen de Valores Faltantes Después de Iteración {iteracion}" + df_filtrado_base, + titulo=f"Resumen de Valores Faltantes Después de Iteración {iteracion}", ) # Verificar condición de salida - no_similitud = (reporte_similitud is None or len(reporte_similitud) == 0) - no_correlacion = (reporte_correlacion is None or len(reporte_correlacion) == 0) + no_similitud = reporte_similitud is None or len(reporte_similitud) == 0 + no_correlacion = reporte_correlacion is None or len(reporte_correlacion) == 0 if no_similitud and no_correlacion: print("\033[1mNo se realizaron nuevas imputaciones. Finalizando...\033[0m") # Retornar resultados actuales antes de salir return df_procesado_base, pd.DataFrame(resumen_imputaciones) - print("\n" + "="*80) + print("\n" + "=" * 80) print(f"\033[1m=== FIN DE ITERACIÓN {iteracion} ===\033[0m") - print("="*80) + print("=" * 80) # Si se terminan las iteraciones sin break: df_resumen = pd.DataFrame(resumen_imputaciones) - print("\n" + "="*80) + print("\n" + "=" * 80) print("\033[1m=== RESUMEN FINAL ===\033[0m") - print("="*80) + print("=" * 80) convertir_a_html( - df_procesado_base, - titulo="DataFrame Procesado Final (df_procesado_base)" - ) - convertir_a_html( - df_resumen, - titulo="Resumen Final de Imputaciones (df_resumen)" + df_procesado_base, titulo="DataFrame Procesado Final (df_procesado_base)" ) + convertir_a_html(df_resumen, titulo="Resumen Final de Imputaciones (df_resumen)") print(f"\033[1mTotal de iteraciones realizadas: {iteracion}\033[0m") print(f"\033[1mTotal de valores imputados: {len(resumen_imputaciones)}\033[0m") - return df_procesado_base, df_resumen \ No newline at end of file + return df_procesado_base, df_resumen diff --git a/ADRpy/analisis/Modulos/similarity_imputation.py b/ADRpy/analisis/Modulos/similarity_imputation.py index 28706356..3095f961 100644 --- a/ADRpy/analisis/Modulos/similarity_imputation.py +++ b/ADRpy/analisis/Modulos/similarity_imputation.py @@ -8,7 +8,7 @@ -def imputacion_similitud_con_rango(df_filtrado, df_procesado, rango_min, rango_max, nivel_confianza_min): +def imputacion_similitud_con_rango(df_filtrado, df_procesado, rango_min=0.85, rango_max=1.15, nivel_confianza_min=0.7): """ Ajusta el rango de similitud e imputa valores faltantes en los parámetros de df_filtrado. @@ -17,6 +17,7 @@ def imputacion_similitud_con_rango(df_filtrado, df_procesado, rango_min, rango_m :param df_procesado: DataFrame procesado con todos los datos. :return: Nuevo DataFrame con los valores imputados. """ + print(f"Rango mínimo: {rango_min}, Rango máximo: {rango_max}, Confianza mínima: {nivel_confianza_min}") # Crear una copia para mantener intacto el DataFrame original df_resultado_por_similitud = df_filtrado.copy() From 6fd500e518e59806ac3a8194bfb56808c6af1063 Mon Sep 17 00:00:00 2001 From: Delpoo Date: Thu, 10 Apr 2025 19:53:02 -0300 Subject: [PATCH 3/9] automated for debugging --- .vscode/launch.json | 23 + ADRpy/analisis/Modulos/config_and_loading.py | 24 +- .../analisis/Modulos/correlation_analysis.py | 39 +- .../Modulos/correlation_imputation.py | 14 +- ADRpy/analisis/Modulos/data_processing.py | 127 +- ADRpy/analisis/Modulos/excel_export.py | 2 +- ADRpy/analisis/Modulos/imputation_loop.py | 96 +- ADRpy/analisis/Modulos/imputation_utils.py | 34 + .../analisis/Modulos/similarity_imputation.py | 45 +- ADRpy/analisis/Modulos/user_interaction.py | 132 +- ADRpy/analisis/Results/Datos_imputados.xlsx | Bin 0 -> 50391 bytes ADRpy/analisis/aaa.ipynb | 33004 +++++++++++++++- ADRpy/analisis/archivo_imputaciones.xlsx | Bin 0 -> 50353 bytes ADRpy/analisis/main.py | 102 +- 14 files changed, 33323 insertions(+), 319 deletions(-) create mode 100644 ADRpy/analisis/Modulos/imputation_utils.py create mode 100644 ADRpy/analisis/Results/Datos_imputados.xlsx create mode 100644 ADRpy/analisis/archivo_imputaciones.xlsx diff --git a/.vscode/launch.json b/.vscode/launch.json index 85b64e34..040e905e 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -32,6 +32,29 @@ "request": "launch", "program": "${workspaceFolder}/custom_script.py", "args": ["--custom-arg", "value"], "console": "integratedTerminal" + }, + { + "name": "🚀 Debug ADRpy automatizado", + "type": "debugpy", + "request": "launch", + "program": "${workspaceFolder}/ADRpy/analisis/main.py", + "console": "integratedTerminal", + "justMyCode": false, + "args": [ + "--ruta_archivo", "${workspaceFolder}/ADRpy/analisis/Data/Datos_aeronaves.xlsx", + "--archivo_destino", "${workspaceFolder}/ADRpy/analisis/Results/archivo_salida.xlsx", + "--debug_mode", + "--umbral_heat_map", "0.7", + "--nivel_confianza_min_similitud", "0.5", + "--rango_min", "0.85", + "--rango_max", "1.15", + "--parametros", "4, 5, 7, 8, 9, 12, 13, 14, 15, 16, 18, 19, 20, 25", + "--columna", "Stalker XE", + "--umbral_correlacion", "0.5", + "--nivel_confianza_min_correlacion", "0.5" + ] + } + ] } \ No newline at end of file diff --git a/ADRpy/analisis/Modulos/config_and_loading.py b/ADRpy/analisis/Modulos/config_and_loading.py index 0cf27d97..9efb2c11 100644 --- a/ADRpy/analisis/Modulos/config_and_loading.py +++ b/ADRpy/analisis/Modulos/config_and_loading.py @@ -1,7 +1,7 @@ import pandas as pd import tkinter as tk from tkinter import simpledialog, messagebox -from openpyxl import load_workbook +import sys def configurar_entorno(max_rows=20, max_columns=10): @@ -12,20 +12,26 @@ def configurar_entorno(max_rows=20, max_columns=10): """ pd.set_option("display.max_rows", max_rows) pd.set_option("display.max_columns", max_columns) - + def cargar_datos(ruta_archivo=None): """ Carga los datos desde un archivo Excel y realiza validaciones. Devuelve el DataFrame cargado y la ruta utilizada. """ - # Solicitar al usuario la ruta del archivo si no se proporciona - if ruta_archivo is None: - ruta_archivo = input( - r"Ingrese la ruta del archivo Excel original (o presione Enter para usar 'C:\Users\delpi\OneDrive\Tesis\ADRpy-VTOL\ADRpy\analisis\data\Datos_aeronaves.xlsx'): " - ).strip() - if not ruta_archivo: - ruta_archivo = r"C:\Users\delpi\OneDrive\Tesis\ADRpy-VTOL\ADRpy\analisis\data\Datos_aeronaves.xlsx" # Asignar valor predeterminado +# Detectar si se está ejecutando en modo debug + modo_debug = "--debug_mode" in sys.argv + + if not ruta_archivo: + if modo_debug: + ruta_archivo = r"C:\Users\delpi\OneDrive\Tesis\ADRpy-VTOL\ADRpy\analisis\Data\Datos_aeronaves.xlsx" + print(f"DEBUG MODE ACTIVADO: usando ruta predeterminada: {ruta_archivo}") + else: + ruta_archivo = input( + r"Ingrese la ruta del archivo Excel original (o presione Enter para usar la predeterminada): " + ).strip() or r"C:\Users\delpi\OneDrive\Tesis\ADRpy-VTOL\ADRpy\analisis\Data\Datos_aeronaves.xlsx" + + print(f"DEBUG: ruta_archivo antes de validar: '{ruta_archivo}'") # Validar el formato del archivo if not ruta_archivo.endswith((".xlsx", ".xlsm")): diff --git a/ADRpy/analisis/Modulos/correlation_analysis.py b/ADRpy/analisis/Modulos/correlation_analysis.py index 89ebd64b..b7690c6e 100644 --- a/ADRpy/analisis/Modulos/correlation_analysis.py +++ b/ADRpy/analisis/Modulos/correlation_analysis.py @@ -6,13 +6,8 @@ from .user_interaction import solicitar_umbral -def calcular_correlaciones_y_generar_heatmap_con_resumen( - df_procesado, - parametros_seleccionados, - umbral=None, - valor_por_defecto=0.7, - devolver_tabla=False, -): +def calcular_correlaciones_y_generar_heatmap_con_resumen(df_procesado, parametros_seleccionados, umbral_heat_map=None, devolver_tabla=False): + valor_por_defecto = 0.7 """ Calcula las correlaciones completas y filtradas entre variables seleccionadas, genera tablas en HTML con un resumen agregado, y crea un heatmap. @@ -45,10 +40,11 @@ def agregar_resumen_a_tabla(tabla, titulo): convertir_a_html(resumen, titulo="Resumen de la Tabla", mostrar=True) try: - # === Paso 1: Obtener umbral desde el argumento o pedirlo al usuario === - if umbral is None: - umbral = solicitar_umbral(valor_por_defecto) - print(f"\nUmbral seleccionado para correlaciones significativas: {umbral}") + # === Paso 1: Obtener umbral_heat_map desde el argumento o pedirlo al usuario === + if umbral_heat_map is None: + umbral_heat_map = solicitar_umbral(valor_por_defecto) + + print(f"\nUmbral seleccionado para correlaciones significativas: {umbral_heat_map}") # === Validación de parámetros seleccionados === parametros_no_encontrados = [ @@ -67,11 +63,11 @@ def agregar_resumen_a_tabla(tabla, titulo): "Tabla de Correlaciones con todos los parametros(tabla_completa)", ) - # Filtrar correlaciones por el umbral + # Filtrar correlaciones por el umbral_heat_map tabla_completa_significativa = tabla_completa[ - (tabla_completa.abs() >= umbral) & (tabla_completa != 1) + (tabla_completa.abs() >= umbral_heat_map) & (tabla_completa != 1) ] - # agregar_resumen_a_tabla(tabla_completa_significativa.round(3), f"Tabla de Correlaciones Significativas (Umbral >= {umbral})") + # agregar_resumen_a_tabla(tabla_completa_significativa.round(3), f"Tabla de Correlaciones Significativas (Umbral >= {umbral_heat_map})") # === Filtrar datos seleccionados === print("\n=== Filtrando datos seleccionados ===") @@ -86,13 +82,13 @@ def agregar_resumen_a_tabla(tabla, titulo): "Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)", ) - # Filtrar correlaciones por el umbral para la tabla filtrada + # Filtrar correlaciones por el umbral_heat_map para la tabla filtrada tabla_filtrada_significativa = tabla_filtrada[ - (tabla_filtrada.abs() >= umbral) & (tabla_filtrada != 1) + (tabla_filtrada.abs() >= umbral_heat_map) & (tabla_filtrada != 1) ] agregar_resumen_a_tabla( tabla_filtrada_significativa.round(3), - f"Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= {umbral})", + f"Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= {umbral_heat_map})", ) # Preparar datos para el heatmap @@ -116,7 +112,7 @@ def agregar_resumen_a_tabla(tabla, titulo): vmax=1, ) plt.title( - f"Heatmap de Correlaciones de Variables Seleccionadas (Umbral >= {umbral})" + f"Heatmap de Correlaciones de Variables Seleccionadas (Umbral >= {umbral_heat_map})" ) plt.show() @@ -127,4 +123,9 @@ def agregar_resumen_a_tabla(tabla, titulo): f"Error: {e}. Asegúrate de que las variables seleccionadas existen en los datos." ) - return tabla_completa + if devolver_tabla: + return tabla_completa + else: + return None + + diff --git a/ADRpy/analisis/Modulos/correlation_imputation.py b/ADRpy/analisis/Modulos/correlation_imputation.py index 252e460a..9dbe7dba 100644 --- a/ADRpy/analisis/Modulos/correlation_imputation.py +++ b/ADRpy/analisis/Modulos/correlation_imputation.py @@ -11,11 +11,11 @@ def Imputacion_por_correlacion( df_correlacion, parametros_preseleccionados, tabla_completa, + parametros_seleccionados, min_datos_validos=5, max_lineas_consola=250, umbral_correlacion=0.7, nivel_confianza_min_correlacion=0.5, - reduccion_confianza=0.05 ): # Lógica de la función """ @@ -55,7 +55,13 @@ def Imputacion_por_correlacion( # === PASO 1: CÁLCULO DE CORRELACIONES === print("\n=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===") - tabla_completa = calcular_correlaciones_y_generar_heatmap_con_resumen(df, parametros_seleccionados, valor_por_defecto=0.7) + tabla_completa = tabla_completa = calcular_correlaciones_y_generar_heatmap_con_resumen( + df, + parametros_seleccionados, + umbral_heat_map=umbral_correlacion, + devolver_tabla=True +) + correlaciones = tabla_completa.copy() indices_validos = df.index @@ -75,7 +81,7 @@ def Imputacion_por_correlacion( # Mostrar correlaciones aceptables en HTML convertir_a_html( datos_procesados=correlaciones_aceptables, - titulo="Tabla de correlaciones con filtro de umbral", + titulo="Tabla de correlaciones con filtro de umbral de correlación", mostrar=True ) #print("Parámetros disponibles en el índice del DataFrame:") @@ -199,7 +205,7 @@ def evaluar_confianza(puntaje): else: print("La columna 'Nivel de Confianza' no está presente en df_reporte.") # Maneja el caso sin filtro, por ejemplo: - return df_procesado, [] + return df, [] # Resumen de imputaciones diff --git a/ADRpy/analisis/Modulos/data_processing.py b/ADRpy/analisis/Modulos/data_processing.py index 560cd863..5d5fcd99 100644 --- a/ADRpy/analisis/Modulos/data_processing.py +++ b/ADRpy/analisis/Modulos/data_processing.py @@ -114,110 +114,47 @@ def procesar_datos_y_manejar_duplicados(df, respuesta_global=None): raise ValueError(f"Error durante el procesamiento y manejo de duplicados: {e}") -def mostrar_celdas_faltantes_con_seleccion(df): +def mostrar_celdas_faltantes_con_seleccion(df, columna_seleccionada=None, debug_mode=False): """ - Permite al usuario seleccionar una columna para analizar y muestra las celdas faltantes. - Si el usuario no selecciona ninguna columna, utiliza una columna predeterminada. - Maneja columnas con más de 26 posiciones generando etiquetas en formato Excel. - :param df: DataFrame procesado. - :return: DataFrame con los detalles de las celdas faltantes (si las hay). - """ - - def seleccionar_columna(df, columna_numero=None): - """ - Permite al usuario seleccionar una columna específica para validar. - Si no se selecciona ninguna, retorna la primera columna como predeterminada. - :param df: DataFrame a analizar - :param columna_numero: (opcional) número de columna a seleccionar automáticamente (modo debug) - """ - columnas_dict = {i + 1: col for i, col in enumerate(df.columns)} - - if columna_numero is not None: - if columna_numero not in columnas_dict: - raise ValueError("Número de columna predefinido fuera de rango.") - return columnas_dict[columna_numero] - - opciones_texto = "\n".join( - [f"{num}: {col}" for num, col in columnas_dict.items()] - ) - - try: - seleccion_usuario = simpledialog.askstring( - "Selección de columna", - f"Selecciona el número correspondiente a la columna que deseas validar:\n\n{opciones_texto}", - ) - - if not seleccion_usuario: - print( - "No se seleccionó ninguna columna. Usando la primera columna como predeterminada." - ) - return df.columns[0] - - seleccion_usuario = int(seleccion_usuario) - - if seleccion_usuario not in columnas_dict: - raise ValueError("Número ingresado fuera del rango válido.") + Muestra las celdas faltantes de una columna específica elegida por el usuario o automáticamente. - return columnas_dict[seleccion_usuario] - - except ValueError as e: - print(f"Error: {e}. Finalizando la ejecución.") - exit() - - def indice_a_columna_excel(indice): - """ - Convierte un índice numérico de columna en una etiqueta al estilo Excel (A, B, ..., Z, AA, AB, ...). - :param indice: Índice numérico de la columna (0 para A, 1 para B, ..., 25 para Z, 26 para AA, etc.). - :return: Etiqueta de columna en formato Excel. - """ - etiqueta = "" - while indice >= 0: - etiqueta = chr(indice % 26 + ord("A")) + etiqueta - indice = indice // 26 - 1 - return etiqueta - - try: - # Selección de columna - columna_prueba = seleccionar_columna(df) + :param df: DataFrame a analizar. + :param columna_seleccionada: Nombre de la columna a analizar. Si None, se pedirá al usuario o se usará el modo automático. + :param debug_mode: Si True, selecciona automáticamente la primera columna con datos faltantes si no se pasa ninguna. + :return: DataFrame con las celdas faltantes de la columna seleccionada. + """ + columnas_con_nulos = df.columns[df.isnull().any()].tolist() - # Identificar celdas faltantes en la columna seleccionada - print( - f"\n=== Analizando celdas faltantes en la columna: '{columna_prueba}' ===" - ) - missing_indices = df[df[columna_prueba].isna()].index.tolist() + if not columnas_con_nulos: + print("✅ No hay columnas con valores faltantes.") + return pd.DataFrame() - if not missing_indices: - print( - f"No se encontraron valores faltantes en la columna '{columna_prueba}'." - ) - return pd.DataFrame() # Devuelve un DataFrame vacío si no hay faltantes + if debug_mode and not columna_seleccionada: + columna_seleccionada = columnas_con_nulos[0] + print(f"[DEBUG] Seleccionando automáticamente la primera columna con nulos: '{columna_seleccionada}'") - # Crear un DataFrame para almacenar los resultados - resultados = [] + elif not columna_seleccionada: + print("\n=== Columnas con datos faltantes ===") + for i, col in enumerate(columnas_con_nulos, start=1): + print(f"{i}. {col}") - for idx in missing_indices: - fila_excel = ( - df.index.get_loc(idx) + 2 - ) # +2 para ajustarse al formato Excel (encabezado en fila 1) - columna_excel = indice_a_columna_excel(df.columns.get_loc(columna_prueba)) - celda_excel = f"{columna_excel}{fila_excel}" - resultados.append( - { - "Índice": idx, - "Celda": celda_excel, - "Columna": columna_prueba, - "Valor Actual": "NaN", - } - ) + seleccion = input("Selecciona el número de la columna a analizar (presiona Enter para seleccionar la primera): ").strip() - # Convertir resultados a DataFrame - df_resultados = pd.DataFrame(resultados) + if not seleccion.isdigit(): + print("🔁 Entrada inválida o vacía. Seleccionando la primera columna por defecto.") + columna_seleccionada = columnas_con_nulos[0] + else: + seleccion = int(seleccion) - 1 + if 0 <= seleccion < len(columnas_con_nulos): + columna_seleccionada = columnas_con_nulos[seleccion] + else: + print("🔁 Número fuera de rango. Seleccionando la primera columna por defecto.") + columna_seleccionada = columnas_con_nulos[0] - return df_resultados + print(f"\n=== Analizando celdas faltantes en la columna: '{columna_seleccionada}' ===") + celdas_faltantes = df[df[columna_seleccionada].isnull()][[columna_seleccionada]] - except Exception as e: - print(f"Error al analizar celdas faltantes: {e}") - raise + return celdas_faltantes def generar_resumen_faltantes( diff --git a/ADRpy/analisis/Modulos/excel_export.py b/ADRpy/analisis/Modulos/excel_export.py index 05216195..510ba196 100644 --- a/ADRpy/analisis/Modulos/excel_export.py +++ b/ADRpy/analisis/Modulos/excel_export.py @@ -6,7 +6,7 @@ -def exportar_excel_con_imputaciones(archivo_origen, df_procesado, resumen_imputaciones, archivo_destino="archivo_imputaciones.xlsx"): +def exportar_excel_con_imputaciones(archivo_origen, df_procesado, resumen_imputaciones, archivo_destino= r"C:\Users\delpi\OneDrive\Tesis\ADRpy-VTOL\ADRpy\analisis\Results\Datos_imputados.xlsx"): """ Exporta el DataFrame procesado a un archivo Excel manteniendo el formato original. Agrega colores y comentarios a las celdas imputadas por similitud y correlación. diff --git a/ADRpy/analisis/Modulos/imputation_loop.py b/ADRpy/analisis/Modulos/imputation_loop.py index be466cee..a0331d94 100644 --- a/ADRpy/analisis/Modulos/imputation_loop.py +++ b/ADRpy/analisis/Modulos/imputation_loop.py @@ -4,14 +4,22 @@ from .html_utils import convertir_a_html from .data_processing import generar_resumen_faltantes - def bucle_imputacion_similitud_correlacion( df_procesado, + df_filtrado, parametros_preseleccionados, tabla_completa, + parametros_seleccionados, + umbral_correlacion=0.7, + rango_min=0.85, + rango_max=1.15, + nivel_confianza_min_similitud=0.7, + max_iteraciones=10, reduccion_confianza=0.05, - max_iteraciones=7, + nivel_confianza_min_correlacion=None, + debug_mode=False ): + """ Realiza un bucle alternando imputaciones por similitud y correlación, consolidando los resultados. Ahora se evita actualizar los DataFrames inmediatamente, y se eligen las imputaciones finales @@ -45,72 +53,38 @@ def bucle_imputacion_similitud_correlacion( # Configuración inicial para imputaciones por similitud print("\n=== Configuración Inicial ===") - try: - rango_min = ( - float( - input("Ingrese el rango mínimo de MTOW (1-200, predeterminado 85): ") - or 85 - ) - / 100 - ) - rango_max = ( - float( - input("Ingrese el rango máximo de MTOW (1-200, predeterminado 115): ") - or 115 - ) - / 100 - ) - nivel_confianza_min = float( - input("Ingrese el nivel mínimo de confianza (0-1, predeterminado 0.5): ") - or 0.5 - ) + + from Modulos.user_interaction import ( + solicitar_rango_min, + solicitar_rango_max, + solicitar_confianza_min_similitud, + ) + + rango_min = rango_min if debug_mode else solicitar_rango_min() + rango_max = rango_max if debug_mode else solicitar_rango_max() + nivel_confianza_min_similitud = nivel_confianza_min_similitud if debug_mode else solicitar_confianza_min_similitud() - if not (0.01 <= rango_min <= 2.00 and 0.01 <= rango_max <= 2.00): - raise ValueError("Los rangos deben estar entre 1% y 200%.") - if rango_min >= rango_max: - raise ValueError( - "El rango mínimo no puede ser mayor o igual al rango máximo." - ) - if not (0 <= nivel_confianza_min <= 1): - raise ValueError("El nivel de confianza debe estar entre 0 y 1.") - except ValueError as e: - print( - f"Error: {e}. Usando valores predeterminados (85% mínimo, 115% máximo, 0.5 confianza mínima)." - ) - rango_min, rango_max, nivel_confianza_min = 0.85, 1.15, 0.5 print( - f"\nValores configurados: Rango MTOW [{rango_min*100:.0f}% - {rango_max*100:.0f}%], Confianza Mínima: {nivel_confianza_min:.2f}" + f"\nValores configurados: Rango MTOW [{rango_min*100:.0f}% - {rango_max*100:.0f}%], Confianza Mínima: {nivel_confianza_min_similitud:.2f}" ) # Configuración inicial para imputaciones por correlación - try: - umbral_correlacion = float( - input("Ingrese el umbral mínimo de correlación (0-1, predeterminado 0.7): ") - or 0.7 - ) - nivel_confianza_min_correlacion = float( - input( - "Ingrese el nivel mínimo de confianza para correlación (0-1, predeterminado 0.5): " - ) - or 0.5 - ) + from Modulos.user_interaction import ( + solicitar_umbral_correlacion, + solicitar_confianza_min_correlacion, + ) - if not (0 <= umbral_correlacion <= 1): - raise ValueError("El umbral de correlación debe estar entre 0 y 1.") - if not (0 <= nivel_confianza_min_correlacion <= 1): - raise ValueError("El nivel de confianza debe estar entre 0 y 1.") - except ValueError as e: - print( - f"Error: {e}. Usando valores predeterminados (umbral = 0.7, confianza mínima = 0.5)." - ) - umbral_correlacion, nivel_confianza_min_correlacion = 0.7, 0.5 + umbral_correlacion = ( + umbral_correlacion if debug_mode and umbral_correlacion is not None else solicitar_umbral_correlacion() + ) + nivel_confianza_min_correlacion = ( + nivel_confianza_min_correlacion if debug_mode and nivel_confianza_min_correlacion is not None + else solicitar_confianza_min_correlacion() + ) # Definir valores predeterminados para correlación min_datos_validos = 5 # Cantidad mínima de datos requeridos por parámetro - umbral_correlacion = 0.7 - nivel_confianza_min_correlacion = 0.5 - reduccion_confianza = 0.05 max_lineas_consola = 40000000 for iteracion in range(1, max_iteraciones + 1): @@ -137,7 +111,7 @@ def bucle_imputacion_similitud_correlacion( df_procesado=df_procesado_base, rango_min=rango_min, rango_max=rango_max, - nivel_confianza_min=nivel_confianza_min, + nivel_confianza_min_similitud=nivel_confianza_min_similitud, ) if reporte_similitud and len(reporte_similitud) > 0: @@ -165,11 +139,11 @@ def bucle_imputacion_similitud_correlacion( df_correlacion, parametros_preseleccionados, tabla_completa, + parametros_seleccionados, min_datos_validos=min_datos_validos, max_lineas_consola=max_lineas_consola, umbral_correlacion=umbral_correlacion, - nivel_confianza_min_correlacion=nivel_confianza_min_correlacion, - reduccion_confianza=reduccion_confianza, + nivel_confianza_min_correlacion=nivel_confianza_min_correlacion, ) if reporte_correlacion and len(reporte_correlacion) > 0: diff --git a/ADRpy/analisis/Modulos/imputation_utils.py b/ADRpy/analisis/Modulos/imputation_utils.py new file mode 100644 index 00000000..a141d073 --- /dev/null +++ b/ADRpy/analisis/Modulos/imputation_utils.py @@ -0,0 +1,34 @@ +import pandas as pd +from .html_utils import convertir_a_html + +def imprimir_detalles_imputacion(numero_valor_imputado, parametro, aeronave, mtow_actual, rango_min, rango_max, candidatas_validas, detalles_ajustes, valores_ajustados, valor_imputado, confianza, calculos_confianza): + """ + Imprime un resumen claro y organizado del proceso de imputación en consola. + """ + negrita = "\033[1m" + reset = "\033[0m" + + print(f"\n{negrita}======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #{numero_valor_imputado} ========================{reset}") + print(f"{negrita}Parámetro:{reset} {parametro}") + print(f"{negrita}Aeronave a imputar:{reset} {aeronave}") + print(f"{negrita}MTOW actual:{reset} {mtow_actual} kg") + print(f"{negrita}Rango Similitud:{reset} {rango_min*100:.0f}% - {rango_max*100:.0f}%") + print(f"{negrita}Candidatas dentro del rango:{reset} {', '.join(candidatas_validas.index)}") + + print("\nAeronaves Válidas para el Cálculo:") + print("-----------------------------------------------------------------------------------------------") + print("Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado") + print("------------|----------------|------------------------------|-------------------|----------------|---------------") + for detalle in detalles_ajustes: + print(f"{detalle['Aeronave']:12}| {detalle['MTOW Candidata']:14}| {detalle['Relación MTOW']:<30.3f}| {detalle['Ajuste Individual']:<19.4f}| {detalle['Valor Original']:<16.2f}| {detalle['Valor Ajustado']:<13.2f}") + print("-----------------------------------------------------------------------------------------------") + + print("\nCálculo del Valor Final:") + print(f"{negrita}- Se tomó la mediana de los valores ajustados {valores_ajustados} = {valor_imputado:.2f}{reset}") + print(f"{negrita}- Nivel de Confianza calculado:{reset} {confianza:.2f}") + print(f"{negrita}- Valor Imputado Final:{reset} {valor_imputado:.2f}") + + print("\nDetalle del Cálculo de Confianza:") + for key, value in calculos_confianza.items(): + print(f"{negrita}- {key}:{reset} {value:.2f}") + print(f"{negrita}============================================================================================{reset}\n") diff --git a/ADRpy/analisis/Modulos/similarity_imputation.py b/ADRpy/analisis/Modulos/similarity_imputation.py index 3095f961..960468e1 100644 --- a/ADRpy/analisis/Modulos/similarity_imputation.py +++ b/ADRpy/analisis/Modulos/similarity_imputation.py @@ -2,13 +2,13 @@ import numpy as np from sklearn.metrics import r2_score from .html_utils import convertir_a_html -from .imputation_loop import imprimir_detalles_imputacion # si la función está ahí, si no corregimos +from .imputation_utils import imprimir_detalles_imputacion -def imputacion_similitud_con_rango(df_filtrado, df_procesado, rango_min=0.85, rango_max=1.15, nivel_confianza_min=0.7): +def imputacion_similitud_con_rango(df_filtrado, df_procesado, rango_min=0.85, rango_max=1.15, nivel_confianza_min_similitud=0.7): """ Ajusta el rango de similitud e imputa valores faltantes en los parámetros de df_filtrado. @@ -17,7 +17,7 @@ def imputacion_similitud_con_rango(df_filtrado, df_procesado, rango_min=0.85, ra :param df_procesado: DataFrame procesado con todos los datos. :return: Nuevo DataFrame con los valores imputados. """ - print(f"Rango mínimo: {rango_min}, Rango máximo: {rango_max}, Confianza mínima: {nivel_confianza_min}") + print(f"Rango mínimo: {rango_min}, Rango máximo: {rango_max}, Confianza mínima: {nivel_confianza_min_similitud}") # Crear una copia para mantener intacto el DataFrame original df_resultado_por_similitud = df_filtrado.copy() @@ -209,7 +209,7 @@ def imputar_por_similitud(datos, parametro, aeronave, rango_min, rango_max, nume ) # Verificar si se realizó una imputación válida - if valor_imputado is not None and confianza is not None and confianza >= nivel_confianza_min: + if valor_imputado is not None and confianza is not None and confianza >= nivel_confianza_min_similitud: # Incrementar el contador SOLO aquí numero_valor_imputado += 1 # Asignar el valor imputado al DataFrame @@ -222,8 +222,8 @@ def imputar_por_similitud(datos, parametro, aeronave, rango_min, rango_max, nume "Nivel de Confianza": confianza }) - elif confianza is not None and confianza < nivel_confianza_min: - print(f"Imputación descartada por baja confianza: {confianza:.3f} < {nivel_confianza_min}.") + elif confianza is not None and confianza < nivel_confianza_min_similitud: + print(f"Imputación descartada por baja confianza: {confianza:.3f} < {nivel_confianza_min_similitud}.") else: print(f"No se pudo imputar: {parametro} para {aeronave}.") @@ -234,7 +234,7 @@ def imputar_por_similitud(datos, parametro, aeronave, rango_min, rango_max, nume if reporte_imputaciones: df_reporte = pd.DataFrame(reporte_imputaciones) #print("Contenido de reporte_imputaciones:", reporte_imputaciones) - df_reporte = df_reporte[df_reporte["Nivel de Confianza"] >= nivel_confianza_min] + df_reporte = df_reporte[df_reporte["Nivel de Confianza"] >= nivel_confianza_min_similitud] convertir_a_html(df_reporte, titulo="Reporte Final de Imputaciones", mostrar=True) else: print("No se realizaron imputaciones con el nivel de confianza aceptable.") @@ -263,34 +263,3 @@ def imputar_por_similitud(datos, parametro, aeronave, rango_min, rango_max, nume # Retornar DataFrame imputado y lista de diccionarios return df_resultado_final, reporte_imputaciones -def imprimir_detalles_imputacion(numero_valor_imputado, parametro, aeronave, mtow_actual, rango_min, rango_max, candidatas_validas, detalles_ajustes, valores_ajustados, valor_imputado, confianza, calculos_confianza): - """ - Imprime un resumen claro y organizado del proceso de imputación en consola. - """ - negrita = "\033[1m" - reset = "\033[0m" - - print(f"\n{negrita}======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #{numero_valor_imputado} ========================{reset}") - print(f"{negrita}Parámetro:{reset} {parametro}") - print(f"{negrita}Aeronave a imputar:{reset} {aeronave}") - print(f"{negrita}MTOW actual:{reset} {mtow_actual} kg") - print(f"{negrita}Rango Similitud:{reset} {rango_min*100:.0f}% - {rango_max*100:.0f}%") - print(f"{negrita}Candidatas dentro del rango:{reset} {', '.join(candidatas_validas.index)}") - - print("\nAeronaves Válidas para el Cálculo:") - print("-----------------------------------------------------------------------------------------------") - print("Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado") - print("------------|----------------|------------------------------|-------------------|----------------|---------------") - for detalle in detalles_ajustes: - print(f"{detalle['Aeronave']:12}| {detalle['MTOW Candidata']:14}| {detalle['Relación MTOW']:<30.3f}| {detalle['Ajuste Individual']:<19.4f}| {detalle['Valor Original']:<16.2f}| {detalle['Valor Ajustado']:<13.2f}") - print("-----------------------------------------------------------------------------------------------") - - print("\nCálculo del Valor Final:") - print(f"{negrita}- Se tomó la mediana de los valores ajustados {valores_ajustados} = {valor_imputado:.2f}{reset}") - print(f"{negrita}- Nivel de Confianza calculado:{reset} {confianza:.2f}") - print(f"{negrita}- Valor Imputado Final:{reset} {valor_imputado:.2f}") - - print("\nDetalle del Cálculo de Confianza:") - for key, value in calculos_confianza.items(): - print(f"{negrita}- {key}:{reset} {value:.2f}") - print(f"{negrita}============================================================================================{reset}\n") diff --git a/ADRpy/analisis/Modulos/user_interaction.py b/ADRpy/analisis/Modulos/user_interaction.py index 3af78070..35fb8b0a 100644 --- a/ADRpy/analisis/Modulos/user_interaction.py +++ b/ADRpy/analisis/Modulos/user_interaction.py @@ -1,54 +1,44 @@ -from tkinter import simpledialog - - - -def seleccionar_parametros_por_indices(elementos, predeterminados): - """ - Permite seleccionar parámetros desde los índices usando números en lugar de nombres. - :param elementos: Lista de nombres de parámetros disponibles (índices). - :param predeterminados: Lista de parámetros preseleccionados por defecto. - :return: Lista de parámetros seleccionados válidos. - """ +def seleccionar_parametros_por_indices(parametros_disponibles, parametros_preseleccionados, entrada_indices=None): print("\n=== Selección de Parámetros ===") print("Parámetros disponibles:") - for i, elem in enumerate(elementos, 1): - print(f"{i}. {elem}") + for i, parametro in enumerate(parametros_disponibles, 1): + print(f"{i}. {parametro}") - # Mostrar preseleccionados - preseleccion_indices = [elementos.index(param) + 1 for param in predeterminados if param in elementos] - print("\nPreseleccionados: ", ", ".join([f"{i}" for i in preseleccion_indices])) + print(f"\nPreseleccionados: {', '.join(str(parametros_disponibles.index(p) + 1) for p in parametros_preseleccionados)}") - # Entrada del usuario - seleccion = input("\nIngresa los números separados por coma (o presiona Enter para usar los preseleccionados): ") + if entrada_indices is None: + indices = input("Ingresa los números separados por coma (o presiona Enter para usar los preseleccionados): ") + else: + indices = entrada_indices - # Manejar casos según la entrada del usuario - if seleccion.strip(): # Si el usuario ingresó algo + if not indices.strip(): + seleccionados = parametros_preseleccionados + else: try: - indices_seleccionados = [int(num.strip()) - 1 for num in seleccion.split(",")] - except ValueError: - print("⚠️ Entrada inválida. Usando parámetros preseleccionados.") - indices_seleccionados = [i - 1 for i in preseleccion_indices] - else: # Si el usuario presiona Enter sin ingresar nada - indices_seleccionados = [i - 1 for i in preseleccion_indices] - - # Construir la lista de seleccionados a partir de los índices válidos - seleccionados = [elementos[i] for i in indices_seleccionados if 0 <= i < len(elementos)] + indices = [int(i.strip()) - 1 for i in indices.split(",")] + seleccionados = [parametros_disponibles[i] for i in indices] + except Exception as e: + print(f"Error al interpretar los índices: {e}") + seleccionados = parametros_preseleccionados - # Validar parámetros seleccionados contra elementos disponibles - seleccionados_validos = [p for p in seleccionados if p in elementos] - if len(seleccionados) > len(seleccionados_validos): - print(f"⚠️ Algunos parámetros seleccionados no son válidos y fueron eliminados: {set(seleccionados) - set(seleccionados_validos)}") + print("Parámetros seleccionados después de filtrar:") + print(seleccionados) + return seleccionados - # Retornar solo los parámetros válidos - return seleccionados_validos -def solicitar_umbral(valor_por_defecto=0.7): +def solicitar_umbral(valor_por_defecto=0.7, umbral_manual=None): """ Solicita al usuario ingresar un umbral para las correlaciones significativas. Si el usuario no proporciona un valor válido, se usa el valor por defecto. :param valor_por_defecto: Valor predeterminado del umbral si el usuario no ingresa ninguno. :return: Umbral de correlación como flotante. """ + if umbral_manual is not None: + if not (0 < umbral_manual < 1): + print(f"⚠️ El umbral_manual ({umbral_manual}) está fuera de rango. Se usará el valor por defecto ({valor_por_defecto})") + return valor_por_defecto + return umbral_manual + try: umbral = float(input(f"Ingrese el umbral mínimo de correlación significativa (por defecto {valor_por_defecto}): ") or valor_por_defecto) if not (0 < umbral < 1): @@ -56,4 +46,72 @@ def solicitar_umbral(valor_por_defecto=0.7): return umbral except ValueError as e: print(f"Valor inválido: {e}. Se usará el umbral por defecto de {valor_por_defecto}.") - return valor_por_defecto \ No newline at end of file + return valor_por_defecto + +def solicitar_rango_min(valor_por_defecto=0.85, valor=None): + try: + if valor is None: + valor = input(f"Ingrese el rango mínimo de MTOW (0-2, predeterminado {valor_por_defecto * 100:.0f}): ") + valor = float(valor) / 100 if valor else valor_por_defecto + if not (0.01 <= valor <= 2.00): + raise ValueError + return valor + except ValueError: + print("Valor inválido. Usando el valor predeterminado.") + return valor_por_defecto + + +def solicitar_rango_max(valor_por_defecto=1.15, valor=None): + try: + if valor is None: + valor = input(f"Ingrese el rango máximo de MTOW (0-2, predeterminado {valor_por_defecto * 100:.0f}): ") + valor = float(valor) / 100 if valor else valor_por_defecto + if not (0.01 <= valor <= 2.00): + raise ValueError + return valor + except ValueError: + print("Valor inválido. Usando el valor predeterminado.") + return valor_por_defecto + + +def solicitar_confianza_min_similitud(valor_por_defecto=0.5, valor=None): + try: + if valor is None: + valor = input(f"Ingrese el nivel mínimo de confianza (0-1, predeterminado {valor_por_defecto}): ") + valor = float(valor) if valor else valor_por_defecto + if not (0 <= valor <= 1): + raise ValueError + return valor + except ValueError: + print("Valor inválido. Usando el valor predeterminado.") + return valor_por_defecto + +def solicitar_umbral_correlacion(valor_por_defecto=0.7): + """ + Solicita al usuario el umbral mínimo de correlación. + Si no se proporciona, se utiliza un valor predeterminado. + """ + try: + valor = input(f"Ingrese el umbral mínimo de correlación (0-1, predeterminado {valor_por_defecto}): ") + umbral = float(valor) if valor else valor_por_defecto + if not 0 <= umbral <= 1: + raise ValueError("El umbral de correlación debe estar entre 0 y 1.") + return umbral + except ValueError: + print("Entrada inválida. Usando el valor predeterminado.") + return valor_por_defecto + +def solicitar_confianza_min_correlacion(valor_por_defecto=0.5): + """ + Solicita al usuario el nivel mínimo de confianza para correlación. + Si no se proporciona, se utiliza un valor predeterminado. + """ + try: + valor = input(f"Ingrese el nivel mínimo de confianza para correlación (0-1, predeterminado {valor_por_defecto}): ") + confianza = float(valor) if valor else valor_por_defecto + if not 0 <= confianza <= 1: + raise ValueError("El nivel de confianza debe estar entre 0 y 1.") + return confianza + except ValueError: + print("Entrada inválida. Usando el valor predeterminado.") + return valor_por_defecto diff --git a/ADRpy/analisis/Results/Datos_imputados.xlsx b/ADRpy/analisis/Results/Datos_imputados.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..338876317d2cbec21f06b09eca2ebc108a2e351e GIT binary patch literal 50391 zcmcG$bx_>P)-Fs!u;6YX5Zv9}2@u@f-3JDj;O_1g+$Bhm2@u@f-Q9JNkL+W6zvtYy z&aL|HAH)2nr>dv>S!+E@x)<`&knb=cARu5Ns58Sf#d~EwC%%3fetn_5zKrdR2qkKd3)`NFbVN9loq4OBJ(52wfP1Z>^h`tbAU zLy5^7QpC>bw`^FLdl3a^NFMkH2Xe!5w>&DDmVF~?@Q}X|euV7Gvzh%G$b`T-w9#0- zKSeKY1{_TicB$o7An)2xXXUP;AS_$@@E&E?os2&mU79PI2I?hTX%JH>YiGdj(HIH@ z^8;J{FI&hCQ66xB3H3|VDi zVziKS6FaT&Ii`lPdR{);&y%Xk8+)nqqjd{X>g3!o43%|jes8mnoUC}!owIC(s}Vji zu;(<78g+^~PIC>5c>Z9k)%Lt#BMLi5oIq*X^38{2tV3rah*kXZxdnI(5%X3E9`DOp z$kw~e+BX?DYB11dQYWoxie9U`UQAntBz`N&qg5jJUvLSVP-A8iF}`rFFC%TkBO^Z1@9{tg}91OD=AuN4zHe21!ns?bFIb5MBDQtdoJ*LJTjkjdxm zfoy5wLp0~0eZWSLqAHsBIqo@ZahYp-61e(9AowXU^Vt;o_~Pgq`LE#l%UJiNLPJ3G 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Stalker XE\n", + "2. Stalker VXE30\n", + "3. Aerosonde® Mk. 4.7 Fixed Wing\n", + "4. Aerosonde® Mk. 4.7 VTOL\n", + "5. Aerosonde® Mk. 4.8 Fixed wing\n", + "6. Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "7. AAI Aerosonde\n", + "8. Fulmar X\n", + "9. Orbiter 4\n", + "10. Orbiter 3\n", + "11. Mantis\n", + "12. ScanEagle\n", + "13. Integrator\n", + "14. Integrator VTOL\n", + "15. Integrator Extended Range (ER)\n", + "16. ScanEagle 3\n", + "17. RQNan21A Blackjack\n", + "18. DeltaQuad Evo\n", + "19. DeltaQuad Pro #MAP\n", + "20. DeltaQuad Pro #CARGO\n", + "21. V21\n", + "22. V25\n", + "23. V32\n", + "24. V35\n", + "25. V39\n", + "26. Volitation VT370\n", + "27. Skyeye 2600\n", + "28. Skyeye 2930 VTOL\n", + "29. Skyeye 3600\n", + "30. Skyeye 3600 VTOL\n", + "31. Skyeye 5000\n", + "32. Skyeye 5000 VTOL\n", + "33. Skyeye 5000 VTOL octo\n", + "34. Volitation VT510\n", + "35. Ascend\n", + "36. Transition\n", + "37. Reach\n", + "🔁 Entrada inválida o vacía. Seleccionando la primera columna por defecto.\n", + "\n", + "=== Analizando celdas faltantes en la columna: 'Stalker XE' ===\n" ] }, { @@ -5190,32 +5233,12 @@ " \n", " \n", " \n", - " Índice\n", - " Celda\n", - " Columna\n", - " Valor Actual\n", + " Stalker XE\n", " \n", " \n", " \n", " \n", - " 0\n", - " Alcance de la aeronave\n", - " C8\n", - " Aerosonde® Mk. 4.7 Fixed Wing\n", - " NaN\n", - " \n", - " \n", - " 1\n", - " Velocidad de pérdida (KCAS)\n", - " C11\n", - " Aerosonde® Mk. 4.7 Fixed Wing\n", - " NaN\n", - " \n", - " \n", - " 2\n", - " Empty weight\n", - " C15\n", - " Aerosonde® Mk. 4.7 Fixed Wing\n", + " Velocidad de pérdida (KCAS)\n", " NaN\n", " \n", " \n", @@ -5538,6 +5561,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "=== Calculando correlaciones y generando heatmap ===\n", "\n", "=== Calculando correlaciones y generando heatmap ===\n", "\n", @@ -9356,15 +9381,32924 @@ "output_type": "display_data" }, { - "ename": "NameError", - "evalue": "name 'df_filtrado' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32m~\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\main.py:126\u001b[0m\n\u001b[0;32m 117\u001b[0m tabla_completa \u001b[38;5;241m=\u001b[39m calcular_correlaciones_y_generar_heatmap_con_resumen(df_procesado, parametros_seleccionados)\n\u001b[0;32m 119\u001b[0m \u001b[38;5;66;03m# Paso 10: Ajustar rango e imputar valores faltantes\u001b[39;00m\n\u001b[0;32m 120\u001b[0m \u001b[38;5;66;03m#print(\"\\n=== Paso 8: Imputación con ajuste de rango ===\")\u001b[39;00m\n\u001b[0;32m 121\u001b[0m \u001b[38;5;66;03m#imputacion_similitud_con_rango(df_filtrado, df_procesado)\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 124\u001b[0m \n\u001b[0;32m 125\u001b[0m \u001b[38;5;66;03m# Paso 10: Llamar a la función principal\u001b[39;00m\n\u001b[1;32m--> 126\u001b[0m df_procesado_actualizado, resumen_imputaciones \u001b[38;5;241m=\u001b[39m bucle_imputacion_similitud_correlacion(df_procesado, parametros_preseleccionados, tabla_completa, reduccion_confianza\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.05\u001b[39m, max_iteraciones\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m7\u001b[39m)\n\u001b[0;32m 128\u001b[0m \u001b[38;5;66;03m# Paso 11: Exportar resultados a Excel\u001b[39;00m\n\u001b[0;32m 129\u001b[0m archivo_destino \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIngrese la ruta donde desea guardar el archivo con las imputaciones (incluya .xlsx): \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[1;32m~\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\config_and_loading.py:1022\u001b[0m, in \u001b[0;36mbucle_imputacion_similitud_correlacion\u001b[1;34m(df_procesado, parametros_preseleccionados, tabla_completa, reduccion_confianza, max_iteraciones)\u001b[0m\n\u001b[0;32m 1011\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1012\u001b[0m \u001b[38;5;124;03mRealiza un bucle alternando imputaciones por similitud y correlación, consolidando los resultados.\u001b[39;00m\n\u001b[0;32m 1013\u001b[0m \u001b[38;5;124;03mAhora se evita actualizar los DataFrames inmediatamente, y se eligen las imputaciones finales\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1018\u001b[0m \u001b[38;5;124;03m df_resumen (pd.DataFrame): Detalle consolidado de imputaciones realizadas.\u001b[39;00m\n\u001b[0;32m 1019\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1021\u001b[0m df_procesado_base \u001b[38;5;241m=\u001b[39m df_procesado\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;66;03m# Copia base del DataFrame original\u001b[39;00m\n\u001b[1;32m-> 1022\u001b[0m df_filtrado_base \u001b[38;5;241m=\u001b[39m df_filtrado\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;66;03m# Copia base del DataFrame original\u001b[39;00m\n\u001b[0;32m 1024\u001b[0m convertir_a_html(df_procesado_base, titulo\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdf_procesado_base\u001b[39m\u001b[38;5;124m\"\u001b[39m, ancho\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m100\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m, alto\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m400px\u001b[39m\u001b[38;5;124m\"\u001b[39m, mostrar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 1025\u001b[0m convertir_a_html(df_filtrado_base, titulo\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdf_filtrado_base\u001b[39m\u001b[38;5;124m\"\u001b[39m, ancho\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m100\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m, alto\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m400px\u001b[39m\u001b[38;5;124m\"\u001b[39m, mostrar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "\u001b[1;31mNameError\u001b[0m: name 'df_filtrado' is not defined" + "data": { + "text/html": [ + "\n", + " \n", + "

df_procesado_base

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Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Distancia de carrera requerida para despegue0.00.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
Tasa de ascensoNaNNaNNaNNaNNaNNaN2.49936NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5.0NaN5.0NaNNaNNaNNaNNaNNaNNaN5.0NaNNaNNaN
Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.0
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NaNNaN30.406584NaNNaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaNNaN17000.010000.013000.016000.0
Velocidad de pérdida limpia (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaNNaN25.0NaNNaNNaN
Área del ala0.871.1582831.551.551.55NaN0.57NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.712
Profundidad del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Ancho del fuselaje0.2110.20.2770.2770.277NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.3750.3750.375NaNNaNNaNNaN
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0NaNNaNNaNNaN3270.0800.0150.050.025.0NaNNaNNaN500.0NaN92.6270.0100.0100.0NaNNaNNaNNaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaNNaN
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0NaN30.0NaN33.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.0
Radio de giroNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100.0120.0150.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
envergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.352NaN0.196552NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
payload2.4947562.49475614.511.317.722.7NaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
duracion en VTOL2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050.050.05
Crucero KIAS15.4333216.09342225.025.025.0NaNNaN27.8NaNNaN16.728.028.3NaNNaN23.530.916.5416.016.018.020.020.025.025.025.033.024.0NaN30.033.028.035.030.020.020.025.0
RTF (dry weight)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.011.854.0
RTF (Including fuel & Batteries)NaNNaN27.742.236.770.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8.916.584.0
Empty weight10.88620817.463292NaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
Maximum CrosswindNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.0NaNNaNNaN45.050.050.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15.015.015.0
Rango de comunicación59.0161.0140.0140.0140.0NaN150.0NaN150.050.025.0101.8692.6NaNNaNNaN92.6NaN50.030.030.030.030.030.030.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Wing LoadingNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.524.025.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Potencia/PesoNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Capacidad combustibleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.0NaNNaN11.511.528.028.028.025.0NaNNaNNaN
ConsumoNaNNaN0.60.6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.96NaNNaNNaNNaN1.2NaNNaN5.0NaNNaNNaN
Potencia(W)NaNNaN2980.02980.0NaNNaN1280.0NaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Potencia(HP)NaNNaN4.04.0NaNNaN1.74NaNNaNNaNNaNNaNNaNNaNNaNNaN8.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
PrecioNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.0NaNNaNNaN
Tiempo de emergencia en vueloNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.1080.1080.108NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Distancia de aterrizajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Battery Power SupplyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Modelo Motor Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Modelo Motor VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
PortabilidadNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
CámaraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
DespegueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Datalink banksNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Motor recomendadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Hélice recomendada VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Hélice recomendada Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Sistema de controlNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Características adicionalesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
EmpresaNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
kjbkNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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df_filtrado_base

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Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NaNNaN30.406584NaNNaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaNNaN17000.010000.013000.016000.0
Área del ala0.871.1582831.551.551.55NaN0.57NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.712
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0NaNNaNNaNNaN3270.0800.0150.050.025.0NaNNaNNaN500.0NaN92.6270.0100.0100.0NaNNaNNaNNaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaNNaN
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0NaN30.0NaN33.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.0
envergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.352NaN0.196552NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
payload2.4947562.49475614.511.317.722.7NaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
Empty weight10.88620817.463292NaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Configuración Inicial ===\n", + "\n", + "Valores configurados: Rango MTOW [85% - 115%], Confianza Mínima: 0.50\n", + "\n", + "================================================================================\n", + "\u001b[1m=== INICIO DE ITERACIÓN 1 ===\u001b[0m\n", + "================================================================================\n", + "\n", + "=== Iteración 1: Resumen antes de imputaciones ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de Valores Faltantes Antes de Iteración 1

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ColumnaValores Faltantes
0Stalker XE33.000
1Stalker VXE3034.000
2Aerosonde® Mk. 4.7 Fixed Wing33.000
3Aerosonde® Mk. 4.7 VTOL32.000
4Aerosonde® Mk. 4.8 Fixed wing36.000
5Aerosonde® Mk. 4.8 VTOL FTUAS44.000
6AAI Aerosonde34.000
7Fulmar X42.000
8Orbiter 443.000
9Orbiter 343.000
10Mantis42.000
11ScanEagle41.000
12Integrator41.000
13Integrator VTOL45.000
14Integrator Extended Range (ER)43.000
15ScanEagle 341.000
16RQNan21A Blackjack39.000
17DeltaQuad Evo34.000
18DeltaQuad Pro #MAP38.000
19DeltaQuad Pro #CARGO38.000
20V2132.000
21V2532.000
22V3233.000
23V3538.000
24V3940.000
25Volitation VT37037.000
26Skyeye 260039.000
27Skyeye 2930 VTOL37.000
28Skyeye 360039.000
29Skyeye 3600 VTOL35.000
30Skyeye 500034.000
31Skyeye 5000 VTOL37.000
32Skyeye 5000 VTOL octo38.000
33Volitation VT51035.000
34Ascend35.000
35Transition35.000
36Reach35.000
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Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes1387.000
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Datos Filtrados por aeronaves seleccionadas antes de imputar(df_resultado_por_similitud)

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Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NaNNaN30.406584NaNNaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaNNaN17000.010000.013000.016000.0
Área del ala0.871.1582831.551.551.55NaN0.57NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.712
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0NaNNaNNaNNaN3270.0800.0150.050.025.0NaNNaNNaN500.0NaN92.6270.0100.0100.0NaNNaNNaNNaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaNNaN
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0NaN30.0NaN33.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.0
envergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.352NaN0.196552NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
payload2.4947562.49475614.511.317.722.7NaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
Empty weight10.88620817.463292NaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #0 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Volitation VT510, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 36.09 | 35.80 \n", + "Skyeye 5000 VTOL| 100.0| 1.075 | 0.0188 | 30.63 | 31.20 \n", + "Volitation VT510| 100.0| 1.075 | 0.0188 | 32.81 | 33.43 \n", + "Reach | 91.0| 0.978 | -0.0054 | 27.34 | 27.20 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [35.803064717345336, 31.201619674835236, 33.430306794466325, 27.19703938863795] = 32.32\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 32.32\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.11\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #1 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m AAI Aerosonde\n", + "\u001b[1mMTOW actual:\u001b[0m 13.1 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, V25, Skyeye 2600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker XE | 13.6| 1.038 | 0.0095 | 16.88 | 17.04 \n", + "V25 | 12.5| 0.954 | -0.0115 | 21.88 | 21.62 \n", + "Skyeye 2600 | 15.0| 1.145 | 0.0363 | 36.09 | 37.40 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [17.041451525056257, 21.624760478782665, 37.402903766342334] = 21.62\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.95\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 21.62\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.95\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #2 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 4\n", + "\u001b[1mMTOW actual:\u001b[0m 55.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing, RQNan21A Blackjack\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.973 | -0.0068 | 27.34 | 27.16 \n", + "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.989 | -0.0027 | 27.34 | 27.27 \n", + "RQNan21A Blackjack| 61.0| 1.109 | 0.0273 | 33.80 | 34.72 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [27.157613705003314, 27.26947572941752, 34.718989390850375] = 27.27\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.96\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 27.27\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.97\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.96\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Advertencia: Ponderación del modelo (-0.016) fuera de rango. Revisar lógica previa.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #3 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 3\n", + "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m ScanEagle 3, V35, Skyeye 2930 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "ScanEagle 3 | 36.3| 1.134 | 0.0336 | 25.70 | 26.57 \n", + "V35 | 32.0| 1.000 | 0.0000 | 27.34 | 27.34 \n", + "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 26.25 | 25.43 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [26.566881229546517, 27.344050412360318, 25.429966883495094] = 26.57\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.55\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 26.57\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.92\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.55\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.55\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #4 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Integrator VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 75.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Integrator | 74.8| 0.997 | -0.0007 | 30.95 | 30.93 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [30.93282942341402] = 30.93\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 30.93\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #5 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Integrator Extended Range (ER)\n", + "\u001b[1mMTOW actual:\u001b[0m 74.8 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Integrator | 74.8| 1.000 | 0.0000 | 30.95 | 30.95 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [30.953465066791882] = 30.95\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 30.95\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #6 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600\n", + "\u001b[1mMTOW actual:\u001b[0m 28.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m ScanEagle, V35, V39, Skyeye 2930 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "ScanEagle | 26.5| 0.946 | -0.0134 | 30.63 | 30.22 \n", + "V35 | 32.0| 1.143 | 0.0357 | 27.34 | 28.32 \n", + "V39 | 24.0| 0.857 | -0.0357 | 27.34 | 26.37 \n", + "Skyeye 2930 VTOL| 28.0| 1.000 | 0.0000 | 26.25 | 26.25 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [30.21517570565815, 28.32062364137318, 26.36747718334745, 26.250288395865905] = 27.34\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.97\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 27.34\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.11\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.92\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.81\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.88\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.97\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #7 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL octo\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Volitation VT510, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.900 | -0.0250 | 36.09 | 35.19 \n", + "Skyeye 5000 VTOL| 100.0| 1.000 | 0.0000 | 30.63 | 30.63 \n", + "Volitation VT510| 100.0| 1.000 | 0.0000 | 32.81 | 32.81 \n", + "Reach | 91.0| 0.910 | -0.0225 | 27.34 | 26.73 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [35.19179288070773, 30.625336461843556, 32.812860494832385, 26.72880927808221] = 31.72\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 31.72\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.11\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.97\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #8 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", + "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 4\n", + "\u001b[1mMTOW actual:\u001b[0m 55.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing, RQNan21A Blackjack\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.973 | -0.0068 | 9700.00 | 9633.86 \n", + "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.989 | -0.0027 | 18200.00 | 18150.36 \n", + "RQNan21A Blackjack| 61.0| 1.109 | 0.0273 | 20.00 | 20.55 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [9633.863636363636, 18150.363636363636, 20.545454545454547] = 9633.86\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.97\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 9633.86\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.97\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #9 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", + "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 3\n", + "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m ScanEagle 3, V35\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "ScanEagle 3 | 36.3| 1.134 | 0.0336 | 20.00 | 20.67 \n", + "V35 | 32.0| 1.000 | 0.0000 | 16000.00 | 16000.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [20.671875, 16000.0] = 8010.34\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.86\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 8010.34\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.86\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #10 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", + "\u001b[1mAeronave a imputar:\u001b[0m Mantis\n", + "\u001b[1mMTOW actual:\u001b[0m 6.5 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m DeltaQuad Pro #MAP, DeltaQuad Pro #CARGO\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "DeltaQuad Pro #MAP| 6.2| 0.954 | -0.0115 | 13.12 | 12.97 \n", + "DeltaQuad Pro #CARGO| 6.2| 0.954 | -0.0115 | 13.12 | 12.97 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [12.97158076923077, 12.97158076923077] = 12.97\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.51\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.97\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.57\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.51\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #11 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", + "\u001b[1mAeronave a imputar:\u001b[0m Integrator VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 75.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator, Integrator Extended Range (ER)\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Integrator | 74.8| 0.997 | -0.0007 | 19500.00 | 19487.00 \n", + "Integrator Extended Range (ER)| 74.8| 0.997 | -0.0007 | 19500.00 | 19487.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [19487.0, 19487.0] = 19487.00\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.53\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 19487.00\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.60\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.53\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #12 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2600\n", + "\u001b[1mMTOW actual:\u001b[0m 15.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker XE | 13.6| 0.907 | -0.0233 | 12000.00 | 11720.00 \n", + "AAI Aerosonde| 13.1| 0.873 | -0.0317 | 15000.00 | 14525.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [11720.0, 14524.999999999998] = 13122.50\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.81\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 13122.50\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.93\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.91\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.81\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #13 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2930 VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 28.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m ScanEagle, V35, V39\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "ScanEagle | 26.5| 0.946 | -0.0134 | 19500.00 | 19238.84 \n", + "V35 | 32.0| 1.143 | 0.0357 | 16000.00 | 16571.43 \n", + "V39 | 24.0| 0.857 | -0.0357 | 16000.00 | 15428.57 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [19238.839285714286, 16571.42857142857, 15428.57142857143] = 16571.43\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.90\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 16571.43\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.89\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.91\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.90\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.90\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #14 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600\n", + "\u001b[1mMTOW actual:\u001b[0m 28.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m ScanEagle, V35, V39\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "ScanEagle | 26.5| 0.946 | -0.0134 | 19500.00 | 19238.84 \n", + "V35 | 32.0| 1.143 | 0.0357 | 16000.00 | 16571.43 \n", + "V39 | 24.0| 0.857 | -0.0357 | 16000.00 | 15428.57 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [19238.839285714286, 16571.42857142857, 15428.57142857143] = 16571.43\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.90\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 16571.43\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.89\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.91\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.90\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.90\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #15 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600 VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing, ScanEagle 3, Volitation VT370\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 Fixed Wing| 42.2| 1.055 | 0.0138 | 14700.00 | 14902.12 \n", + "ScanEagle 3 | 36.3| 0.907 | -0.0231 | 20.00 | 19.54 \n", + "Volitation VT370| 40.0| 1.000 | 0.0000 | 17000.00 | 17000.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [14902.124999999998, 19.537499999999998, 17000.0] = 14902.12\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.97\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 14902.12\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.97\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #16 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000\n", + "\u001b[1mMTOW actual:\u001b[0m 90.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS, Volitation VT510, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 1.033 | 0.0083 | 15000.00 | 15125.00 \n", + "Volitation VT510| 100.0| 1.111 | 0.0278 | 17000.00 | 17472.22 \n", + "Reach | 91.0| 1.011 | 0.0028 | 16000.00 | 16044.44 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [15125.0, 17472.22222222222, 16044.444444444443] = 16044.44\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.92\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 16044.44\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.88\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.92\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.92\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #17 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS, Volitation VT510, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 0.930 | -0.0175 | 15000.00 | 14737.50 \n", + "Volitation VT510| 100.0| 1.000 | 0.0000 | 17000.00 | 17000.00 \n", + "Reach | 91.0| 0.910 | -0.0225 | 16000.00 | 15640.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [14737.5, 17000.0, 15640.0] = 15640.00\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.93\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 15640.00\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.90\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.93\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.93\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #18 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL octo\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS, Volitation VT510, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 0.930 | -0.0175 | 15000.00 | 14737.50 \n", + "Volitation VT510| 100.0| 1.000 | 0.0000 | 17000.00 | 17000.00 \n", + "Reach | 91.0| 0.910 | -0.0225 | 16000.00 | 15640.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [14737.5, 17000.0, 15640.0] = 15640.00\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.93\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 15640.00\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.90\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.93\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.93\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #19 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Skyeye 5000 VTOL octo\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 2.62 | 2.59 \n", + "Skyeye 5000 VTOL| 100.0| 1.075 | 0.0188 | 2.62 | 2.66 \n", + "Skyeye 5000 VTOL octo| 100.0| 1.075 | 0.0188 | 2.62 | 2.66 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [2.593911290322581, 2.664206989247312, 2.664206989247312] = 2.66\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.56\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 2.66\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.56\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.56\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #20 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Fulmar X\n", + "\u001b[1mMTOW actual:\u001b[0m 20.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker VXE30| 19.958047999999998| 0.998 | -0.0005 | 1.16 | 1.16 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [1.15767579628992] = 1.16\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1.16\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #21 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 4\n", + "\u001b[1mMTOW actual:\u001b[0m 55.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.973 | -0.0068 | 1.55 | 1.54 \n", + "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.989 | -0.0027 | 1.55 | 1.55 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [1.5394318181818183, 1.5457727272727273] = 1.54\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.52\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1.54\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.59\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.52\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #22 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 3\n", + "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 1.00 | 0.97 \n", + "Skyeye 3600 | 28.0| 0.875 | -0.0312 | 1.33 | 1.29 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [0.96875, 1.2884375000000001] = 1.13\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.81\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1.13\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.88\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.95\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.91\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.81\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Mantis'para el parametro 'Área del ala'.\n", + "No se pudo imputar: Área del ala para Mantis.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #23 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle\n", + "\u001b[1mMTOW actual:\u001b[0m 26.5 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 2930 VTOL| 28.0| 1.057 | 0.0142 | 1.00 | 1.01 \n", + "Skyeye 3600 | 28.0| 1.057 | 0.0142 | 1.33 | 1.35 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [1.0141509433962264, 1.348820754716981] = 1.18\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.86\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1.18\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.86\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator'para el parametro 'Área del ala'.\n", + "No se pudo imputar: Área del ala para Integrator.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator VTOL'para el parametro 'Área del ala'.\n", + "No se pudo imputar: Área del ala para Integrator VTOL.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator Extended Range (ER)'para el parametro 'Área del ala'.\n", + "No se pudo imputar: Área del ala para Integrator Extended Range (ER).\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #24 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle 3\n", + "\u001b[1mMTOW actual:\u001b[0m 36.3 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 3600 VTOL| 40.0| 1.102 | 0.0255 | 1.32 | 1.35 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [1.3536363636363637] = 1.35\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1.35\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #25 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m RQNan21A Blackjack\n", + "\u001b[1mMTOW actual:\u001b[0m 61.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.877 | -0.0307 | 1.55 | 1.50 \n", + "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.892 | -0.0270 | 1.55 | 1.51 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [1.5023565573770492, 1.5080737704918032] = 1.51\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.48\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1.51\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.89\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.53\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.48\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Imputación descartada por baja confianza: 0.475 < 0.5.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #MAP'para el parametro 'Área del ala'.\n", + "No se pudo imputar: Área del ala para DeltaQuad Pro #MAP.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #CARGO'para el parametro 'Área del ala'.\n", + "No se pudo imputar: Área del ala para DeltaQuad Pro #CARGO.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V32'para el parametro 'Área del ala'.\n", + "No se pudo imputar: Área del ala para V32.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #25 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m V35\n", + "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 1.00 | 0.97 \n", + "Skyeye 3600 | 28.0| 0.875 | -0.0312 | 1.33 | 1.29 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [0.96875, 1.2884375000000001] = 1.13\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.81\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1.13\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.88\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.95\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.91\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.81\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V39'para el parametro 'Área del ala'.\n", + "No se pudo imputar: Área del ala para V39.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #26 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT370\n", + "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing, Skyeye 3600 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 Fixed Wing| 42.2| 1.055 | 0.0138 | 1.55 | 1.57 \n", + "Skyeye 3600 VTOL| 40.0| 1.000 | 0.0000 | 1.32 | 1.32 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [1.5713125, 1.32] = 1.45\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.87\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1.45\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.97\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.98\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.87\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #27 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT510\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Skyeye 5000 VTOL octo\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.900 | -0.0250 | 2.62 | 2.55 \n", + "Skyeye 5000 VTOL| 100.0| 1.000 | 0.0000 | 2.62 | 2.62 \n", + "Skyeye 5000 VTOL octo| 100.0| 1.000 | 0.0000 | 2.62 | 2.62 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [2.5496250000000003, 2.615, 2.615] = 2.62\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 2.62\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.97\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.58\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #28 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Ascend\n", + "\u001b[1mMTOW actual:\u001b[0m 9.5 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m DeltaQuad Evo, V21\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "DeltaQuad Evo| 10.0| 1.053 | 0.0132 | 0.84 | 0.85 \n", + "V21 | 10.0| 1.053 | 0.0132 | 0.80 | 0.81 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [0.8510526315789473, 0.8105263157894737] = 0.83\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.76\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 0.83\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.71\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.85\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.76\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #29 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Transition\n", + "\u001b[1mMTOW actual:\u001b[0m 18.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker VXE30| 19.958047999999998| 1.109 | 0.0272 | 1.16 | 1.19 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [1.1897828403221333] = 1.19\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.57\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1.19\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.57\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #30 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Área del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Reach\n", + "\u001b[1mMTOW actual:\u001b[0m 91.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Skyeye 5000 VTOL octo\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.989 | -0.0027 | 2.62 | 2.61 \n", + "Skyeye 5000 VTOL| 100.0| 1.099 | 0.0247 | 2.62 | 2.68 \n", + "Skyeye 5000 VTOL octo| 100.0| 1.099 | 0.0247 | 2.62 | 2.68 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [2.6078159340659344, 2.6796565934065932, 2.6796565934065932] = 2.68\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.56\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 2.68\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.56\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.56\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #31 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Fulmar X\n", + "\u001b[1mMTOW actual:\u001b[0m 20.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker VXE30| 19.958047999999998| 0.998 | -0.0005 | 15.33 | 15.32 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [15.318411644882964] = 15.32\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 15.32\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #32 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 4\n", + "\u001b[1mMTOW actual:\u001b[0m 55.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.973 | -0.0068 | 12.50 | 12.41 \n", + "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.989 | -0.0027 | 12.50 | 12.47 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [12.414772727272728, 12.465909090909093] = 12.44\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.52\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.44\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.59\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.52\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Orbiter 3'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para Orbiter 3.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Mantis'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para Mantis.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'ScanEagle'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para ScanEagle.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para Integrator.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator VTOL'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para Integrator VTOL.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator Extended Range (ER)'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para Integrator Extended Range (ER).\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'ScanEagle 3'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para ScanEagle 3.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #33 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m RQNan21A Blackjack\n", + "\u001b[1mMTOW actual:\u001b[0m 61.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.877 | -0.0307 | 12.50 | 12.12 \n", + "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.892 | -0.0270 | 12.50 | 12.16 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [12.11577868852459, 12.161885245901642] = 12.14\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.48\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.14\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.89\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.53\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.48\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Imputación descartada por baja confianza: 0.475 < 0.5.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Evo'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para DeltaQuad Evo.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #MAP'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para DeltaQuad Pro #MAP.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #CARGO'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para DeltaQuad Pro #CARGO.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V21'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para V21.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #33 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m V25\n", + "\u001b[1mMTOW actual:\u001b[0m 12.5 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker XE | 13.6| 1.088 | 0.0220 | 15.30 | 15.64 \n", + "AAI Aerosonde| 13.1| 1.048 | 0.0120 | 14.75 | 14.93 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [15.637882845188285, 14.93143859649123] = 15.28\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.51\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 15.28\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.03\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.57\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.51\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V32'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para V32.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V35'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para V35.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V39'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para V39.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #34 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT370\n", + "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 Fixed Wing| 42.2| 1.055 | 0.0138 | 12.50 | 12.67 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [12.671875000000002] = 12.67\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.67\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Advertencia: Ponderación del modelo (-1.312) fuera de rango. Revisar lógica previa.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #35 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2600\n", + "\u001b[1mMTOW actual:\u001b[0m 15.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker XE | 13.6| 0.907 | -0.0233 | 15.30 | 14.94 \n", + "AAI Aerosonde| 13.1| 0.873 | -0.0317 | 14.75 | 14.29 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [14.944225941422594, 14.28716374269006] = 14.62\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.48\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 14.62\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.54\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.48\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Imputación descartada por baja confianza: 0.478 < 0.5.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 2930 VTOL'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para Skyeye 2930 VTOL.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 3600'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para Skyeye 3600.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #35 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600 VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 Fixed Wing| 42.2| 1.055 | 0.0138 | 12.50 | 12.67 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [12.671875000000002] = 12.67\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.67\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #36 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000\n", + "\u001b[1mMTOW actual:\u001b[0m 90.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 1.033 | 0.0083 | 12.50 | 12.60 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [12.604166666666668] = 12.60\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.61\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.60\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.97\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.78\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.61\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #37 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 0.930 | -0.0175 | 12.50 | 12.28 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [12.281250000000002] = 12.28\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.59\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.28\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.76\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.59\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #38 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL octo\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 0.930 | -0.0175 | 12.50 | 12.28 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [12.281250000000002] = 12.28\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.59\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.28\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.76\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.59\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #39 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT510\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 0.930 | -0.0175 | 12.50 | 12.28 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [12.281250000000002] = 12.28\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.59\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.28\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.76\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.59\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Ascend'para el parametro 'Relación de aspecto del ala'.\n", + "No se pudo imputar: Relación de aspecto del ala para Ascend.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #40 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Transition\n", + "\u001b[1mMTOW actual:\u001b[0m 18.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker VXE30| 19.958047999999998| 1.109 | 0.0272 | 15.33 | 15.74 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [15.743253313648973] = 15.74\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.57\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 15.74\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.57\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #41 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", + "\u001b[1mAeronave a imputar:\u001b[0m Reach\n", + "\u001b[1mMTOW actual:\u001b[0m 91.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 1.022 | 0.0055 | 12.50 | 12.57 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [12.568681318681321] = 12.57\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.61\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.57\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.79\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.61\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #42 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Longitud del fuselaje\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Skyeye 5000 VTOL octo, Volitation VT510, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 3.50 | 3.47 \n", + "Skyeye 5000 VTOL| 100.0| 1.075 | 0.0188 | 3.50 | 3.57 \n", + "Skyeye 5000 VTOL octo| 100.0| 1.075 | 0.0188 | 3.50 | 3.57 \n", + "Volitation VT510| 100.0| 1.075 | 0.0188 | 2.90 | 2.96 \n", + "Reach | 91.0| 0.978 | -0.0054 | 4.71 | 4.69 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [3.471774193548387, 3.5658602150537635, 3.5658602150537635, 2.9596639784946235, 4.6866666666666665] = 3.57\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 3.57\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.22\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #43 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Longitud del fuselaje\n", + "\u001b[1mAeronave a imputar:\u001b[0m Integrator VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 75.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator, Integrator Extended Range (ER)\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Integrator | 74.8| 0.997 | -0.0007 | 2.50 | 2.50 \n", + "Integrator Extended Range (ER)| 74.8| 0.997 | -0.0007 | 2.50 | 2.50 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [2.498333333333333, 2.498333333333333] = 2.50\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.53\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 2.50\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.60\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.53\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #44 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Longitud del fuselaje\n", + "\u001b[1mAeronave a imputar:\u001b[0m V39\n", + "\u001b[1mMTOW actual:\u001b[0m 24.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m ScanEagle, V32\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "ScanEagle | 26.5| 1.104 | 0.0260 | 1.71 | 1.75 \n", + "V32 | 23.5| 0.979 | -0.0052 | 1.00 | 0.99 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [1.75453125, 0.9947916666666666] = 1.37\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.85\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1.37\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.85\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #45 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", + "\u001b[1mMTOW actual:\u001b[0m 42.2 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 3600 VTOL| 40.0| 0.948 | -0.0130 | 300.00 | 296.09 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [296.09004739336496] = 296.09\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 296.09\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #46 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.7 VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 53.5 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 4, RQNan21A Blackjack\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Orbiter 4 | 55.0| 1.028 | 0.0070 | 150.00 | 151.05 \n", + "RQNan21A Blackjack| 61.0| 1.140 | 0.0350 | 92.60 | 95.85 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [151.05140186915887, 95.84532710280374] = 123.45\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.84\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 123.45\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.92\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.84\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #47 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 Fixed wing\n", + "\u001b[1mMTOW actual:\u001b[0m 54.4 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 4, RQNan21A Blackjack\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Orbiter 4 | 55.0| 1.011 | 0.0028 | 150.00 | 150.41 \n", + "RQNan21A Blackjack| 61.0| 1.121 | 0.0303 | 92.60 | 95.41 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [150.41360294117646, 95.40863970588234] = 122.91\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.85\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 122.91\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.85\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #48 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 VTOL| 100.0| 1.075 | 0.0188 | 800.00 | 815.05 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [815.0537634408602] = 815.05\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.59\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 815.05\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.76\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.59\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'ScanEagle'para el parametro 'Alcance de la aeronave'.\n", + "No se pudo imputar: Alcance de la aeronave para ScanEagle.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #49 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Integrator\n", + "\u001b[1mMTOW actual:\u001b[0m 74.8 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator Extended Range (ER)\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Integrator Extended Range (ER)| 74.8| 1.000 | 0.0000 | 500.00 | 500.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [500.0] = 500.00\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 500.00\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #50 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Integrator VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 75.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator Extended Range (ER)\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Integrator Extended Range (ER)| 74.8| 0.997 | -0.0007 | 500.00 | 499.67 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [499.66666666666663] = 499.67\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 499.67\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #51 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle 3\n", + "\u001b[1mMTOW actual:\u001b[0m 36.3 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 3, Skyeye 3600 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Orbiter 3 | 32.0| 0.882 | -0.0296 | 50.00 | 48.52 \n", + "Skyeye 3600 VTOL| 40.0| 1.102 | 0.0255 | 300.00 | 307.64 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [48.519283746556475, 307.6446280991736] = 178.08\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.83\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 178.08\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.94\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.83\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #52 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m V21\n", + "\u001b[1mMTOW actual:\u001b[0m 10.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m DeltaQuad Evo\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "DeltaQuad Evo| 10.0| 1.000 | 0.0000 | 270.00 | 270.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [270.0] = 270.00\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 270.00\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #53 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m V25\n", + "\u001b[1mMTOW actual:\u001b[0m 12.5 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker XE | 13.6| 1.088 | 0.0220 | 370.00 | 378.14 \n", + "AAI Aerosonde| 13.1| 1.048 | 0.0120 | 3270.00 | 3309.24 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [378.14, 3309.2400000000002] = 1843.69\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.85\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1843.69\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.85\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #54 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m V32\n", + "\u001b[1mMTOW actual:\u001b[0m 23.5 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Fulmar X\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Fulmar X | 20.0| 0.851 | -0.0372 | 800.00 | 770.21 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [770.2127659574468] = 770.21\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.56\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 770.21\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.86\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.72\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.56\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #55 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m V35\n", + "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 3\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Orbiter 3 | 32.0| 1.000 | 0.0000 | 50.00 | 50.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [50.0] = 50.00\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 50.00\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V39'para el parametro 'Alcance de la aeronave'.\n", + "No se pudo imputar: Alcance de la aeronave para V39.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #56 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT370\n", + "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 3600 VTOL| 40.0| 1.000 | 0.0000 | 300.00 | 300.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [300.0] = 300.00\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 300.00\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #57 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2600\n", + "\u001b[1mMTOW actual:\u001b[0m 15.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker XE | 13.6| 0.907 | -0.0233 | 370.00 | 361.37 \n", + "AAI Aerosonde| 13.1| 0.873 | -0.0317 | 3270.00 | 3166.45 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [361.3666666666667, 3166.45] = 1763.91\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.83\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1763.91\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.94\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.83\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #58 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2930 VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 28.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 3\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Orbiter 3 | 32.0| 1.143 | 0.0357 | 50.00 | 51.79 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [51.78571428571428] = 51.79\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.56\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 51.79\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.87\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.72\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.56\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #59 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600\n", + "\u001b[1mMTOW actual:\u001b[0m 28.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 3\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Orbiter 3 | 32.0| 1.143 | 0.0357 | 50.00 | 51.79 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [51.78571428571428] = 51.79\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.56\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 51.79\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.87\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.72\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.56\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #60 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000\n", + "\u001b[1mMTOW actual:\u001b[0m 90.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 VTOL| 100.0| 1.111 | 0.0278 | 800.00 | 822.22 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [822.2222222222222] = 822.22\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.57\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 822.22\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.89\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.57\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #61 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL octo\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 VTOL| 100.0| 1.000 | 0.0000 | 800.00 | 800.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [800.0] = 800.00\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 800.00\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #62 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT510\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 VTOL| 100.0| 1.000 | 0.0000 | 800.00 | 800.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [800.0] = 800.00\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 800.00\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #63 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Ascend\n", + "\u001b[1mMTOW actual:\u001b[0m 9.5 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m DeltaQuad Evo\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "DeltaQuad Evo| 10.0| 1.053 | 0.0132 | 270.00 | 273.55 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [273.55263157894734] = 273.55\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 273.55\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #64 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Transition\n", + "\u001b[1mMTOW actual:\u001b[0m 18.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30, Fulmar X\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker VXE30| 19.958047999999998| 1.109 | 0.0272 | 433.00 | 444.78 \n", + "Fulmar X | 20.0| 1.111 | 0.0278 | 800.00 | 822.22 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [444.7754831111111, 822.2222222222222] = 633.50\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.83\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 633.50\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.93\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.83\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #65 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Reach\n", + "\u001b[1mMTOW actual:\u001b[0m 91.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 VTOL| 100.0| 1.099 | 0.0247 | 800.00 | 819.78 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [819.7802197802197] = 819.78\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 819.78\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.91\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #66 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Autonomía de la aeronave\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL octo\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS, Skyeye 5000, Skyeye 5000 VTOL, Volitation VT510, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 0.930 | -0.0175 | 14.00 | 13.76 \n", + "Skyeye 5000 | 90.0| 0.900 | -0.0250 | 8.00 | 7.80 \n", + "Skyeye 5000 VTOL| 100.0| 1.000 | 0.0000 | 8.00 | 8.00 \n", + "Volitation VT510| 100.0| 1.000 | 0.0000 | 5.00 | 5.00 \n", + "Reach | 91.0| 0.910 | -0.0225 | 20.00 | 19.55 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [13.755, 7.8, 8.0, 5.0, 19.55] = 8.00\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 8.00\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.22\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #67 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Skyeye 5000 VTOL octo, Volitation VT510, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 42.00 | 41.66 \n", + "Skyeye 5000 VTOL| 100.0| 1.075 | 0.0188 | 42.00 | 42.79 \n", + "Skyeye 5000 VTOL octo| 100.0| 1.075 | 0.0188 | 38.00 | 38.72 \n", + "Volitation VT510| 100.0| 1.075 | 0.0188 | 50.00 | 50.94 \n", + "Reach | 91.0| 0.978 | -0.0054 | 35.00 | 34.81 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [41.66129032258065, 42.79032258064516, 38.71505376344086, 50.94086021505376, 34.81182795698925] = 41.66\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 41.66\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.22\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #68 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Integrator VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 75.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator, Integrator Extended Range (ER)\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Integrator | 74.8| 0.997 | -0.0007 | 46.30 | 46.27 \n", + "Integrator Extended Range (ER)| 74.8| 0.997 | -0.0007 | 46.30 | 46.27 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [46.26913333333333, 46.26913333333333] = 46.27\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.53\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 46.27\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.60\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.53\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #69 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m DeltaQuad Evo\n", + "\u001b[1mMTOW actual:\u001b[0m 10.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m V21, Ascend\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "V21 | 10.0| 1.000 | 0.0000 | 33.00 | 33.00 \n", + "Ascend | 9.5| 0.950 | -0.0125 | 30.00 | 29.62 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [33.0, 29.625] = 31.31\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.86\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 31.31\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.97\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.86\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #70 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m DeltaQuad Pro #MAP\n", + "\u001b[1mMTOW actual:\u001b[0m 6.2 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Mantis\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Mantis | 6.5| 1.048 | 0.0121 | 25.60 | 25.91 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [25.909677419354843] = 25.91\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 25.91\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #71 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m DeltaQuad Pro #CARGO\n", + "\u001b[1mMTOW actual:\u001b[0m 6.2 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Mantis\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Mantis | 6.5| 1.048 | 0.0121 | 25.60 | 25.91 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [25.909677419354843] = 25.91\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 25.91\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #72 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2600\n", + "\u001b[1mMTOW actual:\u001b[0m 15.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker XE | 13.6| 0.907 | -0.0233 | 20.00 | 19.53 \n", + "AAI Aerosonde| 13.1| 0.873 | -0.0317 | 30.85 | 29.87 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [19.533333333333335, 29.86894348051936] = 24.70\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.83\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 24.70\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.93\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.83\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #73 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600\n", + "\u001b[1mMTOW actual:\u001b[0m 28.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 3, ScanEagle, V35, V39, Skyeye 2930 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Orbiter 3 | 32.0| 1.143 | 0.0357 | 36.00 | 37.29 \n", + "ScanEagle | 26.5| 0.946 | -0.0134 | 41.20 | 40.65 \n", + "V35 | 32.0| 1.143 | 0.0357 | 33.00 | 34.18 \n", + "V39 | 24.0| 0.857 | -0.0357 | 33.00 | 31.82 \n", + "Skyeye 2930 VTOL| 28.0| 1.000 | 0.0000 | 30.00 | 30.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [37.28571428571428, 40.64821428571429, 34.17857142857142, 31.821428571428573, 30.0] = 34.18\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 34.18\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.22\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.91\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.93\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.92\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #74 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Stalker XE\n", + "\u001b[1mMTOW actual:\u001b[0m 13.6 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m V25, Skyeye 2600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "V25 | 12.5| 0.919 | -0.0202 | 15.50 | 15.19 \n", + "Skyeye 2600 | 15.0| 1.103 | 0.0257 | 10.00 | 10.26 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [15.18658088235294, 10.257352941176471] = 12.72\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.84\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.72\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.91\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.94\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.84\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #75 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Stalker VXE30\n", + "\u001b[1mMTOW actual:\u001b[0m 19.958047999999998 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Transition\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Transition | 18.0| 0.902 | -0.0245 | 13.00 | 12.68 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [12.68114837683525] = 12.68\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.68\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.91\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #76 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", + "\u001b[1mMTOW actual:\u001b[0m 42.2 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 3600 VTOL| 40.0| 0.948 | -0.0130 | 24.00 | 23.69 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [23.687203791469194] = 23.69\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 23.69\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.7 VTOL'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Aerosonde® Mk. 4.7 VTOL.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.8 Fixed wing'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Aerosonde® Mk. 4.8 Fixed wing.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #77 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL octo, Volitation VT510, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 15.00 | 14.88 \n", + "Skyeye 5000 VTOL octo| 100.0| 1.075 | 0.0188 | 24.00 | 24.45 \n", + "Volitation VT510| 100.0| 1.075 | 0.0188 | 25.00 | 25.47 \n", + "Reach | 91.0| 0.978 | -0.0054 | 13.00 | 12.93 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [14.879032258064516, 24.451612903225808, 25.47043010752688, 12.93010752688172] = 19.67\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 19.67\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.11\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #78 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m AAI Aerosonde\n", + "\u001b[1mMTOW actual:\u001b[0m 13.1 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m V25, Skyeye 2600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "V25 | 12.5| 0.954 | -0.0115 | 15.50 | 15.32 \n", + "Skyeye 2600 | 15.0| 1.145 | 0.0363 | 10.00 | 10.36 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [15.322519083969466, 10.36259541984733] = 12.84\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.84\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.84\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.91\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.94\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.84\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #79 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Fulmar X\n", + "\u001b[1mMTOW actual:\u001b[0m 20.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Transition\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Transition | 18.0| 0.900 | -0.0250 | 13.00 | 12.67 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [12.674999999999999] = 12.67\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 12.67\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Orbiter 4'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Orbiter 4.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #80 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 3\n", + "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 18.00 | 17.44 \n", + "Skyeye 3600 | 28.0| 0.875 | -0.0312 | 12.50 | 12.11 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [17.4375, 12.109375] = 14.77\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.82\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 14.77\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.88\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.97\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.92\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.82\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Mantis'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Mantis.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #81 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle\n", + "\u001b[1mMTOW actual:\u001b[0m 26.5 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m V32, Skyeye 2930 VTOL, Skyeye 3600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "V32 | 23.5| 0.887 | -0.0283 | 17.00 | 16.52 \n", + "Skyeye 2930 VTOL| 28.0| 1.057 | 0.0142 | 18.00 | 18.25 \n", + "Skyeye 3600 | 28.0| 1.057 | 0.0142 | 12.50 | 12.68 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [16.5188679245283, 18.254716981132074, 12.67688679245283] = 16.52\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.95\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 16.52\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.95\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Integrator.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator VTOL'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Integrator VTOL.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator Extended Range (ER)'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Integrator Extended Range (ER).\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #82 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle 3\n", + "\u001b[1mMTOW actual:\u001b[0m 36.3 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 3600 VTOL| 40.0| 1.102 | 0.0255 | 24.00 | 24.61 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [24.611570247933887] = 24.61\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 24.61\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'RQNan21A Blackjack'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para RQNan21A Blackjack.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #83 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m DeltaQuad Evo\n", + "\u001b[1mMTOW actual:\u001b[0m 10.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m V21, Ascend\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "V21 | 10.0| 1.000 | 0.0000 | 14.00 | 14.00 \n", + "Ascend | 9.5| 0.950 | -0.0125 | 13.00 | 12.84 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [14.0, 12.8375] = 13.42\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.86\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 13.42\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.95\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.86\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #MAP'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para DeltaQuad Pro #MAP.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #CARGO'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para DeltaQuad Pro #CARGO.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #84 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m V35\n", + "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 18.00 | 17.44 \n", + "Skyeye 3600 | 28.0| 0.875 | -0.0312 | 12.50 | 12.11 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [17.4375, 12.109375] = 14.77\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.82\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 14.77\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.88\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.97\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.92\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.82\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #85 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m V39\n", + "\u001b[1mMTOW actual:\u001b[0m 24.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m V32\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "V32 | 23.5| 0.979 | -0.0052 | 17.00 | 16.91 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [16.911458333333332] = 16.91\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.61\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 16.91\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.79\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.61\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #86 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT370\n", + "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 3600 VTOL| 40.0| 1.000 | 0.0000 | 24.00 | 24.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [24.0] = 24.00\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 24.00\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #87 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL octo, Volitation VT510, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.900 | -0.0250 | 15.00 | 14.62 \n", + "Skyeye 5000 VTOL octo| 100.0| 1.000 | 0.0000 | 24.00 | 24.00 \n", + "Volitation VT510| 100.0| 1.000 | 0.0000 | 25.00 | 25.00 \n", + "Reach | 91.0| 0.910 | -0.0225 | 13.00 | 12.71 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [14.625, 24.0, 25.0, 12.7075] = 19.31\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 19.31\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.11\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #88 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m envergadura\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Skyeye 5000 VTOL octo, Volitation VT510, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 5.00 | 4.96 \n", + "Skyeye 5000 VTOL| 100.0| 1.075 | 0.0188 | 5.00 | 5.09 \n", + "Skyeye 5000 VTOL octo| 100.0| 1.075 | 0.0188 | 5.00 | 5.09 \n", + "Volitation VT510| 100.0| 1.075 | 0.0188 | 5.10 | 5.20 \n", + "Reach | 91.0| 0.978 | -0.0054 | 6.00 | 5.97 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [4.959677419354839, 5.094086021505376, 5.094086021505376, 5.195967741935483, 5.967741935483871] = 5.09\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 5.09\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.22\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.96\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #89 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m envergadura\n", + "\u001b[1mAeronave a imputar:\u001b[0m Integrator VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 75.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator, Integrator Extended Range (ER)\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Integrator | 74.8| 0.997 | -0.0007 | 4.80 | 4.80 \n", + "Integrator Extended Range (ER)| 74.8| 0.997 | -0.0007 | 4.80 | 4.80 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [4.796799999999999, 4.796799999999999] = 4.80\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.53\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 4.80\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.60\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.53\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.8 VTOL FTUAS'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Aerosonde® Mk. 4.8 VTOL FTUAS.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #90 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Cuerda\n", + "\u001b[1mAeronave a imputar:\u001b[0m Fulmar X\n", + "\u001b[1mMTOW actual:\u001b[0m 20.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker VXE30| 19.958047999999998| 0.998 | -0.0005 | 0.32 | 0.32 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [0.318028178521024] = 0.32\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 0.32\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #91 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Cuerda\n", + "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 4\n", + "\u001b[1mMTOW actual:\u001b[0m 55.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.973 | -0.0068 | 0.35 | 0.35 \n", + "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.989 | -0.0027 | 0.35 | 0.35 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [0.34959999999999997, 0.35104] = 0.35\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.52\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 0.35\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.59\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.52\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Orbiter 3'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Orbiter 3.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Mantis'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Mantis.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'ScanEagle'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para ScanEagle.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Integrator.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator VTOL'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Integrator VTOL.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator Extended Range (ER)'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Integrator Extended Range (ER).\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'ScanEagle 3'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para ScanEagle 3.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #92 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Cuerda\n", + "\u001b[1mAeronave a imputar:\u001b[0m RQNan21A Blackjack\n", + "\u001b[1mMTOW actual:\u001b[0m 61.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.877 | -0.0307 | 0.35 | 0.34 \n", + "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.892 | -0.0270 | 0.35 | 0.34 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [0.34118032786885244, 0.3424786885245901] = 0.34\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.48\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 0.34\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.89\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.53\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.48\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Imputación descartada por baja confianza: 0.475 < 0.5.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Evo'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para DeltaQuad Evo.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #MAP'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para DeltaQuad Pro #MAP.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #CARGO'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para DeltaQuad Pro #CARGO.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V21'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para V21.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #92 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Cuerda\n", + "\u001b[1mAeronave a imputar:\u001b[0m V25\n", + "\u001b[1mMTOW actual:\u001b[0m 12.5 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker XE | 13.6| 1.088 | 0.0220 | 0.24 | 0.24 \n", + "AAI Aerosonde| 13.1| 1.048 | 0.0120 | 0.20 | 0.20 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [0.244258, 0.19891034482758618] = 0.22\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.84\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 0.22\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.96\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.84\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V32'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para V32.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V35'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para V35.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V39'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para V39.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #93 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Cuerda\n", + "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT370\n", + "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 Fixed Wing| 42.2| 1.055 | 0.0138 | 0.35 | 0.36 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [0.35683999999999994] = 0.36\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 0.36\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #94 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Cuerda\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2600\n", + "\u001b[1mMTOW actual:\u001b[0m 15.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker XE | 13.6| 0.907 | -0.0233 | 0.24 | 0.23 \n", + "AAI Aerosonde| 13.1| 0.873 | -0.0317 | 0.20 | 0.19 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [0.23342333333333332, 0.19032758620689652] = 0.21\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.81\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 0.21\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.92\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.91\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.81\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 2930 VTOL'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Skyeye 2930 VTOL.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 3600'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Skyeye 3600.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #95 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Cuerda\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600 VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Aerosonde® Mk. 4.7 Fixed Wing| 42.2| 1.055 | 0.0138 | 0.35 | 0.36 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [0.35683999999999994] = 0.36\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 0.36\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 5000'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Skyeye 5000.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 5000 VTOL'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Skyeye 5000 VTOL.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 5000 VTOL octo'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Skyeye 5000 VTOL octo.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Volitation VT510'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Volitation VT510.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Ascend'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Ascend.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #96 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Cuerda\n", + "\u001b[1mAeronave a imputar:\u001b[0m Transition\n", + "\u001b[1mMTOW actual:\u001b[0m 18.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker VXE30| 19.958047999999998| 1.109 | 0.0272 | 0.32 | 0.33 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [0.3268483894678044] = 0.33\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.57\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 0.33\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.57\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Reach'para el parametro 'Cuerda'.\n", + "No se pudo imputar: Cuerda para Reach.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #97 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m payload\n", + "\u001b[1mAeronave a imputar:\u001b[0m AAI Aerosonde\n", + "\u001b[1mMTOW actual:\u001b[0m 13.1 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, V25, Skyeye 2600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker XE | 13.6| 1.038 | 0.0095 | 2.49 | 2.52 \n", + "V25 | 12.5| 0.954 | -0.0115 | 2.20 | 2.17 \n", + "Skyeye 2600 | 15.0| 1.145 | 0.0363 | 4.00 | 4.15 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [2.518560923664122, 2.174809160305344, 4.145038167938932] = 2.52\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.95\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 2.52\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.95\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #98 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m payload\n", + "\u001b[1mAeronave a imputar:\u001b[0m Fulmar X\n", + "\u001b[1mMTOW actual:\u001b[0m 20.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30, Transition\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker VXE30| 19.958047999999998| 0.998 | -0.0005 | 2.49 | 2.49 \n", + "Transition | 18.0| 0.900 | -0.0250 | 1.50 | 1.46 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [2.4934477499536, 1.4625] = 1.98\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.86\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1.98\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.86\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #99 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m payload\n", + "\u001b[1mAeronave a imputar:\u001b[0m Mantis\n", + "\u001b[1mMTOW actual:\u001b[0m 6.5 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m DeltaQuad Pro #MAP, DeltaQuad Pro #CARGO\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "DeltaQuad Pro #MAP| 6.2| 0.954 | -0.0115 | 1.20 | 1.19 \n", + "DeltaQuad Pro #CARGO| 6.2| 0.954 | -0.0115 | 1.20 | 1.19 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [1.1861538461538461, 1.1861538461538461] = 1.19\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.51\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 1.19\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.57\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.51\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #100 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Empty weight\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", + "\u001b[1mMTOW actual:\u001b[0m 42.2 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 3600 VTOL| 40.0| 0.948 | -0.0130 | 11.00 | 10.86 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [10.856635071090047] = 10.86\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 10.86\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.7 VTOL'para el parametro 'Empty weight'.\n", + "No se pudo imputar: Empty weight para Aerosonde® Mk. 4.7 VTOL.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.8 Fixed wing'para el parametro 'Empty weight'.\n", + "No se pudo imputar: Empty weight para Aerosonde® Mk. 4.8 Fixed wing.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #101 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Empty weight\n", + "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL octo, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 32.00 | 31.74 \n", + "Skyeye 5000 VTOL octo| 100.0| 1.075 | 0.0188 | 35.00 | 35.66 \n", + "Reach | 91.0| 0.978 | -0.0054 | 31.00 | 30.83 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [31.741935483870968, 35.65860215053763, 30.833333333333332] = 31.74\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.95\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 31.74\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.96\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.94\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.95\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #102 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Empty weight\n", + "\u001b[1mAeronave a imputar:\u001b[0m Fulmar X\n", + "\u001b[1mMTOW actual:\u001b[0m 20.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30, Transition\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Stalker VXE30| 19.958047999999998| 0.998 | -0.0005 | 17.46 | 17.45 \n", + "Transition | 18.0| 0.900 | -0.0250 | 5.80 | 5.65 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [17.4541342496752, 5.654999999999999] = 11.55\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.86\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 11.55\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.86\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Orbiter 4'para el parametro 'Empty weight'.\n", + "No se pudo imputar: Empty weight para Orbiter 4.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #103 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Empty weight\n", + "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 3\n", + "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 7.10 | 6.88 \n", + "Skyeye 3600 | 28.0| 0.875 | -0.0312 | 11.50 | 11.14 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [6.878125, 11.140625] = 9.01\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.82\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 9.01\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.88\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.92\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.82\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Mantis'para el parametro 'Empty weight'.\n", + "No se pudo imputar: Empty weight para Mantis.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #104 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Empty weight\n", + "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle\n", + "\u001b[1mMTOW actual:\u001b[0m 26.5 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m V32, Skyeye 2930 VTOL, Skyeye 3600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "V32 | 23.5| 0.887 | -0.0283 | 6.45 | 6.27 \n", + "Skyeye 2930 VTOL| 28.0| 1.057 | 0.0142 | 7.10 | 7.20 \n", + "Skyeye 3600 | 28.0| 1.057 | 0.0142 | 11.50 | 11.66 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [6.267452830188679, 7.200471698113207, 11.662735849056602] = 7.20\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.95\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 7.20\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.95\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator'para el parametro 'Empty weight'.\n", + "No se pudo imputar: Empty weight para Integrator.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator VTOL'para el parametro 'Empty weight'.\n", + "No se pudo imputar: Empty weight para Integrator VTOL.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator Extended Range (ER)'para el parametro 'Empty weight'.\n", + "No se pudo imputar: Empty weight para Integrator Extended Range (ER).\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #105 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Empty weight\n", + "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle 3\n", + "\u001b[1mMTOW actual:\u001b[0m 36.3 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 3600 VTOL| 40.0| 1.102 | 0.0255 | 11.00 | 11.28 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [11.280303030303031] = 11.28\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 11.28\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'RQNan21A Blackjack'para el parametro 'Empty weight'.\n", + "No se pudo imputar: Empty weight para RQNan21A Blackjack.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #MAP'para el parametro 'Empty weight'.\n", + "No se pudo imputar: Empty weight para DeltaQuad Pro #MAP.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #CARGO'para el parametro 'Empty weight'.\n", + "No se pudo imputar: Empty weight para DeltaQuad Pro #CARGO.\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #106 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Empty weight\n", + "\u001b[1mAeronave a imputar:\u001b[0m V35\n", + "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 7.10 | 6.88 \n", + "Skyeye 3600 | 28.0| 0.875 | -0.0312 | 11.50 | 11.14 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [6.878125, 11.140625] = 9.01\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.82\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 9.01\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.88\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.92\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.82\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #107 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Empty weight\n", + "\u001b[1mAeronave a imputar:\u001b[0m V39\n", + "\u001b[1mMTOW actual:\u001b[0m 24.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m V32\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "V32 | 23.5| 0.979 | -0.0052 | 6.45 | 6.42 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [6.41640625] = 6.42\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.61\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 6.42\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.79\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.61\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #108 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Empty weight\n", + "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT370\n", + "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 3600 VTOL| 40.0| 1.000 | 0.0000 | 11.00 | 11.00 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [11.0] = 11.00\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 11.00\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #109 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Empty weight\n", + "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL octo, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.900 | -0.0250 | 32.00 | 31.20 \n", + "Skyeye 5000 VTOL octo| 100.0| 1.000 | 0.0000 | 35.00 | 35.00 \n", + "Reach | 91.0| 0.910 | -0.0225 | 31.00 | 30.30 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [31.2, 35.0, 30.302500000000002] = 31.20\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.91\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 31.20\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.87\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.91\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.91\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #110 ========================\u001b[0m\n", + "\u001b[1mParámetro:\u001b[0m Empty weight\n", + "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT510\n", + "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", + "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", + "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL octo, Reach\n", + "\n", + "Aeronaves Válidas para el Cálculo:\n", + "-----------------------------------------------------------------------------------------------\n", + "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", + "------------|----------------|------------------------------|-------------------|----------------|---------------\n", + "Skyeye 5000 | 90.0| 0.900 | -0.0250 | 32.00 | 31.20 \n", + "Skyeye 5000 VTOL octo| 100.0| 1.000 | 0.0000 | 35.00 | 35.00 \n", + "Reach | 91.0| 0.910 | -0.0225 | 31.00 | 30.30 \n", + "-----------------------------------------------------------------------------------------------\n", + "\n", + "Cálculo del Valor Final:\n", + "\u001b[1m- Se tomó la mediana de los valores ajustados [31.2, 35.0, 30.302500000000002] = 31.20\u001b[0m\n", + "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.91\n", + "\u001b[1m- Valor Imputado Final:\u001b[0m 31.20\n", + "\n", + "Detalle del Cálculo de Confianza:\n", + "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", + "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", + "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.87\n", + "\u001b[1m- Confianza Base:\u001b[0m 0.91\n", + "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.91\n", + "\u001b[1m============================================================================================\u001b[0m\n", + "\n", + "\n", + "=== Generando reporte final ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Reporte Final de Imputaciones

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AeronaveParámetroValor ImputadoNivel de Confianza
0Aerosonde® Mk. 4.8 VTOL FTUASVelocidad a la que se realiza el crucero (KTAS)32.3161.000
1AAI AerosondeVelocidad a la que se realiza el crucero (KTAS)21.6250.953
2Orbiter 4Velocidad a la que se realiza el crucero (KTAS)27.2690.959
3Orbiter 3Velocidad a la que se realiza el crucero (KTAS)26.5670.551
4Integrator VTOLVelocidad a la que se realiza el crucero (KTAS)30.9330.621
5Integrator Extended Range (ER)Velocidad a la que se realiza el crucero (KTAS)30.9530.622
6Skyeye 3600Velocidad a la que se realiza el crucero (KTAS)27.3440.972
7Skyeye 5000 VTOL octoVelocidad a la que se realiza el crucero (KTAS)31.7191.000
8Orbiter 4Techo de servicio máximo9633.8640.972
9Orbiter 3Techo de servicio máximo8010.3360.855
10MantisTecho de servicio máximo12.9720.509
11Integrator VTOLTecho de servicio máximo19487.0000.532
12Skyeye 2600Techo de servicio máximo13122.5000.809
13Skyeye 2930 VTOLTecho de servicio máximo16571.4290.901
14Skyeye 3600Techo de servicio máximo16571.4290.901
15Skyeye 3600 VTOLTecho de servicio máximo14902.1250.972
16Skyeye 5000Techo de servicio máximo16044.4440.922
17Skyeye 5000 VTOLTecho de servicio máximo15640.0000.930
18Skyeye 5000 VTOL octoTecho de servicio máximo15640.0000.930
19Aerosonde® Mk. 4.8 VTOL FTUASÁrea del ala2.6640.565
20Fulmar XÁrea del ala1.1580.621
21Orbiter 4Área del ala1.5430.523
22Orbiter 3Área del ala1.1290.809
23ScanEagleÁrea del ala1.1810.856
24ScanEagle 3Área del ala1.3540.577
25V35Área del ala1.1290.809
26Volitation VT370Área del ala1.4460.869
27Volitation VT510Área del ala2.6150.581
28AscendÁrea del ala0.8310.758
29TransitionÁrea del ala1.1900.574
30ReachÁrea del ala2.6800.560
31Fulmar XRelación de aspecto del ala15.3180.621
32Orbiter 4Relación de aspecto del ala12.4400.523
33V25Relación de aspecto del ala15.2850.510
34Volitation VT370Relación de aspecto del ala12.6720.597
35Skyeye 3600 VTOLRelación de aspecto del ala12.6720.597
36Skyeye 5000Relación de aspecto del ala12.6040.607
37Skyeye 5000 VTOLRelación de aspecto del ala12.2810.591
38Skyeye 5000 VTOL octoRelación de aspecto del ala12.2810.591
39Volitation VT510Relación de aspecto del ala12.2810.591
40TransitionRelación de aspecto del ala15.7430.574
41ReachRelación de aspecto del ala12.5690.612
42Aerosonde® Mk. 4.8 VTOL FTUASLongitud del fuselaje3.5661.000
43Integrator VTOLLongitud del fuselaje2.4980.532
44V39Longitud del fuselaje1.3750.854
45Aerosonde® Mk. 4.7 Fixed WingAlcance de la aeronave296.0900.599
46Aerosonde® Mk. 4.7 VTOLAlcance de la aeronave123.4480.844
47Aerosonde® Mk. 4.8 Fixed wingAlcance de la aeronave122.9110.854
48Aerosonde® Mk. 4.8 VTOL FTUASAlcance de la aeronave815.0540.588
49IntegratorAlcance de la aeronave500.0000.622
50Integrator VTOLAlcance de la aeronave499.6670.621
51ScanEagle 3Alcance de la aeronave178.0820.833
52V21Alcance de la aeronave270.0000.622
53V25Alcance de la aeronave1843.6900.854
54V32Alcance de la aeronave770.2130.558
55V35Alcance de la aeronave50.0000.622
56Volitation VT370Alcance de la aeronave300.0000.622
57Skyeye 2600Alcance de la aeronave1763.9080.832
58Skyeye 2930 VTOLAlcance de la aeronave51.7860.560
59Skyeye 3600Alcance de la aeronave51.7860.560
60Skyeye 5000Alcance de la aeronave822.2220.573
61Skyeye 5000 VTOL octoAlcance de la aeronave800.0000.622
62Volitation VT510Alcance de la aeronave800.0000.622
63AscendAlcance de la aeronave273.5530.598
64TransitionAlcance de la aeronave633.4990.830
65ReachAlcance de la aeronave819.7800.578
66Skyeye 5000 VTOL octoAutonomía de la aeronave8.0001.000
67Aerosonde® Mk. 4.8 VTOL FTUASVelocidad máxima (KIAS)41.6611.000
68Integrator VTOLVelocidad máxima (KIAS)46.2690.532
69DeltaQuad EvoVelocidad máxima (KIAS)31.3120.865
70DeltaQuad Pro #MAPVelocidad máxima (KIAS)25.9100.600
71DeltaQuad Pro #CARGOVelocidad máxima (KIAS)25.9100.600
72Skyeye 2600Velocidad máxima (KIAS)24.7010.826
73Skyeye 3600Velocidad máxima (KIAS)34.1791.000
74Stalker XEVelocidad de pérdida (KCAS)12.7220.838
75Stalker VXE30Velocidad de pérdida (KCAS)12.6810.579
76Aerosonde® Mk. 4.7 Fixed WingVelocidad de pérdida (KCAS)23.6870.599
77Aerosonde® Mk. 4.8 VTOL FTUASVelocidad de pérdida (KCAS)19.6651.000
78AAI AerosondeVelocidad de pérdida (KCAS)12.8430.837
79Fulmar XVelocidad de pérdida (KCAS)12.6750.578
80Orbiter 3Velocidad de pérdida (KCAS)14.7730.815
81ScanEagleVelocidad de pérdida (KCAS)16.5190.949
82ScanEagle 3Velocidad de pérdida (KCAS)24.6120.577
83DeltaQuad EvoVelocidad de pérdida (KCAS)13.4190.857
84V35Velocidad de pérdida (KCAS)14.7730.815
85V39Velocidad de pérdida (KCAS)16.9110.613
86Volitation VT370Velocidad de pérdida (KCAS)24.0000.622
87Skyeye 5000 VTOLVelocidad de pérdida (KCAS)19.3121.000
88Aerosonde® Mk. 4.8 VTOL FTUASenvergadura5.0941.000
89Integrator VTOLenvergadura4.7970.532
90Fulmar XCuerda0.3180.621
91Orbiter 4Cuerda0.3500.523
92V25Cuerda0.2220.841
93Volitation VT370Cuerda0.3570.597
94Skyeye 2600Cuerda0.2120.806
95Skyeye 3600 VTOLCuerda0.3570.597
96TransitionCuerda0.3270.574
97AAI Aerosondepayload2.5190.952
98Fulmar Xpayload1.9780.862
99Mantispayload1.1860.509
100Aerosonde® Mk. 4.7 Fixed WingEmpty weight10.8570.599
101Aerosonde® Mk. 4.8 VTOL FTUASEmpty weight31.7420.951
102Fulmar XEmpty weight11.5550.863
103Orbiter 3Empty weight9.0090.820
104ScanEagleEmpty weight7.2000.955
105ScanEagle 3Empty weight11.2800.577
106V35Empty weight9.0090.820
107V39Empty weight6.4160.613
108Volitation VT370Empty weight11.0000.622
109Skyeye 5000 VTOLEmpty weight31.2000.912
110Volitation VT510Empty weight31.2000.912
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Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Distancia de carrera requerida para despegue0.00.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
Tasa de ascensoNaNNaNNaNNaNNaNNaN2.49936NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5.0NaN5.0NaNNaNNaNNaNNaNNaNNaN5.0NaNNaNNaN
Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.0
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NaNNaN30.406584NaNNaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaNNaN17000.010000.013000.016000.0
Velocidad de pérdida limpia (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaNNaN25.0NaNNaNNaN
Área del ala0.871.1582831.551.551.55NaN0.57NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.712
Profundidad del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Ancho del fuselaje0.2110.20.2770.2770.277NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.3750.3750.375NaNNaNNaNNaN
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0NaNNaNNaNNaN3270.0800.0150.050.025.0NaNNaNNaN500.0NaN92.6270.0100.0100.0NaNNaNNaNNaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaNNaN
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0NaN30.0NaN33.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.0
Radio de giroNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100.0120.0150.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
envergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.352NaN0.196552NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
payload2.4947562.49475614.511.317.722.7NaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
duracion en VTOL2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050.050.05
Crucero KIAS15.4333216.09342225.025.025.0NaNNaN27.8NaNNaN16.728.028.3NaNNaN23.530.916.5416.016.018.020.020.025.025.025.033.024.0NaN30.033.028.035.030.020.020.025.0
RTF (dry weight)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.011.854.0
RTF (Including fuel & Batteries)NaNNaN27.742.236.770.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8.916.584.0
Empty weight10.88620817.463292NaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
Maximum CrosswindNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.0NaNNaNNaN45.050.050.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15.015.015.0
Rango de comunicación59.0161.0140.0140.0140.0NaN150.0NaN150.050.025.0101.8692.6NaNNaNNaN92.6NaN50.030.030.030.030.030.030.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Wing LoadingNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.524.025.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Potencia/PesoNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Capacidad combustibleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.0NaNNaN11.511.528.028.028.025.0NaNNaNNaN
ConsumoNaNNaN0.60.6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.96NaNNaNNaNNaN1.2NaNNaN5.0NaNNaNNaN
Potencia(W)NaNNaN2980.02980.0NaNNaN1280.0NaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Potencia(HP)NaNNaN4.04.0NaNNaN1.74NaNNaNNaNNaNNaNNaNNaNNaNNaN8.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
PrecioNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.0NaNNaNNaN
Tiempo de emergencia en vueloNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.1080.1080.108NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Distancia de aterrizajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Battery Power SupplyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Modelo Motor Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Modelo Motor VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
PortabilidadNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
CámaraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
DespegueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Datalink banksNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Motor recomendadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Hélice recomendada VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Hélice recomendada Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Sistema de controlNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Características adicionalesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
EmpresaNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
kjbkNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Convertir todo a numérico ===\n", + "\n", + "\n", + "=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===\n", + "\n", + "Umbral seleccionado para correlaciones significativas: 0.7\n", + "\n", + "=== Cálculo de tabla completa ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Tabla de Correlaciones con todos los parametros(tabla_completa)

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Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecio
Distancia de carrera requerida para despegue1.0000.0630.467nan-0.5050.363nan0.2600.4250.168nan-0.0680.316-0.3080.145nan0.229nan0.389nan0.357nannan-0.018-0.240-0.156
Altitud a la que se realiza el crucero0.0631.000nan-0.038nan0.301-0.3510.081nan-0.095-0.952-0.2800.128nan0.1360.761-0.183nannannan0.0600.038-0.325nannannan
Velocidad a la que se realiza el crucero (KTAS)0.467nan1.0000.0410.1280.587-0.9990.5350.9360.6630.4070.3360.8150.2570.4720.8460.696-0.6941.0000.7230.426-0.8550.3590.4910.461-0.296
Techo de servicio máximonan-0.0380.0411.000-0.502-0.152-0.3140.0820.3690.1370.4630.079-0.111-0.0710.0570.0170.087-0.8750.0410.579-0.138-0.961-0.120nan0.515-0.257
Velocidad de pérdida limpia (KCAS)-0.505nan0.128-0.5021.0000.097nan0.260nan0.546nan0.4010.4461.0000.505nan0.536nan0.128nan0.038nannan0.0681.0000.163
Área del ala0.3630.3010.587-0.1520.0971.000-0.8310.8670.9840.977-0.3010.0810.7370.4230.8410.9840.899-1.0000.6750.9230.941nan0.5951.0001.0000.899
Relación de aspecto del alanan-0.351-0.999-0.314nan-0.8311.000-0.790-0.996-0.823-0.998-0.305-0.859nan-0.349-0.744-0.888nan-0.999nan0.622nan-0.298nannannan
Longitud del fuselaje0.2600.0810.5350.0820.2600.867-0.7901.0000.9380.7860.1290.3890.2560.1800.6930.9950.599-0.6170.5760.9400.880-0.7180.6760.9290.036-0.210
Ancho del fuselaje0.425nan0.9360.369nan0.984-0.9960.9381.0000.9860.982-0.0900.940nan0.6710.7600.868nan0.944nan0.955nan0.323nan1.000nan
Peso máximo al despegue (MTOW)0.168-0.0950.6630.1370.5460.977-0.8230.7860.9861.000-0.0510.4340.6780.5390.7910.8580.875-0.4010.7080.9790.947-0.4640.5140.9760.7580.052
Alcance de la aeronavenan-0.9520.4070.463nan-0.301-0.9980.1290.982-0.0511.0000.578-0.062nan-0.107-0.7550.554-1.0000.407nan-0.059-1.0000.5081.000nan1.000
Autonomía de la aeronave-0.068-0.2800.3360.0790.4010.081-0.3050.389-0.0900.4340.5781.0000.297-0.1640.532-0.2010.461-0.5940.3360.6340.428-0.7150.802-0.113-0.7320.011
Velocidad máxima (KIAS)0.3160.1280.815-0.1110.4460.737-0.8590.2560.9400.678-0.0620.2971.0000.5390.4000.5120.715-0.9270.7750.8570.517nan0.0940.7050.9100.015
Velocidad de pérdida (KCAS)-0.308nan0.257-0.0711.0000.423nan0.180nan0.539nan-0.1640.5391.0000.401nan0.627nan0.417nan0.321nannan0.2301.0000.160
envergadura0.1450.1360.4720.0570.5050.841-0.3490.6930.6710.791-0.1070.5320.4000.4011.0000.8850.734-0.2580.5010.9360.924-0.4520.6480.2970.0850.032
Cuerdanan0.7610.8460.017nan0.984-0.7440.9950.7600.858-0.755-0.2010.512nan0.8851.0000.776nan0.846nan0.971nan0.354nannannan
payload0.229-0.1830.6960.0870.5360.899-0.8880.5990.8680.8750.5540.4610.7150.6270.7340.7761.000-0.0240.6940.5590.778-0.1420.5460.7110.846-0.008
duracion en VTOLnannan-0.694-0.875nan-1.000nan-0.617nan-0.401-1.000-0.594-0.927nan-0.258nan-0.0241.000-0.694-0.402-0.3151.000nannannannan
Crucero KIAS0.389nan1.0000.0410.1280.675-0.9990.5760.9440.7080.4070.3360.7750.4170.5010.8460.694-0.6941.0000.7230.583-0.8550.3590.5810.461-0.243
RTF (Including fuel & Batteries)nannan0.7230.579nan0.923nan0.940nan0.979nan0.6340.857nan0.936nan0.559-0.4020.7231.0000.996-0.402nannannannan
Empty weight0.3570.0600.426-0.1380.0380.9410.6220.8800.9550.947-0.0590.4280.5170.3210.9240.9710.778-0.3150.5830.9961.000-0.3190.8320.995nan-0.029
Maximum Crosswindnan0.038-0.855-0.961nannannan-0.718nan-0.464-1.000-0.715nannan-0.452nan-0.1421.000-0.855-0.402-0.3191.000nannannannan
Rango de comunicaciónnan-0.3250.359-0.120nan0.595-0.2980.6760.3230.5140.5080.8020.094nan0.6480.3540.546nan0.359nan0.832nan1.000nannannan
Capacidad combustible-0.018nan0.491nan0.0681.000nan0.929nan0.9761.000-0.1130.7050.2300.297nan0.711nan0.581nan0.995nannan1.0000.3770.817
Consumo-0.240nan0.4610.5151.0001.000nan0.0361.0000.758nan-0.7320.9101.0000.085nan0.846nan0.461nannannannan0.3771.0000.998
Precio-0.156nan-0.296-0.2570.1630.899nan-0.210nan0.0521.0000.0110.0150.1600.032nan-0.008nan-0.243nan-0.029nannan0.8170.9981.000
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Resumen de la Tabla

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ResumenCantidad
0Total de valores676.000
1Valores numéricos532.000
2Valores NaN144.000
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Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)1.0000.0410.587-0.9990.5350.6630.4070.3360.8150.2570.4720.8460.6960.426
Techo de servicio máximo0.0411.000-0.152-0.3140.0820.1370.4630.079-0.111-0.0710.0570.0170.087-0.138
Área del ala0.587-0.1521.000-0.8310.8670.977-0.3010.0810.7370.4230.8410.9840.8990.941
Relación de aspecto del ala-0.999-0.314-0.8311.000-0.790-0.823-0.998-0.305-0.859nan-0.349-0.744-0.8880.622
Longitud del fuselaje0.5350.0820.867-0.7901.0000.7860.1290.3890.2560.1800.6930.9950.5990.880
Peso máximo al despegue (MTOW)0.6630.1370.977-0.8230.7861.000-0.0510.4340.6780.5390.7910.8580.8750.947
Alcance de la aeronave0.4070.463-0.301-0.9980.129-0.0511.0000.578-0.062nan-0.107-0.7550.554-0.059
Autonomía de la aeronave0.3360.0790.081-0.3050.3890.4340.5781.0000.297-0.1640.532-0.2010.4610.428
Velocidad máxima (KIAS)0.815-0.1110.737-0.8590.2560.678-0.0620.2971.0000.5390.4000.5120.7150.517
Velocidad de pérdida (KCAS)0.257-0.0710.423nan0.1800.539nan-0.1640.5391.0000.401nan0.6270.321
envergadura0.4720.0570.841-0.3490.6930.791-0.1070.5320.4000.4011.0000.8850.7340.924
Cuerda0.8460.0170.984-0.7440.9950.858-0.755-0.2010.512nan0.8851.0000.7760.971
payload0.6960.0870.899-0.8880.5990.8750.5540.4610.7150.6270.7340.7761.0000.778
Empty weight0.426-0.1380.9410.6220.8800.947-0.0590.4280.5170.3210.9240.9710.7781.000
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Resumen de la Tabla

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ResumenCantidad
0Total de valores196.000
1Valores numéricos190.000
2Valores NaN6.000
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Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)nannannan-0.999nannannannan0.815nannan0.846nannan
Techo de servicio máximonannannannannannannannannannannannannannan
Área del alanannannan-0.8310.8670.977nannan0.737nan0.8410.9840.8990.941
Relación de aspecto del ala-0.999nan-0.831nan-0.790-0.823-0.998nan-0.859nannan-0.744-0.888nan
Longitud del fuselajenannan0.867-0.790nan0.786nannannannannan0.995nan0.880
Peso máximo al despegue (MTOW)nannan0.977-0.8230.786nannannannannan0.7910.8580.8750.947
Alcance de la aeronavenannannan-0.998nannannannannannannan-0.755nannan
Autonomía de la aeronavenannannannannannannannannannannannannannan
Velocidad máxima (KIAS)0.815nan0.737-0.859nannannannannannannannan0.715nan
Velocidad de pérdida (KCAS)nannannannannannannannannannannannannannan
envergaduranannan0.841nannan0.791nannannannannan0.8850.7340.924
Cuerda0.846nan0.984-0.7440.9950.858-0.755nannannan0.885nan0.7760.971
payloadnannan0.899-0.888nan0.875nannan0.715nan0.7340.776nan0.778
Empty weightnannan0.941nan0.8800.947nannannannan0.9240.9710.778nan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores196.000
1Valores numéricos64.000
2Valores NaN132.000
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Tabla de correlaciones con filtro de umbral de correlación

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Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecio
Distancia de carrera requerida para despeguenannannannannannannannannannannannannannannannannannannannannannannannannannan
Altitud a la que se realiza el cruceronannannannannannannannannannan-0.952nannannannan0.761nannannannannannannannannannan
Velocidad a la que se realiza el crucero (KTAS)nannannannannannan-0.999nan0.936nannannan0.815nannan0.846nannan1.0000.723nan-0.855nannannannan
Techo de servicio máximonannannannannannannannannannannannannannannannannan-0.875nannannan-0.961nannannannan
Velocidad de pérdida limpia (KCAS)nannannannannannannannannannannannannannannannannannannannannannannannannannan
Área del alanannannannannannan-0.8310.8670.9840.977nannan0.737nan0.8410.9840.899nannan0.9230.941nannan1.000nan0.899
Relación de aspecto del alanannan-0.999nannan-0.831nan-0.790-0.996-0.823-0.998nan-0.859nannan-0.744-0.888nan-0.999nannannannannannannan
Longitud del fuselajenannannannannan0.867-0.790nan0.9380.786nannannannannan0.995nannannan0.9400.880-0.718nan0.929nannan
Ancho del fuselajenannan0.936nannan0.984-0.9960.938nan0.9860.982nan0.940nannan0.7600.868nan0.944nan0.955nannannannannan
Peso máximo al despegue (MTOW)nannannannannan0.977-0.8230.7860.986nannannannannan0.7910.8580.875nan0.7080.9790.947nannan0.9760.758nan
Alcance de la aeronavenan-0.952nannannannan-0.998nan0.982nannannannannannan-0.755nannannannannannannannannannan
Autonomía de la aeronavenannannannannannannannannannannannannannannannannannannannannan-0.7150.802nan-0.732nan
Velocidad máxima (KIAS)nannan0.815nannan0.737-0.859nan0.940nannannannannannannan0.715-0.9270.7750.857nannannan0.7050.910nan
Velocidad de pérdida (KCAS)nannannannannannannannannannannannannannannannannannannannannannannannannannan
envergaduranannannannannan0.841nannannan0.791nannannannannan0.8850.734nannan0.9360.924nannannannannan
Cuerdanan0.7610.846nannan0.984-0.7440.9950.7600.858-0.755nannannan0.885nan0.776nan0.846nan0.971nannannannannan
payloadnannannannannan0.899-0.888nan0.8680.875nannan0.715nan0.7340.776nannannannan0.778nannan0.7110.846nan
duracion en VTOLnannannan-0.875nannannannannannannannan-0.927nannannannannannannannannannannannannan
Crucero KIASnannan1.000nannannan-0.999nan0.9440.708nannan0.775nannan0.846nannannan0.723nan-0.855nannannannan
RTF (Including fuel & Batteries)nannan0.723nannan0.923nan0.940nan0.979nannan0.857nan0.936nannannan0.723nan0.996nannannannannan
Empty weightnannannannannan0.941nan0.8800.9550.947nannannannan0.9240.9710.778nannan0.996nannan0.8320.995nannan
Maximum Crosswindnannan-0.855-0.961nannannan-0.718nannannan-0.715nannannannannannan-0.855nannannannannannannan
Rango de comunicaciónnannannannannannannannannannannan0.802nannannannannannannannan0.832nannannannannan
Capacidad combustiblenannannannannan1.000nan0.929nan0.976nannan0.705nannannan0.711nannannan0.995nannannannan0.817
Consumonannannannannannannannannan0.758nan-0.7320.910nannannan0.846nannannannannannannannan0.998
Precionannannannannan0.899nannannannannannannannannannannannannannannannannan0.8170.998nan
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", + "\n", + "=== Imputación para el parámetro: **Velocidad a la que se realiza el crucero (KTAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5]\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344]\n", + "Ecuación de regresión: y = -3.588x + 72.195\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 27.34\n", + "\tR²: 0.9951683800002252, Desviación Estándar: 0.3316703651380593, Varianza: 0.1100052311108136, Incertidumbre: 0.19148997459468003\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.723) ---\n", + "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [27.344, 27.344, 27.344, 18.091, 21.875, 21.875, 27.344]\n", + "Ecuación de regresión: y = 0.104x + 21.164\n", + "Valor del parámetro correlacionado para la aeronave: 70.3\n", + "Predicción obtenida: 28.443\n", + "\tR²: 0.5234387684031696, Desviación Estándar: 2.436503148855875, Varianza: 5.936547594384592, Incertidumbre: 0.9209116286426334\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Relación de aspecto del ala: 27.34', 'RTF (Including fuel & Batteries): 28.443']\n", + "**Mediana calculada:** 27.892\n", + "\n", + "--- Imputación para aeronave: **AAI Aerosonde** ---\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5]\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892]\n", + "Ecuación de regresión: y = -3.686x + 73.696\n", + "Valor del parámetro correlacionado para la aeronave: 14.754\n", + "Predicción obtenida: 19.306\n", + "\tR²: 0.9955392528843181, Desviación Estándar: 0.3471824284258566, Varianza: 0.12053563860767504, Incertidumbre: 0.1735912142129283\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 30.407, 18.266, 30.625, 30.953, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", + "Ecuación de regresión: y = 0.607x + 4.971\n", + "Valor del parámetro correlacionado para la aeronave: 30.846\n", + "Predicción obtenida: 23.695\n", + "\tR²: 0.6883167394784258, Desviación Estándar: 3.20056844074234, Varianza: 10.243638343875855, Incertidumbre: 0.7342607576358634\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352]\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344]\n", + "Ecuación de regresión: y = 77.731x + -2.949\n", + "Valor del parámetro correlacionado para la aeronave: 0.197\n", + "Predicción obtenida: 12.33\n", + "\tR²: 0.5951449532870101, Desviación Estándar: 3.036057989288316, Varianza: 9.217648114321413, Incertidumbre: 1.7528688973909234\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Relación de aspecto del ala: 19.306', 'Velocidad máxima (KIAS): 23.695', 'Cuerda: 12.33']\n", + "**Mediana calculada:** 19.306\n", + "\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 18.266, 30.625, 30.953, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", + "Ecuación de regresión: y = 0.625x + 4.123\n", + "Valor del parámetro correlacionado para la aeronave: 36.0\n", + "Predicción obtenida: 26.611\n", + "\tR²: 0.6860861539695317, Desviación Estándar: 3.2600537047404123, Varianza: 10.627950157791688, Incertidumbre: 0.728970169409959\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad máxima (KIAS): 26.611']\n", + "**Mediana calculada:** 26.611\n", + "\n", + "--- Imputación para aeronave: **Orbiter 3** ---\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", + "Ecuación de regresión: y = 0.625x + 4.123\n", + "Valor del parámetro correlacionado para la aeronave: 36.0\n", + "Predicción obtenida: 26.611\n", + "\tR²: 0.6861096464084044, Desviación Estándar: 3.181486648407253, Varianza: 10.121857293993614, Incertidumbre: 0.6942573042294092\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad máxima (KIAS): 26.611']\n", + "**Mediana calculada:** 26.611\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Maximum Crosswind (r = -0.855) ---\n", + "Aeronaves utilizadas: ['DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Ascend', 'Reach']\n", + "Valores para Maximum Crosswind: [45.0, 50.0, 15.0, 15.0]\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [18.091, 17.5, 21.875, 27.344]\n", + "Ecuación de regresión: y = -0.209x + 27.721\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 21.463\n", + "\tR²: 0.755405189129301, Desviación Estándar: 1.9401971688154946, Varianza: 3.764365053879661, Incertidumbre: 0.9700985844077473\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Maximum Crosswind: 21.463']\n", + "**Mediana calculada:** 21.463\n", + "\n", + "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", + "Ecuación de regresión: y = 0.625x + 4.123\n", + "Valor del parámetro correlacionado para la aeronave: 46.3\n", + "Predicción obtenida: 33.045\n", + "\tR²: 0.6861096464084044, Desviación Estándar: 3.181486648407253, Varianza: 10.121857293993614, Incertidumbre: 0.6942573042294092\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad máxima (KIAS): 33.045']\n", + "**Mediana calculada:** 33.045\n", + "\n", + "--- Imputación para aeronave: **Skyeye 3600** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad a la que se realiza el crucero (KTAS)' para la aeronave 'Skyeye 3600'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = 0.936) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 36.094, 30.625]\n", + "Ecuación de regresión: y = 93.547x + -1.195\n", + "Valor del parámetro correlacionado para la aeronave: 0.375\n", + "Predicción obtenida: 33.885\n", + "\tR²: 0.9095474448825779, Desviación Estándar: 2.2440186570446126, Varianza: 5.035619733164306, Incertidumbre: 1.0035556519859081\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", + "Ecuación de regresión: y = 0.625x + 4.123\n", + "Valor del parámetro correlacionado para la aeronave: 38.0\n", + "Predicción obtenida: 27.86\n", + "\tR²: 0.7045474134573646, Desviación Estándar: 3.108339246562038, Varianza: 9.661772871717856, Incertidumbre: 0.6627001540432849\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Crucero KIAS (r = 1.0) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 33.0, 24.0, 30.0]\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 30.407, 18.266, 30.625, 30.953, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 36.094, 26.25, 32.813]\n", + "Ecuación de regresión: y = 1.094x + 0.0\n", + "Valor del parámetro correlacionado para la aeronave: 35.0\n", + "Predicción obtenida: 38.282\n", + "\tR²: 1.0, Desviación Estándar: 3.3158330576972436e-15, Varianza: 1.0994748866517852e-29, Incertidumbre: 8.289582644243109e-16\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "Valores imputados: ['Ancho del fuselaje: 33.885', 'Velocidad máxima (KIAS): 27.86', 'Crucero KIAS: 38.282']\n", + "**Mediana calculada:** 33.885\n", + "\n", + "=== Imputación para el parámetro: **Techo de servicio máximo** ===\n", + "\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Orbiter 4'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Orbiter 3** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Orbiter 3'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Mantis** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Mantis'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Maximum Crosswind (r = -0.961) ---\n", + "Aeronaves utilizadas: ['DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Maximum Crosswind: [45.0, 50.0, 15.0, 15.0, 15.0]\n", + "Valores para Techo de servicio máximo: [13.0, 13.123, 10000.0, 13000.0, 16000.0]\n", + "Ecuación de regresión: y = -395.696x + 18884.7\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 7013.834\n", + "\tR²: 0.9093584510325812, Desviación Estándar: 1998.8363831923273, Varianza: 3995346.886773384, Incertidumbre: 893.9068057435724\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Maximum Crosswind: 7013.834']\n", + "**Mediana calculada:** 7013.834\n", + "\n", + "--- Imputación para aeronave: **Skyeye 2600** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 2600'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Skyeye 2930 VTOL** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 2930 VTOL'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Skyeye 3600** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 3600'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Skyeye 3600 VTOL** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 3600 VTOL'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Skyeye 5000** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 5000'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 5000 VTOL'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 5000 VTOL octo'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Imputación para el parámetro: **Área del ala** ===\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57]\n", + "Ecuación de regresión: y = -0.219x + 4.203\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 1.468\n", + "\tR²: 0.4921072065029338, Desviación Estándar: 0.2579064700616838, Varianza: 0.06651574729967821, Incertidumbre: 0.1289532350308419\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.022x + 0.512\n", + "Valor del parámetro correlacionado para la aeronave: 93.0\n", + "Predicción obtenida: 2.528\n", + "\tR²: 0.9424993971942859, Desviación Estándar: 0.1480022127547218, Varianza: 0.02190465498029394, Incertidumbre: 0.038214007013392295\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.084x + 0.605\n", + "Valor del parámetro correlacionado para la aeronave: 22.7\n", + "Predicción obtenida: 2.503\n", + "\tR²: 0.8079809975569326, Desviación Estándar: 0.2933466953816814, Varianza: 0.08605228369135297, Incertidumbre: 0.07574179105854881\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Relación de aspecto del ala: 1.468', 'Peso máximo al despegue (MTOW): 2.528', 'payload: 2.503']\n", + "**Mediana calculada:** 2.503\n", + "\n", + "--- Imputación para aeronave: **Fulmar X** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.546x + 0.007\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.662\n", + "\tR²: 0.6801892039390519, Desviación Estándar: 0.30468415865316995, Varianza: 0.09283243653419003, Incertidumbre: 0.08795474050811117\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.022x + 0.514\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 0.945\n", + "\tR²: 0.9537986325795035, Desviación Estándar: 0.14339927817107342, Varianza: 0.020563352979984892, Incertidumbre: 0.035849819542768356\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.082x + -1.305\n", + "Valor del parámetro correlacionado para la aeronave: 41.7\n", + "Predicción obtenida: 2.11\n", + "\tR²: 0.4361157359439931, Desviación Estándar: 0.5418933308069339, Varianza: 0.29364838197303306, Incertidumbre: 0.17136171742050005\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.46x + -0.446\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 0.935\n", + "\tR²: 0.606437991532139, Desviación Estándar: 0.33799470646338436, Varianza: 0.11424042159726937, Incertidumbre: 0.09757066738065176\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Longitud del fuselaje: 0.662', 'Peso máximo al despegue (MTOW): 0.945', 'Velocidad máxima (KIAS): 2.11', 'envergadura: 0.935']\n", + "**Mediana calculada:** 0.94\n", + "\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.521x + 0.078\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.703\n", + "\tR²: 0.6641973297028555, Desviación Estándar: 0.30126149307901384, Varianza: 0.09075848721219672, Incertidumbre: 0.08355490466301725\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.022x + 0.513\n", + "Valor del parámetro correlacionado para la aeronave: 55.0\n", + "Predicción obtenida: 1.701\n", + "\tR²: 0.9548206367081808, Desviación Estándar: 0.1391232519560287, Varianza: 0.01935527923482064, Incertidumbre: 0.033742344870242656\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.057x + -0.607\n", + "Valor del parámetro correlacionado para la aeronave: 36.0\n", + "Predicción obtenida: 1.456\n", + "\tR²: 0.2635152280829376, Desviación Estándar: 0.5971883487670894, Varianza: 0.3566339239031628, Incertidumbre: 0.18005906200293956\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.46x + -0.445\n", + "Valor del parámetro correlacionado para la aeronave: 5.2\n", + "Predicción obtenida: 1.948\n", + "\tR²: 0.6098226097328528, Desviación Estándar: 0.32473761841891496, Varianza: 0.10545452081638883, Incertidumbre: 0.09006601032934286\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.084x + 0.605\n", + "Valor del parámetro correlacionado para la aeronave: 12.0\n", + "Predicción obtenida: 1.608\n", + "\tR²: 0.8352230239464027, Desviación Estándar: 0.2840317214888726, Varianza: 0.08067401881193251, Incertidumbre: 0.07100793037221816\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 0.703', 'Peso máximo al despegue (MTOW): 1.701', 'Velocidad máxima (KIAS): 1.456', 'envergadura: 1.948', 'payload: 1.608']\n", + "**Mediana calculada:** 1.608\n", + "\n", + "--- Imputación para aeronave: **Orbiter 3** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.45x + 0.279\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.819\n", + "\tR²: 0.49444751735768744, Desviación Estándar: 0.36781526151231503, Varianza: 0.1352880666013727, Incertidumbre: 0.09830276358612447\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.512\n", + "Valor del parámetro correlacionado para la aeronave: 32.0\n", + "Predicción obtenida: 1.2\n", + "\tR²: 0.9541873450036837, Desviación Estándar: 0.1368279656080401, Varianza: 0.018721892172435, Incertidumbre: 0.03225066077913495\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.058x + -0.629\n", + "Valor del parámetro correlacionado para la aeronave: 36.0\n", + "Predicción obtenida: 1.472\n", + "\tR²: 0.2740142010052371, Desviación Estándar: 0.5732623728312028, Varianza: 0.328629748104061, Incertidumbre: 0.1654865926351893\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.417x + -0.317\n", + "Valor del parámetro correlacionado para la aeronave: 4.4\n", + "Predicción obtenida: 1.516\n", + "\tR²: 0.6121745707536088, Desviación Estándar: 0.3221549155644233, Varianza: 0.10378378962232071, Incertidumbre: 0.08609952282194\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.084x + 0.605\n", + "Valor del parámetro correlacionado para la aeronave: 5.5\n", + "Predicción obtenida: 1.065\n", + "\tR²: 0.8355353911029019, Desviación Estándar: 0.27555125326306473, Varianza: 0.07592849317484565, Incertidumbre: 0.06683099543970229\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 0.819', 'Peso máximo al despegue (MTOW): 1.2', 'Velocidad máxima (KIAS): 1.472', 'envergadura: 1.516', 'payload: 1.065']\n", + "**Mediana calculada:** 1.2\n", + "\n", + "--- Imputación para aeronave: **Mantis** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.423x + 0.354\n", + "Valor del parámetro correlacionado para la aeronave: 1.48\n", + "Predicción obtenida: 0.981\n", + "\tR²: 0.46052122430771714, Desviación Estándar: 0.36722158071804406, Varianza: 0.13485168934505895, Incertidumbre: 0.09481620443259649\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.512\n", + "Valor del parámetro correlacionado para la aeronave: 6.5\n", + "Predicción obtenida: 0.651\n", + "\tR²: 0.9543119129686386, Desviación Estándar: 0.13317860331268058, Varianza: 0.017736540380316333, Incertidumbre: 0.03055326701483504\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.057x + -0.594\n", + "Valor del parámetro correlacionado para la aeronave: 25.6\n", + "Predicción obtenida: 0.857\n", + "\tR²: 0.26284243485241887, Desviación Estándar: 0.5554395050127472, Varianza: 0.30851304372880556, Incertidumbre: 0.1540512012109075\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.398x + -0.27\n", + "Valor del parámetro correlacionado para la aeronave: 2.1\n", + "Predicción obtenida: 0.565\n", + "\tR²: 0.5889263637114722, Desviación Estándar: 0.32055372387545367, Varianza: 0.10275468989042061, Incertidumbre: 0.08276661560896019\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Longitud del fuselaje: 0.981', 'Peso máximo al despegue (MTOW): 0.651', 'Velocidad máxima (KIAS): 0.857', 'envergadura: 0.565']\n", + "**Mediana calculada:** 0.754\n", + "\n", + "--- Imputación para aeronave: **ScanEagle** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.432x + 0.325\n", + "Valor del parámetro correlacionado para la aeronave: 1.71\n", + "Predicción obtenida: 1.063\n", + "\tR²: 0.46837090280435856, Desviación Estándar: 0.35970679644924114, Varianza: 0.1293889794117758, Incertidumbre: 0.08992669911231028\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.524\n", + "Valor del parámetro correlacionado para la aeronave: 26.5\n", + "Predicción obtenida: 1.088\n", + "\tR²: 0.9550142996680715, Desviación Estándar: 0.1316020238299309, Varianza: 0.0173190926761337, Incertidumbre: 0.029427107126027266\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.058x + -0.649\n", + "Valor del parámetro correlacionado para la aeronave: 41.2\n", + "Predicción obtenida: 1.746\n", + "\tR²: 0.29571820868524923, Desviación Estándar: 0.5358228587115724, Varianza: 0.2871061359178417, Incertidumbre: 0.14320468266432035\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.381x + -0.202\n", + "Valor del parámetro correlacionado para la aeronave: 3.1\n", + "Predicción obtenida: 0.98\n", + "\tR²: 0.5966556307390867, Desviación Estándar: 0.31331578218830264, Varianza: 0.0981667793682679, Incertidumbre: 0.07832894554707566\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.083x + 0.619\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 1.034\n", + "\tR²: 0.8350524298607005, Desviación Estándar: 0.2695310649903611, Varianza: 0.07264699499483826, Incertidumbre: 0.0635290812650388\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 1.063', 'Peso máximo al despegue (MTOW): 1.088', 'Velocidad máxima (KIAS): 1.746', 'envergadura: 0.98', 'payload: 1.034']\n", + "**Mediana calculada:** 1.063\n", + "\n", + "--- Imputación para aeronave: **Integrator** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.432x + 0.325\n", + "Valor del parámetro correlacionado para la aeronave: 2.5\n", + "Predicción obtenida: 1.404\n", + "\tR²: 0.4688208790784759, Desviación Estándar: 0.34896686225957346, Varianza: 0.1217778709552921, Incertidumbre: 0.08463689605509357\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.522\n", + "Valor del parámetro correlacionado para la aeronave: 74.8\n", + "Predicción obtenida: 2.117\n", + "\tR²: 0.9552685671013039, Desviación Estándar: 0.1285440575853638, Varianza: 0.016523574740509323, Incertidumbre: 0.028050613048652005\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.049x + -0.376\n", + "Valor del parámetro correlacionado para la aeronave: 46.3\n", + "Predicción obtenida: 1.872\n", + "\tR²: 0.23291649773094203, Desviación Estándar: 0.5418077102312508, Varianza: 0.29355559486603106, Incertidumbre: 0.13989414923816057\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.38x + -0.191\n", + "Valor del parámetro correlacionado para la aeronave: 4.8\n", + "Predicción obtenida: 1.631\n", + "\tR²: 0.5953583197437493, Desviación Estándar: 0.30457832800349305, Varianza: 0.09276795788940341, Incertidumbre: 0.07387109515484842\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.083x + 0.622\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 2.113\n", + "\tR²: 0.8382132873731523, Desviación Estándar: 0.26242037008429453, Varianza: 0.06886445063517811, Incertidumbre: 0.060203361785472836\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 1.404', 'Peso máximo al despegue (MTOW): 2.117', 'Velocidad máxima (KIAS): 1.872', 'envergadura: 1.631', 'payload: 2.113']\n", + "**Mediana calculada:** 1.872\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.53\n", + "Valor del parámetro correlacionado para la aeronave: 75.0\n", + "Predicción obtenida: 2.091\n", + "\tR²: 0.9505024269111185, Desviación Estándar: 0.13477094387403904, Varianza: 0.018163207312699377, Incertidumbre: 0.02873326177786689\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.081x + 0.628\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 2.088\n", + "\tR²: 0.8352123461571666, Desviación Estándar: 0.2607982244843819, Varianza: 0.06801571389420606, Incertidumbre: 0.05831625583583279\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Peso máximo al despegue (MTOW): 2.091', 'payload: 2.088']\n", + "**Mediana calculada:** 2.09\n", + "\n", + "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.46x + 0.297\n", + "Valor del parámetro correlacionado para la aeronave: 2.5\n", + "Predicción obtenida: 1.447\n", + "\tR²: 0.48817664948956896, Desviación Estándar: 0.3550488365774619, Varianza: 0.12605967635500923, Incertidumbre: 0.08368581333210588\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.53\n", + "Valor del parámetro correlacionado para la aeronave: 74.8\n", + "Predicción obtenida: 2.087\n", + "\tR²: 0.9537268168880683, Desviación Estándar: 0.13180876658609073, Varianza: 0.017373550948946544, Incertidumbre: 0.027484027732284446\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.049x + -0.376\n", + "Valor del parámetro correlacionado para la aeronave: 46.3\n", + "Predicción obtenida: 1.872\n", + "\tR²: 0.2800468648798645, Desviación Estándar: 0.5246030655173786, Varianza: 0.27520837635023104, Incertidumbre: 0.13115076637934464\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.397x + -0.238\n", + "Valor del parámetro correlacionado para la aeronave: 4.8\n", + "Predicción obtenida: 1.666\n", + "\tR²: 0.6331001883328717, Desviación Estándar: 0.30060886965213346, Varianza: 0.09036569251353338, Incertidumbre: 0.07085419007194885\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.081x + 0.628\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 2.088\n", + "\tR²: 0.8421455166049525, Desviación Estándar: 0.2545135233705542, Varianza: 0.06477713357849366, Incertidumbre: 0.055539404106451286\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 1.447', 'Peso máximo al despegue (MTOW): 2.087', 'Velocidad máxima (KIAS): 1.872', 'envergadura: 1.666', 'payload: 2.088']\n", + "**Mediana calculada:** 1.872\n", + "\n", + "--- Imputación para aeronave: **ScanEagle 3** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.46x + 0.297\n", + "Valor del parámetro correlacionado para la aeronave: 2.4\n", + "Predicción obtenida: 1.401\n", + "\tR²: 0.48817664948956896, Desviación Estándar: 0.3550488365774619, Varianza: 0.12605967635500923, Incertidumbre: 0.08368581333210588\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.53\n", + "Valor del parámetro correlacionado para la aeronave: 36.3\n", + "Predicción obtenida: 1.286\n", + "\tR²: 0.9537268168880683, Desviación Estándar: 0.13180876658609073, Varianza: 0.017373550948946544, Incertidumbre: 0.027484027732284446\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.049x + -0.376\n", + "Valor del parámetro correlacionado para la aeronave: 41.2\n", + "Predicción obtenida: 1.625\n", + "\tR²: 0.2800468648798645, Desviación Estándar: 0.5246030655173786, Varianza: 0.27520837635023104, Incertidumbre: 0.13115076637934464\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.397x + -0.238\n", + "Valor del parámetro correlacionado para la aeronave: 4.0\n", + "Predicción obtenida: 1.349\n", + "\tR²: 0.6331001883328717, Desviación Estándar: 0.30060886965213346, Varianza: 0.09036569251353338, Incertidumbre: 0.07085419007194885\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.081x + 0.628\n", + "Valor del parámetro correlacionado para la aeronave: 8.6\n", + "Predicción obtenida: 1.326\n", + "\tR²: 0.8421455166049525, Desviación Estándar: 0.2545135233705542, Varianza: 0.06477713357849366, Incertidumbre: 0.055539404106451286\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 1.401', 'Peso máximo al despegue (MTOW): 1.286', 'Velocidad máxima (KIAS): 1.625', 'envergadura: 1.349', 'payload: 1.326']\n", + "**Mediana calculada:** 1.349\n", + "\n", + "--- Imputación para aeronave: **RQNan21A Blackjack** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.458x + 0.299\n", + "Valor del parámetro correlacionado para la aeronave: 2.5\n", + "Predicción obtenida: 1.443\n", + "\tR²: 0.49146211696239317, Desviación Estándar: 0.3457725283158643, Varianza: 0.11955864133794519, Incertidumbre: 0.0793256583358636\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.533\n", + "Valor del parámetro correlacionado para la aeronave: 61.0\n", + "Predicción obtenida: 1.802\n", + "\tR²: 0.9532832420705458, Desviación Estándar: 0.12965091612376284, Varianza: 0.016809360051730986, Incertidumbre: 0.026464882432299938\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.046x + -0.31\n", + "Valor del parámetro correlacionado para la aeronave: 46.3\n", + "Predicción obtenida: 1.828\n", + "\tR²: 0.2696364235152151, Desviación Estándar: 0.5128023719074978, Varianza: 0.2629662726339556, Incertidumbre: 0.12437284379069904\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.397x + -0.238\n", + "Valor del parámetro correlacionado para la aeronave: 4.8\n", + "Predicción obtenida: 1.666\n", + "\tR²: 0.6358629871775192, Desviación Estándar: 0.2925911882905274, Varianza: 0.08560960346526285, Incertidumbre: 0.06712502218573105\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.081x + 0.63\n", + "Valor del parámetro correlacionado para la aeronave: 17.7\n", + "Predicción obtenida: 2.064\n", + "\tR²: 0.8424950545541062, Desviación Estándar: 0.24870967530649968, Varianza: 0.0618565025910645, Incertidumbre: 0.05302508093991701\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 1.443', 'Peso máximo al despegue (MTOW): 1.802', 'Velocidad máxima (KIAS): 1.828', 'envergadura: 1.666', 'payload: 2.064']\n", + "**Mediana calculada:** 1.802\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.476x + 0.281\n", + "Valor del parámetro correlacionado para la aeronave: 0.9\n", + "Predicción obtenida: 0.709\n", + "\tR²: 0.5067722199589602, Desviación Estándar: 0.34569438035947625, Varianza: 0.11950460461212224, Incertidumbre: 0.0772996133923457\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.533\n", + "Valor del parámetro correlacionado para la aeronave: 6.2\n", + "Predicción obtenida: 0.662\n", + "\tR²: 0.9542933408598602, Desviación Estándar: 0.12703143691612878, Varianza: 0.016136985964976404, Incertidumbre: 0.025406287383225756\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.405x + -0.261\n", + "Valor del parámetro correlacionado para la aeronave: 2.35\n", + "Predicción obtenida: 0.691\n", + "\tR²: 0.6610034143057635, Desviación Estándar: 0.2865934213089743, Varianza: 0.08213578913758324, Incertidumbre: 0.06408423719511033\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.08x + 0.635\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.73\n", + "\tR²: 0.8368924216256921, Desviación Estándar: 0.24881616391859884, Varianza: 0.061909483427167046, Incertidumbre: 0.05188175662741571\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 0.709', 'Peso máximo al despegue (MTOW): 0.662', 'envergadura: 0.691', 'payload: 0.73']\n", + "**Mediana calculada:** 0.7\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.477x + 0.279\n", + "Valor del parámetro correlacionado para la aeronave: 0.9\n", + "Predicción obtenida: 0.708\n", + "\tR²: 0.530038527273323, Desviación Estándar: 0.3373686112553851, Varianza: 0.11381757986038715, Incertidumbre: 0.0736198665799968\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.537\n", + "Valor del parámetro correlacionado para la aeronave: 6.2\n", + "Predicción obtenida: 0.666\n", + "\tR²: 0.9563099090104202, Desviación Estándar: 0.12476254599075952, Varianza: 0.015565692882096385, Incertidumbre: 0.024467948329707695\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.405x + -0.259\n", + "Valor del parámetro correlacionado para la aeronave: 2.35\n", + "Predicción obtenida: 0.692\n", + "\tR²: 0.676989457910925, Desviación Estándar: 0.2796931909209141, Varianza: 0.0782282810475229, Incertidumbre: 0.0610340580361345\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.08x + 0.631\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.727\n", + "\tR²: 0.8477300129544219, Desviación Estándar: 0.24364789703392117, Varianza: 0.05936429772905225, Incertidumbre: 0.04973441871960682\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 0.708', 'Peso máximo al despegue (MTOW): 0.666', 'envergadura: 0.692', 'payload: 0.727']\n", + "**Mediana calculada:** 0.7\n", + "\n", + "--- Imputación para aeronave: **V32** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.477x + 0.279\n", + "Valor del parámetro correlacionado para la aeronave: 1.0\n", + "Predicción obtenida: 0.756\n", + "\tR²: 0.530038527273323, Desviación Estándar: 0.3373686112553851, Varianza: 0.11381757986038715, Incertidumbre: 0.0736198665799968\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.537\n", + "Valor del parámetro correlacionado para la aeronave: 23.5\n", + "Predicción obtenida: 1.025\n", + "\tR²: 0.9563099090104202, Desviación Estándar: 0.12476254599075952, Varianza: 0.015565692882096385, Incertidumbre: 0.024467948329707695\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.046x + -0.3\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 1.213\n", + "\tR²: 0.2987388890678494, Desviación Estándar: 0.49838437168601263, Varianza: 0.24838698194086156, Incertidumbre: 0.1174703229521921\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.405x + -0.259\n", + "Valor del parámetro correlacionado para la aeronave: 3.2\n", + "Predicción obtenida: 1.036\n", + "\tR²: 0.676989457910925, Desviación Estándar: 0.2796931909209141, Varianza: 0.0782282810475229, Incertidumbre: 0.0610340580361345\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.08x + 0.631\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 1.03\n", + "\tR²: 0.8477300129544219, Desviación Estándar: 0.24364789703392117, Varianza: 0.05936429772905225, Incertidumbre: 0.04973441871960682\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0]\n", + "Valores para Área del ala: [0.87, 1.158, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.063x + 0.414\n", + "Valor del parámetro correlacionado para la aeronave: 6.45\n", + "Predicción obtenida: 0.818\n", + "\tR²: 0.8862827411226012, Desviación Estándar: 0.22697772477286166, Varianza: 0.05151888754306494, Incertidumbre: 0.06552282524883024\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Precio (r = 0.899) ---\n", + "Aeronaves utilizadas: ['V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Precio: [3999.0, 4679.0, 2299.0, 6799.0, 4999.0, 6999.0, 9999.0, 13900.0, 15999.0]\n", + "Valores para Área del ala: [0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.0x + 0.241\n", + "Valor del parámetro correlacionado para la aeronave: 69999.0\n", + "Predicción obtenida: 11.824\n", + "\tR²: 0.8076686704270842, Desviación Estándar: 0.35422576262319455, Varianza: 0.12547589090598377, Incertidumbre: 0.11807525420773152\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 0.756', 'Peso máximo al despegue (MTOW): 1.025', 'Velocidad máxima (KIAS): 1.213', 'envergadura: 1.036', 'payload: 1.03', 'Empty weight: 0.818', 'Precio: 11.824']\n", + "**Mediana calculada:** 1.03\n", + "\n", + "--- Imputación para aeronave: **V35** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.459x + 0.325\n", + "Valor del parámetro correlacionado para la aeronave: 1.88\n", + "Predicción obtenida: 1.188\n", + "\tR²: 0.5187592344985992, Desviación Estándar: 0.33423029414950794, Varianza: 0.11170988952726661, Incertidumbre: 0.07125813814042148\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.538\n", + "Valor del parámetro correlacionado para la aeronave: 32.0\n", + "Predicción obtenida: 1.201\n", + "\tR²: 0.9567615844762626, Desviación Estándar: 0.12243454518963205, Varianza: 0.014990217855792052, Incertidumbre: 0.023562539207781154\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.046x + -0.324\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 1.203\n", + "\tR²: 0.3019978039605754, Desviación Estándar: 0.4868033988346396, Varianza: 0.23697754911695715, Incertidumbre: 0.1116803589943326\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.405x + -0.26\n", + "Valor del parámetro correlacionado para la aeronave: 3.5\n", + "Predicción obtenida: 1.157\n", + "\tR²: 0.678309022659344, Desviación Estándar: 0.27326502283958154, Varianza: 0.07467377270751702, Incertidumbre: 0.058260298624331026\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.08x + 0.631\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 1.429\n", + "\tR²: 0.850762300542554, Desviación Estándar: 0.23872521083313977, Varianza: 0.05698972628732704, Incertidumbre: 0.047745042166627956\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Precio (r = 0.899) ---\n", + "Aeronaves utilizadas: ['V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Precio: [3999.0, 4679.0, 69999.0, 2299.0, 6799.0, 4999.0, 6999.0, 9999.0, 13900.0, 15999.0]\n", + "Valores para Área del ala: [0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.0x + 1.468\n", + "Valor del parámetro correlacionado para la aeronave: 7999.0\n", + "Predicción obtenida: 1.471\n", + "\tR²: 5.510644397854758e-05, Desviación Estándar: 0.7803061547023157, Varianza: 0.6088776950663142, Incertidumbre: 0.24675447211070242\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Longitud del fuselaje: 1.188', 'Peso máximo al despegue (MTOW): 1.201', 'Velocidad máxima (KIAS): 1.203', 'envergadura: 1.157', 'payload: 1.429', 'Precio: 1.471']\n", + "**Mediana calculada:** 1.202\n", + "\n", + "--- Imputación para aeronave: **V39** ---\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.538\n", + "Valor del parámetro correlacionado para la aeronave: 24.0\n", + "Predicción obtenida: 1.035\n", + "\tR²: 0.9568416510162402, Desviación Estándar: 0.12022845817986676, Varianza: 0.014454882156307969, Incertidumbre: 0.022721042918332626\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.046x + -0.324\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 1.203\n", + "\tR²: 0.30291737528467566, Desviación Estándar: 0.474477281198844, Varianza: 0.2251286903738469, Incertidumbre: 0.10609634545398981\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.405x + -0.257\n", + "Valor del parámetro correlacionado para la aeronave: 3.9\n", + "Predicción obtenida: 1.32\n", + "\tR²: 0.6779800432691134, Desviación Estándar: 0.26741850007254947, Varianza: 0.07151265418105215, Incertidumbre: 0.05576061185064946\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.08x + 0.622\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 1.021\n", + "\tR²: 0.8465859657242829, Desviación Estándar: 0.23811937696573565, Varianza: 0.05670083768655011, Incertidumbre: 0.0466990519120324\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Precio (r = 0.899) ---\n", + "Aeronaves utilizadas: ['V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Precio: [3999.0, 4679.0, 69999.0, 7999.0, 2299.0, 6799.0, 4999.0, 6999.0, 9999.0, 13900.0, 15999.0]\n", + "Valores para Área del ala: [0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.0x + 1.439\n", + "Valor del parámetro correlacionado para la aeronave: 8999.0\n", + "Predicción obtenida: 1.445\n", + "\tR²: 0.00029183893894446644, Desviación Estándar: 0.7479568811282364, Varianza: 0.5594394960270789, Incertidumbre: 0.22551748491516507\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Peso máximo al despegue (MTOW): 1.035', 'Velocidad máxima (KIAS): 1.203', 'envergadura: 1.32', 'payload: 1.021', 'Precio: 1.445']\n", + "**Mediana calculada:** 1.203\n", + "\n", + "--- Imputación para aeronave: **Volitation VT370** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.459x + 0.326\n", + "Valor del parámetro correlacionado para la aeronave: 2.02\n", + "Predicción obtenida: 1.253\n", + "\tR²: 0.5188052941313532, Desviación Estándar: 0.3268966538966882, Varianza: 0.10686142232885117, Incertidumbre: 0.06816266424448632\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.548\n", + "Valor del parámetro correlacionado para la aeronave: 40.0\n", + "Predicción obtenida: 1.373\n", + "\tR²: 0.9540497205736983, Desviación Estándar: 0.12200219933246954, Varianza: 0.014884536641959632, Incertidumbre: 0.022655239663387023\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.046x + -0.324\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 1.203\n", + "\tR²: 0.3037289741090303, Desviación Estándar: 0.46304242133609924, Varianza: 0.21440828395679767, Incertidumbre: 0.10104414027372904\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.403x + -0.255\n", + "Valor del parámetro correlacionado para la aeronave: 6.5\n", + "Predicción obtenida: 2.362\n", + "\tR²: 0.6754640443421005, Desviación Estándar: 0.262831271883581, Varianza: 0.06908027747994087, Incertidumbre: 0.053650208713457374\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.079x + 0.636\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 2.06\n", + "\tR²: 0.8442705988266823, Desviación Estándar: 0.23612835689484077, Varianza: 0.05575660092985729, Incertidumbre: 0.04544292347218011\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Capacidad combustible (r = 1.0) ---\n", + "Aeronaves utilizadas: ['Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Capacidad combustible: [11.5, 11.5, 28.0]\n", + "Valores para Área del ala: [1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.078x + 0.426\n", + "Valor del parámetro correlacionado para la aeronave: 13.0\n", + "Predicción obtenida: 1.442\n", + "\tR²: 0.9999549326242733, Desviación Estándar: 0.004082482904638634, Varianza: 1.6666666666666698e-05, Incertidumbre: 0.002357022603955161\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Precio (r = 0.899) ---\n", + "Aeronaves utilizadas: ['V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Precio: [3999.0, 4679.0, 69999.0, 7999.0, 8999.0, 2299.0, 6799.0, 4999.0, 6999.0, 9999.0, 13900.0, 15999.0]\n", + "Valores para Área del ala: [0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.0x + 1.415\n", + "Valor del parámetro correlacionado para la aeronave: 8999.0\n", + "Predicción obtenida: 1.424\n", + "\tR²: 0.0005527163843784821, Desviación Estándar: 0.7192113906007205, Varianza: 0.5172650243698221, Incertidumbre: 0.20761844498378554\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Longitud del fuselaje: 1.253', 'Peso máximo al despegue (MTOW): 1.373', 'Velocidad máxima (KIAS): 1.203', 'envergadura: 2.362', 'payload: 2.06', 'Capacidad combustible: 1.442', 'Precio: 1.424']\n", + "**Mediana calculada:** 1.424\n", + "\n", + "--- Imputación para aeronave: **Volitation VT510** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.461x + 0.329\n", + "Valor del parámetro correlacionado para la aeronave: 2.905\n", + "Predicción obtenida: 1.668\n", + "\tR²: 0.5190019194394964, Desviación Estándar: 0.32183760586109134, Varianza: 0.10357944454639917, Incertidumbre: 0.06569482619988655\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.021x + 0.549\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 2.612\n", + "\tR²: 0.9538216857828705, Desviación Estándar: 0.12030701287802409, Varianza: 0.014473777347633054, Incertidumbre: 0.02196495492645277\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.046x + -0.299\n", + "Valor del parámetro correlacionado para la aeronave: 50.0\n", + "Predicción obtenida: 1.993\n", + "\tR²: 0.2984442398036834, Desviación Estándar: 0.45473235019931246, Varianza: 0.20678151031779013, Incertidumbre: 0.09694926281256372\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.313x + 0.036\n", + "Valor del parámetro correlacionado para la aeronave: 5.1\n", + "Predicción obtenida: 1.632\n", + "\tR²: 0.5619559117531061, Desviación Estándar: 0.30093688814319053, Varianza: 0.09056301064530718, Incertidumbre: 0.06018737762863811\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.076x + 0.65\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 2.539\n", + "\tR²: 0.8057229726084794, Desviación Estándar: 0.25898874198312066, Varianza: 0.06707516847399946, Incertidumbre: 0.04894427168948646\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Capacidad combustible (r = 1.0) ---\n", + "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Capacidad combustible: [13.0, 11.5, 11.5, 28.0]\n", + "Valores para Área del ala: [1.424, 1.33, 1.32, 2.615]\n", + "Ecuación de regresión: y = 0.078x + 0.417\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 2.378\n", + "\tR²: 0.9997609226468374, Desviación Estándar: 0.008439335502332609, Varianza: 7.122238372093159e-05, Incertidumbre: 0.004219667751166304\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Precio (r = 0.899) ---\n", + "Aeronaves utilizadas: ['V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Precio: [3999.0, 4679.0, 69999.0, 7999.0, 8999.0, 8999.0, 2299.0, 6799.0, 4999.0, 6999.0, 9999.0, 13900.0, 15999.0]\n", + "Valores para Área del ala: [0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.0x + 1.415\n", + "Valor del parámetro correlacionado para la aeronave: 16599.0\n", + "Predicción obtenida: 1.431\n", + "\tR²: 0.0005545122547352399, Desviación Estándar: 0.69099596127085, Varianza: 0.4774754184926259, Incertidumbre: 0.19164779765389064\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Longitud del fuselaje: 1.668', 'Peso máximo al despegue (MTOW): 2.612', 'Velocidad máxima (KIAS): 1.993', 'envergadura: 1.632', 'payload: 2.539', 'Capacidad combustible: 2.378', 'Precio: 1.431']\n", + "**Mediana calculada:** 1.993\n", + "\n", + "--- Imputación para aeronave: **Ascend** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993]\n", + "Ecuación de regresión: y = 0.485x + 0.296\n", + "Valor del parámetro correlacionado para la aeronave: 1.562\n", + "Predicción obtenida: 1.054\n", + "\tR²: 0.5551210410771654, Desviación Estándar: 0.32120350506612844, Varianza: 0.10317169166676639, Incertidumbre: 0.06424070101322568\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993]\n", + "Ecuación de regresión: y = 0.019x + 0.589\n", + "Valor del parámetro correlacionado para la aeronave: 9.5\n", + "Predicción obtenida: 0.771\n", + "\tR²: 0.9237785089671043, Desviación Estándar: 0.15544994346429547, Varianza: 0.02416468492305266, Incertidumbre: 0.027919634045949875\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993]\n", + "Ecuación de regresión: y = 0.046x + -0.299\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 1.076\n", + "\tR²: 0.3456241929431657, Desviación Estándar: 0.44473701805432836, Varianza: 0.197791015227856, Incertidumbre: 0.09273407872908897\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993]\n", + "Ecuación de regresión: y = 0.329x + -0.009\n", + "Valor del parámetro correlacionado para la aeronave: 2.0\n", + "Predicción obtenida: 0.649\n", + "\tR²: 0.58926011492781, Desviación Estándar: 0.3026438405729111, Varianza: 0.09159329423672163, Incertidumbre: 0.05935334033653606\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993]\n", + "Ecuación de regresión: y = 0.071x + 0.685\n", + "Valor del parámetro correlacionado para la aeronave: 0.6\n", + "Predicción obtenida: 0.727\n", + "\tR²: 0.7863972465064352, Desviación Estándar: 0.27091760618201804, Varianza: 0.07339634933939503, Incertidumbre: 0.0503081364980872\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.923) ---\n", + "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 6.8]\n", + "Valores para Área del ala: [1.55, 1.55, 1.55, 2.503, 0.84]\n", + "Ecuación de regresión: y = 0.025x + 0.682\n", + "Valor del parámetro correlacionado para la aeronave: 8.9\n", + "Predicción obtenida: 0.904\n", + "\tR²: 0.9483245120012642, Desviación Estándar: 0.12030916806448211, Varianza: 0.014474295920367803, Incertidumbre: 0.05380389562172576\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0]\n", + "Valores para Área del ala: [0.87, 1.158, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Ecuación de regresión: y = 0.062x + 0.442\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 0.627\n", + "\tR²: 0.8795539331981305, Desviación Estándar: 0.22504777591475647, Varianza: 0.05064650144417845, Incertidumbre: 0.0624170227299834\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 1.054', 'Peso máximo al despegue (MTOW): 0.771', 'Velocidad máxima (KIAS): 1.076', 'envergadura: 0.649', 'payload: 0.727', 'RTF (Including fuel & Batteries): 0.904', 'Empty weight: 0.627']\n", + "**Mediana calculada:** 0.771\n", + "\n", + "--- Imputación para aeronave: **Transition** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771]\n", + "Ecuación de regresión: y = 0.492x + 0.273\n", + "Valor del parámetro correlacionado para la aeronave: 2.3\n", + "Predicción obtenida: 1.404\n", + "\tR²: 0.5564924093650789, Desviación Estándar: 0.31959359329400444, Varianza: 0.10214006487457353, Incertidumbre: 0.0626774603317448\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771]\n", + "Ecuación de regresión: y = 0.019x + 0.589\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 0.933\n", + "\tR²: 0.9262301602367577, Desviación Estándar: 0.15300176981950134, Varianza: 0.023409541567899674, Incertidumbre: 0.02704714724322816\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771]\n", + "Ecuación de regresión: y = 0.047x + -0.36\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 1.056\n", + "\tR²: 0.36011776983130794, Desviación Estándar: 0.43951432810167035, Varianza: 0.19317284460666273, Incertidumbre: 0.08971548654094016\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771]\n", + "Ecuación de regresión: y = 0.323x + 0.017\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 0.986\n", + "\tR²: 0.600080995980541, Desviación Estándar: 0.29780984654529935, Varianza: 0.08869070469933475, Incertidumbre: 0.05731353169008324\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771]\n", + "Ecuación de regresión: y = 0.07x + 0.689\n", + "Valor del parámetro correlacionado para la aeronave: 1.5\n", + "Predicción obtenida: 0.795\n", + "\tR²: 0.7955552594057932, Desviación Estándar: 0.26647289638938826, Varianza: 0.07100780451014965, Incertidumbre: 0.04865107210540163\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.923) ---\n", + "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 6.8, 8.9]\n", + "Valores para Área del ala: [1.55, 1.55, 1.55, 2.503, 0.84, 0.771]\n", + "Ecuación de regresión: y = 0.026x + 0.624\n", + "Valor del parámetro correlacionado para la aeronave: 16.5\n", + "Predicción obtenida: 1.054\n", + "\tR²: 0.9575373795814451, Desviación Estándar: 0.11811377037897985, Varianza: 0.01395086275313838, Incertidumbre: 0.04821974483745979\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0]\n", + "Valores para Área del ala: [0.87, 1.158, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771]\n", + "Ecuación de regresión: y = 0.061x + 0.463\n", + "Valor del parámetro correlacionado para la aeronave: 5.8\n", + "Predicción obtenida: 0.816\n", + "\tR²: 0.8799346208461828, Desviación Estándar: 0.21981540136860422, Varianza: 0.04831881067884058, Incertidumbre: 0.0587481371612512\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 1.404', 'Peso máximo al despegue (MTOW): 0.933', 'Velocidad máxima (KIAS): 1.056', 'envergadura: 0.986', 'payload: 0.795', 'RTF (Including fuel & Batteries): 1.054', 'Empty weight: 0.816']\n", + "**Mediana calculada:** 0.986\n", + "\n", + "--- Imputación para aeronave: **Reach** ---\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986]\n", + "Ecuación de regresión: y = 0.48x + 0.28\n", + "Valor del parámetro correlacionado para la aeronave: 4.712\n", + "Predicción obtenida: 2.54\n", + "\tR²: 0.5321565310192382, Desviación Estándar: 0.3232764059484733, Varianza: 0.1045076346429621, Incertidumbre: 0.06221457333233526\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986]\n", + "Ecuación de regresión: y = 0.019x + 0.592\n", + "Valor del parámetro correlacionado para la aeronave: 91.0\n", + "Predicción obtenida: 2.329\n", + "\tR²: 0.9268130919795654, Desviación Estándar: 0.15093208149961143, Varianza: 0.02278049322580535, Incertidumbre: 0.026273902955966707\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986]\n", + "Ecuación de regresión: y = 0.047x + -0.373\n", + "Valor del parámetro correlacionado para la aeronave: 35.0\n", + "Predicción obtenida: 1.289\n", + "\tR²: 0.36780246644034287, Desviación Estándar: 0.4308491629268742, Varianza: 0.18563100119478818, Incertidumbre: 0.08616983258537483\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0, 3.0]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986]\n", + "Ecuación de regresión: y = 0.323x + 0.017\n", + "Valor del parámetro correlacionado para la aeronave: 6.0\n", + "Predicción obtenida: 1.954\n", + "\tR²: 0.6029729846601317, Desviación Estándar: 0.2924434664063362, Varianza: 0.0855231810437539, Incertidumbre: 0.05526662033263126\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5]\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986]\n", + "Ecuación de regresión: y = 0.069x + 0.705\n", + "Valor del parámetro correlacionado para la aeronave: 7.0\n", + "Predicción obtenida: 1.191\n", + "\tR²: 0.7961115317688139, Desviación Estándar: 0.26421267929509595, Varianza: 0.06980833990029324, Incertidumbre: 0.047453998064099126\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.923) ---\n", + "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend', 'Transition']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 6.8, 8.9, 16.5]\n", + "Valores para Área del ala: [1.55, 1.55, 1.55, 2.503, 0.84, 0.771, 0.986]\n", + "Ecuación de regresión: y = 0.026x + 0.605\n", + "Valor del parámetro correlacionado para la aeronave: 84.0\n", + "Predicción obtenida: 2.82\n", + "\tR²: 0.9596137014057883, Desviación Estándar: 0.11174694285275365, Varianza: 0.01248737923693659, Incertidumbre: 0.04223637436573326\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8]\n", + "Valores para Área del ala: [0.87, 1.158, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986]\n", + "Ecuación de regresión: y = 0.06x + 0.482\n", + "Valor del parámetro correlacionado para la aeronave: 31.0\n", + "Predicción obtenida: 2.346\n", + "\tR²: 0.8758975698949775, Desviación Estándar: 0.21647900912469786, Varianza: 0.046863161391611015, Incertidumbre: 0.05589463980956255\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Longitud del fuselaje: 2.54', 'Peso máximo al despegue (MTOW): 2.329', 'Velocidad máxima (KIAS): 1.289', 'envergadura: 1.954', 'payload: 1.191', 'RTF (Including fuel & Batteries): 2.82', 'Empty weight: 2.346']\n", + "**Mediana calculada:** 2.329\n", + "\n", + "=== Imputación para el parámetro: **Relación de aspecto del ala** ===\n", + "\n", + "--- Imputación para aeronave: **Fulmar X** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754]\n", + "Ecuación de regresión: y = -0.27x + 19.967\n", + "Valor del parámetro correlacionado para la aeronave: 30.407\n", + "Predicción obtenida: 11.753\n", + "\tR²: 0.9958397104049683, Desviación Estándar: 0.08406165080066717, Varianza: 0.007066361135333309, Incertidumbre: 0.03759351309822828\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754]\n", + "Ecuación de regresión: y = -1.556x + 16.146\n", + "Valor del parámetro correlacionado para la aeronave: 0.94\n", + "Predicción obtenida: 14.684\n", + "\tR²: 0.6391425255554871, Desviación Estándar: 0.7828958406065879, Varianza: 0.6129258972390959, Incertidumbre: 0.3501216637796341\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", + "Ecuación de regresión: y = -1.519x + 18.038\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 16.214\n", + "\tR²: 0.4138028760447283, Desviación Estándar: 0.8884554790463566, Varianza: 0.789353138247491, Incertidumbre: 0.4442277395231783\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754]\n", + "Ecuación de regresión: y = -0.04x + 15.296\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 14.489\n", + "\tR²: 0.6775655448926923, Desviación Estándar: 0.7447916715878835, Varianza: 0.5547146340666738, Incertidumbre: 0.28150479165337583\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", + "Ecuación de regresión: y = -0.176x + 19.272\n", + "Valor del parámetro correlacionado para la aeronave: 41.7\n", + "Predicción obtenida: 11.946\n", + "\tR²: 0.6228198639466289, Desviación Estándar: 0.7126696016612697, Varianza: 0.5078979611320329, Incertidumbre: 0.35633480083063485\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5]\n", + "Ecuación de regresión: y = -0.303x + 20.091\n", + "Valor del parámetro correlacionado para la aeronave: 27.8\n", + "Predicción obtenida: 11.659\n", + "\tR²: 0.9951683800002252, Desviación Estándar: 0.0922049930648979, Varianza: 0.00850176074609787, Incertidumbre: 0.05323457756664639\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 11.753', 'Área del ala: 14.684', 'Longitud del fuselaje: 16.214', 'Peso máximo al despegue (MTOW): 14.489', 'Velocidad máxima (KIAS): 11.946', 'Crucero KIAS: 11.659']\n", + "**Mediana calculada:** 13.218\n", + "\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218]\n", + "Ecuación de regresión: y = -0.211x + 18.836\n", + "Valor del parámetro correlacionado para la aeronave: 26.611\n", + "Predicción obtenida: 13.222\n", + "\tR²: 0.86788574295378, Desviación Estándar: 0.44779548895963533, Varianza: 0.2005207999325989, Incertidumbre: 0.1828117428452008\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218]\n", + "Ecuación de regresión: y = -1.355x + 15.647\n", + "Valor del parámetro correlacionado para la aeronave: 1.608\n", + "Predicción obtenida: 13.469\n", + "\tR²: 0.4774673753859955, Desviación Estándar: 0.8905567020216546, Varianza: 0.793091239515686, Incertidumbre: 0.3635682511614764\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218]\n", + "Ecuación de regresión: y = -0.157x + 14.552\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 14.364\n", + "\tR²: 0.0074891649212437406, Desviación Estándar: 1.1481791111587527, Varianza: 1.3183152713013033, Incertidumbre: 0.5134813085792517\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218]\n", + "Ecuación de regresión: y = -0.036x + 14.967\n", + "Valor del parámetro correlacionado para la aeronave: 55.0\n", + "Predicción obtenida: 12.99\n", + "\tR²: 0.5739892405979949, Desviación Estándar: 0.8056365874036895, Varianza: 0.6490503109634626, Incertidumbre: 0.28483554706256875\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218]\n", + "Ecuación de regresión: y = -0.122x + 17.914\n", + "Valor del parámetro correlacionado para la aeronave: 36.0\n", + "Predicción obtenida: 13.511\n", + "\tR²: 0.6167320222408146, Desviación Estándar: 0.7134988554846888, Varianza: 0.5090806167779609, Incertidumbre: 0.3190863885464126\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5]\n", + "Ecuación de regresión: y = -0.158x + 15.309\n", + "Valor del parámetro correlacionado para la aeronave: 12.0\n", + "Predicción obtenida: 13.417\n", + "\tR²: 0.7878676953385522, Desviación Estándar: 0.6109489630792406, Varianza: 0.3732586354875993, Incertidumbre: 0.24941886973776978\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.222', 'Área del ala: 13.469', 'Longitud del fuselaje: 14.364', 'Peso máximo al despegue (MTOW): 12.99', 'Velocidad máxima (KIAS): 13.511', 'payload: 13.417']\n", + "**Mediana calculada:** 13.443\n", + "\n", + "--- Imputación para aeronave: **Orbiter 3** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443]\n", + "Ecuación de regresión: y = -0.208x + 18.787\n", + "Valor del parámetro correlacionado para la aeronave: 26.611\n", + "Predicción obtenida: 13.263\n", + "\tR²: 0.866540858244522, Desviación Estándar: 0.42137143710599334, Varianza: 0.17755388800877012, Incertidumbre: 0.1592634331669075\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443]\n", + "Ecuación de regresión: y = -1.358x + 15.647\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 14.018\n", + "\tR²: 0.48897119697787084, Desviación Estándar: 0.8245435883924694, Varianza: 0.67987212915913, Incertidumbre: 0.31164818285989687\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443]\n", + "Ecuación de regresión: y = 0.102x + 13.89\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 14.012\n", + "\tR²: 0.003963308805261412, Desviación Estándar: 1.089046175187781, Varianza: 1.1860215716911349, Incertidumbre: 0.44460123925662026\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443]\n", + "Ecuación de regresión: y = -0.035x + 14.97\n", + "Valor del parámetro correlacionado para la aeronave: 32.0\n", + "Predicción obtenida: 13.857\n", + "\tR²: 0.5601880164889009, Desviación Estándar: 0.7722572050409231, Varianza: 0.5963811907376184, Incertidumbre: 0.25741906834697437\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443]\n", + "Ecuación de regresión: y = 0.0x + 14.293\n", + "Valor del parámetro correlacionado para la aeronave: 50.0\n", + "Predicción obtenida: 14.299\n", + "\tR²: 0.021248458324993225, Desviación Estándar: 0.896822690408026, Varianza: 0.8042909380306901, Incertidumbre: 0.4010712999033189\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443]\n", + "Ecuación de regresión: y = -0.123x + 17.936\n", + "Valor del parámetro correlacionado para la aeronave: 36.0\n", + "Predicción obtenida: 13.495\n", + "\tR²: 0.6432306766680158, Desviación Estándar: 0.6517820231226291, Varianza: 0.4248198056658274, Incertidumbre: 0.2660888966948914\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443]\n", + "Ecuación de regresión: y = -0.158x + 15.313\n", + "Valor del parámetro correlacionado para la aeronave: 5.5\n", + "Predicción obtenida: 14.446\n", + "\tR²: 0.787811738102038, Desviación Estándar: 0.565704012619775, Varianza: 0.3200210298941146, Incertidumbre: 0.21381601900903846\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.263', 'Área del ala: 14.018', 'Longitud del fuselaje: 14.012', 'Peso máximo al despegue (MTOW): 13.857', 'Alcance de la aeronave: 14.299', 'Velocidad máxima (KIAS): 13.495', 'payload: 14.446']\n", + "**Mediana calculada:** 14.012\n", + "\n", + "--- Imputación para aeronave: **Mantis** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012]\n", + "Ecuación de regresión: y = -0.198x + 18.646\n", + "Valor del parámetro correlacionado para la aeronave: 18.266\n", + "Predicción obtenida: 15.033\n", + "\tR²: 0.8162307529020697, Desviación Estándar: 0.463001126334729, Varianza: 0.2143700429872277, Incertidumbre: 0.16369561806414812\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012]\n", + "Ecuación de regresión: y = -1.357x + 15.646\n", + "Valor del parámetro correlacionado para la aeronave: 0.754\n", + "Predicción obtenida: 14.623\n", + "\tR²: 0.4900269781864812, Desviación Estándar: 0.7712927169798122, Varianza: 0.5948924552661006, Incertidumbre: 0.2726931552281109\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012]\n", + "Ecuación de regresión: y = 0.102x + 13.89\n", + "Valor del parámetro correlacionado para la aeronave: 1.48\n", + "Predicción obtenida: 14.041\n", + "\tR²: 0.0046993987852630426, Desviación Estándar: 1.008260850059533, Varianza: 1.0165899417627717, Incertidumbre: 0.3810867808485868\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012]\n", + "Ecuación de regresión: y = -0.035x + 14.994\n", + "Valor del parámetro correlacionado para la aeronave: 6.5\n", + "Predicción obtenida: 14.767\n", + "\tR²: 0.5649822868921843, Desviación Estándar: 0.7340836887623863, Varianza: 0.538878862106992, Incertidumbre: 0.23213764496672915\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012]\n", + "Ecuación de regresión: y = 0.0x + 14.219\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 14.223\n", + "\tR²: 0.03697239299019073, Desviación Estándar: 0.8249333692250005, Varianza: 0.680515063660911, Incertidumbre: 0.3367776377327012\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012]\n", + "Ecuación de regresión: y = -0.117x + 17.798\n", + "Valor del parámetro correlacionado para la aeronave: 25.6\n", + "Predicción obtenida: 14.81\n", + "\tR²: 0.6133897598737705, Desviación Estándar: 0.6283946045669089, Varianza: 0.3948797790488017, Incertidumbre: 0.23751083555697344\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218]\n", + "Ecuación de regresión: y = -0.214x + 18.598\n", + "Valor del parámetro correlacionado para la aeronave: 16.7\n", + "Predicción obtenida: 15.024\n", + "\tR²: 0.8546770471425846, Desviación Estándar: 0.4778243263623709, Varianza: 0.2283160868636535, Incertidumbre: 0.23891216318118544\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 15.033', 'Área del ala: 14.623', 'Longitud del fuselaje: 14.041', 'Peso máximo al despegue (MTOW): 14.767', 'Alcance de la aeronave: 14.223', 'Velocidad máxima (KIAS): 14.81', 'Crucero KIAS: 15.024']\n", + "**Mediana calculada:** 14.767\n", + "\n", + "--- Imputación para aeronave: **ScanEagle** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767]\n", + "Ecuación de regresión: y = -0.192x + 18.474\n", + "Valor del parámetro correlacionado para la aeronave: 30.625\n", + "Predicción obtenida: 12.602\n", + "\tR²: 0.8235797866498076, Desviación Estándar: 0.4433751181910776, Varianza: 0.19658149543095202, Incertidumbre: 0.14779170606369255\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767]\n", + "Ecuación de regresión: y = -1.383x + 15.694\n", + "Valor del parámetro correlacionado para la aeronave: 1.063\n", + "Predicción obtenida: 14.224\n", + "\tR²: 0.5237760149013296, Desviación Estándar: 0.7284550833069877, Varianza: 0.5306468083957905, Incertidumbre: 0.24281836110232924\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767]\n", + "Ecuación de regresión: y = 0.031x + 14.11\n", + "Valor del parámetro correlacionado para la aeronave: 1.71\n", + "Predicción obtenida: 14.162\n", + "\tR²: 0.00041867860015110114, Desviación Estándar: 0.9721354662815678, Varianza: 0.9450473648024812, Incertidumbre: 0.34370179021982145\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767]\n", + "Ecuación de regresión: y = -0.035x + 14.994\n", + "Valor del parámetro correlacionado para la aeronave: 26.5\n", + "Predicción obtenida: 14.067\n", + "\tR²: 0.6041711076219263, Desviación Estándar: 0.6999213418164504, Varianza: 0.48988988473014045, Incertidumbre: 0.21103422486975046\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767]\n", + "Ecuación de regresión: y = -0.116x + 17.772\n", + "Valor del parámetro correlacionado para la aeronave: 41.2\n", + "Predicción obtenida: 12.99\n", + "\tR²: 0.6343488080766746, Desviación Estándar: 0.5879646533380922, Varianza: 0.3457024335749829, Incertidumbre: 0.2078768967366813\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012]\n", + "Ecuación de regresión: y = -0.151x + 15.186\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 14.43\n", + "\tR²: 0.779730301455002, Desviación Estándar: 0.5464452282253737, Varianza: 0.29860238745028067, Incertidumbre: 0.19319756321259615\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767]\n", + "Ecuación de regresión: y = -0.207x + 18.409\n", + "Valor del parámetro correlacionado para la aeronave: 28.0\n", + "Predicción obtenida: 12.607\n", + "\tR²: 0.8557406648350283, Desviación Estándar: 0.43818596791937064, Varianza: 0.19200694248143574, Incertidumbre: 0.19596272221085095\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.602', 'Área del ala: 14.224', 'Longitud del fuselaje: 14.162', 'Peso máximo al despegue (MTOW): 14.067', 'Velocidad máxima (KIAS): 12.99', 'payload: 14.43', 'Crucero KIAS: 12.607']\n", + "**Mediana calculada:** 14.067\n", + "\n", + "--- Imputación para aeronave: **Integrator** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067]\n", + "Ecuación de regresión: y = -0.157x + 17.776\n", + "Valor del parámetro correlacionado para la aeronave: 30.953\n", + "Predicción obtenida: 12.923\n", + "\tR²: 0.6641740677951478, Desviación Estándar: 0.5805266763901159, Varianza: 0.3370112220005544, Incertidumbre: 0.18357865398802614\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067]\n", + "Ecuación de regresión: y = -1.374x + 15.667\n", + "Valor del parámetro correlacionado para la aeronave: 1.872\n", + "Predicción obtenida: 13.095\n", + "\tR²: 0.5219103690121566, Desviación Estándar: 0.6926593947898028, Varianza: 0.47977703719057585, Incertidumbre: 0.2190381330249543\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067]\n", + "Ecuación de regresión: y = 0.033x + 14.095\n", + "Valor del parámetro correlacionado para la aeronave: 2.5\n", + "Predicción obtenida: 14.178\n", + "\tR²: 0.000490016744001398, Desviación Estándar: 0.9170252395244427, Varianza: 0.8409352899248614, Incertidumbre: 0.3056750798414809\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067]\n", + "Ecuación de regresión: y = -0.035x + 14.994\n", + "Valor del parámetro correlacionado para la aeronave: 74.8\n", + "Predicción obtenida: 12.376\n", + "\tR²: 0.6075184827366995, Desviación Estándar: 0.6701236710904337, Varianza: 0.4490657345557197, Incertidumbre: 0.19344804094720108\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067]\n", + "Ecuación de regresión: y = -0.094x + 17.194\n", + "Valor del parámetro correlacionado para la aeronave: 46.3\n", + "Predicción obtenida: 12.823\n", + "\tR²: 0.526405227591237, Desviación Estándar: 0.6312351449660243, Varianza: 0.39845780824027766, Incertidumbre: 0.21041171498867475\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067]\n", + "Ecuación de regresión: y = -0.146x + 15.095\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 12.462\n", + "\tR²: 0.7754905637643377, Desviación Estándar: 0.5266918070184679, Varianza: 0.27740425958037895, Incertidumbre: 0.1755639356728226\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067]\n", + "Ecuación de regresión: y = -0.155x + 17.54\n", + "Valor del parámetro correlacionado para la aeronave: 28.3\n", + "Predicción obtenida: 13.14\n", + "\tR²: 0.663687056459504, Desviación Estándar: 0.6116803288548116, Varianza: 0.3741528247079305, Incertidumbre: 0.24971744856535041\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.923', 'Área del ala: 13.095', 'Longitud del fuselaje: 14.178', 'Peso máximo al despegue (MTOW): 12.376', 'Velocidad máxima (KIAS): 12.823', 'payload: 12.462', 'Crucero KIAS: 13.14']\n", + "**Mediana calculada:** 12.923\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923]\n", + "Ecuación de regresión: y = -0.157x + 17.776\n", + "Valor del parámetro correlacionado para la aeronave: 21.463\n", + "Predicción obtenida: 14.411\n", + "\tR²: 0.6955131309495535, Desviación Estándar: 0.5535104680562337, Varianza: 0.3063738382478309, Incertidumbre: 0.16688968546150232\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923]\n", + "Ecuación de regresión: y = -1.406x + 15.693\n", + "Valor del parámetro correlacionado para la aeronave: 2.09\n", + "Predicción obtenida: 12.754\n", + "\tR²: 0.5643813137066285, Desviación Estándar: 0.6620561206988881, Varianza: 0.4383183069548606, Incertidumbre: 0.1996174311378599\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923]\n", + "Ecuación de regresión: y = -0.033x + 14.943\n", + "Valor del parámetro correlacionado para la aeronave: 75.0\n", + "Predicción obtenida: 12.498\n", + "\tR²: 0.608454130768421, Desviación Estándar: 0.6573629374382771, Varianza: 0.4321260315174802, Incertidumbre: 0.18231967519410266\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923]\n", + "Ecuación de regresión: y = -0.139x + 15.064\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 12.555\n", + "\tR²: 0.7681447903007532, Desviación Estándar: 0.516359328939539, Varianza: 0.266626956582891, Incertidumbre: 0.16328715705250396\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.411', 'Área del ala: 12.754', 'Peso máximo al despegue (MTOW): 12.498', 'payload: 12.555']\n", + "**Mediana calculada:** 12.654\n", + "\n", + "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654]\n", + "Ecuación de regresión: y = -0.14x + 17.225\n", + "Valor del parámetro correlacionado para la aeronave: 33.045\n", + "Predicción obtenida: 12.59\n", + "\tR²: 0.510023026717956, Desviación Estándar: 0.7136489244448807, Varianza: 0.509294787361335, Incertidumbre: 0.2060126993175694\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654]\n", + "Ecuación de regresión: y = -1.426x + 15.711\n", + "Valor del parámetro correlacionado para la aeronave: 1.872\n", + "Predicción obtenida: 13.042\n", + "\tR²: 0.6128293293992375, Desviación Estándar: 0.6343778425999941, Varianza: 0.40243524718182283, Incertidumbre: 0.18312910909652033\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923]\n", + "Ecuación de regresión: y = -0.175x + 14.359\n", + "Valor del parámetro correlacionado para la aeronave: 2.5\n", + "Predicción obtenida: 13.92\n", + "\tR²: 0.013083522010913673, Desviación Estándar: 0.93913737855047, Varianza: 0.8819790157906487, Incertidumbre: 0.29698131520192456\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654]\n", + "Ecuación de regresión: y = -0.032x + 14.931\n", + "Valor del parámetro correlacionado para la aeronave: 74.8\n", + "Predicción obtenida: 12.534\n", + "\tR²: 0.6320455971220006, Desviación Estándar: 0.6345600288026622, Varianza: 0.40266643015403547, Incertidumbre: 0.16959330136578327\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767]\n", + "Ecuación de regresión: y = 0.0x + 14.332\n", + "Valor del parámetro correlacionado para la aeronave: 500.0\n", + "Predicción obtenida: 14.381\n", + "\tR²: 0.017282284063206976, Desviación Estándar: 0.7854344289958513, Varianza: 0.6169072422520389, Incertidumbre: 0.2968663100387202\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923]\n", + "Ecuación de regresión: y = -0.092x + 17.137\n", + "Valor del parámetro correlacionado para la aeronave: 46.3\n", + "Predicción obtenida: 12.859\n", + "\tR²: 0.5980072707351919, Desviación Estándar: 0.5993741728944746, Varianza: 0.3592493991329355, Incertidumbre: 0.18953875570260967\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654]\n", + "Ecuación de regresión: y = -0.138x + 15.058\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 12.572\n", + "\tR²: 0.7800961130119188, Desviación Estándar: 0.4930778276036819, Varianza: 0.24312574407436624, Incertidumbre: 0.14866855878226887\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.59', 'Área del ala: 13.042', 'Longitud del fuselaje: 13.92', 'Peso máximo al despegue (MTOW): 12.534', 'Alcance de la aeronave: 14.381', 'Velocidad máxima (KIAS): 12.859', 'payload: 12.572']\n", + "**Mediana calculada:** 12.859\n", + "\n", + "--- Imputación para aeronave: **ScanEagle 3** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859]\n", + "Ecuación de regresión: y = -0.135x + 17.109\n", + "Valor del parámetro correlacionado para la aeronave: 25.703\n", + "Predicción obtenida: 13.643\n", + "\tR²: 0.535314561075031, Desviación Estándar: 0.6887523223637181, Varianza: 0.4743797615614151, Incertidumbre: 0.19102552418286836\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859]\n", + "Ecuación de regresión: y = -1.448x + 15.728\n", + "Valor del parámetro correlacionado para la aeronave: 1.349\n", + "Predicción obtenida: 13.774\n", + "\tR²: 0.6339313870790875, Desviación Estándar: 0.6113146322958206, Varianza: 0.37370557965897433, Incertidumbre: 0.16954817324492275\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859]\n", + "Ecuación de regresión: y = -0.322x + 14.544\n", + "Valor del parámetro correlacionado para la aeronave: 2.4\n", + "Predicción obtenida: 13.772\n", + "\tR²: 0.042345280621575165, Desviación Estándar: 0.9416884437522549, Varianza: 0.8867771250965437, Incertidumbre: 0.2839297488490841\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859]\n", + "Ecuación de regresión: y = -0.031x + 14.911\n", + "Valor del parámetro correlacionado para la aeronave: 36.3\n", + "Predicción obtenida: 13.783\n", + "\tR²: 0.6385049587577492, Desviación Estándar: 0.6178453553338438, Varianza: 0.3817328831076036, Incertidumbre: 0.15952698478263871\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859]\n", + "Ecuación de regresión: y = -0.092x + 17.137\n", + "Valor del parámetro correlacionado para la aeronave: 41.2\n", + "Predicción obtenida: 13.33\n", + "\tR²: 0.6473061906505011, Desviación Estándar: 0.5714808508233791, Varianza: 0.32659036285781323, Incertidumbre: 0.17230795973220137\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859]\n", + "Ecuación de regresión: y = -0.135x + 15.044\n", + "Valor del parámetro correlacionado para la aeronave: 8.6\n", + "Predicción obtenida: 13.883\n", + "\tR²: 0.7797023232836269, Desviación Estándar: 0.4782732644237212, Varianza: 0.22874531546252275, Incertidumbre: 0.13806559898061826\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923]\n", + "Ecuación de regresión: y = -0.161x + 17.637\n", + "Valor del parámetro correlacionado para la aeronave: 23.5\n", + "Predicción obtenida: 13.85\n", + "\tR²: 0.7175450779766854, Desviación Estándar: 0.5704794589847608, Varianza: 0.3254468131235454, Incertidumbre: 0.21562096807776418\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.643', 'Área del ala: 13.774', 'Longitud del fuselaje: 13.772', 'Peso máximo al despegue (MTOW): 13.783', 'Velocidad máxima (KIAS): 13.33', 'payload: 13.883', 'Crucero KIAS: 13.85']\n", + "**Mediana calculada:** 13.774\n", + "\n", + "--- Imputación para aeronave: **RQNan21A Blackjack** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774]\n", + "Ecuación de regresión: y = -0.135x + 17.114\n", + "Valor del parámetro correlacionado para la aeronave: 33.797\n", + "Predicción obtenida: 12.563\n", + "\tR²: 0.5342243863228833, Desviación Estándar: 0.6645511147608912, Varianza: 0.44162818412994326, Incertidumbre: 0.17760875624528186\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774]\n", + "Ecuación de regresión: y = -1.448x + 15.728\n", + "Valor del parámetro correlacionado para la aeronave: 1.802\n", + "Predicción obtenida: 13.119\n", + "\tR²: 0.6340136541211301, Desviación Estándar: 0.5890775326049247, Varianza: 0.3470123394199061, Incertidumbre: 0.15743759294669885\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774]\n", + "Ecuación de regresión: y = -0.322x + 14.544\n", + "Valor del parámetro correlacionado para la aeronave: 2.5\n", + "Predicción obtenida: 13.74\n", + "\tR²: 0.04429674165169528, Desviación Estándar: 0.9015982192102218, Varianza: 0.8128793488830431, Incertidumbre: 0.2602689872809544\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774]\n", + "Ecuación de regresión: y = -0.031x + 14.91\n", + "Valor del parámetro correlacionado para la aeronave: 61.0\n", + "Predicción obtenida: 13.014\n", + "\tR²: 0.6395237688067317, Desviación Estándar: 0.598229831780838, Varianza: 0.35787893163252976, Incertidumbre: 0.1495574579452095\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859]\n", + "Ecuación de regresión: y = 0.0x + 14.115\n", + "Valor del parámetro correlacionado para la aeronave: 92.6\n", + "Predicción obtenida: 14.128\n", + "\tR²: 0.022722235303102134, Desviación Estándar: 0.889761474449699, Varianza: 0.7916754814149023, Incertidumbre: 0.31457818611096156\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774]\n", + "Ecuación de regresión: y = -0.089x + 17.059\n", + "Valor del parámetro correlacionado para la aeronave: 46.3\n", + "Predicción obtenida: 12.932\n", + "\tR²: 0.6311618765895944, Desviación Estándar: 0.5601049538041426, Varianza: 0.3137175592759407, Incertidumbre: 0.16168837292663232\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774]\n", + "Ecuación de regresión: y = -0.134x + 15.028\n", + "Valor del parámetro correlacionado para la aeronave: 17.7\n", + "Predicción obtenida: 12.649\n", + "\tR²: 0.7813316684515454, Desviación Estándar: 0.4604041822404213, Varianza: 0.21197201102447108, Incertidumbre: 0.12769314511583893\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774]\n", + "Ecuación de regresión: y = -0.161x + 17.634\n", + "Valor del parámetro correlacionado para la aeronave: 30.9\n", + "Predicción obtenida: 12.645\n", + "\tR²: 0.7186963836121465, Desviación Estándar: 0.5342166106536912, Varianza: 0.28538738709831746, Incertidumbre: 0.18887409400785932\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.563', 'Área del ala: 13.119', 'Longitud del fuselaje: 13.74', 'Peso máximo al despegue (MTOW): 13.014', 'Alcance de la aeronave: 14.128', 'Velocidad máxima (KIAS): 12.932', 'payload: 12.649', 'Crucero KIAS: 12.645']\n", + "**Mediana calculada:** 12.973\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Evo** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973]\n", + "Ecuación de regresión: y = -0.128x + 16.957\n", + "Valor del parámetro correlacionado para la aeronave: 18.091\n", + "Predicción obtenida: 14.65\n", + "\tR²: 0.5421915391164227, Desviación Estándar: 0.6489139037011813, Varianza: 0.42108925441670597, Incertidumbre: 0.16754884947714124\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973]\n", + "Ecuación de regresión: y = -1.461x + 15.737\n", + "Valor del parámetro correlacionado para la aeronave: 0.84\n", + "Predicción obtenida: 14.509\n", + "\tR²: 0.6465018935699594, Desviación Estándar: 0.5702151055687861, Varianza: 0.3251452666188218, Incertidumbre: 0.14722890717492082\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973]\n", + "Ecuación de regresión: y = -0.403x + 14.649\n", + "Valor del parámetro correlacionado para la aeronave: 0.75\n", + "Predicción obtenida: 14.347\n", + "\tR²: 0.06842483962479096, Desviación Estándar: 0.8886738964344558, Varianza: 0.7897412942039979, Incertidumbre: 0.2464737923662158\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973]\n", + "Ecuación de regresión: y = -0.031x + 14.911\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 14.599\n", + "\tR²: 0.6468495695110492, Desviación Estándar: 0.5804480353649806, Varianza: 0.3369199217590657, Incertidumbre: 0.14077932705835708\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973]\n", + "Ecuación de regresión: y = 0.0x + 13.94\n", + "Valor del parámetro correlacionado para la aeronave: 270.0\n", + "Predicción obtenida: 13.997\n", + "\tR²: 0.04703203479609108, Desviación Estándar: 0.9111851551421984, Varianza: 0.8302583869515122, Incertidumbre: 0.30372838504739946\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973]\n", + "Ecuación de regresión: y = -0.131x + 15.014\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 14.62\n", + "\tR²: 0.7771803589437556, Desviación Estándar: 0.45102082932177623, Varianza: 0.20341978848210282, Incertidumbre: 0.1205403869729095\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973]\n", + "Ecuación de regresión: y = -0.153x + 17.471\n", + "Valor del parámetro correlacionado para la aeronave: 16.54\n", + "Predicción obtenida: 14.941\n", + "\tR²: 0.7387286606060905, Desviación Estándar: 0.5118777159980322, Varianza: 0.26201879613536216, Incertidumbre: 0.1706259053326774\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.65', 'Área del ala: 14.509', 'Longitud del fuselaje: 14.347', 'Peso máximo al despegue (MTOW): 14.599', 'Alcance de la aeronave: 13.997', 'payload: 14.62', 'Crucero KIAS: 14.941']\n", + "**Mediana calculada:** 14.599\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599]\n", + "Ecuación de regresión: y = -0.127x + 16.936\n", + "Valor del parámetro correlacionado para la aeronave: 17.5\n", + "Predicción obtenida: 14.717\n", + "\tR²: 0.5673252249031574, Desviación Estándar: 0.6284176728117805, Varianza: 0.39490877150217396, Incertidumbre: 0.15710441820294513\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599]\n", + "Ecuación de regresión: y = -1.472x + 15.757\n", + "Valor del parámetro correlacionado para la aeronave: 0.7\n", + "Predicción obtenida: 14.727\n", + "\tR²: 0.6655463732035445, Desviación Estándar: 0.5525041655219294, Varianza: 0.3052608529190835, Incertidumbre: 0.13812604138048235\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599]\n", + "Ecuación de regresión: y = -0.452x + 14.76\n", + "Valor del parámetro correlacionado para la aeronave: 0.9\n", + "Predicción obtenida: 14.353\n", + "\tR²: 0.10767788693788138, Desviación Estándar: 0.8582189379484234, Varianza: 0.7365397454533199, Incertidumbre: 0.22936865918885752\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599]\n", + "Ecuación de regresión: y = -0.031x + 14.91\n", + "Valor del parámetro correlacionado para la aeronave: 6.2\n", + "Predicción obtenida: 14.717\n", + "\tR²: 0.668742738945738, Desviación Estándar: 0.5640940957956807, Varianza: 0.3182021489115466, Incertidumbre: 0.1329582534548066\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599]\n", + "Ecuación de regresión: y = 0.0x + 14.014\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 14.033\n", + "\tR²: 0.03658191201096572, Desviación Estándar: 0.8828624043127778, Varianza: 0.7794460249489389, Incertidumbre: 0.2791856058160841\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599]\n", + "Ecuación de regresión: y = -0.131x + 15.009\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 14.852\n", + "\tR²: 0.7989376635956842, Desviación Estándar: 0.43575473105263046, Varianza: 0.1898821856347503, Incertidumbre: 0.11251138775986198\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599]\n", + "Ecuación de regresión: y = -0.146x + 17.287\n", + "Valor del parámetro correlacionado para la aeronave: 16.0\n", + "Predicción obtenida: 14.945\n", + "\tR²: 0.7422196460742506, Desviación Estándar: 0.4948955953255141, Varianza: 0.244921650272595, Incertidumbre: 0.15649972852136038\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.717', 'Área del ala: 14.727', 'Longitud del fuselaje: 14.353', 'Peso máximo al despegue (MTOW): 14.717', 'Alcance de la aeronave: 14.033', 'payload: 14.852', 'Crucero KIAS: 14.945']\n", + "**Mediana calculada:** 14.717\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = -0.127x + 16.936\n", + "Valor del parámetro correlacionado para la aeronave: 17.5\n", + "Predicción obtenida: 14.717\n", + "\tR²: 0.5929127107679053, Desviación Estándar: 0.6096546936267196, Varianza: 0.37167884546108937, Incertidumbre: 0.14786298217509053\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = -1.471x + 15.755\n", + "Valor del parámetro correlacionado para la aeronave: 0.7\n", + "Predicción obtenida: 14.726\n", + "\tR²: 0.685319780926233, Desviación Estándar: 0.5360124441675181, Varianza: 0.28730934030243677, Incertidumbre: 0.13000211317342153\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = -0.501x + 14.874\n", + "Valor del parámetro correlacionado para la aeronave: 0.9\n", + "Predicción obtenida: 14.424\n", + "\tR²: 0.14520304942233875, Desviación Estándar: 0.8333946066823736, Varianza: 0.6945465704472683, Incertidumbre: 0.2151815621666609\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = -0.031x + 14.91\n", + "Valor del parámetro correlacionado para la aeronave: 6.2\n", + "Predicción obtenida: 14.717\n", + "\tR²: 0.6901941411051212, Desviación Estándar: 0.5490488826768337, Varianza: 0.3014546755686795, Incertidumbre: 0.12596045235011777\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = 0.0x + 14.096\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 14.111\n", + "\tR²: 0.0237018252029767, Desviación Estándar: 0.8638914663706774, Varianza: 0.7463084656680793, Incertidumbre: 0.2604730775946788\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = -0.129x + 14.981\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 14.826\n", + "\tR²: 0.8169712264348787, Desviación Estándar: 0.4230085386335259, Varianza: 0.17893622375687118, Incertidumbre: 0.10575213465838147\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = -0.143x + 17.182\n", + "Valor del parámetro correlacionado para la aeronave: 16.0\n", + "Predicción obtenida: 14.9\n", + "\tR²: 0.7519794290280211, Desviación Estándar: 0.4758638703382313, Varianza: 0.22644642309328103, Incertidumbre: 0.14347835538165868\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.717', 'Área del ala: 14.726', 'Longitud del fuselaje: 14.424', 'Peso máximo al despegue (MTOW): 14.717', 'Alcance de la aeronave: 14.111', 'payload: 14.826', 'Crucero KIAS: 14.9']\n", + "**Mediana calculada:** 14.717\n", + "\n", + "--- Imputación para aeronave: **V21** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = -0.127x + 16.936\n", + "Valor del parámetro correlacionado para la aeronave: 19.688\n", + "Predicción obtenida: 14.439\n", + "\tR²: 0.5929127107679053, Desviación Estándar: 0.6096546936267196, Varianza: 0.37167884546108937, Incertidumbre: 0.14786298217509053\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = -1.471x + 15.755\n", + "Valor del parámetro correlacionado para la aeronave: 0.8\n", + "Predicción obtenida: 14.578\n", + "\tR²: 0.685319780926233, Desviación Estándar: 0.5360124441675181, Varianza: 0.28730934030243677, Incertidumbre: 0.13000211317342153\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = -0.501x + 14.874\n", + "Valor del parámetro correlacionado para la aeronave: 0.93\n", + "Predicción obtenida: 14.409\n", + "\tR²: 0.14520304942233875, Desviación Estándar: 0.8333946066823736, Varianza: 0.6945465704472683, Incertidumbre: 0.2151815621666609\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = -0.031x + 14.91\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 14.599\n", + "\tR²: 0.6901941411051212, Desviación Estándar: 0.5490488826768337, Varianza: 0.3014546755686795, Incertidumbre: 0.12596045235011777\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973]\n", + "Ecuación de regresión: y = -0.089x + 17.046\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 14.119\n", + "\tR²: 0.6582796109191538, Desviación Estándar: 0.5382312625669818, Varianza: 0.2896928920044473, Incertidumbre: 0.14927849348022884\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = -0.129x + 14.981\n", + "Valor del parámetro correlacionado para la aeronave: 1.5\n", + "Predicción obtenida: 14.787\n", + "\tR²: 0.8169712264348787, Desviación Estándar: 0.4230085386335259, Varianza: 0.17893622375687118, Incertidumbre: 0.10575213465838147\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = -0.143x + 17.182\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 14.615\n", + "\tR²: 0.7519794290280211, Desviación Estándar: 0.4758638703382313, Varianza: 0.22644642309328103, Incertidumbre: 0.14347835538165868\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.439', 'Área del ala: 14.578', 'Longitud del fuselaje: 14.409', 'Peso máximo al despegue (MTOW): 14.599', 'Velocidad máxima (KIAS): 14.119', 'payload: 14.787', 'Crucero KIAS: 14.615']\n", + "**Mediana calculada:** 14.578\n", + "\n", + "--- Imputación para aeronave: **V25** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578]\n", + "Ecuación de regresión: y = -0.128x + 16.971\n", + "Valor del parámetro correlacionado para la aeronave: 21.875\n", + "Predicción obtenida: 14.173\n", + "\tR²: 0.606745390877248, Desviación Estándar: 0.5932915224926394, Varianza: 0.351994830661634, Incertidumbre: 0.13984015292501215\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578]\n", + "Ecuación de regresión: y = -1.471x + 15.755\n", + "Valor del parámetro correlacionado para la aeronave: 0.52\n", + "Predicción obtenida: 14.99\n", + "\tR²: 0.6968457130521275, Desviación Estándar: 0.5209104729685736, Varianza: 0.2713477208483431, Incertidumbre: 0.12277977594239006\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578]\n", + "Ecuación de regresión: y = -0.519x + 14.918\n", + "Valor del parámetro correlacionado para la aeronave: 0.93\n", + "Predicción obtenida: 14.435\n", + "\tR²: 0.16853217384488628, Desviación Estándar: 0.807864743048595, Varianza: 0.6526454430609725, Incertidumbre: 0.20196618576214875\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578]\n", + "Ecuación de regresión: y = -0.031x + 14.908\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 14.519\n", + "\tR²: 0.703248032143867, Desviación Estándar: 0.5351650687571093, Varianza: 0.2864016508178016, Incertidumbre: 0.11966654729242453\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578]\n", + "Ecuación de regresión: y = -0.09x + 17.131\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 14.156\n", + "\tR²: 0.6564776133589556, Desviación Estándar: 0.5318209559369983, Varianza: 0.28283352917374266, Incertidumbre: 0.14213512915877488\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578]\n", + "Ecuación de regresión: y = -0.127x + 14.946\n", + "Valor del parámetro correlacionado para la aeronave: 2.2\n", + "Predicción obtenida: 14.666\n", + "\tR²: 0.8258810341686761, Desviación Estándar: 0.41302279972648664, Varianza: 0.1705878330939055, Incertidumbre: 0.10017274288591961\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578]\n", + "Ecuación de regresión: y = -0.142x + 17.171\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 14.326\n", + "\tR²: 0.7588352909196386, Desviación Estándar: 0.4557148035481193, Varianza: 0.20767598217290095, Incertidumbre: 0.13155353225110206\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.173', 'Área del ala: 14.99', 'Longitud del fuselaje: 14.435', 'Peso máximo al despegue (MTOW): 14.519', 'Velocidad máxima (KIAS): 14.156', 'payload: 14.666', 'Crucero KIAS: 14.326']\n", + "**Mediana calculada:** 14.435\n", + "\n", + "--- Imputación para aeronave: **V32** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", + "Ecuación de regresión: y = -0.129x + 17.012\n", + "Valor del parámetro correlacionado para la aeronave: 21.875\n", + "Predicción obtenida: 14.189\n", + "\tR²: 0.6110785668236963, Desviación Estándar: 0.5803950706423994, Varianza: 0.3368584380259958, Incertidumbre: 0.13315176106628257\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", + "Ecuación de regresión: y = -1.399x + 15.635\n", + "Valor del parámetro correlacionado para la aeronave: 1.03\n", + "Predicción obtenida: 14.194\n", + "\tR²: 0.6872612258181625, Desviación Estándar: 0.5204555466744641, Varianza: 0.2708739760642153, Incertidumbre: 0.11940069118732659\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", + "Ecuación de regresión: y = -0.519x + 14.918\n", + "Valor del parámetro correlacionado para la aeronave: 1.0\n", + "Predicción obtenida: 14.399\n", + "\tR²: 0.18078496325464366, Desviación Estándar: 0.7837439261959693, Varianza: 0.614254541849073, Incertidumbre: 0.19008582300836904\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", + "Ecuación de regresión: y = -0.031x + 14.898\n", + "Valor del parámetro correlacionado para la aeronave: 23.5\n", + "Predicción obtenida: 14.171\n", + "\tR²: 0.7106634123093627, Desviación Estándar: 0.5225600649328873, Varianza: 0.27306902146266343, Incertidumbre: 0.11403195489123787\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435]\n", + "Ecuación de regresión: y = -0.091x + 17.178\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 14.177\n", + "\tR²: 0.6583687729951575, Desviación Estándar: 0.5184390004821812, Varianza: 0.26877899722096305, Incertidumbre: 0.1338603743261271\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", + "Ecuación de regresión: y = -0.125x + 14.913\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 14.287\n", + "\tR²: 0.8297829718090136, Desviación Estándar: 0.4046033990551174, Varianza: 0.16370391052695454, Incertidumbre: 0.09536593572100009\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", + "Ecuación de regresión: y = -0.143x + 17.19\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 14.335\n", + "\tR²: 0.760755459593193, Desviación Estándar: 0.43879195181776326, Varianza: 0.19253837698004228, Incertidumbre: 0.12169899088768228\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.189', 'Área del ala: 14.194', 'Longitud del fuselaje: 14.399', 'Peso máximo al despegue (MTOW): 14.171', 'Velocidad máxima (KIAS): 14.177', 'payload: 14.287', 'Crucero KIAS: 14.335']\n", + "**Mediana calculada:** 14.194\n", + "\n", + "--- Imputación para aeronave: **V35** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", + "Ecuación de regresión: y = -0.129x + 17.012\n", + "Valor del parámetro correlacionado para la aeronave: 27.344\n", + "Predicción obtenida: 13.484\n", + "\tR²: 0.6135185471717993, Desviación Estándar: 0.5657000289670463, Varianza: 0.320016522773317, Incertidumbre: 0.12649437196439156\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", + "Ecuación de regresión: y = -1.399x + 15.635\n", + "Valor del parámetro correlacionado para la aeronave: 1.202\n", + "Predicción obtenida: 13.953\n", + "\tR²: 0.6892242356314006, Desviación Estándar: 0.5072773180656356, Varianza: 0.25733027742386405, Incertidumbre: 0.11343065666385432\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", + "Ecuación de regresión: y = -0.503x + 14.88\n", + "Valor del parámetro correlacionado para la aeronave: 1.88\n", + "Predicción obtenida: 13.934\n", + "\tR²: 0.17980664787964395, Desviación Estándar: 0.7630256503950571, Varianza: 0.5822081431607998, Incertidumbre: 0.1798468705378736\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", + "Ecuación de regresión: y = -0.031x + 14.9\n", + "Valor del parámetro correlacionado para la aeronave: 32.0\n", + "Predicción obtenida: 13.909\n", + "\tR²: 0.7135650005961087, Desviación Estándar: 0.5105679278629256, Varianza: 0.26067960896224157, Incertidumbre: 0.10885344796857536\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194]\n", + "Ecuación de regresión: y = -0.091x + 17.181\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 14.178\n", + "\tR²: 0.6602446946044866, Desviación Estándar: 0.5019934524987022, Varianza: 0.25199742635156674, Incertidumbre: 0.12549836312467555\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", + "Ecuación de regresión: y = -0.125x + 14.903\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 13.656\n", + "\tR²: 0.8320650835497453, Desviación Estándar: 0.394342588657032, Varianza: 0.1555060772287291, Incertidumbre: 0.09046839437317004\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", + "Ecuación de regresión: y = -0.142x + 17.168\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 13.613\n", + "\tR²: 0.7592272998904299, Desviación Estándar: 0.4243796984927565, Varianza: 0.18009812849280288, Incertidumbre: 0.11342024526159432\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.484', 'Área del ala: 13.953', 'Longitud del fuselaje: 13.934', 'Peso máximo al despegue (MTOW): 13.909', 'Velocidad máxima (KIAS): 14.178', 'payload: 13.656', 'Crucero KIAS: 13.613']\n", + "**Mediana calculada:** 13.909\n", + "\n", + "--- Imputación para aeronave: **V39** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", + "Ecuación de regresión: y = -0.127x + 16.984\n", + "Valor del parámetro correlacionado para la aeronave: 27.344\n", + "Predicción obtenida: 13.51\n", + "\tR²: 0.6032988200454845, Desviación Estándar: 0.5593319783823425, Varianza: 0.3128522620411053, Incertidumbre: 0.12205624426411849\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", + "Ecuación de regresión: y = -1.399x + 15.632\n", + "Valor del parámetro correlacionado para la aeronave: 1.203\n", + "Predicción obtenida: 13.95\n", + "\tR²: 0.6891267288117411, Desviación Estándar: 0.4951418045804114, Varianza: 0.24516540664314632, Incertidumbre: 0.10804879996317941\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", + "Ecuación de regresión: y = -0.031x + 14.9\n", + "Valor del parámetro correlacionado para la aeronave: 24.0\n", + "Predicción obtenida: 14.157\n", + "\tR²: 0.713892022390023, Desviación Estándar: 0.4993452842769774, Varianza: 0.24934571292965538, Incertidumbre: 0.10412068936317738\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909]\n", + "Ecuación de regresión: y = -0.09x + 17.141\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 14.161\n", + "\tR²: 0.6545775391356355, Desviación Estándar: 0.49107571880076345, Varianza: 0.2411553615956865, Incertidumbre: 0.11910335639950966\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", + "Ecuación de regresión: y = -0.125x + 14.915\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 14.292\n", + "\tR²: 0.8291128061127491, Desviación Estándar: 0.38828624580477394, Varianza: 0.15076620868116533, Incertidumbre: 0.0868234440347667\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", + "Ecuación de regresión: y = -0.14x + 17.137\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 13.64\n", + "\tR²: 0.7524328660064801, Desviación Estándar: 0.4163959134902486, Varianza: 0.1733855567713786, Incertidumbre: 0.10751296255843715\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.51', 'Área del ala: 13.95', 'Peso máximo al despegue (MTOW): 14.157', 'Velocidad máxima (KIAS): 14.161', 'payload: 14.292', 'Crucero KIAS: 13.64']\n", + "**Mediana calculada:** 14.054\n", + "\n", + "--- Imputación para aeronave: **Volitation VT370** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054]\n", + "Ecuación de regresión: y = -0.125x + 16.949\n", + "Valor del parámetro correlacionado para la aeronave: 27.344\n", + "Predicción obtenida: 13.542\n", + "\tR²: 0.5871778945845514, Desviación Estándar: 0.5579448935987363, Varianza: 0.3113025042929052, Incertidumbre: 0.11895425100220543\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054]\n", + "Ecuación de regresión: y = -1.4x + 15.638\n", + "Valor del parámetro correlacionado para la aeronave: 1.424\n", + "Predicción obtenida: 13.645\n", + "\tR²: 0.6890363238184001, Desviación Estándar: 0.4842444656090505, Varianza: 0.2344927024729949, Incertidumbre: 0.10324126695908932\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", + "Ecuación de regresión: y = -0.504x + 14.879\n", + "Valor del parámetro correlacionado para la aeronave: 2.02\n", + "Predicción obtenida: 13.862\n", + "\tR²: 0.18057230208723418, Desviación Estándar: 0.7426949723818971, Varianza: 0.5515958220013469, Incertidumbre: 0.1703859121309695\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054]\n", + "Ecuación de regresión: y = -0.031x + 14.893\n", + "Valor del parámetro correlacionado para la aeronave: 40.0\n", + "Predicción obtenida: 13.657\n", + "\tR²: 0.7145635232521227, Desviación Estándar: 0.4892582472099815, Varianza: 0.2393736324629834, Incertidumbre: 0.09986942150941053\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054]\n", + "Ecuación de regresión: y = -0.09x + 17.126\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 14.154\n", + "\tR²: 0.6540169951205086, Desviación Estándar: 0.47786020803476664, Varianza: 0.22835037842303046, Incertidumbre: 0.11263273118686595\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054]\n", + "Ecuación de regresión: y = -0.124x + 14.893\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 12.67\n", + "\tR²: 0.8273716345066311, Desviación Estándar: 0.38222671429420746, Varianza: 0.1460972611201457, Incertidumbre: 0.08340870718511739\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054]\n", + "Ecuación de regresión: y = -0.137x + 17.097\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 13.675\n", + "\tR²: 0.7375431593293715, Desviación Estándar: 0.41513917051541654, Varianza: 0.1723405308962281, Incertidumbre: 0.10378479262885414\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.542', 'Área del ala: 13.645', 'Longitud del fuselaje: 13.862', 'Peso máximo al despegue (MTOW): 13.657', 'Velocidad máxima (KIAS): 14.154', 'payload: 12.67', 'Crucero KIAS: 13.675']\n", + "**Mediana calculada:** 13.657\n", + "\n", + "--- Imputación para aeronave: **Skyeye 2600** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", + "Ecuación de regresión: y = -0.124x + 16.942\n", + "Valor del parámetro correlacionado para la aeronave: 36.094\n", + "Predicción obtenida: 12.462\n", + "\tR²: 0.5876976210919462, Desviación Estándar: 0.5461828235331814, Varianza: 0.29831567672267834, Incertidumbre: 0.11388699141705999\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", + "Ecuación de regresión: y = -1.399x + 15.638\n", + "Valor del parámetro correlacionado para la aeronave: 0.88\n", + "Predicción obtenida: 14.407\n", + "\tR²: 0.6899904903283296, Desviación Estándar: 0.4736064692670731, Varianza: 0.22430308773162308, Incertidumbre: 0.09875377543286375\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657]\n", + "Ecuación de regresión: y = -0.51x + 14.88\n", + "Valor del parámetro correlacionado para la aeronave: 2.05\n", + "Predicción obtenida: 13.834\n", + "\tR²: 0.18539329015630057, Desviación Estándar: 0.725250008930158, Varianza: 0.5259875754531943, Incertidumbre: 0.16217083206501628\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", + "Ecuación de regresión: y = -0.031x + 14.893\n", + "Valor del parámetro correlacionado para la aeronave: 15.0\n", + "Predicción obtenida: 14.429\n", + "\tR²: 0.714747401512647, Desviación Estándar: 0.4793732254945338, Varianza: 0.22979868932103314, Incertidumbre: 0.09587464509890677\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", + "Ecuación de regresión: y = -0.116x + 14.861\n", + "Valor del parámetro correlacionado para la aeronave: 4.0\n", + "Predicción obtenida: 14.398\n", + "\tR²: 0.7784792227330228, Desviación Estándar: 0.42305623788558205, Varianza: 0.17897658041390221, Incertidumbre: 0.09019589297591563\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", + "Ecuación de regresión: y = -0.137x + 17.098\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 12.578\n", + "\tR²: 0.7417224511187792, Desviación Estándar: 0.40276642319311073, Varianza: 0.16222079165177197, Incertidumbre: 0.09768520619278931\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.462', 'Área del ala: 14.407', 'Longitud del fuselaje: 13.834', 'Peso máximo al despegue (MTOW): 14.429', 'payload: 14.398', 'Crucero KIAS: 12.578']\n", + "**Mediana calculada:** 14.116\n", + "\n", + "--- Imputación para aeronave: **Skyeye 2930 VTOL** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116]\n", + "Ecuación de regresión: y = -0.1x + 16.412\n", + "Valor del parámetro correlacionado para la aeronave: 26.25\n", + "Predicción obtenida: 13.779\n", + "\tR²: 0.45776737032064685, Desviación Estándar: 0.6141620658053559, Varianza: 0.37719504307430235, Incertidumbre: 0.12536530671639556\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116]\n", + "Ecuación de regresión: y = -1.382x + 15.604\n", + "Valor del parámetro correlacionado para la aeronave: 1.0\n", + "Predicción obtenida: 14.223\n", + "\tR²: 0.686240688916129, Desviación Estándar: 0.4671845538323762, Varianza: 0.21826140733955643, Incertidumbre: 0.0953636477165951\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116]\n", + "Ecuación de regresión: y = -0.501x + 14.877\n", + "Valor del parámetro correlacionado para la aeronave: 2.03\n", + "Predicción obtenida: 13.861\n", + "\tR²: 0.1804114233108679, Desviación Estándar: 0.7102825050039291, Varianza: 0.5045012369146565, Incertidumbre: 0.1549963497134931\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116]\n", + "Ecuación de regresión: y = -0.03x + 14.866\n", + "Valor del parámetro correlacionado para la aeronave: 28.0\n", + "Predicción obtenida: 14.013\n", + "\tR²: 0.7118496958649173, Desviación Estándar: 0.47380475199318356, Varianza: 0.2244909430113222, Incertidumbre: 0.09292075677100896\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", + "Ecuación de regresión: y = -0.089x + 17.061\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 14.392\n", + "\tR²: 0.6368881023118692, Desviación Estándar: 0.47811022561640054, Varianza: 0.2285893878389654, Incertidumbre: 0.10968600828080638\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116]\n", + "Ecuación de regresión: y = -0.114x + 14.834\n", + "Valor del parámetro correlacionado para la aeronave: 6.0\n", + "Predicción obtenida: 14.148\n", + "\tR²: 0.776389170737342, Desviación Estándar: 0.41760392510378513, Varianza: 0.17439303826208777, Incertidumbre: 0.0870764377509503\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116]\n", + "Ecuación de regresión: y = -0.107x + 16.512\n", + "Valor del parámetro correlacionado para la aeronave: 24.0\n", + "Predicción obtenida: 13.932\n", + "\tR²: 0.5759571817973563, Desviación Estándar: 0.5016106192563546, Varianza: 0.25161321335074355, Incertidumbre: 0.11823075679711727\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.779', 'Área del ala: 14.223', 'Longitud del fuselaje: 13.861', 'Peso máximo al despegue (MTOW): 14.013', 'Velocidad máxima (KIAS): 14.392', 'payload: 14.148', 'Crucero KIAS: 13.932']\n", + "**Mediana calculada:** 14.013\n", + "\n", + "--- Imputación para aeronave: **Skyeye 3600** ---\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013]\n", + "Ecuación de regresión: y = -1.374x + 15.586\n", + "Valor del parámetro correlacionado para la aeronave: 1.33\n", + "Predicción obtenida: 13.759\n", + "\tR²: 0.684015300708086, Desviación Estándar: 0.45956965580498865, Varianza: 0.21120426853671576, Incertidumbre: 0.09191393116099773\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013]\n", + "Ecuación de regresión: y = -0.496x + 14.877\n", + "Valor del parámetro correlacionado para la aeronave: 2.488\n", + "Predicción obtenida: 13.641\n", + "\tR²: 0.17871439167546932, Desviación Estándar: 0.6946713690287201, Varianza: 0.4825683109482361, Incertidumbre: 0.14810443350865574\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013]\n", + "Ecuación de regresión: y = -0.03x + 14.866\n", + "Valor del parámetro correlacionado para la aeronave: 28.0\n", + "Predicción obtenida: 14.013\n", + "\tR²: 0.7125790322691798, Desviación Estándar: 0.4649478109590719, Varianza: 0.21617646691563286, Incertidumbre: 0.08947924793878247\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013]\n", + "Ecuación de regresión: y = -0.114x + 14.824\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 13.686\n", + "\tR²: 0.7764391254855179, Desviación Estándar: 0.4096931753354857, Varianza: 0.16784849791647305, Incertidumbre: 0.08362826922271187\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Área del ala: 13.759', 'Longitud del fuselaje: 13.641', 'Peso máximo al despegue (MTOW): 14.013', 'payload: 13.686']\n", + "**Mediana calculada:** 13.722\n", + "\n", + "--- Imputación para aeronave: **Skyeye 3600 VTOL** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013]\n", + "Ecuación de regresión: y = -0.1x + 16.413\n", + "Valor del parámetro correlacionado para la aeronave: 32.813\n", + "Predicción obtenida: 13.132\n", + "\tR²: 0.45509872448190547, Desviación Estándar: 0.6034999397625862, Varianza: 0.36421217729344524, Incertidumbre: 0.12069998795251724\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722]\n", + "Ecuación de regresión: y = -1.374x + 15.585\n", + "Valor del parámetro correlacionado para la aeronave: 1.32\n", + "Predicción obtenida: 13.771\n", + "\tR²: 0.6844716239481952, Desviación Estándar: 0.45070169488409495, Varianza: 0.2031320177713958, Incertidumbre: 0.08838987450089776\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722]\n", + "Ecuación de regresión: y = -0.491x + 14.87\n", + "Valor del parámetro correlacionado para la aeronave: 2.42\n", + "Predicción obtenida: 13.682\n", + "\tR²: 0.18314058790644772, Desviación Estándar: 0.6795903299866143, Varianza: 0.4618430166113153, Incertidumbre: 0.14170437945601297\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722]\n", + "Ecuación de regresión: y = -0.03x + 14.851\n", + "Valor del parámetro correlacionado para la aeronave: 40.0\n", + "Predicción obtenida: 13.637\n", + "\tR²: 0.7086441586316434, Desviación Estándar: 0.4597355870860578, Varianza: 0.2113568100333622, Incertidumbre: 0.08688185944828475\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717]\n", + "Ecuación de regresión: y = 0.0x + 14.096\n", + "Valor del parámetro correlacionado para la aeronave: 300.0\n", + "Predicción obtenida: 14.141\n", + "\tR²: 0.0237018252029767, Desviación Estándar: 0.8638914663706774, Varianza: 0.7463084656680793, Incertidumbre: 0.2604730775946788\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013]\n", + "Ecuación de regresión: y = -0.087x + 16.976\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 14.103\n", + "\tR²: 0.6259343281303104, Desviación Estándar: 0.47309816529550813, Varianza: 0.22382187400597592, Incertidumbre: 0.1057879657631188\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722]\n", + "Ecuación de regresión: y = -0.114x + 14.826\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 13.687\n", + "\tR²: 0.7763699681724744, Desviación Estándar: 0.40147919692479167, Varianza: 0.16118554556337567, Incertidumbre: 0.08029583938495834\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013]\n", + "Ecuación de regresión: y = -0.107x + 16.512\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 13.292\n", + "\tR²: 0.5754628990444985, Desviación Estándar: 0.48856755387819806, Varianza: 0.23869825470252598, Incertidumbre: 0.11208508391831203\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.132', 'Área del ala: 13.771', 'Longitud del fuselaje: 13.682', 'Peso máximo al despegue (MTOW): 13.637', 'Alcance de la aeronave: 14.141', 'Velocidad máxima (KIAS): 14.103', 'payload: 13.687', 'Crucero KIAS: 13.292']\n", + "**Mediana calculada:** 13.684\n", + "\n", + "--- Imputación para aeronave: **Skyeye 5000** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684]\n", + "Ecuación de regresión: y = -0.095x + 16.308\n", + "Valor del parámetro correlacionado para la aeronave: 36.094\n", + "Predicción obtenida: 12.878\n", + "\tR²: 0.4401977193966343, Desviación Estándar: 0.6005761559399635, Varianza: 0.36069171908362335, Incertidumbre: 0.11778267455909297\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684]\n", + "Ecuación de regresión: y = -1.375x + 15.584\n", + "Valor del parámetro correlacionado para la aeronave: 2.615\n", + "Predicción obtenida: 11.987\n", + "\tR²: 0.6847809364305605, Desviación Estándar: 0.4425842972237917, Varianza: 0.19588086014907763, Incertidumbre: 0.08517538771375249\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684]\n", + "Ecuación de regresión: y = -0.491x + 14.87\n", + "Valor del parámetro correlacionado para la aeronave: 3.5\n", + "Predicción obtenida: 13.153\n", + "\tR²: 0.18887857116834583, Desviación Estándar: 0.6652816445821408, Varianza: 0.44259966661791794, Incertidumbre: 0.13580004703882315\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = -0.996) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5]\n", + "Ecuación de regresión: y = -38.708x + 23.253\n", + "Valor del parámetro correlacionado para la aeronave: 0.375\n", + "Predicción obtenida: 8.737\n", + "\tR²: 0.9845301318279952, Desviación Estándar: 0.16498762115286075, Varianza: 0.02722091513367991, Incertidumbre: 0.09525564748556016\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684]\n", + "Ecuación de regresión: y = -0.03x + 14.852\n", + "Valor del parámetro correlacionado para la aeronave: 90.0\n", + "Predicción obtenida: 12.123\n", + "\tR²: 0.7086905843429328, Desviación Estándar: 0.4518198530718025, Varianza: 0.20414117962982523, Incertidumbre: 0.08390084041127426\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684]\n", + "Ecuación de regresión: y = -0.086x + 16.93\n", + "Valor del parámetro correlacionado para la aeronave: 42.0\n", + "Predicción obtenida: 13.305\n", + "\tR²: 0.6139919598950958, Desviación Estándar: 0.4702102654821254, Varianza: 0.22109769376477087, Incertidumbre: 0.10260829210081529\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684]\n", + "Ecuación de regresión: y = -0.114x + 14.826\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 12.548\n", + "\tR²: 0.7763982091486223, Desviación Estándar: 0.39368317088871674, Varianza: 0.15498643904099454, Incertidumbre: 0.07720762194364038\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684]\n", + "Ecuación de regresión: y = -0.103x + 16.426\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 13.035\n", + "\tR²: 0.5683584083709616, Desviación Estándar: 0.4831583112495382, Varianza: 0.2334419537295056, Incertidumbre: 0.10803748278479687\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.878', 'Área del ala: 11.987', 'Longitud del fuselaje: 13.153', 'Ancho del fuselaje: 8.737', 'Peso máximo al despegue (MTOW): 12.123', 'Velocidad máxima (KIAS): 13.305', 'payload: 12.548', 'Crucero KIAS: 13.035']\n", + "**Mediana calculada:** 12.713\n", + "\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713]\n", + "Ecuación de regresión: y = -0.097x + 16.349\n", + "Valor del parámetro correlacionado para la aeronave: 30.625\n", + "Predicción obtenida: 13.383\n", + "\tR²: 0.47992913317978514, Desviación Estándar: 0.590075692205931, Varianza: 0.34818932253230866, Incertidumbre: 0.11356011991244967\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713]\n", + "Ecuación de regresión: y = -1.253x + 15.452\n", + "Valor del parámetro correlacionado para la aeronave: 2.615\n", + "Predicción obtenida: 12.175\n", + "\tR²: 0.6860448617033243, Desviación Estándar: 0.4503815543594209, Varianza: 0.202843544507208, Incertidumbre: 0.08511411342326757\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713]\n", + "Ecuación de regresión: y = -0.545x + 14.955\n", + "Valor del parámetro correlacionado para la aeronave: 3.5\n", + "Predicción obtenida: 13.046\n", + "\tR²: 0.2643820653371962, Desviación Estándar: 0.6563198967362653, Varianza: 0.43075580685190196, Incertidumbre: 0.13126397934725306\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = -0.996) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.713]\n", + "Ecuación de regresión: y = -16.3x + 18.292\n", + "Valor del parámetro correlacionado para la aeronave: 0.375\n", + "Predicción obtenida: 12.179\n", + "\tR²: 0.700384799125958, Desviación Estándar: 0.742124944233944, Varianza: 0.5507494328542346, Incertidumbre: 0.371062472116972\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713]\n", + "Ecuación de regresión: y = -0.029x + 14.809\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 11.945\n", + "\tR²: 0.7105088370955781, Desviación Estándar: 0.45476821765148717, Varianza: 0.20681413178591043, Incertidumbre: 0.08302893708137953\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717, 13.684]\n", + "Ecuación de regresión: y = 0.0x + 14.051\n", + "Valor del parámetro correlacionado para la aeronave: 800.0\n", + "Predicción obtenida: 14.182\n", + "\tR²: 0.027074923206067303, Desviación Estándar: 0.8366422137567701, Varianza: 0.699970193839829, Incertidumbre: 0.24151780366393782\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713]\n", + "Ecuación de regresión: y = -0.09x + 17.04\n", + "Valor del parámetro correlacionado para la aeronave: 42.0\n", + "Predicción obtenida: 13.251\n", + "\tR²: 0.6306274945350463, Desviación Estándar: 0.47489583792466356, Varianza: 0.22552605687816832, Incertidumbre: 0.10124813283983614\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713]\n", + "Ecuación de regresión: y = -0.112x + 14.818\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 12.006\n", + "\tR²: 0.7868817244212251, Desviación Estándar: 0.3874643871417889, Varianza: 0.15012865130316205, Incertidumbre: 0.07456755607256839\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713]\n", + "Ecuación de regresión: y = -0.107x + 16.516\n", + "Valor del parámetro correlacionado para la aeronave: 28.0\n", + "Predicción obtenida: 13.514\n", + "\tR²: 0.6195025656212503, Desviación Estándar: 0.4757969201229366, Varianza: 0.22638270919847214, Incertidumbre: 0.10382740009001022\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.383', 'Área del ala: 12.175', 'Longitud del fuselaje: 13.046', 'Ancho del fuselaje: 12.179', 'Peso máximo al despegue (MTOW): 11.945', 'Alcance de la aeronave: 14.182', 'Velocidad máxima (KIAS): 13.251', 'payload: 12.006', 'Crucero KIAS: 13.514']\n", + "**Mediana calculada:** 13.046\n", + "\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046]\n", + "Ecuación de regresión: y = -0.098x + 16.379\n", + "Valor del parámetro correlacionado para la aeronave: 33.885\n", + "Predicción obtenida: 13.042\n", + "\tR²: 0.4912217506212547, Desviación Estándar: 0.5827288503285376, Varianza: 0.33957291300521925, Incertidumbre: 0.11012540141084928\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046]\n", + "Ecuación de regresión: y = -1.137x + 15.327\n", + "Valor del parámetro correlacionado para la aeronave: 2.615\n", + "Predicción obtenida: 12.354\n", + "\tR²: 0.6639527907853231, Desviación Estándar: 0.4654542390243011, Varianza: 0.21664764862569122, Incertidumbre: 0.08643268232155993\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046]\n", + "Ecuación de regresión: y = -0.545x + 14.955\n", + "Valor del parámetro correlacionado para la aeronave: 3.5\n", + "Predicción obtenida: 13.046\n", + "\tR²: 0.30031202594013184, Desviación Estándar: 0.643574609531079, Varianza: 0.4141882780330809, Incertidumbre: 0.12621536509430112\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = -0.996) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.713, 13.046]\n", + "Ecuación de regresión: y = -13.682x + 17.712\n", + "Valor del parámetro correlacionado para la aeronave: 0.375\n", + "Predicción obtenida: 12.582\n", + "\tR²: 0.6751786720121111, Desviación Estándar: 0.7218724853677485, Varianza: 0.5210998851310104, Incertidumbre: 0.3228311896738016\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046]\n", + "Ecuación de regresión: y = -0.026x + 14.728\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 12.163\n", + "\tR²: 0.672459148806959, Desviación Estándar: 0.48114314921165147, Varianza: 0.2314987300333055, Incertidumbre: 0.08641586063227229\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046]\n", + "Ecuación de regresión: y = -0.091x + 17.075\n", + "Valor del parámetro correlacionado para la aeronave: 38.0\n", + "Predicción obtenida: 13.6\n", + "\tR²: 0.6449500428703554, Desviación Estándar: 0.4662598472902855, Varianza: 0.21739824519516038, Incertidumbre: 0.09722189885607506\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046]\n", + "Ecuación de regresión: y = -0.102x + 14.742\n", + "Valor del parámetro correlacionado para la aeronave: 15.0\n", + "Predicción obtenida: 13.216\n", + "\tR²: 0.7459460023089699, Desviación Estándar: 0.41981136981986106, Varianza: 0.17624158623002814, Incertidumbre: 0.0793368915786228\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046]\n", + "Ecuación de regresión: y = -0.11x + 16.561\n", + "Valor del parámetro correlacionado para la aeronave: 35.0\n", + "Predicción obtenida: 12.711\n", + "\tR²: 0.6278560933803895, Desviación Estándar: 0.47470648197778126, Varianza: 0.22534624403172154, Incertidumbre: 0.10120776201631464\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.042', 'Área del ala: 12.354', 'Longitud del fuselaje: 13.046', 'Ancho del fuselaje: 12.582', 'Peso máximo al despegue (MTOW): 12.163', 'Velocidad máxima (KIAS): 13.6', 'payload: 13.216', 'Crucero KIAS: 12.711']\n", + "**Mediana calculada:** 12.876\n", + "\n", + "--- Imputación para aeronave: **Volitation VT510** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876]\n", + "Ecuación de regresión: y = -0.1x + 16.407\n", + "Valor del parámetro correlacionado para la aeronave: 32.813\n", + "Predicción obtenida: 13.134\n", + "\tR²: 0.5120481249262255, Desviación Estándar: 0.5733507354534118, Varianza: 0.32873106584496814, Incertidumbre: 0.1064685587140827\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876]\n", + "Ecuación de regresión: y = -1.079x + 15.265\n", + "Valor del parámetro correlacionado para la aeronave: 1.993\n", + "Predicción obtenida: 13.114\n", + "\tR²: 0.6668961846597724, Desviación Estándar: 0.46579848190753764, Varianza: 0.21696822574736668, Incertidumbre: 0.08504277859747346\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876]\n", + "Ecuación de regresión: y = -0.56x + 14.977\n", + "Valor del parámetro correlacionado para la aeronave: 2.905\n", + "Predicción obtenida: 13.351\n", + "\tR²: 0.3416103143456418, Desviación Estándar: 0.632254792757014, Varianza: 0.3997461229642147, Incertidumbre: 0.12167749159823103\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876]\n", + "Ecuación de regresión: y = -0.024x + 14.684\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 12.281\n", + "\tR²: 0.6638393025961682, Desviación Estándar: 0.4873610039590521, Varianza: 0.23752074817997518, Incertidumbre: 0.08615406769633238\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876]\n", + "Ecuación de regresión: y = -0.093x + 17.103\n", + "Valor del parámetro correlacionado para la aeronave: 50.0\n", + "Predicción obtenida: 12.449\n", + "\tR²: 0.632488199457406, Desviación Estándar: 0.4786898436308466, Varianza: 0.2291439663953244, Incertidumbre: 0.09771215516235353\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876]\n", + "Ecuación de regresión: y = -0.103x + 14.741\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 12.172\n", + "\tR²: 0.7481817504133035, Desviación Estándar: 0.41708148056338223, Varianza: 0.173956961428943, Incertidumbre: 0.07745008658060275\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876]\n", + "Ecuación de regresión: y = -0.108x + 16.515\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 13.281\n", + "\tR²: 0.6540354444609318, Desviación Estándar: 0.4653103999726643, Varianza: 0.21651376832272087, Incertidumbre: 0.09702392540496321\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.134', 'Área del ala: 13.114', 'Longitud del fuselaje: 13.351', 'Peso máximo al despegue (MTOW): 12.281', 'Velocidad máxima (KIAS): 12.449', 'payload: 12.172', 'Crucero KIAS: 13.281']\n", + "**Mediana calculada:** 13.114\n", + "\n", + "--- Imputación para aeronave: **Ascend** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114]\n", + "Ecuación de regresión: y = -0.1x + 16.409\n", + "Valor del parámetro correlacionado para la aeronave: 21.875\n", + "Predicción obtenida: 14.225\n", + "\tR²: 0.5225539026607045, Desviación Estándar: 0.5637250248651999, Varianza: 0.31778590365927023, Incertidumbre: 0.10292163744961022\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114]\n", + "Ecuación de regresión: y = -1.079x + 15.265\n", + "Valor del parámetro correlacionado para la aeronave: 0.771\n", + "Predicción obtenida: 14.433\n", + "\tR²: 0.6740457304881754, Desviación Estándar: 0.4582240184158411, Varianza: 0.20996925105316108, Incertidumbre: 0.08229946322349961\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114]\n", + "Ecuación de regresión: y = -0.571x + 14.991\n", + "Valor del parámetro correlacionado para la aeronave: 1.562\n", + "Predicción obtenida: 14.1\n", + "\tR²: 0.3593175354516496, Desviación Estándar: 0.6223578212520778, Varianza: 0.3873292576736333, Incertidumbre: 0.11761457296635622\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114]\n", + "Ecuación de regresión: y = -0.022x + 14.64\n", + "Valor del parámetro correlacionado para la aeronave: 9.5\n", + "Predicción obtenida: 14.427\n", + "\tR²: 0.6427542977157497, Desviación Estándar: 0.4983661335008928, Varianza: 0.24836880302062972, Incertidumbre: 0.08675440832753979\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114]\n", + "Ecuación de regresión: y = -0.086x + 16.872\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 14.293\n", + "\tR²: 0.6198077286916189, Desviación Estándar: 0.4840831085935595, Varianza: 0.23433645602560393, Incertidumbre: 0.0968166217187119\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114]\n", + "Ecuación de regresión: y = -0.095x + 14.682\n", + "Valor del parámetro correlacionado para la aeronave: 0.6\n", + "Predicción obtenida: 14.626\n", + "\tR²: 0.7144699709998399, Desviación Estándar: 0.4395959045047289, Varianza: 0.19324455925733075, Incertidumbre: 0.08025886436137562\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114]\n", + "Ecuación de regresión: y = -0.109x + 16.535\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 14.357\n", + "\tR²: 0.6656187134909644, Desviación Estándar: 0.45668281214871304, Varianza: 0.20855919091205674, Incertidumbre: 0.0932199886719708\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.225', 'Área del ala: 14.433', 'Longitud del fuselaje: 14.1', 'Peso máximo al despegue (MTOW): 14.427', 'Velocidad máxima (KIAS): 14.293', 'payload: 14.626', 'Crucero KIAS: 14.357']\n", + "**Mediana calculada:** 14.357\n", + "\n", + "--- Imputación para aeronave: **Transition** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", + "Ecuación de regresión: y = -0.1x + 16.428\n", + "Valor del parámetro correlacionado para la aeronave: 21.875\n", + "Predicción obtenida: 14.232\n", + "\tR²: 0.5298654936239107, Desviación Estándar: 0.5550399694090079, Varianza: 0.30806936764155246, Incertidumbre: 0.09968812134263677\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", + "Ecuación de regresión: y = -1.075x + 15.258\n", + "Valor del parámetro correlacionado para la aeronave: 0.986\n", + "Predicción obtenida: 14.197\n", + "\tR²: 0.6793546401299544, Desviación Estándar: 0.45119534421783186, Varianza: 0.2035772386438478, Incertidumbre: 0.07976082188405685\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", + "Ecuación de regresión: y = -0.577x + 15.013\n", + "Valor del parámetro correlacionado para la aeronave: 2.3\n", + "Predicción obtenida: 13.686\n", + "\tR²: 0.3655687526307442, Desviación Estándar: 0.6133106646584037, Varianza: 0.37614997138373285, Incertidumbre: 0.11388893128133473\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0, 9.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", + "Ecuación de regresión: y = -0.022x + 14.635\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 14.233\n", + "\tR²: 0.6493187979493635, Desviación Estándar: 0.4911193487224634, Varianza: 0.2411982146895766, Incertidumbre: 0.08422627344201288\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", + "Ecuación de regresión: y = -0.086x + 16.884\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 14.297\n", + "\tR²: 0.6276324914013882, Desviación Estándar: 0.4748361245225159, Varianza: 0.22546934515156222, Incertidumbre: 0.09312302556534931\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", + "Ecuación de regresión: y = -0.093x + 14.657\n", + "Valor del parámetro correlacionado para la aeronave: 1.5\n", + "Predicción obtenida: 14.517\n", + "\tR²: 0.7184307080462207, Desviación Estándar: 0.43488206123514683, Varianza: 0.18912240718412998, Incertidumbre: 0.07810712395416497\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", + "Ecuación de regresión: y = -0.109x + 16.535\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 14.357\n", + "\tR²: 0.670906735462538, Desviación Estándar: 0.44745594752331586, Varianza: 0.20021682497398838, Incertidumbre: 0.08949118950466317\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.232', 'Área del ala: 14.197', 'Longitud del fuselaje: 13.686', 'Peso máximo al despegue (MTOW): 14.233', 'Velocidad máxima (KIAS): 14.297', 'payload: 14.517', 'Crucero KIAS: 14.357']\n", + "**Mediana calculada:** 14.233\n", + "\n", + "--- Imputación para aeronave: **Reach** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 21.875]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", + "Ecuación de regresión: y = -0.1x + 16.428\n", + "Valor del parámetro correlacionado para la aeronave: 27.344\n", + "Predicción obtenida: 13.683\n", + "\tR²: 0.5344512568573817, Desviación Estándar: 0.5462986887260068, Varianza: 0.2984422573037545, Incertidumbre: 0.09657287683786958\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = -0.831) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", + "Ecuación de regresión: y = -1.077x + 15.26\n", + "Valor del parámetro correlacionado para la aeronave: 2.329\n", + "Predicción obtenida: 12.753\n", + "\tR²: 0.6824488751728104, Desviación Estándar: 0.444348541369244, Varianza: 0.19744562621697476, Incertidumbre: 0.07735115250889142\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", + "Ecuación de regresión: y = -0.569x + 15.015\n", + "Valor del parámetro correlacionado para la aeronave: 4.712\n", + "Predicción obtenida: 12.334\n", + "\tR²: 0.3541273256305937, Desviación Estándar: 0.6109197380467731, Varianza: 0.3732229263351379, Incertidumbre: 0.11153817378445491\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0, 9.5, 18.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", + "Ecuación de regresión: y = -0.022x + 14.635\n", + "Valor del parámetro correlacionado para la aeronave: 91.0\n", + "Predicción obtenida: 12.602\n", + "\tR²: 0.6533839808972084, Desviación Estándar: 0.4840525196077455, Varianza: 0.2343068417386068, Incertidumbre: 0.08181980929170896\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", + "Ecuación de regresión: y = -0.086x + 16.872\n", + "Valor del parámetro correlacionado para la aeronave: 35.0\n", + "Predicción obtenida: 13.864\n", + "\tR²: 0.6317983869828936, Desviación Estándar: 0.46611492225485973, Varianza: 0.21726312074865395, Incertidumbre: 0.08970385861238159\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", + "Ecuación de regresión: y = -0.092x + 14.633\n", + "Valor del parámetro correlacionado para la aeronave: 7.0\n", + "Predicción obtenida: 13.991\n", + "\tR²: 0.7193105569936625, Desviación Estándar: 0.4307419939368637, Varianza: 0.18553866534070512, Incertidumbre: 0.07614514621364275\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = -0.999) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 20.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", + "Ecuación de regresión: y = -0.108x + 16.516\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 13.808\n", + "\tR²: 0.6725895092170291, Desviación Estándar: 0.4393983706690954, Varianza: 0.19307092814665575, Incertidumbre: 0.08617311024163819\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.683', 'Área del ala: 12.753', 'Longitud del fuselaje: 12.334', 'Peso máximo al despegue (MTOW): 12.602', 'Velocidad máxima (KIAS): 13.864', 'payload: 13.991', 'Crucero KIAS: 13.808']\n", + "**Mediana calculada:** 13.683\n", + "\n", + "=== Imputación para el parámetro: **Longitud del fuselaje** ===\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = 1.343x + 0.345\n", + "Valor del parámetro correlacionado para la aeronave: 2.503\n", + "Predicción obtenida: 3.707\n", + "\tR²: 0.6086218910264541, Desviación Estándar: 0.5492734618495684, Varianza: 0.3017013358922092, Incertidumbre: 0.10380292727296299\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = -0.648x + 11.093\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 2.991\n", + "\tR²: 0.281158048672818, Desviación Estándar: 0.7756999486858921, Varianza: 0.6017104103912956, Incertidumbre: 0.13931982356588346\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.786) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = 0.025x + 1.186\n", + "Valor del parámetro correlacionado para la aeronave: 93.0\n", + "Predicción obtenida: 3.482\n", + "\tR²: 0.5830919989131762, Desviación Estándar: 0.5850219632837886, Varianza: 0.3422506975244185, Incertidumbre: 0.10507304640788168\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.94) ---\n", + "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", + "Valores para Longitud del fuselaje: [3.0, 3.0, 3.0, 0.75, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = 0.044x + 1.202\n", + "Valor del parámetro correlacionado para la aeronave: 70.3\n", + "Predicción obtenida: 4.329\n", + "\tR²: 0.8836944979649586, Desviación Estándar: 0.39788597040864104, Varianza: 0.15831324544802597, Incertidumbre: 0.150386761123267\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Área del ala: 3.707', 'Relación de aspecto del ala: 2.991', 'Peso máximo al despegue (MTOW): 3.482', 'RTF (Including fuel & Batteries): 4.329']\n", + "**Mediana calculada:** 3.594\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = 1.328x + 0.361\n", + "Valor del parámetro correlacionado para la aeronave: 2.09\n", + "Predicción obtenida: 3.135\n", + "\tR²: 0.6482130318947588, Desviación Estándar: 0.5400425508620562, Varianza: 0.29164595674159655, Incertidumbre: 0.10028338411232726\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = -0.69x + 11.683\n", + "Valor del parámetro correlacionado para la aeronave: 12.654\n", + "Predicción obtenida: 2.958\n", + "\tR²: 0.3242880012094823, Desviación Estándar: 0.7699928935334471, Varianza: 0.5928890560920104, Incertidumbre: 0.13611679912073793\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.786) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = 0.025x + 1.181\n", + "Valor del parámetro correlacionado para la aeronave: 75.0\n", + "Predicción obtenida: 3.049\n", + "\tR²: 0.6148826876335616, Desviación Estándar: 0.5761037227120962, Varianza: 0.3318954993227359, Incertidumbre: 0.10184171224913441\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Maximum Crosswind (r = -0.718) ---\n", + "Aeronaves utilizadas: ['DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Maximum Crosswind: [45.0, 50.0, 15.0, 15.0, 15.0]\n", + "Valores para Longitud del fuselaje: [0.75, 0.9, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = -0.062x + 3.771\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 1.922\n", + "\tR²: 0.46781484146630226, Desviación Estándar: 1.0520768032800647, Varianza: 1.1068656, Incertidumbre: 0.47050304993697967\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 3.135', 'Relación de aspecto del ala: 2.958', 'Peso máximo al despegue (MTOW): 3.049', 'Maximum Crosswind: 1.922']\n", + "**Mediana calculada:** 3.004\n", + "\n", + "--- Imputación para aeronave: **V39** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.867) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = 1.317x + 0.37\n", + "Valor del parámetro correlacionado para la aeronave: 1.203\n", + "Predicción obtenida: 1.954\n", + "\tR²: 0.6599199989051069, Desviación Estándar: 0.5314530046626257, Varianza: 0.2824422961649329, Incertidumbre: 0.09702959963587277\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.79) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = -0.692x + 11.72\n", + "Valor del parámetro correlacionado para la aeronave: 14.054\n", + "Predicción obtenida: 1.993\n", + "\tR²: 0.3405258074673375, Desviación Estándar: 0.758274848598365, Varianza: 0.5749807460168733, Incertidumbre: 0.13199870821416507\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.786) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = 0.025x + 1.181\n", + "Valor del parámetro correlacionado para la aeronave: 24.0\n", + "Predicción obtenida: 1.778\n", + "\tR²: 0.6242739779894806, Desviación Estándar: 0.5673572653887533, Varianza: 0.32189426658940423, Incertidumbre: 0.09876422284830895\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Área del ala: 1.954', 'Relación de aspecto del ala: 1.993', 'Peso máximo al despegue (MTOW): 1.778']\n", + "**Mediana calculada:** 1.954\n", + "\n", + "=== Peso máximo al despegue (MTOW): No hay valores faltantes para imputar. ===\n", + "\n", + "=== Imputación para el parámetro: **Alcance de la aeronave** ===\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 Fixed Wing** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.978x + 36149.4\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 280.964\n", + "\tR²: 0.936732753505972, Desviación Estándar: 214.69795555899483, Varianza: 46095.21212121212, Incertidumbre: 61.97796121822399\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 118.915x + -1120.395\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 366.039\n", + "\tR²: 0.01562311222731727, Desviación Estándar: 816.7749459018694, Varianza: 667121.3122530016, Incertidumbre: 226.53261138180895\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 280.964', 'Relación de aspecto del ala: 366.039']\n", + "**Mediana calculada:** 323.501\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 VTOL** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.971x + 36110.407\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 284.508\n", + "\tR²: 0.9368275882693853, Desviación Estándar: 206.58415814438524, Varianza: 42677.01439622437, Incertidumbre: 57.29613652985713\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 124.04x + -1194.892\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 355.606\n", + "\tR²: 0.020800876574606608, Desviación Estándar: 787.1259997898155, Varianza: 619567.3395451166, Incertidumbre: 210.3682722453848\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 284.508', 'Relación de aspecto del ala: 355.606']\n", + "**Mediana calculada:** 320.057\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 Fixed wing** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.966x + 36080.328\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 287.243\n", + "\tR²: 0.9369459484148608, Desviación Estándar: 199.27864216054778, Varianza: 39711.97722135165, Incertidumbre: 53.25945739044961\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 127.479x + -1244.887\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 348.604\n", + "\tR²: 0.02541188671800909, Desviación Estándar: 760.4804439476538, Varianza: 578330.5056268207, Incertidumbre: 196.3552063017124\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = 0.982) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.277, 0.375]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 800.0]\n", + "Ecuación de regresión: y = 2097.004x + -112.685\n", + "Valor del parámetro correlacionado para la aeronave: 0.277\n", + "Predicción obtenida: 468.185\n", + "\tR²: 0.5286444916758515, Desviación Estándar: 123.60775336921141, Varianza: 15278.876692983793, Incertidumbre: 55.279067815917074\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'AAI Aerosonde']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.197]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 3270.0]\n", + "Ecuación de regresión: y = -14120.22x + 5060.052\n", + "Valor del parámetro correlacionado para la aeronave: 0.352\n", + "Predicción obtenida: 89.735\n", + "\tR²: 0.582808463771481, Desviación Estándar: 751.8715055818252, Varianza: 565310.7609058806, Incertidumbre: 336.2471593652147\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 287.243', 'Relación de aspecto del ala: 348.604', 'Ancho del fuselaje: 468.185', 'Cuerda: 89.735']\n", + "**Mediana calculada:** 317.924\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.961x + 36056.221\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 289.434\n", + "\tR²: 0.9370683744644833, Desviación Estándar: 192.6727401288566, Varianza: 37122.7847887619, Incertidumbre: 49.74788758581407\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 129.959x + -1280.934\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 343.556\n", + "\tR²: 0.029508936760294757, Desviación Estándar: 736.3653975548689, Varianza: 542233.99871614, Incertidumbre: 184.09134938871722\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 289.434', 'Relación de aspecto del ala: 343.556']\n", + "**Mediana calculada:** 316.495\n", + "\n", + "--- Imputación para aeronave: **ScanEagle** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.958x + 36036.377\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 291.238\n", + "\tR²: 0.9371872042106679, Desviación Estándar: 186.6690299794889, Varianza: 34845.32675348333, Incertidumbre: 46.667257494872224\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 131.837x + -1308.237\n", + "Valor del parámetro correlacionado para la aeronave: 14.067\n", + "Predicción obtenida: 546.322\n", + "\tR²: 0.03315730870908751, Desviación Estándar: 714.4052565199124, Varianza: 510374.87054328184, Incertidumbre: 173.26872541929876\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 291.238', 'Relación de aspecto del ala: 546.322']\n", + "**Mediana calculada:** 418.78\n", + "\n", + "--- Imputación para aeronave: **Integrator** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 500.0, 92.6, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.942x + 35948.692\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 299.21\n", + "\tR²: 0.9354925794506871, Desviación Estándar: 183.555632392381, Varianza: 33692.67018296691, Incertidumbre: 44.51878003122549\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 129.193x + -1279.073\n", + "Valor del parámetro correlacionado para la aeronave: 12.923\n", + "Predicción obtenida: 390.493\n", + "\tR²: 0.03207230824276419, Desviación Estándar: 694.8866935211308, Varianza: 482867.5168327301, Incertidumbre: 163.78636438169661\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 299.21', 'Relación de aspecto del ala: 390.493']\n", + "**Mediana calculada:** 344.852\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 500.0, 92.6, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.936x + 35919.159\n", + "Valor del parámetro correlacionado para la aeronave: 5000.0\n", + "Predicción obtenida: 6238.105\n", + "\tR²: 0.9353863278088558, Desviación Estándar: 178.6890627548751, Varianza: 31929.78114821569, Incertidumbre: 42.11741599928025\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 131.099x + -1307.522\n", + "Valor del parámetro correlacionado para la aeronave: 12.654\n", + "Predicción obtenida: 351.406\n", + "\tR²: 0.034068577542197054, Desviación Estándar: 676.4273006220689, Varianza: 457553.8930268587, Incertidumbre: 155.183065582295\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 6238.105', 'Relación de aspecto del ala: 351.406']\n", + "**Mediana calculada:** 3294.755\n", + "\n", + "--- Imputación para aeronave: **ScanEagle 3** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 92.6, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -3.54x + 21578.882\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 336.12\n", + "\tR²: 0.8611383209194157, Desviación Estándar: 346.9605684495537, Varianza: 120381.63605883742, Incertidumbre: 79.59821343500164\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = -19.565x + 891.271\n", + "Valor del parámetro correlacionado para la aeronave: 13.774\n", + "Predicción obtenida: 621.78\n", + "\tR²: 0.0004364512856569469, Desviación Estándar: 908.1994401400673, Varianza: 824826.2230707316, Incertidumbre: 203.07956852804415\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 336.12', 'Relación de aspecto del ala: 621.78']\n", + "**Mediana calculada:** 478.95\n", + "\n", + "--- Imputación para aeronave: **V21** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -3.531x + 21530.226\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 343.967\n", + "\tR²: 0.8601185501470221, Desviación Estándar: 339.5976064847731, Varianza: 115326.53433018681, Incertidumbre: 75.93633330961761\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = -20.697x + 899.894\n", + "Valor del parámetro correlacionado para la aeronave: 14.578\n", + "Predicción obtenida: 598.169\n", + "\tR²: 0.0004884279001442504, Desviación Estándar: 886.8330331916383, Varianza: 786472.8287598814, Incertidumbre: 193.52283349466512\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 343.967', 'Relación de aspecto del ala: 598.169']\n", + "**Mediana calculada:** 471.068\n", + "\n", + "--- Imputación para aeronave: **V25** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 300.0]\n", + "Ecuación de regresión: y = -3.523x + 21489.183\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 350.587\n", + "\tR²: 0.8593439743794986, Desviación Estándar: 332.51164306024987, Varianza: 110563.99277062701, Incertidumbre: 72.55998922751108\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 300.0, 800.0]\n", + "Ecuación de regresión: y = -26.566x + 974.331\n", + "Valor del parámetro correlacionado para la aeronave: 14.435\n", + "Predicción obtenida: 590.85\n", + "\tR²: 0.0008406961271735236, Desviación Estándar: 866.829936711758, Varianza: 751394.1391797104, Incertidumbre: 184.80876346543525\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 350.587', 'Relación de aspecto del ala: 590.85']\n", + "**Mediana calculada:** 470.718\n", + "\n", + "--- Imputación para aeronave: **V32** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 300.0]\n", + "Ecuación de regresión: y = -3.516x + 21452.311\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 356.534\n", + "\tR²: 0.8586543866843019, Desviación Estándar: 325.82488395990157, Varianza: 106161.85500748333, Incertidumbre: 69.46609866673465\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 300.0, 800.0]\n", + "Ecuación de regresión: y = -30.914x + 1028.68\n", + "Valor del parámetro correlacionado para la aeronave: 14.194\n", + "Predicción obtenida: 589.888\n", + "\tR²: 0.0011695676261578303, Desviación Estándar: 848.1204843005861, Varianza: 719308.3558902608, Incertidumbre: 176.8453458337392\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 356.534', 'Relación de aspecto del ala: 589.888']\n", + "**Mediana calculada:** 473.211\n", + "\n", + "--- Imputación para aeronave: **V35** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 300.0]\n", + "Ecuación de regresión: y = -3.509x + 21418.189\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 362.038\n", + "\tR²: 0.8579938416630444, Desviación Estándar: 319.5466969999151, Varianza: 102110.09156355557, Incertidumbre: 66.63009228881091\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 300.0, 800.0]\n", + "Ecuación de regresión: y = -33.601x + 1060.685\n", + "Valor del parámetro correlacionado para la aeronave: 13.909\n", + "Predicción obtenida: 593.334\n", + "\tR²: 0.0013961398337015707, Desviación Estándar: 830.5869011959195, Varianza: 689874.6004382401, Incertidumbre: 169.54284124745584\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 362.038', 'Relación de aspecto del ala: 593.334']\n", + "**Mediana calculada:** 477.686\n", + "\n", + "--- Imputación para aeronave: **V39** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 300.0]\n", + "Ecuación de regresión: y = -3.503x + 21385.89\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 367.247\n", + "\tR²: 0.857327553569526, Desviación Estándar: 313.6680914974852, Varianza: 98387.67162367476, Incertidumbre: 64.02723106345546\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 300.0, 800.0]\n", + "Ecuación de regresión: y = -34.612x + 1069.947\n", + "Valor del parámetro correlacionado para la aeronave: 14.054\n", + "Predicción obtenida: 583.507\n", + "\tR²: 0.001482621639041004, Desviación Estándar: 814.1206017478742, Varianza: 662792.3541903207, Incertidumbre: 162.82412034957483\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 367.247', 'Relación de aspecto del ala: 583.507']\n", + "**Mediana calculada:** 475.377\n", + "\n", + "--- Imputación para aeronave: **Volitation VT370** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 300.0]\n", + "Ecuación de regresión: y = -3.498x + 21356.994\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 371.908\n", + "\tR²: 0.8567535916512802, Desviación Estándar: 308.05792801553093, Varianza: 94899.68701322205, Incertidumbre: 61.611585603106185\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 300.0, 800.0]\n", + "Ecuación de regresión: y = -36.248x + 1088.263\n", + "Valor del parámetro correlacionado para la aeronave: 13.657\n", + "Predicción obtenida: 593.228\n", + "\tR²: 0.0016327181254005563, Desviación Estándar: 798.5803788241736, Varianza: 637730.6214429607, Incertidumbre: 156.61449749218386\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 371.908', 'Relación de aspecto del ala: 593.228']\n", + "**Mediana calculada:** 482.568\n", + "\n", + "--- Imputación para aeronave: **Skyeye 2600** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 300.0]\n", + "Ecuación de regresión: y = -3.492x + 21328.643\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 376.481\n", + "\tR²: 0.8561269585544401, Desviación Estándar: 302.82209622289093, Varianza: 91701.22196082582, Incertidumbre: 59.38829914567665\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 300.0, 800.0]\n", + "Ecuación de regresión: y = -35.81x + 1078.147\n", + "Valor del parámetro correlacionado para la aeronave: 14.116\n", + "Predicción obtenida: 572.657\n", + "\tR²: 0.0015929682850825966, Desviación Estándar: 783.9308450787514, Varianza: 614547.5698658854, Incertidumbre: 150.86756147742264\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 376.481', 'Relación de aspecto del ala: 572.657']\n", + "**Mediana calculada:** 474.569\n", + "\n", + "--- Imputación para aeronave: **Skyeye 2930 VTOL** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 300.0]\n", + "Ecuación de regresión: y = -3.487x + 21304.51\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 380.373\n", + "\tR²: 0.8556590461143002, Desviación Estándar: 297.7366014374206, Varianza: 88647.08383550543, Incertidumbre: 57.2994356624997\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 300.0, 800.0]\n", + "Ecuación de regresión: y = -37.527x + 1098.259\n", + "Valor del parámetro correlacionado para la aeronave: 14.013\n", + "Predicción obtenida: 572.395\n", + "\tR²: 0.0017597629068930587, Desviación Estándar: 770.0185720471395, Varianza: 592928.6012975158, Incertidumbre: 145.51983189555736\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 380.373', 'Relación de aspecto del ala: 572.395']\n", + "**Mediana calculada:** 476.384\n", + "\n", + "--- Imputación para aeronave: **Skyeye 3600** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 300.0]\n", + "Ecuación de regresión: y = -3.483x + 21281.79\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 384.038\n", + "\tR²: 0.8552047822769252, Desviación Estándar: 292.9125600732201, Varianza: 85797.76784864777, Incertidumbre: 55.355270702929126\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 300.0, 800.0]\n", + "Ecuación de regresión: y = -38.687x + 1110.907\n", + "Valor del parámetro correlacionado para la aeronave: 13.722\n", + "Predicción obtenida: 580.05\n", + "\tR²: 0.0018749021692411327, Desviación Estándar: 756.8280680927438, Varianza: 572788.7246529949, Incertidumbre: 140.5394440463669\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 384.038', 'Relación de aspecto del ala: 580.05']\n", + "**Mediana calculada:** 482.044\n", + "\n", + "--- Imputación para aeronave: **Skyeye 5000** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0]\n", + "Ecuación de regresión: y = -3.479x + 21259.45\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 387.641\n", + "\tR²: 0.8547134757322701, Desviación Estándar: 288.37174235566516, Varianza: 83158.26178924213, Incertidumbre: 53.54928821744092\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 800.0]\n", + "Ecuación de regresión: y = -38.509x + 1105.191\n", + "Valor del parámetro correlacionado para la aeronave: 12.713\n", + "Predicción obtenida: 615.631\n", + "\tR²: 0.0018568156686025183, Desviación Estándar: 744.3152856692884, Varianza: 554005.2444809544, Incertidumbre: 135.89275728565698\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = 0.982) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Skyeye 5000 VTOL']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.277, 0.277, 0.375]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 800.0]\n", + "Ecuación de regresión: y = 2039.363x + -122.195\n", + "Valor del parámetro correlacionado para la aeronave: 0.375\n", + "Predicción obtenida: 642.567\n", + "\tR²: 0.4608128378817138, Desviación Estándar: 125.92632805453587, Varianza: 15857.440097298588, Incertidumbre: 51.409208152655864\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 387.641', 'Relación de aspecto del ala: 615.631', 'Ancho del fuselaje: 642.567']\n", + "**Mediana calculada:** 615.631\n", + "\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631]\n", + "Ecuación de regresión: y = -3.469x + 21209.325\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 395.726\n", + "\tR²: 0.8517129591346262, Desviación Estándar: 286.4568510267121, Varianza: 82057.52750013994, Incertidumbre: 52.29962635307571\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0]\n", + "Ecuación de regresión: y = -38.509x + 1105.19\n", + "Valor del parámetro correlacionado para la aeronave: 12.876\n", + "Predicción obtenida: 609.354\n", + "\tR²: 0.0019510827495129446, Desviación Estándar: 732.2117914662525, Varianza: 536134.1075622188, Incertidumbre: 131.5091199538622\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = 0.982) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.277, 0.277, 0.375, 0.375]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 615.631, 800.0]\n", + "Ecuación de regresión: y = 1955.637x + -102.217\n", + "Valor del parámetro correlacionado para la aeronave: 0.375\n", + "Predicción obtenida: 631.147\n", + "\tR²: 0.5379529179260039, Desviación Estándar: 116.84090594745173, Varianza: 13651.797302621262, Incertidumbre: 44.16171144234927\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 395.726', 'Relación de aspecto del ala: 609.354', 'Ancho del fuselaje: 631.147']\n", + "**Mediana calculada:** 609.354\n", + "\n", + "--- Imputación para aeronave: **Volitation VT510** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 609.354]\n", + "Ecuación de regresión: y = -3.46x + 21163.965\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 403.042\n", + "\tR²: 0.8490722687449495, Desviación Estándar: 284.3101426544714, Varianza: 80832.2572162059, Incertidumbre: 51.0636090407318\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354]\n", + "Ecuación de regresión: y = -38.509x + 1105.19\n", + "Valor del parámetro correlacionado para la aeronave: 13.114\n", + "Predicción obtenida: 600.189\n", + "\tR²: 0.002013297608376652, Desviación Estándar: 720.6801764312006, Varianza: 519379.91670090647, Incertidumbre: 127.39945995530485\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 403.042', 'Relación de aspecto del ala: 600.189']\n", + "**Mediana calculada:** 501.616\n", + "\n", + "--- Imputación para aeronave: **Ascend** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 609.354, 501.616]\n", + "Ecuación de regresión: y = -3.456x + 21143.728\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 406.306\n", + "\tR²: 0.848548280437885, Desviación Estándar: 280.35664298808086, Varianza: 78599.84726754623, Incertidumbre: 49.56052085189198\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354, 501.616]\n", + "Ecuación de regresión: y = -36.069x + 1068.826\n", + "Valor del parámetro correlacionado para la aeronave: 14.357\n", + "Predicción obtenida: 550.983\n", + "\tR²: 0.00179166089086924, Desviación Estándar: 709.8750011918696, Varianza: 503922.5173171569, Incertidumbre: 123.57337622902887\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 406.306', 'Relación de aspecto del ala: 550.983']\n", + "**Mediana calculada:** 478.644\n", + "\n", + "--- Imputación para aeronave: **Transition** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 609.354, 501.616, 478.644]\n", + "Ecuación de regresión: y = -3.453x + 21129.353\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 408.624\n", + "\tR²: 0.8483145072965955, Desviación Estándar: 276.3539789058695, Varianza: 76371.52165710577, Incertidumbre: 48.10705286196413\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354, 501.616, 478.644]\n", + "Ecuación de regresión: y = -38.096x + 1094.475\n", + "Valor del parámetro correlacionado para la aeronave: 14.233\n", + "Predicción obtenida: 552.251\n", + "\tR²: 0.0020361613553377955, Desviación Estándar: 699.4625257506076, Varianza: 489247.82492941944, Incertidumbre: 119.95683352643485\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 408.624', 'Relación de aspecto del ala: 552.251']\n", + "**Mediana calculada:** 480.438\n", + "\n", + "--- Imputación para aeronave: **Reach** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 609.354, 501.616, 478.644, 480.438]\n", + "Ecuación de regresión: y = -3.451x + 21115.526\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 410.855\n", + "\tR²: 0.8480781165196773, Desviación Estándar: 272.52939729083585, Varianza: 74272.27238770625, Incertidumbre: 46.73840604511903\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354, 501.616, 478.644, 480.438]\n", + "Ecuación de regresión: y = -39.663x + 1113.914\n", + "Valor del parámetro correlacionado para la aeronave: 13.683\n", + "Predicción obtenida: 571.205\n", + "\tR²: 0.002231863535378964, Desviación Estándar: 689.5003938693345, Varianza: 475410.79314596736, Incertidumbre: 116.54683830313121\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 410.855', 'Relación de aspecto del ala: 571.205']\n", + "**Mediana calculada:** 491.03\n", + "\n", + "=== Imputación para el parámetro: **Autonomía de la aeronave** ===\n", + "\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n" + 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Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Autonomía de la aeronave' para la aeronave 'Skyeye 5000 VTOL octo'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Imputación para el parámetro: **Velocidad máxima (KIAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.072x + 7.262\n", + "Valor del parámetro correlacionado para la aeronave: 27.892\n", + "Predicción obtenida: 37.163\n", + "\tR²: 0.6785150773380106, Desviación Estándar: 4.266538520956066, Varianza: 18.20335095080197, Incertidumbre: 0.8896347797490924\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.786x + 25.969\n", + "Valor del parámetro correlacionado para la aeronave: 2.503\n", + "Predicción obtenida: 42.955\n", + "\tR²: 0.32230308247682127, Desviación Estándar: 5.586912076398391, Varianza: 31.213586549406177, Incertidumbre: 1.0956836037800992\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -7.338x + 136.927\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 45.197\n", + "\tR²: 0.6301640775969515, Desviación Estándar: 4.242607000059795, Varianza: 17.99971415695638, Incertidumbre: 0.8017773594814295\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 36.0, 36.0, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.629x + 28.996\n", + "Valor del parámetro correlacionado para la aeronave: 22.7\n", + "Predicción obtenida: 43.264\n", + "\tR²: 0.4900615381264627, Desviación Estándar: 4.799869922083653, Varianza: 23.03875126892334, Incertidumbre: 0.9797693450697835\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.857) ---\n", + "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 8.9, 16.5, 84.0]\n", + "Valores para Velocidad máxima (KIAS): [33.439, 33.439, 33.439, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.067x + 30.152\n", + "Valor del parámetro correlacionado para la aeronave: 70.3\n", + "Predicción obtenida: 34.84\n", + "\tR²: 0.733701886636262, Desviación Estándar: 0.9740665033476542, Varianza: 0.9488055529439255, Incertidumbre: 0.39766098478979256\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 37.163', 'Área del ala: 42.955', 'Relación de aspecto del ala: 45.197', 'payload: 43.264', 'RTF (Including fuel & Batteries): 34.84']\n", + "**Mediana calculada:** 42.955\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.078x + 7.331\n", + "Valor del parámetro correlacionado para la aeronave: 21.463\n", + "Predicción obtenida: 30.477\n", + "\tR²: 0.6650554114482713, Desviación Estándar: 4.333938589551439, Varianza: 18.78302369800311, Incertidumbre: 0.8846615100798704\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.786x + 25.969\n", + "Valor del parámetro correlacionado para la aeronave: 2.09\n", + "Predicción obtenida: 40.152\n", + "\tR²: 0.3552344429430916, Desviación Estándar: 5.48247460485476, Varianza: 30.057527792877355, Incertidumbre: 1.0551027296460609\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -7.178x + 134.644\n", + "Valor del parámetro correlacionado para la aeronave: 12.654\n", + "Predicción obtenida: 43.812\n", + "\tR²: 0.6405623970503131, Desviación Estándar: 4.186974758785706, Varianza: 17.530757630708617, Incertidumbre: 0.7775016937714918\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.626x + 29.01\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 40.28\n", + "\tR²: 0.5133079427191782, Desviación Estándar: 4.703245083983008, Varianza: 22.12051432001033, Incertidumbre: 0.9406490167966016\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 30.477', 'Área del ala: 40.152', 'Relación de aspecto del ala: 43.812', 'payload: 40.28']\n", + "**Mediana calculada:** 40.216\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Evo** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.013x + 9.471\n", + "Valor del parámetro correlacionado para la aeronave: 18.091\n", + "Predicción obtenida: 27.799\n", + "\tR²: 0.6038420321629009, Desviación Estándar: 4.640881006314369, Varianza: 21.537776514769465, Incertidumbre: 0.9281762012628738\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.79x + 25.965\n", + "Valor del parámetro correlacionado para la aeronave: 0.84\n", + "Predicción obtenida: 31.669\n", + "\tR²: 0.3670434992715419, Desviación Estándar: 5.3836956297320535, Varianza: 28.98417863359601, Incertidumbre: 1.0174228407668775\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -6.974x + 131.719\n", + "Valor del parámetro correlacionado para la aeronave: 14.599\n", + "Predicción obtenida: 29.911\n", + "\tR²: 0.6369164293284615, Desviación Estándar: 4.163774471385446, Varianza: 17.337017848561153, Incertidumbre: 0.760197734113986\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.626x + 29.011\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 30.888\n", + "\tR²: 0.5208842290733458, Desviación Estándar: 4.611927331533329, Varianza: 21.26987371134414, Incertidumbre: 0.904473363798475\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.775) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.065x + 9.368\n", + "Valor del parámetro correlacionado para la aeronave: 16.54\n", + "Predicción obtenida: 26.99\n", + "\tR²: 0.6084339451187571, Desviación Estándar: 4.787548453222305, Varianza: 22.920620191951283, Incertidumbre: 1.0705283786979045\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.857) ---\n", + "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 8.9, 16.5, 84.0]\n", + "Valores para Velocidad máxima (KIAS): [33.439, 33.439, 33.439, 42.955, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.119x + 29.159\n", + "Valor del parámetro correlacionado para la aeronave: 6.8\n", + "Predicción obtenida: 29.971\n", + "\tR²: 0.5655356424763149, Desviación Estándar: 2.661463952850547, Varianza: 7.0833903723228575, Incertidumbre: 1.0059388203722117\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 27.799', 'Área del ala: 31.669', 'Relación de aspecto del ala: 29.911', 'payload: 30.888', 'Crucero KIAS: 26.99', 'RTF (Including fuel & Batteries): 29.971']\n", + "**Mediana calculada:** 29.941\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.993x + 10.098\n", + "Valor del parámetro correlacionado para la aeronave: 17.5\n", + "Predicción obtenida: 27.468\n", + "\tR²: 0.61316433334016, Desviación Estándar: 4.56779675046761, Varianza: 20.864767153582456, Incertidumbre: 0.8958186447984638\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.879x + 25.784\n", + "Valor del parámetro correlacionado para la aeronave: 0.7\n", + "Predicción obtenida: 30.599\n", + "\tR²: 0.3790835645732169, Desviación Estándar: 5.299182924288052, Varianza: 28.081339665066075, Incertidumbre: 0.9840335651877286\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -6.972x + 131.702\n", + "Valor del parámetro correlacionado para la aeronave: 14.717\n", + "Predicción obtenida: 29.09\n", + "\tR²: 0.6458059150589619, Desviación Estándar: 4.096069672218646, Varianza: 16.777786759669365, Incertidumbre: 0.7356758306015428\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.631x + 28.924\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 29.681\n", + "\tR²: 0.5346940437266139, Desviación Estándar: 4.529105693357664, Varianza: 20.51279838160481, Incertidumbre: 0.8716267970827714\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.775) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 29.941, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.031x + 10.348\n", + "Valor del parámetro correlacionado para la aeronave: 16.0\n", + "Predicción obtenida: 26.848\n", + "\tR²: 0.6135394527933391, Desviación Estándar: 4.710154634609254, Varianza: 22.185556681931036, Incertidumbre: 1.0278400070497131\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 27.468', 'Área del ala: 30.599', 'Relación de aspecto del ala: 29.09', 'payload: 29.681', 'Crucero KIAS: 26.848']\n", + "**Mediana calculada:** 29.09\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.978x + 10.544\n", + "Valor del parámetro correlacionado para la aeronave: 17.5\n", + "Predicción obtenida: 27.656\n", + "\tR²: 0.6252054405389704, Desviación Estándar: 4.492004783483308, Varianza: 20.178106974836926, Incertidumbre: 0.8644867236483915\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.969x + 25.611\n", + "Valor del parámetro correlacionado para la aeronave: 0.7\n", + "Predicción obtenida: 30.489\n", + "\tR²: 0.3944990966454992, Desviación Estándar: 5.216865762262041, Varianza: 27.215688381461913, Incertidumbre: 0.9524650191557678\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -6.972x + 131.702\n", + "Valor del parámetro correlacionado para la aeronave: 14.717\n", + "Predicción obtenida: 29.09\n", + "\tR²: 0.6561408230541541, Desviación Estándar: 4.031560606462846, Varianza: 16.25348092358307, Incertidumbre: 0.712685960898607\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.634x + 28.868\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 29.629\n", + "\tR²: 0.5509617835923953, Desviación Estándar: 4.448771765099154, Varianza: 19.791570217943438, Incertidumbre: 0.8407388378670155\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.775) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.007x + 11.03\n", + "Valor del parámetro correlacionado para la aeronave: 16.0\n", + "Predicción obtenida: 27.147\n", + "\tR²: 0.6229712190050097, Desviación Estándar: 4.623329335321638, Varianza: 21.375174142845616, Incertidumbre: 0.9856971262384814\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 27.656', 'Área del ala: 30.489', 'Relación de aspecto del ala: 29.09', 'payload: 29.629', 'Crucero KIAS: 27.147']\n", + "**Mediana calculada:** 29.09\n", + "\n", + "--- Imputación para aeronave: **Skyeye 2600** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.978x + 10.544\n", + "Valor del parámetro correlacionado para la aeronave: 36.094\n", + "Predicción obtenida: 45.837\n", + "\tR²: 0.6252054405389704, Desviación Estándar: 4.492004783483308, Varianza: 20.178106974836926, Incertidumbre: 0.8644867236483915\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.969x + 25.611\n", + "Valor del parámetro correlacionado para la aeronave: 0.88\n", + "Predicción obtenida: 31.744\n", + "\tR²: 0.3944990966454992, Desviación Estándar: 5.216865762262041, Varianza: 27.215688381461913, Incertidumbre: 0.9524650191557678\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -6.972x + 131.702\n", + "Valor del parámetro correlacionado para la aeronave: 14.116\n", + "Predicción obtenida: 33.28\n", + "\tR²: 0.6561408230541541, Desviación Estándar: 4.031560606462846, Varianza: 16.25348092358307, Incertidumbre: 0.712685960898607\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.634x + 28.868\n", + "Valor del parámetro correlacionado para la aeronave: 4.0\n", + "Predicción obtenida: 31.404\n", + "\tR²: 0.5509617835923953, Desviación Estándar: 4.448771765099154, Varianza: 19.791570217943438, Incertidumbre: 0.8407388378670155\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.775) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.007x + 11.03\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 44.27\n", + "\tR²: 0.6229712190050097, Desviación Estándar: 4.623329335321638, Varianza: 21.375174142845616, Incertidumbre: 0.9856971262384814\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 45.837', 'Área del ala: 31.744', 'Relación de aspecto del ala: 33.28', 'payload: 31.404', 'Crucero KIAS: 44.27']\n", + "**Mediana calculada:** 33.28\n", + "\n", + "--- Imputación para aeronave: **Skyeye 3600** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.28, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.906x + 25.745\n", + "Valor del parámetro correlacionado para la aeronave: 1.33\n", + "Predicción obtenida: 34.93\n", + "\tR²: 0.3942174961747027, Desviación Estándar: 5.139063743940044, Varianza: 26.409976164279065, Incertidumbre: 0.9230030958652002\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.28, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -6.972x + 131.702\n", + "Valor del parámetro correlacionado para la aeronave: 13.722\n", + "Predicción obtenida: 36.028\n", + "\tR²: 0.6573526772786173, Desviación Estándar: 3.9700064488669455, Varianza: 15.760951204045135, Incertidumbre: 0.6910894167477619\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.28, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.627x + 29.005\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 35.274\n", + "\tR²: 0.5501540266725506, Desviación Estándar: 4.384458563749019, Varianza: 19.223476897232118, Incertidumbre: 0.8141735157186453\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Capacidad combustible (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Capacidad combustible: [13.0, 11.5, 28.0, 28.0, 25.0]\n", + "Valores para Velocidad máxima (KIAS): [33.0, 33.0, 42.0, 38.0, 50.0]\n", + "Ecuación de regresión: y = 0.607x + 26.384\n", + "Valor del parámetro correlacionado para la aeronave: 11.5\n", + "Predicción obtenida: 33.369\n", + "\tR²: 0.487882729041738, Desviación Estándar: 4.5575735331497516, Varianza: 20.77147651006711, Incertidumbre: 2.038208846515347\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 34.93', 'Relación de aspecto del ala: 36.028', 'payload: 35.274', 'Capacidad combustible: 33.369']\n", + "**Mediana calculada:** 35.102\n", + "\n", + "=== Imputación para el parámetro: **Velocidad de pérdida (KCAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Stalker XE** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Stalker XE'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Stalker VXE30** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Stalker VXE30'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 Fixed Wing** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde® Mk. 4.7 Fixed Wing'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 VTOL** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde® Mk. 4.7 VTOL'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 Fixed wing** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde® Mk. 4.8 Fixed wing'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde® Mk. 4.8 VTOL FTUAS'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **AAI Aerosonde** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'AAI Aerosonde'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Fulmar X** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Fulmar X'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Orbiter 4'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Orbiter 3** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Orbiter 3'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Mantis** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Mantis'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **ScanEagle** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Integrator** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator VTOL'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator Extended Range (ER)'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **ScanEagle 3** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle 3'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **RQNan21A Blackjack** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'RQNan21A Blackjack'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **DeltaQuad Evo** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Evo'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Pro #MAP'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Pro #CARGO'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **V35** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'V35'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **V39** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'V39'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Volitation VT370** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Volitation VT370'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Skyeye 5000 VTOL'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Imputación para el parámetro: **envergadura** ===\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 1.904x + 1.375\n", + "Valor del parámetro correlacionado para la aeronave: 2.503\n", + "Predicción obtenida: 6.14\n", + "\tR²: 0.6550202920419386, Desviación Estándar: 0.6922900390325586, Varianza: 0.47926549814370145, Incertidumbre: 0.1285550329147554\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.791) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 0.032x + 2.598\n", + "Valor del parámetro correlacionado para la aeronave: 93.0\n", + "Predicción obtenida: 5.614\n", + "\tR²: 0.6048248274212248, Desviación Estándar: 0.7257069176330723, Varianza: 0.5266505303004948, Incertidumbre: 0.12828807065308317\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = 0.734) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.2, 4.4, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 0.114x + 2.88\n", + "Valor del parámetro correlacionado para la aeronave: 22.7\n", + "Predicción obtenida: 5.476\n", + "\tR²: 0.5142554083484681, Desviación Estándar: 0.8073085344120734, Varianza: 0.6517470697345699, Incertidumbre: 0.1499134313108701\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.936) ---\n", + "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", + "Valores para envergadura: [4.4, 4.4, 4.4, 2.69, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 0.048x + 2.326\n", + "Valor del parámetro correlacionado para la aeronave: 70.3\n", + "Predicción obtenida: 5.674\n", + "\tR²: 0.8762967968830506, Desviación Estándar: 0.44130001156979004, Varianza: 0.1947457002114968, Incertidumbre: 0.16679572631194156\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Área del ala: 6.14', 'Peso máximo al despegue (MTOW): 5.614', 'payload: 5.476', 'RTF (Including fuel & Batteries): 5.674']\n", + "**Mediana calculada:** 5.644\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 1.835x + 1.446\n", + "Valor del parámetro correlacionado para la aeronave: 2.09\n", + "Predicción obtenida: 5.281\n", + "\tR²: 0.6787677993689816, Desviación Estándar: 0.6854337532513997, Varianza: 0.46981943009630067, Incertidumbre: 0.12514250944374053\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.791) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 0.032x + 2.596\n", + "Valor del parámetro correlacionado para la aeronave: 75.0\n", + "Predicción obtenida: 5.033\n", + "\tR²: 0.6331651979245666, Desviación Estándar: 0.7146435397246005, Varianza: 0.5107153888701067, Incertidumbre: 0.12440347223914108\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = 0.734) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 5.2, 4.4, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 0.116x + 2.873\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 4.954\n", + "\tR²: 0.5454308932616603, Desviación Estándar: 0.794258196699784, Varianza: 0.6308460830247928, Incertidumbre: 0.14501104360528233\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Área del ala: 5.281', 'Peso máximo al despegue (MTOW): 5.033', 'payload: 4.954']\n", + "**Mediana calculada:** 5.033\n", + "\n", + "=== Imputación para el parámetro: **Cuerda** ===\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 5500.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", + "Ecuación de regresión: y = 0.0x + -0.975\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.303\n", + "\tR²: 0.5584422980021864, Desviación Estándar: 0.04101181707874288, Varianza: 0.0016819691401002662, Incertidumbre: 0.02050590853937144\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", + "Ecuación de regresión: y = 0.009x + 0.094\n", + "Valor del parámetro correlacionado para la aeronave: 27.892\n", + "Predicción obtenida: 0.345\n", + "\tR²: 0.37089288437785595, Desviación Estándar: 0.04895280456579205, Varianza: 0.0023963770748566308, Incertidumbre: 0.024476402282896024\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", + "Ecuación de regresión: y = 0.167x + 0.103\n", + "Valor del parámetro correlacionado para la aeronave: 2.503\n", + "Predicción obtenida: 0.521\n", + "\tR²: 0.9567278474032922, Desviación Estándar: 0.012838655021740699, Varianza: 0.00016483106276726767, Incertidumbre: 0.006419327510870349\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", + "Ecuación de regresión: y = -0.031x + 0.725\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 0.338\n", + "\tR²: 0.33955370790059436, Desviación Estándar: 0.050157286426532284, Varianza: 0.0025157533816731995, Incertidumbre: 0.025078643213266142\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", + "Ecuación de regresión: y = 0.125x + -0.016\n", + "Valor del parámetro correlacionado para la aeronave: 3.594\n", + "Predicción obtenida: 0.432\n", + "\tR²: 0.9863480800941506, Desviación Estándar: 0.007211276455846646, Varianza: 5.2002508122648166e-05, Incertidumbre: 0.003605638227923323\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.197]\n", + "Ecuación de regresión: y = 0.003x + 0.204\n", + "Valor del parámetro correlacionado para la aeronave: 93.0\n", + "Predicción obtenida: 0.481\n", + "\tR²: 0.736966419457739, Desviación Estándar: 0.03164979554992154, Varianza: 0.0010017095583518332, Incertidumbre: 0.012920974926786248\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 3270.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.197]\n", + "Ecuación de regresión: y = -0.0x + 0.338\n", + "Valor del parámetro correlacionado para la aeronave: 316.495\n", + "Predicción obtenida: 0.325\n", + "\tR²: 0.5966603871244396, Desviación Estándar: 0.0391922998443847, Varianza: 0.0015360363670921572, Incertidumbre: 0.01600018941081718\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", + "Ecuación de regresión: y = 0.074x + -0.018\n", + "Valor del parámetro correlacionado para la aeronave: 5.644\n", + "Predicción obtenida: 0.402\n", + "\tR²: 0.8190779024483189, Desviación Estándar: 0.026251920869893277, Varianza: 0.0006891633493591382, Incertidumbre: 0.013125960434946638\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352]\n", + "Ecuación de regresión: y = 0.005x + 0.27\n", + "Valor del parámetro correlacionado para la aeronave: 22.7\n", + "Predicción obtenida: 0.394\n", + "\tR²: 0.6020929401336428, Desviación Estándar: 0.027642283534153083, Varianza: 0.0007640958389825106, Incertidumbre: 0.012362005007137885\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.345', 'Área del ala: 0.521', 'Relación de aspecto del ala: 0.338', 'Longitud del fuselaje: 0.432', 'Peso máximo al despegue (MTOW): 0.481', 'Alcance de la aeronave: 0.325', 'envergadura: 0.402', 'payload: 0.394']\n", + "**Mediana calculada:** 0.394\n", + "\n", + "--- Imputación para aeronave: **Fulmar X** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = 0.0x + -1.225\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.326\n", + "\tR²: 0.5082545132230825, Desviación Estándar: 0.05085224756183586, Varianza: 0.002585951082090241, Incertidumbre: 0.022741816471382584\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = 0.012x + 0.047\n", + "Valor del parámetro correlacionado para la aeronave: 30.407\n", + "Predicción obtenida: 0.4\n", + "\tR²: 0.5916034080971878, Desviación Estándar: 0.0463426661702313, Varianza: 0.0021476427077655007, Incertidumbre: 0.020725070363043403\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = 0.1x + 0.166\n", + "Valor del parámetro correlacionado para la aeronave: 0.94\n", + "Predicción obtenida: 0.261\n", + "\tR²: 0.8603589273995997, Desviación Estándar: 0.027098580959037145, Varianza: 0.0007343330899934905, Incertidumbre: 0.0121188538236377\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = -0.041x + 0.884\n", + "Valor del parámetro correlacionado para la aeronave: 13.218\n", + "Predicción obtenida: 0.336\n", + "\tR²: 0.5556998392410524, Desviación Estándar: 0.048336833461136076, Varianza: 0.0023364494690496043, Incertidumbre: 0.02161688908723734\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = 0.108x + 0.02\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.15\n", + "\tR²: 0.9729483577129217, Desviación Estándar: 0.011927152694253538, Varianza: 0.00014225697139203943, Incertidumbre: 0.005333984840474135\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = 0.002x + 0.229\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 0.27\n", + "\tR²: 0.7196970118630204, Desviación Estándar: 0.034754348458380345, Varianza: 0.0012078647367665242, Incertidumbre: 0.013135908999850775\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = -0.0x + 0.352\n", + "Valor del parámetro correlacionado para la aeronave: 800.0\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.561672057764841, Desviación Estándar: 0.04346051474042987, Varianza: 0.001888816341503122, Incertidumbre: 0.016426530550576326\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = 0.072x + -0.01\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 0.207\n", + "\tR²: 0.8941942208767555, Desviación Estándar: 0.023588192102840765, Varianza: 0.0005564028066805194, Incertidumbre: 0.010548960201655131\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352]\n", + "Ecuación de regresión: y = 0.008x + 0.145\n", + "Valor del parámetro correlacionado para la aeronave: 27.8\n", + "Predicción obtenida: 0.378\n", + "\tR²: 0.5951449532870101, Desviación Estándar: 0.03013201386753742, Varianza: 0.0009079382597134674, Incertidumbre: 0.017396726317648267\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.326', 'Velocidad a la que se realiza el crucero (KTAS): 0.4', 'Área del ala: 0.261', 'Relación de aspecto del ala: 0.336', 'Longitud del fuselaje: 0.15', 'Peso máximo al despegue (MTOW): 0.27', 'Alcance de la aeronave: 0.313', 'envergadura: 0.207', 'Crucero KIAS: 0.378']\n", + "**Mediana calculada:** 0.313\n", + "\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313]\n", + "Ecuación de regresión: y = 0.0x + -1.197\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.323\n", + "\tR²: 0.5059374879835691, Desviación Estándar: 0.04665619290169541, Varianza: 0.0021768003360802136, Incertidumbre: 0.019047310991669373\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313]\n", + "Ecuación de regresión: y = 0.008x + 0.114\n", + "Valor del parámetro correlacionado para la aeronave: 26.611\n", + "Predicción obtenida: 0.329\n", + "\tR²: 0.4394712393952719, Desviación Estándar: 0.04969552664305181, Varianza: 0.002469645368330272, Incertidumbre: 0.020288113795727262\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313]\n", + "Ecuación de regresión: y = 0.093x + 0.184\n", + "Valor del parámetro correlacionado para la aeronave: 1.608\n", + "Predicción obtenida: 0.334\n", + "\tR²: 0.779679530445362, Desviación Estándar: 0.031156292404129815, Varianza: 0.0009707145563716373, Incertidumbre: 0.012719503111178239\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313]\n", + "Ecuación de regresión: y = -0.04x + 0.855\n", + "Valor del parámetro correlacionado para la aeronave: 13.443\n", + "Predicción obtenida: 0.322\n", + "\tR²: 0.5431294809153299, Desviación Estándar: 0.04486574816022439, Varianza: 0.0020129353579766787, Incertidumbre: 0.01831636498679382\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313]\n", + "Ecuación de regresión: y = 0.058x + 0.165\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.235\n", + "\tR²: 0.4883148244734046, Desviación Estándar: 0.047480989850069766, Varianza: 0.002254444397142428, Incertidumbre: 0.019384032935823015\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313]\n", + "Ecuación de regresión: y = 0.002x + 0.24\n", + "Valor del parámetro correlacionado para la aeronave: 55.0\n", + "Predicción obtenida: 0.346\n", + "\tR²: 0.6707432653466199, Desviación Estándar: 0.035236075624649296, Varianza: 0.0012415810254260045, Incertidumbre: 0.012457834008295764\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313]\n", + "Ecuación de regresión: y = -0.0x + 0.352\n", + "Valor del parámetro correlacionado para la aeronave: 150.0\n", + "Predicción obtenida: 0.344\n", + "\tR²: 0.5617138568209437, Desviación Estándar: 0.04065360565285314, Varianza: 0.0016527156525776928, Incertidumbre: 0.014373220118408109\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313]\n", + "Ecuación de regresión: y = 0.053x + 0.087\n", + "Valor del parámetro correlacionado para la aeronave: 5.2\n", + "Predicción obtenida: 0.361\n", + "\tR²: 0.6218579555553649, Desviación Estándar: 0.040817446367584644, Varianza: 0.0016660639279706489, Incertidumbre: 0.01666365270066685\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394]\n", + "Ecuación de regresión: y = 0.005x + 0.27\n", + "Valor del parámetro correlacionado para la aeronave: 12.0\n", + "Predicción obtenida: 0.335\n", + "\tR²: 0.7240526671985225, Desviación Estándar: 0.02523396237425335, Varianza: 0.0006367528571052337, Incertidumbre: 0.010301722000918372\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.323', 'Velocidad a la que se realiza el crucero (KTAS): 0.329', 'Área del ala: 0.334', 'Relación de aspecto del ala: 0.322', 'Longitud del fuselaje: 0.235', 'Peso máximo al despegue (MTOW): 0.346', 'Alcance de la aeronave: 0.344', 'envergadura: 0.361', 'payload: 0.335']\n", + "**Mediana calculada:** 0.334\n", + "\n", + "--- Imputación para aeronave: **Orbiter 3** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334]\n", + "Ecuación de regresión: y = 0.0x + -1.217\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.325\n", + "\tR²: 0.518160981799697, Desviación Estándar: 0.04335452050989663, Varianza: 0.001879614448643048, Incertidumbre: 0.016386468497090814\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334]\n", + "Ecuación de regresión: y = 0.008x + 0.113\n", + "Valor del parámetro correlacionado para la aeronave: 26.611\n", + "Predicción obtenida: 0.33\n", + "\tR²: 0.45672010492785353, Desviación Estándar: 0.046035749855102136, Varianza: 0.0021192902647215366, Incertidumbre: 0.017399877933568286\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334]\n", + "Ecuación de regresión: y = 0.093x + 0.184\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.296\n", + "\tR²: 0.7867060876798443, Desviación Estándar: 0.028845138922881017, Varianza: 0.0008320420394803054, Incertidumbre: 0.010902437731864672\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334]\n", + "Ecuación de regresión: y = -0.04x + 0.865\n", + "Valor del parámetro correlacionado para la aeronave: 14.012\n", + "Predicción obtenida: 0.301\n", + "\tR²: 0.5529769564137459, Desviación Estándar: 0.041758833679074646, Varianza: 0.001743800190236619, Incertidumbre: 0.015783355564991417\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334]\n", + "Ecuación de regresión: y = 0.038x + 0.223\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.269\n", + "\tR²: 0.2664980716684373, Desviación Estándar: 0.05349140864008716, Varianza: 0.0028613307983007914, Incertidumbre: 0.020217852077171767\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334]\n", + "Ecuación de regresión: y = 0.002x + 0.24\n", + "Valor del parámetro correlacionado para la aeronave: 32.0\n", + "Predicción obtenida: 0.301\n", + "\tR²: 0.6702878820787533, Desviación Estándar: 0.033427806129933846, Varianza: 0.0011174182226604426, Incertidumbre: 0.011142602043311281\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334]\n", + "Ecuación de regresión: y = -0.0x + 0.35\n", + "Valor del parámetro correlacionado para la aeronave: 50.0\n", + "Predicción obtenida: 0.348\n", + "\tR²: 0.5635487874211478, Desviación Estándar: 0.038459910779187215, Varianza: 0.0014791647371430407, Incertidumbre: 0.012819970259729072\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334]\n", + "Ecuación de regresión: y = 0.049x + 0.099\n", + "Valor del parámetro correlacionado para la aeronave: 4.4\n", + "Predicción obtenida: 0.315\n", + "\tR²: 0.6143266200258906, Desviación Estándar: 0.03878762933713736, Varianza: 0.0015044801895951585, Incertidumbre: 0.014660345881688361\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334]\n", + "Ecuación de regresión: y = 0.005x + 0.269\n", + "Valor del parámetro correlacionado para la aeronave: 5.5\n", + "Predicción obtenida: 0.299\n", + "\tR²: 0.7239573172494923, Desviación Estándar: 0.02336635054679909, Varianza: 0.0005459863378758981, Incertidumbre: 0.008831650370569787\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.325', 'Velocidad a la que se realiza el crucero (KTAS): 0.33', 'Área del ala: 0.296', 'Relación de aspecto del ala: 0.301', 'Longitud del fuselaje: 0.269', 'Peso máximo al despegue (MTOW): 0.301', 'Alcance de la aeronave: 0.348', 'envergadura: 0.315', 'payload: 0.299']\n", + "**Mediana calculada:** 0.301\n", + "\n", + "--- Imputación para aeronave: **Mantis** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", + "Ecuación de regresión: y = 0.0x + -1.179\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.322\n", + "\tR²: 0.5005474478510711, Desviación Estándar: 0.04131034033551268, Varianza: 0.001706544218635886, Incertidumbre: 0.014605410892182586\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", + "Ecuación de regresión: y = 0.008x + 0.119\n", + "Valor del parámetro correlacionado para la aeronave: 18.266\n", + "Predicción obtenida: 0.261\n", + "\tR²: 0.43089375042529565, Desviación Estándar: 0.04409692764157503, Varianza: 0.0019445390274263038, Incertidumbre: 0.015590618282425106\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", + "Ecuación de regresión: y = 0.093x + 0.185\n", + "Valor del parámetro correlacionado para la aeronave: 0.754\n", + "Predicción obtenida: 0.255\n", + "\tR²: 0.7861394448447504, Desviación Estándar: 0.027031926600075157, Varianza: 0.0007307250557118509, Incertidumbre: 0.009557229303725078\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", + "Ecuación de regresión: y = -0.04x + 0.865\n", + "Valor del parámetro correlacionado para la aeronave: 14.767\n", + "Predicción obtenida: 0.27\n", + "\tR²: 0.553435246087135, Desviación Estándar: 0.03906194604657353, Varianza: 0.0015258356289454217, Incertidumbre: 0.013810483467937597\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", + "Ecuación de regresión: y = 0.033x + 0.237\n", + "Valor del parámetro correlacionado para la aeronave: 1.48\n", + "Predicción obtenida: 0.286\n", + "\tR²: 0.2386332689505225, Desviación Estándar: 0.05100451268256031, Varianza: 0.002601460313985455, Incertidumbre: 0.01803281839447683\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", + "Ecuación de regresión: y = 0.002x + 0.24\n", + "Valor del parámetro correlacionado para la aeronave: 6.5\n", + "Predicción obtenida: 0.252\n", + "\tR²: 0.6726769802088182, Desviación Estándar: 0.03171272006589414, Varianza: 0.0010056966139777646, Incertidumbre: 0.010028442620755052\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", + "Ecuación de regresión: y = -0.0x + 0.343\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 0.342\n", + "\tR²: 0.5063410592901572, Desviación Estándar: 0.03894562904032464, Varianza: 0.001516762021346578, Incertidumbre: 0.012315689267542349\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", + "Ecuación de regresión: y = 0.049x + 0.099\n", + "Valor del parámetro correlacionado para la aeronave: 2.1\n", + "Predicción obtenida: 0.201\n", + "\tR²: 0.6088855002504914, Desviación Estándar: 0.036556416184119006, Varianza: 0.0013363715642265182, Incertidumbre: 0.012924644889834101\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313]\n", + "Ecuación de regresión: y = 0.005x + 0.208\n", + "Valor del parámetro correlacionado para la aeronave: 16.7\n", + "Predicción obtenida: 0.285\n", + "\tR²: 0.37218727831824217, Desviación Estándar: 0.03267385114997177, Varianza: 0.0010675805489705115, Incertidumbre: 0.016336925574985884\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.322', 'Velocidad a la que se realiza el crucero (KTAS): 0.261', 'Área del ala: 0.255', 'Relación de aspecto del ala: 0.27', 'Longitud del fuselaje: 0.286', 'Peso máximo al despegue (MTOW): 0.252', 'Alcance de la aeronave: 0.342', 'envergadura: 0.201', 'Crucero KIAS: 0.285']\n", + "**Mediana calculada:** 0.27\n", + "\n", + "--- Imputación para aeronave: **ScanEagle** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", + "Ecuación de regresión: y = 0.0x + -1.108\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.315\n", + "\tR²: 0.4389240659793703, Desviación Estándar: 0.042140017620528936, Varianza: 0.001775781085058489, Incertidumbre: 0.014046672540176311\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", + "Ecuación de regresión: y = 0.008x + 0.125\n", + "Valor del parámetro correlacionado para la aeronave: 30.625\n", + "Predicción obtenida: 0.356\n", + "\tR²: 0.4515433062124382, Desviación Estándar: 0.04166343402845906, Varianza: 0.0017358417350437604, Incertidumbre: 0.013887811342819687\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", + "Ecuación de regresión: y = 0.09x + 0.19\n", + "Valor del parámetro correlacionado para la aeronave: 1.063\n", + "Predicción obtenida: 0.286\n", + "\tR²: 0.7885748390015039, Desviación Estándar: 0.025867962774747247, Varianza: 0.0006691514981157094, Incertidumbre: 0.008622654258249082\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", + "Ecuación de regresión: y = -0.04x + 0.865\n", + "Valor del parámetro correlacionado para la aeronave: 14.067\n", + "Predicción obtenida: 0.298\n", + "\tR²: 0.5714606764166177, Desviación Estándar: 0.03682809308680053, Varianza: 0.0013563084404100449, Incertidumbre: 0.01227603102893351\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", + "Ecuación de regresión: y = 0.035x + 0.232\n", + "Valor del parámetro correlacionado para la aeronave: 1.71\n", + "Predicción obtenida: 0.292\n", + "\tR²: 0.26160299392487774, Desviación Estándar: 0.04834247216917272, Varianza: 0.0023369946154272393, Incertidumbre: 0.01611415738972424\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", + "Ecuación de regresión: y = 0.002x + 0.244\n", + "Valor del parámetro correlacionado para la aeronave: 26.5\n", + "Predicción obtenida: 0.293\n", + "\tR²: 0.683688240941903, Desviación Estándar: 0.03060819392233029, Varianza: 0.000936861535186977, Incertidumbre: 0.00922871770461874\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", + "Ecuación de regresión: y = -0.0x + 0.334\n", + "Valor del parámetro correlacionado para la aeronave: 418.78\n", + "Predicción obtenida: 0.317\n", + "\tR²: 0.39569557326663285, Desviación Estándar: 0.042306580420532675, Varianza: 0.0017898467468789986, Incertidumbre: 0.012755913947082096\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", + "Ecuación de regresión: y = 0.037x + 0.154\n", + "Valor del parámetro correlacionado para la aeronave: 3.1\n", + "Predicción obtenida: 0.268\n", + "\tR²: 0.5307410102435243, Desviación Estándar: 0.03853809056250838, Varianza: 0.0014851844242040975, Incertidumbre: 0.012846030187502792\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301]\n", + "Ecuación de regresión: y = 0.005x + 0.27\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 0.297\n", + "\tR²: 0.7420743779557686, Desviación Estándar: 0.02186230804623738, Varianza: 0.0004779605131085757, Incertidumbre: 0.0077294931359418355\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27]\n", + "Ecuación de regresión: y = 0.005x + 0.196\n", + "Valor del parámetro correlacionado para la aeronave: 28.0\n", + "Predicción obtenida: 0.338\n", + "\tR²: 0.4325324299944552, Desviación Estándar: 0.02977768882484922, Varianza: 0.0008867107517495501, Incertidumbre: 0.013316987285039737\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.315', 'Velocidad a la que se realiza el crucero (KTAS): 0.356', 'Área del ala: 0.286', 'Relación de aspecto del ala: 0.298', 'Longitud del fuselaje: 0.292', 'Peso máximo al despegue (MTOW): 0.293', 'Alcance de la aeronave: 0.317', 'envergadura: 0.268', 'payload: 0.297', 'Crucero KIAS: 0.338']\n", + "**Mediana calculada:** 0.298\n", + "\n", + "--- Imputación para aeronave: **Integrator** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", + "Ecuación de regresión: y = 0.0x + -1.087\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.43003046301994163, Desviación Estándar: 0.040303167474752155, Varianza: 0.0016243453084979203, Incertidumbre: 0.012744980613943357\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", + "Ecuación de regresión: y = 0.006x + 0.152\n", + "Valor del parámetro correlacionado para la aeronave: 30.953\n", + "Predicción obtenida: 0.344\n", + "\tR²: 0.3626732282190418, Desviación Estándar: 0.042618134717395714, Varianza: 0.0018163054067900897, Incertidumbre: 0.013477037533486688\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", + "Ecuación de regresión: y = 0.09x + 0.192\n", + "Valor del parámetro correlacionado para la aeronave: 1.872\n", + "Predicción obtenida: 0.36\n", + "\tR²: 0.78420367371009, Desviación Estándar: 0.02479906919480939, Varianza: 0.0006149938329289441, Incertidumbre: 0.007842154250771557\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", + "Ecuación de regresión: y = -0.04x + 0.865\n", + "Valor del parámetro correlacionado para la aeronave: 12.923\n", + "Predicción obtenida: 0.345\n", + "\tR²: 0.5716670205992819, Desviación Estándar: 0.03493848859723169, Varianza: 0.0012206979854588887, Incertidumbre: 0.011048520197107344\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", + "Ecuación de regresión: y = 0.034x + 0.233\n", + "Valor del parámetro correlacionado para la aeronave: 2.5\n", + "Predicción obtenida: 0.32\n", + "\tR²: 0.2607143040447977, Desviación Estándar: 0.045900721121043486, Varianza: 0.0021068761994318076, Incertidumbre: 0.014515082498669469\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", + "Ecuación de regresión: y = 0.002x + 0.245\n", + "Valor del parámetro correlacionado para la aeronave: 74.8\n", + "Predicción obtenida: 0.381\n", + "\tR²: 0.6843844359896646, Desviación Estándar: 0.029343086738118862, Varianza: 0.000861016739320767, Incertidumbre: 0.008470619513553731\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", + "Ecuación de regresión: y = -0.0x + 0.332\n", + "Valor del parámetro correlacionado para la aeronave: 344.852\n", + "Predicción obtenida: 0.319\n", + "\tR²: 0.38810758348194185, Desviación Estándar: 0.040856779150680644, Varianza: 0.0016692764025674925, Incertidumbre: 0.01179433622043328\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", + "Ecuación de regresión: y = 0.035x + 0.165\n", + "Valor del parámetro correlacionado para la aeronave: 4.8\n", + "Predicción obtenida: 0.332\n", + "\tR²: 0.5045938666189362, Desviación Estándar: 0.03757457234940652, Varianza: 0.0014118484872407847, Incertidumbre: 0.011882123073090872\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298]\n", + "Ecuación de regresión: y = 0.005x + 0.27\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 0.368\n", + "\tR²: 0.7571933066563912, Desviación Estándar: 0.02061381414434731, Varianza: 0.0004249293335776932, Incertidumbre: 0.006871271381449103\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298]\n", + "Ecuación de regresión: y = 0.004x + 0.22\n", + "Valor del parámetro correlacionado para la aeronave: 28.3\n", + "Predicción obtenida: 0.323\n", + "\tR²: 0.3103699929495912, Desviación Estándar: 0.029966938700525643, Varianza: 0.0008980174150810615, Incertidumbre: 0.01223395149492497\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.313', 'Velocidad a la que se realiza el crucero (KTAS): 0.344', 'Área del ala: 0.36', 'Relación de aspecto del ala: 0.345', 'Longitud del fuselaje: 0.32', 'Peso máximo al despegue (MTOW): 0.381', 'Alcance de la aeronave: 0.319', 'envergadura: 0.332', 'payload: 0.368', 'Crucero KIAS: 0.323']\n", + "**Mediana calculada:** 0.338\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.0x + -1.114\n", + "Valor del parámetro correlacionado para la aeronave: 5000.0\n", + "Predicción obtenida: 0.077\n", + "\tR²: 0.4346058124858284, Desviación Estándar: 0.03907455742847307, Varianza: 0.0015268210382310403, Incertidumbre: 0.011781422349039958\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.006x + 0.155\n", + "Valor del parámetro correlacionado para la aeronave: 21.463\n", + "Predicción obtenida: 0.285\n", + "\tR²: 0.3877365879710747, Desviación Estándar: 0.040661889117598014, Varianza: 0.0016533892266118358, Incertidumbre: 0.012260020860918912\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.086x + 0.195\n", + "Valor del parámetro correlacionado para la aeronave: 2.09\n", + "Predicción obtenida: 0.374\n", + "\tR²: 0.7800260398164971, Desviación Estándar: 0.024372721993982313, Varianza: 0.0005940295773959491, Incertidumbre: 0.007348652179425639\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", + "Ecuación de regresión: y = -0.04x + 0.857\n", + "Valor del parámetro correlacionado para la aeronave: 12.654\n", + "Predicción obtenida: 0.354\n", + "\tR²: 0.5878792449550165, Desviación Estándar: 0.03336035330709108, Varianza: 0.0011129131727739424, Incertidumbre: 0.010058524981210274\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.036x + 0.232\n", + "Valor del parámetro correlacionado para la aeronave: 3.004\n", + "Predicción obtenida: 0.34\n", + "\tR²: 0.2807426506714811, Desviación Estándar: 0.044071776010299636, Varianza: 0.0019423214407020225, Incertidumbre: 0.01328814044279547\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.002x + 0.249\n", + "Valor del parámetro correlacionado para la aeronave: 75.0\n", + "Predicción obtenida: 0.371\n", + "\tR²: 0.6495168189859627, Desviación Estándar: 0.030035459570872714, Varianza: 0.0009021288316335293, Incertidumbre: 0.008330337658839016\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", + "Ecuación de regresión: y = -0.0x + 0.334\n", + "Valor del parámetro correlacionado para la aeronave: 3294.755\n", + "Predicción obtenida: 0.204\n", + "\tR²: 0.3910118106632742, Desviación Estándar: 0.03959178893133063, Varianza: 0.0015675097507830348, Incertidumbre: 0.01098078654455848\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.035x + 0.164\n", + "Valor del parámetro correlacionado para la aeronave: 5.033\n", + "Predicción obtenida: 0.341\n", + "\tR²: 0.5236198578383562, Desviación Estándar: 0.03586701404683303, Varianza: 0.0012864426966357181, Incertidumbre: 0.010814311631250163\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.005x + 0.272\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 0.362\n", + "\tR²: 0.713775236159582, Desviación Estándar: 0.02131006366987268, Varianza: 0.00045411881361402743, Incertidumbre: 0.006738833828000416\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.077', 'Velocidad a la que se realiza el crucero (KTAS): 0.285', 'Área del ala: 0.374', 'Relación de aspecto del ala: 0.354', 'Longitud del fuselaje: 0.34', 'Peso máximo al despegue (MTOW): 0.371', 'Alcance de la aeronave: 0.204', 'envergadura: 0.341', 'payload: 0.362']\n", + "**Mediana calculada:** 0.341\n", + "\n", + "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.0x + 0.183\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.015512130588261508, Desviación Estándar: 0.0503497708973624, Varianza: 0.002535099429416882, Incertidumbre: 0.014534726890614086\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.006x + 0.172\n", + "Valor del parámetro correlacionado para la aeronave: 33.045\n", + "Predicción obtenida: 0.355\n", + "\tR²: 0.3206948627028712, Desviación Estándar: 0.041823922738469466, Varianza: 0.0017492405132334633, Incertidumbre: 0.01207352652581073\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.079x + 0.201\n", + "Valor del parámetro correlacionado para la aeronave: 1.872\n", + "Predicción obtenida: 0.349\n", + "\tR²: 0.7610795576292256, Desviación Estándar: 0.02480384274196969, Varianza: 0.0006152306147683624, Incertidumbre: 0.007160252642006673\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = -0.039x + 0.839\n", + "Valor del parámetro correlacionado para la aeronave: 12.859\n", + "Predicción obtenida: 0.344\n", + "\tR²: 0.5993334622949501, Desviación Estándar: 0.03212061490919684, Varianza: 0.0010317339021449184, Incertidumbre: 0.009272422832180553\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.036x + 0.232\n", + "Valor del parámetro correlacionado para la aeronave: 2.5\n", + "Predicción obtenida: 0.322\n", + "\tR²: 0.3085407372763075, Desviación Estándar: 0.04219642069079446, Varianza: 0.001780537919114507, Incertidumbre: 0.012181057422334439\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.002x + 0.251\n", + "Valor del parámetro correlacionado para la aeronave: 74.8\n", + "Predicción obtenida: 0.365\n", + "\tR²: 0.6361662614209418, Desviación Estándar: 0.029827965241118803, Varianza: 0.0008897075104253915, Incertidumbre: 0.00797185903406204\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = -0.0x + 0.327\n", + "Valor del parámetro correlacionado para la aeronave: 500.0\n", + "Predicción obtenida: 0.318\n", + "\tR²: 0.12689970642513138, Desviación Estándar: 0.04620662103794288, Varianza: 0.002135051827744066, Incertidumbre: 0.01234923892317737\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.035x + 0.164\n", + "Valor del parámetro correlacionado para la aeronave: 4.8\n", + "Predicción obtenida: 0.333\n", + "\tR²: 0.5420506031887378, Desviación Estándar: 0.0343400608030557, Varianza: 0.0011792397759575624, Incertidumbre: 0.009913121674316162\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.005x + 0.273\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 0.358\n", + "\tR²: 0.6939116284562865, Desviación Estándar: 0.021115874286876224, Varianza: 0.00044588014689916043, Incertidumbre: 0.006366675648171076\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.311', 'Velocidad a la que se realiza el crucero (KTAS): 0.355', 'Área del ala: 0.349', 'Relación de aspecto del ala: 0.344', 'Longitud del fuselaje: 0.322', 'Peso máximo al despegue (MTOW): 0.365', 'Alcance de la aeronave: 0.318', 'envergadura: 0.333', 'payload: 0.358']\n", + "**Mediana calculada:** 0.344\n", + "\n", + "--- Imputación para aeronave: **ScanEagle 3** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", + "Ecuación de regresión: y = 0.0x + 0.165\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.314\n", + "\tR²: 0.02067125389564617, Desviación Estándar: 0.04917798086065571, Varianza: 0.002418473801531019, Incertidumbre: 0.013639517816683143\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", + "Ecuación de regresión: y = 0.005x + 0.177\n", + "Valor del parámetro correlacionado para la aeronave: 25.703\n", + "Predicción obtenida: 0.314\n", + "\tR²: 0.34315816593670323, Desviación Estándar: 0.04027512016870371, Varianza: 0.0016220853046035242, Incertidumbre: 0.011170308530287316\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", + "Ecuación de regresión: y = 0.078x + 0.202\n", + "Valor del parámetro correlacionado para la aeronave: 1.349\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.7692709885882784, Desviación Estándar: 0.023870281740101265, Varianza: 0.0005697903503518118, Incertidumbre: 0.0066204249825928384\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", + "Ecuación de regresión: y = -0.039x + 0.839\n", + "Valor del parámetro correlacionado para la aeronave: 13.774\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.61434850961859, Desviación Estándar: 0.030860570433120724, Varianza: 0.000952374807457605, Incertidumbre: 0.008559182237437285\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", + "Ecuación de regresión: y = 0.037x + 0.232\n", + "Valor del parámetro correlacionado para la aeronave: 2.4\n", + "Predicción obtenida: 0.32\n", + "\tR²: 0.3208426833611422, Desviación Estándar: 0.04095355757307659, Varianza: 0.0016771938778912987, Incertidumbre: 0.011358473210953401\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", + "Ecuación de regresión: y = 0.001x + 0.253\n", + "Valor del parámetro correlacionado para la aeronave: 36.3\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.6342327638509584, Desviación Estándar: 0.02924076794795075, Varianza: 0.0008550225101859038, Incertidumbre: 0.007549933819515259\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", + "Ecuación de regresión: y = -0.0x + 0.329\n", + "Valor del parámetro correlacionado para la aeronave: 478.95\n", + "Predicción obtenida: 0.32\n", + "\tR²: 0.1300366441647196, Desviación Estándar: 0.045095878725376695, Varianza: 0.0020336382780138827, Incertidumbre: 0.011643705819064886\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", + "Ecuación de regresión: y = 0.036x + 0.163\n", + "Valor del parámetro correlacionado para la aeronave: 4.0\n", + "Predicción obtenida: 0.305\n", + "\tR²: 0.5556874366655828, Desviación Estándar: 0.0331246073597043, Varianza: 0.0010972396127345763, Incertidumbre: 0.009187113101155824\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344]\n", + "Ecuación de regresión: y = 0.005x + 0.274\n", + "Valor del parámetro correlacionado para la aeronave: 8.6\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.6868798679185576, Desviación Estándar: 0.020578680684602713, Varianza: 0.00042348209871884076, Incertidumbre: 0.005940553416411365\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.004x + 0.214\n", + "Valor del parámetro correlacionado para la aeronave: 23.5\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.39462657346290686, Desviación Estándar: 0.028144814550039556, Varianza: 0.0007921305860561183, Incertidumbre: 0.01063773999934813\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.314', 'Velocidad a la que se realiza el crucero (KTAS): 0.314', 'Área del ala: 0.308', 'Relación de aspecto del ala: 0.308', 'Longitud del fuselaje: 0.32', 'Peso máximo al despegue (MTOW): 0.306', 'Alcance de la aeronave: 0.32', 'envergadura: 0.305', 'payload: 0.313', 'Crucero KIAS: 0.308']\n", + "**Mediana calculada:** 0.31\n", + "\n", + "--- Imputación para aeronave: **RQNan21A Blackjack** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", + "Ecuación de regresión: y = 0.0x + 0.166\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.02032631380959693, Desviación Estándar: 0.04739774340606181, Varianza: 0.0022465460799868755, Incertidumbre: 0.012667579766550504\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", + "Ecuación de regresión: y = 0.005x + 0.177\n", + "Valor del parámetro correlacionado para la aeronave: 33.797\n", + "Predicción obtenida: 0.356\n", + "\tR²: 0.34279456368656225, Desviación Estándar: 0.03882107144365238, Varianza: 0.0015070755880331622, Incertidumbre: 0.010375367766401489\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", + "Ecuación de regresión: y = 0.078x + 0.202\n", + "Valor del parámetro correlacionado para la aeronave: 1.802\n", + "Predicción obtenida: 0.343\n", + "\tR²: 0.7691040437079356, Desviación Estándar: 0.023010449764320217, Varianza: 0.0005294807983563044, Incertidumbre: 0.0061498013809756745\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", + "Ecuación de regresión: y = -0.039x + 0.84\n", + "Valor del parámetro correlacionado para la aeronave: 12.973\n", + "Predicción obtenida: 0.339\n", + "\tR²: 0.6142874133845568, Desviación Estándar: 0.029740539979987123, Varianza: 0.0008844997183012125, Incertidumbre: 0.007948493650197468\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", + "Ecuación de regresión: y = 0.037x + 0.232\n", + "Valor del parámetro correlacionado para la aeronave: 2.5\n", + "Predicción obtenida: 0.323\n", + "\tR²: 0.3177456232267585, Desviación Estándar: 0.039553972672793015, Varianza: 0.0015645167542000567, Incertidumbre: 0.010571243859100757\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", + "Ecuación de regresión: y = 0.001x + 0.253\n", + "Valor del parámetro correlacionado para la aeronave: 61.0\n", + "Predicción obtenida: 0.342\n", + "\tR²: 0.6340700925234075, Desviación Estándar: 0.028333016378777862, Varianza: 0.0008027598171200946, Incertidumbre: 0.007083254094694466\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", + "Ecuación de regresión: y = -0.0x + 0.328\n", + "Valor del parámetro correlacionado para la aeronave: 92.6\n", + "Predicción obtenida: 0.326\n", + "\tR²: 0.12800904485497755, Desviación Estándar: 0.04373708652402515, Varianza: 0.0019129327376100622, Incertidumbre: 0.010934271631006288\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", + "Ecuación de regresión: y = 0.036x + 0.163\n", + "Valor del parámetro correlacionado para la aeronave: 4.8\n", + "Predicción obtenida: 0.334\n", + "\tR²: 0.5550912173963352, Desviación Estándar: 0.03194128864413451, Varianza: 0.0010202459202479162, Incertidumbre: 0.008536668471314606\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31]\n", + "Ecuación de regresión: y = 0.005x + 0.273\n", + "Valor del parámetro correlacionado para la aeronave: 17.7\n", + "Predicción obtenida: 0.355\n", + "\tR²: 0.6934350662495181, Desviación Estándar: 0.019790488440900297, Varianza: 0.00039166343272940834, Incertidumbre: 0.005488893910780262\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31]\n", + "Ecuación de regresión: y = 0.004x + 0.214\n", + "Valor del parámetro correlacionado para la aeronave: 30.9\n", + "Predicción obtenida: 0.338\n", + "\tR²: 0.39635942215753095, Desviación Estándar: 0.026334104723749873, Varianza: 0.0006934850716014253, Incertidumbre: 0.009310512013320114\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.313', 'Velocidad a la que se realiza el crucero (KTAS): 0.356', 'Área del ala: 0.343', 'Relación de aspecto del ala: 0.339', 'Longitud del fuselaje: 0.323', 'Peso máximo al despegue (MTOW): 0.342', 'Alcance de la aeronave: 0.326', 'envergadura: 0.334', 'payload: 0.355', 'Crucero KIAS: 0.338']\n", + "**Mediana calculada:** 0.338\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Evo** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", + "Ecuación de regresión: y = 0.0x + 0.166\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.02032631380959693, Desviación Estándar: 0.04739774340606181, Varianza: 0.0022465460799868755, Incertidumbre: 0.012667579766550504\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338]\n", + "Ecuación de regresión: y = 0.005x + 0.184\n", + "Valor del parámetro correlacionado para la aeronave: 18.091\n", + "Predicción obtenida: 0.274\n", + "\tR²: 0.3487363165953775, Desviación Estándar: 0.037739302980328356, Varianza: 0.0014242549894410209, Incertidumbre: 0.00974424612935251\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338]\n", + "Ecuación de regresión: y = 0.078x + 0.202\n", + "Valor del parámetro correlacionado para la aeronave: 0.84\n", + "Predicción obtenida: 0.268\n", + "\tR²: 0.7732658196981622, Desviación Estándar: 0.02226762719794307, Varianza: 0.0004958472210265739, Incertidumbre: 0.005749476619812589\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338]\n", + "Ecuación de regresión: y = -0.038x + 0.838\n", + "Valor del parámetro correlacionado para la aeronave: 14.599\n", + "Predicción obtenida: 0.277\n", + "\tR²: 0.622452460235022, Desviación Estándar: 0.02873434266164246, Varianza: 0.0008256624481966859, Incertidumbre: 0.007419175372850569\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", + "Ecuación de regresión: y = 0.037x + 0.232\n", + "Valor del parámetro correlacionado para la aeronave: 0.75\n", + "Predicción obtenida: 0.259\n", + "\tR²: 0.3177456232267585, Desviación Estándar: 0.039553972672793015, Varianza: 0.0015645167542000567, Incertidumbre: 0.010571243859100757\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338]\n", + "Ecuación de regresión: y = 0.001x + 0.253\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 0.268\n", + "\tR²: 0.6384849427043453, Desviación Estándar: 0.027500732010490584, Varianza: 0.0007562902611128215, Incertidumbre: 0.0066699072271210235\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338]\n", + "Ecuación de regresión: y = -0.0x + 0.329\n", + "Valor del parámetro correlacionado para la aeronave: 270.0\n", + "Predicción obtenida: 0.324\n", + "\tR²: 0.135868427381171, Desviación Estándar: 0.04251782061135894, Varianza: 0.001807765069539699, Incertidumbre: 0.01031208619715862\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", + "Ecuación de regresión: y = 0.036x + 0.163\n", + "Valor del parámetro correlacionado para la aeronave: 2.69\n", + "Predicción obtenida: 0.259\n", + "\tR²: 0.5550912173963352, Desviación Estándar: 0.03194128864413451, Varianza: 0.0010202459202479162, Incertidumbre: 0.008536668471314606\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338]\n", + "Ecuación de regresión: y = 0.004x + 0.274\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 0.287\n", + "\tR²: 0.6800173021314551, Desviación Estándar: 0.0195304404540393, Varianza: 0.00038143810432877473, Incertidumbre: 0.005219729770843197\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338]\n", + "Ecuación de regresión: y = 0.004x + 0.214\n", + "Valor del parámetro correlacionado para la aeronave: 16.54\n", + "Predicción obtenida: 0.28\n", + "\tR²: 0.45458778022246527, Desviación Estándar: 0.02482812939297037, Varianza: 0.0006164360091540792, Incertidumbre: 0.008276043130990124\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.313', 'Velocidad a la que se realiza el crucero (KTAS): 0.274', 'Área del ala: 0.268', 'Relación de aspecto del ala: 0.277', 'Longitud del fuselaje: 0.259', 'Peso máximo al despegue (MTOW): 0.268', 'Alcance de la aeronave: 0.324', 'envergadura: 0.259', 'payload: 0.287', 'Crucero KIAS: 0.28']\n", + "**Mediana calculada:** 0.276\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276]\n", + "Ecuación de regresión: y = 0.0x + 0.184\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.31\n", + "\tR²: 0.0147264484768026, Desviación Estándar: 0.04671466282025224, Varianza: 0.002182259722409857, Incertidumbre: 0.012061674075102113\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276]\n", + "Ecuación de regresión: y = 0.005x + 0.185\n", + "Valor del parámetro correlacionado para la aeronave: 17.5\n", + "Predicción obtenida: 0.272\n", + "\tR²: 0.3724780157591121, Desviación Estándar: 0.03654331382237015, Varianza: 0.0013354137851202294, Incertidumbre: 0.009135828455592538\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276]\n", + "Ecuación de regresión: y = 0.077x + 0.204\n", + "Valor del parámetro correlacionado para la aeronave: 0.7\n", + "Predicción obtenida: 0.258\n", + "\tR²: 0.7798191830856929, Desviación Estándar: 0.021646276131902738, Varianza: 0.0004685612703785822, Incertidumbre: 0.0054115690329756844\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276]\n", + "Ecuación de regresión: y = -0.039x + 0.839\n", + "Valor del parámetro correlacionado para la aeronave: 14.717\n", + "Predicción obtenida: 0.272\n", + "\tR²: 0.6362481550089567, Desviación Estándar: 0.02782249399108439, Varianza: 0.000774091171883927, Incertidumbre: 0.006955623497771097\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276]\n", + "Ecuación de regresión: y = 0.034x + 0.238\n", + "Valor del parámetro correlacionado para la aeronave: 0.9\n", + "Predicción obtenida: 0.268\n", + "\tR²: 0.33427169926483846, Desviación Estándar: 0.03839930074605216, Varianza: 0.0014745062977857622, Incertidumbre: 0.009914656819698003\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276]\n", + "Ecuación de regresión: y = 0.001x + 0.254\n", + "Valor del parámetro correlacionado para la aeronave: 6.2\n", + "Predicción obtenida: 0.263\n", + "\tR²: 0.6523551364390465, Desviación Estándar: 0.02678966328890024, Varianza: 0.0007176860591326491, Incertidumbre: 0.006314384192428556\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276]\n", + "Ecuación de regresión: y = -0.0x + 0.325\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 0.324\n", + "\tR²: 0.11453431205818987, Desviación Estándar: 0.042754833055918345, Varianza: 0.0018279757496394483, Incertidumbre: 0.010077410794112875\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276]\n", + "Ecuación de regresión: y = 0.034x + 0.17\n", + "Valor del parámetro correlacionado para la aeronave: 2.35\n", + "Predicción obtenida: 0.251\n", + "\tR²: 0.5630614192360759, Desviación Estándar: 0.03110892493285136, Varianza: 0.000967765210477781, Incertidumbre: 0.00803228987889\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276]\n", + "Ecuación de regresión: y = 0.005x + 0.272\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.277\n", + "\tR²: 0.7184184742433418, Desviación Estándar: 0.019057576632793602, Varianza: 0.0003631912271148008, Incertidumbre: 0.004920645127858749\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276]\n", + "Ecuación de regresión: y = 0.004x + 0.211\n", + "Valor del parámetro correlacionado para la aeronave: 16.0\n", + "Predicción obtenida: 0.277\n", + "\tR²: 0.49980090608872974, Desviación Estándar: 0.023585011510380487, Varianza: 0.0005562527679447801, Incertidumbre: 0.0074582355014090294\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.31', 'Velocidad a la que se realiza el crucero (KTAS): 0.272', 'Área del ala: 0.258', 'Relación de aspecto del ala: 0.272', 'Longitud del fuselaje: 0.268', 'Peso máximo al despegue (MTOW): 0.263', 'Alcance de la aeronave: 0.324', 'envergadura: 0.251', 'payload: 0.277', 'Crucero KIAS: 0.277']\n", + "**Mediana calculada:** 0.272\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.0x + 0.201\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.010249079511174597, Desviación Estándar: 0.04616981560043625, Varianza: 0.0021316518725782866, Incertidumbre: 0.011542453900109063\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.005x + 0.185\n", + "Valor del parámetro correlacionado para la aeronave: 17.5\n", + "Predicción obtenida: 0.272\n", + "\tR²: 0.3967685182755928, Desviación Estándar: 0.03545237044618538, Varianza: 0.0012568705702535586, Incertidumbre: 0.008598462825185189\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.075x + 0.207\n", + "Valor del parámetro correlacionado para la aeronave: 0.7\n", + "Predicción obtenida: 0.26\n", + "\tR²: 0.7836164664637315, Desviación Estándar: 0.021233196730497346, Varianza: 0.0004508486433960032, Incertidumbre: 0.005149806640550596\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = -0.039x + 0.839\n", + "Valor del parámetro correlacionado para la aeronave: 14.717\n", + "Predicción obtenida: 0.272\n", + "\tR²: 0.6503309577441012, Desviación Estándar: 0.026991801495547467, Varianza: 0.0007285573479750386, Incertidumbre: 0.006546473446579232\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.034x + 0.239\n", + "Valor del parámetro correlacionado para la aeronave: 0.9\n", + "Predicción obtenida: 0.269\n", + "\tR²: 0.35785791364325414, Desviación Estándar: 0.037188678863664754, Varianza: 0.0013829978356247857, Incertidumbre: 0.009297169715916188\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.001x + 0.256\n", + "Valor del parámetro correlacionado para la aeronave: 6.2\n", + "Predicción obtenida: 0.265\n", + "\tR²: 0.6662199673897068, Desviación Estándar: 0.02614021892802073, Varianza: 0.0006833110456048533, Incertidumbre: 0.005996977508909025\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = -0.0x + 0.322\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 0.32\n", + "\tR²: 0.08980696350170814, Desviación Estándar: 0.04316639678565242, Varianza: 0.0018633378114563837, Incertidumbre: 0.009903050597128307\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.032x + 0.179\n", + "Valor del parámetro correlacionado para la aeronave: 2.35\n", + "Predicción obtenida: 0.255\n", + "\tR²: 0.5680260142996714, Desviación Estándar: 0.030501695390844595, Varianza: 0.0009303534217158704, Incertidumbre: 0.007625423847711149\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.005x + 0.271\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.276\n", + "\tR²: 0.7521146796518272, Desviación Estándar: 0.018488012959664887, Varianza: 0.00034180662319673684, Incertidumbre: 0.004622003239916222\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.004x + 0.209\n", + "Valor del parámetro correlacionado para la aeronave: 16.0\n", + "Predicción obtenida: 0.276\n", + "\tR²: 0.5394259957381201, Desviación Estándar: 0.02253063800021863, Varianza: 0.0005076296486968957, Incertidumbre: 0.0067932429576407745\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.308', 'Velocidad a la que se realiza el crucero (KTAS): 0.272', 'Área del ala: 0.26', 'Relación de aspecto del ala: 0.272', 'Longitud del fuselaje: 0.269', 'Peso máximo al despegue (MTOW): 0.265', 'Alcance de la aeronave: 0.32', 'envergadura: 0.255', 'payload: 0.276', 'Crucero KIAS: 0.276']\n", + "**Mediana calculada:** 0.272\n", + "\n", + "--- Imputación para aeronave: **V21** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.0x + 0.201\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.010249079511174597, Desviación Estándar: 0.04616981560043625, Varianza: 0.0021316518725782866, Incertidumbre: 0.011542453900109063\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.005x + 0.185\n", + "Valor del parámetro correlacionado para la aeronave: 19.688\n", + "Predicción obtenida: 0.282\n", + "\tR²: 0.3967685182755928, Desviación Estándar: 0.03545237044618538, Varianza: 0.0012568705702535586, Incertidumbre: 0.008598462825185189\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.075x + 0.207\n", + "Valor del parámetro correlacionado para la aeronave: 0.8\n", + "Predicción obtenida: 0.268\n", + "\tR²: 0.7836164664637315, Desviación Estándar: 0.021233196730497346, Varianza: 0.0004508486433960032, Incertidumbre: 0.005149806640550596\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = -0.039x + 0.839\n", + "Valor del parámetro correlacionado para la aeronave: 14.578\n", + "Predicción obtenida: 0.277\n", + "\tR²: 0.6503309577441012, Desviación Estándar: 0.026991801495547467, Varianza: 0.0007285573479750386, Incertidumbre: 0.006546473446579232\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.034x + 0.239\n", + "Valor del parámetro correlacionado para la aeronave: 0.93\n", + "Predicción obtenida: 0.27\n", + "\tR²: 0.35785791364325414, Desviación Estándar: 0.037188678863664754, Varianza: 0.0013829978356247857, Incertidumbre: 0.009297169715916188\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.001x + 0.256\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 0.27\n", + "\tR²: 0.6662199673897068, Desviación Estándar: 0.02614021892802073, Varianza: 0.0006833110456048533, Incertidumbre: 0.005996977508909025\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = -0.0x + 0.322\n", + "Valor del parámetro correlacionado para la aeronave: 471.068\n", + "Predicción obtenida: 0.315\n", + "\tR²: 0.08980696350170814, Desviación Estándar: 0.04316639678565242, Varianza: 0.0018633378114563837, Incertidumbre: 0.009903050597128307\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.032x + 0.179\n", + "Valor del parámetro correlacionado para la aeronave: 2.15\n", + "Predicción obtenida: 0.248\n", + "\tR²: 0.5680260142996714, Desviación Estándar: 0.030501695390844595, Varianza: 0.0009303534217158704, Incertidumbre: 0.007625423847711149\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.005x + 0.271\n", + "Valor del parámetro correlacionado para la aeronave: 1.5\n", + "Predicción obtenida: 0.278\n", + "\tR²: 0.7521146796518272, Desviación Estándar: 0.018488012959664887, Varianza: 0.00034180662319673684, Incertidumbre: 0.004622003239916222\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.004x + 0.209\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 0.285\n", + "\tR²: 0.5394259957381201, Desviación Estándar: 0.02253063800021863, Varianza: 0.0005076296486968957, Incertidumbre: 0.0067932429576407745\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.308', 'Velocidad a la que se realiza el crucero (KTAS): 0.282', 'Área del ala: 0.268', 'Relación de aspecto del ala: 0.277', 'Longitud del fuselaje: 0.27', 'Peso máximo al despegue (MTOW): 0.27', 'Alcance de la aeronave: 0.315', 'envergadura: 0.248', 'payload: 0.278', 'Crucero KIAS: 0.285']\n", + "**Mediana calculada:** 0.278\n", + "\n", + "--- Imputación para aeronave: **V25** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.0x + 0.213\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.007624668273402024, Desviación Estándar: 0.04532987951030284, Varianza: 0.0020547979764185733, Incertidumbre: 0.010994110659852962\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.005x + 0.184\n", + "Valor del parámetro correlacionado para la aeronave: 21.875\n", + "Predicción obtenida: 0.293\n", + "\tR²: 0.41034339711447, Desviación Estándar: 0.03446795427732797, Varianza: 0.0011880398720639715, Incertidumbre: 0.008124174734375492\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.074x + 0.209\n", + "Valor del parámetro correlacionado para la aeronave: 0.52\n", + "Predicción obtenida: 0.248\n", + "\tR²: 0.7859555597017408, Desviación Estándar: 0.020766719698698297, Varianza: 0.0004312566470443038, Incertidumbre: 0.004894762773983275\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = -0.038x + 0.839\n", + "Valor del parámetro correlacionado para la aeronave: 14.435\n", + "Predicción obtenida: 0.283\n", + "\tR²: 0.6584792308147298, Desviación Estándar: 0.02623158211259663, Varianza: 0.0006880959001298995, Incertidumbre: 0.006182843197689607\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.033x + 0.24\n", + "Valor del parámetro correlacionado para la aeronave: 0.93\n", + "Predicción obtenida: 0.271\n", + "\tR²: 0.3698412259519205, Desviación Estándar: 0.03612198254213046, Varianza: 0.001304797622773978, Incertidumbre: 0.008760867613407119\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.001x + 0.257\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 0.274\n", + "\tR²: 0.6742199186237361, Desviación Estándar: 0.025535772267198973, Varianza: 0.0006520756652822481, Incertidumbre: 0.005709972264741082\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = -0.0x + 0.32\n", + "Valor del parámetro correlacionado para la aeronave: 470.718\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.08349677643343056, Desviación Estándar: 0.04283055788146815, Varianza: 0.0018344566884377933, Incertidumbre: 0.009577203893720215\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.03x + 0.189\n", + "Valor del parámetro correlacionado para la aeronave: 2.45\n", + "Predicción obtenida: 0.263\n", + "\tR²: 0.5567962058793969, Desviación Estándar: 0.03029342092653269, Varianza: 0.0009176913514320887, Incertidumbre: 0.007347233778905335\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.005x + 0.271\n", + "Valor del parámetro correlacionado para la aeronave: 2.2\n", + "Predicción obtenida: 0.281\n", + "\tR²: 0.7713758958351147, Desviación Estándar: 0.017936323591463472, Varianza: 0.00032171170397768915, Incertidumbre: 0.004350197453109518\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.004x + 0.207\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 0.292\n", + "\tR²: 0.5559821413548826, Desviación Estándar: 0.021644010407351307, Varianza: 0.0004684631865135317, Incertidumbre: 0.00624808761751367\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278]\n", + "Ecuación de regresión: y = 0.002x + 0.247\n", + "Valor del parámetro correlacionado para la aeronave: 3.45\n", + "Predicción obtenida: 0.253\n", + "\tR²: 0.037864541216720005, Desviación Estándar: 0.04025536882356717, Varianza: 0.001620494719121424, Incertidumbre: 0.018002748229764387\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.293', 'Área del ala: 0.248', 'Relación de aspecto del ala: 0.283', 'Longitud del fuselaje: 0.271', 'Peso máximo al despegue (MTOW): 0.274', 'Alcance de la aeronave: 0.313', 'envergadura: 0.263', 'payload: 0.281', 'Crucero KIAS: 0.292', 'Empty weight: 0.253']\n", + "**Mediana calculada:** 0.281\n", + "\n", + "--- Imputación para aeronave: **V32** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.0x + 0.222\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.304\n", + "\tR²: 0.005879006239630669, Desviación Estándar: 0.044414071195000175, Varianza: 0.0019726097201145446, Incertidumbre: 0.010468496974028912\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.005x + 0.182\n", + "Valor del parámetro correlacionado para la aeronave: 21.875\n", + "Predicción obtenida: 0.292\n", + "\tR²: 0.41638810258008374, Desviación Estándar: 0.03365333043822183, Varianza: 0.0011325466495841479, Incertidumbre: 0.007720603499673174\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.07x + 0.217\n", + "Valor del parámetro correlacionado para la aeronave: 1.03\n", + "Predicción obtenida: 0.288\n", + "\tR²: 0.764143155594072, Desviación Estándar: 0.021393912896673372, Varianza: 0.0004576995090304473, Incertidumbre: 0.004908100227553197\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = -0.039x + 0.84\n", + "Valor del parámetro correlacionado para la aeronave: 14.194\n", + "Predicción obtenida: 0.292\n", + "\tR²: 0.663976576642759, Desviación Estándar: 0.02553587580427515, Varianza: 0.000652080953091365, Incertidumbre: 0.0058583316876653045\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.033x + 0.242\n", + "Valor del parámetro correlacionado para la aeronave: 1.0\n", + "Predicción obtenida: 0.275\n", + "\tR²: 0.3766225687592737, Desviación Estándar: 0.03517033453390517, Varianza: 0.0012369524312268022, Incertidumbre: 0.00828972734850792\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.001x + 0.258\n", + "Valor del parámetro correlacionado para la aeronave: 23.5\n", + "Predicción obtenida: 0.29\n", + "\tR²: 0.6799609284870747, Desviación Estándar: 0.02496000967487211, Varianza: 0.0006230020829697093, Incertidumbre: 0.0054467206515205506\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = -0.0x + 0.318\n", + "Valor del parámetro correlacionado para la aeronave: 473.211\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.07878328572710314, Desviación Estándar: 0.04234714665946332, Varianza: 0.0017932808301980953, Incertidumbre: 0.00924090500154224\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.029x + 0.194\n", + "Valor del parámetro correlacionado para la aeronave: 3.2\n", + "Predicción obtenida: 0.287\n", + "\tR²: 0.5552407456896347, Desviación Estándar: 0.02970731891220461, Varianza: 0.0008825247969514297, Incertidumbre: 0.007002082217897084\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.005x + 0.271\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 0.294\n", + "\tR²: 0.7842231008826372, Desviación Estándar: 0.017431002224719133, Varianza: 0.00030383983855816337, Incertidumbre: 0.004108526625325565\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.004x + 0.205\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 0.291\n", + "\tR²: 0.5586324629674886, Desviación Estándar: 0.021009020466072784, Varianza: 0.00044137894094386517, Incertidumbre: 0.005826853887515214\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.001x + 0.259\n", + "Valor del parámetro correlacionado para la aeronave: 6.45\n", + "Predicción obtenida: 0.264\n", + "\tR²: 0.009256571042910555, Desviación Estándar: 0.03798189451706237, Varianza: 0.0014426243111052524, Incertidumbre: 0.015506043505169485\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.304', 'Velocidad a la que se realiza el crucero (KTAS): 0.292', 'Área del ala: 0.288', 'Relación de aspecto del ala: 0.292', 'Longitud del fuselaje: 0.275', 'Peso máximo al despegue (MTOW): 0.29', 'Alcance de la aeronave: 0.311', 'envergadura: 0.287', 'payload: 0.294', 'Crucero KIAS: 0.291', 'Empty weight: 0.264']\n", + "**Mediana calculada:** 0.291\n", + "\n", + "--- Imputación para aeronave: **V35** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291]\n", + "Ecuación de regresión: y = 0.0x + 0.227\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.303\n", + "\tR²: 0.00512115327712892, Desviación Estándar: 0.043329805954573745, Varianza: 0.0018774720840610147, Incertidumbre: 0.009940539231537572\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", + "Ecuación de regresión: y = 0.005x + 0.182\n", + "Valor del parámetro correlacionado para la aeronave: 27.344\n", + "Predicción obtenida: 0.32\n", + "\tR²: 0.4192571699084816, Desviación Estándar: 0.032802224457642644, Varianza: 0.0010759859293695694, Incertidumbre: 0.007334800370049512\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", + "Ecuación de regresión: y = 0.07x + 0.217\n", + "Valor del parámetro correlacionado para la aeronave: 1.202\n", + "Predicción obtenida: 0.301\n", + "\tR²: 0.7651489951552293, Desviación Estándar: 0.02085967698747946, Varianza: 0.0004351261240219801, Incertidumbre: 0.00466436557326921\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", + "Ecuación de regresión: y = -0.039x + 0.84\n", + "Valor del parámetro correlacionado para la aeronave: 13.909\n", + "Predicción obtenida: 0.303\n", + "\tR²: 0.6656139724811423, Desviación Estándar: 0.02489060361542516, Varianza: 0.000619542148340216, Incertidumbre: 0.005565708168509269\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291]\n", + "Ecuación de regresión: y = 0.032x + 0.245\n", + "Valor del parámetro correlacionado para la aeronave: 1.88\n", + "Predicción obtenida: 0.304\n", + "\tR²: 0.37241241226406685, Desviación Estándar: 0.03441429034951141, Varianza: 0.0011843433802604744, Incertidumbre: 0.007895179676167752\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", + "Ecuación de regresión: y = 0.001x + 0.258\n", + "Valor del parámetro correlacionado para la aeronave: 32.0\n", + "Predicción obtenida: 0.302\n", + "\tR²: 0.682451786672694, Desviación Estándar: 0.024387054383523732, Varianza: 0.0005947284215049441, Incertidumbre: 0.005199337464370489\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", + "Ecuación de regresión: y = -0.0x + 0.317\n", + "Valor del parámetro correlacionado para la aeronave: 477.686\n", + "Predicción obtenida: 0.31\n", + "\tR²: 0.0764508060932585, Desviación Estándar: 0.04158957925260002, Varianza: 0.0017296931024082978, Incertidumbre: 0.008866928089582757\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291]\n", + "Ecuación de regresión: y = 0.029x + 0.195\n", + "Valor del parámetro correlacionado para la aeronave: 3.5\n", + "Predicción obtenida: 0.296\n", + "\tR²: 0.5565499378750433, Desviación Estándar: 0.028928372807546923, Varianza: 0.0008368507532924203, Incertidumbre: 0.00663662387732458\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", + "Ecuación de regresión: y = 0.005x + 0.27\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 0.317\n", + "\tR²: 0.7895139994440685, Desviación Estándar: 0.0169782050018478, Varianza: 0.0002882594450847696, Incertidumbre: 0.003895067360303755\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", + "Ecuación de regresión: y = 0.004x + 0.205\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.5604899238980319, Desviación Estándar: 0.020244967299597776, Varianza: 0.0004098587009617832, Incertidumbre: 0.005410695102966922\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.32', 'Área del ala: 0.301', 'Relación de aspecto del ala: 0.303', 'Longitud del fuselaje: 0.304', 'Peso máximo al despegue (MTOW): 0.302', 'Alcance de la aeronave: 0.31', 'envergadura: 0.296', 'payload: 0.317', 'Crucero KIAS: 0.313']\n", + "**Mediana calculada:** 0.304\n", + "\n", + "--- Imputación para aeronave: **V39** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = 0.0x + 0.227\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.303\n", + "\tR²: 0.005176120015587982, Desviación Estándar: 0.04223283027110972, Varianza: 0.001783611952708361, Incertidumbre: 0.00944354793684122\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = 0.005x + 0.183\n", + "Valor del parámetro correlacionado para la aeronave: 27.344\n", + "Predicción obtenida: 0.319\n", + "\tR²: 0.4130031365050483, Desviación Estándar: 0.03218362230740495, Varianza: 0.0010357855448256935, Incertidumbre: 0.00702304216007393\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = 0.07x + 0.217\n", + "Valor del parámetro correlacionado para la aeronave: 1.203\n", + "Predicción obtenida: 0.301\n", + "\tR²: 0.7648474342748249, Desviación Estándar: 0.0203700405430836, Varianza: 0.0004149385517268696, Incertidumbre: 0.004445107271333332\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = -0.039x + 0.84\n", + "Valor del parámetro correlacionado para la aeronave: 14.054\n", + "Predicción obtenida: 0.298\n", + "\tR²: 0.6655940393757499, Desviación Estándar: 0.024291483455634172, Varianza: 0.0005900761684753486, Incertidumbre: 0.005300836270391003\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = 0.032x + 0.245\n", + "Valor del parámetro correlacionado para la aeronave: 1.954\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.3724510383824907, Desviación Estándar: 0.03354292212178016, Varianza: 0.0011251276244678085, Incertidumbre: 0.007500425402828191\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = 0.001x + 0.258\n", + "Valor del parámetro correlacionado para la aeronave: 24.0\n", + "Predicción obtenida: 0.291\n", + "\tR²: 0.6824856544856703, Desviación Estándar: 0.02385554248252102, Varianza: 0.0005690869071353651, Incertidumbre: 0.004974224462756526\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = -0.0x + 0.317\n", + "Valor del parámetro correlacionado para la aeronave: 475.377\n", + "Predicción obtenida: 0.31\n", + "\tR²: 0.07597157149437128, Desviación Estándar: 0.04069586533624133, Varianza: 0.001656153455465489, Incertidumbre: 0.008485674515131983\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = 0.029x + 0.195\n", + "Valor del parámetro correlacionado para la aeronave: 3.9\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.5548768238027968, Desviación Estándar: 0.02824991788942962, Varianza: 0.0007980578607595157, Incertidumbre: 0.006316873675955201\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = 0.005x + 0.27\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 0.293\n", + "\tR²: 0.7844930543930171, Desviación Estándar: 0.016790498176847062, Varianza: 0.0002819208290267045, Incertidumbre: 0.0037544695299516315\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = 0.004x + 0.206\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.5561091968076497, Desviación Estándar: 0.019679701564958057, Varianza: 0.00038729065368581255, Incertidumbre: 0.005081277094627639\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.319', 'Área del ala: 0.301', 'Relación de aspecto del ala: 0.298', 'Longitud del fuselaje: 0.306', 'Peso máximo al despegue (MTOW): 0.291', 'Alcance de la aeronave: 0.31', 'envergadura: 0.308', 'payload: 0.293', 'Crucero KIAS: 0.312']\n", + "**Mediana calculada:** 0.304\n", + "\n", + "--- Imputación para aeronave: **Volitation VT370** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = 0.0x + 0.227\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.303\n", + "\tR²: 0.005176120015587982, Desviación Estándar: 0.04223283027110972, Varianza: 0.001783611952708361, Incertidumbre: 0.00944354793684122\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = 0.005x + 0.183\n", + "Valor del parámetro correlacionado para la aeronave: 27.344\n", + "Predicción obtenida: 0.319\n", + "\tR²: 0.4130031365050483, Desviación Estándar: 0.03218362230740495, Varianza: 0.0010357855448256935, Incertidumbre: 0.00702304216007393\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", + "Ecuación de regresión: y = 0.07x + 0.217\n", + "Valor del parámetro correlacionado para la aeronave: 1.424\n", + "Predicción obtenida: 0.316\n", + "\tR²: 0.764585109854175, Desviación Estándar: 0.019912811825215336, Varianza: 0.0003965200747864357, Incertidumbre: 0.00424542574579038\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", + "Ecuación de regresión: y = -0.039x + 0.839\n", + "Valor del parámetro correlacionado para la aeronave: 13.657\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.6645264748338366, Desviación Estándar: 0.023770852184850387, Varianza: 0.0005650534135940064, Incertidumbre: 0.00506796271419343\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", + "Ecuación de regresión: y = 0.032x + 0.245\n", + "Valor del parámetro correlacionado para la aeronave: 2.02\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.37232112301089393, Desviación Estándar: 0.032738858426763454, Varianza: 0.0010718328510876604, Incertidumbre: 0.007144204614623162\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", + "Ecuación de regresión: y = 0.001x + 0.259\n", + "Valor del parámetro correlacionado para la aeronave: 40.0\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.6786409596766527, Desviación Estándar: 0.023499467179320575, Varianza: 0.0005522249577119649, Incertidumbre: 0.004796808651384641\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", + "Ecuación de regresión: y = -0.0x + 0.316\n", + "Valor del parámetro correlacionado para la aeronave: 482.568\n", + "Predicción obtenida: 0.31\n", + "\tR²: 0.0755252570158883, Desviación Estándar: 0.03985752064225148, Varianza: 0.0015886219517475032, Incertidumbre: 0.00813588233216365\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", + "Ecuación de regresión: y = 0.029x + 0.195\n", + "Valor del parámetro correlacionado para la aeronave: 6.5\n", + "Predicción obtenida: 0.383\n", + "\tR²: 0.5544873995856561, Desviación Estándar: 0.027581937552100175, Varianza: 0.0007607632791279538, Incertidumbre: 0.006018872221240193\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", + "Ecuación de regresión: y = 0.005x + 0.271\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 0.354\n", + "\tR²: 0.7813065689753644, Desviación Estándar: 0.016547337226329974, Varianza: 0.0002738143692818858, Incertidumbre: 0.003610925018553225\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = 0.004x + 0.206\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.5561091968076497, Desviación Estándar: 0.019679701564958057, Varianza: 0.00038729065368581255, Incertidumbre: 0.005081277094627639\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.319', 'Área del ala: 0.316', 'Relación de aspecto del ala: 0.313', 'Longitud del fuselaje: 0.308', 'Peso máximo al despegue (MTOW): 0.313', 'Alcance de la aeronave: 0.31', 'envergadura: 0.383', 'payload: 0.354', 'Crucero KIAS: 0.312']\n", + "**Mediana calculada:** 0.313\n", + "\n", + "--- Imputación para aeronave: **Skyeye 2600** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Ecuación de regresión: y = 0.0x + 0.227\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.303\n", + "\tR²: 0.005176120015587982, Desviación Estándar: 0.04223283027110972, Varianza: 0.001783611952708361, Incertidumbre: 0.00944354793684122\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313]\n", + "Ecuación de regresión: y = 0.005x + 0.183\n", + "Valor del parámetro correlacionado para la aeronave: 36.094\n", + "Predicción obtenida: 0.362\n", + "\tR²: 0.41332607845278324, Desviación Estándar: 0.03146617256189972, Varianza: 0.0009901200156952508, Incertidumbre: 0.006708610531166274\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", + "Ecuación de regresión: y = 0.07x + 0.217\n", + "Valor del parámetro correlacionado para la aeronave: 0.88\n", + "Predicción obtenida: 0.278\n", + "\tR²: 0.7647651611026156, Desviación Estándar: 0.019487043559427698, Varianza: 0.00037974486668703253, Incertidumbre: 0.004063329469498759\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", + "Ecuación de regresión: y = -0.039x + 0.839\n", + "Valor del parámetro correlacionado para la aeronave: 14.116\n", + "Predicción obtenida: 0.295\n", + "\tR²: 0.6651927379024003, Desviación Estándar: 0.023248373246514756, Varianza: 0.000540486858609263, Incertidumbre: 0.004847620925277197\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", + "Ecuación de regresión: y = 0.032x + 0.245\n", + "Valor del parámetro correlacionado para la aeronave: 2.05\n", + "Predicción obtenida: 0.31\n", + "\tR²: 0.3735742379948259, Desviación Estándar: 0.03200010007033279, Varianza: 0.0010240064045113127, Incertidumbre: 0.006822444258446171\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", + "Ecuación de regresión: y = 0.001x + 0.259\n", + "Valor del parámetro correlacionado para la aeronave: 15.0\n", + "Predicción obtenida: 0.279\n", + "\tR²: 0.6788110572536825, Desviación Estándar: 0.023024764599624735, Varianza: 0.0005301397848681324, Incertidumbre: 0.004604952919924947\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", + "Ecuación de regresión: y = -0.0x + 0.317\n", + "Valor del parámetro correlacionado para la aeronave: 474.569\n", + "Predicción obtenida: 0.31\n", + "\tR²: 0.07577157816533575, Desviación Estándar: 0.039057511178466925, Varianza: 0.001525489179456069, Incertidumbre: 0.007811502235693385\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", + "Ecuación de regresión: y = 0.023x + 0.215\n", + "Valor del parámetro correlacionado para la aeronave: 2.6\n", + "Predicción obtenida: 0.274\n", + "\tR²: 0.456917343395016, Desviación Estándar: 0.02979541908647188, Varianza: 0.0008877669985384927, Incertidumbre: 0.006352404693351432\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", + "Ecuación de regresión: y = 0.004x + 0.272\n", + "Valor del parámetro correlacionado para la aeronave: 4.0\n", + "Predicción obtenida: 0.289\n", + "\tR²: 0.7248069548381981, Desviación Estándar: 0.018136863954327922, Varianza: 0.0003289458340977995, Incertidumbre: 0.0038667923875069623\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313]\n", + "Ecuación de regresión: y = 0.004x + 0.206\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 0.346\n", + "\tR²: 0.5625917571382832, Desviación Estándar: 0.01905614616302398, Varianza: 0.00036313670658653355, Incertidumbre: 0.004764036540755995\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291]\n", + "Ecuación de regresión: y = 0.0x + 0.265\n", + "Valor del parámetro correlacionado para la aeronave: 6.5\n", + "Predicción obtenida: 0.268\n", + "\tR²: 0.003649740403420809, Desviación Estándar: 0.03643263340678523, Varianza: 0.0013273367769532033, Incertidumbre: 0.013770241085933945\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.362', 'Área del ala: 0.278', 'Relación de aspecto del ala: 0.295', 'Longitud del fuselaje: 0.31', 'Peso máximo al despegue (MTOW): 0.279', 'Alcance de la aeronave: 0.31', 'envergadura: 0.274', 'payload: 0.289', 'Crucero KIAS: 0.346', 'Empty weight: 0.268']\n", + "**Mediana calculada:** 0.295\n", + "\n", + "--- Imputación para aeronave: **Skyeye 2930 VTOL** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295]\n", + "Ecuación de regresión: y = 0.0x + 0.23\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.303\n", + "\tR²: 0.004769951987118937, Desviación Estándar: 0.04125452439215927, Varianza: 0.0017019357828232645, Incertidumbre: 0.009002475275546283\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295]\n", + "Ecuación de regresión: y = 0.004x + 0.205\n", + "Valor del parámetro correlacionado para la aeronave: 26.25\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.31968876592912787, Desviación Estándar: 0.03317908352641865, Varianza: 0.0011008515836530652, Incertidumbre: 0.006918317160461927\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", + "Ecuación de regresión: y = 0.069x + 0.219\n", + "Valor del parámetro correlacionado para la aeronave: 1.0\n", + "Predicción obtenida: 0.288\n", + "\tR²: 0.7583363925473515, Desviación Estándar: 0.01935863479576612, Varianza: 0.0003747567411558467, Incertidumbre: 0.003951564780542887\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", + "Ecuación de regresión: y = -0.039x + 0.839\n", + "Valor del parámetro correlacionado para la aeronave: 14.013\n", + "Predicción obtenida: 0.299\n", + "\tR²: 0.6659813449336012, Desviación Estándar: 0.02275906736360552, Varianza: 0.000517975147261134, Incertidumbre: 0.0046456751718719266\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", + "Ecuación de regresión: y = 0.031x + 0.245\n", + "Valor del parámetro correlacionado para la aeronave: 2.03\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.36900063185025456, Desviación Estándar: 0.03143801363195717, Varianza: 0.0009883487011231248, Incertidumbre: 0.006555278991585878\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", + "Ecuación de regresión: y = 0.001x + 0.26\n", + "Valor del parámetro correlacionado para la aeronave: 28.0\n", + "Predicción obtenida: 0.298\n", + "\tR²: 0.674546027930947, Desviación Estándar: 0.022774473008881708, Varianza: 0.0005186766208322815, Incertidumbre: 0.00446644162631077\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", + "Ecuación de regresión: y = -0.0x + 0.316\n", + "Valor del parámetro correlacionado para la aeronave: 476.384\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.074422965024465, Desviación Estándar: 0.03840695118941138, Varianza: 0.0014750938996658278, Incertidumbre: 0.00753222282970824\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", + "Ecuación de regresión: y = 0.022x + 0.219\n", + "Valor del parámetro correlacionado para la aeronave: 2.93\n", + "Predicción obtenida: 0.284\n", + "\tR²: 0.44716444549394097, Desviación Estándar: 0.029426500593546755, Varianza: 0.0008659189371820075, Incertidumbre: 0.006135849529013565\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", + "Ecuación de regresión: y = 0.004x + 0.273\n", + "Valor del parámetro correlacionado para la aeronave: 6.0\n", + "Predicción obtenida: 0.298\n", + "\tR²: 0.7276037635441446, Desviación Estándar: 0.01777586747096132, Varianza: 0.00031598146434518075, Incertidumbre: 0.0037065245900637154\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295]\n", + "Ecuación de regresión: y = 0.003x + 0.226\n", + "Valor del parámetro correlacionado para la aeronave: 24.0\n", + "Predicción obtenida: 0.304\n", + "\tR²: 0.41805913592782484, Desviación Estándar: 0.021338848229013108, Varianza: 0.00045534644374085583, Incertidumbre: 0.005175430892779141\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295]\n", + "Ecuación de regresión: y = 0.0x + 0.27\n", + "Valor del parámetro correlacionado para la aeronave: 7.1\n", + "Predicción obtenida: 0.272\n", + "\tR²: 0.001017512275129695, Desviación Estándar: 0.03522803474762737, Varianza: 0.001241014432180041, Incertidumbre: 0.012454991128961318\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.309', 'Área del ala: 0.288', 'Relación de aspecto del ala: 0.299', 'Longitud del fuselaje: 0.308', 'Peso máximo al despegue (MTOW): 0.298', 'Alcance de la aeronave: 0.309', 'envergadura: 0.284', 'payload: 0.298', 'Crucero KIAS: 0.304', 'Empty weight: 0.272']\n", + "**Mediana calculada:** 0.299\n", + "\n", + "--- Imputación para aeronave: **Skyeye 3600** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299]\n", + "Ecuación de regresión: y = 0.0x + 0.231\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.303\n", + "\tR²: 0.0046024842532983445, Desviación Estándar: 0.040314820357117226, Varianza: 0.0016252847404266335, Incertidumbre: 0.008595148579885139\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", + "Ecuación de regresión: y = 0.068x + 0.22\n", + "Valor del parámetro correlacionado para la aeronave: 1.33\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.7551997675782928, Desviación Estándar: 0.0190968572628113, Varianza: 0.0003646899573161887, Incertidumbre: 0.0038193714525622596\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", + "Ecuación de regresión: y = -0.039x + 0.839\n", + "Valor del parámetro correlacionado para la aeronave: 13.722\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.6662095809690938, Desviación Estándar: 0.022299386888745724, Varianza: 0.0004972626556139646, Incertidumbre: 0.004459877377749145\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", + "Ecuación de regresión: y = 0.031x + 0.244\n", + "Valor del parámetro correlacionado para la aeronave: 2.488\n", + "Predicción obtenida: 0.322\n", + "\tR²: 0.3669311075827396, Desviación Estándar: 0.030832212553008696, Varianza: 0.0009506253309139069, Incertidumbre: 0.006293599032992129\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", + "Ecuación de regresión: y = 0.001x + 0.26\n", + "Valor del parámetro correlacionado para la aeronave: 28.0\n", + "Predicción obtenida: 0.298\n", + "\tR²: 0.6751025711511651, Desviación Estándar: 0.0223500545446565, Varianza: 0.0004995249381491208, Incertidumbre: 0.00430127000258675\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", + "Ecuación de regresión: y = -0.0x + 0.315\n", + "Valor del parámetro correlacionado para la aeronave: 482.044\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.07362847696573804, Desviación Estándar: 0.03773966564722344, Varianza: 0.001424282363164217, Incertidumbre: 0.007263002040183641\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", + "Ecuación de regresión: y = 0.022x + 0.221\n", + "Valor del parámetro correlacionado para la aeronave: 3.6\n", + "Predicción obtenida: 0.299\n", + "\tR²: 0.44117112031756955, Desviación Estándar: 0.02896800757279921, Varianza: 0.0008391454627377525, Incertidumbre: 0.0059130697848697586\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", + "Ecuación de regresión: y = 0.004x + 0.273\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 0.315\n", + "\tR²: 0.7298053627667499, Desviación Estándar: 0.01740232203287305, Varianza: 0.0003028408121358188, Incertidumbre: 0.0035522341100110203\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299]\n", + "Ecuación de regresión: y = 0.0x + 0.274\n", + "Valor del parámetro correlacionado para la aeronave: 11.5\n", + "Predicción obtenida: 0.275\n", + "\tR²: 0.000345064333019951, Desviación Estándar: 0.03430275891623664, Varianza: 0.0011766792692654523, Incertidumbre: 0.01143425297207888\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Área del ala: 0.311', 'Relación de aspecto del ala: 0.311', 'Longitud del fuselaje: 0.322', 'Peso máximo al despegue (MTOW): 0.298', 'Alcance de la aeronave: 0.309', 'envergadura: 0.299', 'payload: 0.315', 'Empty weight: 0.275']\n", + "**Mediana calculada:** 0.309\n", + "\n", + "--- Imputación para aeronave: **Skyeye 3600 VTOL** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309]\n", + "Ecuación de regresión: y = 0.0x + 0.229\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.303\n", + "\tR²: 0.004885808051522722, Desviación Estándar: 0.03944852241805123, Varianza: 0.0015561859209674905, Incertidumbre: 0.008225585537417332\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299]\n", + "Ecuación de regresión: y = 0.004x + 0.205\n", + "Valor del parámetro correlacionado para la aeronave: 32.813\n", + "Predicción obtenida: 0.334\n", + "\tR²: 0.3176237816578882, Desviación Estándar: 0.03254110628346504, Varianza: 0.0010589235981517682, Incertidumbre: 0.006642425505013738\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", + "Ecuación de regresión: y = 0.068x + 0.22\n", + "Valor del parámetro correlacionado para la aeronave: 1.32\n", + "Predicción obtenida: 0.31\n", + "\tR²: 0.7552867180720797, Desviación Estándar: 0.018728748952686985, Varianza: 0.0003507660373327738, Incertidumbre: 0.003673009860574284\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", + "Ecuación de regresión: y = -0.039x + 0.839\n", + "Valor del parámetro correlacionado para la aeronave: 13.684\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.6663591073897637, Desviación Estándar: 0.021868532210116903, Varianza: 0.0004782327010249204, Incertidumbre: 0.004288772018193017\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", + "Ecuación de regresión: y = 0.031x + 0.245\n", + "Valor del parámetro correlacionado para la aeronave: 2.42\n", + "Predicción obtenida: 0.319\n", + "\tR²: 0.3631026056008778, Desviación Estándar: 0.03031715002312391, Varianza: 0.0009191295855246022, Incertidumbre: 0.006063430004624782\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", + "Ecuación de regresión: y = 0.001x + 0.261\n", + "Valor del parámetro correlacionado para la aeronave: 40.0\n", + "Predicción obtenida: 0.314\n", + "\tR²: 0.6721978753808997, Desviación Estándar: 0.02204574905530395, Varianza: 0.00048601505140943494, Incertidumbre: 0.004166254961890813\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", + "Ecuación de regresión: y = -0.0x + 0.315\n", + "Valor del parámetro correlacionado para la aeronave: 300.0\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.07367366337637671, Desviación Estándar: 0.037059621839270106, Varianza: 0.0013734155708697057, Incertidumbre: 0.007003610219200486\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", + "Ecuación de regresión: y = 0.022x + 0.221\n", + "Valor del parámetro correlacionado para la aeronave: 3.6\n", + "Predicción obtenida: 0.3\n", + "\tR²: 0.43919899422207664, Desviación Estándar: 0.02844841198320688, Varianza: 0.0008093121443662688, Incertidumbre: 0.005689682396641376\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", + "Ecuación de regresión: y = 0.004x + 0.272\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 0.315\n", + "\tR²: 0.7285836979639178, Desviación Estándar: 0.017095423279435584, Varianza: 0.0002922534971030681, Incertidumbre: 0.003419084655887117\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299]\n", + "Ecuación de regresión: y = 0.003x + 0.226\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 0.323\n", + "\tR²: 0.41660766587250275, Desviación Estándar: 0.020763504630057248, Varianza: 0.0004311231245224087, Incertidumbre: 0.004894004975037253\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309]\n", + "Ecuación de regresión: y = 0.001x + 0.272\n", + "Valor del parámetro correlacionado para la aeronave: 11.0\n", + "Predicción obtenida: 0.281\n", + "\tR²: 0.00958847480002567, Desviación Estándar: 0.03395743305078767, Varianza: 0.0011531072593987265, Incertidumbre: 0.010738283193316921\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.334', 'Área del ala: 0.31', 'Relación de aspecto del ala: 0.312', 'Longitud del fuselaje: 0.319', 'Peso máximo al despegue (MTOW): 0.314', 'Alcance de la aeronave: 0.311', 'envergadura: 0.3', 'payload: 0.315', 'Crucero KIAS: 0.323', 'Empty weight: 0.281']\n", + "**Mediana calculada:** 0.312\n", + "\n", + "--- Imputación para aeronave: **Skyeye 5000** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312]\n", + "Ecuación de regresión: y = 0.0x + 0.227\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.304\n", + "\tR²: 0.005277507804714032, Desviación Estándar: 0.03865833333585721, Varianza: 0.0014944667363062491, Incertidumbre: 0.007891099248272937\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312]\n", + "Ecuación de regresión: y = 0.004x + 0.209\n", + "Valor del parámetro correlacionado para la aeronave: 36.094\n", + "Predicción obtenida: 0.344\n", + "\tR²: 0.3066672465334307, Desviación Estándar: 0.032165160212105026, Varianza: 0.0010345975314703841, Incertidumbre: 0.006433032042421005\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", + "Ecuación de regresión: y = 0.068x + 0.22\n", + "Valor del parámetro correlacionado para la aeronave: 2.615\n", + "Predicción obtenida: 0.398\n", + "\tR²: 0.7555609689379138, Desviación Estándar: 0.018382843455420142, Varianza: 0.0003379289335064831, Incertidumbre: 0.003537779872485856\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", + "Ecuación de regresión: y = -0.039x + 0.839\n", + "Valor del parámetro correlacionado para la aeronave: 12.713\n", + "Predicción obtenida: 0.349\n", + "\tR²: 0.6668851212909376, Desviación Estándar: 0.0214597386033369, Varianza: 0.00046052038092354804, Incertidumbre: 0.004129928619791854\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", + "Ecuación de regresión: y = 0.03x + 0.245\n", + "Valor del parámetro correlacionado para la aeronave: 3.5\n", + "Predicción obtenida: 0.352\n", + "\tR²: 0.36315238206647993, Desviación Estándar: 0.02976054264839433, Varianza: 0.0008856898987268976, Incertidumbre: 0.005836522603818192\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = 0.76) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352]\n", + "Ecuación de regresión: y = 0.883x + 0.101\n", + "Valor del parámetro correlacionado para la aeronave: 0.375\n", + "Predicción obtenida: 0.432\n", + "\tR²: 0.40209852192250706, Desviación Estándar: 0.03661787969369935, Varianza: 0.001340869113262239, Incertidumbre: 0.02114134269831065\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", + "Ecuación de regresión: y = 0.001x + 0.261\n", + "Valor del parámetro correlacionado para la aeronave: 90.0\n", + "Predicción obtenida: 0.381\n", + "\tR²: 0.67222934329996, Desviación Estándar: 0.021666156030378594, Varianza: 0.00046942231713271076, Incertidumbre: 0.004023304171057925\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", + "Ecuación de regresión: y = -0.0x + 0.315\n", + "Valor del parámetro correlacionado para la aeronave: 615.631\n", + "Predicción obtenida: 0.307\n", + "\tR²: 0.07408169434278955, Desviación Estándar: 0.03641523910867908, Varianza: 0.0013260696393422703, Incertidumbre: 0.006762140141084367\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", + "Ecuación de regresión: y = 0.022x + 0.222\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 0.33\n", + "\tR²: 0.4363264574290574, Desviación Estándar: 0.027998638079495427, Varianza: 0.0007839237343065714, Incertidumbre: 0.005490984689283431\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", + "Ecuación de regresión: y = 0.004x + 0.272\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 0.358\n", + "\tR²: 0.7282681597919514, Desviación Estándar: 0.016773711285579456, Varianza: 0.00028135739029197564, Incertidumbre: 0.0032895954292515833\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312]\n", + "Ecuación de regresión: y = 0.003x + 0.228\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 0.331\n", + "\tR²: 0.4160144196233093, Desviación Estándar: 0.020338026156813578, Varianza: 0.00041363530795523323, Incertidumbre: 0.004665863196244067\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312]\n", + "Ecuación de regresión: y = 0.001x + 0.271\n", + "Valor del parámetro correlacionado para la aeronave: 32.0\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.02251377090831741, Desviación Estándar: 0.033563109667469955, Varianza: 0.0011264823305506154, Incertidumbre: 0.010119658324049803\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.304', 'Velocidad a la que se realiza el crucero (KTAS): 0.344', 'Área del ala: 0.398', 'Relación de aspecto del ala: 0.349', 'Longitud del fuselaje: 0.352', 'Ancho del fuselaje: 0.432', 'Peso máximo al despegue (MTOW): 0.381', 'Alcance de la aeronave: 0.307', 'envergadura: 0.33', 'payload: 0.358', 'Crucero KIAS: 0.331', 'Empty weight: 0.311']\n", + "**Mediana calculada:** 0.346\n", + "\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346]\n", + "Ecuación de regresión: y = 0.0x + 0.215\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.305\n", + "\tR²: 0.0069178491819321675, Desviación Estándar: 0.03877743810622532, Varianza: 0.0015036897060821354, Incertidumbre: 0.007755487621245063\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346]\n", + "Ecuación de regresión: y = 0.004x + 0.209\n", + "Valor del parámetro correlacionado para la aeronave: 30.625\n", + "Predicción obtenida: 0.324\n", + "\tR²: 0.3363378708178395, Desviación Estándar: 0.03154190924564428, Varianza: 0.0009948920388604602, Incertidumbre: 0.006185877336135111\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", + "Ecuación de regresión: y = 0.059x + 0.229\n", + "Valor del parámetro correlacionado para la aeronave: 2.615\n", + "Predicción obtenida: 0.385\n", + "\tR²: 0.7140844954505441, Desviación Estándar: 0.019953273741700576, Varianza: 0.00039813313301123777, Incertidumbre: 0.003770814297295355\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", + "Ecuación de regresión: y = -0.038x + 0.836\n", + "Valor del parámetro correlacionado para la aeronave: 13.046\n", + "Predicción obtenida: 0.336\n", + "\tR²: 0.6808265849574946, Desviación Estándar: 0.021081847262580024, Varianza: 0.00044444428400275285, Incertidumbre: 0.003984094645331039\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", + "Ecuación de regresión: y = 0.03x + 0.246\n", + "Valor del parámetro correlacionado para la aeronave: 3.5\n", + "Predicción obtenida: 0.351\n", + "\tR²: 0.3920963576822901, Desviación Estándar: 0.02922109638467694, Varianza: 0.0008538724739225799, Incertidumbre: 0.0056236026212364105\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = 0.76) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.346]\n", + "Ecuación de regresión: y = 0.4x + 0.207\n", + "Valor del parámetro correlacionado para la aeronave: 0.375\n", + "Predicción obtenida: 0.358\n", + "\tR²: 0.38245795781242664, Desviación Estándar: 0.0353855035263058, Varianza: 0.0012521338598102006, Incertidumbre: 0.0176927517631529\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", + "Ecuación de regresión: y = 0.001x + 0.263\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 0.387\n", + "\tR²: 0.6598630268437224, Desviación Estándar: 0.02206649940336974, Varianza: 0.0004869303959189171, Incertidumbre: 0.004028773162799966\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", + "Ecuación de regresión: y = -0.0x + 0.317\n", + "Valor del parámetro correlacionado para la aeronave: 800.0\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.07051034083815089, Desviación Estándar: 0.03647781384637196, Varianza: 0.0013306309030105662, Incertidumbre: 0.0066599071640440705\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", + "Ecuación de regresión: y = 0.022x + 0.22\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 0.332\n", + "\tR²: 0.4566675873516166, Desviación Estándar: 0.027625614100153503, Varianza: 0.0007631745544106001, Incertidumbre: 0.005316551912417449\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", + "Ecuación de regresión: y = 0.004x + 0.273\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 0.377\n", + "\tR²: 0.7340964250736124, Desviación Estándar: 0.01659158262598554, Varianza: 0.0002752806140349053, Incertidumbre: 0.0031930515651315567\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346]\n", + "Ecuación de regresión: y = 0.003x + 0.224\n", + "Valor del parámetro correlacionado para la aeronave: 28.0\n", + "Predicción obtenida: 0.317\n", + "\tR²: 0.4797563623056019, Desviación Estándar: 0.02006710184638167, Varianza: 0.0004026885765130546, Incertidumbre: 0.004487140383992096\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.305', 'Velocidad a la que se realiza el crucero (KTAS): 0.324', 'Área del ala: 0.385', 'Relación de aspecto del ala: 0.336', 'Longitud del fuselaje: 0.351', 'Ancho del fuselaje: 0.358', 'Peso máximo al despegue (MTOW): 0.387', 'Alcance de la aeronave: 0.306', 'envergadura: 0.332', 'payload: 0.377', 'Crucero KIAS: 0.317']\n", + "**Mediana calculada:** 0.336\n", + "\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", + "Ecuación de regresión: y = 0.0x + 0.207\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.307\n", + "\tR²: 0.008204928681858958, Desviación Estándar: 0.038476561679856074, Varianza: 0.001480445798703769, Incertidumbre: 0.007545874570059327\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336]\n", + "Ecuación de regresión: y = 0.004x + 0.207\n", + "Valor del parámetro correlacionado para la aeronave: 33.885\n", + "Predicción obtenida: 0.337\n", + "\tR²: 0.3474595488240295, Desviación Estándar: 0.03103314635842597, Varianza: 0.000963056172903487, Incertidumbre: 0.005972331801279429\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", + "Ecuación de regresión: y = 0.053x + 0.236\n", + "Valor del parámetro correlacionado para la aeronave: 2.615\n", + "Predicción obtenida: 0.375\n", + "\tR²: 0.6729975157002732, Desviación Estándar: 0.021200760020167443, Varianza: 0.0004494722254327303, Incertidumbre: 0.003936882301555514\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", + "Ecuación de regresión: y = -0.038x + 0.836\n", + "Valor del parámetro correlacionado para la aeronave: 12.876\n", + "Predicción obtenida: 0.343\n", + "\tR²: 0.6878032793029674, Desviación Estándar: 0.020715244926136148, Varianza: 0.00042912137234980943, Incertidumbre: 0.0038467244119793098\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", + "Ecuación de regresión: y = 0.029x + 0.248\n", + "Valor del parámetro correlacionado para la aeronave: 3.5\n", + "Predicción obtenida: 0.348\n", + "\tR²: 0.4021070843542891, Desviación Estándar: 0.02880802670233707, Varianza: 0.0008299024024825657, Incertidumbre: 0.005444205315492288\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = 0.76) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.346, 0.336]\n", + "Ecuación de regresión: y = 0.335x + 0.222\n", + "Valor del parámetro correlacionado para la aeronave: 0.375\n", + "Predicción obtenida: 0.348\n", + "\tR²: 0.3819496237120672, Desviación Estándar: 0.032423341688487206, Varianza: 0.0010512730862483922, Incertidumbre: 0.01450015921463204\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", + "Ecuación de regresión: y = 0.001x + 0.267\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 0.377\n", + "\tR²: 0.61787092141232, Desviación Estándar: 0.023196525022263995, Varianza: 0.0005380787731085197, Incertidumbre: 0.004166218882595367\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", + "Ecuación de regresión: y = -0.0x + 0.317\n", + "Valor del parámetro correlacionado para la aeronave: 609.354\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.0654907654203285, Desviación Estándar: 0.03627518909145652, Varianza: 0.0013158893436209262, Incertidumbre: 0.006515216292849419\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", + "Ecuación de regresión: y = 0.022x + 0.22\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 0.332\n", + "\tR²: 0.46936427126981495, Desviación Estándar: 0.027139388088225875, Varianza: 0.0007365463858033365, Incertidumbre: 0.005128862258279595\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", + "Ecuación de regresión: y = 0.004x + 0.276\n", + "Valor del parámetro correlacionado para la aeronave: 15.0\n", + "Predicción obtenida: 0.332\n", + "\tR²: 0.6901097817245, Desviación Estándar: 0.01772848171904924, Varianza: 0.00031429906406266314, Incertidumbre: 0.0033503681250970823\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336]\n", + "Ecuación de regresión: y = 0.003x + 0.222\n", + "Valor del parámetro correlacionado para la aeronave: 35.0\n", + "Predicción obtenida: 0.342\n", + "\tR²: 0.4932580403474792, Desviación Estándar: 0.019996932601760886, Varianza: 0.00039987731347936734, Incertidumbre: 0.004363688443547595\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346]\n", + "Ecuación de regresión: y = 0.002x + 0.263\n", + "Valor del parámetro correlacionado para la aeronave: 35.0\n", + "Predicción obtenida: 0.344\n", + "\tR²: 0.23082193042213683, Desviación Estándar: 0.03253007300095766, Varianza: 0.0010582056494476341, Incertidumbre: 0.00939062320193054\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.307', 'Velocidad a la que se realiza el crucero (KTAS): 0.337', 'Área del ala: 0.375', 'Relación de aspecto del ala: 0.343', 'Longitud del fuselaje: 0.348', 'Ancho del fuselaje: 0.348', 'Peso máximo al despegue (MTOW): 0.377', 'Alcance de la aeronave: 0.309', 'envergadura: 0.332', 'payload: 0.332', 'Crucero KIAS: 0.342', 'Empty weight: 0.344']\n", + "**Mediana calculada:** 0.342\n", + "\n", + "--- Imputación para aeronave: **Volitation VT510** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", + "Ecuación de regresión: y = 0.0x + 0.199\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.009671200460906637, Desviación Estándar: 0.038341789857909814, Varianza: 0.0014700928495081159, Incertidumbre: 0.007378880898558765\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342]\n", + "Ecuación de regresión: y = 0.004x + 0.207\n", + "Valor del parámetro correlacionado para la aeronave: 32.813\n", + "Predicción obtenida: 0.334\n", + "\tR²: 0.3657078235083421, Desviación Estándar: 0.030486197686657987, Varianza: 0.0009294082493899907, Incertidumbre: 0.005761349821346404\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", + "Ecuación de regresión: y = 0.049x + 0.24\n", + "Valor del parámetro correlacionado para la aeronave: 1.993\n", + "Predicción obtenida: 0.339\n", + "\tR²: 0.6607991636850806, Desviación Estándar: 0.021542829923793395, Varianza: 0.00046409352112548814, Incertidumbre: 0.003933164633919647\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", + "Ecuación de regresión: y = -0.038x + 0.835\n", + "Valor del parámetro correlacionado para la aeronave: 13.114\n", + "Predicción obtenida: 0.334\n", + "\tR²: 0.6968007721860683, Desviación Estándar: 0.020367528893078666, Varianza: 0.00041483623321039427, Incertidumbre: 0.003718585005125804\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", + "Ecuación de regresión: y = 0.028x + 0.249\n", + "Valor del parámetro correlacionado para la aeronave: 2.905\n", + "Predicción obtenida: 0.331\n", + "\tR²: 0.41997296733972356, Desviación Estándar: 0.028328973372175076, Varianza: 0.0008025307323214044, Incertidumbre: 0.005260558290554748\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", + "Ecuación de regresión: y = 0.001x + 0.269\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 0.371\n", + "\tR²: 0.6038191426117239, Desviación Estándar: 0.02351157571063262, Varianza: 0.0005527941923968098, Incertidumbre: 0.004156298655342311\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", + "Ecuación de regresión: y = -0.0x + 0.318\n", + "Valor del parámetro correlacionado para la aeronave: 501.616\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.06342608491681201, Desviación Estándar: 0.03614981016848977, Varianza: 0.0013068087752178463, Incertidumbre: 0.006390443977186381\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", + "Ecuación de regresión: y = 0.023x + 0.219\n", + "Valor del parámetro correlacionado para la aeronave: 5.1\n", + "Predicción obtenida: 0.335\n", + "\tR²: 0.4836792530977432, Desviación Estándar: 0.026728002730142274, Varianza: 0.0007143861299424928, Incertidumbre: 0.004963265505770935\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", + "Ecuación de regresión: y = 0.004x + 0.276\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 0.37\n", + "\tR²: 0.6941742799016253, Desviación Estándar: 0.01751465394968648, Varianza: 0.00030676310297726825, Incertidumbre: 0.0032523895882410683\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342]\n", + "Ecuación de regresión: y = 0.003x + 0.222\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 0.325\n", + "\tR²: 0.5326016124134147, Desviación Estándar: 0.019537228035800464, Varianza: 0.0003817032793228677, Incertidumbre: 0.004165351012835574\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.308', 'Velocidad a la que se realiza el crucero (KTAS): 0.334', 'Área del ala: 0.339', 'Relación de aspecto del ala: 0.334', 'Longitud del fuselaje: 0.331', 'Peso máximo al despegue (MTOW): 0.371', 'Alcance de la aeronave: 0.312', 'envergadura: 0.335', 'payload: 0.37', 'Crucero KIAS: 0.325']\n", + "**Mediana calculada:** 0.334\n", + "\n", + "--- Imputación para aeronave: **Ascend** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", + "Ecuación de regresión: y = 0.0x + 0.199\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.009671200460906637, Desviación Estándar: 0.038341789857909814, Varianza: 0.0014700928495081159, Incertidumbre: 0.007378880898558765\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342, 0.334]\n", + "Ecuación de regresión: y = 0.004x + 0.207\n", + "Valor del parámetro correlacionado para la aeronave: 21.875\n", + "Predicción obtenida: 0.291\n", + "\tR²: 0.3754279357663507, Desviación Estándar: 0.02995609491552544, Varianza: 0.0008973676225879692, Incertidumbre: 0.005562707175802361\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", + "Ecuación de regresión: y = 0.049x + 0.241\n", + "Valor del parámetro correlacionado para la aeronave: 0.771\n", + "Predicción obtenida: 0.278\n", + "\tR²: 0.6655989150459374, Desviación Estándar: 0.021207046925550192, Varianza: 0.00044973883930248784, Incertidumbre: 0.0038088980681594306\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", + "Ecuación de regresión: y = -0.038x + 0.836\n", + "Valor del parámetro correlacionado para la aeronave: 14.357\n", + "Predicción obtenida: 0.286\n", + "\tR²: 0.7014972579081873, Desviación Estándar: 0.02003643818748548, Varianza: 0.0004014588552409264, Incertidumbre: 0.003598650532204136\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", + "Ecuación de regresión: y = 0.028x + 0.248\n", + "Valor del parámetro correlacionado para la aeronave: 1.562\n", + "Predicción obtenida: 0.293\n", + "\tR²: 0.4296493321511522, Desviación Estándar: 0.027858702470545647, Varianza: 0.0007761073033423862, Incertidumbre: 0.005086279921980917\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", + "Ecuación de regresión: y = 0.001x + 0.271\n", + "Valor del parámetro correlacionado para la aeronave: 9.5\n", + "Predicción obtenida: 0.28\n", + "\tR²: 0.582320619650718, Desviación Estándar: 0.023909852818436363, Varianza: 0.0005716810617992893, Incertidumbre: 0.0041621711328791375\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354, 501.616]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", + "Ecuación de regresión: y = -0.0x + 0.319\n", + "Valor del parámetro correlacionado para la aeronave: 478.644\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.06366520088212502, Desviación Estándar: 0.03579900590384107, Varianza: 0.0012815688237032483, Incertidumbre: 0.006231807033284842\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 5.1]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", + "Ecuación de regresión: y = 0.023x + 0.219\n", + "Valor del parámetro correlacionado para la aeronave: 2.0\n", + "Predicción obtenida: 0.265\n", + "\tR²: 0.4924880946399838, Desviación Estándar: 0.026279253552868982, Varianza: 0.000690599167295977, Incertidumbre: 0.004797913321768041\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", + "Ecuación de regresión: y = 0.003x + 0.278\n", + "Valor del parámetro correlacionado para la aeronave: 0.6\n", + "Predicción obtenida: 0.28\n", + "\tR²: 0.6596231201497137, Desviación Estándar: 0.018262602460999124, Varianza: 0.0003335226486484913, Incertidumbre: 0.0033342797755461936\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342, 0.334]\n", + "Ecuación de regresión: y = 0.003x + 0.221\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 0.291\n", + "\tR²: 0.5479865342925803, Desviación Estándar: 0.019192420183738294, Varianza: 0.0003683489925091651, Incertidumbre: 0.004001896248949366\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342]\n", + "Ecuación de regresión: y = 0.002x + 0.263\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 0.27\n", + "\tR²: 0.3428931453094083, Desviación Estándar: 0.031257301972751485, Varianza: 0.000977018926615774, Incertidumbre: 0.008669215768878245\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.308', 'Velocidad a la que se realiza el crucero (KTAS): 0.291', 'Área del ala: 0.278', 'Relación de aspecto del ala: 0.286', 'Longitud del fuselaje: 0.293', 'Peso máximo al despegue (MTOW): 0.28', 'Alcance de la aeronave: 0.313', 'envergadura: 0.265', 'payload: 0.28', 'Crucero KIAS: 0.291', 'Empty weight: 0.27']\n", + "**Mediana calculada:** 0.286\n", + "\n", + "--- Imputación para aeronave: **Transition** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.286]\n", + "Ecuación de regresión: y = 0.0x + 0.204\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.307\n", + "\tR²: 0.008560595514286895, Desviación Estándar: 0.03787184264181916, Varianza: 0.0014342764650867124, Incertidumbre: 0.0071571055230017794\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", + "Ecuación de regresión: y = 0.004x + 0.206\n", + "Valor del parámetro correlacionado para la aeronave: 21.875\n", + "Predicción obtenida: 0.291\n", + "\tR²: 0.38249062220586694, Desviación Estándar: 0.029467308778454444, Varianza: 0.0008683222866447784, Incertidumbre: 0.0053799699089765\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", + "Ecuación de regresión: y = 0.049x + 0.241\n", + "Valor del parámetro correlacionado para la aeronave: 0.986\n", + "Predicción obtenida: 0.289\n", + "\tR²: 0.6683533020403162, Desviación Estándar: 0.020914188913517928, Varianza: 0.00043740329791031626, Incertidumbre: 0.00369714120094126\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", + "Ecuación de regresión: y = -0.038x + 0.836\n", + "Valor del parámetro correlacionado para la aeronave: 14.233\n", + "Predicción obtenida: 0.291\n", + "\tR²: 0.7051191690209833, Desviación Estándar: 0.019720887637784245, Varianza: 0.0003889134092221115, Incertidumbre: 0.003486193344923798\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", + "Ecuación de regresión: y = 0.029x + 0.248\n", + "Valor del parámetro correlacionado para la aeronave: 2.3\n", + "Predicción obtenida: 0.314\n", + "\tR²: 0.4350653976176332, Desviación Estándar: 0.027431632448403815, Varianza: 0.000752494458784321, Incertidumbre: 0.004926866630983046\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0, 9.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", + "Ecuación de regresión: y = 0.001x + 0.272\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 0.288\n", + "\tR²: 0.5874164948558036, Desviación Estándar: 0.023575730786020113, Varianza: 0.0005558150820948966, Incertidumbre: 0.004043204473503137\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354, 501.616, 478.644]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", + "Ecuación de regresión: y = -0.0x + 0.318\n", + "Valor del parámetro correlacionado para la aeronave: 480.438\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.06137710301442201, Desviación Estándar: 0.035559431175592925, Varianza: 0.0012644731455317298, Incertidumbre: 0.006098392135086606\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", + "Ecuación de regresión: y = 0.022x + 0.224\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 0.289\n", + "\tR²: 0.4884294242049745, Desviación Estándar: 0.026103896782224575, Varianza: 0.0006814134272170345, Incertidumbre: 0.004688398265647203\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", + "Ecuación de regresión: y = 0.003x + 0.279\n", + "Valor del parámetro correlacionado para la aeronave: 1.5\n", + "Predicción obtenida: 0.284\n", + "\tR²: 0.6689077573598308, Desviación Estándar: 0.017992987405351088, Varianza: 0.00032374759576912293, Incertidumbre: 0.0032316359373020394\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", + "Ecuación de regresión: y = 0.004x + 0.22\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 0.29\n", + "\tR²: 0.5566215944971976, Desviación Estándar: 0.018811661715774402, Varianza: 0.00035387861650873226, Incertidumbre: 0.0038399143681247003\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286]\n", + "Ecuación de regresión: y = 0.002x + 0.265\n", + "Valor del parámetro correlacionado para la aeronave: 5.8\n", + "Predicción obtenida: 0.278\n", + "\tR²: 0.33191540406967635, Desviación Estándar: 0.030388935680947273, Varianza: 0.0009234874118207502, Incertidumbre: 0.008121784690486755\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.307', 'Velocidad a la que se realiza el crucero (KTAS): 0.291', 'Área del ala: 0.289', 'Relación de aspecto del ala: 0.291', 'Longitud del fuselaje: 0.314', 'Peso máximo al despegue (MTOW): 0.288', 'Alcance de la aeronave: 0.312', 'envergadura: 0.289', 'payload: 0.284', 'Crucero KIAS: 0.29', 'Empty weight: 0.278']\n", + "**Mediana calculada:** 0.29\n", + "\n", + "--- Imputación para aeronave: **Reach** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.286, 0.29]\n", + "Ecuación de regresión: y = 0.0x + 0.208\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.307\n", + "\tR²: 0.0077920393265346055, Desviación Estándar: 0.03734493173285593, Varianza: 0.0013946439261316697, Incertidumbre: 0.006934779727331597\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 21.875]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", + "Ecuación de regresión: y = 0.004x + 0.206\n", + "Valor del parámetro correlacionado para la aeronave: 27.344\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.38722452002086527, Desviación Estándar: 0.028988626049815485, Varianza: 0.0008403404402560409, Incertidumbre: 0.005206510937018345\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", + "Ecuación de regresión: y = 0.049x + 0.241\n", + "Valor del parámetro correlacionado para la aeronave: 2.329\n", + "Predicción obtenida: 0.355\n", + "\tR²: 0.6708663750067654, Desviación Estándar: 0.02059524463122736, Varianza: 0.00042416410142009946, Incertidumbre: 0.0035851719092382297\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", + "Ecuación de regresión: y = -0.038x + 0.836\n", + "Valor del parámetro correlacionado para la aeronave: 13.683\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.7073494265597657, Desviación Estándar: 0.01942028138024239, Varianza: 0.00037714732888778933, Incertidumbre: 0.0033806370606726797\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", + "Ecuación de regresión: y = 0.028x + 0.248\n", + "Valor del parámetro correlacionado para la aeronave: 4.712\n", + "Predicción obtenida: 0.381\n", + "\tR²: 0.4260780462787932, Desviación Estándar: 0.027309060641999183, Varianza: 0.0007457847931483888, Incertidumbre: 0.004827605491948068\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0, 9.5, 18.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", + "Ecuación de regresión: y = 0.001x + 0.272\n", + "Valor del parámetro correlacionado para la aeronave: 91.0\n", + "Predicción obtenida: 0.357\n", + "\tR²: 0.5911374094922912, Desviación Estándar: 0.02323782088030035, Varianza: 0.0005399963192649228, Incertidumbre: 0.003927908637521\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354, 501.616, 478.644, 480.438]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", + "Ecuación de regresión: y = -0.0x + 0.318\n", + "Valor del parámetro correlacionado para la aeronave: 491.03\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.059744190947153664, Desviación Estándar: 0.03523949892053134, Varianza: 0.0012418222841701293, Incertidumbre: 0.005956562489437605\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", + "Ecuación de regresión: y = 0.022x + 0.224\n", + "Valor del parámetro correlacionado para la aeronave: 6.0\n", + "Predicción obtenida: 0.354\n", + "\tR²: 0.491966702458294, Desviación Estándar: 0.025693687153767065, Varianza: 0.0006601655595556547, Incertidumbre: 0.004542045105028593\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", + "Ecuación de regresión: y = 0.003x + 0.279\n", + "Valor del parámetro correlacionado para la aeronave: 7.0\n", + "Predicción obtenida: 0.303\n", + "\tR²: 0.6746565418595589, Desviación Estándar: 0.01774066062848476, Varianza: 0.0003147310395350693, Incertidumbre: 0.0031361353582826925\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 20.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", + "Ecuación de regresión: y = 0.004x + 0.22\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.5621478971519628, Desviación Estándar: 0.018431786174629827, Varianza: 0.0003397307415872752, Incertidumbre: 0.0036863572349259653\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Empty weight (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition']\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29]\n", + "Ecuación de regresión: y = 0.002x + 0.267\n", + "Valor del parámetro correlacionado para la aeronave: 31.0\n", + "Predicción obtenida: 0.333\n", + "\tR²: 0.32516791212624563, Desviación Estándar: 0.029506663193114138, Varianza: 0.0008706431727918767, Incertidumbre: 0.007618587676605494\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.307', 'Velocidad a la que se realiza el crucero (KTAS): 0.312', 'Área del ala: 0.355', 'Relación de aspecto del ala: 0.312', 'Longitud del fuselaje: 0.381', 'Peso máximo al despegue (MTOW): 0.357', 'Alcance de la aeronave: 0.311', 'envergadura: 0.354', 'payload: 0.303', 'Crucero KIAS: 0.308', 'Empty weight: 0.333']\n", + "**Mediana calculada:** 0.312\n", + "\n", + "=== Imputación para el parámetro: **payload** ===\n", + "\n", + "--- Imputación para aeronave: **AAI Aerosonde** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 10.406x + -4.881\n", + "Valor del parámetro correlacionado para la aeronave: 0.57\n", + "Predicción obtenida: 1.05\n", + "\tR²: 0.7012035885121172, Desviación Estándar: 4.05696673307922, Varianza: 16.458979073311475, Incertidumbre: 0.7171771720021375\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = -7.847x + 117.681\n", + "Valor del parámetro correlacionado para la aeronave: 14.754\n", + "Predicción obtenida: 1.896\n", + "\tR²: 0.7149957845485787, Desviación Estándar: 3.9667001777935313, Varianza: 15.734710300507235, Incertidumbre: 0.6905138688293478\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.875) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 55.0, 32.0, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.204x + 1.036\n", + "Valor del parámetro correlacionado para la aeronave: 13.1\n", + "Predicción obtenida: 3.714\n", + "\tR²: 0.7186101745846443, Desviación Estándar: 3.7341950529878645, Varianza: 13.944212693759042, Incertidumbre: 0.6706812303187741\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.28, 30.0, 35.102, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.878x + -20.785\n", + "Valor del parámetro correlacionado para la aeronave: 30.846\n", + "Predicción obtenida: 6.284\n", + "\tR²: 0.5501898082120762, Desviación Estándar: 5.100088235913723, Varianza: 26.01090001410555, Incertidumbre: 0.9311444573588917\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.734) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 5.2, 4.4, 3.1, 4.8, 5.033, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 4.794x + -9.245\n", + "Valor del parámetro correlacionado para la aeronave: 2.9\n", + "Predicción obtenida: 4.659\n", + "\tR²: 0.5558518681245512, Desviación Estándar: 5.025362155962813, Varianza: 25.25426479858321, Incertidumbre: 0.9025816878156258\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29, 0.312]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 198.875x + -52.199\n", + "Valor del parámetro correlacionado para la aeronave: 0.197\n", + "Predicción obtenida: -13.11\n", + "\tR²: 0.6722158191575872, Desviación Estándar: 4.2540023843702155, Varianza: 18.096536286227476, Incertidumbre: 0.7405267635011167\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Empty weight (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Empty weight: [10.886, 17.463, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Valores para payload: [2.495, 2.495, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.388x + 1.17\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 5.053\n", + "\tR²: 0.6045881019855628, Desviación Estándar: 3.3770579595870056, Varianza: 11.404520462409948, Incertidumbre: 0.8719526157771782\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 1.05', 'Relación de aspecto del ala: 1.896', 'Peso máximo al despegue (MTOW): 3.714', 'Velocidad máxima (KIAS): 6.284', 'envergadura: 4.659', 'Cuerda: -13.11', 'Empty weight: 5.053']\n", + "**Mediana calculada:** 3.714\n", + "\n", + "--- Imputación para aeronave: **Fulmar X** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 10.219x + -4.535\n", + "Valor del parámetro correlacionado para la aeronave: 0.94\n", + "Predicción obtenida: 5.071\n", + "\tR²: 0.7043549197034407, Desviación Estándar: 4.019442149271862, Varianza: 16.155915191343205, Incertidumbre: 0.699695067594778\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = -7.761x + 116.556\n", + "Valor del parámetro correlacionado para la aeronave: 13.218\n", + "Predicción obtenida: 13.965\n", + "\tR²: 0.7200402040647889, Desviación Estándar: 3.9193674746851532, Varianza: 15.361441401619876, Incertidumbre: 0.6721659765620716\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.875) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 55.0, 32.0, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.204x + 1.036\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 5.125\n", + "\tR²: 0.7248470361708765, Desviación Estándar: 3.675385156868235, Varianza: 13.508456051327343, Incertidumbre: 0.649722441973478\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 30.846, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.28, 30.0, 35.102, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.887x + -21.192\n", + "Valor del parámetro correlacionado para la aeronave: 41.7\n", + "Predicción obtenida: 15.784\n", + "\tR²: 0.5574907931449841, Desviación Estándar: 5.037319182991233, Varianza: 25.374584551331463, Incertidumbre: 0.9047292332664074\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.734) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 5.2, 4.4, 3.1, 4.8, 5.033, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 4.819x + -9.371\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 5.085\n", + "\tR²: 0.5654022085753986, Desviación Estándar: 4.9488715578269735, Varianza: 24.491329695868778, Incertidumbre: 0.8748451594401715\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29, 0.312]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 155.509x + -38.199\n", + "Valor del parámetro correlacionado para la aeronave: 0.313\n", + "Predicción obtenida: 10.475\n", + "\tR²: 0.5775590357480013, Desviación Estándar: 4.814499686360729, Varianza: 23.179407229967552, Incertidumbre: 0.8256798843799175\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 5.071', 'Relación de aspecto del ala: 13.965', 'Peso máximo al despegue (MTOW): 5.125', 'Velocidad máxima (KIAS): 15.784', 'envergadura: 5.085', 'Cuerda: 10.475']\n", + "**Mediana calculada:** 7.8\n", + "\n", + "--- Imputación para aeronave: **Mantis** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.899) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 10.116x + -4.31\n", + "Valor del parámetro correlacionado para la aeronave: 0.754\n", + "Predicción obtenida: 3.318\n", + "\tR²: 0.7011908920545251, Desviación Estándar: 3.986164457115577, Varianza: 15.889507079171521, Incertidumbre: 0.6836215645406782\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.888) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = -7.633x + 114.627\n", + "Valor del parámetro correlacionado para la aeronave: 14.767\n", + "Predicción obtenida: 1.904\n", + "\tR²: 0.701365229774406, Desviación Estándar: 3.9958441696300517, Varianza: 15.96677062796648, Incertidumbre: 0.6754209402389949\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.875) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.202x + 1.199\n", + "Valor del parámetro correlacionado para la aeronave: 6.5\n", + "Predicción obtenida: 2.515\n", + "\tR²: 0.7210133339229867, Desviación Estándar: 3.6477290781179126, Varianza: 13.305927427346957, Incertidumbre: 0.6349881274802048\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad máxima (KIAS) (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.28, 30.0, 35.102, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.849x + -20.097\n", + "Valor del parámetro correlacionado para la aeronave: 25.6\n", + "Predicción obtenida: 1.635\n", + "\tR²: 0.5252245995470497, Desviación Estándar: 5.143299088683377, Varianza: 26.453525515651258, Incertidumbre: 0.9092154158196514\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.734) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 3.1, 4.8, 5.033, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 4.759x + -9.051\n", + "Valor del parámetro correlacionado para la aeronave: 2.1\n", + "Predicción obtenida: 0.943\n", + "\tR²: 0.5626551847696982, Desviación Estándar: 4.894993387759511, Varianza: 23.960960266209337, Incertidumbre: 0.852108974859816\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29, 0.312]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 155.416x + -38.247\n", + "Valor del parámetro correlacionado para la aeronave: 0.27\n", + "Predicción obtenida: 3.715\n", + "\tR²: 0.5751366916639009, Desviación Estándar: 4.766098029962729, Varianza: 22.715690431214608, Incertidumbre: 0.8056176056952402\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 3.318', 'Relación de aspecto del ala: 1.904', 'Peso máximo al despegue (MTOW): 2.515', 'Velocidad máxima (KIAS): 1.635', 'envergadura: 0.943', 'Cuerda: 3.715']\n", + "**Mediana calculada:** 2.21\n", + "\n", + "=== Imputación para el parámetro: **Empty weight** ===\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 Fixed Wing** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.994x + -5.988\n", + "Valor del parámetro correlacionado para la aeronave: 1.55\n", + "Predicción obtenida: 17.253\n", + "\tR²: 0.8989608005826268, Desviación Estándar: 3.3152777023765134, Varianza: 10.991066243874894, Incertidumbre: 0.8288194255941284\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.792x + -6.58\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 19.796\n", + "\tR²: 0.77484990052306, Desviación Estándar: 4.948926867225632, Varianza: 24.491877137147704, Incertidumbre: 1.237231716806408\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.325x + 1.819\n", + "Valor del parámetro correlacionado para la aeronave: 42.2\n", + "Predicción obtenida: 15.517\n", + "\tR²: 0.8966456395362715, Desviación Estándar: 3.3530448803440964, Varianza: 11.242909969601754, Incertidumbre: 0.8382612200860241\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.55x + -17.327\n", + "Valor del parámetro correlacionado para la aeronave: 4.4\n", + "Predicción obtenida: 20.292\n", + "\tR²: 0.854653857560669, Desviación Estándar: 3.9762778085330774, Varianza: 15.810785210632615, Incertidumbre: 0.9940694521332694\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 168.652x + -36.821\n", + "Valor del parámetro correlacionado para la aeronave: 0.352\n", + "Predicción obtenida: 22.545\n", + "\tR²: 0.3232917045220116, Desviación Estándar: 8.579771360926884, Varianza: 73.61247660578115, Incertidumbre: 2.144942840231721\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 3.714, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.551x + 3.249\n", + "Valor del parámetro correlacionado para la aeronave: 14.5\n", + "Predicción obtenida: 25.745\n", + "\tR²: 0.6053918907187015, Desviación Estándar: 6.551759806427765, Varianza: 42.92555656112239, Incertidumbre: 1.6379399516069413\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 150.0, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 10.0, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.074x + 2.821\n", + "Valor del parámetro correlacionado para la aeronave: 140.0\n", + "Predicción obtenida: 13.161\n", + "\tR²: 0.6929626692113744, Desviación Estándar: 2.790886287308021, Varianza: 7.78904626868395, Incertidumbre: 1.1393745556725372\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 17.253', 'Longitud del fuselaje: 19.796', 'Peso máximo al despegue (MTOW): 15.517', 'envergadura: 20.292', 'Cuerda: 22.545', 'payload: 25.745', 'Rango de comunicación: 13.161']\n", + "**Mediana calculada:** 19.796\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 VTOL** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.104x + -5.974\n", + "Valor del parámetro correlacionado para la aeronave: 1.55\n", + "Predicción obtenida: 17.436\n", + "\tR²: 0.8985057224718374, Desviación Estándar: 3.270696585516228, Varianza: 10.697456154507513, Incertidumbre: 0.7932604406723784\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.792x + -6.58\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 19.796\n", + "\tR²: 0.7812972847516897, Desviación Estándar: 4.801164285068728, Varianza: 23.051178492219506, Incertidumbre: 1.1644533807812627\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = 0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 32.0, 35.0]\n", + "Ecuación de regresión: y = 113.452x + -9.6\n", + "Valor del parámetro correlacionado para la aeronave: 0.277\n", + "Predicción obtenida: 21.827\n", + "\tR²: 0.9023931014122297, Desviación Estándar: 2.838256825639311, Varianza: 8.055701808288138, Incertidumbre: 1.2693070399464534\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.327x + 1.985\n", + "Valor del parámetro correlacionado para la aeronave: 53.5\n", + "Predicción obtenida: 19.49\n", + "\tR²: 0.8900447434647047, Desviación Estándar: 3.404297276853743, Varianza: 11.58923994919381, Incertidumbre: 0.8256633678512089\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.53x + -17.284\n", + "Valor del parámetro correlacionado para la aeronave: 4.4\n", + "Predicción obtenida: 20.246\n", + "\tR²: 0.8586914485406882, Desviación Estándar: 3.859257009089604, Varianza: 14.893864662207235, Incertidumbre: 0.9360073108753961\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 161.947x + -35.001\n", + "Valor del parámetro correlacionado para la aeronave: 0.352\n", + "Predicción obtenida: 22.004\n", + "\tR²: 0.3392826165393894, Desviación Estándar: 8.345019309965002, Varianza: 69.63934728368875, Incertidumbre: 2.02396447428263\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 3.714, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.457x + 3.507\n", + "Valor del parámetro correlacionado para la aeronave: 11.3\n", + "Predicción obtenida: 19.967\n", + "\tR²: 0.600650247472835, Desviación Estándar: 6.487781114213371, Varianza: 42.091303785943694, Incertidumbre: 1.5735180476346566\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.996) ---\n", + "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 6.8, 8.9, 16.5, 84.0]\n", + "Valores para Empty weight: [19.796, 4.8, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.356x + 2.621\n", + "Valor del parámetro correlacionado para la aeronave: 42.2\n", + "Predicción obtenida: 17.662\n", + "\tR²: 0.8796352899520189, Desviación Estándar: 3.766112128353929, Varianza: 14.18360056333456, Incertidumbre: 1.6842565459771597\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.09x + 2.413\n", + "Valor del parámetro correlacionado para la aeronave: 140.0\n", + "Predicción obtenida: 14.967\n", + "\tR²: 0.6992353116426389, Desviación Estándar: 3.354572744999882, Varianza: 11.253158301496043, Incertidumbre: 1.267909319734997\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 17.436', 'Longitud del fuselaje: 19.796', 'Ancho del fuselaje: 21.827', 'Peso máximo al despegue (MTOW): 19.49', 'envergadura: 20.246', 'Cuerda: 22.004', 'payload: 19.967', 'RTF (Including fuel & Batteries): 17.662', 'Rango de comunicación: 14.967']\n", + "**Mediana calculada:** 19.796\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 Fixed wing** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.104x + -5.974\n", + "Valor del parámetro correlacionado para la aeronave: 1.55\n", + "Predicción obtenida: 17.436\n", + "\tR²: 0.8985057224718374, Desviación Estándar: 3.270696585516228, Varianza: 10.697456154507513, Incertidumbre: 0.7932604406723784\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.792x + -6.58\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 19.796\n", + "\tR²: 0.7812972847516897, Desviación Estándar: 4.801164285068728, Varianza: 23.051178492219506, Incertidumbre: 1.1644533807812627\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = 0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 32.0, 35.0]\n", + "Ecuación de regresión: y = 113.452x + -9.6\n", + "Valor del parámetro correlacionado para la aeronave: 0.277\n", + "Predicción obtenida: 21.827\n", + "\tR²: 0.9023931014122297, Desviación Estándar: 2.838256825639311, Varianza: 8.055701808288138, Incertidumbre: 1.2693070399464534\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.328x + 1.989\n", + "Valor del parámetro correlacionado para la aeronave: 54.4\n", + "Predicción obtenida: 19.809\n", + "\tR²: 0.8927270391262186, Desviación Estándar: 3.309108053534251, Varianza: 10.95019610996524, Incertidumbre: 0.7799642481110287\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.53x + -17.284\n", + "Valor del parámetro correlacionado para la aeronave: 4.4\n", + "Predicción obtenida: 20.246\n", + "\tR²: 0.8586914485406882, Desviación Estándar: 3.859257009089604, Varianza: 14.893864662207235, Incertidumbre: 0.9360073108753961\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 161.947x + -35.001\n", + "Valor del parámetro correlacionado para la aeronave: 0.352\n", + "Predicción obtenida: 22.004\n", + "\tR²: 0.3392826165393894, Desviación Estándar: 8.345019309965002, Varianza: 69.63934728368875, Incertidumbre: 2.02396447428263\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 3.714, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.455x + 3.508\n", + "Valor del parámetro correlacionado para la aeronave: 17.7\n", + "Predicción obtenida: 29.263\n", + "\tR²: 0.6105487089694287, Desviación Estándar: 6.305105946732535, Varianza: 39.75436099952198, Incertidumbre: 1.4861277236780677\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.996) ---\n", + "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 6.8, 8.9, 16.5, 84.0]\n", + "Valores para Empty weight: [19.796, 19.796, 4.8, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.362x + 2.802\n", + "Valor del parámetro correlacionado para la aeronave: 36.7\n", + "Predicción obtenida: 16.09\n", + "\tR²: 0.8814477024665971, Desviación Estándar: 3.525541028723161, Varianza: 12.429439545210364, Incertidumbre: 1.4392960979364398\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.09x + 2.413\n", + "Valor del parámetro correlacionado para la aeronave: 140.0\n", + "Predicción obtenida: 14.967\n", + "\tR²: 0.6992353116426389, Desviación Estándar: 3.354572744999882, Varianza: 11.253158301496043, Incertidumbre: 1.267909319734997\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 17.436', 'Longitud del fuselaje: 19.796', 'Ancho del fuselaje: 21.827', 'Peso máximo al despegue (MTOW): 19.809', 'envergadura: 20.246', 'Cuerda: 22.004', 'payload: 29.263', 'RTF (Including fuel & Batteries): 16.09', 'Rango de comunicación: 14.967']\n", + "**Mediana calculada:** 19.809\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.199x + -5.963\n", + "Valor del parámetro correlacionado para la aeronave: 2.503\n", + "Predicción obtenida: 32.08\n", + "\tR²: 0.8981760566509686, Desviación Estándar: 3.2241182006984137, Varianza: 10.394938172074777, Incertidumbre: 0.7599319476869396\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.793x + -6.581\n", + "Valor del parámetro correlacionado para la aeronave: 3.594\n", + "Predicción obtenida: 25.02\n", + "\tR²: 0.7867457415163526, Desviación Estándar: 4.665893862545674, Varianza: 21.770565536541387, Incertidumbre: 1.0997617301675797\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.328x + 1.989\n", + "Valor del parámetro correlacionado para la aeronave: 93.0\n", + "Predicción obtenida: 32.453\n", + "\tR²: 0.8950667903037404, Desviación Estándar: 3.220849267243446, Varianza: 10.373870002302642, Incertidumbre: 0.7389134983311162\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.513x + -17.25\n", + "Valor del parámetro correlacionado para la aeronave: 5.644\n", + "Predicción obtenida: 30.798\n", + "\tR²: 0.8621171121245332, Desviación Estándar: 3.751813006995635, Varianza: 14.076100839461628, Incertidumbre: 0.8843108063301686\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 157.473x + -33.787\n", + "Valor del parámetro correlacionado para la aeronave: 0.394\n", + "Predicción obtenida: 28.257\n", + "\tR²: 0.35355225050821804, Desviación Estándar: 8.123678231024497, Varianza: 65.9941480012213, Incertidumbre: 1.9147693217783197\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 3.714, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.302x + 4.121\n", + "Valor del parámetro correlacionado para la aeronave: 22.7\n", + "Predicción obtenida: 33.679\n", + "\tR²: 0.5820027961619212, Desviación Estándar: 6.42836622157291, Varianza: 41.32389227865957, Incertidumbre: 1.4747683543108792\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.996) ---\n", + "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", + "Valores para Empty weight: [19.796, 19.796, 19.809, 4.8, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.366x + 3.198\n", + "Valor del parámetro correlacionado para la aeronave: 70.3\n", + "Predicción obtenida: 28.95\n", + "\tR²: 0.8686932854431193, Desviación Estándar: 3.5123478434354816, Varianza: 12.336587373285877, Incertidumbre: 1.3275427016691874\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 32.08', 'Longitud del fuselaje: 25.02', 'Peso máximo al despegue (MTOW): 32.453', 'envergadura: 30.798', 'Cuerda: 28.257', 'payload: 33.679', 'RTF (Including fuel & Batteries): 28.95']\n", + "**Mediana calculada:** 30.798\n", + "\n", + "--- Imputación para aeronave: **Fulmar X** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.024x + -5.798\n", + "Valor del parámetro correlacionado para la aeronave: 0.94\n", + "Predicción obtenida: 8.325\n", + "\tR²: 0.9115417035933906, Desviación Estándar: 3.1489544926591027, Varianza: 9.915914396837946, Incertidumbre: 0.7224197058583904\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 9.152x + -7.113\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 3.87\n", + "\tR²: 0.8024007898945031, Desviación Estándar: 4.706409380156019, Varianza: 22.15028925362057, Incertidumbre: 1.0797243618437888\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.323x + 2.09\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 8.545\n", + "\tR²: 0.9077472362486133, Desviación Estándar: 3.1564995773900493, Varianza: 9.963489582063561, Incertidumbre: 0.7058147625993508\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.513x + -17.25\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 8.289\n", + "\tR²: 0.8810383235111052, Desviación Estándar: 3.6517466273004446, Varianza: 13.335253430000172, Incertidumbre: 0.8377681324114802\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 164.068x + -35.656\n", + "Valor del parámetro correlacionado para la aeronave: 0.313\n", + "Predicción obtenida: 15.697\n", + "\tR²: 0.4401012275044295, Desviación Estándar: 7.922310441477786, Varianza: 62.76300273114796, Incertidumbre: 1.8175026638820404\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.254x + 4.367\n", + "Valor del parámetro correlacionado para la aeronave: 7.8\n", + "Predicción obtenida: 14.145\n", + "\tR²: 0.6338082409254504, Desviación Estándar: 6.288836818337574, Varianza: 39.54946852767826, Incertidumbre: 1.406226662520631\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 8.325', 'Longitud del fuselaje: 3.87', 'Peso máximo al despegue (MTOW): 8.545', 'envergadura: 8.289', 'Cuerda: 15.697', 'payload: 14.145']\n", + "**Mediana calculada:** 8.435\n", + "\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.019x + -5.786\n", + "Valor del parámetro correlacionado para la aeronave: 1.608\n", + "Predicción obtenida: 18.365\n", + "\tR²: 0.9128101315191424, Desviación Estándar: 3.069313976161959, Varianza: 9.420688284263136, Incertidumbre: 0.6863194694988309\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.927x + -6.373\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 4.34\n", + "\tR²: 0.7965950223618128, Desviación Estándar: 4.688016094166544, Varianza: 21.977494899164537, Incertidumbre: 1.0482722666169448\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.323x + 2.081\n", + "Valor del parámetro correlacionado para la aeronave: 55.0\n", + "Predicción obtenida: 19.838\n", + "\tR²: 0.909185787337549, Desviación Estándar: 3.080516387452075, Varianza: 9.489581213360784, Incertidumbre: 0.6722237869071928\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.51x + -17.23\n", + "Valor del parámetro correlacionado para la aeronave: 5.2\n", + "Predicción obtenida: 27.02\n", + "\tR²: 0.882742078622475, Desviación Estándar: 3.5594213516060673, Varianza: 12.669480358269164, Incertidumbre: 0.7959108102755347\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 162.214x + -35.455\n", + "Valor del parámetro correlacionado para la aeronave: 0.334\n", + "Predicción obtenida: 18.724\n", + "\tR²: 0.4250328821813467, Desviación Estándar: 7.881879841341832, Varianza: 62.124029833350754, Incertidumbre: 1.7624419115725594\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.255x + 4.083\n", + "Valor del parámetro correlacionado para la aeronave: 12.0\n", + "Predicción obtenida: 19.144\n", + "\tR²: 0.6253889798074073, Desviación Estándar: 6.256578342524824, Varianza: 39.14477255615067, Incertidumbre: 1.3652973260019692\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.099x + 2.179\n", + "Valor del parámetro correlacionado para la aeronave: 150.0\n", + "Predicción obtenida: 16.991\n", + "\tR²: 0.7177783310839329, Desviación Estándar: 3.4856268169459126, Varianza: 12.149594307012496, Incertidumbre: 1.2323551794740677\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 18.365', 'Longitud del fuselaje: 4.34', 'Peso máximo al despegue (MTOW): 19.838', 'envergadura: 27.02', 'Cuerda: 18.724', 'payload: 19.144', 'Rango de comunicación: 16.991']\n", + "**Mediana calculada:** 18.724\n", + "\n", + "--- Imputación para aeronave: **Orbiter 3** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.031x + -5.784\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 12.253\n", + "\tR²: 0.9136497392693784, Desviación Estándar: 2.996311656277053, Varianza: 8.977883541541738, Incertidumbre: 0.6538488081222905\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.282x + -4.258\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 5.68\n", + "\tR²: 0.7127494541740498, Desviación Estándar: 5.464942394654294, Varianza: 29.865595376889807, Incertidumbre: 1.192548199622692\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.322x + 2.067\n", + "Valor del parámetro correlacionado para la aeronave: 32.0\n", + "Predicción obtenida: 12.368\n", + "\tR²: 0.9094761068700505, Desviación Estándar: 3.0184944982890896, Varianza: 9.111309036201503, Incertidumbre: 0.6435451893507028\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.083x + -16.034\n", + "Valor del parámetro correlacionado para la aeronave: 4.4\n", + "Predicción obtenida: 19.531\n", + "\tR²: 0.8563105110981454, Desviación Estándar: 3.8651616084856775, Varianza: 14.93947425971159, Incertidumbre: 0.8434474116248993\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 162.214x + -35.455\n", + "Valor del parámetro correlacionado para la aeronave: 0.301\n", + "Predicción obtenida: 13.371\n", + "\tR²: 0.4309374492611131, Desviación Estándar: 7.691927112179356, Varianza: 59.16574269907985, Incertidumbre: 1.6785161062688088\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.253x + 4.079\n", + "Valor del parámetro correlacionado para la aeronave: 5.5\n", + "Predicción obtenida: 10.972\n", + "\tR²: 0.6286868594150173, Desviación Estándar: 6.113345437183864, Varianza: 37.37299243433677, Incertidumbre: 1.3033696265369021\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'V21', 'V25', 'V32']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.102x + 2.062\n", + "Valor del parámetro correlacionado para la aeronave: 50.0\n", + "Predicción obtenida: 7.156\n", + "\tR²: 0.7467331385836531, Desviación Estándar: 3.326478501613054, Varianza: 11.065459221693828, Incertidumbre: 1.1088261672043513\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 12.253', 'Longitud del fuselaje: 5.68', 'Peso máximo al despegue (MTOW): 12.368', 'envergadura: 19.531', 'Cuerda: 13.371', 'payload: 10.972', 'Rango de comunicación: 7.156']\n", + "**Mediana calculada:** 12.253\n", + "\n", + "--- Imputación para aeronave: **Mantis** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.031x + -5.784\n", + "Valor del parámetro correlacionado para la aeronave: 0.754\n", + "Predicción obtenida: 5.549\n", + "\tR²: 0.913778739631991, Desviación Estándar: 2.9274217216946905, Varianza: 8.569797936649906, Incertidumbre: 0.6241284081402825\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.012x + -3.374\n", + "Valor del parámetro correlacionado para la aeronave: 1.48\n", + "Predicción obtenida: 8.484\n", + "\tR²: 0.695106219357537, Desviación Estándar: 5.50493747043158, Varianza: 30.304336553361637, Incertidumbre: 1.1736566121888548\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.322x + 2.061\n", + "Valor del parámetro correlacionado para la aeronave: 6.5\n", + "Predicción obtenida: 4.154\n", + "\tR²: 0.9096439599147235, Desviación Estándar: 2.952238871692774, Varianza: 8.715714355533823, Incertidumbre: 0.6155843584875383\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.922x + -15.761\n", + "Valor del parámetro correlacionado para la aeronave: 2.1\n", + "Predicción obtenida: 0.876\n", + "\tR²: 0.8337456079246974, Desviación Estándar: 4.065038574534986, Varianza: 16.524538612457434, Incertidumbre: 0.8666691361040485\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 162.348x + -35.547\n", + "Valor del parámetro correlacionado para la aeronave: 0.27\n", + "Predicción obtenida: 8.287\n", + "\tR²: 0.43124218687960514, Desviación Estándar: 7.518683358339971, Varianza: 56.530599442978435, Incertidumbre: 1.6029886780490887\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.25x + 4.165\n", + "Valor del parámetro correlacionado para la aeronave: 2.21\n", + "Predicción obtenida: 6.926\n", + "\tR²: 0.6286967759718571, Desviación Estándar: 5.984629370732705, Varianza: 35.81578870503653, Incertidumbre: 1.2478814865870902\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'V21', 'V25', 'V32']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.095x + 3.264\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 5.627\n", + "\tR²: 0.6914792906735672, Desviación Estándar: 3.4830980292244655, Varianza: 12.131971881187356, Incertidumbre: 1.1014523085993035\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 5.549', 'Longitud del fuselaje: 8.484', 'Peso máximo al despegue (MTOW): 4.154', 'envergadura: 0.876', 'Cuerda: 8.287', 'payload: 6.926', 'Rango de comunicación: 5.627']\n", + "**Mediana calculada:** 5.627\n", + "\n", + "--- Imputación para aeronave: **ScanEagle** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.026x + -5.775\n", + "Valor del parámetro correlacionado para la aeronave: 1.063\n", + "Predicción obtenida: 10.198\n", + "\tR²: 0.9163529546189084, Desviación Estándar: 2.863117876012014, Varianza: 8.197443971939547, Incertidumbre: 0.5970013462929816\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.091x + -3.666\n", + "Valor del parámetro correlacionado para la aeronave: 1.71\n", + "Predicción obtenida: 10.169\n", + "\tR²: 0.7008193572895111, Desviación Estándar: 5.414778194269882, Varianza: 29.319822893140607, Incertidumbre: 1.12905930242721\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.32x + 2.202\n", + "Valor del parámetro correlacionado para la aeronave: 26.5\n", + "Predicción obtenida: 10.676\n", + "\tR²: 0.9116020723584356, Desviación Estándar: 2.90434700702091, Varianza: 8.435231537191319, Incertidumbre: 0.5928473502650619\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.684x + -14.677\n", + "Valor del parámetro correlacionado para la aeronave: 3.1\n", + "Predicción obtenida: 9.144\n", + "\tR²: 0.8299304395789938, Desviación Estándar: 4.0825096549566275, Varianza: 16.66688508281408, Incertidumbre: 0.8512621085856318\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 164.778x + -36.401\n", + "Valor del parámetro correlacionado para la aeronave: 0.298\n", + "Predicción obtenida: 12.703\n", + "\tR²: 0.44533403577557296, Desviación Estándar: 7.372752999831995, Varianza: 54.35748679653168, Incertidumbre: 1.5373252717475088\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.257x + 4.049\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 10.336\n", + "\tR²: 0.6396223521327932, Desviación Estándar: 5.8641677511942225, Varianza: 34.38846341414631, Incertidumbre: 1.1970182297091787\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'V21', 'V25', 'V32']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.095x + 3.264\n", + "Valor del parámetro correlacionado para la aeronave: 101.86\n", + "Predicción obtenida: 12.891\n", + "\tR²: 0.7190956882541144, Desviación Estándar: 3.321003665545642, Varianza: 11.029065346567588, Incertidumbre: 1.0013202805463481\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 10.198', 'Longitud del fuselaje: 10.169', 'Peso máximo al despegue (MTOW): 10.676', 'envergadura: 9.144', 'Cuerda: 12.703', 'payload: 10.336', 'Rango de comunicación: 12.891']\n", + "**Mediana calculada:** 10.336\n", + "\n", + "--- Imputación para aeronave: **Integrator** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.023x + -5.765\n", + "Valor del parámetro correlacionado para la aeronave: 1.872\n", + "Predicción obtenida: 22.358\n", + "\tR²: 0.9167346130104, Desviación Estándar: 2.8029703167934756, Varianza: 7.856642596825316, Incertidumbre: 0.5721539200259446\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.088x + -3.653\n", + "Valor del parámetro correlacionado para la aeronave: 2.5\n", + "Predicción obtenida: 16.567\n", + "\tR²: 0.702201492451173, Desviación Estándar: 5.300874285112574, Varianza: 28.099268186567745, Incertidumbre: 1.0820364324305303\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.32x + 2.183\n", + "Valor del parámetro correlacionado para la aeronave: 74.8\n", + "Predicción obtenida: 26.113\n", + "\tR²: 0.9120249989617861, Desviación Estándar: 2.846441429999798, Varianza: 8.102228814419295, Incertidumbre: 0.5692882859999596\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.663x + -14.551\n", + "Valor del parámetro correlacionado para la aeronave: 4.8\n", + "Predicción obtenida: 22.234\n", + "\tR²: 0.8301280134060601, Desviación Estándar: 4.003567260287771, Varianza: 16.02855080764813, Incertidumbre: 0.8172247448847872\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 165.132x + -36.607\n", + "Valor del parámetro correlacionado para la aeronave: 0.338\n", + "Predicción obtenida: 19.208\n", + "\tR²: 0.44554931519439944, Desviación Estándar: 7.232987396297254, Varianza: 52.316106674994934, Incertidumbre: 1.476427369742511\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.257x + 4.049\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 26.683\n", + "\tR²: 0.6415415848013848, Desviación Estándar: 5.74568750261759, Varianza: 33.01292487773596, Incertidumbre: 1.149137500523518\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'V21', 'V25', 'V32']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 10.336, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.094x + 3.136\n", + "Valor del parámetro correlacionado para la aeronave: 92.6\n", + "Predicción obtenida: 11.799\n", + "\tR²: 0.7062398188700879, Desviación Estándar: 3.2566903822374726, Varianza: 10.606032245758056, Incertidumbre: 0.9401255344260351\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 22.358', 'Longitud del fuselaje: 16.567', 'Peso máximo al despegue (MTOW): 26.113', 'envergadura: 22.234', 'Cuerda: 19.208', 'payload: 26.683', 'Rango de comunicación: 11.799']\n", + "**Mediana calculada:** 22.234\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.016x + -5.76\n", + "Valor del parámetro correlacionado para la aeronave: 2.09\n", + "Predicción obtenida: 25.622\n", + "\tR²: 0.9193277412989592, Desviación Estándar: 2.7464421762175815, Varianza: 7.542944627306766, Incertidumbre: 0.5492884352435163\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.172x + -3.606\n", + "Valor del parámetro correlacionado para la aeronave: 3.004\n", + "Predicción obtenida: 20.942\n", + "\tR²: 0.6983801297433365, Desviación Estándar: 5.310535615782573, Varianza: 28.201788526495196, Incertidumbre: 1.0621071231565147\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.313x + 2.281\n", + "Valor del parámetro correlacionado para la aeronave: 75.0\n", + "Predicción obtenida: 25.783\n", + "\tR²: 0.9088807177212644, Desviación Estándar: 2.882816344795206, Varianza: 8.310630077818393, Incertidumbre: 0.5653667998544224\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.663x + -14.551\n", + "Valor del parámetro correlacionado para la aeronave: 5.033\n", + "Predicción obtenida: 24.019\n", + "\tR²: 0.835430712623076, Desviación Estándar: 3.922678776357705, Varianza: 15.387408782487181, Incertidumbre: 0.7845357552715411\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 167.757x + -37.286\n", + "Valor del parámetro correlacionado para la aeronave: 0.341\n", + "Predicción obtenida: 19.919\n", + "\tR²: 0.45920799755943364, Desviación Estándar: 7.110882159300929, Varianza: 50.56464508346425, Incertidumbre: 1.4221764318601857\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.215x + 4.222\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 26.093\n", + "\tR²: 0.6447227222524704, Desviación Estándar: 5.692401793705153, Varianza: 32.403438180977645, Incertidumbre: 1.1163718394351212\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 25.622', 'Longitud del fuselaje: 20.942', 'Peso máximo al despegue (MTOW): 25.783', 'envergadura: 24.019', 'Cuerda: 19.919', 'payload: 26.093']\n", + "**Mediana calculada:** 24.82\n", + "\n", + "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.956x + -5.711\n", + "Valor del parámetro correlacionado para la aeronave: 1.872\n", + "Predicción obtenida: 22.286\n", + "\tR²: 0.9228857881228267, Desviación Estándar: 2.697264613869004, Varianza: 7.275236397229909, Incertidumbre: 0.5289771115169755\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.3x + -3.735\n", + "Valor del parámetro correlacionado para la aeronave: 2.5\n", + "Predicción obtenida: 17.015\n", + "\tR²: 0.7068456492535431, Desviación Estándar: 5.25901627247607, Varianza: 27.657252154168102, Incertidumbre: 1.031377945986826\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.312x + 2.303\n", + "Valor del parámetro correlacionado para la aeronave: 74.8\n", + "Predicción obtenida: 25.631\n", + "\tR²: 0.912609810603614, Desviación Estándar: 2.834428027141342, Varianza: 8.033982241044361, Incertidumbre: 0.5454859281562245\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.693x + -14.63\n", + "Valor del parámetro correlacionado para la aeronave: 4.8\n", + "Predicción obtenida: 22.295\n", + "\tR²: 0.8429342446881969, Desviación Estándar: 3.8494359348535636, Varianza: 14.818157016541928, Incertidumbre: 0.7549364980055233\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 172.075x + -38.42\n", + "Valor del parámetro correlacionado para la aeronave: 0.344\n", + "Predicción obtenida: 20.773\n", + "\tR²: 0.4755347143919866, Desviación Estándar: 7.034200066776385, Varianza: 49.479970579436895, Incertidumbre: 1.3795201308849407\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.204x + 4.266\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 25.944\n", + "\tR²: 0.6600077706694365, Desviación Estándar: 5.590730903029895, Varianza: 31.256272030093463, Incertidumbre: 1.0759366639436898\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 22.286', 'Longitud del fuselaje: 17.015', 'Peso máximo al despegue (MTOW): 25.631', 'envergadura: 22.295', 'Cuerda: 20.773', 'payload: 25.944']\n", + "**Mediana calculada:** 22.29\n", + "\n", + "--- Imputación para aeronave: **ScanEagle 3** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.956x + -5.712\n", + "Valor del parámetro correlacionado para la aeronave: 1.349\n", + "Predicción obtenida: 14.464\n", + "\tR²: 0.9247777030959305, Desviación Estándar: 2.6468441167859273, Varianza: 7.005783778564275, Incertidumbre: 0.5093853877764442\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.366x + -3.684\n", + "Valor del parámetro correlacionado para la aeronave: 2.4\n", + "Predicción obtenida: 16.395\n", + "\tR²: 0.7034262913324543, Desviación Estándar: 5.255588982695431, Varianza: 27.6212155550296, Incertidumbre: 1.0114385713030796\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.307x + 2.373\n", + "Valor del parámetro correlacionado para la aeronave: 36.3\n", + "Predicción obtenida: 13.524\n", + "\tR²: 0.9106032203613208, Desviación Estándar: 2.8482011972816106, Varianza: 8.1122500601964, Incertidumbre: 0.5382594322773969\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.692x + -14.629\n", + "Valor del parámetro correlacionado para la aeronave: 4.0\n", + "Predicción obtenida: 16.14\n", + "\tR²: 0.8467876896895489, Desviación Estándar: 3.7774776218534605, Varianza: 14.269337183603675, Incertidumbre: 0.7269759072782942\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 173.426x + -38.78\n", + "Valor del parámetro correlacionado para la aeronave: 0.31\n", + "Predicción obtenida: 14.982\n", + "\tR²: 0.4875507277051119, Desviación Estándar: 6.908448268587538, Varianza: 47.726657479750145, Incertidumbre: 1.329531489183873\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.177x + 4.379\n", + "Valor del parámetro correlacionado para la aeronave: 8.6\n", + "Predicción obtenida: 14.499\n", + "\tR²: 0.6631553407358013, Desviación Estándar: 5.528719549623086, Varianza: 30.566739858384498, Incertidumbre: 1.044829785494551\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 14.464', 'Longitud del fuselaje: 16.395', 'Peso máximo al despegue (MTOW): 13.524', 'envergadura: 16.14', 'Cuerda: 14.982', 'payload: 14.499']\n", + "**Mediana calculada:** 14.74\n", + "\n", + "--- Imputación para aeronave: **RQNan21A Blackjack** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.956x + -5.701\n", + "Valor del parámetro correlacionado para la aeronave: 1.802\n", + "Predicción obtenida: 21.249\n", + "\tR²: 0.9247492382330068, Desviación Estándar: 2.59965345007093, Varianza: 6.758198060465689, Incertidumbre: 0.4912883231313392\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.353x + -3.713\n", + "Valor del parámetro correlacionado para la aeronave: 2.5\n", + "Predicción obtenida: 17.168\n", + "\tR²: 0.7023810376902668, Desviación Estándar: 5.16999718239943, Varianza: 26.728870866018045, Incertidumbre: 0.9770376302523951\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.307x + 2.423\n", + "Valor del parámetro correlacionado para la aeronave: 61.0\n", + "Predicción obtenida: 21.149\n", + "\tR²: 0.9100414966060446, Desviación Estándar: 2.8074428350263565, Varianza: 7.881735271940825, Incertidumbre: 0.5213290466629605\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.685x + -14.651\n", + "Valor del parámetro correlacionado para la aeronave: 4.8\n", + "Predicción obtenida: 22.237\n", + "\tR²: 0.8460378541791183, Desviación Estándar: 3.7184931434823216, Varianza: 13.827191258125037, Incertidumbre: 0.7027291506823602\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 173.412x + -38.784\n", + "Valor del parámetro correlacionado para la aeronave: 0.338\n", + "Predicción obtenida: 19.829\n", + "\tR²: 0.48753313542114285, Desviación Estándar: 6.784110329613901, Varianza: 46.024152964374025, Incertidumbre: 1.2820763427844863\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.177x + 4.387\n", + "Valor del parámetro correlacionado para la aeronave: 17.7\n", + "Predicción obtenida: 25.215\n", + "\tR²: 0.6631333634856997, Desviación Estándar: 5.432738626672287, Varianza: 29.514648985737086, Incertidumbre: 1.0088342365074427\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'V32']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 10.336, 22.234, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.095x + 3.847\n", + "Valor del parámetro correlacionado para la aeronave: 92.6\n", + "Predicción obtenida: 12.606\n", + "\tR²: 0.5786689229639067, Desviación Estándar: 4.18551671444084, Varianza: 17.51855016686364, Incertidumbre: 1.160853471402155\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 21.249', 'Longitud del fuselaje: 17.168', 'Peso máximo al despegue (MTOW): 21.149', 'envergadura: 22.237', 'Cuerda: 19.829', 'payload: 25.215', 'Rango de comunicación: 12.606']\n", + "**Mediana calculada:** 21.149\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.952x + -5.699\n", + "Valor del parámetro correlacionado para la aeronave: 0.7\n", + "Predicción obtenida: 4.767\n", + "\tR²: 0.9259690891919676, Desviación Estándar: 2.554502626662271, Varianza: 6.525483669624441, Incertidumbre: 0.4743592291322177\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.399x + -3.679\n", + "Valor del parámetro correlacionado para la aeronave: 0.9\n", + "Predicción obtenida: 3.88\n", + "\tR²: 0.7012558792426988, Desviación Estándar: 5.131560941639433, Varianza: 26.332917697759388, Incertidumbre: 0.9529069444338854\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.307x + 2.423\n", + "Valor del parámetro correlacionado para la aeronave: 6.2\n", + "Predicción obtenida: 4.326\n", + "\tR²: 0.9114139885923979, Desviación Estándar: 2.760255561189478, Varianza: 7.61901076307744, Incertidumbre: 0.5039514117808528\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.657x + -14.58\n", + "Valor del parámetro correlacionado para la aeronave: 2.35\n", + "Predicción obtenida: 3.413\n", + "\tR²: 0.8481057631774891, Desviación Estándar: 3.659067788491134, Varianza: 13.388777080773398, Incertidumbre: 0.6794718303966184\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 174.337x + -39.024\n", + "Valor del parámetro correlacionado para la aeronave: 0.272\n", + "Predicción obtenida: 8.395\n", + "\tR²: 0.49522154420699915, Desviación Estándar: 6.670374425052742, Varianza: 44.493894970397704, Incertidumbre: 1.2386574346277268\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.15x + 4.498\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 5.878\n", + "\tR²: 0.6624428309267374, Desviación Estándar: 5.388158809639675, Varianza: 29.032255357897643, Incertidumbre: 0.9837387078199448\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQNan21A Blackjack', 'V21', 'V25', 'V32']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 10.336, 22.234, 21.149, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.095x + 4.388\n", + "Valor del parámetro correlacionado para la aeronave: 50.0\n", + "Predicción obtenida: 9.156\n", + "\tR²: 0.518357595351121, Desviación Estándar: 4.594228397572152, Varianza: 21.106934569058385, Incertidumbre: 1.2278591871644609\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 4.767', 'Longitud del fuselaje: 3.88', 'Peso máximo al despegue (MTOW): 4.326', 'envergadura: 3.413', 'Cuerda: 8.395', 'payload: 5.878', 'Rango de comunicación: 9.156']\n", + "**Mediana calculada:** 4.767\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.952x + -5.699\n", + "Valor del parámetro correlacionado para la aeronave: 0.7\n", + "Predicción obtenida: 4.767\n", + "\tR²: 0.9286912161672043, Desviación Estándar: 2.5115667515700397, Varianza: 6.3079675475920824, Incertidumbre: 0.4585472548382948\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.358x + -3.56\n", + "Valor del parámetro correlacionado para la aeronave: 0.9\n", + "Predicción obtenida: 3.962\n", + "\tR²: 0.7119717257129345, Desviación Estándar: 5.047667874355293, Varianza: 25.47895096979848, Incertidumbre: 0.9215738525261822\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.306x + 2.46\n", + "Valor del parámetro correlacionado para la aeronave: 6.2\n", + "Predicción obtenida: 4.359\n", + "\tR²: 0.9146821995211808, Desviación Estándar: 2.7164377856628272, Varianza: 7.379034243376764, Incertidumbre: 0.4878866289309157\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.607x + -14.346\n", + "Valor del parámetro correlacionado para la aeronave: 2.35\n", + "Predicción obtenida: 3.53\n", + "\tR²: 0.8530601734425255, Desviación Estándar: 3.605312977255013, Varianza: 12.998281663963407, Incertidumbre: 0.6582370815028935\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 177.343x + -40.07\n", + "Valor del parámetro correlacionado para la aeronave: 0.272\n", + "Predicción obtenida: 8.167\n", + "\tR²: 0.5091333952756019, Desviación Estándar: 6.589537801021175, Varianza: 43.42200843108698, Incertidumbre: 1.203079499050762\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.156x + 4.406\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 5.794\n", + "\tR²: 0.6747265207014675, Desviación Estándar: 5.304009156303675, Varianza: 28.132513130153214, Incertidumbre: 0.952628166470701\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQNan21A Blackjack', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 50.0, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 10.336, 22.234, 21.149, 4.767, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.1x + 3.711\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 6.705\n", + "\tR²: 0.5369948479446686, Desviación Estándar: 4.566261608746338, Varianza: 20.850745079510695, Incertidumbre: 1.17900367767339\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 4.767', 'Longitud del fuselaje: 3.962', 'Peso máximo al despegue (MTOW): 4.359', 'envergadura: 3.53', 'Cuerda: 8.167', 'payload: 5.794', 'Rango de comunicación: 6.705']\n", + "**Mediana calculada:** 4.767\n", + "\n", + "--- Imputación para aeronave: **V35** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.952x + -5.699\n", + "Valor del parámetro correlacionado para la aeronave: 1.202\n", + "Predicción obtenida: 12.273\n", + "\tR²: 0.9286912161672043, Desviación Estándar: 2.5115667515700397, Varianza: 6.3079675475920824, Incertidumbre: 0.4585472548382948\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.358x + -3.56\n", + "Valor del parámetro correlacionado para la aeronave: 1.88\n", + "Predicción obtenida: 12.153\n", + "\tR²: 0.7119717257129345, Desviación Estándar: 5.047667874355293, Varianza: 25.47895096979848, Incertidumbre: 0.9215738525261822\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.306x + 2.46\n", + "Valor del parámetro correlacionado para la aeronave: 32.0\n", + "Predicción obtenida: 12.265\n", + "\tR²: 0.9146821995211808, Desviación Estándar: 2.7164377856628272, Varianza: 7.379034243376764, Incertidumbre: 0.4878866289309157\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.607x + -14.346\n", + "Valor del parámetro correlacionado para la aeronave: 3.5\n", + "Predicción obtenida: 12.278\n", + "\tR²: 0.8530601734425255, Desviación Estándar: 3.605312977255013, Varianza: 12.998281663963407, Incertidumbre: 0.6582370815028935\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 177.343x + -40.07\n", + "Valor del parámetro correlacionado para la aeronave: 0.304\n", + "Predicción obtenida: 13.842\n", + "\tR²: 0.5091333952756019, Desviación Estándar: 6.589537801021175, Varianza: 43.42200843108698, Incertidumbre: 1.203079499050762\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.156x + 4.406\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 15.967\n", + "\tR²: 0.6747265207014675, Desviación Estándar: 5.304009156303675, Varianza: 28.132513130153214, Incertidumbre: 0.952628166470701\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQNan21A Blackjack', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'V21', 'V25', 'V32']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 50.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 10.336, 22.234, 21.149, 4.767, 4.767, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.102x + 3.372\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 6.444\n", + "\tR²: 0.5669527647105395, Desviación Estándar: 4.444173630434083, Varianza: 19.750679257445654, Incertidumbre: 1.1110434076085207\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 12.273', 'Longitud del fuselaje: 12.153', 'Peso máximo al despegue (MTOW): 12.265', 'envergadura: 12.278', 'Cuerda: 13.842', 'payload: 15.967', 'Rango de comunicación: 6.444']\n", + "**Mediana calculada:** 12.273\n", + "\n", + "--- Imputación para aeronave: **V39** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.952x + -5.699\n", + "Valor del parámetro correlacionado para la aeronave: 1.203\n", + "Predicción obtenida: 12.288\n", + "\tR²: 0.9288175658243848, Desviación Estándar: 2.470725546458319, Varianza: 6.104484725921759, Incertidumbre: 0.4437554079969149\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.357x + -3.554\n", + "Valor del parámetro correlacionado para la aeronave: 1.954\n", + "Predicción obtenida: 12.775\n", + "\tR²: 0.7124768203038293, Desviación Estándar: 4.965631866447997, Varianza: 24.657499833083815, Incertidumbre: 0.8918538111271705\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.306x + 2.46\n", + "Valor del parámetro correlacionado para la aeronave: 24.0\n", + "Predicción obtenida: 9.814\n", + "\tR²: 0.9148561084451423, Desviación Estándar: 2.6736569224974254, Varianza: 7.148441339218403, Incertidumbre: 0.47264023511607123\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.607x + -14.346\n", + "Valor del parámetro correlacionado para la aeronave: 3.9\n", + "Predicción obtenida: 15.321\n", + "\tR²: 0.8533205216960276, Desviación Estándar: 3.5466862107514427, Varianza: 12.578983077534426, Incertidumbre: 0.6370036480762108\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 177.47x + -40.16\n", + "Valor del parámetro correlacionado para la aeronave: 0.304\n", + "Predicción obtenida: 13.791\n", + "\tR²: 0.5091069267607173, Desviación Estándar: 6.488309232157318, Varianza: 42.098156692097895, Incertidumbre: 1.1653347392847273\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.153x + 4.318\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 10.083\n", + "\tR²: 0.6704748222680529, Desviación Estándar: 5.259848463722186, Varianza: 27.666005861320635, Incertidumbre: 0.9298186291779004\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = 0.832) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQNan21A Blackjack', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 50.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 10.336, 22.234, 21.149, 4.767, 4.767, 2.65, 3.45, 6.45, 12.273]\n", + "Ecuación de regresión: y = 0.095x + 4.271\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 7.135\n", + "\tR²: 0.5259965377098035, Desviación Estándar: 4.511169408629526, Varianza: 20.35064943335487, Incertidumbre: 1.0941192921667469\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 12.288', 'Longitud del fuselaje: 12.775', 'Peso máximo al despegue (MTOW): 9.814', 'envergadura: 15.321', 'Cuerda: 13.791', 'payload: 10.083', 'Rango de comunicación: 7.135']\n", + "**Mediana calculada:** 12.288\n", + "\n", + "--- Imputación para aeronave: **Volitation VT370** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.952x + -5.699\n", + "Valor del parámetro correlacionado para la aeronave: 1.424\n", + "Predicción obtenida: 15.592\n", + "\tR²: 0.9289339624984088, Desviación Estándar: 2.4318140515483493, Varianza: 5.913719581307998, Incertidumbre: 0.4298880516086425\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.36x + -3.576\n", + "Valor del parámetro correlacionado para la aeronave: 2.02\n", + "Predicción obtenida: 13.311\n", + "\tR²: 0.7128606994643418, Desviación Estándar: 4.888162469550296, Varianza: 23.894132328720048, Incertidumbre: 0.8641132074401486\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.305x + 2.59\n", + "Valor del parámetro correlacionado para la aeronave: 40.0\n", + "Predicción obtenida: 14.79\n", + "\tR²: 0.9128312102468352, Desviación Estándar: 2.6664750643886856, Varianza: 7.110089269006646, Incertidumbre: 0.4641737288731009\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.598x + -14.407\n", + "Valor del parámetro correlacionado para la aeronave: 6.5\n", + "Predicción obtenida: 34.979\n", + "\tR²: 0.8502155711682761, Desviación Estándar: 3.5304709885139887, Varianza: 12.46422540073894, Incertidumbre: 0.6241049941901538\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 177.588x + -40.243\n", + "Valor del parámetro correlacionado para la aeronave: 0.313\n", + "Predicción obtenida: 15.342\n", + "\tR²: 0.5090877540596378, Desviación Estándar: 6.391477230982371, Varianza: 40.85098119416608, Incertidumbre: 1.129864222956763\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.147x + 4.438\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 25.083\n", + "\tR²: 0.6693635088936598, Desviación Estándar: 5.193168453194595, Varianza: 26.968998583255537, Incertidumbre: 0.9040145913151921\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 15.592', 'Longitud del fuselaje: 13.311', 'Peso máximo al despegue (MTOW): 14.79', 'envergadura: 34.979', 'Cuerda: 15.342', 'payload: 25.083']\n", + "**Mediana calculada:** 15.467\n", + "\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.951x + -5.702\n", + "Valor del parámetro correlacionado para la aeronave: 2.615\n", + "Predicción obtenida: 33.395\n", + "\tR²: 0.928961023163967, Desviación Estándar: 2.394780806275851, Varianza: 5.734975110107215, Incertidumbre: 0.41687782928419626\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.351x + -3.491\n", + "Valor del parámetro correlacionado para la aeronave: 3.5\n", + "Predicción obtenida: 25.737\n", + "\tR²: 0.7113023376551475, Desviación Estándar: 4.827686425855041, Varianza: 23.306556226385016, Incertidumbre: 0.8403923367019864\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Ancho del fuselaje (r = 0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.277, 0.375, 0.375]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 32.0, 35.0]\n", + "Ecuación de regresión: y = 114.066x + -10.111\n", + "Valor del parámetro correlacionado para la aeronave: 0.375\n", + "Predicción obtenida: 32.663\n", + "\tR²: 0.8963698345140692, Desviación Estándar: 2.697515152995758, Varianza: 7.276588000641728, Incertidumbre: 1.1012559497108843\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.305x + 2.609\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 33.112\n", + "\tR²: 0.9126951112649697, Desviación Estándar: 2.6294562235715246, Varianza: 6.914040031679023, Incertidumbre: 0.45094802203668083\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 6.49x + -10.713\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 21.735\n", + "\tR²: 0.7330823891778183, Desviación Estándar: 4.642009599703794, Varianza: 21.548253123742178, Incertidumbre: 0.8080701500402837\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 177.604x + -40.244\n", + "Valor del parámetro correlacionado para la aeronave: 0.336\n", + "Predicción obtenida: 19.431\n", + "\tR²: 0.5093083046494281, Desviación Estándar: 6.293928048096407, Varianza: 39.61353027461465, Incertidumbre: 1.0956322413664432\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.088x + 4.692\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 31.883\n", + "\tR²: 0.6380380391473043, Desviación Estándar: 5.353997072660648, Varianza: 28.665284654058784, Incertidumbre: 0.9182029228184352\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Capacidad combustible (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Capacidad combustible: [13.0, 11.5, 11.5, 28.0, 28.0]\n", + "Valores para Empty weight: [15.467, 11.5, 11.0, 32.0, 35.0]\n", + "Ecuación de regresión: y = 1.31x + -3.112\n", + "Valor del parámetro correlacionado para la aeronave: 28.0\n", + "Predicción obtenida: 33.57\n", + "\tR²: 0.98508534505441, Desviación Estándar: 1.2666533895780039, Varianza: 1.6044108093294462, Incertidumbre: 0.566464616605388\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Área del ala: 33.395', 'Longitud del fuselaje: 25.737', 'Ancho del fuselaje: 32.663', 'Peso máximo al despegue (MTOW): 33.112', 'envergadura: 21.735', 'Cuerda: 19.431', 'payload: 31.883', 'Capacidad combustible: 33.57']\n", + "**Mediana calculada:** 32.273\n", + "\n", + "--- Imputación para aeronave: **Volitation VT510** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.941) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 0.771, 0.986, 2.329]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 32.273, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.841x + -5.584\n", + "Valor del parámetro correlacionado para la aeronave: 1.993\n", + "Predicción obtenida: 23.995\n", + "\tR²: 0.9360251057500628, Desviación Estándar: 2.3659573340086495, Varianza: 5.597754106349317, Incertidumbre: 0.4057583352900256\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 32.273, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.649x + -3.948\n", + "Valor del parámetro correlacionado para la aeronave: 2.905\n", + "Predicción obtenida: 21.176\n", + "\tR²: 0.7283975003859964, Desviación Estándar: 4.874936932144367, Varianza: 23.765010092385136, Incertidumbre: 0.8360447865217215\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 32.273, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.303x + 2.653\n", + "Valor del parámetro correlacionado para la aeronave: 100.0\n", + "Predicción obtenida: 32.99\n", + "\tR²: 0.9213940860587302, Desviación Estándar: 2.5949358780767224, Varianza: 6.733692211329811, Incertidumbre: 0.4386242196208442\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.924) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 2.0, 3.0, 6.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 32.273, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 6.733x + -11.353\n", + "Valor del parámetro correlacionado para la aeronave: 5.1\n", + "Predicción obtenida: 22.986\n", + "\tR²: 0.7256919483141002, Desviación Estándar: 4.899157460266217, Varianza: 24.001743820482126, Incertidumbre: 0.8401985728260232\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Cuerda (r = 0.971) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 32.273, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 185.703x + -42.364\n", + "Valor del parámetro correlacionado para la aeronave: 0.334\n", + "Predicción obtenida: 19.661\n", + "\tR²: 0.5077463639535145, Desviación Estándar: 6.562912085594877, Varianza: 43.07181504324729, Incertidumbre: 1.1255301370941933\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 32.273, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.091x + 4.669\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 31.949\n", + "\tR²: 0.674893773503893, Desviación Estándar: 5.277298544902269, Varianza: 27.849879932027605, Incertidumbre: 0.8920262637393525\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Capacidad combustible (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Capacidad combustible: [13.0, 11.5, 11.5, 28.0, 28.0, 28.0]\n", + "Valores para Empty weight: [15.467, 11.5, 11.0, 32.0, 32.273, 35.0]\n", + "Ecuación de regresión: y = 1.283x + -2.79\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 29.289\n", + "\tR²: 0.9857968254239403, Desviación Estándar: 1.2345889100883332, Varianza: 1.5242097769130982, Incertidumbre: 0.504018811969206\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Área del ala: 23.995', 'Longitud del fuselaje: 21.176', 'Peso máximo al despegue (MTOW): 32.99', 'envergadura: 22.986', 'Cuerda: 19.661', 'payload: 31.949', 'Capacidad combustible: 29.289']\n", + "**Mediana calculada:** 23.995\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Reporte Final de Imputaciones

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AeronaveParámetroValor ImputadoNivel de Confianza
8Aerosonde® Mk. 4.8 VTOL FTUASÁrea del ala2.5030.804
9Fulmar XÁrea del ala0.9400.804
10Orbiter 4Área del ala1.6080.804
11Orbiter 3Área del ala1.2000.804
12MantisÁrea del ala0.7540.804
13ScanEagleÁrea del ala1.0630.804
14IntegratorÁrea del ala1.8720.804
15Integrator VTOLÁrea del ala2.0900.804
16Integrator Extended Range (ER)Área del ala1.8720.804
17ScanEagle 3Área del ala1.3490.804
18RQNan21A BlackjackÁrea del ala1.8020.804
19DeltaQuad Pro #MAPÁrea del ala0.7000.804
20DeltaQuad Pro #CARGOÁrea del ala0.7000.804
21V32Área del ala1.0300.804
29Fulmar XRelación de aspecto del ala13.2180.963
30Orbiter 4Relación de aspecto del ala13.4430.752
31Orbiter 3Relación de aspecto del ala14.0120.752
32MantisRelación de aspecto del ala14.7670.752
33ScanEagleRelación de aspecto del ala14.0670.777
34IntegratorRelación de aspecto del ala12.9230.631
35Integrator VTOLRelación de aspecto del ala12.6540.676
36Integrator Extended Range (ER)Relación de aspecto del ala12.8590.676
37ScanEagle 3Relación de aspecto del ala13.7740.676
38RQNan21A BlackjackRelación de aspecto del ala12.9730.695
39DeltaQuad EvoRelación de aspecto del ala14.5990.716
40DeltaQuad Pro #MAPRelación de aspecto del ala14.7170.726
41DeltaQuad Pro #CARGORelación de aspecto del ala14.7170.740
42V21Relación de aspecto del ala14.5780.740
43V25Relación de aspecto del ala14.4350.752
44V32Relación de aspecto del ala14.1940.761
45V35Relación de aspecto del ala13.9090.767
46V39Relación de aspecto del ala14.0530.768
47Volitation VT370Relación de aspecto del ala13.6570.764
48Skyeye 2600Relación de aspecto del ala14.1160.771
49Skyeye 2930 VTOLRelación de aspecto del ala14.0130.669
50Skyeye 3600Relación de aspecto del ala13.7230.675
51Skyeye 3600 VTOLRelación de aspecto del ala13.6840.675
52Skyeye 5000Relación de aspecto del ala12.7130.675
53Skyeye 5000 VTOLRelación de aspecto del ala13.0460.699
54Skyeye 5000 VTOL octoRelación de aspecto del ala12.8770.703
55Volitation VT510Relación de aspecto del ala13.1140.718
56AscendRelación de aspecto del ala14.3570.726
57TransitionRelación de aspecto del ala14.2330.732
58ReachRelación de aspecto del ala13.6830.736
59Aerosonde® Mk. 4.8 VTOL FTUASLongitud del fuselaje3.5950.831
60Integrator VTOLLongitud del fuselaje3.0030.831
61V39Longitud del fuselaje1.9540.831
92Aerosonde® Mk. 4.8 VTOL FTUASenvergadura5.6440.805
93Integrator VTOLenvergadura5.0330.805
94Aerosonde® Mk. 4.8 VTOL FTUASCuerda0.3940.827
95Fulmar XCuerda0.3130.827
96Orbiter 4Cuerda0.3340.737
97Orbiter 3Cuerda0.3010.737
98MantisCuerda0.2700.737
99ScanEagleCuerda0.2970.763
100IntegratorCuerda0.3380.714
101Integrator VTOLCuerda0.3410.749
102Integrator Extended Range (ER)Cuerda0.3440.749
103ScanEagle 3Cuerda0.3100.749
104RQNan21A BlackjackCuerda0.3390.750
105DeltaQuad EvoCuerda0.2760.774
106DeltaQuad Pro #MAPCuerda0.2720.793
107DeltaQuad Pro #CARGOCuerda0.2720.809
108V21Cuerda0.2780.809
109V25Cuerda0.2810.602
110V32Cuerda0.2910.591
111V35Cuerda0.3030.590
112V39Cuerda0.3040.590
113Volitation VT370Cuerda0.3130.590
114Skyeye 2600Cuerda0.2950.590
115Skyeye 2930 VTOLCuerda0.2990.590
116Skyeye 3600Cuerda0.3090.590
117Skyeye 3600 VTOLCuerda0.3120.594
118Skyeye 5000Cuerda0.3460.599
119Skyeye 5000 VTOLCuerda0.3360.683
120Skyeye 5000 VTOL octoCuerda0.3430.683
121Volitation VT510Cuerda0.3340.728
122AscendCuerda0.2860.728
123TransitionCuerda0.2900.724
124ReachCuerda0.3120.722
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Resumen de Imputaciones

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AeronaveCantidad de Valores Imputados
0Aerosonde® Mk. 4.8 VTOL FTUAS4.000
1Ascend2.000
2DeltaQuad Evo2.000
3DeltaQuad Pro #CARGO3.000
4DeltaQuad Pro #MAP3.000
5Fulmar X3.000
6Integrator3.000
7Integrator Extended Range (ER)3.000
8Integrator VTOL5.000
9Mantis3.000
10Orbiter 33.000
11Orbiter 43.000
12RQNan21A Blackjack3.000
13Reach2.000
14ScanEagle3.000
15ScanEagle 33.000
16Skyeye 26002.000
17Skyeye 2930 VTOL2.000
18Skyeye 36002.000
19Skyeye 3600 VTOL2.000
20Skyeye 50002.000
21Skyeye 5000 VTOL2.000
22Skyeye 5000 VTOL octo2.000
23Transition2.000
24V212.000
25V252.000
26V323.000
27V352.000
28V393.000
29Volitation VT3702.000
30Volitation VT5102.000
TotalTotal80.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m>>> RESULTADOS DE IMPUTACIÓN POR CORRELACIÓN\u001b[0m\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Aerosonde® Mk. 4.8 VTOL FTUAS = 32.31596323465078 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - AAI Aerosonde = 21.624760478782665 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Orbiter 4 = 27.26947572941752 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Orbiter 3 = 26.566881229546517 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Integrator VTOL = 30.93282942341402 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Integrator Extended Range (ER) = 30.953465066791882 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Skyeye 3600 = 27.344050412360318 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Skyeye 5000 VTOL octo = 31.71909847833797 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Orbiter 4 = 9633.863636363636 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Orbiter 3 = 8010.3359375 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Mantis = 12.97158076923077 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Integrator VTOL = 19487.0 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 2600 = 13122.5 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 2930 VTOL = 16571.42857142857 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 3600 = 16571.42857142857 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 3600 VTOL = 14902.124999999998 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 = 16044.444444444443 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 VTOL = 15640.0 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 VTOL octo = 15640.0 (Similitud)\n", + "Imputación final aplicada: Área del ala - Aerosonde® Mk. 4.8 VTOL FTUAS = 2.503 (Correlación)\n", + "Imputación final aplicada: Área del ala - Fulmar X = 0.94 (Correlación)\n", + "Imputación final aplicada: Área del ala - Orbiter 4 = 1.608 (Correlación)\n", + "Imputación final aplicada: Área del ala - Orbiter 3 = 1.12859375 (Similitud)\n", + "Imputación final aplicada: Área del ala - ScanEagle = 1.1814858490566036 (Similitud)\n", + "Imputación final aplicada: Área del ala - ScanEagle 3 = 1.349 (Correlación)\n", + "Imputación final aplicada: Área del ala - V35 = 1.12859375 (Similitud)\n", + "Imputación final aplicada: Área del ala - Volitation VT370 = 1.4456562499999999 (Similitud)\n", + "Imputación final aplicada: Área del ala - Volitation VT510 = 2.615 (Similitud)\n", + "Imputación final aplicada: Área del ala - Ascend = 0.8307894736842105 (Similitud)\n", + "Imputación final aplicada: Área del ala - Transition = 1.1897828403221333 (Similitud)\n", + "Imputación final aplicada: Área del ala - Reach = 2.6796565934065932 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Fulmar X = 13.217500000000001 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Orbiter 4 = 13.443 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - V25 = 14.435 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Volitation VT370 = 13.657 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 3600 VTOL = 13.6845 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 = 12.713000000000001 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 VTOL = 13.046 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 VTOL octo = 12.8765 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Volitation VT510 = 13.114 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Transition = 14.233 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Reach = 13.683 (Correlación)\n", + "Imputación final aplicada: Longitud del fuselaje - Aerosonde® Mk. 4.8 VTOL FTUAS = 3.5658602150537635 (Similitud)\n", + "Imputación final aplicada: Longitud del fuselaje - Integrator VTOL = 3.0035 (Correlación)\n", + "Imputación final aplicada: Longitud del fuselaje - V39 = 1.3746614583333334 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Aerosonde® Mk. 4.7 Fixed Wing = 296.09004739336496 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Aerosonde® Mk. 4.7 VTOL = 123.4483644859813 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Aerosonde® Mk. 4.8 Fixed wing = 122.9111213235294 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Aerosonde® Mk. 4.8 VTOL FTUAS = 815.0537634408602 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Integrator = 500.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Integrator VTOL = 499.66666666666663 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - ScanEagle 3 = 178.08195592286503 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - V21 = 270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - V25 = 1843.69 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - V32 = 770.2127659574468 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - V35 = 50.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Volitation VT370 = 300.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 2600 = 1763.9083333333333 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 2930 VTOL = 51.78571428571428 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 3600 = 51.78571428571428 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 5000 = 822.2222222222222 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 5000 VTOL octo = 800.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Volitation VT510 = 800.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Ascend = 273.55263157894734 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Transition = 633.4988526666666 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Reach = 819.7802197802197 (Similitud)\n", + "Imputación final aplicada: Autonomía de la aeronave - Skyeye 5000 VTOL octo = 8.0 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - Aerosonde® Mk. 4.8 VTOL FTUAS = 41.66129032258065 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - Integrator VTOL = 46.26913333333333 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Evo = 31.3125 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Pro #MAP = 25.909677419354843 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Pro #CARGO = 25.909677419354843 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - Skyeye 2600 = 24.701138406926347 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - Skyeye 3600 = 34.17857142857142 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Stalker XE = 12.721966911764707 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Stalker VXE30 = 12.68114837683525 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde® Mk. 4.7 Fixed Wing = 23.687203791469194 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde® Mk. 4.8 VTOL FTUAS = 19.66532258064516 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - AAI Aerosonde = 12.842557251908397 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Fulmar X = 12.674999999999999 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Orbiter 3 = 14.7734375 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - ScanEagle = 16.5188679245283 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - ScanEagle 3 = 24.611570247933887 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - DeltaQuad Evo = 13.41875 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - V35 = 14.7734375 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - V39 = 16.911458333333332 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Volitation VT370 = 24.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Skyeye 5000 VTOL = 19.3125 (Similitud)\n", + "Imputación final aplicada: envergadura - Aerosonde® Mk. 4.8 VTOL FTUAS = 5.094086021505376 (Similitud)\n", + "Imputación final aplicada: envergadura - Integrator VTOL = 5.033 (Correlación)\n", + "Imputación final aplicada: Cuerda - Fulmar X = 0.313 (Correlación)\n", + "Imputación final aplicada: Cuerda - Orbiter 4 = 0.334 (Correlación)\n", + "Imputación final aplicada: Cuerda - V25 = 0.22158417241379308 (Similitud)\n", + "Imputación final aplicada: Cuerda - Volitation VT370 = 0.35683999999999994 (Similitud)\n", + "Imputación final aplicada: Cuerda - Skyeye 2600 = 0.2118754597701149 (Similitud)\n", + "Imputación final aplicada: Cuerda - Skyeye 3600 VTOL = 0.35683999999999994 (Similitud)\n", + "Imputación final aplicada: Cuerda - Transition = 0.29 (Correlación)\n", + "Imputación final aplicada: payload - AAI Aerosonde = 2.518560923664122 (Similitud)\n", + "Imputación final aplicada: payload - Fulmar X = 1.9779738749767999 (Similitud)\n", + "Imputación final aplicada: payload - Mantis = 1.1861538461538461 (Similitud)\n", + "Imputación final aplicada: Empty weight - Aerosonde® Mk. 4.7 Fixed Wing = 10.856635071090047 (Similitud)\n", + "Imputación final aplicada: Empty weight - Aerosonde® Mk. 4.8 VTOL FTUAS = 31.741935483870968 (Similitud)\n", + "Imputación final aplicada: Empty weight - Fulmar X = 11.5545671248376 (Similitud)\n", + "Imputación final aplicada: Empty weight - Orbiter 3 = 9.009375 (Similitud)\n", + "Imputación final aplicada: Empty weight - ScanEagle = 7.200471698113207 (Similitud)\n", + "Imputación final aplicada: Empty weight - ScanEagle 3 = 11.280303030303031 (Similitud)\n", + "Imputación final aplicada: Empty weight - V35 = 9.009375 (Similitud)\n", + "Imputación final aplicada: Empty weight - V39 = 6.41640625 (Similitud)\n", + "Imputación final aplicada: Empty weight - Volitation VT370 = 11.0 (Similitud)\n", + "Imputación final aplicada: Empty weight - Skyeye 5000 VTOL = 31.2 (Similitud)\n", + "Imputación final aplicada: Empty weight - Volitation VT510 = 31.2 (Similitud)\n", + "Imputación final aplicada: Área del ala - Mantis = 0.754 (Correlación)\n", + "Imputación final aplicada: Área del ala - Integrator = 1.872 (Correlación)\n", + "Imputación final aplicada: Área del ala - Integrator VTOL = 2.0895 (Correlación)\n", + "Imputación final aplicada: Área del ala - Integrator Extended Range (ER) = 1.872 (Correlación)\n", + "Imputación final aplicada: Área del ala - RQNan21A Blackjack = 1.802 (Correlación)\n", + "Imputación final aplicada: Área del ala - DeltaQuad Pro #MAP = 0.7 (Correlación)\n", + "Imputación final aplicada: Área del ala - DeltaQuad Pro #CARGO = 0.7 (Correlación)\n", + "Imputación final aplicada: Área del ala - V32 = 1.03 (Correlación)\n", + "Imputación final aplicada: Área del ala - V39 = 1.203 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Orbiter 3 = 14.012 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Mantis = 14.767 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - ScanEagle = 14.067 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Integrator = 12.923 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Integrator VTOL = 12.654499999999999 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Integrator Extended Range (ER) = 12.859 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - ScanEagle 3 = 13.774 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - RQNan21A Blackjack = 12.972999999999999 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - DeltaQuad Evo = 14.599 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - DeltaQuad Pro #MAP = 14.717 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - DeltaQuad Pro #CARGO = 14.717 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - V21 = 14.578 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - V32 = 14.194 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - V35 = 13.909 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - V39 = 14.0535 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 2600 = 14.116 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 2930 VTOL = 14.013 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 3600 = 13.7225 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Ascend = 14.357 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - ScanEagle = 418.78 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - V39 = 475.37699999999995 (Correlación)\n", + "Imputación final aplicada: Cuerda - Aerosonde® Mk. 4.8 VTOL FTUAS = 0.394 (Correlación)\n", + "Imputación final aplicada: Cuerda - Orbiter 3 = 0.301 (Correlación)\n", + "Imputación final aplicada: Cuerda - Mantis = 0.27 (Correlación)\n", + "Imputación final aplicada: Cuerda - ScanEagle = 0.2975 (Correlación)\n", + "Imputación final aplicada: Cuerda - Integrator = 0.33799999999999997 (Correlación)\n", + "Imputación final aplicada: Cuerda - Integrator VTOL = 0.341 (Correlación)\n", + "Imputación final aplicada: Cuerda - Integrator Extended Range (ER) = 0.344 (Correlación)\n", + "Imputación final aplicada: Cuerda - ScanEagle 3 = 0.3105 (Correlación)\n", + "Imputación final aplicada: Cuerda - RQNan21A Blackjack = 0.3385 (Correlación)\n", + "Imputación final aplicada: Cuerda - DeltaQuad Evo = 0.2755 (Correlación)\n", + "Imputación final aplicada: Cuerda - DeltaQuad Pro #MAP = 0.272 (Correlación)\n", + "Imputación final aplicada: Cuerda - DeltaQuad Pro #CARGO = 0.272 (Correlación)\n", + "Imputación final aplicada: Cuerda - V21 = 0.2775 (Correlación)\n", + "Imputación final aplicada: Cuerda - V32 = 0.291 (Correlación)\n", + "Imputación final aplicada: Cuerda - V35 = 0.3035 (Correlación)\n", + "Imputación final aplicada: Cuerda - V39 = 0.3045 (Correlación)\n", + "Imputación final aplicada: Cuerda - Skyeye 2930 VTOL = 0.299 (Correlación)\n", + "Imputación final aplicada: Cuerda - Skyeye 3600 = 0.309 (Correlación)\n", + "Imputación final aplicada: Cuerda - Skyeye 5000 = 0.3465 (Correlación)\n", + "Imputación final aplicada: Cuerda - Skyeye 5000 VTOL = 0.336 (Correlación)\n", + "Imputación final aplicada: Cuerda - Skyeye 5000 VTOL octo = 0.3425 (Correlación)\n", + "Imputación final aplicada: Cuerda - Volitation VT510 = 0.334 (Correlación)\n", + "Imputación final aplicada: Cuerda - Ascend = 0.286 (Correlación)\n", + "Imputación final aplicada: Cuerda - Reach = 0.312 (Correlación)\n", + "Imputación final aplicada: Empty weight - Aerosonde® Mk. 4.7 VTOL = 19.796 (Correlación)\n", + "Imputación final aplicada: Empty weight - Aerosonde® Mk. 4.8 Fixed wing = 19.809 (Correlación)\n", + "Imputación final aplicada: Empty weight - Orbiter 4 = 18.724 (Correlación)\n", + "Imputación final aplicada: Empty weight - Mantis = 5.627 (Correlación)\n", + "Imputación final aplicada: Empty weight - Integrator = 22.234 (Correlación)\n", + "Imputación final aplicada: Empty weight - Integrator VTOL = 24.8205 (Correlación)\n", + "Imputación final aplicada: Empty weight - Integrator Extended Range (ER) = 22.2905 (Correlación)\n", + "Imputación final aplicada: Empty weight - RQNan21A Blackjack = 21.149 (Correlación)\n", + "Imputación final aplicada: Empty weight - DeltaQuad Pro #MAP = 4.767 (Correlación)\n", + "Imputación final aplicada: Empty weight - DeltaQuad Pro #CARGO = 4.767 (Correlación)\n", + "\n", + "=== Iteración 1: Resumen después de imputaciones ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de Valores Faltantes Después de Iteración 1

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ColumnaValores Faltantes
0Stalker XE0.000
1Stalker VXE300.000
2Aerosonde® Mk. 4.7 Fixed Wing0.000
3Aerosonde® Mk. 4.7 VTOL1.000
4Aerosonde® Mk. 4.8 Fixed wing1.000
5Aerosonde® Mk. 4.8 VTOL FTUAS0.000
6AAI Aerosonde0.000
7Fulmar X0.000
8Orbiter 41.000
9Orbiter 30.000
10Mantis1.000
11ScanEagle0.000
12Integrator1.000
13Integrator VTOL1.000
14Integrator Extended Range (ER)1.000
15ScanEagle 30.000
16RQNan21A Blackjack1.000
17DeltaQuad Evo0.000
18DeltaQuad Pro #MAP1.000
19DeltaQuad Pro #CARGO1.000
20V210.000
21V250.000
22V320.000
23V350.000
24V390.000
25Volitation VT3700.000
26Skyeye 26000.000
27Skyeye 2930 VTOL0.000
28Skyeye 36000.000
29Skyeye 3600 VTOL0.000
30Skyeye 50000.000
31Skyeye 5000 VTOL0.000
32Skyeye 5000 VTOL octo0.000
33Volitation VT5100.000
34Ascend0.000
35Transition0.000
36Reach0.000
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Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes10.000
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Resumen de Valores Faltantes Antes de Iteración 2

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ColumnaValores Faltantes
0Stalker XE32.000
1Stalker VXE3033.000
2Aerosonde® Mk. 4.7 Fixed Wing30.000
3Aerosonde® Mk. 4.7 VTOL30.000
4Aerosonde® Mk. 4.8 Fixed wing34.000
5Aerosonde® Mk. 4.8 VTOL FTUAS35.000
6AAI Aerosonde31.000
7Fulmar X36.000
8Orbiter 437.000
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10Mantis36.000
11ScanEagle35.000
12Integrator36.000
13Integrator VTOL35.000
14Integrator Extended Range (ER)38.000
15ScanEagle 335.000
16RQNan21A Blackjack35.000
17DeltaQuad Evo30.000
18DeltaQuad Pro #MAP33.000
19DeltaQuad Pro #CARGO33.000
20V2129.000
21V2529.000
22V3229.000
23V3532.000
24V3933.000
25Volitation VT37031.000
26Skyeye 260034.000
27Skyeye 2930 VTOL33.000
28Skyeye 360033.000
29Skyeye 3600 VTOL32.000
30Skyeye 500030.000
31Skyeye 5000 VTOL32.000
32Skyeye 5000 VTOL octo32.000
33Volitation VT51030.000
34Ascend31.000
35Transition31.000
36Reach31.000
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Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes1212.000
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Datos Filtrados por aeronaves seleccionadas antes de imputar(df_resultado_por_similitud)

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Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.3440532.31596321.6247630.40658427.26947626.56688118.26582630.62533630.95346530.93282930.95346525.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.25028827.3440532.8128636.09414730.62533631.71909832.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.8429633.8636368010.33593812.97158119500.019500.019487.019500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.013122.516571.42857116571.42857114902.12516044.44444415640.015640.017000.010000.013000.016000.0
Área del ala0.871.1582831.551.551.552.5030.570.941.6081.1285940.7541.1814861.8722.08951.8721.3491.8020.840.70.70.80.521.031.1285941.2031.4456560.881.01.331.322.6152.6152.6152.6150.8307891.1897832.679657
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.75438613.217513.44314.01214.76714.06712.92312.654512.85913.77412.97314.59914.71714.71714.57814.43514.19413.90914.053513.65714.11614.01313.722513.684512.71313.04612.876513.11414.35714.23313.683
Longitud del fuselaje2.12.59083.03.03.03.565861.71.21.21.21.481.712.53.00352.52.42.50.750.90.90.930.931.01.881.3746612.022.052.032.4882.423.53.53.52.9051.5622.34.712
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0296.090047123.448364122.911121815.0537633270.0800.0150.050.025.0418.78500.0499.666667500.0178.08195692.6270.0100.0100.0270.01843.69770.21276650.0475.377300.01763.90833351.78571451.785714300.0822.222222800.0800.0800.0273.552632633.498853819.78022
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.08.05.06.012.020.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.4388641.6612930.84572541.736.036.025.641.246.346.26913346.341.246.331.312525.90967725.90967733.033.033.033.033.033.024.70113830.034.17857133.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)12.72196712.68114823.687204NaNNaN19.66532312.84255712.675NaN14.773438NaN16.518868NaNNaNNaN24.61157NaN13.41875NaNNaN14.015.517.014.77343816.91145824.010.018.012.524.015.019.312524.025.013.013.013.0
envergadura3.6574.87684.44.44.45.0940862.93.05.24.42.13.14.85.0334.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.3520.3940.1965520.3130.3340.3010.270.29750.3380.3410.3440.31050.33850.27550.2720.2720.27750.2215840.2910.30350.30450.356840.2118750.2990.3090.356840.34650.3360.34250.3340.2860.290.312
payload2.4947562.49475614.511.317.722.72.5185611.97797412.05.51.1861545.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
Empty weight10.88620817.46329210.85663519.79619.80931.74193510.011.55456718.7249.0093755.6277.20047222.23424.820522.290511.28030321.1494.84.7674.7672.653.456.459.0093756.41640611.06.57.111.511.032.031.235.031.23.05.831.0
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.7 VTOL'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Aerosonde® Mk. 4.7 VTOL.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.8 Fixed wing'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Aerosonde® Mk. 4.8 Fixed wing.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Orbiter 4'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Orbiter 4.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Mantis'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Mantis.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Integrator.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator VTOL'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Integrator VTOL.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator Extended Range (ER)'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para Integrator Extended Range (ER).\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'RQNan21A Blackjack'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para RQNan21A Blackjack.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #MAP'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para DeltaQuad Pro #MAP.\n", + "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #CARGO'para el parametro 'Velocidad de pérdida (KCAS)'.\n", + "No se pudo imputar: Velocidad de pérdida (KCAS) para DeltaQuad Pro #CARGO.\n", + "\n", + "=== Generando reporte final ===\n", + "No se realizaron imputaciones con el nivel de confianza aceptable.\n", + "No se realizaron imputaciones con éxito.\n", + "\u001b[1mNo se realizaron imputaciones por similitud en esta iteración.\u001b[0m\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN 2 ***\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "\n", + "=== DataFrame inicial ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())

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Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Distancia de carrera requerida para despegue0.00.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
Tasa de ascensoNaNNaNNaNNaNNaNNaN2.49936NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5.0NaN5.0NaNNaNNaNNaNNaNNaNNaN5.0NaNNaNNaN
Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.0
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.3440532.31596321.6247630.40658427.26947626.56688118.26582630.62533630.95346530.93282930.95346525.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.25028827.3440532.8128636.09414730.62533631.71909832.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.8429633.8636368010.33593812.97158119500.019500.019487.019500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.013122.516571.42857116571.42857114902.12516044.44444415640.015640.017000.010000.013000.016000.0
Velocidad de pérdida limpia (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaNNaN25.0NaNNaNNaN
Área del ala0.871.1582831.551.551.552.5030.570.941.6081.1285940.7541.1814861.8722.08951.8721.3491.8020.840.70.70.80.521.031.1285941.2031.4456560.881.01.331.322.6152.6152.6152.6150.8307891.1897832.679657
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.75438613.217513.44314.01214.76714.06712.92312.654512.85913.77412.97314.59914.71714.71714.57814.43514.19413.90914.053513.65714.11614.01313.722513.684512.71313.04612.876513.11414.35714.23313.683
Longitud del fuselaje2.12.59083.03.03.03.565861.71.21.21.21.481.712.53.00352.52.42.50.750.90.90.930.931.01.881.3746612.022.052.032.4882.423.53.53.52.9051.5622.34.712
Profundidad del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Ancho del fuselaje0.2110.20.2770.2770.277NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.3750.3750.375NaNNaNNaNNaN
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0296.090047123.448364122.911121815.0537633270.0800.0150.050.025.0418.78500.0499.666667500.0178.08195692.6270.0100.0100.0270.01843.69770.21276650.0475.377300.01763.90833351.78571451.785714300.0822.222222800.0800.0800.0273.552632633.498853819.78022
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.08.05.06.012.020.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.4388641.6612930.84572541.736.036.025.641.246.346.26913346.341.246.331.312525.90967725.90967733.033.033.033.033.033.024.70113830.034.17857133.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)12.72196712.68114823.687204NaNNaN19.66532312.84255712.675NaN14.773438NaN16.518868NaNNaNNaN24.61157NaN13.41875NaNNaN14.015.517.014.77343816.91145824.010.018.012.524.015.019.312524.025.013.013.013.0
Radio de giroNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100.0120.0150.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
envergadura3.6574.87684.44.44.45.0940862.93.05.24.42.13.14.85.0334.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.3520.3940.1965520.3130.3340.3010.270.29750.3380.3410.3440.31050.33850.27550.2720.2720.27750.2215840.2910.30350.30450.356840.2118750.2990.3090.356840.34650.3360.34250.3340.2860.290.312
payload2.4947562.49475614.511.317.722.72.5185611.97797412.05.51.1861545.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
duracion en VTOL2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050.050.05
Crucero KIAS15.4333216.09342225.025.025.0NaNNaN27.8NaNNaN16.728.028.3NaNNaN23.530.916.5416.016.018.020.020.025.025.025.033.024.0NaN30.033.028.035.030.020.020.025.0
RTF (dry weight)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.011.854.0
RTF (Including fuel & Batteries)NaNNaN27.742.236.770.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8.916.584.0
Empty weight10.88620817.46329210.85663519.79619.80931.74193510.011.55456718.7249.0093755.6277.20047222.23424.820522.290511.28030321.1494.84.7674.7672.653.456.459.0093756.41640611.06.57.111.511.032.031.235.031.23.05.831.0
Maximum CrosswindNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.0NaNNaNNaN45.050.050.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15.015.015.0
Rango de comunicación59.0161.0140.0140.0140.0NaN150.0NaN150.050.025.0101.8692.6NaNNaNNaN92.6NaN50.030.030.030.030.030.030.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Wing LoadingNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.524.025.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Potencia/PesoNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Capacidad combustibleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.0NaNNaN11.511.528.028.028.025.0NaNNaNNaN
ConsumoNaNNaN0.60.6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.96NaNNaNNaNNaN1.2NaNNaN5.0NaNNaNNaN
Potencia(W)NaNNaN2980.02980.0NaNNaN1280.0NaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Potencia(HP)NaNNaN4.04.0NaNNaN1.74NaNNaNNaNNaNNaNNaNNaNNaNNaN8.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
PrecioNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.0NaNNaNNaN
Tiempo de emergencia en vueloNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.1080.1080.108NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Distancia de aterrizajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Battery Power SupplyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Modelo Motor Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Modelo Motor VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
PortabilidadNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
CámaraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
DespegueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Datalink banksNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Motor recomendadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Hélice recomendada VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Hélice recomendada Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Sistema de controlNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Características adicionalesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
EmpresaNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
kjbkNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Convertir todo a numérico ===\n", + "\n", + "\n", + "=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===\n", + "\n", + "Umbral seleccionado para correlaciones significativas: 0.7\n", + "\n", + "=== Cálculo de tabla completa ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Tabla de Correlaciones con todos los parametros(tabla_completa)

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Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecio
Distancia de carrera requerida para despegue1.0000.0630.3460.058-0.5050.230-0.2490.2270.4250.168-0.014-0.0690.207-0.2350.1130.1510.229nan0.389nan0.209nannan-0.018-0.240-0.156
Altitud a la que se realiza el crucero0.0631.000-0.056-0.125nan-0.0590.108-0.096nan-0.095-0.324-0.281-0.1970.170-0.0840.079-0.101nannannan-0.1270.038-0.325nannannan
Velocidad a la que se realiza el crucero (KTAS)0.346-0.0561.0000.1660.1390.672-0.7850.5550.9320.7020.1020.2720.7030.3690.5310.5420.708-0.6940.9820.7970.608-0.6020.3050.5950.461-0.272
Techo de servicio máximo0.058-0.1250.1661.000-0.1660.187-0.1290.1780.5530.2040.1470.0840.158-0.0020.1250.0950.200-0.8750.1440.5790.118-0.798-0.036-0.0900.469-0.138
Velocidad de pérdida limpia (KCAS)-0.505nan0.139-0.1661.0000.439-0.3700.260nan0.546-0.2890.4010.5931.0000.5050.6530.536nan0.128nan0.411nannan0.0681.0000.163
Área del ala0.230-0.0590.6720.1870.4391.000-0.7460.8460.9840.979-0.0490.3460.6840.4620.8090.7230.845-0.4430.6720.9860.949-0.5430.5130.9920.6870.048
Relación de aspecto del ala-0.2490.108-0.785-0.129-0.370-0.7461.000-0.626-0.676-0.7890.108-0.444-0.740-0.572-0.618-0.779-0.8280.521-0.765-0.495-0.6960.429-0.416-0.9700.3020.025
Longitud del fuselaje0.227-0.0960.5550.1780.2600.846-0.6261.0000.9380.8080.0430.3930.3970.3120.7040.5770.665-0.6170.5640.9250.832-0.6960.6820.9290.036-0.186
Ancho del fuselaje0.425nan0.9320.553nan0.984-0.6760.9381.0000.9860.721-0.1940.9400.5470.6710.5360.868nan0.944nan0.882nan0.323nan1.000nan
Peso máximo al despegue (MTOW)0.168-0.0950.7020.2040.5460.979-0.7890.8080.9861.0000.0130.3930.7360.5120.8020.7070.884-0.4010.7080.9790.958-0.4640.5140.9760.7580.052
Alcance de la aeronave-0.014-0.3240.1020.147-0.289-0.0490.1080.0430.7210.0131.0000.2000.001-0.227-0.095-0.492-0.074-0.5780.3320.6240.073-0.7860.1750.9660.6680.037
Autonomía de la aeronave-0.069-0.2810.2720.0840.4010.346-0.4440.393-0.1940.3930.2001.0000.4220.2030.5300.3180.380-0.5940.3010.6340.384-0.7150.802-0.093-0.7320.021
Velocidad máxima (KIAS)0.207-0.1970.7030.1580.5930.684-0.7400.3970.9400.7360.0010.4221.0000.5150.5310.5930.732-0.2020.6640.7840.632-0.3390.1840.7420.9100.114
Velocidad de pérdida (KCAS)-0.2350.1700.369-0.0021.0000.462-0.5720.3120.5470.512-0.2270.2030.5151.0000.4930.6340.6540.5850.3940.1740.3571.0000.0890.0420.3540.136
envergadura0.113-0.0840.5310.1250.5050.809-0.6180.7040.6710.802-0.0950.5300.5310.4931.0000.7190.770-0.2580.5010.9340.793-0.4140.6480.2970.0850.032
Cuerda0.1510.0790.5420.0950.6530.723-0.7790.5770.5360.707-0.4920.3180.5930.6340.7191.0000.754-0.4890.5000.5910.642-0.4810.4120.014-0.9220.075
payload0.229-0.1010.7080.2000.5360.845-0.8280.6650.8680.884-0.0740.3800.7320.6540.7700.7541.000-0.0240.6710.5590.808-0.1420.4750.7110.846-0.008
duracion en VTOLnannan-0.694-0.875nan-0.4430.521-0.617nan-0.401-0.578-0.594-0.2020.585-0.258-0.489-0.0241.000-0.694-0.402-0.3151.000nannannannan
Crucero KIAS0.389nan0.9820.1440.1280.672-0.7650.5640.9440.7080.3320.3010.6640.3940.5010.5000.671-0.6941.0000.7230.620-0.8550.3590.5810.461-0.243
RTF (Including fuel & Batteries)nannan0.7970.579nan0.986-0.4950.925nan0.9790.6240.6340.7840.1740.9340.5910.559-0.4020.7231.0000.973-0.402nannannannan
Empty weight0.209-0.1270.6080.1180.4110.949-0.6960.8320.8820.9580.0730.3840.6320.3570.7930.6420.808-0.3150.6200.9731.000-0.3860.7210.9890.6170.028
Maximum Crosswindnan0.038-0.602-0.798nan-0.5430.429-0.696nan-0.464-0.786-0.715-0.3391.000-0.414-0.481-0.1421.000-0.855-0.402-0.3861.000nannannannan
Rango de comunicaciónnan-0.3250.305-0.036nan0.513-0.4160.6820.3230.5140.1750.8020.1840.0890.6480.4120.475nan0.359nan0.721nan1.000nannannan
Capacidad combustible-0.018nan0.595-0.0900.0680.992-0.9700.929nan0.9760.966-0.0930.7420.0420.2970.0140.711nan0.581nan0.989nannan1.0000.3770.817
Consumo-0.240nan0.4610.4691.0000.6870.3020.0361.0000.7580.668-0.7320.9100.3540.085-0.9220.846nan0.461nan0.617nannan0.3771.0000.998
Precio-0.156nan-0.272-0.1380.1630.0480.025-0.186nan0.0520.0370.0210.1140.1360.0320.075-0.008nan-0.243nan0.028nannan0.8170.9981.000
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Resumen de la Tabla

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ResumenCantidad
0Total de valores676.000
1Valores numéricos602.000
2Valores NaN74.000
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Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)1.0000.1660.672-0.7850.5550.7020.1020.2720.7030.3690.5310.5420.7080.608
Techo de servicio máximo0.1661.0000.187-0.1290.1780.2040.1470.0840.158-0.0020.1250.0950.2000.118
Área del ala0.6720.1871.000-0.7460.8460.979-0.0490.3460.6840.4620.8090.7230.8450.949
Relación de aspecto del ala-0.785-0.129-0.7461.000-0.626-0.7890.108-0.444-0.740-0.572-0.618-0.779-0.828-0.696
Longitud del fuselaje0.5550.1780.846-0.6261.0000.8080.0430.3930.3970.3120.7040.5770.6650.832
Peso máximo al despegue (MTOW)0.7020.2040.979-0.7890.8081.0000.0130.3930.7360.5120.8020.7070.8840.958
Alcance de la aeronave0.1020.147-0.0490.1080.0430.0131.0000.2000.001-0.227-0.095-0.492-0.0740.073
Autonomía de la aeronave0.2720.0840.346-0.4440.3930.3930.2001.0000.4220.2030.5300.3180.3800.384
Velocidad máxima (KIAS)0.7030.1580.684-0.7400.3970.7360.0010.4221.0000.5150.5310.5930.7320.632
Velocidad de pérdida (KCAS)0.369-0.0020.462-0.5720.3120.512-0.2270.2030.5151.0000.4930.6340.6540.357
envergadura0.5310.1250.809-0.6180.7040.802-0.0950.5300.5310.4931.0000.7190.7700.793
Cuerda0.5420.0950.723-0.7790.5770.707-0.4920.3180.5930.6340.7191.0000.7540.642
payload0.7080.2000.845-0.8280.6650.884-0.0740.3800.7320.6540.7700.7541.0000.808
Empty weight0.6080.1180.949-0.6960.8320.9580.0730.3840.6320.3570.7930.6420.8081.000
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Resumen de la Tabla

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ResumenCantidad
0Total de valores196.000
1Valores numéricos196.000
2Valores NaN0.000
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Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)nannannan-0.785nan0.702nannan0.703nannannan0.708nan
Techo de servicio máximonannannannannannannannannannannannannannan
Área del alanannannan-0.7460.8460.979nannannannan0.8090.7230.8450.949
Relación de aspecto del ala-0.785nan-0.746nannan-0.789nannan-0.740nannan-0.779-0.828nan
Longitud del fuselajenannan0.846nannan0.808nannannannan0.704nannan0.832
Peso máximo al despegue (MTOW)0.702nan0.979-0.7890.808nannannan0.736nan0.8020.7070.8840.958
Alcance de la aeronavenannannannannannannannannannannannannannan
Autonomía de la aeronavenannannannannannannannannannannannannannan
Velocidad máxima (KIAS)0.703nannan-0.740nan0.736nannannannannannan0.732nan
Velocidad de pérdida (KCAS)nannannannannannannannannannannannannannan
envergaduranannan0.809nan0.7040.802nannannannannan0.7190.7700.793
Cuerdanannan0.723-0.779nan0.707nannannannan0.719nan0.754nan
payload0.708nan0.845-0.828nan0.884nannan0.732nan0.7700.754nan0.808
Empty weightnannan0.949nan0.8320.958nannannannan0.793nan0.808nan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores196.000
1Valores numéricos58.000
2Valores NaN138.000
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Tabla de correlaciones con filtro de umbral de correlación

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Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecio
Distancia de carrera requerida para despeguenannannannannannannannannannannannannannannannannannannannannannannannannannan
Altitud a la que se realiza el cruceronannannannannannannannannannannannannannannannannannannannannannannannannannan
Velocidad a la que se realiza el crucero (KTAS)nannannannannannan-0.785nan0.9320.702nannan0.703nannannan0.708nan0.9820.797nannannannannannan
Techo de servicio máximonannannannannannannannannannannannannannannannannan-0.875nannannan-0.798nannannannan
Velocidad de pérdida limpia (KCAS)nannannannannannannannannannannannannannannannannannannannannannannannannannan
Área del alanannannannannannan-0.7460.8460.9840.979nannannannan0.8090.7230.845nannan0.9860.949nannan0.992nannan
Relación de aspecto del alanannan-0.785nannan-0.746nannannan-0.789nannan-0.740nannan-0.779-0.828nan-0.765nannannannan-0.970nannan
Longitud del fuselajenannannannannan0.846nannan0.9380.808nannannannan0.704nannannannan0.9250.832nannan0.929nannan
Ancho del fuselajenannan0.932nannan0.984nan0.938nan0.9860.721nan0.940nannannan0.868nan0.944nan0.882nannannannannan
Peso máximo al despegue (MTOW)nannan0.702nannan0.979-0.7890.8080.986nannannan0.736nan0.8020.7070.884nan0.7080.9790.958nannan0.9760.758nan
Alcance de la aeronavenannannannannannannannan0.721nannannannannannannannannannannannan-0.786nan0.966nannan
Autonomía de la aeronavenannannannannannannannannannannannannannannannannannannannannan-0.7150.802nan-0.732nan
Velocidad máxima (KIAS)nannan0.703nannannan-0.740nan0.9400.736nannannannannannan0.732nannan0.784nannannan0.7420.910nan
Velocidad de pérdida (KCAS)nannannannannannannannannannannannannannannannannannannannannannannannannannan
envergaduranannannannannan0.809nan0.704nan0.802nannannannannan0.7190.770nannan0.9340.793nannannannannan
Cuerdanannannannannan0.723-0.779nannan0.707nannannannan0.719nan0.754nannannannannannannan-0.922nan
payloadnannan0.708nannan0.845-0.828nan0.8680.884nannan0.732nan0.7700.754nannannannan0.808nannan0.7110.846nan
duracion en VTOLnannannan-0.875nannannannannannannannannannannannannannannannannannannannannannan
Crucero KIASnannan0.982nannannan-0.765nan0.9440.708nannannannannannannannannan0.723nan-0.855nannannannan
RTF (Including fuel & Batteries)nannan0.797nannan0.986nan0.925nan0.979nannan0.784nan0.934nannannan0.723nan0.973nannannannannan
Empty weightnannannannannan0.949nan0.8320.8820.958nannannannan0.793nan0.808nannan0.973nannan0.7210.989nannan
Maximum Crosswindnannannan-0.798nannannannannannan-0.786-0.715nannannannannannan-0.855nannannannannannannan
Rango de comunicaciónnannannannannannannannannannannan0.802nannannannannannannannan0.721nannannannannan
Capacidad combustiblenannannannannan0.992-0.9700.929nan0.9760.966nan0.742nannannan0.711nannannan0.989nannannannan0.817
Consumonannannannannannannannannan0.758nan-0.7320.910nannan-0.9220.846nannannannannannannannan0.998
Precionannannannannannannannannannannannannannannannannannannannannannannan0.8170.998nan
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", + "\n", + "=== Velocidad a la que se realiza el crucero (KTAS): No hay valores faltantes para imputar. ===\n", + "\n", + "=== Techo de servicio máximo: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Área del ala: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Relación de aspecto del ala: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Longitud del fuselaje: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Peso máximo al despegue (MTOW): No hay valores faltantes para imputar. ===\n", + "\n", + "=== Alcance de la aeronave: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Autonomía de la aeronave: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Velocidad máxima (KIAS): No hay valores faltantes para imputar. ===\n", + "\n", + "=== Imputación para el parámetro: **Velocidad de pérdida (KCAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 VTOL** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde® Mk. 4.7 VTOL'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 Fixed wing** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde® Mk. 4.8 Fixed wing'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Orbiter 4'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Mantis** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Mantis'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Integrator** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator VTOL'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator Extended Range (ER)'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **RQNan21A Blackjack** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'RQNan21A Blackjack'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Pro #MAP'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Pro #CARGO'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== envergadura: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Cuerda: No hay valores faltantes para imputar. ===\n", + "\n", + "=== payload: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Empty weight: No hay valores faltantes para imputar. ===\n", + "La columna 'Nivel de Confianza' no está presente en df_reporte.\n", + "\u001b[1mNo se realizaron imputaciones por correlación en esta iteración.\u001b[0m\n", + "\n", + "=== Iteración 2: Resumen después de imputaciones ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de Valores Faltantes Después de Iteración 2

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ColumnaValores Faltantes
0Stalker XE0.000
1Stalker VXE300.000
2Aerosonde® Mk. 4.7 Fixed Wing0.000
3Aerosonde® Mk. 4.7 VTOL1.000
4Aerosonde® Mk. 4.8 Fixed wing1.000
5Aerosonde® Mk. 4.8 VTOL FTUAS0.000
6AAI Aerosonde0.000
7Fulmar X0.000
8Orbiter 41.000
9Orbiter 30.000
10Mantis1.000
11ScanEagle0.000
12Integrator1.000
13Integrator VTOL1.000
14Integrator Extended Range (ER)1.000
15ScanEagle 30.000
16RQNan21A Blackjack1.000
17DeltaQuad Evo0.000
18DeltaQuad Pro #MAP1.000
19DeltaQuad Pro #CARGO1.000
20V210.000
21V250.000
22V320.000
23V350.000
24V390.000
25Volitation VT3700.000
26Skyeye 26000.000
27Skyeye 2930 VTOL0.000
28Skyeye 36000.000
29Skyeye 3600 VTOL0.000
30Skyeye 50000.000
31Skyeye 5000 VTOL0.000
32Skyeye 5000 VTOL octo0.000
33Volitation VT5100.000
34Ascend0.000
35Transition0.000
36Reach0.000
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Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes10.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mNo se realizaron nuevas imputaciones. Finalizando...\u001b[0m\n", + "=== Exportando datos al archivo: C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Results\\Datos_imputados.xlsx ===\n", + "Exportación completada. El archivo se guardó como 'C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Results\\Datos_imputados.xlsx'.\n", + "\n", + "=== Flujo completado. Verifique el archivo generado. ===\n", + "✅ Script finalizado.\n" ] } ], diff --git a/ADRpy/analisis/archivo_imputaciones.xlsx b/ADRpy/analisis/archivo_imputaciones.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..a4c49d13a6427269538ad5ebd58f20e95fd29abf GIT binary patch literal 50353 zcmcG$byS>LwmwXPC1`M$1b27$0KwgZyA|%i-Q6v?YXOA>2yVgMg1dVGKhoVZ-90n+ zuDNS{-yg+Wb!xFWXUnsnz2A2uFAWKW4gmr22ExKIR8zc9=40Z^r;(S}yO)=dt)V=? z*3N-J-_DNS)!IsCOa{J-5dmhcL(v(S8bvX1z6;PJ8|16WB$BUSg{90W z4iF`K$1$|hh-q(dSTS*aN%~$!s{vJFY}h+%8gl|L&*|zE2_1~TZyB5s-xv{iSwyRPAl_kb*1c(%c?5i&?TMgU_qKYtLWCsy`J5 z0-~Q70s`$td#+Xtj%LQz#=rk$`lY`kjgfFX4)hMJdx4N<9+pN^Fv?FT533zZ4vkt_ zsi=jTLxpl29LW_OwAW-@SBjKh3zCIx`4T1sAradyqu-L&?-7l2usmVUr;(;x2}{kw zUw^!BE6QH+aCa|jw%g-0*o}E3>9$lmxPuQL%ef|k8?7~SB-Alg9iz-iXwYjoQM)aw znN08fG!N~m8%X-$&8DFnA+TJ&WY29e>6N3QTa=t6f*}e0hw>b%-fQ_ak{@_Xpr4#< zJYB4f@)0ERJP#7=^P(M*c>rhWJc?Lqck+1E<8GTR#y0Pnm0HEuBUtW)ZT6CZF!cdyFm 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'4,5,6')", default=None) +parser.add_argument("--columna", type=str, help="Nombre de la columna para analizar celdas faltantes", default=None) +parser.add_argument("--umbral_correlacion", type=float, help="Umbral mínimo de correlación significativa", default=None) +parser.add_argument("--nivel_confianza_min_correlacion", type=float, help="Nivel de confianza mínimo correlacion para imputaciones", default=None) + + +args = parser.parse_args() + + +# Ahora args.ruta_archivo, args.archivo_destino, etc. están disponibles + + # ===================== # # IMPORTACIONES # # ===================== # @@ -24,26 +59,24 @@ # IMPORTAR MÓDULOS # # ===================== # -from ADRpy.analisis.Modulos.config_and_loading import configurar_entorno, cargar_datos -from ADRpy.analisis.Modulos.data_processing import procesar_datos_y_manejar_duplicados -from ADRpy.analisis.Modulos.user_interaction import seleccionar_parametros_por_indices, solicitar_umbral -from ADRpy.analisis.Modulos.correlation_analysis import calcular_correlaciones_y_generar_heatmap_con_resumen -from ADRpy.analisis.Modulos.similarity_imputation import imputacion_similitud_con_rango, imprimir_detalles_imputacion -from ADRpy.analisis.Modulos.correlation_imputation import Imputacion_por_correlacion -from ADRpy.analisis.Modulos.imputation_loop import bucle_imputacion_similitud_correlacion -from ADRpy.analisis.Modulos.excel_export import exportar_excel_con_imputaciones -from ADRpy.analisis.Modulos.html_utils import convertir_a_html - +from Modulos.config_and_loading import configurar_entorno, cargar_datos +from Modulos.data_processing import procesar_datos_y_manejar_duplicados +from Modulos.user_interaction import seleccionar_parametros_por_indices, solicitar_umbral +from Modulos.correlation_analysis import calcular_correlaciones_y_generar_heatmap_con_resumen +from Modulos.similarity_imputation import imputacion_similitud_con_rango, imprimir_detalles_imputacion +from Modulos.correlation_imputation import Imputacion_por_correlacion +from Modulos.imputation_loop import bucle_imputacion_similitud_correlacion +from Modulos.excel_export import exportar_excel_con_imputaciones +from Modulos.html_utils import convertir_a_html +from Modulos.data_processing import mostrar_celdas_faltantes_con_seleccion, generar_resumen_faltantes -# Solicitar la ruta del archivo al usuario -archivo_origen = input("Ingrese la ruta del archivo Excel original: ") # Paso 1: Configurar entorno configurar_entorno(max_rows=20, max_columns=10) # Paso 2: Cargar datos try: - df_inicial, ruta_archivo = cargar_datos(archivo_origen) # Aquí se valida la entrada + df_inicial, ruta_archivo = cargar_datos(ruta_archivo=args.ruta_archivo) print(f"Datos cargados correctamente desde: {ruta_archivo}") except ValueError as e: print(f"Error al cargar datos: {e}") @@ -80,7 +113,7 @@ else: print("\n❌ Los encabezados fueron modificados durante el procesamiento.") -# Paso 5: Mostr en HTML +# Paso 5: Mostrar en HTML print("\n=== Mostrando datos procesados en formato HTML ===") convertir_a_html(df_procesado, titulo="Datos Procesados", mostrar=True) @@ -115,7 +148,7 @@ #print("Parámetros preseleccionados válidos:") #print(parametros_preseleccionados) -parametros_seleccionados = seleccionar_parametros_por_indices(parametros_disponibles, parametros_preseleccionados) +parametros_seleccionados = seleccionar_parametros_por_indices(parametros_disponibles, parametros_preseleccionados, args.parametros) # Imprimir parámetros seleccionados después de filtrar print("Parámetros seleccionados después de filtrar:") print(parametros_seleccionados) @@ -134,7 +167,11 @@ # Paso 7: Mostrar celdas faltantes con selección de columna # Analizar celdas faltantes en la columna seleccionada -df_celdas_faltantes = mostrar_celdas_faltantes_con_seleccion(df_filtrado) +df_celdas_faltantes = mostrar_celdas_faltantes_con_seleccion( + df_filtrado, + columna_seleccionada=args.columna, + debug_mode=args.debug_mode +) # Verificar si hay celdas faltantes if df_celdas_faltantes.empty: @@ -149,23 +186,48 @@ # Paso 9: Calculando correlaciones y generando heatmap print("\n=== Calculando correlaciones y generando heatmap ===") -tabla_completa = calcular_correlaciones_y_generar_heatmap_con_resumen(df_procesado, parametros_seleccionados) +# Paso 9: Calculando correlaciones y generando heatmap +print("\n=== Calculando correlaciones y generando heatmap ===") +tabla_completa = calcular_correlaciones_y_generar_heatmap_con_resumen( + df_procesado, + parametros_seleccionados, + umbral_heat_map=args.umbral_heat_map if args.debug_mode else None, + devolver_tabla=True +) # Paso 10: Ajustar rango e imputar valores faltantes #print("\n=== Paso 8: Imputación con ajuste de rango ===") #imputacion_similitud_con_rango(df_filtrado, df_procesado) #Paso 11: Ajustar rango e imputar valores faltantes por correlación -#Imputacion_por_correlacion(df_procesado, parametros_preseleccionados, tabla_completa, umbral_correlacion=0.7, min_datos_validos=5, max_lineas_consola=250) +#Imputacion_por_correlacion(df_procesado, parametros_preseleccionados, tabla_completa, parametros_seleccionados, umbral_correlacion=0.7, min_datos_validos=5, max_lineas_consola=250) # Paso 10: Llamar a la función principal -df_procesado_actualizado, resumen_imputaciones = bucle_imputacion_similitud_correlacion(df_procesado, parametros_preseleccionados, tabla_completa, reduccion_confianza=0.05, max_iteraciones=7) +df_procesado_actualizado, resumen_imputaciones = bucle_imputacion_similitud_correlacion( + df_procesado=df_procesado, + df_filtrado=df_filtrado, + parametros_preseleccionados=parametros_preseleccionados, + tabla_completa=tabla_completa, + parametros_seleccionados=parametros_seleccionados, + rango_min=args.rango_min if args.debug_mode else None, + rango_max=args.rango_max if args.debug_mode else None, + nivel_confianza_min_similitud=args.nivel_confianza_min_similitud if args.debug_mode else None, + umbral_correlacion=args.umbral_correlacion if args.debug_mode else None, + nivel_confianza_min_correlacion=args.nivel_confianza_min_similitud if args.debug_mode else None, + debug_mode=args.debug_mode +) + + # Paso 11: Exportar resultados a Excel -archivo_destino = input("Ingrese la ruta donde desea guardar el archivo con las imputaciones (incluya .xlsx): ") +archivo_destino = args.archivo_destino +if not archivo_destino: + archivo_destino = input("Ingrese la ruta donde desea guardar el archivo con las imputaciones (incluya .xlsx): ") + exportar_excel_con_imputaciones( archivo_origen=ruta_archivo, df_procesado=df_procesado_actualizado, resumen_imputaciones=resumen_imputaciones ) print("\n=== Flujo completado. Verifique el archivo generado. ===") +print("✅ Script finalizado.") From f21b46719bf795c34399b43202ed3b77137ae162 Mon Sep 17 00:00:00 2001 From: Delpoo <157638420+Delpoo@users.noreply.github.com> Date: Wed, 16 Apr 2025 18:41:05 -0300 Subject: [PATCH 4/9] =?UTF-8?q?Agregado=20Diagrama=20sin=20t=C3=ADtulo.dra?= =?UTF-8?q?wio?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "ADRpy/analisis/Diagrama sin t\303\255tulo.drawio" | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 "ADRpy/analisis/Diagrama sin t\303\255tulo.drawio" diff --git "a/ADRpy/analisis/Diagrama sin t\303\255tulo.drawio" "b/ADRpy/analisis/Diagrama sin t\303\255tulo.drawio" new file mode 100644 index 00000000..24353d76 --- /dev/null +++ "b/ADRpy/analisis/Diagrama sin t\303\255tulo.drawio" @@ -0,0 +1,10 @@ + + + + + + + + + + From 7ac73bd8a1c01dcfb09a838dd2b0fee22a87d91e Mon Sep 17 00:00:00 2001 From: Delpoo Date: Fri, 25 Apr 2025 16:49:34 -0300 Subject: [PATCH 5/9] Imputacion en excel ,imputacion_por_similitud nueva sin finalizar --- .vscode/launch.json | 2 +- ADRpy/analisis/Data/Datos_aeronaves.xlsx | Bin 65069 -> 75687 bytes .../analisis/Modulos/correlation_analysis.py | 6 +- .../Modulos/imputacion_similitud_flexible.py | 251 +++ ADRpy/analisis/Modulos/imputation_loop.py | 42 +- ..._imputados.xlsx => Datos_imputados11.xlsx} | Bin 50391 -> 50392 bytes ADRpy/analisis/Results/archivo_salida.xlsx | Bin 0 -> 60204 bytes ADRpy/analisis/flujo_datos_adrpy.md | 51 + ADRpy/analisis/inicial.drawio | 1391 +++++++++++++++++ ADRpy/analisis/main.py | 30 +- archivo_imputaciones.xlsx | Bin 50354 -> 0 bytes 11 files changed, 1751 insertions(+), 22 deletions(-) create mode 100644 ADRpy/analisis/Modulos/imputacion_similitud_flexible.py rename ADRpy/analisis/Results/{Datos_imputados.xlsx => Datos_imputados11.xlsx} (92%) create mode 100644 ADRpy/analisis/Results/archivo_salida.xlsx create mode 100644 ADRpy/analisis/flujo_datos_adrpy.md create mode 100644 ADRpy/analisis/inicial.drawio delete mode 100644 archivo_imputaciones.xlsx diff --git a/.vscode/launch.json b/.vscode/launch.json index 040e905e..610a5016 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -48,7 +48,7 @@ "--nivel_confianza_min_similitud", "0.5", "--rango_min", "0.85", "--rango_max", "1.15", - "--parametros", "4, 5, 7, 8, 9, 12, 13, 14, 15, 16, 18, 19, 20, 25", + "--parametros", "3, 4, 6, 7, 8, 11, 12, 13, 14, 15, 18, 19, 20, 25", "--columna", "Stalker XE", "--umbral_correlacion", "0.5", "--nivel_confianza_min_correlacion", "0.5" diff --git a/ADRpy/analisis/Data/Datos_aeronaves.xlsx b/ADRpy/analisis/Data/Datos_aeronaves.xlsx index f8349ad5c952e52fa9581cdcfa4b8d544b46237c..3d9e9464c40a2869d6f029d6742353fc90d8e36e 100644 GIT binary patch delta 62004 zcmb5VbzD?$*FH)kA)s`ZNH>CXcXvukNh#eMl$MY#>6Ff)o1sHGM!KZC2RP&R>G%De z-+9g-vza~f8TPf;y4JPgzIQnkv1lBLNKFwD2@ehh?im~$95vkZ$k*mGcsMvmjA|lU z1YpT=kpr(itIi|LSCFpo-JCLR4xwv{J#)PeoCRfAu!W%rTX4g1IEGCrIvUNS^B3v3 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filtrada print("\n=== Cálculo de correlaciones filtradas ===") - tabla_filtrada = datos_filtrados.corr() + tabla_filtrada = df_filtrado_transpuesto.corr() agregar_resumen_a_tabla( tabla_filtrada.round(3), "Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)", @@ -93,7 +93,7 @@ def agregar_resumen_a_tabla(tabla, titulo): # Preparar datos para el heatmap print("\n=== Preparando datos para el heatmap ===") - heatmap_data = datos_filtrados.dropna( + heatmap_data = df_filtrado_transpuesto.dropna( thresh=2 ) # Excluir variables con menos de 2 valores válidos heatmap_correlaciones = heatmap_data.corr() diff --git a/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py b/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py new file mode 100644 index 00000000..c0e92b6b --- /dev/null +++ b/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py @@ -0,0 +1,251 @@ +""" +imputacion_similitud_flexible.py +-------------------------------- +Implementa la lógica de K‑NN con 3 ejes obligatorios (físico, geométrico, prestacional) +y filtrado progresivo de familia (F0‑F3). Diseñado para integrarse sin romper +los nombres ni los flujos que ya existen en tu proyecto ADRpy. + +Uso rápido: + python imputacion_similitud_flexible.py --ruta_excel Datos_aeronaves.xlsx \ + --aeronave "Stalker XE" \ + --parametro "Velocidad a la que se realiza el crucero (KTAS)" +""" + +import argparse +import pandas as pd +import numpy as np +from pathlib import Path + +# ------------------------------------------------------------------ # +# CONFIGURACIÓN DE BLOQUES Y CAPAS DE FAMILIA +# ------------------------------------------------------------------ # + +def configurar_similitud(): + """ + Devuelve las configuraciones necesarias para ejecutar la imputación por similitud flexible: + - bloques de rasgos + - filas de familia + - capas de filtrado jerárquico de familia + """ + + bloques_rasgos = { + "fisico": [ + "Peso máximo al despegue (MTOW)", + "Peso vacío", + ], + "geom": [ + "Área del ala", + "envergadura", + "Longitud del fuselaje", + "Relación de aspecto del ala", + ], + "prest": [ + "Potencia específica (P/W)", + "Autonomía de la aeronave", + "Alcance de la aeronave", + "Velocidad a la que se realiza el crucero (KTAS)", + "Velocidad máxima (KIAS)", + ], + } + + filas_familia = [ + "Misión", + "Despegue", + "Propulsión vertical", + "Propulsión horizontal", + "Cantidad de motores propulsión vertical", + "Cantidad de motores propulsión horizontal", + ] + + capas_familia = [ + { + "Misión": "equals", + "Despegue": "equals", + "Propulsión vertical": "equals", + "Propulsión horizontal": "equals", + "Cantidad de motores propulsión vertical": "equals", + "Cantidad de motores propulsión horizontal": "equals", + }, + { + "Misión": "equals", + "Despegue": "equals", + "Propulsión vertical": "equals", + "Propulsión horizontal": "equals", + }, + { + "Misión": "equals", + "Despegue": "equals", + }, + { + "Misión": "equals", + }, + ] + + return bloques_rasgos, filas_familia, capas_familia + +# ------------------------------------------------------------------ # +# HELPERS +# ------------------------------------------------------------------ # + +def imprimir(msg, bold=False): + prefix = "\033[1m" if bold else "" + suffix = "\033[0m" if bold else "" + print(f"{prefix}{msg}{suffix}") + +import pandas as pd + +def zscore(arr: pd.Series) -> pd.Series: + """ + Si la desviación estándar es cero, devolvemos un + vector de ceros (todos idénticos a la media). + En caso contrario, el Z‐score habitual. + """ + std = arr.std(ddof=0) + if std == 0 or pd.isna(std): + # arr.index preserva el nombre de filas + return pd.Series(0.0, index=arr.index) + return (arr - arr.mean()) / std + + +# ------------------------------------------------------------------ # +# FUNCIÓN PRINCIPAL +# ------------------------------------------------------------------ # +def imputar_por_similitud( + df_parametros: pd.DataFrame, + df_atributos: pd.DataFrame, + aeronave_obj: str, + parametro_objetivo: str, + bloques_rasgos: dict, + capas_familia: list +): + + imprimir(f"\n=== Iniciando imputación por similitud de aeronave {aeronave_obj} y parametro {parametro_objetivo} ===", True) + + # ------------------------ Paso 0. Validaciones ------------------ + if parametro_objetivo not in df_parametros.index: + imprimir(f"Parámetro '{parametro_objetivo}' no encontrado.", True) + return None + + if aeronave_obj not in df_parametros.columns: + imprimir(f"Aeronave '{aeronave_obj}' no encontrada.", True) + return None + + # ------------------------ Paso 1. Vector de X ------------------- + vector_objetivo = {} + for bloque, lista in bloques_rasgos.items(): + for rasgo in lista: + if not pd.isna(df_parametros.at[rasgo, aeronave_obj]): + vector_objetivo[bloque] = rasgo + break + else: # ningún rasgo disponible + imprimir(f"⚠️ Bloque {bloque.upper()} sin datos en '{aeronave_obj}'.", True) + return None + + imprimir("Vector objetivo seleccionado (se buscan parámetros con valores no nulos en celdas para realizar la imputación):") + for bloque, rasgo in vector_objetivo.items(): + imprimir(f" {bloque}: {rasgo}") + + # ------------------------ Paso 2. Iterar capas familia ---------- + for capa_idx, criterios in enumerate(capas_familia): + imprimir(f"\n=== Capa F{capa_idx} ===", True) + + # Filtrado fila a fila + mascara_cols = np.array([True] * df_parametros.shape[1]) + for fila, modo in criterios.items(): + val_obj = df_atributos.at[fila, aeronave_obj] + if modo == "equals": + mascara_cols &= df_atributos.loc[fila] == val_obj + + df_familia = df_parametros.loc[:, mascara_cols] + imprimir("Realizando selección de aeronaves con valores en todos los parámetros físicos, geométricos y prestacionales:") + n_fam = df_familia.shape[1] + imprimir(f"Drones en familia: {n_fam}") + + if n_fam == 0: + imprimir("❌ Sin drones en esta capa. Relajando…") + continue + + # Filtrar los que poseen el parámetro objetivo + cols_with_param = df_familia.columns[ + df_familia.loc[parametro_objetivo].notna() + ] + if cols_with_param.empty: + imprimir(f"❌ Ningún dron en F{capa_idx} tiene '{parametro_objetivo}'.") + continue + + # Además deben poseer los tres ejes + filtros_ejes = [ + df_familia.loc[vector_objetivo[bloque]].notna() + for bloque in ("fisico", "geom", "prest") + ] + mask_all_ejes = filtros_ejes[0] & filtros_ejes[1] & filtros_ejes[2] + cols_validas = cols_with_param[mask_all_ejes[cols_with_param]] + + k = len(cols_validas) + imprimir(f"Vecinos válidos (k)......................: {k}") + + if k == 0: + imprimir("❌ No quedan drones con los 3 ejes. Relajando…") + continue + + imprimir(f"Vecinos válidos nombres: {list(cols_validas)}") + + # ---------------- Paso 3. Distancias y media ponderada ------- + # Matriz con los tres ejes + data_ejes = pd.DataFrame({ + col: [ + df_familia.at[vector_objetivo["fisico"], col], + df_familia.at[vector_objetivo["geom"], col], + df_familia.at[vector_objetivo["prest"], col], + ] + for col in list(cols_validas) + [aeronave_obj] + }, index=["fis", "geo", "pre"]).T + + # Z‑score columna a columna + imprimir("Matriz de datos antes de z-score (data_ejes):") + imprimir(data_ejes) + data_z = data_ejes.apply(zscore) + imprimir("Matriz de datos después de z-score (data_z):") + imprimir(data_z) + + # Distancias + diffs = data_z.loc[cols_validas].values - data_z.loc[aeronave_obj].values + dist = np.linalg.norm(diffs, axis=1) + weights = 1 / (1 + dist) + + valores_vecinos = df_familia.loc[parametro_objetivo, cols_validas].values + valor_imputado = np.sum(weights * valores_vecinos) / np.sum(weights) + + # Confianza simple (CV + distancia media) + cv = valores_vecinos.std(ddof=0) / valores_vecinos.mean() + conf = max(0, 1 - cv - 0.1 * dist.mean()) + + imprimir("Detalle del cálculo del valor imputado (se calcula el valor ponderado basado en la distancia entre aeronaves):") + imprimir(f" Pesos (weights): {weights}") + imprimir(f" Valores vecinos: {valores_vecinos}") + + productos = weights * valores_vecinos + suma_ponderada = np.sum(productos) + suma_pesos = np.sum(weights) + + imprimir(f" Producto elemento a elemento (weights * valores_vecinos): {productos}") + imprimir(f" Suma ponderada (Σ weights * valores_vecinos): {suma_ponderada:.3f}") + imprimir(f" Suma de pesos (Σ weights): {suma_pesos:.3f}") + imprimir(f" División final: {suma_ponderada:.3f} / {suma_pesos:.3f} = {valor_imputado:.3f}") + + imprimir(f"✅ Valor imputado: {valor_imputado:.3f}") + imprimir(f" Confianza.....: {conf:.2f}") + imprimir(f" Vecinos usados: {list(cols_validas)}") + + return { + "valor": valor_imputado, + "confianza": conf, + "vecinos": list(cols_validas) + } + + # Si ninguna capa funcionó + imprimir("⚠️ Sin vecinos en ninguna capa. Se delega a correlación.", True) + return None + + + diff --git a/ADRpy/analisis/Modulos/imputation_loop.py b/ADRpy/analisis/Modulos/imputation_loop.py index a0331d94..02b777e1 100644 --- a/ADRpy/analisis/Modulos/imputation_loop.py +++ b/ADRpy/analisis/Modulos/imputation_loop.py @@ -1,13 +1,17 @@ import pandas as pd -from .similarity_imputation import imputacion_similitud_con_rango +from Modulos.imputacion_similitud_flexible import * from .correlation_imputation import Imputacion_por_correlacion from .html_utils import convertir_a_html from .data_processing import generar_resumen_faltantes def bucle_imputacion_similitud_correlacion( + df_parametros, + df_atributos, + parametros_preseleccionados, + bloques_rasgos, + capas_familia, df_procesado, df_filtrado, - parametros_preseleccionados, tabla_completa, parametros_seleccionados, umbral_correlacion=0.7, @@ -99,21 +103,37 @@ def bucle_imputacion_similitud_correlacion( ) # Crear copias independientes para cada método - df_similitud = df_filtrado_base.copy() + df_similitud = df_procesado_base.copy() df_correlacion = df_procesado_base.copy() # Imputación por similitud (no actualiza todavía) print("\n" + "-" * 80) print(f"\033[1m*** IMPUTACIÓN POR SIMILITUD - ITERACIÓN {iteracion} ***\033[0m") print("-" * 80) - df_resultado_final, reporte_similitud = imputacion_similitud_con_rango( - df_filtrado=df_similitud, - df_procesado=df_procesado_base, - rango_min=rango_min, - rango_max=rango_max, - nivel_confianza_min_similitud=nivel_confianza_min_similitud, - ) - + df_similitud_resultado = df_similitud.copy() + reporte_similitud = [] + + for parametro in parametros_preseleccionados: + for aeronave in df_similitud_resultado.columns: + if pd.isna(df_similitud_resultado.loc[parametro, aeronave]): + resultado = imputar_por_similitud( + df_parametros=df_parametros, + df_atributos=df_atributos, + aeronave_obj=aeronave, + parametro_objetivo=parametro, + bloques_rasgos=bloques_rasgos, + capas_familia=capas_familia + ) + + if resultado is not None: + df_similitud_resultado.loc[parametro, aeronave] = resultado["valor"] + reporte_similitud.append({ + "Aeronave": aeronave, + "Parámetro": parametro, + "Valor Imputado": resultado["valor"], + "Nivel de Confianza": resultado["confianza"] + }) + if reporte_similitud and len(reporte_similitud) > 0: print("\033[1m>>> RESULTADOS DE IMPUTACIÓN POR SIMILITUD\033[0m") # Se guardan las imputaciones de similitud, pero NO se actualiza el DataFrame aún. diff --git a/ADRpy/analisis/Results/Datos_imputados.xlsx b/ADRpy/analisis/Results/Datos_imputados11.xlsx similarity index 92% rename from ADRpy/analisis/Results/Datos_imputados.xlsx rename to ADRpy/analisis/Results/Datos_imputados11.xlsx index 338876317d2cbec21f06b09eca2ebc108a2e351e..d0f0418b8b4c6fd39e431a90103d3959a542b42f 100644 GIT binary patch delta 1037 zcmccK$$X=enK!_jnMH(wfq{eJ_}cD?yazaeR8;<#T_XGwuW8h8oI77>jS0KPoE0rD zDgx%GZ*CJS%gG7PtgN~-VcFWK&c*3z_UE&-^E9rD#jQ0<*vlaPcFG%>r)hHed(1@? 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principal de los datos en el proceso de análisis e imputación de aeronaves. + + +```mermaid +graph TD + A[Archivo Excel original] --> B[Procesamiento y limpieza de datos] + B --> C[Analisis de correlaciones] + B --> D[Imputacion por similitud MTOW] + C --> E[Imputacion por correlacion] + D --> F[Bucle de imputacion hasta completar valores] + E --> F + F --> G[Exportacion a nuevo Excel con formato y comentarios] + G --> H[Visualizacion con Data Wrangler o HTML] +``` + +```mermaid +graph TD + A[📁 Excel original] --> B[🧹 Limpieza de datos] + B --> C[📈 Correlaciones] + B --> D[🧮 Similitud MTOW] + C --> E[🔗 Correlaciones significativas] + D --> F[🔁 Bucle de imputacion] + E --> F + F --> G[📤 Exportar a Excel] + G --> H[🖥️ Visualizar con Data Wrangler] +``` +```mermaid +graph TD + classDef inicio fill=#cce5ff,stroke=#004085,color=#004085,stroke-width:2px; + classDef proceso fill=#e2e3e5,stroke=#383d41,color=#383d41; + classDef importante fill=#fff3cd,stroke=#856404,color=#856404,stroke-width:2px; + classDef salida fill=#d4edda,stroke=#155724,color=#155724; + + A[📁 Excel original]:::inicio --> B[🧹 Limpieza de datos]:::proceso + B --> C[📈 Correlaciones]:::proceso + B --> D[🧮 Similitud MTOW]:::proceso + C --> E[🔗 Correlación significativa]:::importante + D --> F[🔁 Bucle de imputación]:::importante + E --> F + F --> G[📤 Exportación Excel]:::salida + G --> H[🖥️ Visualización HTML o Data Wrangler]:::salida + +--- + +## 📌 Notas: +- Cada bloque representa una etapa clave del proyecto. +- Las flechas indican la evolución del DataFrame a través de funciones. +- Las funciones clave están agrupadas en módulos dentro de la carpeta `Modulos`. + diff --git a/ADRpy/analisis/inicial.drawio b/ADRpy/analisis/inicial.drawio new file mode 100644 index 00000000..61c07fd1 --- /dev/null +++ b/ADRpy/analisis/inicial.drawio @@ -0,0 +1,1391 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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fallando o tiene errores +# ! Esto es importante +# ? Esto es una duda o algo que quiero revisar +# * Esto es algo que quiero hacer +# Normal comment: Este es un comentario normal import argparse @@ -69,8 +75,8 @@ from Modulos.excel_export import exportar_excel_con_imputaciones from Modulos.html_utils import convertir_a_html from Modulos.data_processing import mostrar_celdas_faltantes_con_seleccion, generar_resumen_faltantes - - +from Modulos.imputacion_similitud_flexible import configurar_similitud +from Modulos.imputacion_similitud_flexible import imputar_por_similitud # Paso 1: Configurar entorno configurar_entorno(max_rows=20, max_columns=10) @@ -184,8 +190,6 @@ print("\n=== Generando resumen de valores faltantes por columna ===") resumen_faltantes = generar_resumen_faltantes(df_filtrado, titulo="Resumen de Valores Faltantes de df_filtrado") -# Paso 9: Calculando correlaciones y generando heatmap -print("\n=== Calculando correlaciones y generando heatmap ===") # Paso 9: Calculando correlaciones y generando heatmap print("\n=== Calculando correlaciones y generando heatmap ===") tabla_completa = calcular_correlaciones_y_generar_heatmap_con_resumen( @@ -200,12 +204,23 @@ #imputacion_similitud_con_rango(df_filtrado, df_procesado) #Paso 11: Ajustar rango e imputar valores faltantes por correlación #Imputacion_por_correlacion(df_procesado, parametros_preseleccionados, tabla_completa, parametros_seleccionados, umbral_correlacion=0.7, min_datos_validos=5, max_lineas_consola=250) +# Separar atributos y parámetros como antes: + +# Cargar configuración de similitud +bloques_rasgos, filas_familia, capas_familia = configurar_similitud() + +df_atributos = df_procesado.loc[filas_familia] +df_parametros = df_procesado.drop(index=filas_familia) # Paso 10: Llamar a la función principal df_procesado_actualizado, resumen_imputaciones = bucle_imputacion_similitud_correlacion( + df_parametros=df_parametros, + df_atributos=df_atributos, + parametros_preseleccionados=parametros_preseleccionados, + bloques_rasgos=bloques_rasgos, + capas_familia=capas_familia, df_procesado=df_procesado, df_filtrado=df_filtrado, - parametros_preseleccionados=parametros_preseleccionados, tabla_completa=tabla_completa, parametros_seleccionados=parametros_seleccionados, rango_min=args.rango_min if args.debug_mode else None, @@ -216,7 +231,7 @@ debug_mode=args.debug_mode ) - +print("Hola") # Paso 11: Exportar resultados a Excel archivo_destino = args.archivo_destino @@ -226,7 +241,8 @@ exportar_excel_con_imputaciones( archivo_origen=ruta_archivo, df_procesado=df_procesado_actualizado, - resumen_imputaciones=resumen_imputaciones + resumen_imputaciones=resumen_imputaciones, + archivo_destino=archivo_destino ) print("\n=== Flujo completado. 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2001 From: Delpoo Date: Mon, 5 May 2025 06:54:53 -0300 Subject: [PATCH 6/9] Cambio de logica en similitud flexible al 90% --- .vscode/launch.json | 2 +- ADRpy/analisis/Data/Datos_aeronaves.xlsx | Bin 75687 -> 75912 bytes ADRpy/analisis/Modulos/config_and_loading.py | 29 +- ADRpy/analisis/Modulos/excel_export.py | 9 +- .../Modulos/imputacion_similitud_flexible.py | 380 +- ADRpy/analisis/Results/Datos_imputados.xlsx | Bin 0 -> 76963 bytes ADRpy/analisis/Results/archivo_salida.xlsx | Bin 60204 -> 62070 bytes ADRpy/analisis/aaa.ipynb | 52718 +++++++++++----- ADRpy/analisis/main.py | 13 +- 9 files changed, 36877 insertions(+), 16274 deletions(-) create mode 100644 ADRpy/analisis/Results/Datos_imputados.xlsx diff --git a/.vscode/launch.json b/.vscode/launch.json index 610a5016..a47b4c85 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -48,7 +48,7 @@ "--nivel_confianza_min_similitud", "0.5", "--rango_min", "0.85", "--rango_max", "1.15", - "--parametros", "3, 4, 6, 7, 8, 11, 12, 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b/ADRpy/analisis/Modulos/config_and_loading.py @@ -2,7 +2,7 @@ import tkinter as tk from tkinter import simpledialog, messagebox import sys - +import unicodedata def configurar_entorno(max_rows=20, max_columns=10): """ @@ -76,3 +76,30 @@ def cargar_datos(ruta_archivo=None): raise ValueError("Error: Archivo no encontrado.") except Exception as e: raise ValueError(f"Error al cargar el archivo: {e}") + + + + +def normalizar_encabezados(df): + """ + Normaliza los encabezados del DataFrame: + - Elimina espacios al inicio y al final. + - Convierte a minúsculas. + - Elimina caracteres especiales o no ASCII. + :param df: DataFrame a normalizar. + :return: DataFrame con encabezados normalizados. + """ + def normalizar(texto): + if isinstance(texto, str): + # Eliminar caracteres no ASCII y convertir a minúsculas + texto = ''.join( + c for c in unicodedata.normalize('NFD', texto) + if unicodedata.category(c) != 'Mn' + ) + return texto.strip().lower() # Eliminar espacios y convertir a minúsculas + return texto + + # Normalizar columnas e índices + df.columns = [normalizar(col) for col in df.columns] + df.index = [normalizar(idx) for idx in df.index] + return df \ No newline at end of file diff --git a/ADRpy/analisis/Modulos/excel_export.py b/ADRpy/analisis/Modulos/excel_export.py index 510ba196..ef20ffd1 100644 --- a/ADRpy/analisis/Modulos/excel_export.py +++ b/ADRpy/analisis/Modulos/excel_export.py @@ -57,8 +57,11 @@ def exportar_excel_con_imputaciones(archivo_origen, df_procesado, resumen_imputa elif registro["Método"] == "Correlación": celda.fill = color_correlacion - # Agregar comentario con el nivel de confianza - comentario = f"Nivel de confianza: {registro['Nivel de Confianza']:.2f}" + # Agregar comentario con el nivel de confianza y la iteración + comentario = ( + f"Nivel de confianza: {registro['Nivel de Confianza']*100:.2f}%\n" + f"Iteración: {registro['Iteración']}" + ) celda.comment = Comment(comentario, "Sistema") # Guardar el archivo con las imputaciones @@ -66,6 +69,6 @@ def exportar_excel_con_imputaciones(archivo_origen, df_procesado, resumen_imputa print(f"Exportación completada. El archivo se guardó como '{archivo_destino}'.") except FileNotFoundError: - print(f"Error: El archivo '{archivo_origen}' no fue encontrado.") + print(f"Error: El archivo '{archivo_origen}' o {archivo_destino} no fue encontrado.") except Exception as e: print(f"Error al procesar el archivo: {e}") diff --git a/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py b/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py index c0e92b6b..c3badfc2 100644 --- a/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py +++ b/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py @@ -1,115 +1,53 @@ -""" -imputacion_similitud_flexible.py --------------------------------- -Implementa la lógica de K‑NN con 3 ejes obligatorios (físico, geométrico, prestacional) -y filtrado progresivo de familia (F0‑F3). Diseñado para integrarse sin romper -los nombres ni los flujos que ya existen en tu proyecto ADRpy. - -Uso rápido: - python imputacion_similitud_flexible.py --ruta_excel Datos_aeronaves.xlsx \ - --aeronave "Stalker XE" \ - --parametro "Velocidad a la que se realiza el crucero (KTAS)" -""" - -import argparse import pandas as pd import numpy as np -from pathlib import Path -# ------------------------------------------------------------------ # -# CONFIGURACIÓN DE BLOQUES Y CAPAS DE FAMILIA -# ------------------------------------------------------------------ # +# ------------------------ HELPERS ------------------------ + +def imprimir(msg, bold=False): + prefix = "\033[1m" if bold else "" + suffix = "\033[0m" if bold else "" + print(f"{prefix}{msg}{suffix}") + +# ------------------------ CONFIGURACIÓN ------------------------ def configurar_similitud(): """ - Devuelve las configuraciones necesarias para ejecutar la imputación por similitud flexible: - - bloques de rasgos - - filas de familia - - capas de filtrado jerárquico de familia + Devuelve: + - bloques_rasgos: diccionario de ejes y parámetros + - filas_familia: atributos para clasificación familiar + - capas_familia: filtros progresivos de familia F0, F1, F2 """ - bloques_rasgos = { - "fisico": [ - "Peso máximo al despegue (MTOW)", - "Peso vacío", - ], - "geom": [ - "Área del ala", - "envergadura", - "Longitud del fuselaje", - "Relación de aspecto del ala", - ], + "fisico": ["Peso máximo al despegue (MTOW)", "Peso vacío"], + "geom": ["Área del ala", "envergadura", "Longitud del fuselaje", "Relación de aspecto del ala"], "prest": [ - "Potencia específica (P/W)", - "Autonomía de la aeronave", - "Alcance de la aeronave", - "Velocidad a la que se realiza el crucero (KTAS)", + "Potencia específica (P/W)", "Autonomía de la aeronave", + "Alcance de la aeronave", "Velocidad a la que se realiza el crucero (KTAS)", "Velocidad máxima (KIAS)", ], } - filas_familia = [ - "Misión", - "Despegue", - "Propulsión vertical", - "Propulsión horizontal", - "Cantidad de motores propulsión vertical", - "Cantidad de motores propulsión horizontal", + "Misión", "Despegue", "Propulsión vertical", "Propulsión horizontal", + "Cantidad de motores propulsión vertical", "Cantidad de motores propulsión horizontal", ] - capas_familia = [ - { - "Misión": "equals", - "Despegue": "equals", - "Propulsión vertical": "equals", - "Propulsión horizontal": "equals", - "Cantidad de motores propulsión vertical": "equals", - "Cantidad de motores propulsión horizontal": "equals", - }, - { - "Misión": "equals", - "Despegue": "equals", - "Propulsión vertical": "equals", - "Propulsión horizontal": "equals", - }, - { - "Misión": "equals", - "Despegue": "equals", - }, - { - "Misión": "equals", - }, + {attr: "equals" for attr in filas_familia}, + {attr: "equals" for attr in filas_familia[:4]}, + {attr: "equals" for attr in filas_familia[:2]}, ] - return bloques_rasgos, filas_familia, capas_familia -# ------------------------------------------------------------------ # -# HELPERS -# ------------------------------------------------------------------ # - -def imprimir(msg, bold=False): - prefix = "\033[1m" if bold else "" - suffix = "\033[0m" if bold else "" - print(f"{prefix}{msg}{suffix}") - -import pandas as pd +# ------------------------ FUNCIÓN PRINCIPAL ------------------------ -def zscore(arr: pd.Series) -> pd.Series: +def penalizacion_por_k(k): """ - Si la desviación estándar es cero, devolvemos un - vector de ceros (todos idénticos a la media). - En caso contrario, el Z‐score habitual. + Calcula la penalización por cantidad de vecinos (k) usando una ecuación polinomial. + Para k > 10, la penalización se fija en 1.0 (confianza máxima). """ - std = arr.std(ddof=0) - if std == 0 or pd.isna(std): - # arr.index preserva el nombre de filas - return pd.Series(0.0, index=arr.index) - return (arr - arr.mean()) / std - + if k > 10: + return 1.0 + return max(0, min(1, 0.00002281 * k**5 - 0.00024 * k**4 - 0.0036 * k**3 + 0.046 * k**2 + 0.0095 * k + 0.024)) -# ------------------------------------------------------------------ # -# FUNCIÓN PRINCIPAL -# ------------------------------------------------------------------ # def imputar_por_similitud( df_parametros: pd.DataFrame, df_atributos: pd.DataFrame, @@ -118,134 +56,174 @@ def imputar_por_similitud( bloques_rasgos: dict, capas_familia: list ): + imprimir(f"\n=== Imputación por similitud: {aeronave_obj} - {parametro_objetivo} ===", True) - imprimir(f"\n=== Iniciando imputación por similitud de aeronave {aeronave_obj} y parametro {parametro_objetivo} ===", True) - - # ------------------------ Paso 0. Validaciones ------------------ + # Validaciones if parametro_objetivo not in df_parametros.index: - imprimir(f"Parámetro '{parametro_objetivo}' no encontrado.", True) + imprimir(f"⚠️ Parámetro '{parametro_objetivo}' no encontrado.", True) return None - if aeronave_obj not in df_parametros.columns: - imprimir(f"Aeronave '{aeronave_obj}' no encontrada.", True) + imprimir(f"⚠️ Aeronave '{aeronave_obj}' no encontrada.", True) return None - # ------------------------ Paso 1. Vector de X ------------------- - vector_objetivo = {} - for bloque, lista in bloques_rasgos.items(): - for rasgo in lista: - if not pd.isna(df_parametros.at[rasgo, aeronave_obj]): - vector_objetivo[bloque] = rasgo - break - else: # ningún rasgo disponible - imprimir(f"⚠️ Bloque {bloque.upper()} sin datos en '{aeronave_obj}'.", True) - return None - - imprimir("Vector objetivo seleccionado (se buscan parámetros con valores no nulos en celdas para realizar la imputación):") - for bloque, rasgo in vector_objetivo.items(): - imprimir(f" {bloque}: {rasgo}") - - # ------------------------ Paso 2. Iterar capas familia ---------- + # Iteración por capas de familia for capa_idx, criterios in enumerate(capas_familia): - imprimir(f"\n=== Capa F{capa_idx} ===", True) - - # Filtrado fila a fila - mascara_cols = np.array([True] * df_parametros.shape[1]) + familia = f"F{capa_idx}" + imprimir(f"\n--- Capa {familia}: criterios {list(criterios.keys())} ---", True) + # Filtrar familia + mask = np.ones(df_parametros.shape[1], dtype=bool) for fila, modo in criterios.items(): - val_obj = df_atributos.at[fila, aeronave_obj] - if modo == "equals": - mascara_cols &= df_atributos.loc[fila] == val_obj - - df_familia = df_parametros.loc[:, mascara_cols] - imprimir("Realizando selección de aeronaves con valores en todos los parámetros físicos, geométricos y prestacionales:") - n_fam = df_familia.shape[1] - imprimir(f"Drones en familia: {n_fam}") - - if n_fam == 0: - imprimir("❌ Sin drones en esta capa. Relajando…") + val = df_atributos.at[fila, aeronave_obj] + mask &= (df_atributos.loc[fila] == val).values + df_familia = df_parametros.loc[:, mask] + if df_familia.shape[1] == 0: + imprimir(f"❌ Sin drones en {familia}. Continuando...", True) continue - - # Filtrar los que poseen el parámetro objetivo - cols_with_param = df_familia.columns[ - df_familia.loc[parametro_objetivo].notna() - ] - if cols_with_param.empty: - imprimir(f"❌ Ningún dron en F{capa_idx} tiene '{parametro_objetivo}'.") + # —> Validar que haya vecinos con el parámetro objetivo + cols_validas = df_familia.columns[df_familia.loc[parametro_objetivo].notna()] + if len(cols_validas) == 0: + imprimir(f"❌ Ningún dron en {familia} tiene '{parametro_objetivo}'.", True) continue - # Además deben poseer los tres ejes - filtros_ejes = [ - df_familia.loc[vector_objetivo[bloque]].notna() - for bloque in ("fisico", "geom", "prest") - ] - mask_all_ejes = filtros_ejes[0] & filtros_ejes[1] & filtros_ejes[2] - cols_validas = cols_with_param[mask_all_ejes[cols_with_param]] - k = len(cols_validas) - imprimir(f"Vecinos válidos (k)......................: {k}") + # Parámetros MTOW y filtro ±20% + mtow_obj = df_familia.at["Peso máximo al despegue (MTOW)", aeronave_obj] + mtow_vec = df_familia.loc["Peso máximo al despegue (MTOW)", cols_validas].values + delta_mtow = np.abs(mtow_vec - mtow_obj) / mtow_obj * 100 + mask_mtow = delta_mtow <= 20 + cols_filtrados = cols_validas[mask_mtow] + if len(cols_filtrados) == 0: + imprimir(f"❌ Sin vecinos ±20% MTOW en {familia}.", True) + continue - if k == 0: - imprimir("❌ No quedan drones con los 3 ejes. Relajando…") + # Cálculo de MTOW_score + d = delta_mtow[mask_mtow] + g = -0.002*d**4 + 0.041*d**3 - 0.28135*d**2 + 0.23*d + 99.94 + mtow_scores = g / 100.0 + + # Nueva lógica para calcular los bonos geométricos y prestacionales + + def calcular_bono(tipo): + parametros = bloques_rasgos[tipo] # Obtener los parámetros geométricos o prestacionales + bono_total = 0 # Inicializar el bono total + + for parametro in parametros: + try: + # Valores de la aeronave objetivo y los vecinos + valor_objetivo = df_parametros.at[parametro, aeronave_obj] + valores_vecinos = df_familia.loc[parametro, cols_filtrados].values + + # Si el valor de la aeronave objetivo es NaN, el bono es 0 + if pd.isna(valor_objetivo): + imprimir(f"⚠️ Parámetro '{parametro}' no tiene valor en la aeronave objetivo. Bono = 0.") + continue + + # Calcular las diferencias relativas para los vecinos válidos + diferencias = np.abs(valores_vecinos - valor_objetivo) / valor_objetivo * 100 + + for d, vecino in zip(diferencias, valores_vecinos): + if pd.isna(d): + imprimir(f"⚠️ Diferencia NaN para el parámetro '{parametro}'. Vecino: {vecino}. Bono = 0.") + continue + + # Ajustar la diferencia relativa según el rango + if d > 40: + g = -100 # Máximo bono negativo + elif d > 20: + # Recalcular la diferencia relativa en el rango 20% a 40% + d_ajustada = d - 20 + g = -0.002 * d_ajustada**4 + 0.041 * d_ajustada**3 - 0.28135 * d_ajustada**2 + 0.23 * d_ajustada + 99.94 + g = -g # Cambiar el signo a negativo + else: + # Rango de 1% a 20% + g = -0.002 * d**4 + 0.041 * d**3 - 0.28135 * d**2 + 0.23 * d + 99.94 + + # Calcular el bono para este parámetro y vecino + bono_parametro = (g / 100) * 0.05 + bono_total += bono_parametro + + # Imprimir detalles para depuración + imprimir(f" Parámetro: {parametro}, Vecino: {vecino}, Objetivo: {valor_objetivo}, d: {d:.2f}%, g: {g:.2f}, Bono: {bono_parametro:.5f}") + + except KeyError: + imprimir(f"⚠️ Parámetro '{parametro}' no encontrado en los datos. Ignorando.") + continue + + imprimir(f" Bono total para '{tipo}': {bono_total:.5f}") + return bono_total + + # Calcular los bonos geométricos y prestacionales + bonus_geom = calcular_bono("geom") + bonus_prest = calcular_bono("prest") + imprimir(f" Bono geométrico: {bonus_geom:.3f}") + imprimir(f" Bono prestacional: {bonus_prest:.3f}") + + # Score de familia + family_scores = {0: 0.95, 1: 0.825, 2: 0.70} + fam_score = family_scores[capa_idx] + sim_i = fam_score * mtow_scores + bonus_geom + bonus_prest + + # Mostrar similitudes + for nbr, s in zip(cols_filtrados, sim_i): + imprimir(f" vecino '{nbr}' → sim_i: {s:.3f}") + + # Filtrar por umbral + umbral = 0.0 + mask_sim = sim_i >= umbral + vecinos_val = cols_filtrados[mask_sim] + sim_vals = sim_i[mask_sim] + if len(vecinos_val) == 0 or sim_vals.sum() < 1e-6: + imprimir(f"❌ Sin vecinos ≥{umbral} en {familia}.", True) continue - imprimir(f"Vecinos válidos nombres: {list(cols_validas)}") - - # ---------------- Paso 3. Distancias y media ponderada ------- - # Matriz con los tres ejes - data_ejes = pd.DataFrame({ - col: [ - df_familia.at[vector_objetivo["fisico"], col], - df_familia.at[vector_objetivo["geom"], col], - df_familia.at[vector_objetivo["prest"], col], - ] - for col in list(cols_validas) + [aeronave_obj] - }, index=["fis", "geo", "pre"]).T - - # Z‑score columna a columna - imprimir("Matriz de datos antes de z-score (data_ejes):") - imprimir(data_ejes) - data_z = data_ejes.apply(zscore) - imprimir("Matriz de datos después de z-score (data_z):") - imprimir(data_z) - - # Distancias - diffs = data_z.loc[cols_validas].values - data_z.loc[aeronave_obj].values - dist = np.linalg.norm(diffs, axis=1) - weights = 1 / (1 + dist) - - valores_vecinos = df_familia.loc[parametro_objetivo, cols_validas].values - valor_imputado = np.sum(weights * valores_vecinos) / np.sum(weights) - - # Confianza simple (CV + distancia media) - cv = valores_vecinos.std(ddof=0) / valores_vecinos.mean() - conf = max(0, 1 - cv - 0.1 * dist.mean()) - - imprimir("Detalle del cálculo del valor imputado (se calcula el valor ponderado basado en la distancia entre aeronaves):") - imprimir(f" Pesos (weights): {weights}") - imprimir(f" Valores vecinos: {valores_vecinos}") - - productos = weights * valores_vecinos - suma_ponderada = np.sum(productos) - suma_pesos = np.sum(weights) - - imprimir(f" Producto elemento a elemento (weights * valores_vecinos): {productos}") - imprimir(f" Suma ponderada (Σ weights * valores_vecinos): {suma_ponderada:.3f}") - imprimir(f" Suma de pesos (Σ weights): {suma_pesos:.3f}") - imprimir(f" División final: {suma_ponderada:.3f} / {suma_pesos:.3f} = {valor_imputado:.3f}") - - imprimir(f"✅ Valor imputado: {valor_imputado:.3f}") - imprimir(f" Confianza.....: {conf:.2f}") - imprimir(f" Vecinos usados: {list(cols_validas)}") + # Imputación y confianza + y = df_familia.loc[parametro_objetivo, vecinos_val].values + valor_imp = np.dot(sim_vals, y) / sim_vals.sum() + + # Cálculo de métricas estadísticas + if len(y) > 1: + media_y = np.mean(y) + dispersion = np.std(y, ddof=0) + cv = dispersion / media_y if media_y != 0 else 0 # Coeficiente de variación + else: + media_y = y[0] if len(y) == 1 else 0 # Si hay un solo valor, usarlo; si no hay valores, asignar 0 + cv = 1 # Penalización máxima para k=1 + dispersion = 0 + + # Penalización por cantidad de datos usados + penalizacion_k = penalizacion_por_k(len(vecinos_val)) + # Penalización por la calidad de los datos + confianza_cv = max(0,1-(cv/0.5)) #cuando la desviacion estandar es igual al 50% de la media entonces confianza 0 # Dispersión de los valores + + # Confianza final combinada + beta = 0.7 # Peso para cantidad de datos usados + pesos = sim_vals / sim_vals.sum() # Normalizar los pesos + promedio_sim_i = np.dot(pesos, sim_vals) + confianza_datos = beta * penalizacion_k + (1 - beta) * (confianza_cv) # Confianza basada en K y CV + confianza_final = promedio_sim_i*confianza_datos # Confianza final = promedio de confianza de vecinos * confianza de datos + + # Mostrar detalles del cálculo de confianza final + imprimir("\nDetalles del cálculo de confianza:") + + imprimir(f" Confianza que tan similares son los vecinos (familia x Mtow + Bonos): {[f'{s:.3f}' for s in sim_i]}") + imprimir(f" Promedio ponderado de confianza de similitud de aeronaves: {promedio_sim_i:.3f}") + imprimir(f" Media de valores (y): {media_y:.3f}") + imprimir(f" Coeficiente de variación (CV): {confianza_cv:.3f}") + imprimir(f" Dispersión: {dispersion:.3f}") + imprimir(f" Penalización por cantidad de vecinos (k): {penalizacion_k:.3f}") + imprimir(f" Confianza en base a la calidad y cantidad de datos: {confianza_datos:.3f}") + imprimir(f" Confianza final: {confianza_final*100:.3f}%") + + + # Retornar resultados + imprimir(f"✅ Valor imputado: {valor_imp:.3f} (conf {confianza_final:.3f}, datos {len(vecinos_val)}, familia {familia})") return { - "valor": valor_imputado, - "confianza": conf, - "vecinos": list(cols_validas) + "valor": valor_imp, + "confianza": confianza_final, + "num_vecinos": len(vecinos_val), + "familia": familia } - # Si ninguna capa funcionó - imprimir("⚠️ Sin vecinos en ninguna capa. Se delega a correlación.", True) + imprimir("⚠️ No se pudo imputar en ninguna capa. 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{ "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "b70a4a82", "metadata": {}, "outputs": [ @@ -12,52 +12,53 @@ "text": [ "DEBUG: ruta_archivo antes de validar: 'C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Data\\Datos_aeronaves.xlsx'\n", "=== Cargando datos desde el archivo: C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Data\\Datos_aeronaves.xlsx ===\n", + "Advertencia: Índices nulos encontrados. Reemplazando por 'indice_desconocido'.\n", "\n", "=== Resumen inicial del DataFrame cargado ===\n", "\n", - "Index: 52 entries, Distancia de carrera requerida para despegue to kjbk\n", + "Index: 57 entries, Distancia de carrera requerida para despegue to indice_desconocido\n", "Data columns (total 37 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 Stalker XE 52 non-null object\n", - " 1 Stalker VXE30 52 non-null object\n", - " 2 Aerosonde® Mk. 4.7 Fixed Wing 52 non-null object\n", - " 3 Aerosonde® Mk. 4.7 VTOL 52 non-null object\n", - " 4 Aerosonde® Mk. 4.8 Fixed wing 52 non-null object\n", - " 5 Aerosonde® Mk. 4.8 VTOL FTUAS 52 non-null object\n", - " 6 AAI Aerosonde 52 non-null object\n", - " 7 Fulmar X 52 non-null object\n", - " 8 Orbiter 4 52 non-null object\n", - " 9 Orbiter 3 52 non-null object\n", - " 10 Mantis 52 non-null object\n", - " 11 ScanEagle 52 non-null object\n", - " 12 Integrator 52 non-null object\n", - " 13 Integrator VTOL 52 non-null object\n", - " 14 Integrator Extended Range (ER) 52 non-null object\n", - " 15 ScanEagle 3 52 non-null object\n", - " 16 RQNan21A Blackjack 52 non-null object\n", - " 17 DeltaQuad Evo 52 non-null object\n", - " 18 DeltaQuad Pro #MAP 52 non-null object\n", - " 19 DeltaQuad Pro #CARGO 52 non-null object\n", - " 20 V21 52 non-null object\n", - " 21 V25 52 non-null object\n", - " 22 V32 52 non-null object\n", - " 23 V35 52 non-null object\n", - " 24 V39 52 non-null object\n", - " 25 Volitation VT370 52 non-null object\n", - " 26 Skyeye 2600 52 non-null object\n", - " 27 Skyeye 2930 VTOL 52 non-null object\n", - " 28 Skyeye 3600 52 non-null object\n", - " 29 Skyeye 3600 VTOL 52 non-null object\n", - " 30 Skyeye 5000 52 non-null object\n", - " 31 Skyeye 5000 VTOL 52 non-null object\n", - " 32 Skyeye 5000 VTOL octo 52 non-null object\n", - " 33 Volitation VT510 52 non-null object\n", - " 34 Ascend 52 non-null object\n", - " 35 Transition 52 non-null object\n", - " 36 Reach 52 non-null object\n", + " 0 Stalker XE 57 non-null object\n", + " 1 Stalker VXE30 57 non-null object\n", + " 2 Aerosonde Mk. 4.7 Fixed Wing 57 non-null object\n", + " 3 Aerosonde Mk. 4.7 VTOL 57 non-null object\n", + " 4 Aerosonde Mk. 4.8 Fixed wing 57 non-null object\n", + " 5 Aerosonde Mk. 4.8 VTOL FTUAS 57 non-null object\n", + " 6 AAI Aerosonde 57 non-null object\n", + " 7 Fulmar X 57 non-null object\n", + " 8 Orbiter 4 57 non-null object\n", + " 9 Orbiter 3 57 non-null object\n", + " 10 Mantis 57 non-null object\n", + " 11 ScanEagle 57 non-null object\n", + " 12 Integrator 57 non-null object\n", + " 13 Integrator VTOL 57 non-null object\n", + " 14 Integrator Extended Range (ER) 57 non-null object\n", + " 15 ScanEagle 3 57 non-null object\n", + " 16 RQ Nan 21A Blackjack 57 non-null object\n", + " 17 DeltaQuad Evo 57 non-null object\n", + " 18 DeltaQuad Pro #MAP 57 non-null object\n", + " 19 DeltaQuad Pro #CARGO 57 non-null object\n", + " 20 V21 57 non-null object\n", + " 21 V25 57 non-null object\n", + " 22 V32 57 non-null object\n", + " 23 V35 57 non-null object\n", + " 24 V39 57 non-null object\n", + " 25 Volitation VT370 57 non-null object\n", + " 26 Skyeye 2600 57 non-null object\n", + " 27 Skyeye 2930 VTOL 57 non-null object\n", + " 28 Skyeye 3600 57 non-null object\n", + " 29 Skyeye 3600 VTOL 57 non-null object\n", + " 30 Skyeye 5000 57 non-null object\n", + " 31 Skyeye 5000 VTOL 57 non-null object\n", + " 32 Skyeye 5000 VTOL octo 57 non-null object\n", + " 33 Volitation VT510 57 non-null object\n", + " 34 Ascend 57 non-null object\n", + " 35 Transition 57 non-null object\n", + " 36 Reach 57 non-null object\n", "dtypes: object(37)\n", - "memory usage: 15.4+ KB\n", + "memory usage: 16.9+ KB\n", "None\n", "Datos cargados correctamente desde: C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Data\\Datos_aeronaves.xlsx\n", "\n", @@ -66,7 +67,7 @@ "=== Continuando con el procesamiento de datos ===\n", "\n", "Encabezados iniciales cargados:\n", - "['Stalker XE', 'Stalker VXE30', 'Aerosonde®\\xa0Mk. 4.7 Fixed Wing', 'Aerosonde®\\xa0Mk. 4.7 VTOL', 'Aerosonde®\\xa0Mk. 4.8 Fixed wing', 'Aerosonde®\\xa0Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "\n", "=== Mostrando datos iniciales en formato HTML ===\n" ] @@ -112,10 +113,10 @@ " \n", " Stalker XE\n", " Stalker VXE30\n", - " Aerosonde® Mk. 4.7 Fixed Wing\n", - " Aerosonde® Mk. 4.7 VTOL\n", - " Aerosonde® Mk. 4.8 Fixed wing\n", - " Aerosonde® Mk. 4.8 VTOL FTUAS\n", + " Aerosonde Mk. 4.7 Fixed Wing\n", + " Aerosonde Mk. 4.7 VTOL\n", + " Aerosonde Mk. 4.8 Fixed wing\n", + " Aerosonde Mk. 4.8 VTOL FTUAS\n", " AAI Aerosonde\n", " Fulmar X\n", " Orbiter 4\n", @@ -126,7 +127,7 @@ " Integrator VTOL\n", " Integrator Extended Range (ER)\n", " ScanEagle 3\n", - " RQNan21A Blackjack\n", + " RQ Nan 21A Blackjack\n", " DeltaQuad Evo\n", " DeltaQuad Pro #MAP\n", " DeltaQuad Pro #CARGO\n", @@ -148,6 +149,46 @@ " Transition\n", " Reach\n", " \n", + " \n", + " Modelo\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -191,46 +232,6 @@ " 0\n", " \n", " \n", - " Tasa de ascenso\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " 2.49936\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " 5\n", - " Nan\n", - " 5\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " Nan\n", - " 5\n", - " Nan\n", - " Nan\n", - " Nan\n", - " \n", - " \n", " Altitud a la que se realiza el crucero\n", " 6000\n", " 6000\n", @@ -640,7 +641,7 @@ " Nan\n", " 3270\n", " 800\n", - " 150\n", + " Nan\n", " 50\n", " 25\n", " Nan\n", @@ -648,7 +649,7 @@ " Nan\n", " 500\n", " Nan\n", - " 92.6\n", + " Nan\n", " 270\n", " 100\n", " 100\n", @@ -791,6 +792,46 @@ " 13\n", " \n", " \n", + " Tasa de ascenso\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " \n", + " \n", " Radio de giro\n", " Nan\n", " Nan\n", @@ -1271,7 +1312,7 @@ " Nan\n", " \n", " \n", - " Potencia/Peso\n", + " Potencia específica (P/W)\n", " Nan\n", " Nan\n", " Nan\n", @@ -1391,7 +1432,7 @@ " Nan\n", " \n", " \n", - " Potencia(W)\n", + " Potencia Watts\n", " Nan\n", " Nan\n", " 2980\n", @@ -1431,7 +1472,7 @@ " Nan\n", " \n", " \n", - " Potencia(HP)\n", + " Potencia HP\n", " Nan\n", " Nan\n", " 4\n", @@ -1591,6 +1632,246 @@ " Nan\n", " \n", " \n", + " Despegue\n", + " 1\n", + " 1\n", + " 1\n", + " 2\n", + " 1\n", + " 2\n", + " 2\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 2\n", + " 1\n", + " 1\n", + " 1\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 3\n", + " 2\n", + " 3\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " \n", + " \n", + " Propulsión horizontal\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 1\n", + " 1\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " 2\n", + " \n", + " \n", + " Propulsión vertical\n", + " 5\n", + " 5\n", + " 5\n", + " 1\n", + " 5\n", + " 1\n", + " 1\n", + " 5\n", + " 5\n", + " 5\n", + " 5\n", + " 5\n", + " 5\n", + " 1\n", + " 5\n", + " 5\n", + " 5\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 5\n", + " 1\n", + " 5\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " \n", + " \n", + " Cantidad de motores propulsión vertical\n", + " 0\n", + " 0\n", + " 0\n", + " 4\n", + " 0\n", + " 4\n", + " 4\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 4\n", + " 0\n", + " 0\n", + " 0\n", + " 4\n", + " 4\n", + " 4\n", + " 4\n", + " 4\n", + " 4\n", + " 4\n", + " 4\n", + " 4\n", + " 4\n", + " 4\n", + " 0\n", + " 4\n", + " 0\n", + " 4\n", + " 8\n", + " 4\n", + " 4\n", + " 4\n", + " 4\n", + " \n", + " \n", + " Cantidad de motores propulsión horizontal\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " \n", + " \n", + " Misión\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " \n", + " \n", " Dimensiones de la bahía de carga útil\n", " Nan\n", " Nan\n", @@ -1831,7 +2112,7 @@ " Nan\n", " \n", " \n", - " Despegue\n", + " Despegue todos los tipos\n", " Bungee, rail, VTOL\n", " Bungee, rail, VTOL\n", " Rail\n", @@ -2151,47 +2432,7 @@ " Nan\n", " \n", " \n", - " Empresa\n", - " Lockheed Martin\n", - " Lockheed Martin\n", - " Textron Systems\n", - " Textron Systems\n", - " Textron Systems\n", - " Textron Systems\n", - " Textron Systems\n", - " Thales Group\n", - " Aeronautics Group\n", - " Aeronautics Group\n", - " Indra Sistemas\n", - " Insitu\n", - " Insitu\n", - " Insitu\n", - " Insitu\n", - " Insitu\n", - " Insitu\n", - " Vertical Technologies\n", - " Vertical Technologies\n", - " Vertical Technologies\n", - " Airmobi\n", - " Airmobi\n", - " Airmobi\n", - " Airmobi\n", - " Airmobi\n", - " Airmobi\n", - " Airmobi\n", - " Airmobi\n", - " Airmobi\n", - " Airmobi\n", - " Airmobi\n", - " Airmobi\n", - " Airmobi\n", - " Airmobi\n", - " Alti\n", - " Alti\n", - " Alti\n", - " \n", - " \n", - " kjbk\n", + " indice_desconocido\n", " pdf\n", " pdf\n", " Link\n", @@ -2300,10 +2541,10 @@ " \n", " Stalker XE\n", " Stalker VXE30\n", - " Aerosonde® Mk. 4.7 Fixed Wing\n", - " Aerosonde® Mk. 4.7 VTOL\n", - " Aerosonde® Mk. 4.8 Fixed wing\n", - " Aerosonde® Mk. 4.8 VTOL FTUAS\n", + " Aerosonde Mk. 4.7 Fixed Wing\n", + " Aerosonde Mk. 4.7 VTOL\n", + " Aerosonde Mk. 4.8 Fixed wing\n", + " Aerosonde Mk. 4.8 VTOL FTUAS\n", " AAI Aerosonde\n", " Fulmar X\n", " Orbiter 4\n", @@ -2314,7 +2555,7 @@ " Integrator VTOL\n", " Integrator Extended Range (ER)\n", " ScanEagle 3\n", - " RQNan21A Blackjack\n", + " RQ Nan 21A Blackjack\n", " DeltaQuad Evo\n", " DeltaQuad Pro #MAP\n", " DeltaQuad Pro #CARGO\n", @@ -2336,6 +2577,46 @@ " Transition\n", " Reach\n", " \n", + " \n", + " Modelo\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2379,46 +2660,6 @@ " 0.0\n", " \n", " \n", - " Tasa de ascenso\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 2.49936\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 5.0\n", - " NaN\n", - " 5.0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 5.0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", " Altitud a la que se realiza el crucero\n", " 6000.0\n", " 6000.0\n", @@ -2828,7 +3069,7 @@ " NaN\n", " 3270.0\n", " 800.0\n", - " 150.0\n", + " NaN\n", " 50.0\n", " 25.0\n", " NaN\n", @@ -2836,7 +3077,7 @@ " NaN\n", " 500.0\n", " NaN\n", - " 92.6\n", + " NaN\n", " 270.0\n", " 100.0\n", " 100.0\n", @@ -2979,6 +3220,46 @@ " 13.0\n", " \n", " \n", + " Tasa de ascenso\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", " Radio de giro\n", " NaN\n", " NaN\n", @@ -3459,7 +3740,7 @@ " NaN\n", " \n", " \n", - " Potencia/Peso\n", + " Potencia específica (P/W)\n", " NaN\n", " NaN\n", " NaN\n", @@ -3579,7 +3860,7 @@ " NaN\n", " \n", " \n", - " Potencia(W)\n", + " Potencia Watts\n", " NaN\n", " NaN\n", " 2980.0\n", @@ -3619,7 +3900,7 @@ " NaN\n", " \n", " \n", - " Potencia(HP)\n", + " Potencia HP\n", " NaN\n", " NaN\n", " 4.0\n", @@ -3779,6 +4060,246 @@ " NaN\n", " \n", " \n", + " Despegue\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 2.0\n", + " 1.0\n", + " 2.0\n", + " 2.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 2.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 3.0\n", + " 2.0\n", + " 3.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " \n", + " \n", + " Propulsión horizontal\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 1.0\n", + " 1.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " \n", + " \n", + " Propulsión vertical\n", + " 5.0\n", + " 5.0\n", + " 5.0\n", + " 1.0\n", + " 5.0\n", + " 1.0\n", + " 1.0\n", + " 5.0\n", + " 5.0\n", + " 5.0\n", + " 5.0\n", + " 5.0\n", + " 5.0\n", + " 1.0\n", + " 5.0\n", + " 5.0\n", + " 5.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 5.0\n", + " 1.0\n", + " 5.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " \n", + " \n", + " Cantidad de motores propulsión vertical\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 4.0\n", + " 0.0\n", + " 4.0\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 0.0\n", + " 4.0\n", + " 0.0\n", + " 4.0\n", + " 8.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " \n", + " \n", + " Cantidad de motores propulsión horizontal\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " \n", + " \n", + " Misión\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " \n", + " \n", " Dimensiones de la bahía de carga útil\n", " NaN\n", " NaN\n", @@ -4019,7 +4540,7 @@ " NaN\n", " \n", " \n", - " Despegue\n", + " Despegue todos los tipos\n", " NaN\n", " NaN\n", " NaN\n", @@ -4339,47 +4860,7 @@ " NaN\n", " \n", " \n", - " Empresa\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " kjbk\n", + " indice_desconocido\n", " NaN\n", " NaN\n", " NaN\n", @@ -4433,26 +4914,26 @@ "output_type": "stream", "text": [ "Parámetros disponibles en df_procesado antes de seleccionar:\n", - "['Distancia de carrera requerida para despegue', 'Tasa de ascenso', 'Altitud a la que se realiza el crucero', 'Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Velocidad de pérdida limpia (KCAS)', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Profundidad del fuselaje', 'Ancho del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Radio de giro', 'envergadura', 'Cuerda', 'payload', 'duracion en VTOL', 'Crucero KIAS', 'RTF (dry weight)', 'RTF (Including fuel & Batteries)', 'Empty weight', 'Maximum Crosswind', 'Rango de comunicación', 'Wing Loading', 'Potencia/Peso', 'Capacidad combustible', 'Consumo', 'Potencia(W)', 'Potencia(HP)', 'Precio', 'Tiempo de emergencia en vuelo', 'Distancia de aterrizaje', 'Dimensiones de la bahía de carga útil', 'Battery Power Supply', 'Modelo Motor Fixed Wing', 'Modelo Motor VTOL', 'Portabilidad', 'Cámara', 'Despegue', 'Datalink banks', 'Material del fuselaje', 'Motor recomendado', 'Hélice recomendada VTOL', 'Hélice recomendada Fixed Wing', 'Sistema de control', 'Características adicionales', 'Empresa', 'kjbk']\n", + "['Distancia de carrera requerida para despegue', 'Altitud a la que se realiza el crucero', 'Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Velocidad de pérdida limpia (KCAS)', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Profundidad del fuselaje', 'Ancho del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Tasa de ascenso', 'Radio de giro', 'envergadura', 'Cuerda', 'payload', 'duracion en VTOL', 'Crucero KIAS', 'RTF (dry weight)', 'RTF (Including fuel & Batteries)', 'Empty weight', 'Maximum Crosswind', 'Rango de comunicación', 'Wing Loading', 'Potencia específica (P/W)', 'Capacidad combustible', 'Consumo', 'Potencia Watts', 'Potencia HP', 'Precio', 'Tiempo de emergencia en vuelo', 'Distancia de aterrizaje', 'Despegue', 'Propulsión horizontal', 'Propulsión vertical', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal', 'Misión', 'Dimensiones de la bahía de carga útil', 'Battery Power Supply', 'Modelo Motor Fixed Wing', 'Modelo Motor VTOL', 'Portabilidad', 'Cámara', 'Despegue todos los tipos', 'Datalink banks', 'Material del fuselaje', 'Motor recomendado', 'Hélice recomendada VTOL', 'Hélice recomendada Fixed Wing', 'Sistema de control', 'Características adicionales', 'indice_desconocido']\n", "\n", "=== Selección de Parámetros ===\n", "Parámetros disponibles:\n", "1. Distancia de carrera requerida para despegue\n", - "2. Tasa de ascenso\n", - "3. Altitud a la que se realiza el crucero\n", - "4. Velocidad a la que se realiza el crucero (KTAS)\n", - "5. Techo de servicio máximo\n", - "6. Velocidad de pérdida limpia (KCAS)\n", - "7. Área del ala\n", - "8. Relación de aspecto del ala\n", - "9. Longitud del fuselaje\n", - "10. Profundidad del fuselaje\n", - "11. Ancho del fuselaje\n", - "12. Peso máximo al despegue (MTOW)\n", - "13. Alcance de la aeronave\n", - "14. Autonomía de la aeronave\n", - "15. Velocidad máxima (KIAS)\n", - "16. Velocidad de pérdida (KCAS)\n", + "2. Altitud a la que se realiza el crucero\n", + "3. Velocidad a la que se realiza el crucero (KTAS)\n", + "4. Techo de servicio máximo\n", + "5. Velocidad de pérdida limpia (KCAS)\n", + "6. Área del ala\n", + "7. Relación de aspecto del ala\n", + "8. Longitud del fuselaje\n", + "9. Profundidad del fuselaje\n", + "10. Ancho del fuselaje\n", + "11. Peso máximo al despegue (MTOW)\n", + "12. Alcance de la aeronave\n", + "13. Autonomía de la aeronave\n", + "14. Velocidad máxima (KIAS)\n", + "15. Velocidad de pérdida (KCAS)\n", + "16. Tasa de ascenso\n", "17. Radio de giro\n", "18. envergadura\n", "19. Cuerda\n", @@ -4465,36 +4946,41 @@ "26. Maximum Crosswind\n", "27. Rango de comunicación\n", "28. Wing Loading\n", - "29. Potencia/Peso\n", + "29. Potencia específica (P/W)\n", "30. Capacidad combustible\n", "31. Consumo\n", - "32. Potencia(W)\n", - "33. Potencia(HP)\n", + "32. Potencia Watts\n", + "33. Potencia HP\n", "34. Precio\n", "35. Tiempo de emergencia en vuelo\n", "36. Distancia de aterrizaje\n", - "37. Dimensiones de la bahía de carga útil\n", - "38. Battery Power Supply\n", - "39. Modelo Motor Fixed Wing\n", - "40. Modelo Motor VTOL\n", - "41. Portabilidad\n", - "42. Cámara\n", - "43. Despegue\n", - "44. Datalink banks\n", - "45. Material del fuselaje\n", - "46. Motor recomendado\n", - "47. Hélice recomendada VTOL\n", - "48. Hélice recomendada Fixed Wing\n", - "49. Sistema de control\n", - "50. Características adicionales\n", - "51. Empresa\n", - "52. kjbk\n", - "\n", - "Preseleccionados: 4, 5, 7, 8, 9, 12, 13, 14, 15, 16, 18, 19, 20, 25\n", + "37. Despegue\n", + "38. Propulsión horizontal\n", + "39. Propulsión vertical\n", + "40. Cantidad de motores propulsión vertical\n", + "41. Cantidad de motores propulsión horizontal\n", + "42. Misión\n", + "43. Dimensiones de la bahía de carga útil\n", + "44. Battery Power Supply\n", + "45. Modelo Motor Fixed Wing\n", + "46. Modelo Motor VTOL\n", + "47. Portabilidad\n", + "48. Cámara\n", + "49. Despegue todos los tipos\n", + "50. Datalink banks\n", + "51. Material del fuselaje\n", + "52. Motor recomendado\n", + "53. Hélice recomendada VTOL\n", + "54. Hélice recomendada Fixed Wing\n", + "55. Sistema de control\n", + "56. Características adicionales\n", + "57. indice_desconocido\n", + "\n", + "Preseleccionados: 3, 4, 6, 7, 8, 11, 12, 13, 14, 15, 5, 18, 19, 20, 25\n", "Parámetros seleccionados después de filtrar:\n", - "['Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'envergadura', 'Cuerda', 'payload', 'Empty weight']\n", + "['Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Velocidad de pérdida limpia (KCAS)', 'envergadura', 'Cuerda', 'payload', 'Empty weight']\n", "Parámetros seleccionados después de filtrar:\n", - "['Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'envergadura', 'Cuerda', 'payload', 'Empty weight']\n" + "['Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Velocidad de pérdida limpia (KCAS)', 'envergadura', 'Cuerda', 'payload', 'Empty weight']\n" ] }, { @@ -4538,10 +5024,10 @@ " \n", " Stalker XE\n", " Stalker VXE30\n", - " Aerosonde® Mk. 4.7 Fixed Wing\n", - " Aerosonde® Mk. 4.7 VTOL\n", - " Aerosonde® Mk. 4.8 Fixed wing\n", - " Aerosonde® Mk. 4.8 VTOL FTUAS\n", + " Aerosonde Mk. 4.7 Fixed Wing\n", + " Aerosonde Mk. 4.7 VTOL\n", + " Aerosonde Mk. 4.8 Fixed wing\n", + " Aerosonde Mk. 4.8 VTOL FTUAS\n", " AAI Aerosonde\n", " Fulmar X\n", " Orbiter 4\n", @@ -4552,7 +5038,7 @@ " Integrator VTOL\n", " Integrator Extended Range (ER)\n", " ScanEagle 3\n", - " RQNan21A Blackjack\n", + " RQ Nan 21A Blackjack\n", " DeltaQuad Evo\n", " DeltaQuad Pro #MAP\n", " DeltaQuad Pro #CARGO\n", @@ -4574,6 +5060,46 @@ " Transition\n", " Reach\n", " \n", + " \n", + " Modelo\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4826,7 +5352,7 @@ " NaN\n", " 3270.0\n", " 800.0\n", - " 150.0\n", + " NaN\n", " 50.0\n", " 25.0\n", " NaN\n", @@ -4834,7 +5360,7 @@ " NaN\n", " 500.0\n", " NaN\n", - " 92.6\n", + " NaN\n", " 270.0\n", " 100.0\n", " 100.0\n", @@ -4977,6 +5503,46 @@ " 13.0\n", " \n", " \n", + " Velocidad de pérdida limpia (KCAS)\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 14.0\n", + " 15.5\n", + " 17.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 10.0\n", + " 18.0\n", + " 12.5\n", + " 24.0\n", + " 15.0\n", + " NaN\n", + " NaN\n", + " 25.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", " envergadura\n", " 3.657\n", " 4.8768\n", @@ -5154,10 +5720,10 @@ "=== Columnas con datos faltantes ===\n", "1. Stalker XE\n", "2. Stalker VXE30\n", - "3. Aerosonde® Mk. 4.7 Fixed Wing\n", - "4. Aerosonde® Mk. 4.7 VTOL\n", - "5. Aerosonde® Mk. 4.8 Fixed wing\n", - "6. Aerosonde® Mk. 4.8 VTOL FTUAS\n", + "3. Aerosonde Mk. 4.7 Fixed Wing\n", + "4. Aerosonde Mk. 4.7 VTOL\n", + "5. Aerosonde Mk. 4.8 Fixed wing\n", + "6. Aerosonde Mk. 4.8 VTOL FTUAS\n", "7. AAI Aerosonde\n", "8. Fulmar X\n", "9. Orbiter 4\n", @@ -5168,7 +5734,7 @@ "14. Integrator VTOL\n", "15. Integrator Extended Range (ER)\n", "16. ScanEagle 3\n", - "17. RQNan21A Blackjack\n", + "17. RQ Nan 21A Blackjack\n", "18. DeltaQuad Evo\n", "19. DeltaQuad Pro #MAP\n", "20. DeltaQuad Pro #CARGO\n", @@ -5235,12 +5801,20 @@ " \n", " Stalker XE\n", " \n", + " \n", + " Modelo\n", + " \n", + " \n", " \n", " \n", " \n", " Velocidad de pérdida (KCAS)\n", " NaN\n", " \n", + " \n", + " Velocidad de pérdida limpia (KCAS)\n", + " NaN\n", + " \n", " \n", "" ], @@ -5306,102 +5880,102 @@ " \n", " 0\n", " Stalker XE\n", - " 1.000\n", + " 2.000\n", " \n", " \n", " 1\n", " Stalker VXE30\n", - " 1.000\n", + " 2.000\n", " \n", " \n", " 2\n", - " Aerosonde® Mk. 4.7 Fixed Wing\n", - " 3.000\n", + " Aerosonde Mk. 4.7 Fixed Wing\n", + " 4.000\n", " \n", " \n", " 3\n", - " Aerosonde® Mk. 4.7 VTOL\n", - " 3.000\n", + " Aerosonde Mk. 4.7 VTOL\n", + " 4.000\n", " \n", " \n", " 4\n", - " Aerosonde® Mk. 4.8 Fixed wing\n", - " 3.000\n", + " Aerosonde Mk. 4.8 Fixed wing\n", + " 4.000\n", " \n", " \n", " 5\n", - " Aerosonde® Mk. 4.8 VTOL FTUAS\n", - " 9.000\n", + " Aerosonde Mk. 4.8 VTOL FTUAS\n", + " 10.000\n", " \n", " \n", " 6\n", " AAI Aerosonde\n", - " 3.000\n", + " 4.000\n", " \n", " \n", " 7\n", " Fulmar X\n", - " 6.000\n", + " 7.000\n", " \n", " \n", " 8\n", " Orbiter 4\n", - " 7.000\n", + " 9.000\n", " \n", " \n", " 9\n", " Orbiter 3\n", - " 7.000\n", + " 8.000\n", " \n", " \n", " 10\n", " Mantis\n", - " 7.000\n", + " 8.000\n", " \n", " \n", " 11\n", " ScanEagle\n", - " 6.000\n", + " 7.000\n", " \n", " \n", " 12\n", " Integrator\n", - " 6.000\n", + " 7.000\n", " \n", " \n", " 13\n", " Integrator VTOL\n", - " 11.000\n", + " 12.000\n", " \n", " \n", " 14\n", " Integrator Extended Range (ER)\n", - " 6.000\n", + " 7.000\n", " \n", " \n", " 15\n", " ScanEagle 3\n", - " 6.000\n", + " 7.000\n", " \n", " \n", " 16\n", - " RQNan21A Blackjack\n", - " 5.000\n", + " RQ Nan 21A Blackjack\n", + " 7.000\n", " \n", " \n", " 17\n", " DeltaQuad Evo\n", - " 4.000\n", + " 5.000\n", " \n", " \n", " 18\n", " DeltaQuad Pro #MAP\n", - " 6.000\n", + " 7.000\n", " \n", " \n", " 19\n", " DeltaQuad Pro #CARGO\n", - " 6.000\n", + " 7.000\n", " \n", " \n", " 20\n", @@ -5421,17 +5995,17 @@ " \n", " 23\n", " V35\n", - " 6.000\n", + " 7.000\n", " \n", " \n", " 24\n", " V39\n", - " 7.000\n", + " 8.000\n", " \n", " \n", " 25\n", " Volitation VT370\n", - " 6.000\n", + " 7.000\n", " \n", " \n", " 26\n", @@ -5461,12 +6035,12 @@ " \n", " 31\n", " Skyeye 5000 VTOL\n", - " 5.000\n", + " 6.000\n", " \n", " \n", " 32\n", " Skyeye 5000 VTOL octo\n", - " 6.000\n", + " 7.000\n", " \n", " \n", " 33\n", @@ -5476,17 +6050,17 @@ " \n", " 34\n", " Ascend\n", - " 4.000\n", + " 5.000\n", " \n", " \n", " 35\n", " Transition\n", - " 4.000\n", + " 5.000\n", " \n", " \n", " 36\n", " Reach\n", - " 4.000\n", + " 5.000\n", " \n", " \n", "" @@ -5545,7 +6119,7 @@ " \n", " 0\n", " Total de Valores Faltantes\n", - " 185.000\n", + " 215.000\n", " \n", " \n", "" @@ -5561,8 +6135,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "=== Calculando correlaciones y generando heatmap ===\n", "\n", "=== Calculando correlaciones y generando heatmap ===\n", "\n", @@ -5609,9 +6181,8 @@ "

Tabla de Correlaciones con todos los parametros(tabla_completa)

\n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -5626,6 +6197,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5638,21 +6210,27 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5660,15 +6238,73 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -5684,6 +6320,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5703,60 +6340,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5779,7 +6366,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -5789,11 +6375,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5813,6 +6400,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5834,7 +6426,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -5844,10 +6435,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5868,6 +6460,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5888,7 +6485,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -5899,10 +6495,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5923,6 +6520,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5944,7 +6546,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -5958,6 +6559,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5978,6 +6580,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5998,7 +6605,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6013,6 +6619,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6033,6 +6640,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6053,7 +6665,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6069,6 +6680,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6088,6 +6700,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6108,7 +6725,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6119,10 +6735,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6143,6 +6760,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6214,12 +6836,16 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6234,6 +6860,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6253,6 +6880,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6273,7 +6905,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6284,10 +6915,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6308,6 +6940,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6328,41 +6965,46 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6383,7 +7025,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6394,10 +7035,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6418,6 +7060,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6438,7 +7085,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6449,11 +7095,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6473,6 +7120,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6494,7 +7146,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6508,6 +7159,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6528,6 +7180,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6546,10 +7203,69 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6563,6 +7279,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6584,6 +7301,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6603,7 +7325,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6614,10 +7335,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6638,6 +7360,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6658,7 +7385,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6674,6 +7400,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6693,6 +7420,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6713,7 +7445,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6724,10 +7455,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6748,6 +7480,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6769,7 +7506,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6784,6 +7520,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6803,6 +7540,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6824,7 +7566,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6834,10 +7575,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6858,6 +7600,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6879,7 +7626,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6894,6 +7640,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6914,6 +7661,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6934,7 +7686,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6949,6 +7700,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -6968,6 +7720,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6988,7 +7745,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7003,6 +7759,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -7023,6 +7780,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7043,7 +7805,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7059,6 +7820,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -7079,6 +7841,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7098,7 +7865,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7109,11 +7875,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -7133,6 +7900,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7153,7 +7925,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7168,6 +7939,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -7189,6 +7961,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7206,7 +7983,12 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7264,7 +8046,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7279,6 +8060,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -7298,6 +8080,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7319,7 +8106,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7334,6 +8120,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -7353,6 +8140,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7371,8 +8163,7 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7389,6 +8180,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -7408,6 +8200,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7426,8 +8223,7 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7439,11 +8235,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -7463,6 +8260,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7484,7 +8286,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7498,6 +8299,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -7518,6 +8320,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7589,56 +8396,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7646,7 +8403,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7699,56 +8456,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7756,42 +8463,47 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7811,42 +8523,47 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7866,42 +8583,47 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7921,42 +8643,47 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7976,24 +8703,7 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8029,34 +8739,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8079,14 +8761,14 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8141,9 +8823,7 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8194,10 +8874,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8205,6 +8881,9 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8249,9 +8928,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8265,6 +8941,9 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8304,9 +8983,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8325,6 +9001,9 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8359,10 +9038,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8386,6 +9061,9 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8414,9 +9092,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8446,6 +9121,9 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8469,9 +9147,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8506,6 +9181,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8524,544 +9204,23 @@ " \n", " \n", " \n", - " \n", - " \n", - "
ModeloDistancia de carrera requerida para despegueTasa de ascensoAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdaMaximum CrosswindRango de comunicaciónWing LoadingPotencia/PesoPotencia específica (P/W)Capacidad combustibleConsumoPotencia(W)Potencia(HP)Potencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisiónDimensiones de la bahía de carga útilBattery Power SupplyModelo Motor Fixed WingModelo Motor VTOLPortabilidadCámaraDespegueDespegue todos los tiposDatalink banksMaterial del fuselajeMotor recomendadoHélice recomendada Fixed WingSistema de controlCaracterísticas adicionalesEmpresakjbkindice_desconocido
Modelo
Distancia de carrera requerida para despegue1.000nan0.0630.467nan0.316-0.308nannan0.145nan0.229-0.156nannannannannannannannannannannannannannannannannannan
Tasa de ascensonan1.0001.000nan0.870nannannan0.531nannan0.589nan-0.8660.439nannan0.656nannannannannannannannan-1.000nannannannannannannannan0.7350.1540.671-0.598nannannanAltitud a la que se realiza el crucero0.0631.0001.000nan-0.038nannannan-0.095-0.952-0.955-0.2800.128nannannan0.1360.761-0.183nannannan-0.119-0.1090.187-0.159nannannannanVelocidad a la que se realiza el crucero (KTAS)0.467nannan1.0000.0410.128nan0.9360.6630.4070.6650.3360.8150.257nan0.8030.4720.846-0.296nannan0.1130.6190.126-0.126nannannannan
Techo de servicio máximonan0.870-0.0380.0411.000nan0.3690.1370.4630.4280.079-0.111-0.071nan-0.8030.0570.017-0.257nannan0.1250.007-0.1250.125nannannannanVelocidad de pérdida limpia (KCAS)-0.505nannan0.128-0.5021.0000.4010.4461.000nan0.9930.505nan0.163nannan-0.3450.231-0.3450.345nannannannan
Área del ala0.363nan0.3010.587-0.1520.0810.7370.423nan-1.0000.8410.9840.899nannan0.2360.4530.1720.055nannannannan
Relación de aspecto del alanannan-0.351-0.999-0.314-0.859nannannan-0.349-0.744-0.888nannannan-0.247nan0.247-0.247nannannannan
Longitud del fuselaje0.2600.5310.0810.5350.082nan0.9380.7860.1290.1500.3890.2560.180nan0.9180.6930.995-0.210nannan0.1110.6150.093-0.004nannannannannannannannannannannannan
Ancho del fuselaje0.425nannan0.9360.369nan0.940nannannan0.6710.7600.868nannannan0.794nan-0.5350.574nannannannan
Peso máximo al despegue (MTOW)0.1680.589-0.0950.6630.137nan0.9861.000-0.0510.0250.4340.6780.539nan0.9730.7910.8580.052nannan0.0900.4670.0230.075nannannannan
Alcance de la aeronavenannan-0.9520.4070.463-0.9550.6650.428nan-0.301-0.9980.1290.150nan0.982-0.0510.0251.0000.578-0.0620.8430.042nannan-0.107nan-0.010-0.7550.5540.804-1.0000.4070.665nannan-0.059-1.0000.5080.670nannan1.000nannan-1.000nan1.000nannan0.2620.474-0.2620.262nannannannan
Autonomía de la aeronave-0.068-0.866-0.2800.3360.079nan-0.0900.4340.5780.8431.0000.297-0.164nan0.9540.532-0.2010.011nannan-0.4240.4770.361-0.361nannannannan
Velocidad máxima (KIAS)0.3160.4390.1280.815-0.111nan0.9400.678-0.0620.0420.2971.0000.539nannan0.4000.5120.7150.015nannan-0.0410.2080.174-0.133nannannannanVelocidad de pérdida (KCAS)-0.308nannan0.257-0.0711.000-0.1640.5391.000nan0.9930.401nan0.160nannan-0.2440.154-0.2440.444nannannannannan
Radio de giroTasa de ascensonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Radio de gironannan0.803-0.8030.9930.954nan0.993nan1.0000.992nannannannan0.918nannannannannannannan
envergadura0.1450.6560.1360.4720.057nan0.6710.791-0.107-0.0100.5320.4000.401nan0.9921.0000.8850.032nannan-0.1240.5080.239-0.164nannannannan
Cuerdanannan0.7610.8460.0170.512nannannan0.8851.0000.776nannannan-0.313nan0.313-0.313nannannannan
payload0.229nan-0.1830.6960.087nan0.8680.8750.5540.8040.4610.7150.627nan0.9760.7340.776-0.008nannan-0.0040.4770.162-0.111nannannannanduracion en VTOLnannannan-0.694-0.875nan-0.927nannannan-0.258nan-0.024nannannan-0.188-0.9040.188-0.188nannannannanCrucero KIAS0.389nannan1.0000.0410.128nan0.9440.7080.4070.6650.3360.7750.417nan0.8030.5010.846-0.243nannan0.1430.6080.0650.063nannannannanRTF (dry weight)nannannan0.9150.677nan0.994nannannan0.983nan0.915nannannan0.408nannannannannannannanRTF (Including fuel & Batteries)nannannan0.7230.579nan0.857nannannan0.936nan0.559nannannan0.0970.428-0.0970.097nannannannan
Empty weight0.357nan0.0600.426-0.1380.4280.5170.321nan0.9790.9240.971-0.029nannan0.1820.4040.3070.004nannannannan
Maximum Crosswindnannan0.038-0.855-0.961nannannannan-0.452nan-0.142nannannan-0.943nannannannannannannan
Rango de comunicaciónnan-1.000-0.3250.359-0.120nan0.3230.5140.5080.6700.8020.094nannannan0.6480.3540.546nannannan-0.4300.6040.430-0.430nannannannan
Wing Loadingnannan-0.2150.997-0.9860.268-0.2150.900nan0.8440.780nannannannan0.572nannannannannannannannan
Potencia/PesoPotencia específica (P/W)nannannannannannannannanCapacidad combustible-0.018nannan0.491nan0.0680.7050.230nannan0.297nan0.7110.817nannan-0.080nan-0.0800.270nannannannanConsumo-0.240nannan0.4610.5151.0000.9101.000nannan0.085nan0.8460.998nannan0.113nan-0.3750.375nannannannannan
Potencia(W)nanPotencia Wattsnan0.2771.000-0.624nannannan0.5221.0000.841nannannan0.232nan-0.2320.232nannannannannan
Potencia(HP)nanPotencia HPnan0.6901.000nannan0.855-1.000nan-0.5770.970nannannan0.8351.0000.866nannannan-0.694nan0.694-0.694nannannannanPrecio-0.156nannan-0.296-0.2570.1630.0110.0150.160nan0.9210.032nan1.000nannan-0.1380.217-0.1380.134nannannannannannannan
Distancia de aterrizajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Dimensiones de la bahía de carga útilDistancia de aterrizajenannannannannannan
Battery Power Supplynannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Modelo Motor Fixed WingnannannannannannannannannannannannannannannannannannannannannannannannannannanDespegue0.735-0.1190.1130.125-0.3450.236-0.2470.111nan0.7940.0900.262-0.424-0.041-0.244nannan-0.124-0.313-0.004-0.1880.143nan0.0970.182nan-0.430nannan-0.0800.1130.232-0.694-0.138nannan1.000-0.010-0.6390.610nannannannan
Modelo Motor VTOLnannannannannannannannannannannannannannannannannannannannannannannanPropulsión horizontal0.154-0.1090.6190.0070.2310.453nan0.615nannan0.4670.4740.4770.2080.154nan0.9180.508nan0.477-0.9040.6080.4080.4280.404-0.9430.6040.572nannannannannan0.217nannan-0.0101.0000.118-0.083nannannannan
PortabilidadnannannannannannannannannannannannannannannannannannannannannannannannannannannanPropulsión vertical0.6710.1870.126-0.125-0.3450.1720.2470.093nan-0.5350.023-0.2620.3610.174-0.244nannan0.2390.3130.1620.1880.065nan-0.0970.307nan0.430nannan-0.080-0.375-0.2320.694-0.138nannan-0.6390.1181.000-0.954nannannannan
CámaranannannannannannannannannannannannannannannannannannannannannannannannannannanCantidad de motores propulsión vertical-0.598-0.159-0.1260.1250.3450.055-0.247-0.004nan0.5740.0750.262-0.361-0.1330.444nannan-0.164-0.313-0.111-0.1880.063nan0.0970.004nan-0.430nannan0.2700.3750.232-0.6940.134nannan0.610-0.083-0.9541.000nannannannan
DespeguenannannannannannannannannannannannannannannannannanCantidad de motores propulsión horizontalnannannannannannan
Datalink banksnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Misiónnannannannannan
Material del fuselajenannannannan
Motor recomendadonannanDimensiones de la bahía de carga útilnannannannannannan
Hélice recomendada VTOLnannannannannannannan
Battery Power Supplynannannannannannan
Hélice recomendada Fixed Wingnannannannannannan
Modelo Motor Fixed Wingnannannannannannan
Sistema de controlnannannannannannan
Modelo Motor VTOLnannannannannannan
Características adicionalesnannannannannannannan
Portabilidadnannannannannannan
Empresanannannannannannan
Cámaranannannannannannan
kjbknannannannannannan
Despegue todos los tiposnannannannannannannannan
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Resumen de la Tabla

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ResumenCantidad
0Total de valores2704.000
1Valores numéricos700.000
2Valores NaN2004.000
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Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)1.0000.0410.587-0.9990.5350.6630.4070.3360.8150.2570.4720.8460.6960.426
Techo de servicio máximo0.0411.000-0.152-0.3140.0820.1370.4630.079-0.111-0.0710.0570.0170.087-0.138
Área del ala0.587-0.1521.000-0.8310.8670.977-0.3010.0810.7370.4230.8410.9840.8990.941
Relación de aspecto del ala-0.999-0.314-0.8311.000-0.790-0.823-0.998-0.305-0.859nan-0.349-0.744-0.8880.622
Longitud del fuselaje0.5350.0820.867-0.7901.0000.7860.1290.3890.2560.1800.6930.9950.5990.880
Peso máximo al despegue (MTOW)0.6630.1370.977-0.8230.7861.000-0.0510.4340.6780.5390.7910.8580.8750.947
Alcance de la aeronave0.4070.463-0.301-0.9980.129-0.0511.0000.578-0.062nan-0.107-0.7550.554-0.059
Autonomía de la aeronave0.3360.0790.081-0.3050.3890.4340.5781.0000.297-0.1640.532-0.2010.4610.428
Velocidad máxima (KIAS)0.815-0.1110.737-0.8590.2560.678-0.0620.2971.0000.5390.4000.5120.7150.517
Velocidad de pérdida (KCAS)0.257-0.0710.423nan0.1800.539nan-0.1640.5391.0000.401nan0.6270.321
envergadura0.4720.0570.841-0.3490.6930.791-0.1070.5320.4000.4011.0000.8850.7340.924
Cuerda0.8460.0170.984-0.7440.9950.858-0.755-0.2010.512nan0.8851.0000.7760.971
payload0.6960.0870.899-0.8880.5990.8750.5540.4610.7150.6270.7340.7761.0000.778
Empty weight0.426-0.1380.9410.6220.8800.947-0.0590.4280.5170.3210.9240.9710.7781.000
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

Resumen de la Tabla

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ResumenCantidad
0Total de valores196.000
1Valores numéricos190.000
2Valores NaN6.000
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Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9076,81 +9235,63 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -9158,12 +9299,54 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9180,11 +9363,55 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9193,11 +9420,53 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9214,71 +9483,243 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9338,17 +9779,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "
Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)nannannan-0.999nannannannan0.815nannan0.846nannan
Techo de servicio máximonannannannannannan
Área del alanannannan-0.8310.8670.977nannan0.737nan0.8410.9840.8990.941
Relación de aspecto del ala-0.999Datalink banksnannannannannannannannannannannannannannannannannannannannannannannannan-0.831nan-0.790-0.823-0.998nan-0.859nannan-0.744-0.888nan
Longitud del fuselajenannan0.867-0.790nan0.786nannannannannan0.995nan0.880
Peso máximo al despegue (MTOW)nannan0.977-0.8230.786nannannannannan0.7910.8580.8750.947
Alcance de la aeronavenannannan-0.998nannannannannannan-0.755nannan
Autonomía de la aeronaveMaterial del fuselajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Velocidad máxima (KIAS)0.815Motor recomendadonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan0.737-0.859nannannannannannan0.715nan
Velocidad de pérdida (KCAS)Hélice recomendada VTOLnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
envergaduraHélice recomendada Fixed Wingnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan0.841nannan0.791nannannannannan0.8850.7340.924
Cuerda0.846Sistema de controlnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan0.984-0.7440.9950.858-0.755nannannan0.885nan0.7760.971
payloadCaracterísticas adicionalesnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan0.899-0.888nan0.875nannan0.715nan0.7340.776nan0.778
Empty weightindice_desconocidonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan0.941nan0.8800.947nannannannan0.9240.9710.778nan
0Total de valores196.0003249.000
1Valores numéricos64.000907.000
2Valores NaN132.0002342.000
" @@ -9365,21 +9806,11 @@ "output_type": "stream", "text": [ "\n", - "=== Preparando datos para el heatmap ===\n", + "=== Filtrando datos seleccionados ===\n", "\n", - "=== Generando heatmap ===\n" + "=== Cálculo de correlaciones filtradas ===\n" ] }, - { - "data": { - "image/png": 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Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

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Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReachModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Distancia de carrera requerida para despegue0.00.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
Tasa de ascensoNaNNaNNaNNaNNaNNaN2.49936NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5.0NaN5.0NaNNaNNaNNaNNaNNaNNaN5.0NaNNaNNaN
Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.0Modelo
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NaNNaN30.406584NaNNaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.344051.0000.0410.587-0.9990.5350.6630.6650.3360.8150.2570.1280.4720.8460.6960.426
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaNNaN17000.010000.013000.016000.0
Velocidad de pérdida limpia (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaNNaN25.0NaNNaNNaN0.0411.000-0.152-0.3140.0820.1370.4280.079-0.111-0.071-0.5020.0570.0170.087-0.138
Área del ala0.871.1582831.551.551.55NaN0.57NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN0.587-0.1521.000-0.8310.8670.977-0.3010.0810.7370.4230.0970.8410.9840.8990.941
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-0.999-0.314-0.8311.000-0.790-0.823-0.998-0.305-0.859nannan-0.349-0.744-0.8880.622
Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.7120.5350.0820.867-0.7901.0000.7860.1500.3890.2560.1800.2600.6930.9950.5990.880
Profundidad del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Ancho del fuselaje0.2110.20.2770.2770.277NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.3750.3750.375NaNNaNNaNNaN
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0Peso máximo al despegue (MTOW)0.6630.1370.977-0.8230.7861.0000.0250.4340.6780.5390.5460.7910.8580.8750.947
Alcance de la aeronave370.0433.0NaNNaNNaNNaN3270.0800.0150.050.025.0NaNNaNNaN500.0NaN92.6270.0100.0100.0NaNNaNNaNNaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaNNaN0.6650.428-0.301-0.9980.1500.0251.0000.8430.042nannan-0.010-0.7550.804-0.059
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.00.3360.0790.081-0.3050.3890.4340.8431.0000.297-0.1640.4010.532-0.2010.4610.428
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0NaN30.0NaN33.042.042.038.050.030.030.035.00.815-0.1110.737-0.8590.2560.6780.0420.2971.0000.5390.4460.4000.5120.7150.517
Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.00.257-0.0710.423nan0.1800.539nan-0.1640.5391.0001.0000.401nan0.6270.321
Radio de giroNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100.0120.0150.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNVelocidad de pérdida limpia (KCAS)0.128-0.5020.097nan0.2600.546nan0.4010.4461.0001.0000.505nan0.5360.038
envergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.00.4720.0570.841-0.3490.6930.791-0.0100.5320.4000.4010.5051.0000.8850.7340.924
Cuerda0.2390.3181950.3520.3520.352NaN0.196552NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.8460.0170.984-0.7440.9950.858-0.755-0.2010.512nannan0.8851.0000.7760.971
payload2.4947562.49475614.511.317.722.7NaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.00.6960.0870.899-0.8880.5990.8750.8040.4610.7150.6270.5360.7340.7761.0000.778
duracion en VTOL2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050.050.05Empty weight0.426-0.1380.9410.6220.8800.947-0.0590.4280.5170.3210.0380.9240.9710.7781.000
Crucero KIAS15.4333216.09342225.025.025.0NaNNaN27.8NaNNaN16.728.028.3NaNNaN23.530.916.5416.016.018.020.020.025.025.025.033.024.0NaN30.033.028.035.030.020.020.025.0
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Resumen de la Tabla

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ResumenCantidad
RTF (dry weight)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.011.854.00Total de valores225.000
RTF (Including fuel & Batteries)NaNNaN27.742.236.770.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8.916.584.01Valores numéricos213.000
2Valores NaN12.000
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Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

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ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight10.88620817.463292NaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
Maximum CrosswindNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.0NaNNaNNaN45.050.050.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15.015.015.0Modelo
Rango de comunicación59.0161.0140.0140.0140.0NaN150.0NaN150.050.025.0101.8692.6NaNNaNNaN92.6NaN50.030.030.030.030.030.030.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNVelocidad a la que se realiza el crucero (KTAS)nannannan-0.999nannannannan0.815nannannan0.846nannan
Wing LoadingNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.524.025.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTecho de servicio máximonannannannannannannannannannannannannannannan
Potencia/PesoNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNÁrea del alanannannan-0.8310.8670.977nannan0.737nannan0.8410.9840.8990.941
Capacidad combustibleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNRelación de aspecto del ala-0.999nan-0.831nan-0.790-0.823-0.998nan-0.859nannannan-0.744-0.888nan
Longitud del fuselajenannan0.867-0.790nan0.786nannannannannannan0.995nan0.880
Peso máximo al despegue (MTOW)nannan0.977-0.8230.786nannannannannannan0.7910.8580.8750.947
Alcance de la aeronavenannannan-0.998nannannan0.843nannannannan-0.7550.804nan
Autonomía de la aeronavenannannannannannan0.843nannannannannannannannan
Velocidad máxima (KIAS)0.815nan0.737-0.859nannannannannannannannannan0.715nan
Velocidad de pérdida (KCAS)nannannannannannannannannannannannannannannan
Velocidad de pérdida limpia (KCAS)nannannannannannannannannannannannannannannan
envergaduranannan0.841nannan0.791nannannannannannan0.8850.7340.924
Cuerda0.846nan0.984-0.7440.9950.858-0.755nannannannan0.885nan0.7760.971
payloadnannan0.899-0.888nan0.8750.804nan0.715nannan0.7340.776nan0.778
Empty weightnannan0.941nan0.8800.947nannannannannan0.9240.9710.778nan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores225.000
1Valores numéricos68.000
2Valores NaN157.000
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df_procesado_base

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" \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10716,7 +10967,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -10730,24 +10980,34 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -10757,17 +11017,36 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10778,9 +11057,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10801,27 +11077,49 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10841,9 +11139,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10858,13 +11153,18 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10874,13 +11174,9 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10894,35 +11190,76 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10932,7 +11269,9 @@ " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", @@ -10940,20 +11279,87 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10974,13 +11380,26 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11020,10 +11439,7 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11044,6 +11460,9 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11060,11 +11479,54 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", @@ -11095,12 +11557,51 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11116,6 +11617,8 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11131,16 +11634,52 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11158,6 +11697,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -11174,13 +11714,20 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11190,6 +11737,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -11206,30 +11754,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11240,13 +11777,29 @@ " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11258,14 +11811,15 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11280,12 +11834,37 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11300,17 +11879,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -11324,6 +11900,9 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11340,14 +11919,14 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -11380,14 +11959,7 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11413,21 +11985,25 @@ " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11449,26 +12025,28 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -11477,6 +12055,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -11500,15 +12079,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -11518,6 +12096,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -11539,163 +12118,14 @@ " \n", " \n", " \n", - " \n", - "
Stalker XEStalker VXE30Aerosonde Mk. 4.7 Fixed WingAerosonde Mk. 4.7 VTOLAerosonde Mk. 4.8 Fixed wingAerosonde Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQ Nan 21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Modelo
Distancia de carrera requerida para despegue0.00.0NaN0.0NaNNaN13.0NaNNaN11.511.528.028.028.025.0NaNNaNNaN
ConsumoNaNNaN0.60.60.0NaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.0
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NaNNaN30.406584NaNNaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaN0.96NaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaN1.2NaNNaN5.0NaNNaNNaN17000.010000.013000.016000.0
Potencia(W)NaNNaN2980.02980.0NaNNaN1280.0NaNVelocidad de pérdida limpia (KCAS)NaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaNNaN25.0NaNNaNNaN
Potencia(HP)NaNNaN4.04.0Área del ala0.871.1582831.551.551.55NaN0.57NaN1.74NaNNaNNaNNaNNaNNaN8.00.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NaNNaNNaNNaNNaNNaN
PrecioNaNNaNNaNNaNNaNNaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.0
Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.712
Tiempo de emergencia en vueloNaNProfundidad del fuselajeNaNNaNNaNNaNNaNNaN0.1080.1080.108NaNNaNNaNNaNNaNNaN
Distancia de aterrizajeNaNNaNNaNNaN
Ancho del fuselaje0.2110.20.2770.2770.277NaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.0NaNNaNNaNNaNNaNNaN0.3750.3750.375NaNNaNNaNNaN
Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPeso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0NaNNaNNaNNaN3270.0800.0NaN50.025.0NaNNaNNaN500.0NaNNaN270.0100.0100.0NaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaN
Battery Power SupplyNaNNaNNaNNaNNaNAutonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0NaN30.0NaN33.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.0
Modelo Motor Fixed WingTasa de ascensoNaNNaNNaNNaN
Modelo Motor VTOLNaNNaNNaNRadio de giroNaNNaNNaNNaNNaNNaN100.0120.0150.0NaNNaNNaNNaN
PortabilidadNaNenvergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.352NaN0.196552NaNNaNNaNNaNNaNNaN
payload2.4947562.49475614.511.317.722.7NaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
Cámaraduracion en VTOL2.0NaNNaNNaNNaNNaNNaNNaN4.53NaNNaNNaNNaNNaNNaNNaN0.050.050.05
Crucero KIAS15.4333216.09342225.025.025.0NaNNaN27.8NaNNaN16.728.028.3NaNNaN23.530.916.5416.016.018.020.020.025.025.025.033.024.0NaN30.033.028.035.030.020.020.025.0
DespegueRTF (dry weight)NaNNaNNaNNaNNaNNaN4.8NaNNaNNaNNaNNaNNaN6.011.854.0
RTF (Including fuel & Batteries)NaNNaN27.742.236.770.3NaNNaN
Datalink banksNaNNaNNaNNaNNaNNaN6.8NaNNaNNaNNaNNaNNaN8.916.584.0
Empty weight10.88620817.463292NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
Maximum CrosswindNaNNaNNaNNaNNaNNaN
Motor recomendadoNaNNaN30.0NaNNaNNaN45.050.050.0NaNNaNNaNNaNNaNNaN15.015.015.0
Rango de comunicación59.0161.0140.0140.0140.0NaN150.0NaN150.050.025.0101.8692.6NaNNaNNaN92.6NaN50.030.030.030.030.030.030.0NaNNaNNaNNaN
Hélice recomendada VTOLNaNNaNNaNNaNWing LoadingNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaNNaN12.524.025.0NaNNaNNaNNaN
Hélice recomendada Fixed WingNaNPotencia específica (P/W)NaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaN
Sistema de controlNaNNaNNaNNaNNaNNaNNaNCapacidad combustibleNaNNaNNaNNaNNaNNaN13.0NaNNaN11.511.528.028.028.025.0NaNNaNNaN
Características adicionalesNaNNaNNaNNaNNaNConsumoNaNNaN0.60.6NaNNaNNaNNaNNaNNaN0.96NaNNaNNaNNaN1.2NaNNaN5.0NaNNaNNaN
EmpresaNaNNaNNaNNaNPotencia WattsNaNNaN2980.02980.0NaNNaN1280.0NaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaN
kjbkNaNNaNNaNNaNPotencia HPNaNNaN4.04.0NaNNaN1.74NaNNaNNaNNaNNaNNaN8.0NaNNaNNaNNaNNaN
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df_filtrado_base

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11703,60 +12133,44 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11766,6 +12180,9 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11780,6 +12197,9 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11787,109 +12207,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11899,9 +12231,7 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", @@ -11909,87 +12239,300 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12010,73 +12553,60 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -12109,54 +12639,67 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -12167,511 +12710,46 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - "
Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NaNNaN30.406584NaNNaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288PrecioNaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaN17000.010000.013000.016000.0
Área del ala0.871.1582831.551.551.55NaN0.57NaNNaNNaNNaNNaNNaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.0NaNNaNNaN
Tiempo de emergencia en vueloNaN0.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NaNNaNNaNNaNNaNNaN0.1080.1080.108NaNNaNNaNNaNNaNNaN
Distancia de aterrizajeNaNNaNNaNNaNNaNNaN
Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.712
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0NaNNaNNaN0.0NaN3270.0800.0150.050.025.0NaNNaN0.00.00.0NaN500.0NaN92.6270.0100.0100.0NaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaN
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.0Despegue1.01.01.02.01.02.02.01.01.01.01.01.01.02.01.01.01.02.02.02.02.02.02.02.02.02.02.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.02.02.02.02.02.02.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0Propulsión horizontal2.02.02.02.02.02.02.02.02.01.01.02.02.02.02.02.02.01.01.01.01.01.02.02.02.02.02.02.02.02.02.02.02.02.02.02.02.0
Propulsión vertical5.05.05.01.05.01.01.05.05.05.05.05.05.01.05.05.05.01.01.01.01.01.01.01.01.01.01.01.05.01.05.01.01.01.01.01.01.0
Cantidad de motores propulsión vertical0.00.00.04.00.04.04.00.00.00.00.00.00.04.00.00.00.04.04.04.04.04.04.04.04.04.04.04.00.04.00.04.08.04.04.04.04.0
Cantidad de motores propulsión horizontal1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
Misión1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.0NaN33.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)Battery Power SupplyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.0
envergadura3.6574.87684.44.44.4Modelo Motor Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.352Modelo Motor VTOLNaNNaNNaNNaNNaNNaNNaN0.196552NaNNaNNaNNaN
payload2.4947562.49475614.511.317.722.7PortabilidadNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
Empty weight10.88620817.463292CámaraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Configuración Inicial ===\n", - "\n", - "Valores configurados: Rango MTOW [85% - 115%], Confianza Mínima: 0.50\n", - "\n", - "================================================================================\n", - "\u001b[1m=== INICIO DE ITERACIÓN 1 ===\u001b[0m\n", - "================================================================================\n", - "\n", - "=== Iteración 1: Resumen antes de imputaciones ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

Resumen de Valores Faltantes Antes de Iteración 1

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ColumnaValores Faltantes
0Stalker XE33.000
1Stalker VXE3034.000
2Aerosonde® Mk. 4.7 Fixed Wing33.000
3Aerosonde® Mk. 4.7 VTOL32.000
4Aerosonde® Mk. 4.8 Fixed wing36.000
5Aerosonde® Mk. 4.8 VTOL FTUAS44.000
6AAI Aerosonde34.000
7Fulmar X42.000
8Orbiter 443.000
9Orbiter 343.000
10Mantis42.000
11ScanEagle41.000
12Integrator41.000
13Integrator VTOL45.000
14Integrator Extended Range (ER)43.000
15ScanEagle 341.000
16RQNan21A Blackjack39.000
17DeltaQuad Evo34.000
18DeltaQuad Pro #MAP38.000
19DeltaQuad Pro #CARGO38.000
20V2132.000
21V2532.000
22V3233.000
23V3538.000
24V3940.000
25Volitation VT37037.000
26Skyeye 260039.000
27Skyeye 2930 VTOL37.000
28Skyeye 360039.000
29Skyeye 3600 VTOL35.000
30Skyeye 500034.000
31Skyeye 5000 VTOL37.000
32Skyeye 5000 VTOL octo38.000
33Volitation VT51035.000
34Ascend35.000
35Transition35.000
36Reach35.000
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes1387.000
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Datos Filtrados por aeronaves seleccionadas antes de imputar(df_resultado_por_similitud)

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12679,20 +12757,26 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -12703,36 +12787,26 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12765,107 +12839,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12875,97 +12863,36 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12986,73 +12913,60 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -13085,54 +12999,67 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -13143,26 +13070,13 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", "
Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405Despegue todos los tiposNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.406584NaNNaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaN17000.010000.013000.016000.0
Área del ala0.871.1582831.551.551.55Datalink banksNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.57NaNNaNNaNNaNNaNNaN0.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Longitud del fuselaje2.12.59083.03.03.0Motor recomendadoNaNNaNNaNNaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.712
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0NaNNaNNaNNaN3270.0800.0150.050.025.0NaNNaNNaN500.0NaN92.6270.0100.0100.0NaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaNNaN
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0NaN30.0NaN33.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)Hélice recomendada VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.0
envergadura3.6574.87684.44.44.4Hélice recomendada Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.352Sistema de controlNaNNaNNaNNaNNaNNaNNaN0.196552NaNNaNNaNNaN
payload2.4947562.49475614.511.317.722.7Características adicionalesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
Empty weight10.88620817.463292indice_desconocidoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
" @@ -13174,3494 +13088,6 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #0 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Volitation VT510, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 36.09 | 35.80 \n", - "Skyeye 5000 VTOL| 100.0| 1.075 | 0.0188 | 30.63 | 31.20 \n", - "Volitation VT510| 100.0| 1.075 | 0.0188 | 32.81 | 33.43 \n", - "Reach | 91.0| 0.978 | -0.0054 | 27.34 | 27.20 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [35.803064717345336, 31.201619674835236, 33.430306794466325, 27.19703938863795] = 32.32\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 32.32\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.11\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #1 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m AAI Aerosonde\n", - "\u001b[1mMTOW actual:\u001b[0m 13.1 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, V25, Skyeye 2600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker XE | 13.6| 1.038 | 0.0095 | 16.88 | 17.04 \n", - "V25 | 12.5| 0.954 | -0.0115 | 21.88 | 21.62 \n", - "Skyeye 2600 | 15.0| 1.145 | 0.0363 | 36.09 | 37.40 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [17.041451525056257, 21.624760478782665, 37.402903766342334] = 21.62\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.95\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 21.62\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.95\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #2 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 4\n", - "\u001b[1mMTOW actual:\u001b[0m 55.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing, RQNan21A Blackjack\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.973 | -0.0068 | 27.34 | 27.16 \n", - "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.989 | -0.0027 | 27.34 | 27.27 \n", - "RQNan21A Blackjack| 61.0| 1.109 | 0.0273 | 33.80 | 34.72 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [27.157613705003314, 27.26947572941752, 34.718989390850375] = 27.27\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.96\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 27.27\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.97\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.96\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Advertencia: Ponderación del modelo (-0.016) fuera de rango. Revisar lógica previa.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #3 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 3\n", - "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m ScanEagle 3, V35, Skyeye 2930 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "ScanEagle 3 | 36.3| 1.134 | 0.0336 | 25.70 | 26.57 \n", - "V35 | 32.0| 1.000 | 0.0000 | 27.34 | 27.34 \n", - "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 26.25 | 25.43 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [26.566881229546517, 27.344050412360318, 25.429966883495094] = 26.57\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.55\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 26.57\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.92\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.55\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.55\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #4 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Integrator VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 75.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Integrator | 74.8| 0.997 | -0.0007 | 30.95 | 30.93 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [30.93282942341402] = 30.93\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 30.93\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #5 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Integrator Extended Range (ER)\n", - "\u001b[1mMTOW actual:\u001b[0m 74.8 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Integrator | 74.8| 1.000 | 0.0000 | 30.95 | 30.95 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [30.953465066791882] = 30.95\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 30.95\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #6 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600\n", - "\u001b[1mMTOW actual:\u001b[0m 28.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m ScanEagle, V35, V39, Skyeye 2930 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "ScanEagle | 26.5| 0.946 | -0.0134 | 30.63 | 30.22 \n", - "V35 | 32.0| 1.143 | 0.0357 | 27.34 | 28.32 \n", - "V39 | 24.0| 0.857 | -0.0357 | 27.34 | 26.37 \n", - "Skyeye 2930 VTOL| 28.0| 1.000 | 0.0000 | 26.25 | 26.25 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [30.21517570565815, 28.32062364137318, 26.36747718334745, 26.250288395865905] = 27.34\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.97\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 27.34\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.11\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.92\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.81\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.88\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.97\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #7 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad a la que se realiza el crucero (KTAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL octo\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Volitation VT510, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.900 | -0.0250 | 36.09 | 35.19 \n", - "Skyeye 5000 VTOL| 100.0| 1.000 | 0.0000 | 30.63 | 30.63 \n", - "Volitation VT510| 100.0| 1.000 | 0.0000 | 32.81 | 32.81 \n", - "Reach | 91.0| 0.910 | -0.0225 | 27.34 | 26.73 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [35.19179288070773, 30.625336461843556, 32.812860494832385, 26.72880927808221] = 31.72\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 31.72\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.11\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.97\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #8 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", - "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 4\n", - "\u001b[1mMTOW actual:\u001b[0m 55.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing, RQNan21A Blackjack\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.973 | -0.0068 | 9700.00 | 9633.86 \n", - "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.989 | -0.0027 | 18200.00 | 18150.36 \n", - "RQNan21A Blackjack| 61.0| 1.109 | 0.0273 | 20.00 | 20.55 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [9633.863636363636, 18150.363636363636, 20.545454545454547] = 9633.86\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.97\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 9633.86\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.97\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #9 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", - "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 3\n", - "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m ScanEagle 3, V35\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "ScanEagle 3 | 36.3| 1.134 | 0.0336 | 20.00 | 20.67 \n", - "V35 | 32.0| 1.000 | 0.0000 | 16000.00 | 16000.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [20.671875, 16000.0] = 8010.34\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.86\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 8010.34\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.86\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #10 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", - "\u001b[1mAeronave a imputar:\u001b[0m Mantis\n", - "\u001b[1mMTOW actual:\u001b[0m 6.5 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m DeltaQuad Pro #MAP, DeltaQuad Pro #CARGO\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "DeltaQuad Pro #MAP| 6.2| 0.954 | -0.0115 | 13.12 | 12.97 \n", - "DeltaQuad Pro #CARGO| 6.2| 0.954 | -0.0115 | 13.12 | 12.97 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [12.97158076923077, 12.97158076923077] = 12.97\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.51\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.97\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.57\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.51\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #11 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", - "\u001b[1mAeronave a imputar:\u001b[0m Integrator VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 75.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator, Integrator Extended Range (ER)\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Integrator | 74.8| 0.997 | -0.0007 | 19500.00 | 19487.00 \n", - "Integrator Extended Range (ER)| 74.8| 0.997 | -0.0007 | 19500.00 | 19487.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [19487.0, 19487.0] = 19487.00\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.53\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 19487.00\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.60\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.53\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #12 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2600\n", - "\u001b[1mMTOW actual:\u001b[0m 15.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker XE | 13.6| 0.907 | -0.0233 | 12000.00 | 11720.00 \n", - "AAI Aerosonde| 13.1| 0.873 | -0.0317 | 15000.00 | 14525.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [11720.0, 14524.999999999998] = 13122.50\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.81\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 13122.50\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.93\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.91\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.81\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #13 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2930 VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 28.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m ScanEagle, V35, V39\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "ScanEagle | 26.5| 0.946 | -0.0134 | 19500.00 | 19238.84 \n", - "V35 | 32.0| 1.143 | 0.0357 | 16000.00 | 16571.43 \n", - "V39 | 24.0| 0.857 | -0.0357 | 16000.00 | 15428.57 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [19238.839285714286, 16571.42857142857, 15428.57142857143] = 16571.43\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.90\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 16571.43\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.89\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.91\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.90\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.90\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #14 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600\n", - "\u001b[1mMTOW actual:\u001b[0m 28.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m ScanEagle, V35, V39\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "ScanEagle | 26.5| 0.946 | -0.0134 | 19500.00 | 19238.84 \n", - "V35 | 32.0| 1.143 | 0.0357 | 16000.00 | 16571.43 \n", - "V39 | 24.0| 0.857 | -0.0357 | 16000.00 | 15428.57 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [19238.839285714286, 16571.42857142857, 15428.57142857143] = 16571.43\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.90\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 16571.43\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.89\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.91\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.90\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.90\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #15 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600 VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing, ScanEagle 3, Volitation VT370\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 Fixed Wing| 42.2| 1.055 | 0.0138 | 14700.00 | 14902.12 \n", - "ScanEagle 3 | 36.3| 0.907 | -0.0231 | 20.00 | 19.54 \n", - "Volitation VT370| 40.0| 1.000 | 0.0000 | 17000.00 | 17000.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [14902.124999999998, 19.537499999999998, 17000.0] = 14902.12\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.97\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 14902.12\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.97\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #16 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000\n", - "\u001b[1mMTOW actual:\u001b[0m 90.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS, Volitation VT510, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 1.033 | 0.0083 | 15000.00 | 15125.00 \n", - "Volitation VT510| 100.0| 1.111 | 0.0278 | 17000.00 | 17472.22 \n", - "Reach | 91.0| 1.011 | 0.0028 | 16000.00 | 16044.44 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [15125.0, 17472.22222222222, 16044.444444444443] = 16044.44\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.92\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 16044.44\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.88\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.92\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.92\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #17 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS, Volitation VT510, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 0.930 | -0.0175 | 15000.00 | 14737.50 \n", - "Volitation VT510| 100.0| 1.000 | 0.0000 | 17000.00 | 17000.00 \n", - "Reach | 91.0| 0.910 | -0.0225 | 16000.00 | 15640.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [14737.5, 17000.0, 15640.0] = 15640.00\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.93\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 15640.00\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.90\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.93\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.93\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #18 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Techo de servicio máximo\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL octo\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS, Volitation VT510, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 0.930 | -0.0175 | 15000.00 | 14737.50 \n", - "Volitation VT510| 100.0| 1.000 | 0.0000 | 17000.00 | 17000.00 \n", - "Reach | 91.0| 0.910 | -0.0225 | 16000.00 | 15640.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [14737.5, 17000.0, 15640.0] = 15640.00\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.93\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 15640.00\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.90\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.93\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.93\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #19 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Skyeye 5000 VTOL octo\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 2.62 | 2.59 \n", - "Skyeye 5000 VTOL| 100.0| 1.075 | 0.0188 | 2.62 | 2.66 \n", - "Skyeye 5000 VTOL octo| 100.0| 1.075 | 0.0188 | 2.62 | 2.66 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [2.593911290322581, 2.664206989247312, 2.664206989247312] = 2.66\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.56\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 2.66\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.56\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.56\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #20 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Fulmar X\n", - "\u001b[1mMTOW actual:\u001b[0m 20.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker VXE30| 19.958047999999998| 0.998 | -0.0005 | 1.16 | 1.16 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [1.15767579628992] = 1.16\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1.16\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #21 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 4\n", - "\u001b[1mMTOW actual:\u001b[0m 55.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.973 | -0.0068 | 1.55 | 1.54 \n", - "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.989 | -0.0027 | 1.55 | 1.55 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [1.5394318181818183, 1.5457727272727273] = 1.54\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.52\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1.54\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.59\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.52\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #22 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 3\n", - "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 1.00 | 0.97 \n", - "Skyeye 3600 | 28.0| 0.875 | -0.0312 | 1.33 | 1.29 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [0.96875, 1.2884375000000001] = 1.13\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.81\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1.13\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.88\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.95\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.91\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.81\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Mantis'para el parametro 'Área del ala'.\n", - "No se pudo imputar: Área del ala para Mantis.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #23 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle\n", - "\u001b[1mMTOW actual:\u001b[0m 26.5 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 2930 VTOL| 28.0| 1.057 | 0.0142 | 1.00 | 1.01 \n", - "Skyeye 3600 | 28.0| 1.057 | 0.0142 | 1.33 | 1.35 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [1.0141509433962264, 1.348820754716981] = 1.18\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.86\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1.18\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.86\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator'para el parametro 'Área del ala'.\n", - "No se pudo imputar: Área del ala para Integrator.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator VTOL'para el parametro 'Área del ala'.\n", - "No se pudo imputar: Área del ala para Integrator VTOL.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator Extended Range (ER)'para el parametro 'Área del ala'.\n", - "No se pudo imputar: Área del ala para Integrator Extended Range (ER).\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #24 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle 3\n", - "\u001b[1mMTOW actual:\u001b[0m 36.3 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 3600 VTOL| 40.0| 1.102 | 0.0255 | 1.32 | 1.35 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [1.3536363636363637] = 1.35\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1.35\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #25 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m RQNan21A Blackjack\n", - "\u001b[1mMTOW actual:\u001b[0m 61.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.877 | -0.0307 | 1.55 | 1.50 \n", - "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.892 | -0.0270 | 1.55 | 1.51 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [1.5023565573770492, 1.5080737704918032] = 1.51\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.48\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1.51\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.89\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.53\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.48\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Imputación descartada por baja confianza: 0.475 < 0.5.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #MAP'para el parametro 'Área del ala'.\n", - "No se pudo imputar: Área del ala para DeltaQuad Pro #MAP.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #CARGO'para el parametro 'Área del ala'.\n", - "No se pudo imputar: Área del ala para DeltaQuad Pro #CARGO.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V32'para el parametro 'Área del ala'.\n", - "No se pudo imputar: Área del ala para V32.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #25 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m V35\n", - "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 1.00 | 0.97 \n", - "Skyeye 3600 | 28.0| 0.875 | -0.0312 | 1.33 | 1.29 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [0.96875, 1.2884375000000001] = 1.13\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.81\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1.13\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.88\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.95\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.91\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.81\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V39'para el parametro 'Área del ala'.\n", - "No se pudo imputar: Área del ala para V39.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #26 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT370\n", - "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing, Skyeye 3600 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 Fixed Wing| 42.2| 1.055 | 0.0138 | 1.55 | 1.57 \n", - "Skyeye 3600 VTOL| 40.0| 1.000 | 0.0000 | 1.32 | 1.32 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [1.5713125, 1.32] = 1.45\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.87\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1.45\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.97\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.98\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.87\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #27 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT510\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Skyeye 5000 VTOL octo\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.900 | -0.0250 | 2.62 | 2.55 \n", - "Skyeye 5000 VTOL| 100.0| 1.000 | 0.0000 | 2.62 | 2.62 \n", - "Skyeye 5000 VTOL octo| 100.0| 1.000 | 0.0000 | 2.62 | 2.62 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [2.5496250000000003, 2.615, 2.615] = 2.62\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 2.62\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.97\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.58\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #28 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Ascend\n", - "\u001b[1mMTOW actual:\u001b[0m 9.5 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m DeltaQuad Evo, V21\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "DeltaQuad Evo| 10.0| 1.053 | 0.0132 | 0.84 | 0.85 \n", - "V21 | 10.0| 1.053 | 0.0132 | 0.80 | 0.81 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [0.8510526315789473, 0.8105263157894737] = 0.83\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.76\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 0.83\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.71\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.85\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.76\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #29 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Transition\n", - "\u001b[1mMTOW actual:\u001b[0m 18.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker VXE30| 19.958047999999998| 1.109 | 0.0272 | 1.16 | 1.19 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [1.1897828403221333] = 1.19\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.57\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1.19\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.57\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #30 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Área del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Reach\n", - "\u001b[1mMTOW actual:\u001b[0m 91.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Skyeye 5000 VTOL octo\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.989 | -0.0027 | 2.62 | 2.61 \n", - "Skyeye 5000 VTOL| 100.0| 1.099 | 0.0247 | 2.62 | 2.68 \n", - "Skyeye 5000 VTOL octo| 100.0| 1.099 | 0.0247 | 2.62 | 2.68 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [2.6078159340659344, 2.6796565934065932, 2.6796565934065932] = 2.68\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.56\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 2.68\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.56\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.56\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #31 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Fulmar X\n", - "\u001b[1mMTOW actual:\u001b[0m 20.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker VXE30| 19.958047999999998| 0.998 | -0.0005 | 15.33 | 15.32 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [15.318411644882964] = 15.32\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 15.32\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #32 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 4\n", - "\u001b[1mMTOW actual:\u001b[0m 55.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.973 | -0.0068 | 12.50 | 12.41 \n", - "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.989 | -0.0027 | 12.50 | 12.47 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [12.414772727272728, 12.465909090909093] = 12.44\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.52\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.44\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.59\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.52\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Orbiter 3'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para Orbiter 3.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Mantis'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para Mantis.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'ScanEagle'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para ScanEagle.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para Integrator.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator VTOL'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para Integrator VTOL.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator Extended Range (ER)'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para Integrator Extended Range (ER).\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'ScanEagle 3'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para ScanEagle 3.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #33 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m RQNan21A Blackjack\n", - "\u001b[1mMTOW actual:\u001b[0m 61.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.877 | -0.0307 | 12.50 | 12.12 \n", - "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.892 | -0.0270 | 12.50 | 12.16 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [12.11577868852459, 12.161885245901642] = 12.14\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.48\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.14\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.89\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.53\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.48\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Imputación descartada por baja confianza: 0.475 < 0.5.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Evo'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para DeltaQuad Evo.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #MAP'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para DeltaQuad Pro #MAP.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #CARGO'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para DeltaQuad Pro #CARGO.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V21'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para V21.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #33 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m V25\n", - "\u001b[1mMTOW actual:\u001b[0m 12.5 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker XE | 13.6| 1.088 | 0.0220 | 15.30 | 15.64 \n", - "AAI Aerosonde| 13.1| 1.048 | 0.0120 | 14.75 | 14.93 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [15.637882845188285, 14.93143859649123] = 15.28\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.51\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 15.28\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.03\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.57\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.51\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V32'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para V32.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V35'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para V35.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V39'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para V39.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #34 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT370\n", - "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 Fixed Wing| 42.2| 1.055 | 0.0138 | 12.50 | 12.67 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [12.671875000000002] = 12.67\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.67\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Advertencia: Ponderación del modelo (-1.312) fuera de rango. Revisar lógica previa.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #35 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2600\n", - "\u001b[1mMTOW actual:\u001b[0m 15.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker XE | 13.6| 0.907 | -0.0233 | 15.30 | 14.94 \n", - "AAI Aerosonde| 13.1| 0.873 | -0.0317 | 14.75 | 14.29 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [14.944225941422594, 14.28716374269006] = 14.62\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.48\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 14.62\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.54\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.48\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Imputación descartada por baja confianza: 0.478 < 0.5.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 2930 VTOL'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para Skyeye 2930 VTOL.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 3600'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para Skyeye 3600.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #35 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600 VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 Fixed Wing| 42.2| 1.055 | 0.0138 | 12.50 | 12.67 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [12.671875000000002] = 12.67\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.67\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #36 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000\n", - "\u001b[1mMTOW actual:\u001b[0m 90.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 1.033 | 0.0083 | 12.50 | 12.60 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [12.604166666666668] = 12.60\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.61\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.60\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.97\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.78\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.61\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #37 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 0.930 | -0.0175 | 12.50 | 12.28 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [12.281250000000002] = 12.28\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.59\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.28\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.76\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.59\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #38 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL octo\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 0.930 | -0.0175 | 12.50 | 12.28 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [12.281250000000002] = 12.28\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.59\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.28\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.76\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.59\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #39 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT510\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 0.930 | -0.0175 | 12.50 | 12.28 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [12.281250000000002] = 12.28\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.59\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.28\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.76\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.59\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Ascend'para el parametro 'Relación de aspecto del ala'.\n", - "No se pudo imputar: Relación de aspecto del ala para Ascend.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #40 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Transition\n", - "\u001b[1mMTOW actual:\u001b[0m 18.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker VXE30| 19.958047999999998| 1.109 | 0.0272 | 15.33 | 15.74 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [15.743253313648973] = 15.74\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.57\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 15.74\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.57\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #41 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Relación de aspecto del ala\n", - "\u001b[1mAeronave a imputar:\u001b[0m Reach\n", - "\u001b[1mMTOW actual:\u001b[0m 91.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 1.022 | 0.0055 | 12.50 | 12.57 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [12.568681318681321] = 12.57\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.61\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.57\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.79\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.61\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #42 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Longitud del fuselaje\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Skyeye 5000 VTOL octo, Volitation VT510, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 3.50 | 3.47 \n", - "Skyeye 5000 VTOL| 100.0| 1.075 | 0.0188 | 3.50 | 3.57 \n", - "Skyeye 5000 VTOL octo| 100.0| 1.075 | 0.0188 | 3.50 | 3.57 \n", - "Volitation VT510| 100.0| 1.075 | 0.0188 | 2.90 | 2.96 \n", - "Reach | 91.0| 0.978 | -0.0054 | 4.71 | 4.69 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [3.471774193548387, 3.5658602150537635, 3.5658602150537635, 2.9596639784946235, 4.6866666666666665] = 3.57\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 3.57\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.22\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #43 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Longitud del fuselaje\n", - "\u001b[1mAeronave a imputar:\u001b[0m Integrator VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 75.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator, Integrator Extended Range (ER)\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Integrator | 74.8| 0.997 | -0.0007 | 2.50 | 2.50 \n", - "Integrator Extended Range (ER)| 74.8| 0.997 | -0.0007 | 2.50 | 2.50 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [2.498333333333333, 2.498333333333333] = 2.50\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.53\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 2.50\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.60\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.53\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #44 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Longitud del fuselaje\n", - "\u001b[1mAeronave a imputar:\u001b[0m V39\n", - "\u001b[1mMTOW actual:\u001b[0m 24.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m ScanEagle, V32\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "ScanEagle | 26.5| 1.104 | 0.0260 | 1.71 | 1.75 \n", - "V32 | 23.5| 0.979 | -0.0052 | 1.00 | 0.99 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [1.75453125, 0.9947916666666666] = 1.37\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.85\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1.37\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.85\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #45 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", - "\u001b[1mMTOW actual:\u001b[0m 42.2 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 3600 VTOL| 40.0| 0.948 | -0.0130 | 300.00 | 296.09 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [296.09004739336496] = 296.09\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 296.09\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #46 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.7 VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 53.5 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 4, RQNan21A Blackjack\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Orbiter 4 | 55.0| 1.028 | 0.0070 | 150.00 | 151.05 \n", - "RQNan21A Blackjack| 61.0| 1.140 | 0.0350 | 92.60 | 95.85 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [151.05140186915887, 95.84532710280374] = 123.45\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.84\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 123.45\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.92\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.84\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #47 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 Fixed wing\n", - "\u001b[1mMTOW actual:\u001b[0m 54.4 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 4, RQNan21A Blackjack\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Orbiter 4 | 55.0| 1.011 | 0.0028 | 150.00 | 150.41 \n", - "RQNan21A Blackjack| 61.0| 1.121 | 0.0303 | 92.60 | 95.41 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [150.41360294117646, 95.40863970588234] = 122.91\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.85\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 122.91\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.85\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #48 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 VTOL| 100.0| 1.075 | 0.0188 | 800.00 | 815.05 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [815.0537634408602] = 815.05\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.59\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 815.05\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.76\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.59\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'ScanEagle'para el parametro 'Alcance de la aeronave'.\n", - "No se pudo imputar: Alcance de la aeronave para ScanEagle.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #49 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Integrator\n", - "\u001b[1mMTOW actual:\u001b[0m 74.8 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator Extended Range (ER)\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Integrator Extended Range (ER)| 74.8| 1.000 | 0.0000 | 500.00 | 500.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [500.0] = 500.00\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 500.00\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #50 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Integrator VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 75.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator Extended Range (ER)\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Integrator Extended Range (ER)| 74.8| 0.997 | -0.0007 | 500.00 | 499.67 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [499.66666666666663] = 499.67\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 499.67\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #51 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle 3\n", - "\u001b[1mMTOW actual:\u001b[0m 36.3 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 3, Skyeye 3600 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Orbiter 3 | 32.0| 0.882 | -0.0296 | 50.00 | 48.52 \n", - "Skyeye 3600 VTOL| 40.0| 1.102 | 0.0255 | 300.00 | 307.64 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [48.519283746556475, 307.6446280991736] = 178.08\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.83\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 178.08\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.94\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.83\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #52 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m V21\n", - "\u001b[1mMTOW actual:\u001b[0m 10.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m DeltaQuad Evo\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "DeltaQuad Evo| 10.0| 1.000 | 0.0000 | 270.00 | 270.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [270.0] = 270.00\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 270.00\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #53 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m V25\n", - "\u001b[1mMTOW actual:\u001b[0m 12.5 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker XE | 13.6| 1.088 | 0.0220 | 370.00 | 378.14 \n", - "AAI Aerosonde| 13.1| 1.048 | 0.0120 | 3270.00 | 3309.24 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [378.14, 3309.2400000000002] = 1843.69\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.85\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1843.69\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.85\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #54 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m V32\n", - "\u001b[1mMTOW actual:\u001b[0m 23.5 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Fulmar X\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Fulmar X | 20.0| 0.851 | -0.0372 | 800.00 | 770.21 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [770.2127659574468] = 770.21\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.56\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 770.21\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.86\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.72\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.56\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #55 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m V35\n", - "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 3\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Orbiter 3 | 32.0| 1.000 | 0.0000 | 50.00 | 50.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [50.0] = 50.00\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 50.00\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V39'para el parametro 'Alcance de la aeronave'.\n", - "No se pudo imputar: Alcance de la aeronave para V39.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #56 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT370\n", - "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 3600 VTOL| 40.0| 1.000 | 0.0000 | 300.00 | 300.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [300.0] = 300.00\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 300.00\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #57 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2600\n", - "\u001b[1mMTOW actual:\u001b[0m 15.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker XE | 13.6| 0.907 | -0.0233 | 370.00 | 361.37 \n", - "AAI Aerosonde| 13.1| 0.873 | -0.0317 | 3270.00 | 3166.45 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [361.3666666666667, 3166.45] = 1763.91\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.83\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1763.91\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.94\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.83\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #58 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2930 VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 28.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 3\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Orbiter 3 | 32.0| 1.143 | 0.0357 | 50.00 | 51.79 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [51.78571428571428] = 51.79\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.56\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 51.79\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.87\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.72\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.56\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #59 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600\n", - "\u001b[1mMTOW actual:\u001b[0m 28.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 3\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Orbiter 3 | 32.0| 1.143 | 0.0357 | 50.00 | 51.79 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [51.78571428571428] = 51.79\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.56\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 51.79\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.87\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.72\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.56\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #60 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000\n", - "\u001b[1mMTOW actual:\u001b[0m 90.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 VTOL| 100.0| 1.111 | 0.0278 | 800.00 | 822.22 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [822.2222222222222] = 822.22\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.57\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 822.22\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.89\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.57\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #61 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL octo\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 VTOL| 100.0| 1.000 | 0.0000 | 800.00 | 800.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [800.0] = 800.00\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 800.00\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #62 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT510\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 VTOL| 100.0| 1.000 | 0.0000 | 800.00 | 800.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [800.0] = 800.00\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 800.00\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #63 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Ascend\n", - "\u001b[1mMTOW actual:\u001b[0m 9.5 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m DeltaQuad Evo\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "DeltaQuad Evo| 10.0| 1.053 | 0.0132 | 270.00 | 273.55 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [273.55263157894734] = 273.55\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 273.55\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #64 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Transition\n", - "\u001b[1mMTOW actual:\u001b[0m 18.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30, Fulmar X\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker VXE30| 19.958047999999998| 1.109 | 0.0272 | 433.00 | 444.78 \n", - "Fulmar X | 20.0| 1.111 | 0.0278 | 800.00 | 822.22 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [444.7754831111111, 822.2222222222222] = 633.50\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.83\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 633.50\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.93\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.83\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #65 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Alcance de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Reach\n", - "\u001b[1mMTOW actual:\u001b[0m 91.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 VTOL| 100.0| 1.099 | 0.0247 | 800.00 | 819.78 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [819.7802197802197] = 819.78\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 819.78\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.91\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #66 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Autonomía de la aeronave\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL octo\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS, Skyeye 5000, Skyeye 5000 VTOL, Volitation VT510, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.8 VTOL FTUAS| 93.0| 0.930 | -0.0175 | 14.00 | 13.76 \n", - "Skyeye 5000 | 90.0| 0.900 | -0.0250 | 8.00 | 7.80 \n", - "Skyeye 5000 VTOL| 100.0| 1.000 | 0.0000 | 8.00 | 8.00 \n", - "Volitation VT510| 100.0| 1.000 | 0.0000 | 5.00 | 5.00 \n", - "Reach | 91.0| 0.910 | -0.0225 | 20.00 | 19.55 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [13.755, 7.8, 8.0, 5.0, 19.55] = 8.00\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 8.00\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.22\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #67 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Skyeye 5000 VTOL octo, Volitation VT510, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 42.00 | 41.66 \n", - "Skyeye 5000 VTOL| 100.0| 1.075 | 0.0188 | 42.00 | 42.79 \n", - "Skyeye 5000 VTOL octo| 100.0| 1.075 | 0.0188 | 38.00 | 38.72 \n", - "Volitation VT510| 100.0| 1.075 | 0.0188 | 50.00 | 50.94 \n", - "Reach | 91.0| 0.978 | -0.0054 | 35.00 | 34.81 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [41.66129032258065, 42.79032258064516, 38.71505376344086, 50.94086021505376, 34.81182795698925] = 41.66\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 41.66\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.22\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #68 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Integrator VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 75.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator, Integrator Extended Range (ER)\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Integrator | 74.8| 0.997 | -0.0007 | 46.30 | 46.27 \n", - "Integrator Extended Range (ER)| 74.8| 0.997 | -0.0007 | 46.30 | 46.27 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [46.26913333333333, 46.26913333333333] = 46.27\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.53\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 46.27\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.60\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.53\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #69 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m DeltaQuad Evo\n", - "\u001b[1mMTOW actual:\u001b[0m 10.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m V21, Ascend\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "V21 | 10.0| 1.000 | 0.0000 | 33.00 | 33.00 \n", - "Ascend | 9.5| 0.950 | -0.0125 | 30.00 | 29.62 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [33.0, 29.625] = 31.31\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.86\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 31.31\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.97\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.86\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #70 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m DeltaQuad Pro #MAP\n", - "\u001b[1mMTOW actual:\u001b[0m 6.2 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Mantis\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Mantis | 6.5| 1.048 | 0.0121 | 25.60 | 25.91 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [25.909677419354843] = 25.91\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 25.91\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #71 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m DeltaQuad Pro #CARGO\n", - "\u001b[1mMTOW actual:\u001b[0m 6.2 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Mantis\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Mantis | 6.5| 1.048 | 0.0121 | 25.60 | 25.91 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [25.909677419354843] = 25.91\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 25.91\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #72 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2600\n", - "\u001b[1mMTOW actual:\u001b[0m 15.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker XE | 13.6| 0.907 | -0.0233 | 20.00 | 19.53 \n", - "AAI Aerosonde| 13.1| 0.873 | -0.0317 | 30.85 | 29.87 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [19.533333333333335, 29.86894348051936] = 24.70\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.83\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 24.70\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.93\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.83\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #73 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad máxima (KIAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600\n", - "\u001b[1mMTOW actual:\u001b[0m 28.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Orbiter 3, ScanEagle, V35, V39, Skyeye 2930 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Orbiter 3 | 32.0| 1.143 | 0.0357 | 36.00 | 37.29 \n", - "ScanEagle | 26.5| 0.946 | -0.0134 | 41.20 | 40.65 \n", - "V35 | 32.0| 1.143 | 0.0357 | 33.00 | 34.18 \n", - "V39 | 24.0| 0.857 | -0.0357 | 33.00 | 31.82 \n", - "Skyeye 2930 VTOL| 28.0| 1.000 | 0.0000 | 30.00 | 30.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [37.28571428571428, 40.64821428571429, 34.17857142857142, 31.821428571428573, 30.0] = 34.18\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 34.18\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.22\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.91\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.93\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.92\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #74 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Stalker XE\n", - "\u001b[1mMTOW actual:\u001b[0m 13.6 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m V25, Skyeye 2600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "V25 | 12.5| 0.919 | -0.0202 | 15.50 | 15.19 \n", - "Skyeye 2600 | 15.0| 1.103 | 0.0257 | 10.00 | 10.26 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [15.18658088235294, 10.257352941176471] = 12.72\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.84\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.72\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.91\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.94\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.84\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #75 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Stalker VXE30\n", - "\u001b[1mMTOW actual:\u001b[0m 19.958047999999998 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Transition\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Transition | 18.0| 0.902 | -0.0245 | 13.00 | 12.68 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [12.68114837683525] = 12.68\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.68\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.91\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #76 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", - "\u001b[1mMTOW actual:\u001b[0m 42.2 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 3600 VTOL| 40.0| 0.948 | -0.0130 | 24.00 | 23.69 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [23.687203791469194] = 23.69\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 23.69\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.7 VTOL'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Aerosonde® Mk. 4.7 VTOL.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.8 Fixed wing'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Aerosonde® Mk. 4.8 Fixed wing.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #77 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL octo, Volitation VT510, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 15.00 | 14.88 \n", - "Skyeye 5000 VTOL octo| 100.0| 1.075 | 0.0188 | 24.00 | 24.45 \n", - "Volitation VT510| 100.0| 1.075 | 0.0188 | 25.00 | 25.47 \n", - "Reach | 91.0| 0.978 | -0.0054 | 13.00 | 12.93 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [14.879032258064516, 24.451612903225808, 25.47043010752688, 12.93010752688172] = 19.67\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 19.67\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.11\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #78 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m AAI Aerosonde\n", - "\u001b[1mMTOW actual:\u001b[0m 13.1 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m V25, Skyeye 2600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "V25 | 12.5| 0.954 | -0.0115 | 15.50 | 15.32 \n", - "Skyeye 2600 | 15.0| 1.145 | 0.0363 | 10.00 | 10.36 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [15.322519083969466, 10.36259541984733] = 12.84\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.84\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.84\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.91\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.94\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.84\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #79 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Fulmar X\n", - "\u001b[1mMTOW actual:\u001b[0m 20.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Transition\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Transition | 18.0| 0.900 | -0.0250 | 13.00 | 12.67 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [12.674999999999999] = 12.67\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 12.67\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Orbiter 4'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Orbiter 4.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #80 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 3\n", - "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 18.00 | 17.44 \n", - "Skyeye 3600 | 28.0| 0.875 | -0.0312 | 12.50 | 12.11 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [17.4375, 12.109375] = 14.77\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.82\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 14.77\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.88\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.97\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.92\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.82\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Mantis'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Mantis.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #81 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle\n", - "\u001b[1mMTOW actual:\u001b[0m 26.5 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m V32, Skyeye 2930 VTOL, Skyeye 3600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "V32 | 23.5| 0.887 | -0.0283 | 17.00 | 16.52 \n", - "Skyeye 2930 VTOL| 28.0| 1.057 | 0.0142 | 18.00 | 18.25 \n", - "Skyeye 3600 | 28.0| 1.057 | 0.0142 | 12.50 | 12.68 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [16.5188679245283, 18.254716981132074, 12.67688679245283] = 16.52\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.95\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 16.52\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.95\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Integrator.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator VTOL'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Integrator VTOL.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator Extended Range (ER)'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Integrator Extended Range (ER).\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #82 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle 3\n", - "\u001b[1mMTOW actual:\u001b[0m 36.3 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 3600 VTOL| 40.0| 1.102 | 0.0255 | 24.00 | 24.61 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [24.611570247933887] = 24.61\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 24.61\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'RQNan21A Blackjack'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para RQNan21A Blackjack.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #83 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m DeltaQuad Evo\n", - "\u001b[1mMTOW actual:\u001b[0m 10.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m V21, Ascend\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "V21 | 10.0| 1.000 | 0.0000 | 14.00 | 14.00 \n", - "Ascend | 9.5| 0.950 | -0.0125 | 13.00 | 12.84 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [14.0, 12.8375] = 13.42\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.86\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 13.42\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.95\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.96\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.86\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #MAP'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para DeltaQuad Pro #MAP.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #CARGO'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para DeltaQuad Pro #CARGO.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #84 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m V35\n", - "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 18.00 | 17.44 \n", - "Skyeye 3600 | 28.0| 0.875 | -0.0312 | 12.50 | 12.11 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [17.4375, 12.109375] = 14.77\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.82\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 14.77\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.88\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.97\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.92\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.82\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #85 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m V39\n", - "\u001b[1mMTOW actual:\u001b[0m 24.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m V32\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "V32 | 23.5| 0.979 | -0.0052 | 17.00 | 16.91 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [16.911458333333332] = 16.91\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.61\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 16.91\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.79\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.61\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #86 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT370\n", - "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 3600 VTOL| 40.0| 1.000 | 0.0000 | 24.00 | 24.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [24.0] = 24.00\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 24.00\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #87 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Velocidad de pérdida (KCAS)\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL octo, Volitation VT510, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.900 | -0.0250 | 15.00 | 14.62 \n", - "Skyeye 5000 VTOL octo| 100.0| 1.000 | 0.0000 | 24.00 | 24.00 \n", - "Volitation VT510| 100.0| 1.000 | 0.0000 | 25.00 | 25.00 \n", - "Reach | 91.0| 0.910 | -0.0225 | 13.00 | 12.71 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [14.625, 24.0, 25.0, 12.7075] = 19.31\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 19.31\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.11\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #88 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m envergadura\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL, Skyeye 5000 VTOL octo, Volitation VT510, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 5.00 | 4.96 \n", - "Skyeye 5000 VTOL| 100.0| 1.075 | 0.0188 | 5.00 | 5.09 \n", - "Skyeye 5000 VTOL octo| 100.0| 1.075 | 0.0188 | 5.00 | 5.09 \n", - "Volitation VT510| 100.0| 1.075 | 0.0188 | 5.10 | 5.20 \n", - "Reach | 91.0| 0.978 | -0.0054 | 6.00 | 5.97 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [4.959677419354839, 5.094086021505376, 5.094086021505376, 5.195967741935483, 5.967741935483871] = 5.09\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 1.00\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 5.09\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.22\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.96\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 1.00\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #89 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m envergadura\n", - "\u001b[1mAeronave a imputar:\u001b[0m Integrator VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 75.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Integrator, Integrator Extended Range (ER)\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Integrator | 74.8| 0.997 | -0.0007 | 4.80 | 4.80 \n", - "Integrator Extended Range (ER)| 74.8| 0.997 | -0.0007 | 4.80 | 4.80 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [4.796799999999999, 4.796799999999999] = 4.80\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.53\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 4.80\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.60\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.53\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.8 VTOL FTUAS'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Aerosonde® Mk. 4.8 VTOL FTUAS.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #90 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Cuerda\n", - "\u001b[1mAeronave a imputar:\u001b[0m Fulmar X\n", - "\u001b[1mMTOW actual:\u001b[0m 20.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker VXE30| 19.958047999999998| 0.998 | -0.0005 | 0.32 | 0.32 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [0.318028178521024] = 0.32\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 0.32\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #91 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Cuerda\n", - "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 4\n", - "\u001b[1mMTOW actual:\u001b[0m 55.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.973 | -0.0068 | 0.35 | 0.35 \n", - "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.989 | -0.0027 | 0.35 | 0.35 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [0.34959999999999997, 0.35104] = 0.35\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.52\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 0.35\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.59\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.52\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Orbiter 3'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Orbiter 3.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Mantis'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Mantis.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'ScanEagle'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para ScanEagle.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Integrator.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator VTOL'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Integrator VTOL.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator Extended Range (ER)'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Integrator Extended Range (ER).\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'ScanEagle 3'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para ScanEagle 3.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #92 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Cuerda\n", - "\u001b[1mAeronave a imputar:\u001b[0m RQNan21A Blackjack\n", - "\u001b[1mMTOW actual:\u001b[0m 61.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 VTOL, Aerosonde® Mk. 4.8 Fixed wing\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 VTOL| 53.5| 0.877 | -0.0307 | 0.35 | 0.34 \n", - "Aerosonde® Mk. 4.8 Fixed wing| 54.4| 0.892 | -0.0270 | 0.35 | 0.34 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [0.34118032786885244, 0.3424786885245901] = 0.34\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.48\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 0.34\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.89\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.53\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.48\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Imputación descartada por baja confianza: 0.475 < 0.5.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Evo'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para DeltaQuad Evo.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #MAP'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para DeltaQuad Pro #MAP.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #CARGO'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para DeltaQuad Pro #CARGO.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V21'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para V21.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #92 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Cuerda\n", - "\u001b[1mAeronave a imputar:\u001b[0m V25\n", - "\u001b[1mMTOW actual:\u001b[0m 12.5 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker XE | 13.6| 1.088 | 0.0220 | 0.24 | 0.24 \n", - "AAI Aerosonde| 13.1| 1.048 | 0.0120 | 0.20 | 0.20 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [0.244258, 0.19891034482758618] = 0.22\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.84\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 0.22\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.96\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.84\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V32'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para V32.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V35'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para V35.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'V39'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para V39.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #93 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Cuerda\n", - "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT370\n", - "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 Fixed Wing| 42.2| 1.055 | 0.0138 | 0.35 | 0.36 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [0.35683999999999994] = 0.36\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 0.36\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #94 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Cuerda\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 2600\n", - "\u001b[1mMTOW actual:\u001b[0m 15.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, AAI Aerosonde\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker XE | 13.6| 0.907 | -0.0233 | 0.24 | 0.23 \n", - "AAI Aerosonde| 13.1| 0.873 | -0.0317 | 0.20 | 0.19 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [0.23342333333333332, 0.19032758620689652] = 0.21\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.81\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 0.21\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.92\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.91\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.81\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 2930 VTOL'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Skyeye 2930 VTOL.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 3600'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Skyeye 3600.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #95 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Cuerda\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 3600 VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Aerosonde® Mk. 4.7 Fixed Wing| 42.2| 1.055 | 0.0138 | 0.35 | 0.36 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [0.35683999999999994] = 0.36\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 0.36\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 5000'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Skyeye 5000.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 5000 VTOL'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Skyeye 5000 VTOL.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Skyeye 5000 VTOL octo'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Skyeye 5000 VTOL octo.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Volitation VT510'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Volitation VT510.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Ascend'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Ascend.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #96 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Cuerda\n", - "\u001b[1mAeronave a imputar:\u001b[0m Transition\n", - "\u001b[1mMTOW actual:\u001b[0m 18.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker VXE30| 19.958047999999998| 1.109 | 0.0272 | 0.32 | 0.33 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [0.3268483894678044] = 0.33\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.57\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 0.33\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.57\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Reach'para el parametro 'Cuerda'.\n", - "No se pudo imputar: Cuerda para Reach.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #97 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m payload\n", - "\u001b[1mAeronave a imputar:\u001b[0m AAI Aerosonde\n", - "\u001b[1mMTOW actual:\u001b[0m 13.1 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker XE, V25, Skyeye 2600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker XE | 13.6| 1.038 | 0.0095 | 2.49 | 2.52 \n", - "V25 | 12.5| 0.954 | -0.0115 | 2.20 | 2.17 \n", - "Skyeye 2600 | 15.0| 1.145 | 0.0363 | 4.00 | 4.15 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [2.518560923664122, 2.174809160305344, 4.145038167938932] = 2.52\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.95\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 2.52\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.99\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.95\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #98 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m payload\n", - "\u001b[1mAeronave a imputar:\u001b[0m Fulmar X\n", - "\u001b[1mMTOW actual:\u001b[0m 20.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30, Transition\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker VXE30| 19.958047999999998| 0.998 | -0.0005 | 2.49 | 2.49 \n", - "Transition | 18.0| 0.900 | -0.0250 | 1.50 | 1.46 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [2.4934477499536, 1.4625] = 1.98\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.86\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1.98\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.86\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #99 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m payload\n", - "\u001b[1mAeronave a imputar:\u001b[0m Mantis\n", - "\u001b[1mMTOW actual:\u001b[0m 6.5 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m DeltaQuad Pro #MAP, DeltaQuad Pro #CARGO\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "DeltaQuad Pro #MAP| 6.2| 0.954 | -0.0115 | 1.20 | 1.19 \n", - "DeltaQuad Pro #CARGO| 6.2| 0.954 | -0.0115 | 1.20 | 1.19 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [1.1861538461538461, 1.1861538461538461] = 1.19\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.51\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 1.19\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.57\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.51\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #100 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Empty weight\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.7 Fixed Wing\n", - "\u001b[1mMTOW actual:\u001b[0m 42.2 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 3600 VTOL| 40.0| 0.948 | -0.0130 | 11.00 | 10.86 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [10.856635071090047] = 10.86\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.60\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 10.86\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.77\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.60\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.7 VTOL'para el parametro 'Empty weight'.\n", - "No se pudo imputar: Empty weight para Aerosonde® Mk. 4.7 VTOL.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.8 Fixed wing'para el parametro 'Empty weight'.\n", - "No se pudo imputar: Empty weight para Aerosonde® Mk. 4.8 Fixed wing.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #101 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Empty weight\n", - "\u001b[1mAeronave a imputar:\u001b[0m Aerosonde® Mk. 4.8 VTOL FTUAS\n", - "\u001b[1mMTOW actual:\u001b[0m 93.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL octo, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.968 | -0.0081 | 32.00 | 31.74 \n", - "Skyeye 5000 VTOL octo| 100.0| 1.075 | 0.0188 | 35.00 | 35.66 \n", - "Reach | 91.0| 0.978 | -0.0054 | 31.00 | 30.83 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [31.741935483870968, 35.65860215053763, 30.833333333333332] = 31.74\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.95\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 31.74\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.96\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.94\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.95\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #102 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Empty weight\n", - "\u001b[1mAeronave a imputar:\u001b[0m Fulmar X\n", - "\u001b[1mMTOW actual:\u001b[0m 20.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Stalker VXE30, Transition\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Stalker VXE30| 19.958047999999998| 0.998 | -0.0005 | 17.46 | 17.45 \n", - "Transition | 18.0| 0.900 | -0.0250 | 5.80 | 5.65 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [17.4541342496752, 5.654999999999999] = 11.55\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.86\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 11.55\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.95\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.97\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.86\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Orbiter 4'para el parametro 'Empty weight'.\n", - "No se pudo imputar: Empty weight para Orbiter 4.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #103 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Empty weight\n", - "\u001b[1mAeronave a imputar:\u001b[0m Orbiter 3\n", - "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 7.10 | 6.88 \n", - "Skyeye 3600 | 28.0| 0.875 | -0.0312 | 11.50 | 11.14 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [6.878125, 11.140625] = 9.01\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.82\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 9.01\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.88\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.92\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.82\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Mantis'para el parametro 'Empty weight'.\n", - "No se pudo imputar: Empty weight para Mantis.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #104 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Empty weight\n", - "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle\n", - "\u001b[1mMTOW actual:\u001b[0m 26.5 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m V32, Skyeye 2930 VTOL, Skyeye 3600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "V32 | 23.5| 0.887 | -0.0283 | 6.45 | 6.27 \n", - "Skyeye 2930 VTOL| 28.0| 1.057 | 0.0142 | 7.10 | 7.20 \n", - "Skyeye 3600 | 28.0| 1.057 | 0.0142 | 11.50 | 11.66 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [6.267452830188679, 7.200471698113207, 11.662735849056602] = 7.20\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.95\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 7.20\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.93\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 1.00\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.95\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.95\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator'para el parametro 'Empty weight'.\n", - "No se pudo imputar: Empty weight para Integrator.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator VTOL'para el parametro 'Empty weight'.\n", - "No se pudo imputar: Empty weight para Integrator VTOL.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator Extended Range (ER)'para el parametro 'Empty weight'.\n", - "No se pudo imputar: Empty weight para Integrator Extended Range (ER).\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #105 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Empty weight\n", - "\u001b[1mAeronave a imputar:\u001b[0m ScanEagle 3\n", - "\u001b[1mMTOW actual:\u001b[0m 36.3 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 3600 VTOL| 40.0| 1.102 | 0.0255 | 11.00 | 11.28 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [11.280303030303031] = 11.28\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.58\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 11.28\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.90\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.74\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.58\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'RQNan21A Blackjack'para el parametro 'Empty weight'.\n", - "No se pudo imputar: Empty weight para RQNan21A Blackjack.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #MAP'para el parametro 'Empty weight'.\n", - "No se pudo imputar: Empty weight para DeltaQuad Pro #MAP.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #CARGO'para el parametro 'Empty weight'.\n", - "No se pudo imputar: Empty weight para DeltaQuad Pro #CARGO.\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #106 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Empty weight\n", - "\u001b[1mAeronave a imputar:\u001b[0m V35\n", - "\u001b[1mMTOW actual:\u001b[0m 32.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 2930 VTOL, Skyeye 3600\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 2930 VTOL| 28.0| 0.875 | -0.0312 | 7.10 | 6.88 \n", - "Skyeye 3600 | 28.0| 0.875 | -0.0312 | 11.50 | 11.14 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [6.878125, 11.140625] = 9.01\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.82\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 9.01\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.89\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.88\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.98\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.92\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.82\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #107 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Empty weight\n", - "\u001b[1mAeronave a imputar:\u001b[0m V39\n", - "\u001b[1mMTOW actual:\u001b[0m 24.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m V32\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "V32 | 23.5| 0.979 | -0.0052 | 6.45 | 6.42 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [6.41640625] = 6.42\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.61\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 6.42\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.98\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.79\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.61\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #108 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Empty weight\n", - "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT370\n", - "\u001b[1mMTOW actual:\u001b[0m 40.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 3600 VTOL\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 3600 VTOL| 40.0| 1.000 | 0.0000 | 11.00 | 11.00 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [11.0] = 11.00\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.62\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 11.00\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 0.78\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 1.00\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.50\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.80\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.62\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #109 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Empty weight\n", - "\u001b[1mAeronave a imputar:\u001b[0m Skyeye 5000 VTOL\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL octo, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.900 | -0.0250 | 32.00 | 31.20 \n", - "Skyeye 5000 VTOL octo| 100.0| 1.000 | 0.0000 | 35.00 | 35.00 \n", - "Reach | 91.0| 0.910 | -0.0225 | 31.00 | 30.30 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [31.2, 35.0, 30.302500000000002] = 31.20\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.91\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 31.20\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.87\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.91\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.91\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "\u001b[1m======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #110 ========================\u001b[0m\n", - "\u001b[1mParámetro:\u001b[0m Empty weight\n", - "\u001b[1mAeronave a imputar:\u001b[0m Volitation VT510\n", - "\u001b[1mMTOW actual:\u001b[0m 100.0 kg\n", - "\u001b[1mRango Similitud:\u001b[0m 85% - 115%\n", - "\u001b[1mCandidatas dentro del rango:\u001b[0m Skyeye 5000, Skyeye 5000 VTOL octo, Reach\n", - "\n", - "Aeronaves Válidas para el Cálculo:\n", - "-----------------------------------------------------------------------------------------------\n", - "Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado\n", - "------------|----------------|------------------------------|-------------------|----------------|---------------\n", - "Skyeye 5000 | 90.0| 0.900 | -0.0250 | 32.00 | 31.20 \n", - "Skyeye 5000 VTOL octo| 100.0| 1.000 | 0.0000 | 35.00 | 35.00 \n", - "Reach | 91.0| 0.910 | -0.0225 | 31.00 | 30.30 \n", - "-----------------------------------------------------------------------------------------------\n", - "\n", - "Cálculo del Valor Final:\n", - "\u001b[1m- Se tomó la mediana de los valores ajustados [31.2, 35.0, 30.302500000000002] = 31.20\u001b[0m\n", - "\u001b[1m- Nivel de Confianza calculado:\u001b[0m 0.91\n", - "\u001b[1m- Valor Imputado Final:\u001b[0m 31.20\n", - "\n", - "Detalle del Cálculo de Confianza:\n", - "\u001b[1m- Penalización por pocos candidatos:\u001b[0m 1.00\n", - "\u001b[1m- Cantidad Ponderada (basada en MTOW):\u001b[0m 0.94\n", - "\u001b[1m- Ponderación del modelo (R² o dispersión):\u001b[0m 0.87\n", - "\u001b[1m- Confianza Base:\u001b[0m 0.91\n", - "\u001b[1m- Confianza Final (tras penalización):\u001b[0m 0.91\n", - "\u001b[1m============================================================================================\u001b[0m\n", - "\n", - "\n", - "=== Generando reporte final ===\n" - ] - }, { "data": { "text/html": [ @@ -16697,893 +13123,5446 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

Reporte Final de Imputaciones

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df_filtrado_base

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AeronaveParámetroValor ImputadoNivel de Confianza
0Aerosonde® Mk. 4.8 VTOL FTUASVelocidad a la que se realiza el crucero (KTAS)32.3161.000
1AAI AerosondeVelocidad a la que se realiza el crucero (KTAS)21.6250.953
2Orbiter 4Velocidad a la que se realiza el crucero (KTAS)27.2690.959
3Orbiter 3Velocidad a la que se realiza el crucero (KTAS)26.5670.551
4Integrator VTOLVelocidad a la que se realiza el crucero (KTAS)30.9330.621
5Integrator Extended Range (ER)Velocidad a la que se realiza el crucero (KTAS)30.9530.622
6Skyeye 3600Velocidad a la que se realiza el crucero (KTAS)27.3440.972Stalker XEStalker VXE30Aerosonde Mk. 4.7 Fixed WingAerosonde Mk. 4.7 VTOLAerosonde Mk. 4.8 Fixed wingAerosonde Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQ Nan 21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
7Skyeye 5000 VTOL octoVelocidad a la que se realiza el crucero (KTAS)31.7191.000Modelo
8Orbiter 4Techo de servicio máximo9633.8640.972Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NaNNaN30.406584NaNNaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
9Orbiter 3Techo de servicio máximo8010.3360.855
10MantisTecho de servicio máximo12.9720.509
11Integrator VTOLTecho de servicio máximo19487.0000.532
12Skyeye 2600Techo de servicio máximo13122.5000.809
13Skyeye 2930 VTOLTecho de servicio máximo16571.4290.901
14Skyeye 3600Techo de servicio máximo16571.4290.901Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaNNaN17000.010000.013000.016000.0
15Skyeye 3600 VTOLTecho de servicio máximo14902.1250.972Área del ala0.871.1582831.551.551.55NaN0.57NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN
16Skyeye 5000Techo de servicio máximo16044.4440.922Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
17Skyeye 5000 VTOLTecho de servicio máximo15640.0000.930Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.712
18Skyeye 5000 VTOL octoTecho de servicio máximo15640.0000.930Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
19Aerosonde® Mk. 4.8 VTOL FTUASÁrea del ala2.6640.565Alcance de la aeronave370.0433.0NaNNaNNaNNaN3270.0800.0NaN50.025.0NaNNaNNaN500.0NaNNaN270.0100.0100.0NaNNaNNaNNaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaNNaN
20Fulmar XÁrea del ala1.1580.621Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.0
21Orbiter 4Área del ala1.5430.523Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0NaN30.0NaN33.042.042.038.050.030.030.035.0
22Orbiter 3Área del ala1.1290.809Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.0
23ScanEagleÁrea del ala1.1810.856Velocidad de pérdida limpia (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaNNaN25.0NaNNaNNaN
24ScanEagle 3Área del ala1.3540.577envergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
25V35Área del ala1.1290.809Cuerda0.2390.3181950.3520.3520.352NaN0.196552NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
26Volitation VT370Área del ala1.4460.869
27Volitation VT510Área del ala2.6150.581payload2.4947562.49475614.511.317.722.7NaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
28AscendÁrea del ala0.8310.758Empty weight10.88620817.463292NaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
29TransitionÁrea del ala1.1900.574
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Configuración Inicial ===\n", + "\n", + "Valores configurados: Rango MTOW [85% - 115%], Confianza Mínima: 0.50\n", + "\n", + "================================================================================\n", + "\u001b[1m=== INICIO DE ITERACIÓN 1 ===\u001b[0m\n", + "================================================================================\n", + "\n", + "=== Iteración 1: Resumen antes de imputaciones ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de Valores Faltantes Antes de Iteración 1

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ColumnaValores Faltantes
30ReachÁrea del ala2.6800.5600Stalker XE32.000
31Fulmar XRelación de aspecto del ala15.3180.6211Stalker VXE3033.000
32Orbiter 4Relación de aspecto del ala12.4400.5232Aerosonde Mk. 4.7 Fixed Wing32.000
33V25Relación de aspecto del ala15.2850.5103Aerosonde Mk. 4.7 VTOL31.000
34Volitation VT370Relación de aspecto del ala12.6720.5974Aerosonde Mk. 4.8 Fixed wing35.000
35Skyeye 3600 VTOLRelación de aspecto del ala12.6720.5975Aerosonde Mk. 4.8 VTOL FTUAS43.000
36Skyeye 5000Relación de aspecto del ala12.6040.6076AAI Aerosonde34.000
37Skyeye 5000 VTOLRelación de aspecto del ala12.2810.5917Fulmar X41.000
38Skyeye 5000 VTOL octoRelación de aspecto del ala12.2810.5918Orbiter 443.000
39Volitation VT510Relación de aspecto del ala12.2810.5919Orbiter 342.000
40TransitionRelación de aspecto del ala15.7430.57410Mantis41.000
41ReachRelación de aspecto del ala12.5690.61211ScanEagle40.000
42Aerosonde® Mk. 4.8 VTOL FTUASLongitud del fuselaje3.5661.00012Integrator40.000
4313Integrator VTOLLongitud del fuselaje2.4980.532
44V39Longitud del fuselaje1.3750.854
45Aerosonde® Mk. 4.7 Fixed WingAlcance de la aeronave296.0900.59944.000
46Aerosonde® Mk. 4.7 VTOLAlcance de la aeronave123.4480.84414Integrator Extended Range (ER)42.000
47Aerosonde® Mk. 4.8 Fixed wingAlcance de la aeronave122.9110.85415ScanEagle 340.000
48Aerosonde® Mk. 4.8 VTOL FTUASAlcance de la aeronave815.0540.58816RQ Nan 21A Blackjack39.000
49IntegratorAlcance de la aeronave500.0000.62217DeltaQuad Evo33.000
50Integrator VTOLAlcance de la aeronave499.6670.62118DeltaQuad Pro #MAP37.000
51ScanEagle 3Alcance de la aeronave178.0820.83319DeltaQuad Pro #CARGO37.000
5220V21Alcance de la aeronave270.0000.62231.000
5321V25Alcance de la aeronave1843.6900.85431.000
5422V32Alcance de la aeronave770.2130.55832.000
5523V35Alcance de la aeronave50.0000.62238.000
5624V3939.000
25Volitation VT370Alcance de la aeronave300.0000.62237.000
5726Skyeye 2600Alcance de la aeronave1763.9080.83238.000
5827Skyeye 2930 VTOLAlcance de la aeronave51.7860.56036.000
5928Skyeye 3600Alcance de la aeronave51.7860.56038.000
6029Skyeye 3600 VTOL34.000
30Skyeye 5000Alcance de la aeronave822.2220.57333.000
6131Skyeye 5000 VTOL36.000
32Skyeye 5000 VTOL octoAlcance de la aeronave800.0000.62237.000
6233Volitation VT510Alcance de la aeronave800.0000.62235.000
6334AscendAlcance de la aeronave273.5530.59834.000
6435TransitionAlcance de la aeronave633.4990.83034.000
6536ReachAlcance de la aeronave819.7800.578
66Skyeye 5000 VTOL octoAutonomía de la aeronave8.0001.00034.000
67Aerosonde® Mk. 4.8 VTOL FTUASVelocidad máxima (KIAS)41.6611.000
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Sumatoria Total de Valores Faltantes

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ResumenCantidad
68Integrator VTOLVelocidad máxima (KIAS)46.2690.5320Total de Valores Faltantes1356.000
69DeltaQuad EvoVelocidad máxima (KIAS)31.3120.865
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m*** IMPUTACIÓN POR SIMILITUD - ITERACIÓN 1 ***\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 14.0, d: 64.29%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.15000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.150\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.770\n", + " vecino 'Volitation VT510' → sim_i: 0.770\n", + " vecino 'Reach' → sim_i: 0.795\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.770', '0.770', '0.795']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.778\n", + " Media de valores (y): 30.261\n", + " Coeficiente de variación (CV): 0.851\n", + " Dispersión: 2.247\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.504\n", + " Confianza final: 39.244%\n", + "✅ Valor imputado: 30.229 (conf 0.392, datos 3, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: AAI Aerosonde - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.88, Objetivo: 0.57, d: 54.39%, g: -100.00, Bono: -0.05000\n", + " Parámetro: envergadura, Vecino: 2.6, Objetivo: 2.9, d: 10.34%, g: 94.70, Bono: 0.04735\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.05, Objetivo: 1.7, d: 20.59%, g: -99.99, Bono: -0.04999\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': -0.05265\n", + "⚠️ Diferencia NaN para el parámetro 'Potencia específica (P/W)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.0, Objetivo: 26.0, d: 92.31%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.053\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 2600' → sim_i: 0.664\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.664']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.664\n", + " Media de valores (y): 36.094\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.517%\n", + "✅ Valor imputado: 36.094 (conf 0.035, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 5.2, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: envergadura, Vecino: 4.8, Objetivo: 5.2, d: 7.69%, g: 96.72, Bono: 0.04836\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 1.2, d: 150.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5, Objetivo: 1.2, d: 108.33%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01457\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 24.0, d: 17.50%, g: 49.96, Bono: 0.02498\n", + " Parámetro: Autonomía de la aeronave, Vecino: 16.0, Objetivo: 24.0, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 36.0, d: 7.11%, g: 96.98, Bono: 0.04849\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 46.3, Objetivo: 36.0, d: 28.61%, g: -96.24, Bono: -0.04812\n", + " Bono total para 'prest': -0.01814\n", + " Bono geométrico: -0.015\n", + " Bono prestacional: -0.018\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 0.916\n", + " vecino 'RQ Nan 21A Blackjack' → sim_i: 0.859\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.916', '0.859']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.889\n", + " Media de valores (y): 30.571\n", + " Coeficiente de variación (CV): 0.789\n", + " Dispersión: 3.227\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.373\n", + " Confianza final: 33.168%\n", + "✅ Valor imputado: 30.466 (conf 0.332, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.1, Objetivo: 4.4, d: 29.55%, g: -95.56, Bono: -0.04778\n", + " Parámetro: envergadura, Vecino: 4.0, Objetivo: 4.4, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.71, Objetivo: 1.2, d: 42.50%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.4, Objetivo: 1.2, d: 100.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.09982\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 18.0, Objetivo: 6.0, d: 200.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 18.0, Objetivo: 6.0, d: 200.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 41.2, Objetivo: 36.0, d: 14.44%, g: 81.06, Bono: 0.04053\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 41.2, Objetivo: 36.0, d: 14.44%, g: 81.06, Bono: 0.04053\n", + " Bono total para 'prest': -0.01894\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.019\n", + " vecino 'ScanEagle' → sim_i: 0.262\n", + " vecino 'ScanEagle 3' → sim_i: 0.487\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.262', '0.487']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.408\n", + " Media de valores (y): 28.164\n", + " Coeficiente de variación (CV): 0.825\n", + " Dispersión: 2.461\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.384\n", + " Confianza final: 15.678%\n", + "✅ Valor imputado: 27.426 (conf 0.157, datos 2, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8, Objetivo: 4.8, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: envergadura, Vecino: 4.8, Objetivo: 4.8, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5, Objetivo: 2.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5, Objetivo: 2.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.19988\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 24.0, Objetivo: 19.0, d: 26.32%, g: -97.32, Bono: -0.04866\n", + " Parámetro: Autonomía de la aeronave, Vecino: 16.0, Objetivo: 19.0, d: 15.79%, g: 70.51, Bono: 0.03526\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 46.3, Objetivo: 46.3, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 46.3, Objetivo: 46.3, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': 0.08654\n", + " Bono geométrico: 0.200\n", + " Bono prestacional: 0.087\n", + " vecino 'Integrator' → sim_i: 1.236\n", + " vecino 'RQ Nan 21A Blackjack' → sim_i: 0.611\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.236', '0.611']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.029\n", + " Media de valores (y): 32.375\n", + " Coeficiente de variación (CV): 0.912\n", + " Dispersión: 1.422\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.410\n", + " Confianza final: 42.216%\n", + "✅ Valor imputado: 31.894 (conf 0.422, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad a la que se realiza el crucero (KTAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 2.615, Objetivo: 2.615, d: 0.00%, g: 99.94, Bono: 0.04997\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 5.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 3.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 3.5, d: 34.63%, g: -79.86, Bono: -0.03993\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.18823\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Autonomía de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 38.0, d: 10.53%, g: 94.45, Bono: 0.04723\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 38.0, d: 31.58%, g: -92.58, Bono: -0.04629\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 38.0, d: 7.89%, g: 96.63, Bono: 0.04831\n", + " Bono total para 'prest': 0.04925\n", + " Bono geométrico: 0.188\n", + " Bono prestacional: 0.049\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 1.062\n", + " vecino 'Volitation VT510' → sim_i: 1.062\n", + " vecino 'Reach' → sim_i: 1.029\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.062', '1.062', '1.029']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.051\n", + " Media de valores (y): 30.261\n", + " Coeficiente de variación (CV): 0.851\n", + " Dispersión: 2.247\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.504\n", + " Confianza final: 53.011%\n", + "✅ Valor imputado: 30.291 (conf 0.530, datos 3, familia F1)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 5.2, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: envergadura, Vecino: 4.8, Objetivo: 5.2, d: 7.69%, g: 96.72, Bono: 0.04836\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 1.2, d: 150.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5, Objetivo: 1.2, d: 108.33%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01457\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 24.0, d: 17.50%, g: 49.96, Bono: 0.02498\n", + " Parámetro: Autonomía de la aeronave, Vecino: 16.0, Objetivo: 24.0, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 36.0, d: 7.11%, g: 96.98, Bono: 0.04849\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 46.3, Objetivo: 36.0, d: 28.61%, g: -96.24, Bono: -0.04812\n", + " Bono total para 'prest': -0.01814\n", + " Bono geométrico: -0.015\n", + " Bono prestacional: -0.018\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 0.916\n", + " vecino 'RQ Nan 21A Blackjack' → sim_i: 0.859\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.916', '0.859']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.889\n", + " Media de valores (y): 9110.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 9090.000\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.137\n", + " Confianza final: 12.136%\n", + "✅ Valor imputado: 9403.635 (conf 0.121, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.1, Objetivo: 4.4, d: 29.55%, g: -95.56, Bono: -0.04778\n", + " Parámetro: envergadura, Vecino: 4.0, Objetivo: 4.4, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.71, Objetivo: 1.2, d: 42.50%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.4, Objetivo: 1.2, d: 100.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.09982\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 18.0, Objetivo: 6.0, d: 200.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 18.0, Objetivo: 6.0, d: 200.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 41.2, Objetivo: 36.0, d: 14.44%, g: 81.06, Bono: 0.04053\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 41.2, Objetivo: 36.0, d: 14.44%, g: 81.06, Bono: 0.04053\n", + " Bono total para 'prest': -0.01894\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.019\n", + " vecino 'ScanEagle' → sim_i: 0.262\n", + " vecino 'ScanEagle 3' → sim_i: 0.487\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.262', '0.487']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.408\n", + " Media de valores (y): 9760.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 9740.000\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.137\n", + " Confianza final: 5.574%\n", + "✅ Valor imputado: 6839.145 (conf 0.056, datos 2, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2600 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.88, d: 35.23%, g: -75.44, Bono: -0.03772\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.6, d: 11.54%, g: 92.67, Bono: 0.04633\n", + " Parámetro: envergadura, Vecino: 3.0, Objetivo: 2.6, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 2.05, d: 17.07%, g: 55.96, Bono: 0.02798\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.3, Objetivo: 2.05, d: 12.20%, g: 91.03, Bono: 0.04551\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.11918\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 2.0, d: 1200.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 12.0, Objetivo: 2.0, d: 500.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 36.09414654431562, d: 39.39%, g: -14.71, Bono: -0.00736\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.10736\n", + " Bono geométrico: 0.119\n", + " Bono prestacional: -0.107\n", + " vecino 'AAI Aerosonde' → sim_i: 0.863\n", + " vecino 'Transition' → sim_i: 0.012\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.863', '0.012']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.851\n", + " Media de valores (y): 14000.000\n", + " Coeficiente de variación (CV): 0.857\n", + " Dispersión: 1000.000\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.394\n", + " Confianza final: 33.507%\n", + "✅ Valor imputado: 14972.956 (conf 0.335, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.2, Objetivo: 2.93, d: 9.22%, g: 95.83, Bono: 0.04791\n", + " Parámetro: envergadura, Vecino: 3.5, Objetivo: 2.93, d: 19.45%, g: 13.34, Bono: 0.00667\n", + " Parámetro: envergadura, Vecino: 3.9, Objetivo: 2.93, d: 33.11%, g: -87.92, Bono: -0.04396\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.0, Objetivo: 2.03, d: 50.74%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.88, Objetivo: 2.03, d: 7.39%, g: 96.86, Bono: 0.04843\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00905\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.5, Objetivo: 3.0, d: 50.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.8, Objetivo: 3.0, d: 6.67%, g: 97.17, Bono: 0.04858\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.5, Objetivo: 3.0, d: 50.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 26.250288395865905, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 26.250288395865905, d: 4.17%, g: 98.38, Bono: 0.04919\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 26.250288395865905, d: 4.17%, g: 98.38, Bono: 0.04919\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 30.0, d: 10.00%, g: 95.11, Bono: 0.04755\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 30.0, d: 10.00%, g: 95.11, Bono: 0.04755\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 30.0, d: 10.00%, g: 95.11, Bono: 0.04755\n", + " Bono total para 'prest': 0.22017\n", + " Bono geométrico: 0.009\n", + " Bono prestacional: 0.220\n", + " vecino 'V32' → sim_i: 0.873\n", + " vecino 'V35' → sim_i: 1.009\n", + " vecino 'V39' → sim_i: 1.009\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.873', '1.009', '1.009']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.968\n", + " Media de valores (y): 16000.000\n", + " Coeficiente de variación (CV): 1.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.549\n", + " Confianza final: 53.099%\n", + "✅ Valor imputado: 16000.000 (conf 0.531, datos 3, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 VTOL - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.5, Objetivo: 3.6, d: 2.78%, g: 99.17, Bono: 0.04958\n", + " Parámetro: envergadura, Vecino: 6.5, Objetivo: 3.6, d: 80.56%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.88, Objetivo: 2.42, d: 22.31%, g: -99.42, Bono: -0.04971\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.02, Objetivo: 2.42, d: 16.53%, g: 62.74, Bono: 0.03137\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01875\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.8, Objetivo: 6.0, d: 53.33%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 15.0, Objetivo: 6.0, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 32.812860494832385, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 32.812860494832385, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': 0.06105\n", + " Bono geométrico: -0.019\n", + " Bono prestacional: 0.061\n", + " vecino 'V35' → sim_i: 0.042\n", + " vecino 'Volitation VT370' → sim_i: 0.992\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.042', '0.992']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.953\n", + " Media de valores (y): 16500.000\n", + " Coeficiente de variación (CV): 0.939\n", + " Dispersión: 500.000\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.418\n", + " Confianza final: 39.867%\n", + "✅ Valor imputado: 16959.092 (conf 0.399, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 3.5, d: 34.63%, g: -79.86, Bono: -0.03993\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.03832\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 14.0, Objetivo: 8.0, d: 75.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 8.0, d: 37.50%, g: -49.96, Bono: -0.02498\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 8.0, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 30.625336461843556, d: 7.14%, g: 96.96, Bono: 0.04848\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 30.625336461843556, d: 10.71%, g: 94.18, Bono: 0.04709\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 42.0, d: 19.05%, g: 22.32, Bono: 0.01116\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 42.0, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Bono total para 'prest': 0.01231\n", + " Bono geométrico: 0.038\n", + " Bono prestacional: 0.012\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 0.972\n", + " vecino 'Volitation VT510' → sim_i: 1.000\n", + " vecino 'Reach' → sim_i: 0.963\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.972', '1.000', '0.963']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.979\n", + " Media de valores (y): 16000.000\n", + " Coeficiente de variación (CV): 0.898\n", + " Dispersión: 816.497\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.518\n", + " Confianza final: 50.706%\n", + "✅ Valor imputado: 16009.436 (conf 0.507, datos 3, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 3.5, d: 34.63%, g: -79.86, Bono: -0.03993\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.03832\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Autonomía de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 38.0, d: 31.58%, g: -92.58, Bono: -0.04629\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 38.0, d: 7.89%, g: 96.63, Bono: 0.04831\n", + " Bono total para 'prest': 0.00202\n", + " Bono geométrico: 0.038\n", + " Bono prestacional: 0.002\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 0.841\n", + " vecino 'Volitation VT510' → sim_i: 0.865\n", + " vecino 'Reach' → sim_i: 0.832\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.841', '0.865', '0.832']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.846\n", + " Media de valores (y): 16000.000\n", + " Coeficiente de variación (CV): 0.898\n", + " Dispersión: 816.497\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.518\n", + " Confianza final: 43.846%\n", + "✅ Valor imputado: 16009.476 (conf 0.438, datos 3, familia F1)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.870\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.870']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.870\n", + " Media de valores (y): 2.615\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.607%\n", + "✅ Valor imputado: 2.615 (conf 0.046, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8768, Objetivo: 3.0, d: 62.56%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5908, Objetivo: 1.2, d: 115.90%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.10000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 8.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Alcance de la aeronave, Vecino: 433.0, Objetivo: 800.0, d: 45.88%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 17.602373279430935, Objetivo: 30.406584058544677, d: 42.11%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 25.034211403264, Objetivo: 41.7, d: 39.97%, g: -0.88, Bono: -0.00044\n", + " Bono total para 'prest': -0.05047\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.050\n", + " vecino 'Stalker VXE30' → sim_i: 0.799\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.799']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.799\n", + " Media de valores (y): 1.158\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.235%\n", + "✅ Valor imputado: 1.158 (conf 0.042, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 5.2, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 1.2, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01293\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 24.0, d: 17.50%, g: 49.96, Bono: 0.02498\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 36.0, d: 7.11%, g: 96.98, Bono: 0.04849\n", + " Bono total para 'prest': 0.07347\n", + " Bono geométrico: -0.013\n", + " Bono prestacional: 0.073\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 1.010\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.010']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.010\n", + " Media de valores (y): 1.550\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.349%\n", + "✅ Valor imputado: 1.550 (conf 0.053, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range (ER) - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 2.4, d: 25.00%, g: -97.93, Bono: -0.04897\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00141\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 18.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 25.7034073876187, d: 6.38%, g: 97.29, Bono: 0.04864\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 41.2, d: 18.84%, g: 26.66, Bono: 0.01333\n", + " Bono total para 'prest': 0.10952\n", + " Bono geométrico: -0.001\n", + " Bono prestacional: 0.110\n", + " vecino 'Aerosonde Mk. 4.7 Fixed Wing' → sim_i: 0.733\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.733']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.733\n", + " Media de valores (y): 1.550\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.885%\n", + "✅ Valor imputado: 1.550 (conf 0.039, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.8, d: 8.33%, g: 96.40, Bono: 0.04820\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 2.5, d: 20.00%, g: 0.00, Bono: 0.00000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.04820\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 16.0, d: 23.75%, g: -98.61, Bono: -0.04931\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 33.797246309677355, d: 19.09%, g: 21.34, Bono: 0.01067\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 46.3, d: 27.78%, g: -96.68, Bono: -0.04834\n", + " Bono total para 'prest': -0.08698\n", + " Bono geométrico: 0.048\n", + " Bono prestacional: -0.087\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 0.854\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.854']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.854\n", + " Media de valores (y): 1.550\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.526%\n", + "✅ Valor imputado: 1.550 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.2, d: 8.44%, g: 96.34, Bono: 0.04817\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.03, Objetivo: 1.0, d: 103.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00183\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.5, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 21.875240329888257, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.00448\n", + " Bono geométrico: -0.002\n", + " Bono prestacional: 0.004\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 0.194\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.194']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.194\n", + " Media de valores (y): 1.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 1.028%\n", + "✅ Valor imputado: 1.000 (conf 0.010, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.5, d: 16.29%, g: 65.47, Bono: 0.03274\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.03, Objetivo: 1.88, d: 7.98%, g: 96.58, Bono: 0.04829\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.08103\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 2.8, d: 7.14%, g: 96.96, Bono: 0.04848\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.14568\n", + " Bono geométrico: 0.081\n", + " Bono prestacional: 0.146\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 1.083\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.083']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.083\n", + " Media de valores (y): 1.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.736%\n", + "✅ Valor imputado: 1.000 (conf 0.057, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.9, d: 24.87%, g: -98.00, Bono: -0.04900\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.04900\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.5, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.05371\n", + " Bono geométrico: -0.049\n", + " Bono prestacional: 0.054\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 0.585\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.585']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.585\n", + " Media de valores (y): 1.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.101%\n", + "✅ Valor imputado: 1.000 (conf 0.031, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.6, Objetivo: 6.5, d: 44.62%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.42, Objetivo: 2.02, d: 19.80%, g: 5.01, Bono: 0.00251\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.04749\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 6.0, Objetivo: 15.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 27.344050412360318, d: 20.00%, g: -99.94, Bono: -0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.047\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 3600 VTOL' → sim_i: 0.852\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.852']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.852\n", + " Media de valores (y): 1.320\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.513%\n", + "✅ Valor imputado: 1.320 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT510 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 5.1, d: 1.96%, g: 99.59, Bono: 0.04979\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 2.905, d: 20.48%, g: -99.99, Bono: -0.04999\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00020\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 5.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 30.625336461843556, Objetivo: 32.812860494832385, d: 6.67%, g: 97.17, Bono: 0.04858\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 50.0, d: 16.00%, g: 68.46, Bono: 0.03423\n", + " Bono total para 'prest': 0.03281\n", + " Bono geométrico: -0.000\n", + " Bono prestacional: 0.033\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.982\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.982']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.982\n", + " Media de valores (y): 2.615\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.203%\n", + "✅ Valor imputado: 2.615 (conf 0.052, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.69, Objetivo: 2.0, d: 34.50%, g: -80.70, Bono: -0.04035\n", + " Parámetro: envergadura, Vecino: 2.15, Objetivo: 2.0, d: 7.50%, g: 96.81, Bono: 0.04840\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.75, Objetivo: 1.562, d: 51.98%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.93, Objetivo: 1.562, d: 40.46%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.09195\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.53, Objetivo: 6.0, d: 24.50%, g: -98.19, Bono: -0.04910\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 6.0, d: 50.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 18.090823752817585, Objetivo: 21.875240329888257, d: 17.30%, g: 52.85, Bono: 0.02643\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 19.68771629689943, Objetivo: 21.875240329888257, d: 10.00%, g: 95.11, Bono: 0.04755\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 30.0, d: 10.00%, g: 95.11, Bono: 0.04755\n", + " Bono total para 'prest': 0.02243\n", + " Bono geométrico: -0.092\n", + " Bono prestacional: 0.022\n", + " vecino 'DeltaQuad Evo' → sim_i: 0.615\n", + " vecino 'V21' → sim_i: 0.615\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.615', '0.615']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.615\n", + " Media de valores (y): 0.820\n", + " Coeficiente de variación (CV): 0.951\n", + " Dispersión: 0.020\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.422\n", + " Confianza final: 25.952%\n", + "✅ Valor imputado: 0.820 (conf 0.260, datos 2, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.6, Objetivo: 3.0, d: 13.33%, g: 86.96, Bono: 0.04348\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.05, Objetivo: 2.3, d: 10.87%, g: 93.93, Bono: 0.04697\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.09045\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.0, Objetivo: 12.0, d: 83.33%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 36.09414654431562, Objetivo: 21.875240329888257, d: 65.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.10000\n", + " Bono geométrico: 0.090\n", + " Bono prestacional: -0.100\n", + " vecino 'Skyeye 2600' → sim_i: 0.571\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.571']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.571\n", + " Media de valores (y): 0.880\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.025%\n", + "✅ Valor imputado: 0.880 (conf 0.030, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Reach - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 6.0, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 4.712, d: 25.72%, g: -97.58, Bono: -0.04879\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01823\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 20.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 30.625336461843556, Objetivo: 27.344050412360318, d: 12.00%, g: 91.56, Bono: 0.04578\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 35.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Bono total para 'prest': -0.00422\n", + " Bono geométrico: -0.018\n", + " Bono prestacional: -0.004\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.882\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.882']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.882\n", + " Media de valores (y): 2.615\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.674%\n", + "✅ Valor imputado: 2.615 (conf 0.047, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8768, Objetivo: 3.0, d: 62.56%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5908, Objetivo: 1.2, d: 115.90%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.10000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 8.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Alcance de la aeronave, Vecino: 433.0, Objetivo: 800.0, d: 45.88%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 17.602373279430935, Objetivo: 30.406584058544677, d: 42.11%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 25.034211403264, Objetivo: 41.7, d: 39.97%, g: -0.88, Bono: -0.00044\n", + " Bono total para 'prest': -0.05047\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.050\n", + " vecino 'Stalker VXE30' → sim_i: 0.799\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.799']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.799\n", + " Media de valores (y): 15.326\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.235%\n", + "✅ Valor imputado: 15.326 (conf 0.042, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 5.2, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 1.2, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01293\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 24.0, d: 17.50%, g: 49.96, Bono: 0.02498\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 36.0, d: 7.11%, g: 96.98, Bono: 0.04849\n", + " Bono total para 'prest': 0.07347\n", + " Bono geométrico: -0.013\n", + " Bono prestacional: 0.073\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 1.010\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.010']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.010\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.349%\n", + "✅ Valor imputado: 12.500 (conf 0.053, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range (ER) - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 2.4, d: 25.00%, g: -97.93, Bono: -0.04897\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00141\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 18.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 25.7034073876187, d: 6.38%, g: 97.29, Bono: 0.04864\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 41.2, d: 18.84%, g: 26.66, Bono: 0.01333\n", + " Bono total para 'prest': 0.10952\n", + " Bono geométrico: -0.001\n", + " Bono prestacional: 0.110\n", + " vecino 'Aerosonde Mk. 4.7 Fixed Wing' → sim_i: 0.733\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.733']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.733\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.885%\n", + "✅ Valor imputado: 12.500 (conf 0.039, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.8, d: 8.33%, g: 96.40, Bono: 0.04820\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 2.5, d: 20.00%, g: 0.00, Bono: 0.00000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.04820\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 16.0, d: 23.75%, g: -98.61, Bono: -0.04931\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 33.797246309677355, d: 19.09%, g: 21.34, Bono: 0.01067\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 46.3, d: 27.78%, g: -96.68, Bono: -0.04834\n", + " Bono total para 'prest': -0.08698\n", + " Bono geométrico: 0.048\n", + " Bono prestacional: -0.087\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 0.854\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.854']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.854\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.526%\n", + "✅ Valor imputado: 12.500 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V21 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V25 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.52, d: 9.62%, g: 95.49, Bono: 0.04775\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.45, d: 18.37%, g: 35.68, Bono: 0.01784\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 0.93, d: 82.80%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.01558\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 4.0, d: 550.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.845724764736, Objetivo: 33.0, d: 6.53%, g: 97.23, Bono: 0.04861\n", + " Bono total para 'prest': -0.00139\n", + " Bono geométrico: 0.016\n", + " Bono prestacional: -0.001\n", + " vecino 'AAI Aerosonde' → sim_i: 0.700\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.700']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.700\n", + " Media de valores (y): 14.754\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.711%\n", + "✅ Valor imputado: 14.754 (conf 0.037, datos 1, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2600 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.88, d: 35.23%, g: -75.44, Bono: -0.03772\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.6, d: 11.54%, g: 92.67, Bono: 0.04633\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 2.05, d: 17.07%, g: 55.96, Bono: 0.02798\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.03660\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 2.0, d: 1200.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.037\n", + " Bono prestacional: -0.050\n", + " vecino 'AAI Aerosonde' → sim_i: 0.837\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.837']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.837\n", + " Media de valores (y): 14.754\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.436%\n", + "✅ Valor imputado: 14.754 (conf 0.044, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 14.0, Objetivo: 8.0, d: 75.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.050\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 0.872\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.872']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.872\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.618%\n", + "✅ Valor imputado: 12.500 (conf 0.046, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Autonomía de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': 0.00000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: 0.000\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 0.800\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.800']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.800\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.241%\n", + "✅ Valor imputado: 12.500 (conf 0.042, datos 1, familia F1)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT510 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 14.0, Objetivo: 5.0, d: 180.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.050\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 0.872\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.872']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.872\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.618%\n", + "✅ Valor imputado: 12.500 (conf 0.046, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Reach - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 14.0, Objetivo: 20.0, d: 30.00%, g: -95.11, Bono: -0.04755\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.04755\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.048\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 0.897\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.897']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.897\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.755%\n", + "✅ Valor imputado: 12.500 (conf 0.048, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Longitud del fuselaje ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 14.0, d: 64.29%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.15000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.150\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.770\n", + " vecino 'Volitation VT510' → sim_i: 0.770\n", + " vecino 'Reach' → sim_i: 0.795\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.770', '0.770', '0.795']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.778\n", + " Media de valores (y): 3.706\n", + " Coeficiente de variación (CV): 0.594\n", + " Dispersión: 0.752\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.427\n", + " Confianza final: 33.237%\n", + "✅ Valor imputado: 3.717 (conf 0.332, datos 3, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Longitud del fuselaje ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Longitud del fuselaje ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.2, Objetivo: 3.9, d: 17.95%, g: 42.93, Bono: 0.02147\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.9, d: 24.87%, g: -98.00, Bono: -0.04900\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.02753\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.5, Objetivo: 4.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.5, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 27.344050412360318, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.15365\n", + " Bono geométrico: -0.028\n", + " Bono prestacional: 0.154\n", + " vecino 'V32' → sim_i: 1.072\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 0.707\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.072', '0.707']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.927\n", + " Media de valores (y): 1.515\n", + " Coeficiente de variación (CV): 0.320\n", + " Dispersión: 0.515\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.233\n", + " Confianza final: 21.554%\n", + "✅ Valor imputado: 1.409 (conf 0.216, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.870\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.870']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.870\n", + " Media de valores (y): 800.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.607%\n", + "✅ Valor imputado: 800.000 (conf 0.046, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8, Objetivo: 4.8, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5, Objetivo: 2.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.09994\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.0, Objetivo: 24.0, d: 20.83%, g: -99.96, Bono: -0.04998\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 46.3, Objetivo: 46.3, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': -0.00001\n", + " Bono geométrico: 0.100\n", + " Bono prestacional: -0.000\n", + " vecino 'Integrator Extended Range (ER)' → sim_i: 1.049\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.049']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.049\n", + " Media de valores (y): 500.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.559%\n", + "✅ Valor imputado: 500.000 (conf 0.056, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.2, Objetivo: 2.4, d: 50.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00245\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 6.0, Objetivo: 18.0, d: 66.67%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 36.0, Objetivo: 41.2, d: 12.62%, g: 89.71, Bono: 0.04485\n", + " Bono total para 'prest': -0.00515\n", + " Bono geométrico: -0.002\n", + " Bono prestacional: -0.005\n", + " vecino 'Orbiter 3' → sim_i: 0.636\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.636']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.636\n", + " Media de valores (y): 50.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.370%\n", + "✅ Valor imputado: 50.000 (conf 0.034, datos 1, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V21 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.84, Objetivo: 0.8, d: 5.00%, g: 97.93, Bono: 0.04897\n", + " Parámetro: envergadura, Vecino: 2.69, Objetivo: 2.15, d: 25.12%, g: -97.87, Bono: -0.04894\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.75, Objetivo: 0.93, d: 19.35%, g: 15.60, Bono: 0.00780\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00783\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.53, Objetivo: 3.0, d: 51.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 18.090823752817585, Objetivo: 19.68771629689943, d: 8.11%, g: 96.52, Bono: 0.04826\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.00174\n", + " Bono geométrico: 0.008\n", + " Bono prestacional: -0.002\n", + " vecino 'DeltaQuad Evo' → sim_i: 0.956\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.956']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.956\n", + " Media de valores (y): 270.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.062%\n", + "✅ Valor imputado: 270.000 (conf 0.051, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V25 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.84, Objetivo: 0.52, d: 61.54%, g: -100.00, Bono: -0.05000\n", + " Parámetro: envergadura, Vecino: 2.69, Objetivo: 2.45, d: 9.80%, g: 95.32, Bono: 0.04766\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.75, Objetivo: 0.93, d: 19.35%, g: 15.60, Bono: 0.00780\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00546\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.53, Objetivo: 4.0, d: 13.25%, g: 87.32, Bono: 0.04366\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 18.090823752817585, Objetivo: 21.875240329888257, d: 17.30%, g: 52.85, Bono: 0.02643\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': 0.07009\n", + " Bono geométrico: 0.005\n", + " Bono prestacional: 0.070\n", + " vecino 'DeltaQuad Evo' → sim_i: 0.076\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.076']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.076\n", + " Media de valores (y): 270.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 0.400%\n", + "✅ Valor imputado: 270.000 (conf 0.004, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.6, Objetivo: 6.5, d: 44.62%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.42, Objetivo: 2.02, d: 19.80%, g: 5.01, Bono: 0.00251\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.04749\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 6.0, Objetivo: 15.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 27.344050412360318, d: 20.00%, g: -99.94, Bono: -0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.047\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 3600 VTOL' → sim_i: 0.852\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.852']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.852\n", + " Media de valores (y): 300.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.513%\n", + "✅ Valor imputado: 300.000 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2600 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.88, d: 35.23%, g: -75.44, Bono: -0.03772\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.6, d: 11.54%, g: 92.67, Bono: 0.04633\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 2.05, d: 17.07%, g: 55.96, Bono: 0.02798\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.03660\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 2.0, d: 1200.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.037\n", + " Bono prestacional: -0.050\n", + " vecino 'AAI Aerosonde' → sim_i: 0.837\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.837']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.837\n", + " Media de valores (y): 3270.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.436%\n", + "✅ Valor imputado: 3270.000 (conf 0.044, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 2.615, Objetivo: 2.615, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 5.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 3.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.14991\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Autonomía de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 38.0, d: 10.53%, g: 94.45, Bono: 0.04723\n", + " Bono total para 'prest': 0.04723\n", + " Bono geométrico: 0.150\n", + " Bono prestacional: 0.047\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 1.022\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.022']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.022\n", + " Media de valores (y): 800.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.412%\n", + "✅ Valor imputado: 800.000 (conf 0.054, datos 1, familia F1)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT510 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 5.1, d: 1.96%, g: 99.59, Bono: 0.04979\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 2.905, d: 20.48%, g: -99.99, Bono: -0.04999\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00020\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 5.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 30.625336461843556, Objetivo: 32.812860494832385, d: 6.67%, g: 97.17, Bono: 0.04858\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 50.0, d: 16.00%, g: 68.46, Bono: 0.03423\n", + " Bono total para 'prest': 0.03281\n", + " Bono geométrico: -0.000\n", + " Bono prestacional: 0.033\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.982\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.982']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.982\n", + " Media de valores (y): 800.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.203%\n", + "✅ Valor imputado: 800.000 (conf 0.052, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.69, Objetivo: 2.0, d: 34.50%, g: -80.70, Bono: -0.04035\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.75, Objetivo: 1.562, d: 51.98%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.09035\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.53, Objetivo: 6.0, d: 24.50%, g: -98.19, Bono: -0.04910\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 18.090823752817585, Objetivo: 21.875240329888257, d: 17.30%, g: 52.85, Bono: 0.02643\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.02267\n", + " Bono geométrico: -0.090\n", + " Bono prestacional: -0.023\n", + " vecino 'DeltaQuad Evo' → sim_i: 0.572\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.572']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.572\n", + " Media de valores (y): 270.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.028%\n", + "✅ Valor imputado: 270.000 (conf 0.030, datos 1, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Reach - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 6.0, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 4.712, d: 25.72%, g: -97.58, Bono: -0.04879\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01823\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 20.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 30.625336461843556, Objetivo: 27.344050412360318, d: 12.00%, g: 91.56, Bono: 0.04578\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 35.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Bono total para 'prest': -0.00422\n", + " Bono geométrico: -0.018\n", + " Bono prestacional: -0.004\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.882\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.882']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.882\n", + " Media de valores (y): 800.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.674%\n", + "✅ Valor imputado: 800.000 (conf 0.047, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Autonomía de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Autonomía de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: Área del ala, Vecino: 2.615, Objetivo: 2.615, d: 0.00%, g: 99.94, Bono: 0.04997\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 5.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 3.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 3.5, d: 34.63%, g: -79.86, Bono: -0.03993\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.18823\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Autonomía de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 38.0, d: 10.53%, g: 94.45, Bono: 0.04723\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 38.0, d: 31.58%, g: -92.58, Bono: -0.04629\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 38.0, d: 7.89%, g: 96.63, Bono: 0.04831\n", + " Bono total para 'prest': 0.04925\n", + " Bono geométrico: 0.188\n", + " Bono prestacional: 0.049\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 1.038\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 1.062\n", + " vecino 'Volitation VT510' → sim_i: 1.062\n", + " vecino 'Reach' → sim_i: 1.029\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.038', '1.062', '1.062', '1.029']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.048\n", + " Media de valores (y): 11.750\n", + " Coeficiente de variación (CV): 0.019\n", + " Dispersión: 5.761\n", + " Penalización por cantidad de vecinos (k): 0.530\n", + " Confianza en base a la calidad y cantidad de datos: 0.376\n", + " Confianza final: 39.457%\n", + "✅ Valor imputado: 11.673 (conf 0.395, datos 4, familia F1)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 14.0, d: 64.29%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.15000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.150\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.770\n", + " vecino 'Volitation VT510' → sim_i: 0.770\n", + " vecino 'Reach' → sim_i: 0.795\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.770', '0.770', '0.795']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.778\n", + " Media de valores (y): 42.333\n", + " Coeficiente de variación (CV): 0.710\n", + " Dispersión: 6.128\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.462\n", + " Confianza final: 35.952%\n", + "✅ Valor imputado: 42.253 (conf 0.360, datos 3, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.8, Objetivo: 0.84, d: 4.76%, g: 98.05, Bono: 0.04903\n", + " Parámetro: envergadura, Vecino: 2.15, Objetivo: 2.69, d: 20.07%, g: -99.96, Bono: -0.04998\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.93, Objetivo: 0.75, d: 24.00%, g: -98.47, Bono: -0.04924\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.05019\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.53, d: 33.77%, g: -84.88, Bono: -0.04244\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 19.68771629689943, Objetivo: 18.090823752817585, d: 8.83%, g: 96.11, Bono: 0.04805\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': 0.00561\n", + " Bono geométrico: -0.050\n", + " Bono prestacional: 0.006\n", + " vecino 'V21' → sim_i: 0.905\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.905']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.905\n", + " Media de valores (y): 33.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.794%\n", + "✅ Valor imputado: 33.000 (conf 0.048, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2600 - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.88, d: 35.23%, g: -75.44, Bono: -0.03772\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.6, d: 11.54%, g: 92.67, Bono: 0.04633\n", + " Parámetro: envergadura, Vecino: 3.0, Objetivo: 2.6, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 2.05, d: 17.07%, g: 55.96, Bono: 0.02798\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.3, Objetivo: 2.05, d: 12.20%, g: 91.03, Bono: 0.04551\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.11918\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 2.0, d: 1200.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 12.0, Objetivo: 2.0, d: 500.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 36.09414654431562, d: 39.39%, g: -14.71, Bono: -0.00736\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.10736\n", + " Bono geométrico: 0.119\n", + " Bono prestacional: -0.107\n", + " vecino 'AAI Aerosonde' → sim_i: 0.863\n", + " vecino 'Transition' → sim_i: 0.012\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.863', '0.012']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.851\n", + " Media de valores (y): 30.423\n", + " Coeficiente de variación (CV): 0.972\n", + " Dispersión: 0.423\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.428\n", + " Confianza final: 36.444%\n", + "✅ Valor imputado: 30.834 (conf 0.364, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker XE - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker VXE30 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 14.0, d: 64.29%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.10000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.100\n", + " vecino 'Volitation VT510' → sim_i: 0.820\n", + " vecino 'Reach' → sim_i: 0.845\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.820', '0.845']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.833\n", + " Media de valores (y): 19.000\n", + " Coeficiente de variación (CV): 0.368\n", + " Dispersión: 6.000\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.247\n", + " Confianza final: 20.573%\n", + "✅ Valor imputado: 18.907 (conf 0.206, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: AAI Aerosonde - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.88, Objetivo: 0.57, d: 54.39%, g: -100.00, Bono: -0.05000\n", + " Parámetro: envergadura, Vecino: 2.6, Objetivo: 2.9, d: 10.34%, g: 94.70, Bono: 0.04735\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.05, Objetivo: 1.7, d: 20.59%, g: -99.99, Bono: -0.04999\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': -0.05265\n", + "⚠️ Diferencia NaN para el parámetro 'Potencia específica (P/W)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.0, Objetivo: 26.0, d: 92.31%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.053\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 2600' → sim_i: 0.664\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.664']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.664\n", + " Media de valores (y): 10.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.517%\n", + "✅ Valor imputado: 10.000 (conf 0.035, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.8, Objetivo: 0.84, d: 4.76%, g: 98.05, Bono: 0.04903\n", + " Parámetro: envergadura, Vecino: 2.15, Objetivo: 2.69, d: 20.07%, g: -99.96, Bono: -0.04998\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.93, Objetivo: 0.75, d: 24.00%, g: -98.47, Bono: -0.04924\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.05019\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.53, d: 33.77%, g: -84.88, Bono: -0.04244\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 19.68771629689943, Objetivo: 18.090823752817585, d: 8.83%, g: 96.11, Bono: 0.04805\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': 0.00561\n", + " Bono geométrico: -0.050\n", + " Bono prestacional: 0.006\n", + " vecino 'V21' → sim_i: 0.905\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.905']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.905\n", + " Media de valores (y): 14.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.794%\n", + "✅ Valor imputado: 14.000 (conf 0.048, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.5, d: 16.29%, g: 65.47, Bono: 0.03274\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.03, Objetivo: 1.88, d: 7.98%, g: 96.58, Bono: 0.04829\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.08103\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 2.8, d: 7.14%, g: 96.96, Bono: 0.04848\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.14568\n", + " Bono geométrico: 0.081\n", + " Bono prestacional: 0.146\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 1.083\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.083']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.083\n", + " Media de valores (y): 18.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.736%\n", + "✅ Valor imputado: 18.000 (conf 0.057, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.2, Objetivo: 3.9, d: 17.95%, g: 42.93, Bono: 0.02147\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.9, d: 24.87%, g: -98.00, Bono: -0.04900\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.02753\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.5, Objetivo: 4.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.5, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 27.344050412360318, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.15365\n", + " Bono geométrico: -0.028\n", + " Bono prestacional: 0.154\n", + " vecino 'V32' → sim_i: 1.072\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 0.707\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.072', '0.707']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.927\n", + " Media de valores (y): 17.500\n", + " Coeficiente de variación (CV): 0.943\n", + " Dispersión: 0.500\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.419\n", + " Confianza final: 38.865%\n", + "✅ Valor imputado: 17.397 (conf 0.389, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.6, Objetivo: 6.5, d: 44.62%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.42, Objetivo: 2.02, d: 19.80%, g: 5.01, Bono: 0.00251\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.04749\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 6.0, Objetivo: 15.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 27.344050412360318, d: 20.00%, g: -99.94, Bono: -0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.047\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 3600 VTOL' → sim_i: 0.852\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.852']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.852\n", + " Media de valores (y): 24.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.513%\n", + "✅ Valor imputado: 24.000 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 3.5, d: 34.63%, g: -79.86, Bono: -0.03993\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.03832\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 8.0, d: 37.50%, g: -49.96, Bono: -0.02498\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 8.0, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 30.625336461843556, d: 7.14%, g: 96.96, Bono: 0.04848\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 30.625336461843556, d: 10.71%, g: 94.18, Bono: 0.04709\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 42.0, d: 19.05%, g: 22.32, Bono: 0.01116\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 42.0, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Bono total para 'prest': 0.06231\n", + " Bono geométrico: 0.038\n", + " Bono prestacional: 0.062\n", + " vecino 'Volitation VT510' → sim_i: 1.050\n", + " vecino 'Reach' → sim_i: 1.013\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.050', '1.013']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.032\n", + " Media de valores (y): 19.000\n", + " Coeficiente de variación (CV): 0.368\n", + " Dispersión: 6.000\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.247\n", + " Confianza final: 25.490%\n", + "✅ Valor imputado: 19.109 (conf 0.255, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker XE - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker VXE30 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 14.0, d: 64.29%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.050\n", + " vecino 'Volitation VT510' → sim_i: 0.870\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.870']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.870\n", + " Media de valores (y): 25.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.607%\n", + "✅ Valor imputado: 25.000 (conf 0.046, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: AAI Aerosonde - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.88, Objetivo: 0.57, d: 54.39%, g: -100.00, Bono: -0.05000\n", + " Parámetro: envergadura, Vecino: 2.6, Objetivo: 2.9, d: 10.34%, g: 94.70, Bono: 0.04735\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.05, Objetivo: 1.7, d: 20.59%, g: -99.99, Bono: -0.04999\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': -0.05265\n", + "⚠️ Diferencia NaN para el parámetro 'Potencia específica (P/W)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.0, Objetivo: 26.0, d: 92.31%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.053\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 2600' → sim_i: 0.664\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.664']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.664\n", + " Media de valores (y): 10.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.517%\n", + "✅ Valor imputado: 10.000 (conf 0.035, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.8, Objetivo: 0.84, d: 4.76%, g: 98.05, Bono: 0.04903\n", + " Parámetro: envergadura, Vecino: 2.15, Objetivo: 2.69, d: 20.07%, g: -99.96, Bono: -0.04998\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.93, Objetivo: 0.75, d: 24.00%, g: -98.47, Bono: -0.04924\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.05019\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.53, d: 33.77%, g: -84.88, Bono: -0.04244\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 19.68771629689943, Objetivo: 18.090823752817585, d: 8.83%, g: 96.11, Bono: 0.04805\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': 0.00561\n", + " Bono geométrico: -0.050\n", + " Bono prestacional: 0.006\n", + " vecino 'V21' → sim_i: 0.905\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.905']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.905\n", + " Media de valores (y): 14.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.794%\n", + "✅ Valor imputado: 14.000 (conf 0.048, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.5, d: 16.29%, g: 65.47, Bono: 0.03274\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.03, Objetivo: 1.88, d: 7.98%, g: 96.58, Bono: 0.04829\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.08103\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 2.8, d: 7.14%, g: 96.96, Bono: 0.04848\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.14568\n", + " Bono geométrico: 0.081\n", + " Bono prestacional: 0.146\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 1.083\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.083']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.083\n", + " Media de valores (y): 18.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.736%\n", + "✅ Valor imputado: 18.000 (conf 0.057, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.2, Objetivo: 3.9, d: 17.95%, g: 42.93, Bono: 0.02147\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.9, d: 24.87%, g: -98.00, Bono: -0.04900\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.02753\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.5, Objetivo: 4.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.5, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 27.344050412360318, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.15365\n", + " Bono geométrico: -0.028\n", + " Bono prestacional: 0.154\n", + " vecino 'V32' → sim_i: 1.072\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 0.707\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.072', '0.707']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.927\n", + " Media de valores (y): 17.500\n", + " Coeficiente de variación (CV): 0.943\n", + " Dispersión: 0.500\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.419\n", + " Confianza final: 38.865%\n", + "✅ Valor imputado: 17.397 (conf 0.389, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.6, Objetivo: 6.5, d: 44.62%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.42, Objetivo: 2.02, d: 19.80%, g: 5.01, Bono: 0.00251\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.04749\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 6.0, Objetivo: 15.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 27.344050412360318, d: 20.00%, g: -99.94, Bono: -0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.047\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 3600 VTOL' → sim_i: 0.852\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.852']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.852\n", + " Media de valores (y): 24.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.513%\n", + "✅ Valor imputado: 24.000 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.07825\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 8.0, d: 37.50%, g: -49.96, Bono: -0.02498\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 30.625336461843556, d: 7.14%, g: 96.96, Bono: 0.04848\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 42.0, d: 19.05%, g: 22.32, Bono: 0.01116\n", + " Bono total para 'prest': 0.03466\n", + " Bono geométrico: 0.078\n", + " Bono prestacional: 0.035\n", + " vecino 'Volitation VT510' → sim_i: 1.062\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.062']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.062\n", + " Media de valores (y): 25.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.628%\n", + "✅ Valor imputado: 25.000 (conf 0.056, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.07825\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Autonomía de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 38.0, d: 31.58%, g: -92.58, Bono: -0.04629\n", + " Bono total para 'prest': -0.04629\n", + " Bono geométrico: 0.078\n", + " Bono prestacional: -0.046\n", + " vecino 'Volitation VT510' → sim_i: 0.856\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.856']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.856\n", + " Media de valores (y): 25.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.537%\n", + "✅ Valor imputado: 25.000 (conf 0.045, datos 1, familia F1)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.15, Objetivo: 2.0, d: 7.50%, g: 96.81, Bono: 0.04840\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.93, Objetivo: 1.562, d: 40.46%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00160\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 6.0, d: 50.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 19.68771629689943, Objetivo: 21.875240329888257, d: 10.00%, g: 95.11, Bono: 0.04755\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 30.0, d: 10.00%, g: 95.11, Bono: 0.04755\n", + " Bono total para 'prest': 0.04511\n", + " Bono geométrico: -0.002\n", + " Bono prestacional: 0.045\n", + " vecino 'V21' → sim_i: 0.728\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.728']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.728\n", + " Media de valores (y): 14.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.857%\n", + "✅ Valor imputado: 14.000 (conf 0.039, datos 1, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.6, Objetivo: 3.0, d: 13.33%, g: 86.96, Bono: 0.04348\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.05, Objetivo: 2.3, d: 10.87%, g: 93.93, Bono: 0.04697\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.09045\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.0, Objetivo: 12.0, d: 83.33%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 36.09414654431562, Objetivo: 21.875240329888257, d: 65.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.10000\n", + " Bono geométrico: 0.090\n", + " Bono prestacional: -0.100\n", + " vecino 'Skyeye 2600' → sim_i: 0.571\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.571']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.571\n", + " Media de valores (y): 10.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.025%\n", + "✅ Valor imputado: 10.000 (conf 0.030, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Reach - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 6.0, d: 15.00%, g: 77.21, Bono: 0.03861\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 4.712, d: 38.35%, g: -36.01, Bono: -0.01801\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.02060\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 20.0, d: 75.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 27.344050412360318, d: 20.00%, g: -99.94, Bono: -0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 35.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + " Bono total para 'prest': -0.14997\n", + " Bono geométrico: 0.021\n", + " Bono prestacional: -0.150\n", + " vecino 'Volitation VT510' → sim_i: 0.775\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.775']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.775\n", + " Media de valores (y): 25.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.107%\n", + "✅ Valor imputado: 25.000 (conf 0.041, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - envergadura ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 14.0, d: 64.29%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.15000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.150\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.770\n", + " vecino 'Volitation VT510' → sim_i: 0.770\n", + " vecino 'Reach' → sim_i: 0.795\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.770', '0.770', '0.795']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.778\n", + " Media de valores (y): 5.367\n", + " Coeficiente de variación (CV): 0.832\n", + " Dispersión: 0.450\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.499\n", + " Confianza final: 38.799%\n", + "✅ Valor imputado: 5.374 (conf 0.388, datos 3, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - envergadura ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8768, Objetivo: 3.0, d: 62.56%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5908, Objetivo: 1.2, d: 115.90%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.10000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 8.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Alcance de la aeronave, Vecino: 433.0, Objetivo: 800.0, d: 45.88%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 17.602373279430935, Objetivo: 30.406584058544677, d: 42.11%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 25.034211403264, Objetivo: 41.7, d: 39.97%, g: -0.88, Bono: -0.00044\n", + " Bono total para 'prest': -0.05047\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.050\n", + " vecino 'Stalker VXE30' → sim_i: 0.799\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.799']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.799\n", + " Media de valores (y): 0.318\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.235%\n", + "✅ Valor imputado: 0.318 (conf 0.042, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 5.2, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 1.2, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01293\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 24.0, d: 17.50%, g: 49.96, Bono: 0.02498\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 36.0, d: 7.11%, g: 96.98, Bono: 0.04849\n", + " Bono total para 'prest': 0.07347\n", + " Bono geométrico: -0.013\n", + " Bono prestacional: 0.073\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 1.010\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.010']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.010\n", + " Media de valores (y): 0.352\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.349%\n", + "✅ Valor imputado: 0.352 (conf 0.053, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range (ER) - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 2.4, d: 25.00%, g: -97.93, Bono: -0.04897\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00141\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 18.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 25.7034073876187, d: 6.38%, g: 97.29, Bono: 0.04864\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 41.2, d: 18.84%, g: 26.66, Bono: 0.01333\n", + " Bono total para 'prest': 0.10952\n", + " Bono geométrico: -0.001\n", + " Bono prestacional: 0.110\n", + " vecino 'Aerosonde Mk. 4.7 Fixed Wing' → sim_i: 0.733\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.733']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.733\n", + " Media de valores (y): 0.352\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.885%\n", + "✅ Valor imputado: 0.352 (conf 0.039, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.8, d: 8.33%, g: 96.40, Bono: 0.04820\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 2.5, d: 20.00%, g: 0.00, Bono: 0.00000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.04820\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 16.0, d: 23.75%, g: -98.61, Bono: -0.04931\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 33.797246309677355, d: 19.09%, g: 21.34, Bono: 0.01067\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 46.3, d: 27.78%, g: -96.68, Bono: -0.04834\n", + " Bono total para 'prest': -0.08698\n", + " Bono geométrico: 0.048\n", + " Bono prestacional: -0.087\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 0.854\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.854']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.854\n", + " Media de valores (y): 0.352\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.526%\n", + "✅ Valor imputado: 0.352 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V21 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V25 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.52, d: 9.62%, g: 95.49, Bono: 0.04775\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.45, d: 18.37%, g: 35.68, Bono: 0.01784\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 0.93, d: 82.80%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.01558\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 4.0, d: 550.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.845724764736, Objetivo: 33.0, d: 6.53%, g: 97.23, Bono: 0.04861\n", + " Bono total para 'prest': -0.00139\n", + " Bono geométrico: 0.016\n", + " Bono prestacional: -0.001\n", + " vecino 'AAI Aerosonde' → sim_i: 0.700\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.700']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.700\n", + " Media de valores (y): 0.197\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.711%\n", + "✅ Valor imputado: 0.197 (conf 0.037, datos 1, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2600 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.88, d: 35.23%, g: -75.44, Bono: -0.03772\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.6, d: 11.54%, g: 92.67, Bono: 0.04633\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 2.05, d: 17.07%, g: 55.96, Bono: 0.02798\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.03660\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 2.0, d: 1200.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.037\n", + " Bono prestacional: -0.050\n", + " vecino 'AAI Aerosonde' → sim_i: 0.837\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.837']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.837\n", + " Media de valores (y): 0.197\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.436%\n", + "✅ Valor imputado: 0.197 (conf 0.044, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT510 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Reach - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: AAI Aerosonde - payload ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.88, Objetivo: 0.57, d: 54.39%, g: -100.00, Bono: -0.05000\n", + " Parámetro: envergadura, Vecino: 2.6, Objetivo: 2.9, d: 10.34%, g: 94.70, Bono: 0.04735\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.05, Objetivo: 1.7, d: 20.59%, g: -99.99, Bono: -0.04999\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': -0.05265\n", + "⚠️ Diferencia NaN para el parámetro 'Potencia específica (P/W)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.0, Objetivo: 26.0, d: 92.31%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.053\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 2600' → sim_i: 0.664\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.664']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.664\n", + " Media de valores (y): 4.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.517%\n", + "✅ Valor imputado: 4.000 (conf 0.035, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - payload ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8768, Objetivo: 3.0, d: 62.56%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5908, Objetivo: 1.2, d: 115.90%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.10000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 8.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Alcance de la aeronave, Vecino: 433.0, Objetivo: 800.0, d: 45.88%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 17.602373279430935, Objetivo: 30.406584058544677, d: 42.11%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 25.034211403264, Objetivo: 41.7, d: 39.97%, g: -0.88, Bono: -0.00044\n", + " Bono total para 'prest': -0.05047\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.050\n", + " vecino 'Stalker VXE30' → sim_i: 0.799\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.799']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.799\n", + " Media de valores (y): 2.495\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.235%\n", + "✅ Valor imputado: 2.495 (conf 0.042, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - payload ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.050\n", + " vecino 'Reach' → sim_i: 0.895\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.895']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.895\n", + " Media de valores (y): 31.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.743%\n", + "✅ Valor imputado: 31.000 (conf 0.047, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8768, Objetivo: 3.0, d: 62.56%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5908, Objetivo: 1.2, d: 115.90%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.10000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 8.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Alcance de la aeronave, Vecino: 433.0, Objetivo: 800.0, d: 45.88%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 17.602373279430935, Objetivo: 30.406584058544677, d: 42.11%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 25.034211403264, Objetivo: 41.7, d: 39.97%, g: -0.88, Bono: -0.00044\n", + " Bono total para 'prest': -0.05047\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.050\n", + " vecino 'Stalker VXE30' → sim_i: 0.799\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.799']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.799\n", + " Media de valores (y): 17.463\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.235%\n", + "✅ Valor imputado: 17.463 (conf 0.042, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range (ER) - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.5, d: 16.29%, g: 65.47, Bono: 0.03274\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.03, Objetivo: 1.88, d: 7.98%, g: 96.58, Bono: 0.04829\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.08103\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 2.8, d: 7.14%, g: 96.96, Bono: 0.04848\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.14568\n", + " Bono geométrico: 0.081\n", + " Bono prestacional: 0.146\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 1.083\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.083']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.083\n", + " Media de valores (y): 7.100\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.736%\n", + "✅ Valor imputado: 7.100 (conf 0.057, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.2, Objetivo: 3.9, d: 17.95%, g: 42.93, Bono: 0.02147\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.9, d: 24.87%, g: -98.00, Bono: -0.04900\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.02753\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.5, Objetivo: 4.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.5, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 27.344050412360318, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.15365\n", + " Bono geométrico: -0.028\n", + " Bono prestacional: 0.154\n", + " vecino 'V32' → sim_i: 1.072\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 0.707\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.072', '0.707']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.927\n", + " Media de valores (y): 6.775\n", + " Coeficiente de variación (CV): 0.904\n", + " Dispersión: 0.325\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.408\n", + " Confianza final: 37.786%\n", + "✅ Valor imputado: 6.708 (conf 0.378, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.6, Objetivo: 6.5, d: 44.62%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.42, Objetivo: 2.02, d: 19.80%, g: 5.01, Bono: 0.00251\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.04749\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 6.0, Objetivo: 15.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 27.344050412360318, d: 20.00%, g: -99.94, Bono: -0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.047\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 3600 VTOL' → sim_i: 0.852\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.852']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.852\n", + " Media de valores (y): 11.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.513%\n", + "✅ Valor imputado: 11.000 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 3.5, d: 34.63%, g: -79.86, Bono: -0.03993\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.03993\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 8.0, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 30.625336461843556, d: 10.71%, g: 94.18, Bono: 0.04709\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 42.0, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Bono total para 'prest': 0.02765\n", + " Bono geométrico: -0.040\n", + " Bono prestacional: 0.028\n", + " vecino 'Reach' → sim_i: 0.900\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.900']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.900\n", + " Media de valores (y): 31.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.766%\n", + "✅ Valor imputado: 31.000 (conf 0.048, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT510 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.1, d: 17.65%, g: 47.74, Bono: 0.02387\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 2.905, d: 62.20%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.02613\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 5.0, d: 300.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 32.812860494832385, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 50.0, d: 30.00%, g: -95.11, Bono: -0.04755\n", + " Bono total para 'prest': -0.06700\n", + " Bono geométrico: -0.026\n", + " Bono prestacional: -0.067\n", + " vecino 'Reach' → sim_i: 0.819\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.819']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.819\n", + " Media de valores (y): 31.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.338%\n", + "✅ Valor imputado: 31.000 (conf 0.043, datos 1, familia F0)\n", + "\u001b[1m>>> RESULTADOS DE IMPUTACIÓN POR SIMILITUD\u001b[0m\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN 1 ***\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "\n", + "=== DataFrame inicial ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())

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Stalker XEStalker VXE30Aerosonde Mk. 4.7 Fixed WingAerosonde Mk. 4.7 VTOLAerosonde Mk. 4.8 Fixed wingAerosonde Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQ Nan 21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
70DeltaQuad Pro #MAPVelocidad máxima (KIAS)25.9100.600
71DeltaQuad Pro #CARGOVelocidad máxima (KIAS)25.9100.600
72Skyeye 2600Velocidad máxima (KIAS)24.7010.826
73Skyeye 3600Velocidad máxima (KIAS)34.1791.000
74Stalker XEVelocidad de pérdida (KCAS)12.7220.838
75Stalker VXE30Velocidad de pérdida (KCAS)12.6810.579
76Aerosonde® Mk. 4.7 Fixed WingVelocidad de pérdida (KCAS)23.6870.599
77Aerosonde® Mk. 4.8 VTOL FTUASVelocidad de pérdida (KCAS)19.6651.000
78AAI AerosondeVelocidad de pérdida (KCAS)12.8430.837
79Fulmar XVelocidad de pérdida (KCAS)12.6750.578
80Orbiter 3Velocidad de pérdida (KCAS)14.7730.815
81ScanEagleVelocidad de pérdida (KCAS)16.5190.949
82ScanEagle 3Velocidad de pérdida (KCAS)24.6120.577
83DeltaQuad EvoVelocidad de pérdida (KCAS)13.4190.857
84V35Velocidad de pérdida (KCAS)14.7730.815
85V39Velocidad de pérdida (KCAS)16.9110.613
86Volitation VT370Velocidad de pérdida (KCAS)24.0000.622
87Skyeye 5000 VTOLVelocidad de pérdida (KCAS)19.3121.000
88Aerosonde® Mk. 4.8 VTOL FTUASenvergadura5.0941.000
89Integrator VTOLenvergadura4.7970.532
90Fulmar XCuerda0.3180.621
91Orbiter 4Cuerda0.3500.523
92V25Cuerda0.2220.841
93Volitation VT370Cuerda0.3570.597
94Skyeye 2600Cuerda0.2120.806
95Skyeye 3600 VTOLCuerda0.3570.597
96TransitionCuerda0.3270.574
97AAI Aerosondepayload2.5190.952
98Fulmar Xpayload1.9780.862
99Mantispayload1.1860.509
100Aerosonde® Mk. 4.7 Fixed WingEmpty weight10.8570.599
101Aerosonde® Mk. 4.8 VTOL FTUASEmpty weight31.7420.951
102Fulmar XEmpty weight11.5550.863
103Orbiter 3Empty weight9.0090.820
104ScanEagleEmpty weight7.2000.955
105ScanEagle 3Empty weight11.2800.577
106V35Empty weight9.0090.820
107V39Empty weight6.4160.613
108Volitation VT370Empty weight11.0000.622
109Skyeye 5000 VTOLEmpty weight31.2000.912
110Volitation VT510Empty weight31.2000.912
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DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())

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Tabla de Correlaciones con todos los parametros(tabla_completa)

ModeloStalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
0.0
Tasa de ascensoNaNNaNNaNNaNNaNNaN2.49936NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5.0NaN5.0NaNNaNNaNNaNNaNNaNNaN5.0NaNNaNNaN
Altitud a la que se realiza el crucero6000.06000.0NaN3270.0800.0150.0NaN50.025.0NaNNaN500.0NaN92.6NaN270.0100.0100.013.0
Tasa de ascensoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Radio de giroNaNNaNNaN
Potencia/PesoPotencia específica (P/W)NaNNaNNaNNaN
Potencia(W)Potencia WattsNaNNaN2980.0NaN
Potencia(HP)Potencia HPNaNNaN4.0NaN
Despegue1.01.01.02.01.02.02.01.01.01.01.01.01.02.01.01.01.02.02.02.02.02.02.02.02.02.02.02.03.02.03.02.02.02.02.02.02.0
Propulsión horizontal2.02.02.02.02.02.02.02.02.01.01.02.02.02.02.02.02.01.01.01.01.01.02.02.02.02.02.02.02.02.02.02.02.02.02.02.02.0
Propulsión vertical5.05.05.01.05.01.01.05.05.05.05.05.05.01.05.05.05.01.01.01.01.01.01.01.01.01.01.01.05.01.05.01.01.01.01.01.01.0
Cantidad de motores propulsión vertical0.00.00.04.00.04.04.00.00.00.00.00.00.04.00.00.00.04.04.04.04.04.04.04.04.04.04.04.00.04.00.04.08.04.04.04.04.0
Cantidad de motores propulsión horizontal1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
Misión1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
Dimensiones de la bahía de carga útilNaNNaNNaN
DespegueDespegue todos los tiposNaNNaNNaNNaN
EmpresaNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
kjbkindice_desconocidoNaNNaNNaN
\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19757,6 +20936,47 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -19788,6 +21008,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -19801,7 +21027,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19817,6 +21043,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -19830,7 +21062,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19846,6 +21078,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -19859,7 +21097,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19875,6 +21113,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -19904,6 +21148,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -19933,6 +21183,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -19962,6 +21218,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -19975,7 +21237,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19991,6 +21253,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20020,6 +21288,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20033,7 +21307,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -20049,35 +21323,47 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20091,7 +21377,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -20107,6 +21393,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20120,7 +21412,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -20136,6 +21428,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20165,6 +21463,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20178,7 +21482,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -20194,6 +21498,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20223,6 +21533,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20236,7 +21552,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -20252,6 +21568,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20281,6 +21603,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20294,7 +21622,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -20310,6 +21638,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20339,6 +21673,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20368,6 +21708,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20397,6 +21743,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20410,7 +21762,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -20426,6 +21778,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20455,6 +21813,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20484,6 +21848,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20513,6 +21883,222 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Capacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Modelo
-0.018-0.240-0.1560.7350.1540.671-0.598nannan
Altitud a la que se realiza el crucero0.081nan-0.095-0.952-0.955-0.2800.128nannannannan-0.119-0.1090.187-0.159nannan
Velocidad a la que se realiza el crucero (KTAS)0.5350.9360.6630.4070.6650.3360.8150.2570.4910.461-0.2960.1130.6190.126-0.126nannan
Techo de servicio máximo0.0820.3690.1370.4630.4280.079-0.111-0.071nan0.515-0.2570.1250.007-0.1250.125nannan
Velocidad de pérdida limpia (KCAS)0.0681.0000.163-0.3450.231-0.3450.345nannan
Área del ala1.0001.0000.8990.2360.4530.1720.055nannan
Relación de aspecto del alanannannan-0.247nan0.247-0.247nannan
Longitud del fuselaje1.0000.9380.7860.1290.1500.3890.2560.1800.9290.036-0.2100.1110.6150.093-0.004nannan
Ancho del fuselajenan1.000nan0.794nan-0.5350.574nannan
Peso máximo al despegue (MTOW)0.7860.9861.000-0.0510.0250.4340.6780.5390.9760.7580.0520.0900.4670.0230.075nannan
Alcance de la aeronavenan-0.9520.4070.463-0.9550.6650.428nan-0.301-0.9980.1290.1500.982-0.0510.0251.0000.578-0.0620.8430.042nan-0.107-0.010-0.7550.5540.804-1.0000.4070.665nan-0.059-1.0000.5080.6701.000nan1.0000.2620.474-0.2620.262nannan
Autonomía de la aeronave0.389-0.0900.4340.5780.8431.0000.297-0.164-0.113-0.7320.011-0.4240.4770.361-0.361nannan
Velocidad máxima (KIAS)0.2560.9400.678-0.0620.0420.2971.0000.5390.7050.9100.015-0.0410.2080.174-0.133nannan
Velocidad de pérdida (KCAS)0.2301.0000.160-0.2440.154-0.2440.444nannan
envergadura0.6930.6710.791-0.107-0.0100.5320.4000.4010.2970.0850.032-0.1240.5080.239-0.164nannan
Cuerdanannannan-0.313nan0.313-0.313nannan
payload0.5990.8680.8750.5540.8040.4610.7150.6270.7110.846-0.008-0.0040.4770.162-0.111nannan
duracion en VTOLnannannan-0.188-0.9040.188-0.188nannan
Crucero KIAS0.5760.9440.7080.4070.6650.3360.7750.4170.5810.461-0.2430.1430.6080.0650.063nannan
RTF (Including fuel & Batteries)nannannan0.0970.428-0.0970.097nannan
Empty weight0.995nan-0.0290.1820.4040.3070.004nannan
Maximum Crosswindnannannannan-0.943nannannannan
Rango de comunicación0.6760.3230.5140.5080.6700.8020.094nannannannan-0.4300.6040.430-0.430nannan
Capacidad combustible1.0000.3770.817-0.080nan-0.0800.270nannan
Consumo0.3771.0000.9980.113nan-0.3750.375nannan
Precio0.8170.9981.000-0.1380.217-0.1380.134nannan
Despegue0.735-0.1190.1130.125-0.3450.236-0.2470.1110.7940.0900.262-0.424-0.041-0.244-0.124-0.313-0.004-0.1880.1430.0970.182nan-0.430-0.0800.113-0.1381.000-0.010-0.6390.610nannan
Propulsión horizontal0.154-0.1090.6190.0070.2310.453nan0.615nan0.4670.4740.4770.2080.1540.508nan0.477-0.9040.6080.4280.404-0.9430.604nannan0.217-0.0101.0000.118-0.083nannan
Propulsión vertical0.6710.1870.126-0.125-0.3450.1720.2470.093-0.5350.023-0.2620.3610.174-0.2440.2390.3130.1620.1880.065-0.0970.307nan0.430-0.080-0.375-0.138-0.6390.1181.000-0.954nannan
Cantidad de motores propulsión vertical-0.598-0.159-0.1260.1250.3450.055-0.247-0.0040.5740.0750.262-0.361-0.1330.444-0.164-0.313-0.111-0.1880.0630.0970.004nan-0.4300.2700.3750.1340.610-0.083-0.9541.000nannan
Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
" @@ -20571,17 +22157,17 @@ " \n", " 0\n", " Total de valores\n", - " 676.000\n", + " 1024.000\n", " \n", " \n", " 1\n", " Valores numéricos\n", - " 532.000\n", + " 740.000\n", " \n", " \n", " 2\n", " Valores NaN\n", - " 144.000\n", + " 284.000\n", " \n", " \n", "" @@ -20641,7 +22227,7 @@ "

Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -20652,11 +22238,30 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20667,10 +22272,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -20684,10 +22290,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -20705,6 +22312,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -20722,6 +22330,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -20735,10 +22344,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -20752,10 +22362,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -20763,19 +22374,20 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -20786,10 +22398,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -20803,10 +22416,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -20824,12 +22438,31 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -20837,10 +22470,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -20858,6 +22492,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -20871,10 +22506,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -20892,6 +22528,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -20954,17 +22591,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "
ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Modelo
-0.9990.5350.6630.4070.6650.3360.8150.2570.1280.4720.8460.696-0.3140.0820.1370.4630.4280.079-0.111-0.071-0.5020.0570.0170.0870.0810.7370.4230.0970.8410.9840.899-0.305-0.859nannan-0.349-0.744-0.888-0.7901.0000.7860.1290.1500.3890.2560.1800.2600.6930.9950.599-0.8230.7861.000-0.0510.0250.4340.6780.5390.5460.7910.8580.875
Alcance de la aeronave0.4070.4630.6650.428-0.301-0.9980.129-0.0510.1500.0251.0000.578-0.0620.8430.042nannan-0.107-0.010-0.7550.5540.804-0.059
-0.3050.3890.4340.5780.8431.0000.297-0.1640.4010.532-0.2010.461-0.8590.2560.678-0.0620.0420.2971.0000.5390.4460.4000.5120.715-0.1640.5391.0001.0000.401nan0.6270.321
Velocidad de pérdida limpia (KCAS)0.128-0.5020.097nan0.2600.546nan0.4010.4461.0001.0000.505nan0.5360.038
envergadura0.4720.057-0.3490.6930.791-0.107-0.0100.5320.4000.4010.5051.0000.8850.734-0.2010.512nannan0.8851.0000.776-0.8880.5990.8750.5540.8040.4610.7150.6270.5360.7340.7761.0000.4280.5170.3210.0380.9240.9710.778
0Total de valores196.000225.000
1Valores numéricos190.000213.000
2Valores NaN6.00012.000
" @@ -21014,7 +22651,7 @@ "

Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -21025,11 +22662,30 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21045,6 +22701,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21065,6 +22722,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21078,6 +22736,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21096,6 +22755,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21113,6 +22773,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21129,6 +22790,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21143,12 +22805,13 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -21159,6 +22822,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21182,6 +22846,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21201,6 +22866,25 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21215,6 +22899,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21231,6 +22916,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21244,10 +22930,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21265,6 +22952,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21327,17 +23015,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "
ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Modelo
0.815nannannan0.846nannannannannannan
Área del alanan0.737nannan0.8410.9840.899-0.859nannannan-0.744-0.888nannannannannan0.995nan0.880nannannannan0.7910.8580.875nannannan0.843nannannannan-0.755nan0.804nan
nannannan0.843nannannannannannannan0.715nan
nannannannan
Velocidad de pérdida limpia (KCAS)nannannannannannannannannannannannannannannan
envergaduranannannannan0.8850.7340.924nannannannan0.885nan0.776-0.888nan0.875nan0.804nan0.715nannan0.7340.776nannannannannan0.9240.9710.778
0Total de valores196.000225.000
1Valores numéricos64.00068.000
2Valores NaN132.000157.000
" @@ -21361,7 +23049,7 @@ }, { "data": { - "image/png": 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nz55M8+bNBW4nSPnxnjp1iuc9PT09pkePHny3Kz9XleunkSNH8pzX4uJiRlVVlQHAvHz5khOemprKCAsLM7NmzeKE1eXaEFS3V1X+HX3//n0GAPP69WvOe1XLRG0kJCQw//zzD9O+fXuGxWIxampqzOTJk5lHjx7xlL/a3IeU/9XExMSEb1mLi4tjADBr166t03HMnz+fAcAEBwfzfT8yMpIBwOzevbtO6RJCfh31PCOEENIojhw5gpCQEJ6/Dh06cMULCAgAi8XCsGHDuJ4Ga2hooFmzZlzDs5KSkjBhwgQ0adIEIiIiEBUV5UyyHB4eXuu8NW/eHNra2pzXFhYWAMqe+Faet6U8vHJPi5ycHMyfPx/GxsYQERGBiIgIZGRkkJubyzcPQ4cO5Xrdrl076OnpcYYp8pObm4uQkBD07dsXEhISnPDynk6V1eXz+1XleR41ahRXeOvWrWFhYYE7d+5whSsqKqJz58580/Lw8ICoqCjn9adPn/DhwwfO51X5WNzc3BAfH8+3d1K52p6Xa9euoVOnTpxzWxu5ubl4+vQp+vfvz9VTQFhYGMOHD8e3b9948lZ5qCtQ0UuhvCzduHEDJSUlGDFiBNexSkhIwNHRkXPelJSUYGRkhE2bNmHr1q149epVrea/+vjxI+Li4jBkyBCuIXN6enpo164dV9yAgAAoKCjA3d2dKy/NmzeHhoZGjWWIxWJh9OjRePPmDac3S2pqKi5fvox+/fpx5isqKSnB2rVrYWlpCTExMYiIiEBMTAyRkZF8r51+/frVeJzA77kma3udGRsbQ1FREfPnz8eePXu4ehDWxM7ODp8+fcL169exaNEitG3bFnfu3MGIESPg4eHB6Yn2K+erttveunULpaWlmDx5ssC0goKCkJWVhUmTJtVp5cS61iMaGhpo3bo1V5iNjQ3PsNwzZ86gffv2kJGR4Xw/HDx4kKsMlO+7ahkYMmQITz5rW15bt26N169fY9KkSbhx4waysrJq9TmUD+MW1KOyrlgsFte8gyIiIjA2NoampiZsbW054UpKSlBTU+M7rLm214aguv3z588YMmQINDQ0ICwsDFFRUTg6OgKo23c0P+rq6pgyZQoePXqEL1++YN68eZye5wYGBlxDQ93d3fnef/D7q43qynddyn5JSQkOHz4MKysrgXM3lpeH79+/1zpdQkj9oAUDCCGENAoLCwu0atWKJ1xeXh5fv37lvE5MTATDMFBXV+ebjqGhIYCy4UbdunVDXFwclixZAmtra0hLS4PNZsPe3r7GoWWVKSkpcb0uXwVQUHjleVaGDBmCO3fuYMmSJbCzs4OcnBznRwu/PJSvpFU1rLrVtNLT08FmswVuW1ltPz9+VFRUICUlhejoaIFxKivPM795mbS0tHh+jAmav4nfe+XD3ubMmYM5c+bw3YbfELxytT0vycnJdZ68PD09HQzDCDxuADznU1lZmet1+Xxy5XkpP147Ozu++yyf44rFYuHOnTtYuXIlNm7ciNmzZ0NJSQlDhw7FmjVrICsry3f78vwIKkMxMTGc14mJicjIyBC4GmZ1n3u50aNHY/ny5fDx8UHLli1x/PhxFBUVca3UOmvWLOzcuRPz58+Ho6MjFBUVISQkhLFjx/K9dqorP5X9jmuytteZvLw87t+/jzVr1mDRokVIT0+HpqYmxo0bh8WLF3M1GPMjKioKFxcXuLi4ACg7j/3790dAQACuXbsGNze3Xzpftd22fP6z6q6V2sThp671SNVrCSi7niqf2/Pnz2PgwIEYMGAA5s6dCw0NDYiIiGD37t2cRSvK9y0iIsKTJr8yUdvyunDhQkhLS+PYsWPYs2cPhIWF4eDggA0bNvD9DixXnkblByTlREREBA5LLB9qWLUsSUlJ8aQlJibG871WHs5v/rDaXhv8zl1OTg46duwICQkJrF69GqamppCSksLXr1/Rt2/fOn1H1yQzMxMZGRnIzMwEAM65KaekpAR5efl62ZeysjLfuiEtLY2zr9q6evUqEhISMH/+fIFxys9hfX5ehJDaocYzQgghfzQVFRWwWCw8fPiQ74T15WFhYWF4/fo1fH19MXLkSM77nz59+m15zczMREBAAJYtW4YFCxZwwsvn2+EnISGBb5ixsbHA/SgqKoLFYgnctrLafn78CAsLo0uXLrh27Rq+fftW44/g8h+c8fHxPHHj4uKgoqLCFVaXp/Xl2y5cuBB9+/blu42gSbXrcl5UVVVrnI+pqvIfZvHx8TzvlfceqXrsNSmPf/bsWU7vSUH09PQ4k7RHRETAz88Py5cvR1FREfbs2cN3m/JzVdsypKysLHBVVUENdJXp6OigW7duOHHiBLZs2QIfHx8YGxvDwcGBE+fYsWMYMWIE1q5dy7VtSkoKFBQUeNKsTY+O33VN1uU6s7a2xqlTp8AwDN68eQNfX1+sXLkSkpKSXHmsDWVlZcyYMQOBgYEICwuDm5vbL52v2m6rqqoKAPj27ZvA+RArx6mLutYjtXHs2DEYGBjg9OnTXOWmsLCQZ98lJSVITU3lakDjVyZqW15FREQwa9YszJo1CxkZGbh9+zYWLVoEFxcXfP36VeAqlOXHya+cqqurC+x5VB4uqCH3V9T22uB3bd69exdxcXEIDAzk9DYDUOPcb7UVERGB06dP49SpU3j//j2MjY0xePBgDBkyBObm5lxxDx8+jNGjR9cqXaaGifmtra1x8uRJlJSUcM17Vr5wRNOmTWt9DAcPHoSYmBiGDx8uME55efiZ64AQ8mto2CYhhJA/Ws+ePcEwDL5//45WrVrx/FlbWwOouFmv+sN17969PGlW7eVTX1gsFhiG4cnDgQMHBPYSOH78ONfroKAgfPnyBU5OTgL3U77SZ9XVxbKzs3H58mWuuLX9/ARZuHAhGIbBuHHjUFRUxPN+cXExZ5/lw3SqTgwfEhKC8PBwdOnSpdp9VcfMzAwmJiZ4/fo13+No1aqVwEaBupyX7t274969e9UOAa1KWloabdq0wfnz57nKFJvNxrFjx6CjowNTU9M6HC3g4uICERERREVFCTxefkxNTbF48WJYW1vj5cuXAtM3MzODpqYmTp48yfXj8MuXLwgKCuKK27NnT6SmpqK0tJRvPmq7EqCXlxfS09OxdOlShIaGYvTo0Vw/slksFs85unLlyi8NT/pd1+TPXGcsFgvNmjXDtm3boKCgUO35Ki4uFtjzrXy4W3kvx185X7Xdtlu3bhAWFsbu3bsFptWuXTvIy8tjz549dVoZsCHqERaLBTExMa7ylpCQwLPaZvnk/FXLwIkTJ/imWdfyqqCggP79+2Py5MlIS0vj6uFZVfnQ8aioKJ73unbtirCwML7Dfv38/CAjI4M2bdoITPtn/cy1Ua4u39G1lZSUhA0bNsDW1hZmZmbYs2cPunXrhmfPniEyMhIrV67kaTgD6nfYZp8+fZCTk4Nz585xhR8+fBhaWlq1Pg8JCQm4evUqevfuzbc3Zbny1U0tLS1rlS4hpP5QzzNCCCF/tPbt22P8+PEYPXo0nj9/DgcHB0hLSyM+Ph6PHj2CtbU1Jk6cCHNzcxgZGWHBggVgGAZKSkq4fPkybt26xZNm+Q/Z7du3Y+TIkRAVFYWZmVmtetBUR05ODg4ODti0aRNUVFSgr6+P+/fv4+DBg3x7zgDA8+fPMXbsWAwYMABfv37F33//DW1tba6VJ/lZtWoVXF1d4ezsjNmzZ6O0tBQbNmyAtLQ0V0+F2n5+gpSvXjpp0iS0bNkSEydOhJWVFYqLi/Hq1Svs27cPTZs2hbu7O8zMzDB+/Hj8888/EBISQvfu3RETE4MlS5agSZMmmDlz5k99ruX27t2L7t27w8XFBaNGjYK2tjbS0tIQHh6Oly9f4syZM3y3q8t5WblyJa5duwYHBwcsWrQI1tbWyMjIwPXr1zFr1iy+P8QAYN26dXB2dkanTp0wZ84ciImJYdeuXQgLC8PJkyfrNO8NAOjr62PlypX4+++/8fnzZ7i6ukJRURGJiYl49uwZpKWlsWLFCrx58wZTpkzBgAEDYGJiAjExMdy9exdv3ryptheTkJAQVq1ahbFjx6JPnz4YN24cMjIysHz5cp6hWYMGDcLx48fh5uaG6dOno3Xr1hAVFcW3b99w79499OrVC3369KnxmDw8PKCiooJNmzZBWFiYq4coUNZw4+vrC3Nzc9jY2ODFixfYtGlTnYf9Vfa7rsnaXmcBAQHYtWsXevfuDUNDQzAMg/PnzyMjIwPOzs4C08/MzIS+vj4GDBiArl27okmTJsjJyUFgYCC2b98OCwsLTo/MXzlftd1WX18fixYtwqpVq5Cfn4/BgwdDXl4e79+/R0pKClasWAEZGRls2bIFY8eORdeuXTFu3Dioq6vj06dPeP36Nf7991++eWiIeqRnz544f/48Jk2ahP79++Pr169YtWoVNDU1ERkZyYnXrVs3ODg4YN68ecjNzUWrVq3w+PFjHD16lG+atSmv7u7uaNq0KVq1agVVVVV8+fIF3t7e0NPTg4mJicA86+jowNDQEE+ePMG0adO43ps+fTqOHDkCJycnTj2Vnp6O06dP4+zZs9i6desvf5/x87PfV0BZY6qioiImTJiAZcuWQVRUFMePH8fr169/Oj9Xr17F+vXr0a9fP2zevBmdOnXiGp4piLKycrUNVHXRvXt3ODs7Y+LEicjKyoKxsTFOnjyJ69ev49ixYxAWFubE9fLywuHDhxEVFcXTo/jw4cMoKSnB2LFjq93fkydPOEN/CSG/2e9eoYAQQsj/tvJVrkJCQvi+36NHD65V7ModOnSIadOmDSMtLc1ISkoyRkZGzIgRI5jnz59z4rx//55xdnZmZGVlGUVFRWbAgAFMbGws31XSFi5cyGhpaTFCQkJcq/MJWsUMADN58mSusPJVFzdt2sQJ+/btG9OvXz9GUVGRkZWVZVxdXZmwsDBGT0+PGTlyJM/ncPPmTWb48OGMgoICIykpybi5uTGRkZE1fIplLl26xNjY2DBiYmKMrq4us379eoGrlNXm86tOaGgoM3LkSEZXV5cRExNjpKWlGVtbW2bp0qVMUlISJ15paSmzYcMGxtTUlBEVFWVUVFSYYcOGMV+/fuVKz9HRkbGysuLZD7/PtLLXr18zAwcOZNTU1BhRUVFGQ0OD6dy5M7Nnzx5OHH4rLtb2vDBM2QqJY8aMYTQ0NBhRUVFGS0uLGThwIJOYmMiVx8qr2TEMwzx8+JDp3Lkz5zO2t7dnLl++zBVHUPkXtErkhQsXmE6dOjFycnKMuLg4o6enx/Tv35+5ffs2wzAMk5iYyIwaNYoxNzdnpKWlGRkZGcbGxobZtm0bU1JSwvczrOzAgQOMiYkJIyYmxpiamjKHDh3iWUmSYcpW5tu8eTPTrFkzRkJCgpGRkWHMzc2Zv/76q9bllWEYZubMmQwAxs3Njee99PR0xsvLi1FTU2OkpKSYDh06MA8fPuRZEbL8szpz5gxPGr9y7utyTfL7jBim5uvsw4cPzODBgxkjIyNGUlKSkZeXZ1q3bs34+vpW+7kVFhYymzdvZrp3787o6uoy4uLijISEBGNhYcHMmzePSU1N5Ypf2/NV9bOty7YMwzBHjhxh7OzsOPFsbW15rourV68yjo6OjLS0NCMlJcVYWloyGzZs4LzPr8761XqE3/lZv349o6+vz4iLizMWFhbM/v37+e47IyODGTNmDKOgoMBISUkxzs7OzIcPH3i+R2pbXrds2cK0a9eOUVFR4dTVXl5eTExMDE++q1qyZAmjqKjIFBQU8LyXkJDATJw4kdHV1WVEREQYWVlZpkOHDnyvi5EjRzLS0tI84YI+v6rfg3W5NgSlyTAMExQUxLRt25aRkpJiVFVVmbFjxzIvX77kqU9ru9pmSkoKU1hYWGO8hpadnc1MmzaN0dDQYMTExBgbGxvm5MmTPPHKVz3lt9K3qakpo6+vX+3KtAzDMB07dmTc3d3rK+uEkDpgMUwd+lETQgghpF74+vpi9OjRCAkJqXbSaEIIIf+b4uLiYGBggCNHjsDT07Oxs0MaWVRUFExMTHDjxo1qe6oSQhoGzXlGCCGEEEIIIX8YLS0tzJgxA2vWrAGbzW7s7JBGtnr1anTp0oUazghpJDTnGSGEEEIIIYT8gRYvXgwpKSl8//5d4Mqm5L+vpKQERkZGWLhwYWNnhZD/WTRskxBCCCGEEEIIIYQQAWjYJiGEEEIIIYQQQgipdw8ePIC7uzu0tLTAYrFw4cKFGre5f/8+WrZsCQkJCRgaGmLPnj08cc6dOwdLS0uIi4vD0tIS/v7+DZD7CtR4RgghhBBCCCGEEELqXW5uLpo1a4Z///23VvGjo6Ph5uaGjh074tWrV1i0aBGmTZuGc+fOceIEBwfD09MTw4cPx+vXrzF8+HAMHDgQT58+bajDoGGbhBBCCCGEEEIIIaRhsVgs+Pv7o3fv3gLjzJ8/H5cuXUJ4eDgnbMKECXj9+jWCg4MBAJ6ensjKysK1a9c4cVxdXaGoqIiTJ082SN6p5xkhhBBCCCGEEEIIqVFhYSGysrK4/goLC+st/eDgYHTr1o0rzMXFBc+fP0dxcXG1cYKCguotH1XRapuEEEIIIYQQQgghf5D2Oyc0dhb4ck7WwIoVK7jCli1bhuXLl9dL+gkJCVBXV+cKU1dXR0lJCVJSUqCpqSkwTkJCQr3kgR9qPCOEkHr0p37J/azHk/cgLiOpsbNR77QU1LD6lk9jZ6NeLXYejXTnPo2djXqleMsfW+43TNf7xjLbcTDmX9nV2NmoVxt6TMKxF9cbOxv1alhLV1wKe9jY2ahXHk07YuiJ5Y2djXp3fMhyBH560djZqFdOxi2xN/hCY2ejXv3VtjcGHl3c2NmoV37DV2PGRe/Gzka98u41A0dCrjZ2NurVCDu3xs7Cf87ChQsxa9YsrjBxcfF63QeLxeJ6XT7bWOVwfnGqhtUnajwjhBBCCCGEEEIIITUSFxev98ayyjQ0NHh6kCUlJUFERATKysrVxqnaG60+0ZxnhBBCCCGEEEIIIX8Q1h/6r6G1bdsWt27d4gq7efMmWrVqBVFR0WrjtGvXrsHyRT3PCCGEEEIIIYQQQki9y8nJwadPnzivo6OjERoaCiUlJejq6mLhwoX4/v07jhw5AqBsZc1///0Xs2bNwrhx4xAcHIyDBw9yraI5ffp0ODg4YMOGDejVqxcuXryI27dv49GjRw12HNTzjBBCCCGEEEIIIYTUu+fPn8PW1ha2trYAgFmzZsHW1hZLly4FAMTHxyM2NpYT38DAAFevXkVgYCCaN2+OVatWYceOHejXrx8nTrt27XDq1Cn4+PjAxsYGvr6+OH36NNq0adNgx0E9zwghhBBCCCGEEEL+IEINOPn97+Tk5MSZ8J8fX19fnjBHR0e8fPmy2nT79++P/v37/2r2ao16nhFCCCGEEEIIIYQQIgA1nhFCCCGEEEIIIYQQIgAN2ySEEEIIIYQQQgj5g7D+I8M2/yuo5xkhhBBCCCGEEEIIIQJQ4xkhhBBCCCGEEEIIIQLQsE1CCCGEEEIIIYSQP8h/ZbXN/wrqeUYIIYQQQgghhBBCiADUeEYIIYQQQgghhBBCiAA0bJMQQgghhBBCCCHkD8ICDdv8k1DPM0IIIYQQQgghhBBCBKDGM0IIIYQQQgghhBBCBKBhm4QQQgghhBBCCCF/EBattvlHoZ5nhBBCCCGEEEIIIYQIQI1nhBBCCCGEEEIIIYQI8Nsbz/T19eHt7f1b02OxWLhw4cIv7Wf58uVo3rz5L6VRVWBgIFgsFjIyMuo1XcKt6ufs6+sLBQWFRs3TqFGj0Lt370bNw58gNTUVampqiImJaeyscPn333/h4eHR2NkghBBCCCGE/I8SYrH+yL//VbWe88zd3R35+fm4ffs2z3vBwcFo164dXrx4gRYtWtRrBmsSEhICaWnp37pP8v+bp6cn3NzcGjsbBMC6devg7u4OfX19AEBMTAwMDAzw6tUrTmN1dnY23N3dkZCQgI8fP1abXnR0NPT19REUFISOHTvC2dkZ169f54l37tw5bNy4ER8+fACbzYauri5cXV2xZcsWAMC4ceOwZs0aPHr0CB06dKjXY/4VzTSNMcS2G8zVdKEirYAFV3fjYfTrxs4WAODCWX+cPnYSqamp0DfQx5SZ02Bj20xg/NCXr7DL+1/ERMdARUUZg4YPgUff3nzj3r15G6uWrEB7hw5YvWkdJ/z1q1CcPnYSER8+IjUlFas2rkEHR4f6PjQuHx+8xLs7T5GfmQMFTRW06tcV6sZN+MZNiPiCWztO8oR7LB4HeQ1lAAC7tBRhN4MR9TQMeRnZkFdXgm2vTtC2NGzQ4/hZoh3sId6jG4RNjCAkL4esCTNRGhXT2Nni8S7wGd7cCEJeZjYUtdTQ1tMVmiZ6fOPGfYxGwJbDPOEDV0yGgqYqT/inZ29x98A56DUzg8vkwfWed0Hs9azgaGgLWXEpJOak4fK7x4hJjxcYX1hICF1N7GCrZQpZcSlkFuTg7qcXeP7tAyeOhIgYXMzaoKmGISRFxZGen42A94/xMTn2dxwSnt96iOCAu8jOyIKqtgZcRvSFrrlRjdt9/fgZh1f9A7Ummhi/bh4nPOlbPO6fuYr46G/ITElDt+F90Ka7UwMeAa+g6/cQePEGstMzoN5ECx6jB8HQ0pRv3OjwSFw5ehbJ3xNQVFQERRVl2HdzgIN7N77xQx89w/Ft+2Bl1xyjFkxpyMPg0tXEDj0s2kFBUhbfM5Nw9MX1astIO31r9LRoDw1ZZeQVF+BN3CeceHUTOUX5AIBORi3QwaAZmiioAQCi0+Jx+vUdfE79/luOp1xgwC3cPB+AzLQMaOlqY+D4ETBpas437svHz/Dg6m18/fwFJcUl0NTThvuQfrBqyf09l5eTiwtH/PAqKAR5OblQUVdF/7FDYW1n+zsOCaF3gvH82n3kZmRDWVsdTkPcoWNmUON23yNj4LduL1S01TF81QxOuN+6vfj28TNPfAMbc/SZNbo+sy5QN9PW8LDqCAVJGXzLSILv86v4kPRFYPwOBs3gYdkBmnLKyCsqRGhcJI6+uMYpf46Gtpjcvh/PdkOPL0cxu6TBjqM67fVt0Nm4JeQkpJGQnQr/t/fxOS2Ob9whtt3QWteSJzw+KxUb7h1t6KwK9PzWIzy5eg85P+pz52G9a1efR3zG0dU7oaqjgXFr53LCP4S8weNLt5CemAJ2KRuK6iqwd3OCdQe7hjwMQqpV68YzLy8v9O3bF1++fIGeHvcN6KFDh9C8efPf3nAGAKqqvDe25L+hqKgIYmJi9Z6upKQkJCUl6z3dxtRQn1VtFBcXQ1RUtM7b5efn4+DBg7h69arAOMnJyejevTsA4Pbt2xARqaiy7OzsMH78eIwbN44TVl4fHDp0CFOnTsWBAwcQGxsLXV1dTpzbt29j0KBBWLt2LTw8PMBisfD+/XvcuXOHE0dcXBxDhgzBP//880c1nkmKiuNT6jdc/RCEtd0nNHZ2OO7euoOd23ZgxrxZaGpjjcv+lzB/5lz4njoKdQ11nvjxcXFYOHMeevRyx98rliDszVt4b9wKeQUFOHZ24oqbEJ+A3Tt2waY5b0NcQX4BjEyM4drTDcsWLG6ow+OIeRGO5+duo7WnC9QMtRHxKBR3d/nBY/FYSCvJC9yu15LxEJWsuD7FZaQ4/w+9/ACfQ96h7ZDukFNXRlz4Z9zffx6us4ZBqYlGgx7Pz2BJiKPk3QcUPQiC9KzJjZ0dvqJCwhB8+jo6DOkBdWNdhD94jms7jmHg8smQUVYQuN3AVVMgJiHOeS0hy/tgLjs1A0/P3oSGiS7Pew3JRtMY7pYdcCHsAb6kJ6CNriXGtO6JrfdPIqMgh+82Q21dICsuibNv7iE1LxPSYpIQFqoYcCDMEsLYNh7IKcrHsZc3kFmQAwUJGRSWFP+WY3oX/BI3jvjDbcwA6Jga4OWdIJzYsAcTNy2EvIqSwO0K8vJxcfcxGFiZIjcrm+u9ksIiKKqpwKKNLW4d82/oQ+AR+vgZLvmcQp9xQ6FvbownNx/g4JrtmOO9EoqqyjzxxcTF0b57Z2jq6UBMQhzR4ZE4t/coxMTFYd/NkStuelIqAg6fgYGFye86HACAva4Vhrdwhc/zK4hIjkVn41aY5zQM867sRGpeJk98U1VdTLTvg2Mvb+Dl949QlJLDGLueGNvGA94PTwMALNT1EfwlDEeef0URuwQ9LdpjQafhmH9lJ9Lzs3nSbAghD4Lht/8IhkwaAyMLUzy4fgf/LNuA5bs3QUlNhSd+5LsPsLC1Ru+RnpCUlkLQ7fvYuXIzFmxdBV0jfQBASXEJvBevg6y8HP5aNB2KKkpIT06F+G+65/z49DUCT1xGlxG9oWWihzf3nsJ/6yGMXDsLcsqKArcrzMvH9X2noWtphLxM7vrEfepwsEtKOa/zc3NxdMl2mNpZN9hxVNZWrylGtXLDgWeX8TEpFl1N7bCo8wjMvLSDb/kzU9XDlHb9cPjFVTz/9hFKknIYZ++BCW37YPP9E5x4eUUFmH7Rm2vbxmo4s9UyRR9rR5x9fRfRaXFop2+Dv9r2xrq7R5HB53o4/zYQl98/4rwWYglhXqeheB0X+Rtzze39k1e4dewCXEf1RxNTA7y8G4RTm/bhrw0LIK8iuOwV5OXj0p4TMLAyQU4m97FKSkuhvYczVLTUISwijMhX73B53ylIycnCyIZ/IzchDa3WwzZ79uwJNTU1+Pr6coXn5eXh9OnT8PLyAgAEBQXBwcEBkpKSaNKkCaZNm4bc3FyB6cbGxqJXr16QkZGBnJwcBg4ciMTERK44ly5dQqtWrSAhIQEVFRX07duX817VYZuRkZFwcHCAhIQELC0tcevWLZ59zp8/H6amppCSkoKhoSGWLFmC4mLum8X169dDXV0dsrKy8PLyQkFBQbWfT2lpKby8vGBgYABJSUmYmZlh+/bt1W5TVWpqKgYPHgwdHR1ISUnB2toaJ0/y9lqoytfXF7q6upCSkkKfPn2wZcsWrmGJ/IYIzpgxA05OTpzXDMNg48aNMDQ0hKSkJJo1a4azZ89Wu99du3bBxMQEEhISUFdXR//+/X8pPX19faxevRqjRo2CvLw8p1GkpjJ17NgxtGrVCrKystDQ0MCQIUOQlJRU7edV+fPR19cHi8Xi+StXm/JS1ffv3+Hp6QlFRUUoKyujV69edR6a+PjxYzg6OkJKSgqKiopwcXFBeno6AMDJyQlTpkzBrFmzoKKiAmdnZ8TExIDFYiE0NJSTRkZGBlgsFgIDAzlh7969Q48ePSAnJwdZWVl07NgRUVFRnPd9fHxgYWEBCQkJmJubY9euXZz3yvfh5+cHJycnSEhI4NixY2Cz2Vi5ciV0dHQgLi6O5s2b8+3xVdm1a9cgIiKCtm3b8n3/69ev6NixI2RlZXHv3j3o6OhAQ0OD8ycsLMw555XDcnNz4efnh4kTJ6Jnz548dVZAQAA6dOiAuXPnwszMDKampujduzf++ecfrngeHh64cOEC8vPzqz2O3+lJ7Dvsf3oJ9z+HNnZWuJw5eRpuHj3Qo5c79Az0MWXWNKipq+HSOf4/Xi+dvwg1DXVMmTUNegb66NHLHd3de8Dv+CmueKWlpVizdCVGjR8DTW1NnnTatLOH14RxcOjkyPNeQ3h/9xmM2zaDSbtmkNdQgV3/rpBSlMPHh6+q3U5CVgqScjKcP6FKDRifn72Ddbe20LYygqyKAsw6toCmhQHe3w1p6MP5KUW376PgmB9KXv4ZPR75eXMrGGYdWsC8Y0soaqqinWd3yCjK4/3959VuJykrDSl5Wc5f5fMEAGw2G3cPnENLj06Qq+bHQEPoaNAMIV/DEfI1HEk56bj8/jEyC3Jgr9eUb3xT1SYwVNbCoZAr+JT6Den52fiWmYQv6QmcOK2aWEBKVBxHnl/Dl/QEZOTnICY9AfHZqb/lmJ5cDYStkz1sO7Xl9DqTU1bE89uPq93uyoHTsGrXEjom+jzvaRnpoevQXmjargWERX7/gvIPLt+CXecOaNPVAeo6Wug1ZhAUlBURfCOQb3xtQ13YdmwDDV1tKKmpoKVjW5g1t0J0OPePYHYpGye270c3Tw8oqf/eh8bdzdsi8PNLBEa9RFxWCo69vI7UvEx0NWnFN76xsg6SczNwI+IpknMzEJEci7ufnsNQSYsTZ1fQedyODMGXjATEZ6XgwLNLEGKxYKXx+3rc3va/ivbdnNDBpRM0dbXhOX4EFFWUcf8q7wgbAPAcPwIu/d2hb2oEdW1N9Bk5CGpaGnjz9CUnzuNbgcjNzsGkJbNgbGkGZTVVGFuZo4kh/16v9e3FjYdo6mAHa8fWUNZSR6ehHpBVksfru0+q3e6273mY2zeHphFvPiVlpCCtIMv5iw2LhKiYKExb2zTUYXDpadkedz+9wN1PL/A9KxmHn19FSl4mupm15hvfVFUHSbkZuPbhCZJz0vEx+QtuR4TAUFmbKx4DBpkFOVx/jcXJuAWefnmHJ7HvkJiTDv+w+8jIz0EHff6fcUFJEbIL8zh/ugrqkBSVwNPYd7855xWeXgtEc6c2sO1kDxVtdXQb3gdyygp4eaf6+vzaoTOwatsC2sb6PO/pWRrD3M4GKtrqUFRXQWtXR6g10cRXPj0h/8v4/Ub9E/7+V9W68UxERAQjRoyAr68vGIbhhJ85cwZFRUUYOnQo3r59CxcXF/Tt2xdv3rzB6dOn8ejRI0yZwr9rOcMw6N27N9LS0nD//n3cunULUVFR8PT05MS5cuUK+vbtix49euDVq1e4c+cOWrXi/4XNZrPRt29fCAsL48mTJ9izZw/mz5/PE09WVha+vr54//49tm/fjv3792Pbtm2c9/38/LBs2TKsWbMGz58/h6amJlcDgqB96+jowM/PD+/fv8fSpUuxaNEi+Pn5VbtdZQUFBWjZsiUCAgIQFhaG8ePHY/jw4Xj69KnAbZ4+fYoxY8Zg0qRJCA0NRadOnbB69epa77Pc4sWL4ePjg927d+Pdu3eYOXMmhg0bhvv37/ON//z5c0ybNg0rV67Ex48fcf36dTg4OPx0euU2bdqEpk2b4sWLF1iyZEmtylRRURFWrVqF169f48KFC4iOjsaoUaNqfewhISGIj49HfHw8vn37Bnt7e3Ts2JHzfk3lpaq8vDx06tQJMjIyePDgAR49egQZGRm4urqiqKioVnkKDQ1Fly5dYGVlheDgYDx69Aju7u4oLa14+nf48GGIiIjg8ePH2Lt3b63S/f79O6dx+e7du3jx4gXGjBmDkpKyp2379+/H33//jTVr1iA8PBxr167FkiVLcPgw95Cm+fPnY9q0aQgPD4eLiwu2b9+OLVu2YPPmzXjz5g1cXFzg4eGByEjBT8EePHgg8Fr++PEj2rdvD3Nzc1y/fh2ysrK1Oj4AOH36NMzMzGBmZoZhw4bBx8eHq87S0NDAu3fvEBYWVm06rVq1QnFxMZ49e1brff8vKi4uRsSHCLRqw30j26q1HcLe8v+M3799h1atubvd29m3xsfwD5yyCABHDvpCQVEBPTx61n/G66i0pBRpXxOgaaHPFa5loY/k6OqHGgVs8MHZRf/g1o6TSIjgHmpSWlICIVHuH/kioiJIivpaL/n+X1NaUoKU2DjoWHIPFdGxNEJiDZ/p+VV7cXTOZgRsPYy4D9E8778MuA9JWWmYd/i9veyFWULQlldFZDJ3/iOSv0JPkbdnJwBYqhvgW2YSHA1tsajLCMxxHIIeFu0gIiRcKY4+vmQkonfTjljcdRRmOniik1ELsNDwN8WlJSWIj/4KQxszrnAjazN8i+D97MuFBj5BelIKHPu5NnQW66ykuATfo77AtLkVV7hpMyt8+RglYCtu3z/HIuZjFAytuId53jpzGdJysmjdtaOALRuGsJAwDJS08DaeO/9vE6JgosJ/uHpkylcoScmhmVZZDzk5CWm0bmKJ0Gp6xYgLi0KYJYTcwt/zsKqkuASxn6JhacvdOGHZwhpR4RG1SoPNZqMgvwDSlXqovnn6AobmJjixywdzhk7AiknzcPX0BbBL2fWaf35KS0qQGPMdek25eybqNTVF3CfBQxzDHoYgIzkNbXt3rdV+3j58DrM2zSAq3vCjHYSFhGGopIXX8Z+4wt/EfYKZKv/evx+TY6EsJQdbrbJrSF5CGvZ6Vnj1nXvaDwkRMezsMwe7+87F/E7DoK/I+4DudxBmCUFHXg0fkrnP0YekL9BXql2e2uhZISI59rf12qyqrD7/BoOm3PW5YVMzfIuMEbjd6/tPkZ6YAoe+LjXug2EYRIdFIC0huVZDQQlpKHV6LDdmzBhs2rQJgYGB6NSpE4Cy4VF9+/aFoqIipk+fjiFDhmDGjBkAABMTE+zYsQOOjo7YvXs3JCQkuNK7ffs23rx5g+joaDRpUvYlfPToUVhZWSEkJAR2dnZYs2YNBg0ahBUrVnC2a9aM/zw6t2/fRnh4OGJiYqCjowMAWLt2LWfYV7nFiyuG9+jr62P27Nk4ffo05s0rmzfD29sbY8aMwdixYwEAq1evxu3bt6vtfSYqKsqVRwMDAwQFBcHPzw8DBw4U/KFWoq2tjTlz5nBeT506FdevX8eZM2fQpk0bvtts374dLi4uWLBgAQDA1NQUQUFBNfb6qSw3Nxdbt27F3bt3Ob2ADA0N8ejRI+zduxeOjry9OmJjYyEtLY2ePXtCVlYWenp6sLW1/en0ynXu3JnrMxgxYkSNZWrMmDGc+IaGhtixYwdat26NnJwcyMjI1Hj8lYf+Tp8+HfHx8QgJqej1UVN5qerUqVMQEhLCgQMHOC3zPj4+UFBQQGBgILp14z+XSWUbN25Eq1atuBptray4b8iNjY2xceNGzuva9GzbuXMn5OXlcerUKc5QS1PTipv0VatWYcuWLZzenQYGBnj//j327t2LkSNHcuLNmDGDqwfo5s2bMX/+fAwaNAgAsGHDBty7dw/e3t7YuXMn37zExMRAS0uL73sjRoxAu3btcO7cOQgLC/ONI8jBgwcxbNgwAICrqytycnJw584ddO1admM4depUPHz4ENbW1tDT04O9vT26deuGoUOHQly8YsiWtLQ0FBQUEBMTw7fMFhYWorCwkCus8vb/KzIzMsEuLYWiEndPHEVlRaQ/SeO7TVpqKhSVuRvbFJUUUVpaisyMDCirqODt6ze4eukKDhw71GB5r4vCnDwwbIZnKJ+ErDQKsvj3rpaUl4H9YFco6WqAXVyKzyFhuPXPSXSbPgTqxmU3/loWhgi/GwJ14yaQVVFE/McYfH0TydXgS2qv4Md5kpTjPk+SctLIy+Lfs0BKXhYdh7tDVVcTpSWliHzyGgHbDsN99ihomuoDABI+xeLjo5fot+T3D5eWEpOAsJAQZ76ecjmFeZAV59+AoSQpB31FTZSUluLI8+uQFpNA76YOkBQVx9k398riSMnBSFIWoXGR8Hl2BSrS8ujV1AFCLCHc+VR9L71flZedC4bNhrS8HFe4tLwsz9CdcqnxSbh76jJGLpsOoTp+L/wOudk5YLPZkK1yTDIKcsjO4B1eVtnqcXORk5UNNrsUzgM90KZrxcPI6A+RCLnzCDO3LG2QfFdHVlwKwkJCyCzgruMy83Mhr8n//ioy5St2BZ3H1Pb9ISosAhEhYbz49gGHnwueomFQ865Iy89GWMLv6VFS9lmzIafAPdxeVkEeWenVn6tyt/yvoKigEC072nPCkhOSkJr4Hm2c2mPq8nlIikvAyd2+YJey0XNI32pS+3X52Xll15Qc93mRkpNBnoBrKj0hBY/OXIfnogm1uqbiP39F6rcEdBvTv8a49UFOXArCQsI8vcIyC3KhIMG//EUkf8WOR2cww8GTU/5Cvobj0LMATpy4rGTsCjqP2IxESIqKw828LVa5jsPcgJ1I+E09b8tJi5cNp88uyOMKzy7Mg5yElICtKsiJS8FCTR9HX1xrqCzWqLw+l5HnftAtLS+LnIwsvtukJSTj3ukADF8ytdqyV5CXjx1Tl6O0pAQsISG4juoPQ2szgfEJaWh1ajwzNzdHu3btcOjQIXTq1AlRUVF4+PAhbt68CQB48eIFPn36hOPHj3O2YRgGbDYb0dHRsLCw4EovPDwcTZo04TScAYClpSUUFBQQHh4OOzs7hIaGcs1pVJ3w8HDo6upyGs4A8B0SdvbsWXh7e+PTp0/IyclBSUkJ5OTkuNKZMIH7Brlt27a4d+9etfvfs2cPDhw4gC9fviA/Px9FRUV1WqGztLQU69evx+nTp/H9+3fOj/PqFkQIDw9Hnz59ePJal8az9+/fo6CgAM7OzlzhRUVFnAaxqpydnaGnpwdDQ0O4urrC1dUVffr0gZSU1E+lV65qT6TalKlXr15h+fLlCA0NRVpaGtjssid8sbGxsLTknVBTkH379uHgwYN4/PgxV4NaTeWlqvI8V+0tVVBQwDU8sjqhoaEYMGBAtXEE9dqqKd2OHTvynaMsOTkZX79+hZeXF9c1V1JSAnl57hvMyvvOyspCXFwc2rdvzxWnffv2eP1a8NCu/Px8ngb1cr169YK/vz/OnTtX68ZnoKzH2rNnz3D+/HkAZT1mPT09cejQIU7jmbS0NK5cuYKoqCjcu3cPT548wezZs7F9+3YEBwdDSqriZkVSUhJ5eXl897Vu3TquBnMAWLZsGfA/Og0jTxduBkA13bqr9m4pbyxisVjIy83D2mWrMWfRPMg38sq4VfH0ymEAQR115NWVIa9eMc+RqqE2ctOz8P72M07jmV3/rgg+eQ2XVu0HWICsiiKM7G0Q9eRNAx1B7Yl1doDUjIrvwpxFq1ASFt6IOao93vIlOK6ChgoUNCrmOFI3aoKc9Cy8vhkETVN9FBUU4t7B8+g43IPvPGi/C0+DKosFQYdVfj2eCr2NgpKyHs8B4UEY1sIFF8IeoIRdChZYyC3Kx7k3gWDA4HtWMuQkpOFg2LzBG884+azyWtDlxGaz4b/zCBz7d4eyptpvyNkv4KkLq6kkfpi0eh4KCwoRG/EZV4+dg4qGGmw7tkFBfgFObj+I/hNHQFqu9j2w6xtTpaSxWICgwqctp4oRLbvDP+w+3sRHQUFSBkOad8OY1j2x/+klnvg9LdqjrZ41Vt/x/f1zTtW2AFbxLDAIAcfPY9KSWVwNcAybgayCHIZNHQshYSHomRgiIy0dN89dafDGMw5+38V8DorNZuPq3pNo29sZihq1u3EJe/AMyjoa0DTk32jfUPjV34LqPm15VYy264Gzb+7hdVwkFCVlMaylK8bZ98Ke4LKpJCJTviEy5Rtnm49JsdjQYxK6m9vDJ+RKAxxB3bFY1X9vlWuta4X84kKe3qGNokrZK7sN5F/2Luw8io79XGusz8UlxDF2zRwUFRYh5l0Ebh+/AEVVZehZGtdnzv9oQr+hNzipvTpPCOHl5YUpU6Zg586d8PHxgZ6eHrp06QKg7GL466+/MG3aNJ7tKk/YXY5hGL4XVeXwukzszu9JfdX0nzx5wunJ5uLiwumFU77K3s/y8/PDzJkzsWXLFrRt2xaysrLYtGlTtUMuq9qyZQu2bdsGb29vWFtbQ1paGjNmzKh2qF9teicICQnxxKs8Z1d5Y9OVK1egrc09J4CgnjSysrJ4+fIlAgMDcfPmTSxduhTLly9HSEjIT6VXrmpDYU1lKjc3F926dUO3bt1w7NgxqKqqIjY2Fi4uLrUeIgkAgYGBmDp1Kk6ePMnVs/FnygubzUbLli25GvzK1XaBi9qU+6qfVfn8PJXPddW52apLt/y87d+/n6enY9XeX/wadKtea4Ku73IqKiqcOdyqWrRoEWxsbDB06FAwDMM1lLs6Bw8eRElJCVe5YxgGoqKiSE9Ph6JiRe8oIyMjGBkZYezYsfj7779hamqK06dPY/ToitWj0tLSBJ6zhQsXYtasWVxh4uLiuHVgeq3y+l8hryAPIWFhpKVy9zJLT0vn6Y1WTklZmSd+RnoGhIWFIScvj5jP0UiIj8eiOQs47zM/ymeXdk444ncc2jrcdUtDE5eRAkuIhfxs7ifgBTm5dWpQUdXXxueQinlJJGSl0Gl8P5QWl6AwNx+S8jJ4dTGw2ontf5ei4Gco+VAxhImdwr8n4Z9E4sd5qtrLrCA7F1JyNfdELqdmoINPT8saMLOS05CdmoEbOysmmy6vZ/dPWAHPlVMhpyZ4gvtflVdUgFI2G7Li3L0QZMQkkVPIv3E/uzAXmQW5nIYzAEjOSYcQiwV5CRmk5mUiuzAXpQybq2EkKScdchLSEGYJoZRpuKFmUrLSYAkJISeTu1dCXmY2pOV5G4mK8gsQ//krEmK+45rvOQA/zgHDYPWwmRi6cCIMrPivaPm7SMuWzWdYtZdZTmY2ZBUEP3ADwJnHTFNPB9mZWbjldwm2HdsgNSEJ6Ukp8FlXMSdnedmbP2A85v6zGioaDdeYmF2Yh1I2m6eXj5yEtMA5ojysOiAiJRZXwoMAAF8zEuFTcgXLnMfgzOu7XAtcuJm3g4dVR6y7ewRfMxL5ptcQZOTK5jSs2sssOzOTpzdaVSEPgnFkxz78tWA6LGy5J82XV1KAsLAwhIQrZsXRbKKNrPQMlBSXQES0zj+7ak1SVgosISHkVulllpedAyl53rqvKL8QidHfkPQlDnePXQRQcU1tG7MQ/eZ4QbdSA0VxYRE+Pn2Ndn1qHj1RX7IK81DKLoWCJHf+5aspf32aOuJjcixnQv3YjEQUPL2MVa7jcCr0FjLyebdjwCAq9Ts0ZHkX9WhouYX5ZfW7RNX6XQrZAur3ytroWuL5t/AGra9rwqnPM2pbnxciPvorEr58x43DZQ+7y8ve2hGzMWT+BOhblQ0/ZgkJQelH466GnjZSvici6PLt/6nGM/JnqXMtPnDgQEyfPh0nTpzA4cOHMW7cOM4P5BYtWuDdu3cwNq5dgba0tERsbCy+fv3K6X32/v17ZGZmcnqp2djY4M6dO1w/aGtKLy4ujjMcLDg4mCvO48ePoaenh7///psT9uUL9zhzCwsLPHnyBCNGjOCEPXlS/WSbDx8+RLt27TBp0iROWG17GVVOo1evXpwhZ2w2G5GRkTw99iqztLTkyVvV16qqqjzzO4WGhnJ6H1laWkJcXByxsbHVDqmsSkREBF27dkXXrl2xbNkyKCgo4O7du3B2dv6p9PipqUy9ffsWKSkpWL9+PacMPX9etyfmnz59Qr9+/bBo0SKuoYhA7coLvzyfPn0aampq1fZQq055ua/as6k65Y088fHxnB5+lRcPKE/38OHDfFfIVFdXh7a2Nj5//oyhQ4fWer9ycnLQ0tLCo0ePuOa9CwoKQuvW/Cd0BQBbW1scO3ZM4PuLFy+GiIgIhg4dCjabjcGDB1ebj5KSEhw5cgRbtmzhGRrbr18/HD9+XOD8i/r6+pCSkuJaiCIqKgoFBQUCe0uKi4v/Tw7TrEpUVBSm5qZ4/iwEHZ0qzv+LZyFo78B/pVJLaysEP+SeRPb502cwszCHiIgIdPV0cegE9zx7B/fsR15eHqbOmg419d/f80RYRBhKTTQQ/yEGus0qhgzEf4iBjnXtV79L+5YISXnexjZhURFIKciCXVqK2NCP0GshuN7/bfILwM5PqDneH0RYRAQqulr4/j4KBrYVn+G38CjoN6v96lypX+M5PzgVNFTQf9lErvdDLtxFcWER2nm6Qlrp5+r52ipl2PiemQwT1SZ4l1gxH5iJig7eJ8bw3SYmLQHWmkYQExZBUWlZbx4VaXmwGTbnR2dMegKaa5mgciciFWkFZBXkNvgPMWEREWgaNMHntx9hblfxwOpz2EeYtuRdxU9cUgJ/beCew/b5rUeIeReJ/jNGQ4HPSpa/m4ioCLSN9BD5+j2s21TMixfx5j2s7JrXOh2GYVBSXHbO1LQ1MXsb933A9RP+KCwoQK8xg6Gg3HCNtgBQyi5FdFocmmoY4fm3D5xwaw0jvKj0ujIxYVGwq5QfzutKD9R6WLRDbysHbLh3DNFpcfWf+WqIiIpA19gA4a/ewrZdxfyb4a/C0My+pcDtngUG4cj2vRg7bwqsW/PeGxhZmiIkMAhsNpvzQDPxezzklRQatOEMKLum1PW1EfsuEiYtKxYS+fIuEka2vKMwxCXFMWL1TK6w13eDEfs+Cu5ThkFelbtsRTx7g9LiUli0q34ESX0qZZfic1ocbDSNEfK1otezjaYxQr7x7wUtLizKU3+Vl7/q5nPUU9T4rQ245UoZNr5lJsFMVZer95iZmi7C4qsfxmysrANVGUU8qTQktTGU1ec6iA6LgLldxTyC0WERMG3Ju6iNuKQ4xq3jnvrmxe3H+PI+En2njYKCavX1Wnn9SEhjqHNNLiMjA09PTyxatAiZmZlcE7PPnz8f9vb2mDx5MsaNGwdpaWmEh4fj1q1bPCvZAUDXrl05vUu8vb1RUlKCSZMmwdHRkTMsbNmyZejSpQuMjIwwaNAglJSU4Nq1a3znm+ratSvMzMwwYsQIbNmyBVlZWVyNHkDZPFGxsbE4deoU7OzscOXKFfj7c68IN336dIwcORKtWrVChw4dcPz4cbx79w6GhoJXATI2NsaRI0dw48YNGBgY4OjRowgJCYGBgUGtP1tjY2OcO3cOQUFBUFRUxNatW5GQkFBt49m0adPQrl07bNy4Eb1798bNmzd5hmx27twZmzZtwpEjR9C2bVscO3YMYWFhnEYBWVlZzJkzBzNnzgSbzUaHDh2QlZWFoKAgyMjIcM11VS4gIACfP3+Gg4MDFBUVcfXqVbDZbJiZmf1UeoLUVKZ0dXUhJiaGf/75BxMmTEBYWBhWrVpV6/Tz8/Ph7u6O5s2bY/z48UhIqPihqKGhUavyUtXQoUOxadMm9OrVi7MCZWxsLM6fP4+5c+dyDSsWZOHChbC2tsakSZMwYcIEiImJ4d69exgwYABUVHiXUAfKepXZ29tj/fr10NfXR0pKCtd8bQAwZcoU/PPPPxg0aBAWLlwIeXl5PHnyBK1bt4aZmRmWL1+OadOmQU5ODt27d0dhYSGeP3+O9PR0nl5Wlc2dOxfLli2DkZERmjdvDh8fH4SGhvLtfVfOxcUFCxcu5OkRVtmCBQsgLCyM4cOHg81mV9uoFxAQgPT0dHh5efEMM+3fvz8OHjyIKVOmYPny5cjLy4Obmxv09PSQkZGBHTt2oLi4mGuo8cOHD2FoaAgjoz9nYlJJUXHoyFf0hNOSU4GJig6yCnKRmMO/F9/vMGCwJ9YtXw0zc3NYWVsh4MIlJCYmwb1vbwDA/p17kJycgkXLy8qjR99euHDmPHZ6/4Oevdzx7u07XL10BYtXLQMAiImLw8CIu76VkS1ryKgcnp+Xh+/fKibrj4+Lx6eISMjKyUFdg/9E6r/CsnNrPD5yGcq6GlA10EbE41DkpmXBtGNZXfryYiDyM7PRfoQ7ACD8XgikleShoKkCdmkpPj97h9jQj3AcWzHUPjkmDvkZ2VDUUUdeRjbeXH0EhmFg1ZX/PJeNjSUrAyE1FbB+/GgX+tEDkJ2WASY9oxFzVsHGuS3uHToPFT0tqBs1QfiDF8hJy4SFY9l9xbPzt5GbkYVOY8oelry9HQxZZQUoaqmhtLQUn568QfTLcDhPKBsyLiIqCiVt7vIkLlU25LxqeEN5GP0ans274FtGEmIzEtG6iSUUJGXxJLbswZirmT3kJKTh9/oOACA0LgJdTFphQLPOuBURAmkxCbiZt8Pzrx9Qwi5beObJl3dor28Nd6sOCIp5CxVpBXQyboHHMW9/yzHZuznhwq5j0DLUhbaJPl7dDUJmSjpadimbAuDOqcvITstE70nDwBISgloT7jkypeVkISImyhVeWlKC5G8JnP9np2UiIeYbxCTEOb0XGpKDuzNO7TgIHSN96JkZ4umtB8hISUPbbk4AgKvHziEzLQODp5WtUP/42l0oqihB9cdqwjEfIvHg0k20794ZACAqJgoNXe5ethLSZT1UqoY3lGsfgjGxbV9Ep8UhMuUrOhu3hLKUPO5Elj2o9GzWBYpScpwhca++R8CrjTu6GLfCm/goKErKYFhLV3xK+YaMHxOa97Roj/42nbAz6BySczMg/6NnW0FJEQpLaj9q4Fd07eMGny27oGdiCENzEzy8fhdpySlwcCsbTePvewoZqWkYPbvsofizwCD4bN0Nz/EjYGBmgsy0DACAmLgYJH+cE0c3Z9y7fBOn9x5BZw8XJH1PwDW/i+js/nsWuGjp0hHX9p2Gur4ONI118TbwGbJTM9CsU9m8bA/PXENOeha6j/cES0gIKjoaXNtLyspARFSEJxwoW1jAuIUlJGV+79D1gPePMbV9f3xO/Y6I5K/oatoKKtLyuBVRNjfxYFtnKEnKYWdQWY/U598+4K+2veFs2pozbHNkKzdEpnzlTKjf36YTIpO/Ij47lTPnmb6SJg4+u/xbj61c4KeXGNrSBV8zEhGTFo+2+tZQlJTF45iy3s89LdpDXlIax1/e5NqujZ4VYtLif/s8bfy06e6Ei7uPQ9OwCXSM9fHqXhAyU9PRoks7AMC90wHITs+Ex4ShP+pz7sUQpOVkICwqwhX++NJtaBo0gaK6MkpLShEVGo63j0LgOqr6aW3+a/6XV7b8E/3UYxAvLy8cPHgQ3bp14xqOaWNjg/v37+Pvv/9Gx44dwTAMjIyMBA65YrFYuHDhAqZOnQoHBwcICQnB1dWVq6HNyckJZ86cwapVq7B+/XrIyclx9W6pTEhICP7+/vDy8kLr1q2hr6+PHTt2wNW14kurV69emDlzJqZMmYLCwkL06NEDS5YswfLlyzlxPD09ERUVhfnz56OgoAD9+vXDxIkTcePGDYGfyYQJExAaGgpPT0+wWCwMHjwYkyZNwrVrtZ/AccmSJYiOjoaLiwukpKQwfvx49O7dG5mZgicvtbe3x4EDB7Bs2TIsX74cXbt2xeLFi7kakFxcXLBkyRLMmzcPBQUFGDNmDEaMGIG3bytukFetWgU1NTWsW7cOnz9/hoKCAlq0aIFFixbx3a+CggLOnz+P5cuXo6CgACYmJjh58iRnUvu6pidITWVKVVUVvr6+WLRoEXbs2IEWLVpg8+bN8PDwqFX6iYmJ+PDhAz58+MAzeT3DMLUqL1VJSUnhwYMHmD9/Pvr27Yvs7Gxoa2ujS5cute6JZmpqips3b2LRokVo3bo1JCUl0aZNmxp7Xx06dAhjxoxBq1atYGZmho0bN3L1wlJWVsbdu3cxd+5cODo6QlhYGM2bN+fMVzZ27FhISUlh06ZNmDdvHqSlpWFtbc1ZsEGQadOmISsrC7Nnz0ZSUhIsLS1x6dIlmJgI7pFjbW2NVq1awc/PD3/99ZfAeHPnzoWwsDBGjhwJNpuN4cOH84138OBBdO3alafhDCjrebZ27Vq8fPkSjo6O2LlzJ0aMGIHExEQoKirC1tYWN2/ehJlZRY+ikydP1nq+xd/FXFUP//apaMSc1qHsBuJqeDDW3D0saLMG19m5C7Iys3DkkC/SUlKhb2iA9ds2QkOz7AY8NTUVSYkVT3Q1tbSwbttG7PL+BxfP+kNZRQVTZ0+HY2enOu33Y/hHzJxUMaR7l/e/AACXHq5YsPRvQZv9NP2WFijMzceba4+Rn5ULBU0VdJ40ADJKZWUuPysHuWkVwxbYJaV46X8XeZk5EBYVKYs/cQC0rSoaZNnFJQgNeIDslAyIiotB28oQ7Uf0hJgU//kAG5toWztIz634zGUWly3wkn/kFAqOnm6sbHExsmuKgtw8vLxyH3mZOVDSUkP3qUMh+2MobF5mNnLSKr5XS0tK8eTsTeRmZENEVASKWmpwnToEutaNOwywsjfxnyAlJo4uJq0gJy6NhJxU+IQEcIYgyYpLcQ1tKiotwYGnl9DLqiOmduiPvKJCvIn/hBsfK6aSyCzIwYGnl+Fu2R4zOnoiqyAXj6PfIDDq1W85Jqu2LZCfk4sH528gJyMTqjqaGDzvL06vg5yMLGSl1u2hQHZ6JvYv2sR5HXzlLoKv3IWehTFGLJlar/nnp3n71sjLzsXtM5eRlZ4JDV0teC2aDkW1sp5xWemZyEip+KHLMAyuHj+PtKQUCAsLQ1ldFd2H9oV9t1/rtV+fnsS+g4y4FPo0dYSCpAy+ZSZhU+BxpOSVXUMKkrJQlqr43n0QHQoJUTF0M22NoS1ckFdUgHeJ0TgVeosTp6uJHUSFRTCjI/fvg3NvA3H+beBvOCrAzqEtcrNycOXkeWSmZUBLTwdTVsyDslpZI2tmWgbSkivO1cPrd8AuLcXJ3T44uduHE962iwNGzSqbG1JJVRnTVy3Amf3HsHLyAigoK6Kzhytc+9funvRXmbVphvycPDy5eAe5mVlQ1tZAn1mjIadS9oAyNyMb2akZdU43PSEZ3yNi0G+OVz3nuGbBX8IgKy6FfjadoCgpi68ZiVh39yhScjMAAIqSslCRVuDEv//5FSRFxeFq1gYjWroit6gA7xI+49jLit9w0mISGG/fGwqSMsgrLkB0WjyW3TiAqNTqV85uKK/iIiAlJgEXM3vIiUshPjsVe59c5DT2yUlIQ1GS+/eDhIgYmmka43zY/cbIMg9Le1vkZefikf8N5GRkQVVHE4Pmjoe8SkV9nplSt/q8uLAI133PIjstEyJiolDWUkOvicNgaf/7ej8SUhWLoSW9/nN8fX0xY8YMZGRkNHZWCKnW1atXMWfOHISFhXGGOPwJwsLC0KVLF0RERPBtjKtO+52/fzW+hvR48h7EZSQ1djbqnZaCGlbf8qk54v8ji51HI925T80R/x9RvOWPLfdPNnY26tVsx8GYf2VXzRH/H9nQYxKOvaj9QkX/Hwxr6YpLYQ8bOxv1yqNpRww9sbyxs1Hvjg9ZjsBPLxo7G/XKybgl9gZfaOxs1Ku/2vbGwKOLa474/4jf8NWYcdG7sbNRr7x7zcCREMGr4/5/NMLOrbGz8NO6HxA88qcxXRu7tbGz0CgadgA+IYRUw83NDZGRkfj+/TvXqruNLS4uDkeOHKlzwxkhhBBCCCGE1AcW68/pXECo8YwQ0simT//zVqesuuAAIYQQQgghhJD/XdSU+R80atQoGrJJCCGEEEIIIYQQUg+o5xkhhBBCCCGEEELIH0SIVtv8o1DPM0IIIYQQQgghhBBCBKDGM0IIIYQQQgghhBBCBKBhm4QQQgghhBBCCCF/EBZo2OafhHqeEUIIIYQQQgghhBAiADWeEUIIIYQQQgghhBAiAA3bJIQQQgghhBBCCPmD0GqbfxbqeUYIIYQQQgghhBBCiADUeEYIIYQQQgghhBBCiAA0bJMQQgghhBBCCCHkD8KiYZt/FOp5RgghhBBCCCGEEEKIANR4RgghhBBCCCGEEEKIADRskxBCCCGEEEIIIeQPQsM2/yzU84wQQgghhBBCCCGEEAGo8YwQQgghhBBCCCGEEAFo2CYhhBBCCCGEEELIH0QINGzzT0I9zwghhBBCCCGEEEIIEYAazwghhBBCCCGEEEIIEYCGbRJCCCGEEEIIIYT8QWi1zT8Li2EYprEzQQghhBBCCCGEEELK9D/yd2Nnga+zI9Y0dhYaBfU8I4SQehSXkdTYWahXWgpqaL9zQmNno949nrwH2R8iGjsb9UrW3BRZt+81djbqlVzXTohO+d7Y2ahXBiraeBcf1djZqFdWmkbY/+RiY2ejXo2z7/WfLHvZ0TGNnY16J2ugj+y4uMbORr2S1dLC1/SExs5GvWqiqIGkrNTGzka9UpNTxueUb42djXplqKKD7MhPjZ2NeiVrYtzYWSD/EdR4RgghhBBCCCGEEPIHEaJhm38UWjCAEEIIIYQQQgghhBABqPGMEEIIIYQQQgghhBABaNgmIYQQQgghhBBCyB+EVtv8s1DPM0IIIYQQQgghhBBCBKDGM0IIIYQQQgghhBBCBKBhm4QQQgghhBBCCCF/EBZo2OafhHqeEUIIIYQQQgghhBAiADWeEUIIIYQQQgghhBAiAA3bJIQQQgghhBBCCPmDCNFqm38U6nlGCCGEEEIIIYQQQogA1HhGCCGEEEIIIYQQQogANGyTEEIIIYQQQggh5A/ComGbfxTqeUYIIYQQQgghhBBCiADUeEYIIYQQQgghhBBCiAA0bJMQQgghhBBCCCHkD0Krbf5ZqOcZIYQQQgghhBBCCCECUOMZIYQQQgghhBBCCCECUOMZIeSPkZeXh1WrVuHLly+NnRVCCCGEEEIIaTSsP/Tf/ypqPCP/L/n6+kJBQeG37MvJyQkzZsz4LfuqT3X9jPT19eHt7d1g+amNqVOnIi4uDnp6enXa7v/rOSKEEEIIIYQQ8uejBQNIg2HVMMHhyJEj4evr+3sy8z/I09MTbm5utY4fEhICaWnpBsxR9U6ePInExERcvHixztueP38eoqKiDZCrhnHhrD9OHzuJ1NRU6BvoY8rMabCxbSYwfujLV9jl/S9iomOgoqKMQcOHwKNvb75x7968jVVLVqC9Qwes3rSOE/76VShOHzuJiA8fkZqSilUb16CDo0N9H1qdNdM0xhDbbjBX04WKtAIWXN2Nh9GvGztbAjEMg32nTsL/xg1k5+bAytQU8/+aACNdwQ2+UbFfsOfEcXyIikJ8UhJmeY3FEI9eXHH2njyB/adOcoUpKyjgxuGjDXIclTEMg/1XA+D/+BGy8/Jgpa+PeQMHw0hLS+A2d0NfwffGNXxNTkZJaSmaqKphWJeucGtjz4nzMjISR2/fxIevsUjJzMSm8RPg1Kx5vef/8vmLOHviNNJSU6FnoI8J0yajaXMbgfHfvHqNff/swpfoGCirqGDAEE/06OPBFcf/9FkE+F9CcmIS5BTk0dHJAaMnjIOYuBgA4NSRE3h8/yG+fYmFmLg4LK2tMGbiODTR06334yt37UIALp46h/TUNDQx0MOYKeNhadOUb9y01DQc3rUfURGfEP8tDm59PeA19S+BaT+6cx9bV21A6/b2WLBmaUMdAo9Xd4IQcvU+cjOzoaKljk5DPaBjZlDjdt8jYnBq3R6o6Khj5KqZXO8V5Obj0bnriHwehoK8fMirKMFpcA8YNrNokGOo7/I3d8pMvH3FWwfatW2DVZvL6vS83Dwc2X8IQQ8eISM9A0amxpgwYwrMLMzr/wB/YBgG+44dg/+1q8jOyYGVmTnmT54MI319gdtExcRgz9Ej+BD5CfFJiZj1118Y0qcvV5yXb9/i6NkzCI+MREpaGjYvXQandu0aJv+HD8M/IADZ2dmwsrDA/OnTYWRQfXm7c/8+9vj44FtcHHS0tDDJywudOnbkvO8+aBDiExN5thvQqxfm/3iod/fBA5y/fBnhERHIzMrC8f37YWZs/MvHdPGsP84cP4XU1DToG+hj0swpsG4u+F7i9ctQ7Nm+EzHRMVBWUYbnsMFw71vxXXQj4Bo2rV7Ps93V+zchJi4OABja2xOJCQk8cTz69ca0uTN5wuvK/8w5nDx2AqkpqdA3NMC0WdPRzLa5wPivXrzCv947EPM5GsoqKhgyYih69+vDeT866jMO7j2Ajx8+ICE+AVNnTsfAIZ5caQzw6IuEeN5j6tO/L2bNn/PLxwQAAecv4uwJP0498de0STXWE/v/2c2pJ/oP8USPPu5ccfxPn8OVSt9THZwcMHrCWM731Mh+Q5CUwFs2e/b1wOTZ0+vluCpjGAb7TpyA/43rZXWEqRnmT5wIo2oeiEd9+YI9x4/hw6dPZfdH48ZhSK/eAuP7+Plh55HDGOzRC7PHj6/3YyCkJtR4RhpMfHw85/+nT5/G0qVL8fHjR06YpKRkY2Tr/73S0lKwWCwICVXfcVRSUrJOn7GqquqvZu2XDB48GIMHD/6pbZWUlOo5Nw3n7q072LltB2bMm4WmNta47H8J82fOhe+po1DXUOeJHx8Xh4Uz56FHL3f8vWIJwt68hffGrZBXUIBjZyeuuAnxCdi9Yxds+Nw8F+QXwMjEGK493bBsweKGOrw6kxQVx6fUb7j6IQhru09o7OzU6PD5czhx8QKWTZ8BXS1tHPQ7jclLl+Lcrt2QlpLiu01BYSF01DXQtV0HbD10QGDahrq62LVyNee1cA3XeH05cusmTty9g6XDR0JXTQ2Hrl/DlH+34+zSFZCWkOC7jbyUFEa7dIe+hgZEhUXwMOwNVh47AkVZWbS1tAIA5BcVwlRHB+5t22H+/r0Nkvf7t+9h7/admDx7OqxsmuLqhctYPGcB9h3zgRqf6ykhLh5L5ixEd3c3zFu6CO/ehGHnlu2QV1BAh05ljcl3b9zGoT37MWvhPFhYW+F77FdsWbMRAPDX9MkAgLehr+HetxdMLczALmXDd99B/D1zHvYd94FEA3y3Pbp7Hz7/7sO4GZNgYW2JG5euYfW8pdh+eA9U1dV44pcUFUNOQR79hg1CwBn/atNOSkiE7+4DsLSxqvd8V+fD01DcO34ZXUf0hrapPl7fe4pzWw5i9LrZkFNWFLhdYV4+ru47BT1LY+RmZXO9V1pSgjOb9kNKTgYeU4ZDRkke2WkZEJMQb5BjaIjyt3TtChQXl3C2ycrMxKRR49CxkyMnzHv9ZsR8jsbcpQuhrKKCOzduYeH0udh3/BBUGui7/PAZP5zwP49ls2ZDV0cHB0+ewORFC3HuwMHq6z4NTXTt6ICte/nXAfkFBTAxMIS7czfMW72qQfIOAIdPncKJM2ewbP586DZpgoNHj2Ly3Lk4d+SIwPy/efcOi1auxIQxY9CpY0fce/gQC1aswMEdO9DU0hIAcGTPHpSy2ZxtoqKjMXnOHHRxcuI6xmZNm6KrkxNWb95cL8dz79Zd7Pb+F9PmzoSVTVNcuXAZC2fOx8GThwXcS8Tj71nz4darJxYs/xvv3oRhx6ZtkFdQgEPnirIlJS0NXz/uhzblDWcAsNNnL9jsUs7r6KhozJ82Gw5V7kd+xp2bt7Fj63bMmj8H1s1scOn8BcydPhtH/Y5DXUODJ37c9zjMmzEb7r09sGTlMrx9/QZbN2yGgqICnDp3AgAUFBRAU1sLTl074Z+tO/jud9/hg2CXVpzD6KjPmDllOjp17fzLxwSU1xO7MHn2NFjaNMXVCwFYMmch9h47JLCeWDpnEVzd3TB36UK8fxOGnVt2QF5Bnut7ymfPfsxcOBeW1lb4FvsNWznfU5MAANsP7AK7Utn88jkai2bM46pL6tPhc2dx4oI/ls2cWXZ/dPo0Ji9ZjHN79tZQR2iga/sO2Hpgf7Xpv4uIgP+N6zDRr/kBy39JTZ1RyO9FwzZJg9HQ0OD8ycvLg8VicYU9ePAALVu2hISEBAwNDbFixQqUlFTcMGZkZGD8+PFQV1eHhIQEmjZtioCAAK593LhxAxYWFpCRkYGrqytXgx2bzcbKlSuho6MDcXFxNG/eHNevX682z7m5uRgxYgRkZGSgqamJLVu28MQpKirCvHnzoK2tDWlpabRp0waBgYHVprt8+XLo6upCXFwcWlpamDZtWq3TKx9+GRAQAEtLS4iLi2P//v2QkJBARkYG136mTZsGR0dHru0qu3TpElq1agUJCQmoqKigb9+KJ8BVh23GxsaiV69ekJGRgZycHAYOHIhEPk9Xy8XExIDFYsHPzw8dO3aEpKQk7OzsEBERgZCQELRq1YpznpKTkznbhYSEwNnZGSoqKpCXl4ejoyNevnzJeT8wMBBiYmJ4+PAhJ2zLli1QUVHhnO+qwzb19fWxevVqzrnU09PDxYsXkZyczDkma2trPH/+nOsYzp07BysrK4iLi0NfX5/v+f9VZ06ehptHD/To5Q49A31MmTUNaupquHSO/w/cS+cvQk1DHVNmTYOegT569HJHd/ce8Dt+iiteaWkp1ixdiVHjx0BTW5MnnTbt7OE1YRwcGuim6Wc9iX2H/U8v4f7n0MbOSo0YhsHJy5cwesBAdG7bDsZ6elgxYyYKigpx/cF9gdtZmZhi+ugxcHFwgFg1PSRFhIWhoqjI+VOUl2+Iw+DCMAxO3ruD0S7d0bm5LYy1tLF8+EgUFBXhRsgzgdu1NDVDp+a2MNDQhI6qKgZ36gJjbW2ERkVx4rS3aoqJ7r3Qubltg+X//OkzcOnZHd09ekBXXw8TZkyBqpoaAvwv8Y1/5cJlqKmrYcKMKdDV10N3jx7o1qM7zp7048QJD3sHK+um6NStCzQ0NdCyjR2cnDsj4kMEJ86arRvQrYcr9A0NYGhihFmL5iEpMQmRHyP47faXXT7jjy5u3eDc0xU6errwmvoXlNVUcePiFb7x1TTV4TV1Ajq5dIFUNT2KS0tL4b16EwaNHgZ1Td56oyE9v/4Q1g52sHFqA2UtdXQe6gFZJQWE3nlS7XY3fc/Doq0tNI15e/m9fRCCgpw89J42Etqm+pBXUYSOqQHUdAX3ovwVDVH+ZOXkoKSsxPl7FfICEuISnAaOwsJCPLr/AF6T/4J182bQ0tHGcK9R0NDUELjfX8UwDE76X8DoQYPQuUMHGOvrY8XsOSgoLMT1e/cEbmdlZobp48bBxclJYN3X3s4Ok0aNQucOHRok78CP/J89i9HDhqGzgwOMDQywYsECFBQU4Prt2wK3O3n2LNq0aoXRQ4dCX1cXo4cOResWLXDi3DlOHEUFBagoKXH+HgUHQ0dLCy2bVTzE6tGtG8aNHInWLVvW2zGdO+kHV3c3uPXqCT0DfUyaORVqaqq4fJ5/7/2A8xehpq6GSTOnQs9AH269esLV3Q1nTnDfS7BYLCgpK3P9VaagqMD13tPHwdDS0UazFs1/+ZhOnziFHr3c4d7bA/oG+pg2ewbU1NXgf5b//dHF8/5Q11DHtNkzoG+gD/feHujh0ROnjp3gxLGwssTk6VPQtZszxMT4l0FFRUUoqyhz/oIePYa2jjaat6if7y7/02fRrWd3uHLqiclQVVPDFf/LfONX1BOToauvB1ePHujWwxXnKtUTH8Lew/LH95S6pgZatmkFJ+dOiPxQ0Umh7FxV1CVPHz+BprYWrKsZ6fCzGIbByYsXMdrTE53btS+rI2bNKqsj7ldzf2RqiuljvODi6Fjt/VFefj6WbN6Ev6dOhayMTL3nn5DaosYz0ihu3LiBYcOGYdq0aXj//j327t0LX19frFmzBkBZw1f37t0RFBSEY8eO4f3791i/fj2EhYU5aeTl5WHz5s04evQoHjx4gNjYWMyZU9G9evv27diyZQs2b96MN2/ewMXFBR4eHoiMjBSYr7lz5+LevXvw9/fHzZs3ERgYiBcvXnDFGT16NB4/foxTp07hzZs3GDBgAFxdXQWme/bsWWzbtg179+5FZGQkLly4AGtr6zqll5eXh3Xr1uHAgQN49+4dhg0bBgUFBZyrdANXWloKPz8/DB06lG8+rly5gr59+6JHjx549eoV7ty5g1atWvGNyzAMevfujbS0NNy/fx+3bt1CVFQUPD09+cavbNmyZVi8eDFevnwJERERDB48GPPmzcP27dvx8OFDREVFYenSimFBWVlZGDFiBB4+fIjg4GAYGhrCzc0N2dllPQrKG8aGDx+OzMxMvH79Gn///Tf2798PzWp+7G3btg3t27fHq1ev0KNHDwwfPhwjRozAsGHD8PLlSxgbG2PEiBFgGAYA8OLFCwwcOBCDBg3C27dvsXz5cixZsqRehxYXFxcj4kMEWrVpzRXeqrUdwt6G8d3m/dt3aNXajivMzr41PoZ/4GpsPnLQFwqKCujh0bPe8ku4fU9MRGp6OuxtK26oxURF0cKqKd58+PDL6cfGxcF11Eh4jPPCwk0b8Y3P0Jj69j01BalZWbC3qBjSJiYqihbGJngT/blWaTAMg2cfPuBLYiJa1MNQpNoqLi5G5McItGjNXY+1aN0K4WHv+G4THvaOJ37LNq0Q+eEj53qyamaNyI8R+Pg+HAAQ/z0OIcFP0bpdG4F5ycvNBVDW8FHfiouLEfXxE5rZteAKb25niw/vwn8p7TNHTkJOQR5de7j8Ujp1VVpSgsSY79BvasoVrt/UBHGfYgRu9/ZBCDKSUtGud1e+70e9eg8tYz3cOeKPXVNXwmfRFjy5fJer90V9aajyV9WNgGtw7NqJ06OxtKQU7FI2xMTEuOKJiYvj3Rv+3yO/6ntCAlLT02DfoqLxR0xMDC2srfEm/H2D7LM+fY+PR2paGuwr3fOIiYmhRbNmePOO/7kCgDfv36NNlfskezs7gdsUFxfj6q1b8OjevUF7ixQXFyPiYwRateG+N2jZxg7vBd1LhL1DyyrxW7WxQ0Q4d9nLz8/HkN4DMci9P/6evaDaBwLFxcW4ff0WXHv++vGW3R99ROsq90d2bVoj7M1bvtu8exsGuyrxW9u3wYf3HwReT7XJx81rN+Dm0bNezqHgeqIl3guoJz6EvUeL1twNrS3a2CHyQwTnuCybNcWnjxH4+L7s3qPse+oZWrez50mvPB/3bt5Gtx6uDVI2vycm/Lg/qvieEhMVRYumTfEm/Ne+pwBgw+7daG9nhzYN+DCOkNqgYZukUaxZswYLFizAyJEjAQCGhoZYtWoV5s2bh2XLluH27dt49uwZwsPDYWpqyolTWXFxMfbs2QMjIyMAwJQpU7By5UrO+5s3b8b8+fMxaNAgAMCGDRtw7949eHt7Y+fOnTx5ysnJwcGDB3HkyBE4OzsDAA4fPgwdHR1OnKioKJw8eRLfvn2D1o/5gObMmYPr16/Dx8cHa9eu5Uk3NjYWGhoa6Nq1K0RFRaGrq4vWrVvXKb3i4mLs2rULzSo9yfT09MSJEyfg5eUFALhz5w7S09MxYMAAgZ/5oEGDsGLFCk5Y5fQqu337Nt68eYPo6Gg0adIEAHD06FFYWVkhJCQEdnZ2fLcrz7+LS9kPsenTp2Pw4MG4c+cO2rdvDwDw8vLiapDq0qUL1/YHDhyAgoIC7t+/j549yxqCVq9ejdu3b2P8+PF49+4dhg8fjj59+qA6bm5u+Ouvsjl+li5dit27d8POzo7z+cyfPx9t27ZFYmIiNDQ0sHXrVnTp0gVLliwBAJiamuL9+/fYtGkTRo0axZN+YWEhCgsLucLExasfGpSZkQl2aSkUlbiHJCkqKyL9SRrfbdJSU6GozH1zqKikiNLSUmRmZEBZRQVvX7/B1UtXcODYoWr3T35Nano6AEBZXoErXFlBAfFJSb+UdlNTU6yYMRN6WtpIzcjAwTOn4TV/Lk7/sxMKDdAgUy41KwsAoCTLvQ8lOTkkpPEvk+Vy8vPhtmgBikqKISwkhPmeg9HGwrLB8lpVVkYm2KVs3utJURFpqfzznp6WDkXFKvE511MmlFWU4dS1MzLTMzB74nQwDIPS0lL07OMBz+FD+KbJMAz27tgFKxtr6BvW/3CS7MwssNlsKCgqcIXLKyoiIy39p9MNf/sOt6/cwNYD//5iDusuPzsXDJsNKXnuHgRS8rLIzczmu016QjIenrmGQX9PhFClB2mVZSanITY8ChZtbdF31hhkJKbg9pELYJeWol1v53o9hoYqf5V9fB+OmM/RmLmw4sGglLQULJpa4oTvUejq6UJBSRGBt+/i4/twaOlo19PRcUtNLzse5Sp5V1ZURHzir9V9v0NqWnX5F9yjPjUtje82qQLqxsBHj5CTkwN3V9dfzHH1Ku4luKesUFQSXPbSUtN4y6qSElfZa6Kvi3mLF8DA2BB5ubk4f/ocZoyfgr1HD0FHV4cnzcf3HyInJwfdenSvh2PKQCm/Y1JWEnhMqalpaK1c9TMoO6aMjAyoqKjUOR8PAx8gJycHbj1rP2dwdQTVEwqKikgXWE+kQUFAPZGVkQklzvdUJuZU+p7q0ccDA4fzn/4k+MFj5OTkwNmtYR6UcO6Pqox4Kbs/SuazRe3duH8fH6I+4cg2719K5/8rIRb1dfqTUOMZaRQvXrxASEgIp6cZUNZzqqCgAHl5eQgNDYWOjg6n4YwfKSkpTsMZAGhqaiLpxw/YrKwsxMXFcRpsyrVv3x6vX/OfjDwqKgpFRUVo27YtJ0xJSQlmZmac1y9fvgTDMDz5KiwshHKVru3lBgwYAG9vbxgaGsLV1RVubm5wd3eHiIhIrdMTExODjQ33xKJDhw5F27ZtERcXBy0tLRw/fhxubm48N+blQkNDMW7cOL7vVRUeHo4mTZpwGs4AwNLSEgoKCggPD6+28axyPtXVy+ZyqNzTTl1dnXOeACA1NRWrV69GYGAgkpKSUFpairy8PMTGxnId/7Fjx2BjYwM9Pb1arQpam3wAQFJSEjQ0NBAeHo5evbgncW/fvj28vb1RWlrK1esRANatW8fVEAmU9bobP2NSjXnjeerHAKjmSWDVJaHLe8uxWCzk5eZh7bLVmLNoHuR/0wq0/yuuBQZi7e6KhnbvJWU9JqueP4ZhfvlJbvuWFU+ljQHYmJuj91/jEHDvLoZVM3luXV179hTrTlYMadk2qWwOL37HVBMpcXEcX/g38goLEfLxA7adPwttFRW0NDWrcdt6VTXvqOF88BxreXBZ+OuXoTh15Dgmz54OcysLxH37jj3bd0LR5yiGjh7Ok9zOrTsQHfUZW3bzn0+nvvDWG8xPLxefn5eH7Ws2Y9LcaZBTaPjhwYLwPSY+547NZiNgz0m06+MMJQ3Bc3oxbAZSsjLoNrofhISEoGGgg5yMLIRcvV/vjWcc9Vz+KrsecA36hgYws+Re7GDukoXYtm4ThvYeCCFhIRibmsDJuQuiIgT3rK+La3fvYu2O7ZzX3ivL5iKrmsOyuq9edlmvrt26hbVbt3Jee68rW2iBp57jE8aDX30vIOrFq1fRrk0bqP5Eo83P4HP51LHsMVzBlk2tYNm0Yu5DKxtrTBw5DhfOnMMUPhPMX7t8Fa3tW0NFtf6Olyf7NZQxnvsjMHzDayvg0mW0aWtf73MH8r/tq+a+T9B38o/wNy9DcfrIcUyePQ1mVhaI+xaHvdt34oSPEobw+Z66EXANrexbQ7meztW1e/ewdmfFgxfvZcsF5Lva29saJSQnY8v+ffh35SqIV+ltS0hjoMYz0ijYbDZWrFjBNedWOQkJiVpNdF91dUUWi8Xzg68uP3Jr82ORzWZDWFgYL1684GlMkREwBr9Jkyb4+PEjbt26hdu3b2PSpEnYtGkT7t+/X+v0JCUlefLdunVrGBkZ4dSpU5g4cSL8/f3h4+MjMO91WTxA0OdUm0aCyuelPG7VsMpDaEaPHo3U1FRs374denp6EBcXh7W1NYqKirjSDQoKAgCkpaUhLS2txpVBa5MPAJy88Du26srEwoULMWvWLK4wcXFxpOZnCtxGXkEeQsLCPE9R09PSeZ5KllNSVuaJn5GeAWFhYcjJyyPmczQS4uOxaM6Cinz/OKYu7ZxwxO84tBuoN8J/nUPr1mhqVtGwXVRcDABIyUiHSqWn42mZmVCq54ZLSQkJGOnp42tcXL2m62DTDE0rTbZb9GMISGpWJlQqzbGWnp0N5Rp6vAkJCaGJWtlk9WZNmiAmMQG+N2/8tsYzOQV5CAkL8Ty9z0jPEHg9KSopIj2tavz0H9dT2fEe2e+Dzi7O6O7RAwBgYGSIgoIC7NiwFYNHDuVarGXX1h148igIm3d6Q1WtYSZql5WXg5CQENKr9DLLzMiAvJLCT6WZ8D0eSQmJWLuw4gFAeX3Xv3NP/Ht0PzT4zJ1YXyRlpcESEkJuBncvs7ysHEjJ8X6XFuUXIjH6G5K+xOHO0YsV+WUYbBm9AAPmjoWupTGkFWQhJCzMdY6UNNWQm5mN0pISCIvU321vQ5W/cgUFBbh/+x5GjB3Fk46WjjY27fRGQX4+cnPzoKyijLVLVkJdk3dS9Z/hYG+PpuYV13FR0Y+6Lz0dKpUe7KVlZEBJwAO7xuTQvj1nQn8AnPuJlLQ07vynp1ebf2UlJZ5eZmkZGXwXKYpPSMCzly+xscpDtYYg6F4iI726ewklPmU1nXMvwY+QkBBMLczw/es3nvcS4xPwKuQFlq2vn0Ue5BUUICzw/oj/olDKykpIS03lCstIKzsm+Z94KJAQH48Xz55j9UbeUSQ/q7yeSEutUn+np0NBYD2hxKeeyOD7PeVa6XuqsCAfOzZsw6Aq31OJCYkIff4Si9cur7fjcmjTBk0rdS7g3B+lV70/yoCSws/XER8+fUJaRgaGz6hovC1ls/HqXRj8Ai4jyP8Cz+8nQhoSNZ6RRtGiRQt8/PgRxgLmx7GxscG3b98QERFRbe8zQeTk5KClpYVHjx7BwcGBEx4UFMQZMlmVsbExREVF8eTJE+jqlk1EnJ6ejoiICM4k/La2tigtLUVSUhI6VlqqvCaSkpLw8PCAh4cHJk+eDHNzc7x9+/an0ys3ZMgQHD9+HDo6OhASEkKPHj0ExrWxscGdO3cwevToGtO1tLREbGwsvn79yul99v79e2RmZsLCwqKGrevm3r172L17N+c8ffnyBSkpKVxxoqKiMHPmTOzfvx9+fn4YMWIE7ty5U+OKo3VhaWmJR48ecYUFBQXB1NSU7xezuLg4/2Ga+YL3ISoqClNzUzx/FoKOThXl8sWzELR34D9ZsqW1FYIfPuYKe/70GcwszCEiIgJdPV0cOnGY6/2De/YjLy8PU2dNhxqflfhI7UhLSXGtEMUwDJQVFfE0NBTmhmW9XouLi/HyXRimjhhZr/suKi5GzLevsLWs32GQ0hISXCtoMgwDZTk5PP0QDrMmZfVecUkJXn6KxNRe1Q+NrophGBSVFNdrfqsjKioKEzNTvAp5gfaOFfXnq5AXsO/Qju82Fk2t8PRxMFfYy2fPYWJuBpEfDSuFhQU8dYuQkBAYhuE0MDEMg11bdyDowSNs/HcbNLQarqFJVFQURmbGeP38Few7VhzX6+ev0Lo9//ltaqKt2wTbDu3iCjt58Ajy8/MxZspfUFZr2J4zwiIiUNfXRsy7SJi0asoJj3kXCWNb3lU/xSXFMXIN98OK0DvB+Br+Ce5ThkNetezHmraJPsKfhIJhs8H6cQ7TE1MgrSBbrw1nQMOVv3IP7gSiuLgInV34z+8GABKSkpCQlER2VjZePAuB16S/fuGIKvCv+5Tw9NVLmP+4bysuLsbLt28xdYxXveyzPvHNv5ISnj5/DnMTEwA/8v/6NaaOHy8wHRtLSzx98QJDK02H8fT5c9hY8ZbRS9evQ1FBAR0qjV5oKKKiojA1M8WLZ8/Rgete4jnaCbqXaGqF4EdBXGHPn4bA1IK37JVjGAZRkZ9gYGTI8971gGtQUFSAvYA5tuqq7P7IDCFPn3EtbBTyLAQdHPjfH1tZN8XjKvdHz54+g7mlucBjqs7Vy1egoKiItu35X78/g7ueqDg3L0NeoG2H9ny3MW9qKaCeMK30PVUIlhD3A18hIWGu76lyt65ch7yiAlq3rZ9zBVRzf/TqFcyNKt0fhYVh6qiaf3cIYtesGU79yz3Vzsrt3tDT0cHIfv3/JxrOaLXNPws1npFGsXTpUvTs2RNNmjTBgAEDICQkhDdv3uDt27dYvXo1HB0d4eDggH79+mHr1q0wNjbGhw8fwGKx4FrLuSTmzp2LZcuWwcjICM2bN4ePjw9CQ0Nx/PhxvvFlZGTg5eWFuXPnQllZGerq6vj777+5fkSZmppi6NChGDFiBLZs2QJbW1ukpKTg7t27sLa2hpsb7xwJvr6+KC0tRZs2bSAlJYWjR49CUlISenp6UFZWrnN6lQ0dOhQrVqzAmjVr0L9/f0hU+lFc1bJly9ClSxcYGRlh0KBBKCkpwbVr1zBv3jyeuF27doWNjQ2GDh0Kb29vlJSUYNKkSXB0dBS4yMDPMjIywuHDh9GyZUtkZmZizpw5XL3kSktLMXz4cHTr1g2jR49G9+7dYW1tjS1btmDu3Ln1lo/Zs2fDzs4Oq1atgqenJ4KDg/Hvv/9i165dNW9cBwMGe2Ld8tUwMzeHlbUVAi5cQmJiEtz79gYA7N+5B8nJKVi0fDEAwKNvL1w4cx47vf9Bz17uePf2Ha5euoLFq5YBKJskuuqNrYxsWc+NyuH5eXn4/u0753V8XDw+RURCVk6O77L2v4ukqDh05Ct67GjJqcBERQdZBblIzPn5+ZwaAovFwmB3D/icPQNdTS000dKCz1k/SIiJw9Wh4mZ/6batUFNWxpQfDWrFxcX4/PXrj/+XIDk1FR8/f4aUpASaaJbNdejtcxAd7VpDQ1UV6RmZOHjmNHLz8tCzcxfejNT3MXXqAp8b19FEVQ1N1NTge+M6JMTE4GJX8aBh2WEfqCooYMqPBjWfG9dhqasLbVVVlJSU4vG7MFx5+gQLBlXMC5ZXUICvlVbWjUtNwcevXyEvLQ0NAT0J6qqv5wBsWrUOJuZmsGhqiWsXA5CUmIgefdwBAId270dqSgrmLlkIAOjR2x2Xzl3A3h270N2jB8LD3uNGwDUs+HG9AUCb9m3hf+osjEyNYW5ZNmzzyH4f2Hdox7lR37llO+7duoNl61dDUkqK01tCWka6xrkPf4b7gD7YsXYLjM1MYGZljpuXryMlMRndPMq+I47t80FqSiqmL6qYGys6smzl04L8fGRlZiI6MgoioqJooq8LMXEx6Bnqc+1D+kdv56rhDaWVa0dc3XsaGgY60DLWxZt7T5GdmoFmnct+4D3wu4ac9Ey4/TUILCEhqOpw96qSkpOGsKgIV3izzm3x8vZj3D1+CbbO7ZGekIKnl++ihTP/H6m/qiHKX7kbAdfQrmMHvr2Cnj8NARgGOrpNEPftOw7s3Asd3Sbo1qNh5tpisVgY3Kc3fE6dgq6WNppoa8Pn1ElIiIvDtVMnTrylmzZCTVkFU8aMAfCj7vsxBUNxSTGSU1LxMSqqrO7TKusRnZefz9XD9ntCAj5GRUFeVhYaavXz8IfFYmFw//7wOX4cujo6aKKjA59jxyAhIQHXrhWNk0vXroWaqiqm/JjiYlC/fhg/fTp8T56EU/v2CHz8GE9fvMDBHdxDtNlsNi5fv46eLi4Q4fNjPjMrCwlJSUj+8WDwy4/PRPnHCp0/o9/ggdiwYg1MLcxg2dQKVy4GICkxCe59PAAAB3btQ0pyMhYs+xsA0LNvL1w864/d3v/CrVdPvA97h+uXr2LRyooFnI4c8IVFU0toN9FBXm4u/P3OISriE6bNmclzvDeuXIOzm2u9Nkp7DhmE1ctWwtzSAlbWTXHJ/yKSEhLRu19vAMCef3cjJTkZi1eU5blX3z4473cO/2zbDvfevfDubRiuXLyMZWsqev8VFxcj5nP0j/+XIDk5GZEfIyApJQWdJhXzuLHZbFy9fAXde3T/qYa36vTx7I/Nq9bDxNz0Rz1xBcmJSXD7UU/47D6A1JQUzFlSNoKgR293XD53Eft27ILrj3riZsA1zF/+NyfNNu3b4nwN31Plx3XrynV07d4NwiIN19DEYrEwuFcv+Jzxg67Wj/ujM35ldYRjpfujLVvK7o9+zCVcdn9UXkeU3x9FQUpCEk20tCAtJQVjfX2ufUmIS0BBVo4nnJDfgRrPSKNwcXFBQEAAVq5ciY0bN0JUVBTm5uYYO3YsJ865c+cwZ84cDB48GLm5uTA2Nsb69etrvY9p06YhKysLs2fPRlJSEiwtLXHp0iWY/HjqyM+mTZuQk5MDDw8PyMrKYvbs2cjM5B6G5+Pjg9WrV2P27Nn4/v07lJWV0bZtW4ENXQoKCli/fj1mzZqF0tJSWFtb4/Lly5w5zeqaXmUmJiaws7NDSEhIjfOAOTk54cyZM1i1ahXWr18POTk5rl55lbFYLFy4cAFTp06Fg4MDhISE4Orqin/++afGPNWVj48Pxo8fD1tbW+jq6mLt2rVcq6auWbMGMTExuHy5bElvDQ0NHDhwAAMHDoSzszOaN29eL/lo0aIF/Pz8sHTpUqxatQqamppYuXIl38UCfkVn5y7IyszCkUO+SEtJhb6hAdZv2wiNH8NtUlNTkVRpAmNNLS2s27YRu7z/wcWz/lBWUcHU2dPh2NmpTvv9GP4RMydN47ze5V02V4VLD1csWPq3oM0anLmqHv7tU9GjZFqHsif8V8ODsebuYUGbNZqRffuhsKgI6/fuRnZODpqamuLfFSu5nsAmpCRDqNIT4eS0NAydWTHk4OgFfxy94I8WTZti35qyuXgSU1Lx9+bNyMjOgqKcHJqamcFn42Zo1tOPx+qMcO6GwuIibDh9Etl5ebDSN8A/U6Zx9VBLSE/jevpZUFSIDadPIikjA+KiotBT18DKUWPQrdLcbeGxXzBh+zbO623nzgIAerSxx/IRo+ol745dOyErKwvHfY4gPTUNeob6WLV5HdQ1yq6ntNQ0JFWa0FxDSxOrNq/D3h07EXD+IpRUlDFxxhR06FRRFw4ZORwsFguH9x1CanIK5BUV0KZ9W4waX9HDJsD/EgBg3hTuH5WzFs1rkAaMDp0dkZ2VDb/DJ5CelgZdA338vWEF1H40fKenpiMlkXtS5tnjpnL+HxXxCQ9vB0JVXQ17T/vWe/5+hnmb5sjPyUPwxdvIzciCirYG+s4aA3mVsiE+uZlZyErLqFOacsoKGDB3HO6duIzDi7dBRkEOLbp1QOseTvV/AGiY8gcA32K/4t2bt1i7bSPf/ebl5MJnz36kJKdARk4WHRw7YtRfXvX+o7+ykQMGorCwCOv//RfZOdloam6Of9eu4677kpK5JrdOTk3F0MkV84AePXcWR8+dRQtrG+zbtAkA8D4iAhPmVzzE27ZvLwCgZ1dnLK90L/DL+R80CIWFhVjv7Y3s7Gw0tbDAv5s2Vcl/EtcD02ZNm2LN0qXYffAg9hw6BB0tLaxbupRrSCgAPHvxAgmJifDozn/i/AdBQVixYQPn9aJVZUMdx40cib9+8h6jk3NnZGVm4tjBI0hLLbuXWLt1A2foblpKKpISKsqeppYm1mzdgN3e/+LSuQtQVlHG5FnT4NC5omEjJycH29ZvRnpqGqRlpGFkaoJte3bA3Ip7xMHLkBdISkhEd/f6mVS/XJduXZGVmQnfA4eQmpIKAyNDbPTeDI0fK6unpqQiMaHi/khLWwsbvbfgn23b4X/mPFRUVTB9zkw4da5o0E1JTsGYYaM4r08dO4FTx06geQtb/LO3okfT82chSExIhFsDrFju2LUTsrOycMLnKNJS06BvqI+Vm9dxHlympaby1BMrN6/Fvh27cPn8JSirKGNClXpi8MhhYLFYOLLPp9L3lD1GjufuCfoq5CWSEpMarGG9spH9+pfVEbt3ld0fmZnh35WruK+xZD73R9Mq7k2Pnj+Po+fPo0VTa+yrw28+Qn4XFlObiZ4IIYTUSlzGn7/yWF1oKaih/c4JjZ2Nevd48h5kf4ho7GzUK1lzU2TdvtfY2ahXcl07ITrle80R/x8xUNHGu/ioxs5GvbLSNML+JxcbOxv1apx9r/9k2cuOjmnsbNQ7WQN9ZNfz/JCNTVZLC1/TExo7G/WqiaIGkrJSa474/4ianDI+p/DOCff/maGKDrIjPzV2NuqVrAn/aYL+Pxjrt66xs8DXgYELGzsLjYLWPiWEEEIIIYQQQgghRABqPCOEEEIIIYQQQgghRACa84wQQgghhBBCCCHkD0Krbf5ZqOcZIYQQQgghhJD/Y+++w5uq3gCOf5N07z0ohRYKbSl77703gvADWQoqojhwIE4UBVEZooKKbJG9lL33KqPMslpKGd17jyS/PwopaROGtrTq+3mePE9ze+7NOcnNSfLe95wjhBDCCAmeCSGEEEIIIYQQQohSMWfOHHx9fbGwsKBBgwYcPHjQaNmRI0eiUCiK3YKCgnRlFi1aZLBMdnZ2qbVBgmdCCCGEEEIIIYQQ5Yih4FB5uD2plStX8uabb/Lhhx9y5swZWrVqRbdu3YiMjDRY/rvvviMqKkp3u3XrFk5OTjz77LN65ezs7PTKRUVFYWFh8Zee68chwTMhhBBCCCGEEEIIUeJmzJjBqFGjGD16NIGBgcyaNQtvb2/mzp1rsLy9vT0eHh6628mTJ0lKSuL555/XK6dQKPTKeXh4lGo7JHgmhBBCCCGEEEIIIR4pJyeH1NRUvVtOTo7Bsrm5uZw6dYrOnTvrbe/cuTNHjhx5rMebP38+HTt2pHLlynrb09PTqVy5MhUrVqRnz56cOXPmrzXoMUnwTAghhBBCCCGEEKIcUaIol7epU6dib2+vd5s6darBNsTHx6NWq3F3d9fb7u7uTnR09COfg6ioKLZu3cro0aP1tgcEBLBo0SL++OMPli9fjoWFBS1atODatWt//Ql/BJNSO7IQQgghhBBCCCGE+NeYOHEi48eP19tmbm7+0H2KzpWm1Wofa/60RYsW4eDgQN++ffW2N23alKZNm+rut2jRgvr16/P9998ze/bsRx73r5DgmRBCCCGEEEIIIYR4JHNz80cGy+5zcXFBpVIVyzKLjY0tlo1WlFarZcGCBQwbNgwzM7OHllUqlTRq1KhUM89k2KYQQgghhBBCCCFEOVLWq2qWxGqbZmZmNGjQgJ07d+pt37lzJ82bN3/ovvv37+f69euMGjXqkY+j1WoJCQnB09Pzier3JCTzTAghhBBCCCGEEEKUuPHjxzNs2DAaNmxIs2bN+OWXX4iMjGTMmDFAwTDQO3fusGTJEr395s+fT5MmTahZs2axY3722Wc0bdqUatWqkZqayuzZswkJCeHHH38stXZI8EwIIYQQQgghhBBClLhBgwaRkJDA559/TlRUFDVr1mTLli261TOjoqKIjIzU2yclJYW1a9fy3XffGTxmcnIyL730EtHR0djb21OvXj0OHDhA48aNS60dEjwTQgghhBBCCCGEKEeUTzhEsjwbO3YsY8eONfi/RYsWFdtmb29PZmam0ePNnDmTmTNnllT1HovMeSaEEEIIIYQQQgghhBESPBNCCCGEEEIIIYQQwggZtimEEEIIIYQQQghRjjzpypaidEnmmRBCCCGEEEIIIYQQRkjwTAghhBBCCCGEEEIII2TYphBCCCGEEEIIIUQ5okCGbZYnknkmhBBCCCGEEEIIIYQRCq1Wqy3rSgghhBBCCCGEEEKIAuPWzyjrKhj0fb/xZV2FMiHDNoUQogR9sXNhWVehRH3U6XnSLl8t62qUONuA6rT4cUxZV6NEHX71J5JfeK2sq1GiHBb8QP/FH5R1NUrU2hFTaD1nbFlXo0QdGDuH1D0HyroaJcqufWtuxN8p62qUKF8XL15Z+01ZV6PEze3/LkuCt5R1NUrU8EbdGfTbx2VdjRK1cujkf2Xf13PBu2VdjRK16YVv6PTLG2VdjRK186XvyroKf5lSVtssV2TYphBCCCGEEEIIIYQQRkjwTAghhBBCCCGEEEIII2TYphBCCCGEEEIIIUQ5opBhm+WKZJ4JIYQQQgghhBBCCGGEBM+EEEIIIYQQQgghhDBChm0KIYQQQgghhBBClCMybLN8kcwzIYQQQgghhBBCCCGMkOCZEEIIIYQQQgghhBBGyLBNIYQQQgghhBBCiHJEiQzbLE8k80wIIYQQQgghhBBCCCMkeCaEEEIIIYQQQgghhBEybFMIIYQQQgghhBCiHJHVNssXyTwTQgghhBBCCCGEEMIICZ4JIYQQQgghhBBCCGGEDNsUQgghhBBCCCGEKEeUMmyzXJHMMyGEEEIIIYQQQgghjJDgmRD/EdnZ2XzxxRfExMSUdVWEEEIIIYQQQoh/DBm2KcR/xMcff0xqairu7u5lXRUhhBBCCCGEEA+hUEiuU3kir4YQ/wE5OTk4Ozsze/bssq6KEEIIIYQQQgjxjyLBMyH+A8zNzXn//fcxNzcv66oUs2/fPhQKBcnJyY+9j4+PD7Nmzfpbj6tQKNiwYcPfOoYQQgghhBBCiH8/GbYpxH/AkSNHaNWqFZ06dWLbtm1lXR1RxJUDp7m4+zhZKek4eLrQsH9H3P28DZaNvnqTnbOXF9ve+6MXsfdwBkCjVnNhx1HCjl8gMzkNe3cn6vVph1eNKqXajqK0Wi2/rFjO+u3bSctIJ6h6dSa8PIaqlSob3Scs8iY//b6My2FhRMXGMn7UaIb07qNX5uflvzNvhf5z4OzgwPbFS0ulHU+qjqcfQ+p1JsCtEi7WDry/ZS4Hb5wt62o9lEWf7pi1aYHCyhJ1+E0yf1uJ5m70Q/cx79QWs3atUDo5ok3PIPfkGbLX/AH5+QCYtW2JebtWKF2cAFDfiSb7z63kn79U6u3p4t+EPkGtcLSy5VZyLAtPbCY0NsJo+Va+dehbszWeds5k5mZz5u41Fp/cQnpOFgCfdRlNTY/i759Tty8zZfeS0mqGnr5BrRlcryNOVvZEJEbx/eHVnIsKM1q+X83WPFOrLR62TsSkJbH09Da2XzmuV+bZ2u3oE9Qad1tHUrIz2Bd2ml+ObSRXnV/azTFKq9Uyb/OfrD90gLTMTIJ8fHnvf0OoWsHL6D57zpxm0bYt3IqLJV+txtvNjaEdO9O9SbNSr++f6zay5veVJCYkUNnXhzGvv0rNurWNlj935iy/fD+HmzcicHZx4dkhg+jRr7demfUr17Bp/R/ExcRi52BPq7ateX7Mi5iZmwFwPuQsa35fybXL10hMSOCTqZ/TvHXLUmtj6yp16VS9EfYWNkSlxrP67B6uJ9wxWHZ4g24086lZbPvd1Hgm71xYbHvDigGMatKLkLvX+PnohpKu+kOd3HmIY1v2kp6ciquXB52G9qVSQNVH7nfrajhLv/gR14oevDjlXd32M3uPcv5gMHG3C/pOD9+KtB3YA6+qxj/zSlrn6o3pVaMlDpY23E6OZfHJrVyOu2m0fEuf2vQOaoWHrROZeTmcvXuNpae2kZ6bVaxs88q1eKPVQIJvhfLt/t9Lsxl6/gt9X/eAZjxTqy1OlrZEJscw7/gfXIy5YbR8j8Dm9AxsjpuNE3EZSaw6u4c91089xRoX16tGS56t3R5nKzsikqKZe3QdF6LDjZbvXaMlfYJa4W7rRGx6Er+f2cmua8EGy7atWo8PO4zkcMQ5Ju2YX1pNKJcUyGqb5YkEz4T4D1iwYAHjxo3j119/JTIykkqVKhktq9VqUavVmJhI9/A0RJwK5eTaXTQe1AW3Kl5cPRTCnjmr6P3RaKyd7I3u1+fjlzC1NNPdN7ex0v0d8ucBwoMv0mxIN+zcnbkbGs7+eevoOn4oTt4epdqeBy1et5bfN27g0zfepFIFL+avWsmrn3zC2jlzsbayMrhPdk4OFd096Ni8JTMW/Gr02FUqVWLO51/o7quU5SeR2tLUnOsJt9ly+QhTuo0p6+o8knm3jph3bkfm/N9Qx8Ri0bMrNu+MI/WDzyE7x+A+pk0bYjGgD5kLlqG+Ho7Sww2rUcMAyF6xDgBNUjJZazaiiY0HwKxFE6zHvUTapK8eGZj7O5r71OL5Rj2Yd/wPLsfepHP1xnzYcQRvbpxFfEZKsfIBbpUZ1/JZFgVv5uTtyzhZ2fFy076Mbf4MX+9dBsA3e5dholTp9rG1sGJ6r3EcjbhQau14UHu/BoxrOYAZB1ZwITqc3jVa8nXPVxm+fDKx6UnFyvcJasVLTfvwzb7fCY2NINDNh/faPkdadiZHbp4HoFO1RrzUtC/T9i7lQnQ43g7uTGxf8Br+cHjtU2mXIUt2bOP33Tv5ZPjzVHJzZ8HWzbw2eyZrJn2BtYWFwX3sra15vlt3fNw9MTVRcfD8OT5fsghHW1ua1SgeyCkp+3ft5efvfuTVt98gqHZNtmz4k4/eeZ9ffluIm0fx+UWj70bx8TsT6darO+998gEXz13gx+nfYe/gQMt2rQHYs30XC36ax/iJ7xFYK4g7kbeY/uXXALz8xqsAZGdl4+tXlU7du/LFh5NKrX0ADSr682yd9qw4s5OwhDu08q3Dqy0H8PmOBSRlpRUrv+rsbjZcOKC7r1Qq+LDDSE7fvlKsrJOVHc/Uasu1uFul2gZDLh07w87fNtB15AC8q/tyes8RVnzzCy9Pex97F0ej+2VnZvHHT7/jG1SN9BT99t8MvU6NZvWpWN0XE1MTjm7aw/JpP/HSVxOwc3Io5RZBs8o1GdGgG/ODN3ElNpKO1Roysf0wxv/5PQmZxfs+f9dKvNq8P4tPbeXUvb7vxSa9eblpX6Yf0L9A5WJtz9D6XQiNiSj1djzov9D3tfKtw4tNejP36HouxUTQLaApkzqPYuy6b4nLSC5WvltAM0Y06Mb3h9dwNf4W/i7evNZyAOk5mZy4FfrU6w/Qpko9XmnWj+8PreZizA16BDZnSrcxjFo1lbiM4q9Tz8AWvNC4FzMPrOBKXCQBbpV4q9X/SM/J5FjkRb2ybjaOvNSkL+eirj+t5ghhVPn5tSGEKBUZGRmsWrWKV155hZ49e7Jo0SK9/98fNrl9+3YaNmyIubk5Bw8eRKvV8vXXX1OlShUsLS2pU6cOa9as0e2nVqsZNWoUvr6+WFpa4u/vz3fffffI+mzZsoXq1atjaWlJu3btiIiIKFbmyJEjtG7dGktLS7y9vXn99dfJyMh47DYHBwfTqVMnXFxcsLe3p02bNpw+ffqh+0yYMIHq1atjZWVFlSpV+Pjjj8nLy3vsx/yrLu05gV+zOlRrXgd7DxcaDeiIlaMdVw6eeeh+FrZWWNrZ6G7KB4JH4ScuUqtzM7yCqmLr4oB/q/p4BvpyaY/hK3qlQavVsvzPP3j+2YG0b9Ycv8qV+ezNt8jOzWHbgf1G9wuqVp03nn+BLq1bY2ZqarSciUqFi6Oj7uZobzzQ+LQdi7zIvON/sD88pKyr8ljMO7Uje9N28k6fRXMnisz5S1GYmWLWpKHRfUyq+pJ/LZy84yfRJCSSf/EyucdPYuJTGJjPP3uB/POX0MTEoomJJXvdn2izczCp6luq7elVoyV7rp9i97WT3EmJY2HwZhIyUuji38Rg+equ3sRlJLHl8lFi05O4HHuTHVdPUNW5MNMpPTeL5Ox03a22px85+Xm6H2OlbWCd9mwOPcLm0CPcTIrm+8NriEtPpm/N1gbLd/Fvwh8XD7Hn+imiUhPYc/0Um0OPMKR+J12ZIA9fLkSHsevaSaLTEgm+Fcruayfxd316WTJFabValu/ZzfNdu9O+Xn38vLyYNOJ5snNz2R583Oh+Dar7065ufXw9Pano6sbg9h3x86pIyPXS/bG1buVquvTsRrfePajkU5kxb76Gq5sbm9b/YbD85g1/4ubuxpg3X6OST2W69e5B5x7dWLN8la5M6IWLBNWqSbvOHfDw9KBBk0a07dSeq5ev6so0ataEkS+NomVbw69/SepQrSFHIs5zOOI80WmJrD63l6TMNFpXqWuwfHZ+Lqk5GbpbZUcPrMwsOHpTP9CsQMHzjXqwKfSwwaB2aTu+dR912zahXrumuHi503lYP+ycHTi9+/BD99u6YDVBzerj5edT7H99xw6jYaeWeFT2wqWCOz1GD0Kr0RJx8VoptUJfj8Dm7Ak7zZ7rp7iTGsfiU1tJyEylc/XGBstXc/EmNiOZbVeOEZeRzJW4SHZdC6aKs36Wp0KhYFyLZ1l9bg8x6YlPoyk6/4W+r2/N1uy8GsyOqye4nRLLvON/EJ+RTPcAw5mz7avWZ+uVYxy8cZaYtEQO3DjLzqvB9K/d7inXvFD/2m3ZduUYW68cIzI5hrlH1xOXnkSvGi0Mlu9YrRGbQw+zP/wM0WkJ7As7w7YrxxhUt6NeOaVCwcT2w1lyaivRqQlPoylCPJQEz4T4l1u5ciX+/v74+/szdOhQFi5ciFarLVbuvffeY+rUqYSGhlK7dm0++ugjFi5cyNy5c7l48SJvvfUWQ4cOZf/+gsCHRqOhYsWKrFq1ikuXLvHJJ5/wwQcfsGrVqmLHvu/WrVs888wzdO/enZCQEEaPHs3777+vV+b8+fN06dKFZ555hnPnzrFy5UoOHTrEa6+99thtTktLY8SIERw8eJBjx45RrVo1unfvTlpa8avk99na2rJo0SIuXbrEd999x7x585g5c+ZjP+Zfoc5Xk3grGs9AH73tFQJ9iLtheEjMfZumLWTNB9+zc/Zyoq/qD8lQ5+ejNNXPHDQxNSE27Old3b8TE0NCUhJN69XTbTMzNaV+UE3OXb78t48fefcuXUeOoPeLo5j4zdfcji69TKZ/M6WrM0oHe/IvPvCa5OeTf+U6Jn7Gh/nmXwvHxMcblW9l3XFMawWRd+6i4R0UCkwbN0BhbkZ+mPGhKH+XiVJFVecKhNzV/7F69u51oz+MrsRG4mxlT32v6gDYW9jQrHJNThnIlLmvQ7WGHI44R05+6QfYTZQqqrtWIrhIRkHwrVBquht+jUyVJuSq9euWo84j0M1Hl6V5LiqM6q6VCHQreF487ZxpWrkmx24+nWw6Q+7Ex5OQmkLTGkG6bWamptSvVp1zYcaHaT1Iq9Vy4nIoN2OiqV+temlVlby8PK5duUr9xvpB5vqNGxJ6wfD7IPTCxWLlGzRpyLXLV8i/N9w5qE4trl25ypVLBa931J27BB89TuPmhoO/pUmlUFLJwYNLRbKNQmMjigVYjGnuU4vLsTdJzEzV294jsDnpOVkciXg6AegHqfPzibpxG9+a/nrbq9T05/a1CKP7nd1/nKSYeFo/0+WxHicvJxeNWoOljeFM65KkUqqo4lShWHbO2ajrVHc1PA3E1bhInK3sqFuhGgD2FtY0qRTEmTv6fd+AWu1Izc5gb9jDL0KWtP9C32eiVOHn7MWZu1f1tp+5c5UAN8OfWaYqE/KKDC/Nzc+juos3qjJYmdFEqaK6i3exz8xTt68Q5G74YpmpyqTYENmc/Dz8XSvptWFo/a4kZ6Wz7cqxkq/4P4RSoSiXt/8qGZclxL/c/PnzGTp0KABdu3YlPT2d3bt307Gj/tWdzz//nE6dCq7MZWRkMGPGDPbs2UOzZgVXvqpUqcKhQ4f4+eefadOmDaampnz22We6/X19fTly5AirVq1i4MCBBusyd+5cqlSpwsyZM1EoFPj7+3P+/HmmTZumK/PNN98wZMgQ3nzzTQCqVavG7NmzadOmDXPnzsXCyLCdB7Vv317v/s8//4yjoyP79++nZ8+eBvf56KOPdH/7+Pjw9ttvs3LlSt577z2D5XNycsjJ0R/S9qQLMuSkZ6LVaLGwtdbbbmFrTXaq4Uw7S3sbmg7uilMlDzR5asKDL7Dz++V0fmMI7n4FWT8VAqsQuicYdz9vbF0ciboSwa1z1wwGTUtLQlJBmr6zvYPedmcHB6JiY//WsWtWr85nb75F5QpeJCQnM3/1SkZNeJeV3/+Ig53d3zr2f43i3vOlSdUPLGtS01A6OxndL+/EKbJsbbCZ+BagQGGiImfPAXK27NQrp/SqgO2Hb4OpCeTkkPHDvFIdsmlrboVKqSIlK11ve3J2Gg6W1QzucyUuklkHVzG+zWBMVSaYKFWciLzE/ON/Gizv51KRyo4ezDmyrsTrb4i9hQ0mSlWxIXKJmak4eRs+30/cukTPwBYcvHGWq3G38HetRPeAZpiqTHCwsCEhM5U910/hYGnLD/3eRoECE5WK9RcOsOzMjqfRLIMSUgsykJxs9dvlZGdHdMLDsw7SszLpPvE9cvPyUSkVTBj8HE0Ca5RaXVOTU9CoNTg66Q/xc3R0JDHBcHZOUmISjo5Fyjs5olarSUlOwdnFmbYd25OSlMzbr7yhm0ahZ7/eDBo2pNTaYoyNuSUqpZK0bP3Po7TsDOzdrY3sVcjOwpog9yosOLFJb3sVZy+a+9Tiy92LS7S+jyszLQOtRoONva3edmt7W9KTUw3ukxgdx96Vmxj28TiUKpXBMkXtXbkJW0d7fINKL4h7n52Rvi8lKx2HCrYG97kaf4vvD6/hzVaDdH1f8K1QFgZv1pXxd61Eu6r1mbBlTqnW35D/Qt9nZ26NykAbk7LSqW9l+HU7fecKnas35ujNC4Ql3MHPuSIdqzfCVGWCnYW1weHUpcne4n4b9N87SVlpOBppw6nbl+kW0JQjEee4Fn+b6i7edPVviqnKBHsLGxKzUgly96Wrf1PGrP36aTRDiMciwTMh/sWuXLnCiRMnWLeu4EeeiYkJgwYNYsGCBcWCZw0bFl4Nv3TpEtnZ2bpg2n25ubnUeyCT6KeffuLXX3/l5s2bZGVlkZubS926dY3WJzQ0lKZNm6J44IrF/eDcfadOneL69essW7ZMt02r1aLRaLhx4waBgYGPbHdsbCyffPIJe/bsISYmBrVaTWZmJpGRkUb3WbNmDbNmzeL69eukp6eTn5+P3UMCMVOnTtULHgJ8+umnmLR48rT/YpOBasHY/KD27s7Yuzvr7rtW8SIjKZVLu07ogmeNBnTk6PKt/DF5HijA1sWRqk1rE3bs3BPX7XFt3bePKXN/1N2f9fEnAHqvNRS8lkW3PakWDQrPVT+gdkAAfV9+kU179zC0T9+/dex/O9OmDbEaPlh3P33W3II/DAVWHxJsNfGvhkXPLmQtXUl++E1U7i5YDh6AJiWVnD8LFyXRRMeQNmkqCisrTBvUxWr0MNKnfVeqATQALfp1f9iEuxXt3RjVuCerz+4h5O41HC1tGd6gKy8362swQNbBryE3k6K5Hn+7xOv9MEWD3wqFolg771t8citOVnb89Mx7oICkzDS2XT7GkPqdUWs1ANStUI1hDbow48AKQmMi8LJ35fWWz5LQoBtLTm0t9fYAbD1xjKm//6a7P3PsOACKdhFarYGNRViZW7Dsg0/IzMkm+MplZq5ZhZeLKw2q+z90v7+taB/HI/q4Yn3i/c0F28+eDmHFkmW8+vYbBAQFcvf2HX767kccFy7lueeHlWjVH1fxs8z4ufegZpVrkpWXzdkHMkHNTUx5vlF3lp3eToaBSemfqmKvXfHPLCjItN/w41Ja9e+Ks6fbYx366KbdXDx6hqEfvoqJmfEpCEpa0VdFoVAYvXDmZe/KyIbdWXt+L2fvXsfR0pbn6ndhdJPe/HxsAxYmZrzWYgC/HN9IWk5m6VfeiH9j31dMkeYoFMY/gleE7MLR0pbpvcahAJKz0tl97SQDardDc6+NZaFofRUKQ31Hgd9Ob8fRypbZfcejoCDQtuPqcQbV7YhGq8HS1JwJ7YYx8+AKUnMef9oWIUqbBM+E+BebP38++fn5eHkVDq/QarWYmpqSlKR/BdzauvAqskZT8OG7efNmvX2hMLtq1apVvPXWW0yfPp1mzZpha2vLN998w/HjxueleZzMJ41Gw8svv8zrr79e7H8PW+jgQSNHjiQuLo5Zs2ZRuXJlzM3NadasGbm5uQbLHzt2jP/973989tlndOnSBXt7e1asWMH06dONPsbEiRMZP3683jZzc3O+OfD4K1CZ21ihUCrIStO/UpydnlEsG+1hXH28CA8uHCZkYWtFu5f6o87LJycjC0t7G85s3IeNs8NjH/NJtW7cmJr+hVfXc+/NFxefnISLU2EGU2JKCk4OJVsPSwsLqlb24dbduyV63H+jvJDzpIVHFG64tzCI0t4OdUrhVWOlnS3aVONXry369SD3yAlyDx4FQHPnLpiZYzViMDmbthd+i1ardQsGqCMiUflWwrxjW7KWrCjZht2TlpOJWqPGwVL/are9hQ3JRTIy7numVhsux95k48WDANxMiiY7P5cvu73M72d2kvzAVXwzlSktfGuzMmRXqdTfkJTsdPI1apys9IP5jpa2JGUafo1y1XlM2/sb3+7/HSdLOxIyU+hVoyUZuVmkZBX8EBnVuBc7rpxgc+gRAMIT72Jhas67bYaw9NS2xwqO/F2ta9elpk/h8Kvce8NgE1JTcXkgazUpLRVn24dnlSqVSrzdCgIb/t6ViIiKYtG2LaUWPLNzsEepUpJUJMssOSm5WDbafY5OjiQlFi2fhEqlws6+oH1L5i2kfZdOdOvdAwDfqlXIzs5m9rQZDB7xnN78lqUtPScLtUaDnYX+55GthRWp2Y8OpjT3qcXxyEu6oAWAq7UjLtYOvNL8Gd22+wGrH/q9zaQd84k3MEl6SbKytUahVBbLMstMScPavnimTG5WDlE3bhF98w7bFxcE1LVaLWi1TBn+NkMmjMEnqDCz9djmvRz+YxdD3n8F90oVSrUt96Xq+j4bve12FtakZBvu+/oGteZqXCR/XiqY5y0yOYbsE7l83uVFVp7dhb2FDW42jrzX9jndPvdfq9+HTOKtP74jxsCk/SXl39z33Zeak4Faoy6WoeVgYaP32fOgXHU+3x1azQ+H1+JgaUtSVipd/JuSmZv9WO/LkpaSXdCGoq+Tg4UtyQ95nabvX86sAytxtLIlMTOV7gHNycjNJiU7gyrOFfC0c2Zylxd1+9w/97aNnsHzK78kKu2/MQfa373gLEqWBM+E+JfKz89nyZIlTJ8+nc6dO+v9r3///ixbtszoPGI1atTA3NycyMhI2rRpY7DMwYMHad68OWPHjtVtC3vEnDQ1atRgw4YNetuOHdOfx6B+/fpcvHgRPz+/hx7rYQ4ePMicOXPo3r07UDDXWnx8vNHyhw8fpnLlynz44Ye6bTdvGl/aHQoCZU86TLMolYkKJ28Poi5HUKlO4Q+8qMsRVKxleIiZIYm3Y7C0Lx5sU5maYOVgi0atJjLkCpXrPzpr76+ytrLSW0FTq9Xi7OjI8ZAQAqpUBQrmCDp98QLjho8o0cfOzcsj4vYt6tUovSFa/xrZOWiKrKCpSU7BpEYA6sh7mVQqFSb+fmSt3mj8OGZmxS8zazVGMyYLKVCU4kq++Ro1YQl3qePpx4nIS7rttSv4EXzrksF9zE1MUWv0r9bfv3pftDktfGphqlKxP/zhC3qUpHyNmqtxkTT0DuTgjbO67Q0rBnAo4uHZpGqNRrdaWwe/hhyJuKD7YWhhYlbsgoZGo0GheHjWQ0mytrDQW0FTq9XibGfP8dBL+HsXXCzJy8/n9LWrjOvX/4mOrUVLbn7+owv+RaamplTzr86Z4FO0aNNKt/1M8CmatmxucJ/AmkEcP3xUb9vpEyepFuCvW+E6Jye7WIBMqVSi1Wqf6tB7ALVWQ2RyNIFulfWyxwruP3wxhmou3rjZOBab0yw6LYHJOxfqbesV1BILEzNWn91DUqbhYZMlSWVigqdvRW5cuEpAo9q67TcuXKV6g+Krs5pbmvPiVP0pHE7tOszNS9d45vWROLgWXiA6umkPhzfuZPCEl6lQ5fEu+JUEtUZNeOJdantU1ZsjrLZHVU7eNjzPqOG+r+AcU6Dgbko87/z5vd7/B9XtiIWJGYtPbiG+lF+rf3Pfd1++Rs31hDvUrVBNb1GNuhWqczzSyByi96i1Gt0qqq1963DiVuhTDfzdl69RczX+FvW9/Dn8wOtSv6L/I+c0VGs1ugVD2lWtz/HIi2jREpkcw4urv9IrO7JRd6xMLZhzZJ3BVUiFeBokeCbEv9SmTZtISkpi1KhR2BdZiXDAgAHMnz/faPDM1taWd955h7feeguNRkPLli1JTU3lyJEj2NjYMGLECPz8/FiyZAnbt2/H19eXpUuXEhwcjK+v8ZX0xowZw/Tp0xk/fjwvv/wyp06dKrb654QJE2jatCmvvvoqL774ItbW1oSGhrJz506+//57wwcuws/Pj6VLl9KwYUNSU1N59913sbS0fGj5yMhIVqxYQaNGjdi8eTPr169/rMf6u2q0b8zhJX/iXMkDV18vrh4OISMxleqtCobHnt64j6yUNFoM7wVA6N5grJ3scfB0QaNWE37iIpEhV2gzup/umHERd8lKTsOxojuZyWmc23IIrVZLUMenN+G0QqFgcK/eLFyzmkqeFfCuUIGFa1ZhYWZO19aFAdlPZs7AzdmZ1+4F1PLy8gi/deve3/nEJSRwJTwcK0sLvD0LruDPWjifVo0a4+HqSlJyCvNXryQjM5Oe7Ts8tfY9jKWpORXtXXX3K9i5UM2lIqnZGaV6lf6vytm5F4uendHExqKOicOiRxe0uXnkHj+pK2M1ehiapBSy1xasIph/9gLmnduhjryNOjwCpZsrFn17khdyXvfLw+KZXuSdv4Q2MQksLDBr0gCTgGpkzCjduXP+vHSI11s+S1jCHa7ERdKpeiNcrO3ZceUEAM/V74yTlR3fHypYPfjkrcuMad6PLv5NCLlzFQdLW15o3JOrcbeKzR3TvlpDTkSGkp7zdIebrTq7hw87jOBK7E0uxtygV40WuNk6svFCQbbcS0374GLtwJR7c0hVtHcj0N2H0Jgb2JpbMbBOB3ydPZmyp3COqSM3zzOwTnuuxt/SDV0a1aQnhyPO635AP20KhYLB7TuwcNsWvN3c8HZ1Z9G2LViYmdGlUWH/9emi+bg6OPJa34LspYXbtlCjsg9eLq7kq/M5fOE8m48d4/3Bzxl7qBLxzKBn+WbyVKoF+BNYswZbN24iNiaGHv0K+usFc+eREB/Pux9PBKBH3178sXYDP8+eQ7fePQi9cIntm7by/qTCOTebtGjG+hVrqFrdj4AaBcM2l8xbSNOWzVHdm2srKzOLu7cLF5WJvhtF2NXr2NrZ4ubhXqJt3H3tJCMb9eBmUjQ3Eu/S0rcOjlZ2umBGn6BWOFjasvjkFr39WvjU4kbCXe6m6l+4yteoi23Lyi0I6BfdXpqadGvLxrnL8KziTUU/H87sPUJKQhL1OxQEPveu3ERaUgq9xzyHQqnEzdtTb39rOxtUpiZ6249u2s3+NVvpO3YY9i5Ousw2MwtzzCz+3sW2x7E59AivNe9PWOJdrsXdokO1hrhY27PzWkHfN7huJ5ys7PjxyFqgYEL3l5r2oVO1RpyNKhi2OaJhN67FF/Z9t1L05yi9P9S26PbS8l/o+zZcOMD41v/jevxtQmNv0tW/Ca42Dmy5XBBoH9GgG87W9sw4UJCxXcHOhequlbgaF4mNmSV9a7amsqMHMw+ufOp1v2/tuX1MaDeUq/GRhMZE0D2wOW42jmwKLchqfKFRT1ys7fl6X8GULF72rgS4VuZy7E1szC3pX7sdPk6euv/nqfOJSIrSe4yMe5+7RbcL8TRJ8EyIf6n58+fTsWPHYoEzKMg8mzJlCqdPG185afLkybi5uTF16lTCw8NxcHCgfv36fPDBB0BBICwkJIRBgwYV/OAZPJixY8eydavx+SIqVarE2rVreeutt5gzZw6NGzdmypQpvPDCC7oytWvXZv/+/Xz44Ye0atUKrVZL1apVGTRo0GO3fcGCBbz00kvUq1ePSpUqMWXKFN555x2j5fv06cNbb73Fa6+9Rk5ODj169ODjjz9m0qRJj/2Yf5VPg0ByMrI4t/UwWakZOHi60H7ss9g4FbxuWanpZCQWXt3V5Ks5vX4PmSnpqExNCsq/8ixeQVULy+TlE7LpAGnxyZiam+EVVIUWw3tiZvXoxRZK0ohn+pOTm8tXP88lLT2dmtWr88Nnn+tlqEXHx6FUFub3xCUm8txbb+juL92wnqUb1lO/Zk1++XIqADHxCXz47bckp6XiaGdHTX9/Fn79LZ5ujzcXTWkLcK3MD/0Kh/S+3vJZALaEHuXLB77Alxc5W3ehMDPDcuggFNZWqMMjSJ/+AzyQoaZ0cgJN4Y+K7D+3odVqsejXE6WjPdq0dPLOXiB7beEk+wp7W6xfHI7C3g5tVjbq23fImDGH/Et/f7XVhzkScR5bcyuerdMeR0tbIpNjmLJ7se5KtaOlLS7WDrrye8NOY2FqTreApoxo2I2M3GzOR4Xx2+ntesf1tHOmhrsPn+1YUKr1N2TP9VPYmVszomF3nK3tuJEQxYRNc4hJLxgC6Gxlh7tN4VBBlVLJoDodqOTgTr5GzZk7Vxm77lui0wqHDC45uRWtVsvoJr1wtXYgOSudIxHnmXf8j6fevgcN79yVnLw8pi3/nbTMDIJ8q/D9uLf0MtSiExP1hrJk5+QwbfkyYpOTMDc1pbKHJ58/P4rODRuVal3bdGxHamoqyxYuISkhkcpVfJj87VTcPTwASExIJDamMMjgUcGTyd9O5efZP7Jp3UacXJx55c3XaNmuta7MkBHDUCgULP5lAQlx8dg7OtCkRTNGvjRKV+bq5StMGFfYx/zyfcHchR27deGdjyaUaBtP3b6CtZklPQKbY2dhTVRqPD8eXqtbPdPewganIkPOLEzMqOdVnVVn95RoXUpSjab1yEzL4ND67aQnp+Ja0ZP/vfsS9i4FWWTpyamkxD/ZxY5Tuw6jzlezdvYive2t+nWhdf+uJVV1o47evICtuRX9a7XF0dKWW8kxfLV3qS6zx8HSBmfrwu+E+8PPYGlqRhf/pgxr0JWM3GwuxtxgWZG+ryz9F/q+gzfOYmtuxf/qdsTJyo6bSdFM2jG/8DPLyg7XBz6zlAol/Wq2xsveFbVGzbmoMN7d9COxZXhxbn/4GewsrBlavwtOVvZEJEbx4dafdXVytrLD7cHXSaFkQO12VHRwQ61RE3L3Gm9snKV7XUWh//LKluWRQvu0c8CFEOJf7Isiw1H+6T7q9Dxpl68+uuA/jG1AdVr8OKasq1GiDr/6E8kvGM4m/adyWPAD/Rd/UNbVKFFrR0yh9Zyxjy74D3Jg7BxS9xwo62qUKLv2rbkRf+fRBf9BfF28eGXtN2VdjRI3t/+7LAne8uiC/yDDG3Vn0G8fl3U1StTKoZP/lX1fzwXvlnU1StSmF76h0y9vPLrgP8jOl74r6yr8ZR9v+6Wsq2DQ5K4vlXUVysTTm3VUCCGEEEIIIYQQQoh/GBm2KYQQQgghhBBCCFGOKB69EpN4iiTzTAghhBBCCCGEEEIIIyR4JoQQQgghhBBCCCGEETJsUwghhBBCCCGEEKIcUchqm+WKZJ4JIYQQQgghhBBCCGGEBM+EEEIIIYQQQgghhDBChm0KIYQQQgghhBBClCNKGbZZrkjmmRBCCCGEEEIIIYQQRkjwTAghhBBCCCGEEEIII2TYphBCCCGEEEIIIUQ5Iqttli+SeSaEEEIIIYQQQgghhBESPBNCCCGEEEIIIYQQwggZtimEEEIIIYQQQghRjiiRYZvliWSeCSGEEEIIIYQQQghhhATPhBBCCCGEEEIIIYQwQoZtCiGEEEIIIYQQQpQjstpm+SKZZ0IIIYQQQgghhBBCGCHBMyGEEEIIIYQQQgghjJBhm0IIIYQQQgghhBDliAzbLF8k80wIIYQQQgghhBBCCCMkeCaEEEIIIYQQQgghhBEKrVarLetKCCGEEEIIIYQQQogCX+1ZUtZVMOj99sPLugplQuY8E0KIEpTUqV9ZV6FEOe5cT+quvWVdjRJn17EdyS+8VtbVKFEOC36gxY9jyroaJerwqz+RPGZ8WVejRDn8NIPkUePKuholymH+9//Ovm/3/rKuRomy69CG5OH/rj4CwGHJT2VdhVKRumVHWVehRNl170zyWx+UdTVKlMPMKST/b1RZV6NEOayYzyfb5pV1NUrU511fLOsqiH8JGbYphBBCCCGEEEIIIYQRknkmhBBCCCGEEEIIUY4okNU2yxPJPBNCCCGEEEIIIYQQwggJngkhhBBCCCGEEEIIYYQM2xRCCCGEEEIIIYQoR5QKGbZZnkjmmRBCCCGEEEIIIYQQRkjwTAghhBBCCCGEEEIII2TYphBCCCGEEEIIIUQ5opBhm+WKZJ4JIYQQQgghhBBCCGGEBM+EEEIIIYQQQgghhDBChm0KIYQQQgghhBBClCMKheQ6lSfyagghhBBCCCGEEEIIYYQEz4QQQgghhBBCCCGEMEKGbQohhBBCCCGEEEKUI0pktc3yRDLPhBBCCCGEEEIIIYQwQoJnQgghhBBCCCGEEEIYIcM2hRBCCCGEEEIIIcoRhUKGbZYnknkmhBBCCCGEEEIIIYQREjwT/wknT55k5syZaDSasq6KEEIIIYQQQggh/kEkeCbKREREBAqFgpCQkBI7pkKhYMOGDcW2x8fHM3DgQGrWrIlSWfKn/KRJk6hbt26JH/e/ZOTIkfTt2/exy+/btw+FQkFycvJffsxFixbh4ODwl/cXQgghhBBCiNKiVCjK5e2/SuY8E09s5MiRLF68GACVSkWFChXo0aMHU6ZMwdHRsczqFRUVVezxtVotw4cP55NPPqFTp05lVLN/B4VCwfr1658oyCVKh2nLppj36IyqWlWU9nakjnkLdVhEWVerGK1Wy7wtm1h/+BBpmZkE+fjw3sDBVK1Qweg+e0LOsGj7Vm7FxZGvVuPt6sbQDh3p3qSprszpa9dYumsHl29FEp+SwjcvjaFtnbpPoUUFLPp0x6xNCxRWlqjDb5L520o0d6Mfuo95p7aYtWuF0skRbXoGuSfPkL3mD8jPB8CsbUvM27VC6eIEgPpONNl/biX//KVSb8/jqOPpx5B6nQlwq4SLtQPvb5nLwRtny7paD2XRswtmLZuisLJCHXGTzOVr0UTFGC1vM34sJtX9im3PO3+JjB9/Lbhjbo5l726Y1q2JwtYW9a3bZK3agPrmrdJqhh6L3t30z71lqx597nVsi1m7lg+ceyFkry0898y7d8K0fh1Unu5oc/NQh90ga/VGNDGxT6NJT+Sf0vcVpdVqmbf5T9YfPnivL/TlvUFDHt4Xnjl9ry+MLegL3dwY2qET3Zs0e4o112fRrydmbVuisLZCHRZB5pLlaO5EPXQf8y7tMWvfGqWzE9q0dHKDz5C9ej3k5evKKBwdsBzYD5M6QShMzdBEx5A5fynqiMjSbtK/jlarZd72raw/epi0rCyCKlXmvf4DqerpaXSfPedCWLRzB7fi48nXqPF2cWVo2/Z0b9RYV+aXbVuYt32r3n5OtrZs/3xKqbXlQRZdOmDWrBEKS0vUkbfIXPsHmmjjfZTNq6Mx8atSbHvepctkzFtSbLt5hzZY9uxCzv7DZG3YXKJ1N8ZiQG/M2rdBYWOF+no4mQuWobl996H7mHfriFmndihd7r2fjp8ke/nawveThQWWA/ti2qg+Cntb1BGRZC1ajjo8otTb08g7kJa+dbAxtyQuPYmtl49xM8n455NKoaStX33qVPDDxtyK1OwM9oed4cydqwA0qOhP3QrVcbMt+G13NyWeXdeCuZMSV+ptEcIYCZ6Jv6Rr164sXLiQ/Px8Ll26xAsvvEBycjLLly8vszp5eHgU26ZQKNiyZUsZ1EaI0qOwMCf/4mVyDxzBevyrZV0do5bs3MHve3bzybARVHJzY8G2rbz2w3es+eQzrC0sDO5jb2XF81264ePhganKhIMXzvH5b0twtLWlWY0gALJyc6hesSK9mjVnwryfn2aTMO/WEfPO7cic/xvqmFgsenbF5p1xpH7wOWTnGNzHtGlDLAb0IXPBMtTXw1F6uGE1ahgA2SvWAaBJSiZrzUY0sfEAmLVogvW4l0ib9NUjgyNPg6WpOdcTbrPl8hGmdBtT1tV5JPPO7THv0IbMxctRx8Zh0a0TNm+MIfXTryDH8OuU8dMiMFHp7iusrbD96B3yThcGCa2GDURVwZOMhb+jTUnFrEkDbN4cQ+pnX6NNTindNt0/9xYsu3fudcHm7ddI/XCy8XOvSUMsBvQmc+Ey1NdvFJx7LwwFIHtlwblnUt2P3L0Hyb9xE5QqLJ/pic3br5L60ZeQm1uqbXpS/5S+r6glO7fz+55dfDJsJJXc3VmwdTOvfT+TNZ9ONt4XWlvzfNfu+Lh7YGqi4uD583y+dDGOtna6vvBpMu/RGfOuHcictxh1VCwWfbph894bpE741Pj516wxFs/2I3P+EtTX7vV9L44AIPv31QAorKyw/ehd8kKvkPHtD2hT01C6uaDNzHxqbfs3WbJnF7/v28snQ56jkqsbC3Zu57WffmDNxI8f8rlrzfOduuDj7o6pSsXBixf5fMWygs/dgEBduSoenvz4ymu6+yrl08k+MW/fGvO2Lcj8fS3quHgsOrXDZswLpE6dATmG+6iMhctAVaQ/f2cceSEXipVVeXth1qwR6kcEgkuSee9umHfvTObcBaijYrB4pic2H7xN6vgPITvb4D6mLZpgMXgAmT8vRH31OkpPD6zGvABA9pKVAFi9PAJVRS8yfvwVbVIyZq2aYvPR26S+/THapORSa09Njyp0C2zGpkuHiUyKoZF3AEMbdOWHQ6tJyc4wuM/Auh2wMbdkw4UDJGamYm1mqZfR5ONUgXNR17kVGkO+Rk1L3zoMb9iNHw6tIS1H+od/ojlz5vDNN98QFRVFUFAQs2bNolWrVgbL7tu3j3bt2hXbHhoaSkBAgO7+2rVr+fjjjwkLC6Nq1ap8+eWX9OvXr9TaIMM2xV9ibm6Oh4cHFStWpHPnzgwaNIgdO3bolVm4cCGBgYFYWFgQEBDAnDlzjB5PrVYzatQofH19sbS0xN/fn++++65YuQULFhAUFIS5uTmenp689lrhh3jRYZvnz5+nffv2WFpa4uzszEsvvUR6erru//eHCn777bd4enri7OzMq6++Sl5e3kPb/tVXX+Hu7o6trS2jRo0i28CH3JO0HWDbtm20bNkSBwcHnJ2d6dmzJ2FhYbr/5+bm8tprr+Hp6YmFhQU+Pj5MnTpVr+1z586lW7duWFpa4uvry+rVq/Ue486dOwwaNAhHR0ecnZ3p06cPERERj/X8+vj4ANCvXz8UCoXuPsDcuXOpWrUqZmZm+Pv7s3Tp0oe2Va1WM378eF1b33vvPbRarV4ZrVbL119/TZUqVbC0tKROnTqsWbPmocctasaMGdSqVQtra2u8vb0ZO3as3utfVFhYGH369MHd3R0bGxsaNWrErl27nugxn5bcXfvJ/m0V+afLb+aPVqtl+d7dPN+lG+3r1sOvgheTho0gOzeX7cEnjO7XoLo/7erWw9fDk4qurgxu1wE/Ly9CHng/tAiqySu9+tC+br2n0RQ95p3akb1pO3mnz6K5E0Xm/KUozEwxa9LQ6D4mVX3JvxZO3vGTaBISC378Hz+JiU8lXZn8sxfIP38JTUwsmphYstf9iTY7B5Oqvk+jWY90LPIi847/wf7wkLKuymMx79Ca7K27yAs5j+ZuNJmLf0dhZoZZ4/pG99FmZqJNTdPdTAP9ITeP3FP33memppjWq03Wuj9RXw9HExdP9qbtaOITMW/dvPTb1LEt2Zt3PHDu/fZ45971cPKOn3rg3Duld+5lzJpL7uHjaO5Go7l9h8wFy1A6O6Hy8S71Nj2pf0LfV5RWq2X5nl0837U77evVL+gLhz9/ry88bnQ/XV/o6UlFVzcGt7/fF15/irUvZN6lA9l/bCXvZAiaO3fJ/GVxwXuqWWOj+5j4VSH/Whh5R4PRxCeQfyGU3GPBmPgWnn/mPTujSUwk69clqMMjCspduqK7kCAen1arZfn+fTzfqTPta9fFz7MCk4YMJTs3j+2nTxrdr4FfNdrVroOvuwcVXVwZ3KYtfp4VCAkP0yunUipxsbPT3RxtbEu7SQCYt2lO9s595J2/WJCV+Pvqgr6vfl2j+2gzs9CmpetuptX9IC+P3LPn9QuamWE1dBBZq9ajzcoq3YY8wLxbR7I3bCYv+HRBvztnPgpzM8xaNDG6j0n1quRfvU7e4eNo4hLIP3eR3CPHManiU1DA1BTTxg3I+n0N6stXC75LrPkDTWw85p2KByFKUnOfWpy+fYXTt68Qn5HM1svHSM1Op1GlGgbL+7lUxMfJk99ObSc84S7JWencSYnjVnJhNuHac3sJvhVKdFoi8RkpbLxwEIVCQRVnr1JtS3mjUCjK5e1JrVy5kjfffJMPP/yQM2fO0KpVK7p160Zk5MMzjK9cuUJUVJTuVq1aNd3/jh49yqBBgxg2bBhnz55l2LBhDBw4kOPHjX+2/l0SPBN/W3h4ONu2bcPU1FS3bd68eXz44Yd8+eWXhIaGMmXKFD7++GPdcM+iNBoNFStWZNWqVVy6dIlPPvmEDz74gFWrVunKzJ07l1dffZWXXnqJ8+fP88cff+DnV3yIDUBmZiZdu3bF0dGR4OBgVq9eza5du/SCbQB79+4lLCyMvXv3snjxYhYtWsSiRYuMtnXVqlV8+umnfPnll5w8eRJPT89igbEnbTtARkYG48ePJzg4mN27d6NUKunXr59ugYPZs2fzxx9/sGrVKq5cucJvv/2mF8AC+Pjjj+nfvz9nz55l6NChDB48mNDQUN3z0a5dO2xsbDhw4ACHDh3CxsaGrl27knsvs+Bhz29wcDBQEBSMiorS3V+/fj1vvPEGb7/9NhcuXODll1/m+eefZ+/evUbbOn36dBYsWMD8+fM5dOgQiYmJrF+/Xq/MRx99xMKFC5k7dy4XL17krbfeYujQoezfv9/ocYtSKpXMnj2bCxcusHjxYvbs2cN7771ntHx6ejrdu3dn165dnDlzhi5dutCrV69HdurCsDsJ8SSkptI0sPCqtZmpKfX9qnHuRvhjHUOr1XLi8mVuxsRQ38h7/WlSujqjdLAn/+Llwo35+eRfuW5weIiuyLVwTHy8UflW1h3HtFYQeecuGt5BocC0cQMU5mbkh90oySb8JyhdnFDa25EfeqVwY76a/GthhT8yHoNZiybknjxTmH2lVKJQqfSGmwFo8/Iw8SvdIKfS5SHn3kMCrPnXwzCp/MC55+KMaa0axs89QGFVkJ2izZAr+yWhsC8s/BFpZmpK/WrVORf+JH1h6L2+sNqjdyhhSleXgvPvQmjhxvx88q9cw6TaQ/q+q9cx8amE6t77TunqgmmdmuSdLcz+Ma1Xh/wbkVi99iJ2P3yNzeQPMGvbsrSa8q92JyGBhLRUmvoXZmWYmZhS38+Pczce77NEq9Vy4uoVbsbFUr+q/ufurfg4un36IX0mf8oHSxZyO770A5xKZ0eUdnbkX7lWuFGtJv/6Db0g7KOYNWlI7plzkKt/gdxqQG/yQi+TfzXMyJ4lT+nmgtLRgfwH++H8fPJDr2BSvarR/fIvX8fEtzKqe32+0s0F03q1yDt9rqCASnXvM0q/jdrcPEwCSu87lEqhxNPOhbD4O3rbr8ffoZKDu8F9Atwqczclnpa+tXmn7RBebzWQLv5NMFGqDJYHMFWZoFIoycoznOkqyrcZM2YwatQoRo8eTWBgILNmzcLb25u5c+c+dD83Nzc8PDx0N9UDGaWzZs2iU6dOTJw4kYCAACZOnEiHDh2YNWtWqbVDhm2Kv2TTpk3Y2NigVqt1mVczZszQ/X/y5MlMnz6dZ555BgBfX18uXbrEzz//zIgRI4odz9TUlM8++0x339fXlyNHjrBq1SoGDhwIwBdffMHbb7/NG2+8oSvXqFEjg/VbtmwZWVlZLFmyBGtrawB++OEHevXqxbRp03B3L+jMHR0d+eGHH1CpVAQEBNCjRw92797Niy++aPC4s2bN4oUXXmD06NG6Ou3atUsv++xJ2w7Qv39/vfvz58/Hzc2NS5cuUbNmTSIjI6lWrRotW7ZEoVBQuXLlYsd49tlndfWaPHkyO3fu5Pvvv2fOnDmsWLECpVLJr7/+qrtasHDhQhwcHNi3bx+dO3d+6PPr6uoKgIODg97w2G+//ZaRI0cyduxYAMaPH8+xY8f49ttvDaba3n8OJ06cqGvzTz/9xPbt23X/z8jIYMaMGezZs4dmzQrmdqlSpQqHDh3i559/pk2bNgaPW9Sbb76p+9vX15fJkyfzyiuvGM0CrFOnDnXq1NHd/+KLL1i/fj1//PFHsaArQE5ODjlFhn+Zm5s/Vt3+CxJSUwFwsrXT2+5kZ0d0YuJD903PyqL7B++Tm5+HSqlkwqDBNAk0fPXyaVLYFbRFk5qmt12TmobS2cnofnknTpFla4PNxLcABQoTFTl7DpCzZadeOaVXBWw/fBtMTSAnh4wf5pWLIZv/NA99nZweb15OlU8lVF6eZC5dWbgxJ4f8sBtY9OhERnRMQXZao/qofCqVepaMwv5+m1L1tj/63DtNlo0NNu+/ie7c23uQnK07je5jOegZ8q+GPXIuK/F4ElKM9IW2dkQnJjx03/SsTLp/MIHcvHt94f+GlElfqDv/UoqcfympunkaDck7fpIsOxtsPnoH3fm3ez85mwo/85WuLpi3b03Otl1k/LkNVRUfLIcORJuXR97h0sse+DdKSDNyrtnYEp30GJ+7kz4iNz+/4FwbMJAmDwThgipX5rMhw6jk6kZCWioLdm5n1OwZrJzwIQ73vmeXBoVtQXabJk1/5IAmPR2lo8NjHUNVqSKqCh5k3huqfp9pvdqovCqQOfPho0NKmsLBHjD2fnI2ul/e0RMF76fP3i84jokJOTv2kvPHvbnosrPJv3odi2d6knEnCm1yCqYtmqDy833o/HB/l5WZBSqlkvRc/QsuGblZ2JhbGtzH0dKWSo7u5GvULD+zEytTC3oGtcDS1JwNFw4Y3KdT9UakZmcQnnDH4P/F02Xsd5Ch30K5ubmcOnWK999/X297586dOXLkyEMfp169emRnZ1OjRg0++ugjvd+XR48e5a233tIr36VLFwmeifKnXbt2zJ07l8zMTH799VeuXr3KuHHjAIiLi+PWrVuMGjVKLwiVn5+Pvb290WP+9NNP/Prrr9y8eZOsrCxyc3N1q1jGxsZy9+5dOnTo8Fj1Cw0NpU6dOrrAGUCLFi3QaDRcuXJFFzwLCgrSi2B7enpy/vz5Ysd78LhjxujP99OsWTNdptVfbXtYWBgff/wxx44dIz4+XpdxFhkZSc2aNRk5ciSdOnXC39+frl270rNnTzp37lysHkXv31/N9NSpU1y/fh1bW/0U++zsbMLCwp74+X3w+XjppZf0trVo0cLgkFuAlJQUoqKi9OpqYmJCw4YNdUM3L126RHZ2drEFHnJzc6lX7/GH6e3du5cpU6Zw6dIlUlNTyc/PJzs7m4yMDL3z4r6MjAw+++wzNm3axN27d8nPzycrK8to5tnUqVP1Ar4An376KW8YLP3XmbVvjdWbhedc+geT9a/+lxNbTxxn6vLfdfdnji2Yj6hoanfRIbqGWJmbs2zih2Tm5BB85TIz163By8WFBtX9S7bSj2DatCFWwwfr7qfPund1zFAbHtIuE/9qWPTsQtbSleSH30Tl7oLl4AFoUlLJ+XObrpwmOoa0SVNRWFlh2qAuVqOHkT7tOwmgPYJp4/pYDXlWdz/9/uT+RV+TJxhmYNa8Ceo7UcUmLM9c+DtWw/+H/bRJaNVq1LfukBd8BlWlkh1GYtqkIVbD/6e7n/7dTwV/FD3NFIpHnHt+Befeb6vID49A5eaK5eD+aHp20Qtg3Gf53LOoKlYg7atZJdCKv+ef0vcVVdAX/qa7P/PeHFFFTz8t2keek1bmFiyb+PG9vjCUmWtX4+XiWup9oWmzxlg9P0R3P336jwV/GHpPPaRLNwmojkWvbmQtXk5+2A1U7m5YDh2IJjmFnI335qNVKlDfuEn2mo0AqG/eQuXliXmHNhI8e4Stp4KZumqF7v7MFwveL0XPqsc718xZ9s77ZObmEHz1CjM3rMfL2YUG9zIdWwQWzrPnRwVq+/jS98vP2Bx8nOfati+ZBgGm9etgNbCv7n66gcn9Czz83HuQWZOGqO9Go468Xbi3gz2W/XqS/tMC3eIppcW0RROsXhyuu58+7d535GJ99yP68xr+WPTrSdb838i/Ho7Kww3LEYPRJPckZ90mADJ//BWrl5/Hfu70gs+oGzfJO3xcl338tBlrzv3vhmvO7SEnvyBTbtvlYwyq25FNlw6Tr1HrlW/pW5tanlVZeGJzsf/925XXlS2N/Q6aNGlSsbLx8fGo1Wrd7+/73N3diY42/B3X09OTX375hQYNGpCTk8PSpUvp0KED+/bto3Xr1gBER0c/0TFLggTPxF9ibW2tG9I3e/Zs2rVrx2effcbkyZN1gZ958+bRpIn+2P0HA1UPWrVqFW+99RbTp0+nWbNm2Nra8s033+jGLFtaGr5yYYxWqzU6HvvB7Q8ONb3/v/v1/yv+StsBevXqhbe3N/PmzaNChQpoNBpq1qypG1JZv359bty4wdatW9m1axcDBw6kY8eOj5wH7H5bNRoNDRo0YNmyZcXKuLq6olT+9RHchoIjf2Us/H33n8PNmzfj5aX/g/RxM7tu3rxJ9+7dGTNmDJMnT8bJyYlDhw4xatQoo3Pavfvuu2zfvp1vv/0WPz8/LC0tGTBggO41KGrixImMHz++WP0ye/7PYPm/KvfoCfIvX9Xd18Q//OpxWWlduw41fQqHj+Xe+zKakJqCywOB46S0NJzt7Irt/yClUom3mxsA/t7eRMREs2jH9qcePMsLOU/agytUmRR8ZCrt7VA/cMVYaWeLtkiW04Ms+vUg98gJcg8eBUBz5y6YmWM1YnBBAOP+t0u1WpfBpI6IROVbCfOObclassLYoQWQd/YiaTceCHLdm/RfaW+H+oHXRWlr89DXScfUFLNGdcl6ILB5nyY+gfQZP4KZGQoLc7SpaViNHlbi78u8s+dJ+yyicIOxc+8RbbLo25Pcow+ee1FgbobV8MHkbN6h98vGcsgATOvWIn3ad6U6sfTj+qf0fUUZ7wtTcbF30G1PSkvD2fYJ+8LoaBZt31rqfWHembOkPThk3PTe+edgb6DvSy26u45F/17kHjlO7v7DAAUrCZqbYfX80IJsGa0WbXJKsYna1XejMW1ofH5CUaB1UC1qvuOju68719JS9T9309NxfsT8ZEqlEu97owz8vSoSERPDol07dMGzoizNzfHzrMCtuJJd+TDvYihp3z6wevH9vs/WRr8/t7FG+5B5bHVMTTGrV5usbfpz2JpUrIDS1gbbBxYeUahUqKr4YNayKSnvfvLQQNaTyDt1lrTrDwQZHnw/PbDQjNLeFm3KQ95PA/uSe/AouXsPAqC5dQfMzbF6cTg56zeDVosmJo70z78GczMUlpZok1OweuPlUs2OzszNRq3RYGNmpbfd2sySjFzD88il5WSSmp2hC5wBxKUno1QosLOwJjGz8Hlo4VOLVlXqsjh4CzHp/4zPgf8CY7+DHuZJfjP6+/vj71/4WdesWTNu3brFt99+qwuePekxS4IEz0SJ+PTTT+nWrRuvvPIKFSpUwMvLi/DwcJ577rnH2v/gwYM0b95cN/wP0Jsw39bWFh8fH3bv3m10OOCDatSoweLFi/WyjA4fPoxSqaR69epP2LpCgYGBHDt2jOHDC68gHTt2TPe3u7v7E7c9ISGB0NBQfv75Z92KI4cOHSpWzs7OjkGDBjFo0CAGDBhA165dSUxMxMnJSVePovW6n6lVv359Vq5ciZubG3ZGAhePen5NTU1Rq/Wv9gQGBnLo0CG9xz1y5AiBD8xz9SB7e3s8PT05duyYruPLz8/n1KlT1K9f8EW5Ro0amJubExkZ+dhDNIs6efIk+fn5TJ8+XRcYfHD+PEMOHjzIyJEjdSu0pKenF1tQ4UHGUpNLfJagrGw0WeU/88jawkJvJS+tVouznR3HL4fi710wL0lefj6nr19jXJ8nWwVHq9WSm//whTxKRXYOmiKryGmSUzCpEVB4BVulwsTfj6zVG40fx8ys+JdwraZ4ekAxChQm8jH9SDk5aOKKvE4pqZgEVkd9697wDpUKk2pVyVq/6ZGHM2tYF0xMyDt+ynih3Fy0ubkorCwxrRFA1ro//0YDDDB67vkXP/fW/GH8OGamxc89jbbYuWc55FlM69cm/evZaOIfPpTwqfmH9H1FGe0LQy/p94XXrjKu7zNPdOyCvrB0s2SAe+efflBEk5yCSVAg6pv3AhsqFSb+1chatd7AAe4xMys43/QOpN/35V8LQ+Wpnzmg9HBHk1BOzsNyzOC5ZmvH8StX8K9YsOBHwefudcb16v1Ex9by8HMtNz+PiJgY6lYxPkfXX5KTiyZHP0CiSU3FxN+vMMiqUmHi50vWn8WzZ4syq1sLTFTknTyjtz3vWhip9zPA7rEa3B9NbBzZuw+UWOAMgOxsNEUWF9MkJWNSq0ZhdrNKhUmgP1m/P+SiuKHvEhqN4azCnFy0ObkorK0wrV2TrN9XFy9TQtRaDVGp8VR18SI0NkK3vaqLF5djbxrcJzIphiCPKpipTMhVF5xnLtb2aLQaUh9YnbOFT23aVK3HkpNbuZsqi4iUJ8Z+Bxni4uKCSqUqlhEWGxtbLHPsYZo2bcpvvxVmdnt4ePztYz4p+VYuSkTbtm0JCgpiypQp/PDDD0yaNInXX38dOzs7unXrRk5ODidPniQpKalYlBrAz8+PJUuWsH37dnx9fVm6dCnBwcH4+hZevZ00aRJjxozBzc2Nbt26kZaWxuHDh3XDRR/03HPP8emnnzJixAgmTZpEXFwc48aNY9iwYX/rDfXGG28wYsQIGjZsSMuWLVm2bBkXL16kSpXCCXOftO33V7/85Zdf8PT0JDIystiY8JkzZ+Lp6UndunVRKpWsXr0aDw8PHBwcdGVWr16tV68TJ04wf/583fPxzTff0KdPHz7//HMqVqxIZGQk69at491336VixYqPfH7vB9datGiBubk5jo6OvPvuuwwcOJD69evToUMH/vzzT9atW/fQVSrfeOMNvvrqK6pVq0ZgYCAzZswgOTlZ939bW1veeecd3nrrLTQaDS1btiQ1NZUjR45gY2NjdN64B1WtWpX8/Hy+//57evXqxeHDh/npp58euo+fnx/r1q2jV69eKBQKPv7447+VhViaFLY2KN1cUNyb60hZsSBDT5OYXC4yRqDgStDgdh1YuH0b3q5ueLu5sWj7NizMzOjSqHB1tk8XL8TVwYHX7gXUFm7fRo1KlfBydSU/X83hixfYfPwY7/+vcAhRZna23tXuuwnxXLl1C3trazycjM+/UxJydu7FomdnNLGxqGPisOjRBW1uHrnHC1cysxo9DE1SCtlrC4Ia+WcvYN65HerI26jDI1C6uWLRtyd5Ied1X4QtnulF3vlLaBOTwMICsyYNMAmoRsaMpzsXizGWpuZUtHfV3a9g50I1l4qkZmcQk55UhjUzLGf3ASy6dkQTG486Ng6Lrh3R5uaSe+K0rozVyMFoklPJ3rBZb1+z5k3IC7lgcMJ8kxr+gAJNTCxKNxcsn+mFOiaW3CPGV5AtKTm79mHRozOamLiCNnXvXPzcGzUMTVIy2feCefrn3k2Ubi5Y9O1BXsgF3blnOXQgZk0akP79PLTZ2SjsCjJUtFnZxSaeLmv/hL6vKIVCweD2HVm4fSvebu4FfeG2rff6wsIM9U8XLSjoC+8F1BZu20qNypXv9YX59/rCo7w/+PEuzJW0nO27sejVFU1MLOroWCx6dy14Tx0tPPetXhpZcP6t3gBAfsh5zLt2QH3zFuqwGyjd3bDo35u8M+d051/Ott3YfPwe5r26knf8FKqqPpi3a0nmguKZ8uLhFAoFg9u0ZeGuHXi7uuLt6sqiXTuwMDOlS/3CVXk/XbYEV3sHXutZEFBbuGsHNbwr4eXsQr46n8Ohl9gcfIL3nx2k22fWxvW0CqqJh6MjSenpzN+xnYzsbHo2alKsHiUtZ/8RLDq2RROXgDouAYuObQv6vtMhujJWQwqmQsjevENvX7OmDck7H4o2s0gGVE4umugY/W25uWgzMotvLwU5W3dh0bcHmugY1FGxWPTrjjYnl9wHhipbjR2FJjGJ7BUFc7Xlnz6LeffOqG9Eor4ejtLDDYuBfck7FaJ7P5nUDgKFAs3daJQeblg+9yzqqGhy9x0u1fYciTjPM7Xb6lbMbOgdgL2FDcGRBUPtO1ZvhJ25NevO7wPgfNR12latR99abdh77RRWZhZ09m/C6dtXdcMyW/rWpn21hqw5u4fkrDRszApGIeWq83QBt/+C0syielrMzMxo0KABO3fu1CUpAOzcuZM+ffo89nHOnDmDp6en7n6zZs3YuXOn3rxnO3bsoHnz0lsBXYJnosSMHz+e559/ngkTJjB69GisrKz45ptveO+997C2tqZWrVp6k7g/aMyYMYSEhDBo0KCCD//Bgxk7dixbt27VlRkxYgTZ2dnMnDmTd955BxcXFwYMGGDweFZWVmzfvp033niDRo0aYWVlRf/+/fUWNfgrBg0aRFhYGBMmTCA7O5v+/fvzyiuv6E14/6RtVyqVrFixgtdff52aNWvi7+/P7Nmzadu2ra6MjY0N06ZN49q1a6hUKho1asSWLVv0hlt+9tlnrFixgrFjx+Lh4cGyZcuoUaOG7vk4cOAAEyZM4JlnniEtLQ0vLy86dOigy0R71PM7ffp0xo8fz7x58/Dy8iIiIoK+ffvy3Xff8c033/D666/j6+vLwoUL9epe1Ntvv01UVBQjR45EqVTywgsv0K9fP1JSClPXJ0+ejJubG1OnTiU8PBwHBwfq16/PBx988FivU926dZkxYwbTpk1j4sSJtG7dmqlTp+plyBU1c+ZMXnjhBZo3b46LiwsTJkwg9SHDUcqSabNGWL/7uu5+wWTMkLVkBdkPTnBexoZ36kxOXi7TVi4nLTOTIB9fvn/tdb0r5dFJiXpfDLJzc5i2cjmxycmYm5pS2d2Dz0e+QOcGhV/8QyNvMua7mbr7M9cWXKnt0aQpk4aPLNU25WzdhcLMDMuhg1BYW6EOjyB9+g/wQJaQ0slJL9si+89taLVaLPr1ROlojzYtnbyzF8heW5itpLC3xfrF4Sjs7dBmZaO+fYeMGXPIv3SZ8iDAtTI/9CsM/r/esmCOsS2hR/lyj/GVhMtKzo49KMxMsRzcH4WVJeobkaTP/hlyHnydHItdxVe6uWJSrUrhHGNFKCwtsOjbA6WDA9rMTPLOnCNrw5aCq/+lLGfrLhSmplgOHVh47s34sci5p9+m7E3b0VIwfFPv3FtXmIFn3q4g49l2gv6MjZkLftP7IVce/FP6vqKGd+pCTm4u01YsK+wLx71ZvC9UFukLV/xObHLSA33hKDo3NLxQUmnL2byjoO8bMRiFlRXq8Bukfz1b//xzdtI//zZuKej7BvRG6ehQcP6dOaeb3wxAfeMmGbN/wvLZvlj06YEmPp6sZavJO1r6Ael/o+HtO5KTl8e0NatIy8okqLIP3495tci5llTkczeXaWtWEZty73PXzZ3Phw6nc70GujKxKcl8tHQRyRkZONrYULOyDwveHI9nKV+wAsjZc6Cg7xvQG4WlJeqbt0n/aSHkFE6roXR0KN6fuzpjUsWH9LkLSr2OTyrnj60Fn1EvDEVhbY36ejjpU2bAAxlqSpci76d1m9BqwWJQX5ROjmhT08g7dZbsBxZCUFhZYjG4f8H/0zMKFixasR7UpTtP2IXocCxNzWnrVx9bcyti0xL57dQ2UrILhtbamlthb1k433CuOp/FJ7fQI7A5LzfvR1ZuNheiw9l9rfBiUKNKNTBRqvhfPf05kPdeP8Xe66cR/yzjx49n2LBhNGzYkGbNmvHLL78QGRmpm0t84sSJ3LlzhyVLCuY5nDVrFj4+PgQFBZGbm8tvv/3G2rVrWbt2re6Yb7zxBq1bt2batGn06dOHjRs3smvXLoMjuEqKQvs4szcLIco1hULB+vXr6du3b1lX5T8vqdOTDUks7xx3rid1196yrkaJs+vYjuQXiq+i+k/msOAHWvw45tEF/0EOv/oTyWOKZ+z+kzn8NIPkUcUzpv/JHOZ//+/s+3bvL+tqlCi7Dm1IHv7v6iMAHJY8PLP8nyp1y45HF/oHsevemeS3Hu8i6D+Fw8wpJP9vVFlXo0Q5rJjPJ9vmlXU1StTnXV98dKFyas6RtY8uVAbGNu//xPvMmTOHr7/+mqioKGrWrMnMmTN10/iMHDmSiIgI9u3bB8DXX3/NL7/8wp07d7C0tCQoKIiJEyfSvXt3vWOuWbOGjz76iPDwcKpWrcqXX37JM8882ZQIT0Iyz4QQQgghhBBCCCHKESV/fVG38mbs2LF685s/aNGiRXr333vvPd57771HHnPAgAFGR6KVhn/PqyGEEEIIIYQQQgghRAmTzDMh/gVk9LUQQgghhBBCCFE6JHgmhBBCCCGEEEIIUY78G1bb/DeRYZtCCCGEEEIIIYQQQhghwTMhhBBCCCGEEEIIIYyQYZtCCCGEEEIIIYQQ5YhShm2WK5J5JoQQQgghhBBCCCGEERI8E0IIIYQQQgghhBDCCBm2KYQQQgghhBBCCFGOKJBhm+WJZJ4JIYQQQgghhBBCCGGEBM+EEEIIIYQQQgghhDBChm0KIYQQQgghhBBClCMKWW2zXJHMMyGEEEIIIYQQQgghjJDgmRBCCCGEEEIIIYQQRsiwTSGEEEIIIYQQQohyRCnDNssVyTwTQgghhBBCCCGEEMIICZ4JIYQQQgghhBBCCGGEDNsUQgghhBBCCCGEKEdktc3yRTLPhBBCCCGEEEIIIYQwQoJnQgghhBBCCCGEEEIYIcM2hRBCCCGEEEIIIcoRJTJsszxRaLVabVlXQgghhBBCCCGEEEIUWBK8payrYNDwRt3LugplQjLPhBCiBE3fv7ysq1Ci3m4zmBvxd8q6GiXO18WL/os/KOtqlKi1I6aQPGZ8WVejRDn8NIMWP44p62qUqMOv/sTzK78s62qUqIWDPiQ0Orysq1GiAj2qsPLMzrKuRokaVK8T+66fKutqlLi2fg04dSu0rKtRohp4B/LWH9+VdTVK1Mzeb/wr+/PPdy4o62qUqE86vUDfRe+XdTVK1IaRX5V1FcS/hATPhBBCCCGEEEIIIcoRWW2zfJEFA4QQQgghhBBCCCGEMEKCZ0IIIYQQQgghhBBCGCHDNoUQQgghhBBCCCHKERm2Wb5I5pkQQgghhBBCCCGEEEZI8EwIIYQQQgghhBBCCCNk2KYQQgghhBBCCCFEOaKUYZvlimSeCSGEEEIIIYQQQghhhATPhBBCCCGEEEIIIYQwQoZtCiGEEEIIIYQQQpQjCmTYZnkimWdCCCGEEEIIIYQQQhghwTMhhBBCCCGEEEIIIYyQYZtCCCGEEEIIIYQQ5Yistlm+SOaZEEIIIYQQQgghhBBGSPBMCCGEEEIIIYQQQggjZNimEEIIIYQQQgghRDmikGGb5YpkngkhhBBCCCGEEEIIYYQEz4QQQgghhBBCCCGEMEKGbQohhBBCCCGEEEKUIzJss3yRzDMhnqKIiAgUCgUhISGlcnyFQsGGDRueaJ+2bdvy5ptvPnb5RYsW4eDg8NAyly9fpmnTplhYWFC3bt0nqs9f9aTtKO3XQgghhBBCCCHEv4Nknon/jJEjR5KcnPzEwaWS5O3tTVRUFC4uLgDs27ePdu3akZSU9MiA1D/Jp59+irW1NVeuXMHGxqasq2NQ0deiLF3cd4Jz24+QmZKGYwU3mg3qime1ygbL3r1yg03TFxfbPvCzV3HwdC22/fqJ8+z5dS2V6/jT5dXBJV73+/5ct5E1v68kMSGByr4+jHn9VWrWrW20/LkzZ/nl+zncvBGBs4sLzw4ZRI9+vfXKrF+5hk3r/yAuJhY7B3tatW3N82NexMzcDIAVS37n8P6D3L4ZiZm5OTVqBfHCKy/iXblSqbWzi38T+gS1wtHKllvJsSw8sZnQ2Aij5Vv51qFvzdZ42jmTmZvNmbvXWHxyC+k5WQB81mU0NT2qFNvv1O3LTNm9pLSaUYxFzy6YtWyKwsoKdcRNMpevRRMVY7S8zfixmFT3K7Y97/wlMn78teCOuTmWvbthWrcmCltb1Lduk7VqA+qbt0qrGU+sjqcfQ+p1JsCtEi7WDry/ZS4Hb5wt62oZ1M6vAd38m+JgacOdlDh+P7OTa/GGn8tRjXvS0rdOse13UuL4aNsvAKgUSnoENqeFb20cLW2JSktg9dk9XIgOL9V2PGjL+k1sWLGGpMREvH0qM+q1lwmqU9Ng2cSERBb+OI+wq9eIun2XHv17M3rcGL0yRw8cZs1vK4m6cxd1fj6eFb3oM/AZ2nXp8DSaA8CJHQc49Odu0pNTcK3oSbfh/fEJLP5eAbh5OYwdv28k/m40eTl5OLg60bBDC5r3aK8ro85Xc2DjDkL2HyctKRlnT3c6D+lDtbo1nlaT2LdpJzvWbSIlMZkKlbwY+NJwqtUMMFj29OETHNiyi1vhN8nPy8ezshe9hvQnqIH++ZiZnsGGJas4cySYzPQMXNxdGTD6OWo1qvc0mgTAzo1b2LR6A8kJSXj5eDN87CgCagUZLJuUkMiynxZy41oY0Xei6NKvB8PHji5WbuvaP9j15zbiY+OxtbelSavmDBo9DDMzs9JuDgAtfGrTrmp97CysiU5LYMOFA4Qn3jVaXqVU0aV6YxpUDMDO3Irk7HR2Xg3mxK1LACgVSjpWa0gj70DsLWyITU9i06XDXI67+VTaA9CvZhuG1OuEs5U9NxLvMvvQas5GXTda/pmabehfqy2eds7EpCWy+NRWtl05rvu/SqlkeP2udAtohou1A5HJMcw9uo7jkZeeRnMMunrgNJd2nyArJR0HTxca9O+Am5+30fLqvHzObz3CjeCLZKdlYOVgS80uzajazPj3rtLWzb8pfWu2Lvh+lBTD/BObuPSQ70etq9SlX802VLBzJiM3mzN3rrLo5BbScjJ1ZXrVaEFX/6a4WDuQlpPBkYgLLD29jTx1/lNokRDFSfBMiKdIpVLh4eFR1tUodWFhYfTo0YPKlQ0HgMqD8vJahAVf4OjKbbQc0gN3v0qEHjjJ1tm/MXDSq9g4Oxjdb+Dk1zCzMNfdt7C1LlYmLSGZ42t24FGt9IJJAPt37eXn737k1bffIKh2TbZs+JOP3nmfX35biJuHe7Hy0Xej+PidiXTr1Z33PvmAi+cu8OP077B3cKBlu9YA7Nm+iwU/zWP8xPcIrBXEnchbTP/yawBefuNVAM6HnKXXM32oHuiPRq1h0S/z+fCt9/hl2UIsLC1LvJ3NfWrxfKMezDv+B5djb9K5emM+7DiCNzfOIj4jpVj5ALfKjGv5LIuCN3Py9mWcrOx4uWlfxjZ/hq/3LgPgm73LMFGqdPvYWlgxvdc4jkZcKPH6G2PeuT3mHdqQuXg56tg4LLp1wuaNMaR++hXk5BjcJ+OnRWBSWG+FtRW2H71D3unCwJPVsIGoKniSsfB3tCmpmDVpgM2bY0j97Gu0ycWfr7JgaWrO9YTbbLl8hCndxjx6hzLS2DuQIXU7sfT0Nq7F3aKtX33Gt/4fH277mcTM1GLlfz+zk9Xn9uruqxRKPu8ymuBbobptz9RqQ7PKtVh0cjNRqQnU9KjCuBYD+HL3YiKTjQdOS8qhPftZ8MPPvPzWqwTUrMH2P7cwecLHfL/4Z1zd3YqVz8vNw97BnmeH/o8/Vq83eEwbW1ueHToIr0remJiacPLoCb6fNgMHRwfqNW5Q2k3i/JFTbF28lp6jBlHJvwrBuw7x21dzeG36Rzi4OBUrb2ZuRpMurfGo5IWpuRmRV8L449cVmJmb0bBjSwB2r/yTs4eC6fPSEFwquHP9bCjLp8/jxc/H4+lr/Md1SQk+cJRV85YwZOwLVA2szoFtu/n+02lMmvsNTm7FLz5du3iZwHq16DtiEJbWVhzZtZ8fP/+W92dMplJVHwDy8/KZ9dFUbO3tePmDN3B0cSIpLgHzUui3jTm69xBL5i7ghddfpnpQALs3b2faxMl8M/97XNyLX4jKz8vD1sGePkOeZevaPwwe89Du/az4dSkvvfMa1YMCiLp9l5++mQ3AsLGjSrU9AHUrVKNvzdasObeXG4l3aV65Fi817cNXe38jOSvN4D4jGnTD1tyKlSG7iMtIxtbcCqWicGBS94BmNKgYwKqzu4lNT8TfrTLPN+7J7IOruJMaV+pt6uDXgDdaPsv0/cs5Fx1G36BWfNvrNYb+/hkx6UnFyvcNas2YZn2Ztvc3QmNvEujmw/vthpKWk8nhiPMAvNSkD12qN2Havt+4mRRNY+8aTO02hpfXfmP0gkRpijgVyqm1u2k0qDOuVby4diiEvXNW0/Oj0Vg72Rnc59CCjWSlZdD0uW7YujqSnZaBVqN5yjUv1MKnNi807snPxzZyOTaCLv5N+LjT84zbMMPg96NAt8q80XIgC4I3EXwrFGcrO8Y068erzfvz1d6lQEFwbViDrvxwaA2X4yKpYOfC6y2fBWBB8Kan2r6ypESGbZYnMmxTiHv2799P48aNMTc3x9PTk/fff5/8/MIrG23btuX111/nvffew8nJCQ8PDyZNmqR3jMuXL9OyZUssLCyoUaMGu3bt0htK+eBQwYiICNq1aweAo6MjCoWCkSNHAuDj48OsWbP0jl23bl29x7t27RqtW7fWPdbOnTsf2caMjAyGDx+OjY0Nnp6eTJ8+vViZ3Nxc3nvvPby8vLC2tqZJkybs27fvkce+T6FQcOrUKT7//HMUCgWTJk1i3759KBQKkpOTdeVCQkJQKBREREQAcPPmTXr16oWjoyPW1tYEBQWxZcsWXflLly7RvXt3bGxscHd3Z9iwYcTHxxutx2+//UbDhg2xtbXFw8ODIUOGEBsbq/u/oWGbT/oYJeHczqP4t6xPQKsGOHq60nxQN2wc7bm0/+RD97O0tcbK3lZ3Uyr1u3ONRsOeX9fSoHc77FwcS7MJrFu5mi49u9Gtdw8q+VRmzJuv4ermxqb1hn9gbN7wJ27ubox58zUq+VSmW+8edO7RjTXLV+nKhF64SFCtmrTr3AEPTw8aNGlE207tuXr5qq7MlzOm0blHV3yq+FKlWlXGf/AesTGxXLty1dDD/m29arRkz/VT7L52kjspcSwM3kxCRgpd/JsYLF/d1Zu4jCS2XD5KbHoSl2NvsuPqCao6e+nKpOdmkZydrrvV9vQjJz+PIzfPl0obDDHv0JrsrbvICzmP5m40mYt/R2Fmhlnj+kb30WZmok1N091MA/0hN4/cU/eCZ6ammNarTda6P1FfD0cTF0/2pu1o4hMxb938KbXs0Y5FXmTe8T/YHx5S1lV5qM7+TThwI4QD4SFEpSWw/MxOErNSaV/V8GuUlZdDanaG7ubj5ImVmSWHHsiqa+ZTi02hhzkXFUZcRjJ7w05zITqcrkbO55K2cdV6OnbvTKeeXfH2qcTocWNwcXVl28bNBsu7e7oz+vUxtOvaESub4hcLAGrVq03T1i3w9qmEp1cFeg3oi08VXy6dv1iaTdE5snkP9ds1o0H75rh6edB9xADsnB0J3nnQYHlPX29qt2iIm7cnjm7O1GnVGL/agdy8HKYrc/bQCVr37Uz1ekE4ubvQuHMr/OoEcnjznqfSpl3rt9Cic1tadmmHZyUvBr00HEcXZ/Zv2WWw/KCXhtNlQC98qlfF3cuTfiP+h1sFD84dP60rc3jnPjLS0hn78Xj8avjj7OaKX1AA3lWe3gW3LWs30rZrR9p174RXZW+Gjx2Ns5sLu/7cZrC8q4c7I14dTevO7bCytjJY5tqlK1SvGUCLDm1w9XCndsN6NG/XivCrxrOkSlLbqvU5HnmR45EXiU1PYsPFAyRnpdPCp5bB8gGulfFzqci84xu5Gn+LpKw0IpNjiEiK0pVp6B3ArmvBhMZGkJCZypGI81yJvUlbP+OfDyVpUN2ObAo9zJ+hh7mZFM13h1YTm5ZEv5ptDJbv6t+EjRcPsvv6Ke6mxrP7+kk2hR7muXpd9MosObWVozcvcDc1ng0XD3A88hKD63Z8Km0q6vKeYKo2q41f8zrYe7jQcEBHrBxtuXrwjMHydy+FE3P9Fu1eeRbPAB9snO1x8amAa5WKT7nmhfoEtWTXtZPsuhbM7ZQ45p/YRHxGCl39mxosX921EnHpSWwOPUJsehKhsTfZceUEfi6F34/8XStxOeYmB26cJTY9iZC71zgYflavjBBPmwTPhADu3LlD9+7dadSoEWfPnmXu3LnMnz+fL774Qq/c4sWLsba25vjx43z99dd8/vnnuqCVRqOhb9++WFlZcfz4cX755Rc+/PBDo4/p7e3N2rVrAbhy5QpRUVF89913j1VfjUbDM888g0ql4tixY/z0009MmDDhkfu9++677N27l/Xr17Njxw727dvHqVOn9Mo8//zzHD58mBUrVnDu3DmeffZZunbtyrVr1x6rblFRUQQFBfH2228TFRXFO++881j7vfrqq+Tk5HDgwAHOnz/PtGnTdEM+o6KiaNOmDXXr1uXkyZNs27aNmJgYBg4caPR4ubm5TJ48mbNnz7JhwwZu3LihC04aq/eTPsbfpc7PJz7yLhVrVNXbXrFGVWLCHn71c93kn1n6zrdsmrGYu5dvFPv/6U37sbS1JqBl6X7BzcvL49qVq9Rv3FBve/3GDQm9YPgHa+iFi8XKN2jSkGuXr+gC1kF1anHtylWuXCrIlIm6c5fgo8dp3Nz4D/vMjAwAbO0MX6n9O0yUKqo6VyDkrv774Ozd6/i7Gv7BdyU2Emcre+p7VQfA3sKGZpVrcur2FaOP06FaQw5HnCMnP6/kKv8QShcnlPZ25Ic+UKd8NfnXwjCp4vPYxzFr0YTck2cgN/fegZUoVCrI0x9aoc3Lw8TPtwRq/t+hUirxcfTkYrT++/xidDhVXR7vx1Jr37pcirlBwgNZaqZKVbGhL7nqfKq5ln42U15eHmFXr1G3kX7/VLdRfS5fKJmhU1qtlrOnznDn1m2CahseClqS8vPzibpxi6q1A/W2+9UOJPJq8T7akKgbt7h1NRyfGtUKj5uXj4mpqV45UzNTIh8IsJWW/Lx8Iq/foEY9/aFgNerXIiz08S5SaDQasrOysX4gO/rc8VNUCajG73MW8s5zY/hs7HtsWbkBjfrpZM7k5+Vx42oYtRvW1dteq0Fdrl66/JeP618zkBtXw7h+7yJPzN1oQk6cpl6Tho/Y8+9TKZRUtHfjSmyk3vYrcTfxcfQ0uE+QRxVuJcfQ3q8hn3YaxcT2w+ldoyWmD2RDmyhV5GvUevvlqfOp4lSh5BtRhIlShb9rJU5EhuptP3Er1OB0BwCmKhNyi3x+5uTnUcPdB9W9i4ymKhNy1cXL1PY0PLy6NKnz1STeisYzUP9z0TPQl/gbdwzuc/v8dZwreXBp13HWffgjf3z2C6fX7SE/9+l8byiq4PuRV7HvRyF3rxHgZvj70eXYmzhb29PAyx+49/3IpyYnbxe+/0JjI6jq4kW1e59z7jZO1K/or1dGiKdNhm0KAcyZMwdvb29++OEHFAoFAQEB3L17lwkTJvDJJ5/osnpq167Np59+CkC1atX44Ycf2L17N506dWLHjh2EhYWxb98+3XDAL7/8kk6dOhl8TJVKhZNTwTAONze3J5rzbNeuXYSGhhIREUHFigUfKlOmTKFbt25G90lPT2f+/PksWbJEV6fFixfr9oeC4ZbLly/n9u3bVKhQ8MXonXfeYdu2bSxcuJApU6Y8sm4eHh6YmJhgY2PzRMMiIyMj6d+/P7VqFVwhrVKl8IvR3LlzqV+/vt7jL1iwAG9vb65evUr16tWLHe+FF17Q/V2lShVmz55N48aNSU9PNzgP25M+Rk5ODjlFhrSZm5vzJLLTM9FqtFja6WdRWNpZk5mabnAfK3tbWg3rhWslT9T5aq4dO8ummYvp9fZIPKv7ABB9PZIrh07T/+PSH4aWmpyCRq3B0Uk/u83R0ZHEhESD+yQlJuHoWKS8kyNqtZqU5BScXZxp27E9KUnJvP3KG2i1WtRqNT379WbQsCEGj6nVavl59hyCatfCp0rJB2dsza1QKVWkZOm/LsnZaThYVjO4z5W4SGYdXMX4NoMxVZlgolRxIvIS84//abC8n0tFKjt6MOfIuhKvvzGKe4FGTar+kB5NahpKp8fLWFT5VELl5Unm0pWFG3NyyA+7gUWPTmRExxRkpzWqj8qnEprY0s3m/LexNbNCpVSSmq1/7qVkZ1DT4tFzStpb2FDLsyo/H9ugt/1CdDhd/JtwNS6S2PQkAt19qedVHeVTWNkrLSUVjVqDQ5FzzN7RgaTE4kOxnkRGegajBgwlLzcPpUrJy2++WixIVxoyU9PRaDTY2Nvqbbe2tyU9ufjQ2gd9O/YjMlLT0ajVtBvQnQbtC7Mz/WoHcmTLHnwC/XB0dyH8whUunzyHRqMtlXY8KD01DY1Gg52Dvd52Wwd7UpMeb+j1zvWbyc3OoUGrwgyUuOhYEmIu0aRtC8ZNeo/Yu9Esn7sIjVpDzyHPlGgbDElLKWiXvaOD3nZ7R3tS/sb517xdK9KSU/jszQ/g3udWx15d6T24/9+s8aNZm1miUir15osCSMvJws7CcKams7Udvk4VyFOrWRi8CWszSwbUboeVmQUrQgoyCy/HRtK2Sj3CEu6QkJFMNddK1PSo8lT6CQcLG0yUKhKz9N8/SVmpOFsZvkh24tYletZoyYEbZ7kSF0mAayV6BDbHVGWCg4UNCZmpHI+8xP/qdiTk7nXupMTRsGIArXzroFQ+/eFxOfe+B1rY6mczWthak5WaYXCf9PhkYsNuozQxofWL/cjJyCJ45Q5yMrNpNrT706i2nvvfj4oODU7JSsPRsvj3cyj4fjTjwAreaTtE9/3oeOQl5h0rHLFw6MY57M1tmNJtDAqFAhOliq2Xj7Lu/P5SbU95o1BIrlN5IsEzIYDQ0FCaNWumtxxwixYtSE9P5/bt21SqVDBnVO3a+ldfPT09dUMBr1y5gre3t17AqHHjxqVW30qVKukFvpo1a/bQfcLCwsjNzdUr5+TkhL+/v+7+6dOn0Wq1BgNFzs7OJVR7w15//XVeeeUVduzYQceOHenfv7/u+T516hR79+41GPQKCwszGDw7c+YMkyZNIiQkhMTERDT35oKIjIykRo3iEy0/6WNMnTqVzz77TG/bp59+im07f56Uosh8BtqH/CZy8HDBwaNwnhn3qt6kJ6VydscRPKv7kJudw97562g1rLfBedBKTZEv0lq0D19eu2h57f3NBdvPng5hxZJlvPr2GwQEBXL39h1++u5HHBcu5bnnhxU73I8zZnMjLJzpc2f/vXY8ghb9F6foa/egivZujGrck9Vn9xBy9xqOlrYMb9CVl5v1NRgg6+DXkJtJ0VyPv13i9b7PtHF9rIY8q7uffn9y/6In3RP8MDJr3gT1nSjUEfoZD5kLf8dq+P+wnzYJrVqN+tYd8oLPoKokQy7+iqLdQsG59+gASkvf2mTmZXP6jn7G4+9ndjKyYXemdBuDFohNT+LQjbMGFxooPUXPs0f0G4/B0sqSmb/+SFZWFudOh7BgzjzcK3hSq95Tmki7WPUf3aZRk94kNzuHW9ci2Ll8I04ertRuUZCt1H3kADb+spzZ4yejUChwdHehXtumnNl3rHTqb0jxl8lAO4s7se8Im5atY+zH4/UCcFqNFlsHO4aOG41SpaRytSokJyaxY+3mpxI80zHYrr9+/l0KOc+G39fwwusvUzWgGjF3o1ny46+sc17JM0MH/a2qPq6in1FQcHHJECUKtMBvp7eRnV+QNbzh4gFGNuzB2nN7ydOoWX9hP4PqdGBi+2FotZCQmcKJW5do7P30FqwoXn+F0Z5vYfAWnKzs+KX/BFBAUmYaWy4fZWj9LqjvHee7g6uY0G4ovw+ZhBYtd1Pi2Hz5CD0CynJKgSLnnVZr9FTUagv6lBYje2FmWXDRVv1Mew7O30CjgZ0wMTM1vOPTplAYPB+h4PvRi016szJkN2fuXsXR0paRDbvzSrN+/HCkYFROTY8qDKjTjp+PbeRaXCQedi6MbtyLpNpprDr3dIatC1GUBM+EoPCDqOg2QG+7aZGhEwqFQheUMXSMv0qpVBb7spCXV5iObeiL0KMe29iXpwdpNBpUKhWnTp1CpVLp/e/vrJp5P3PvwTo82B6A0aNH06VLFzZv3syOHTuYOnUq06dPZ9y4cWg0Gnr16sW0adOKHdvTs/hwhIyMDDp37kznzp357bffcHV1JTIyki5dupB7f1hZEU/6GBMnTmT8+PF628zNzfnh2ONnDVnYWKFQKoplmWWnZWBl9/jPt5tvRa4fPwdAalwiaQnJbP/xd93/7z/v88Z8xqDPx2HnVnzi6r/KzsEepUpJUpEss+Sk5GLZaPc5OjmSlFi0fBIqlQo7+4KryUvmLaR9l050690DAN+qVcjOzmb2tBkMHvGc3hxvc2bM5tihI3z74yxc3YpP9FwS0nIyUWvUOFjqZ5XYW9iQnGU4S/CZWm24HHuTjRcL5ju6mRRNdn4uX3Z7md/P7NS7SmumMqWFb21WhhieR6ik5J29SNqNB4Jc9yb9V9rboX4g+0xpa4M21fAE03pMTTFrVJcsA/MEaeITSJ/xI5iZobAwR5uahtXoYWjiDWckCsPScjNRazTYF8kys7OwIiXbcGbCg1r51uFIxHnURSaTTsvJ5PvDazBRqrAxtyI5K41na7cjPiO5JKtvkK29HUqVkuQi/UBKUgoORbKBnpRSqcSzYkHmdJVqVbl98xZrl60s9eCZlZ0NSqWS9GT9901GSjrWRbLRinK8N/G+eyUv0lPS2Ltmiy54Zm1ny5B3XiIvN4+s9AxsHe3Z+ftGHNxK94IWgI1dwXyaRbPM0lJSimWjFRV84ChLZv/Cy++/QWA9/Tm37J0cUKlUKFWF/bintxepScn3hqmW7s8T23vzhKYkJuttT0lOKZaN9iRWL/qdlh3b0q57QXZ/pSo+5GRn8+vMOfQd8myxuUlLUkZuFmqNBjtz/YtmtuaWxbLR7kvNySAlO10XOAOISUtEqVBgb2lLfEYyGblZLAjehIlShbWZBSnZGfQMbGFwoZKSlpydTr5GjbOV/rnmaGlr9PFz1XlM3bOUr/ctw8nSjoTMFHrXaEVGbpYuczw5O52JW3/CTGWCnYUN8RnJvNKsH1FpTz8r2vze98DsNP2+PDs90+gFUEs7GyztbXSBMwB7D2fQQmZyWol+x3scf+X70YDabQmNjWDDxQNAwfejn49tYGr3V1h2ZgdJWWkMqdeJfWGn2XUtTy5RrwABAABJREFUuKBMcgwWJqaMbf4Mq8/tNRqYE6I0SR6gEECNGjU4cuSIXnDnyJEj2Nra4uX1eFkSAQEBREZGEhNTuEpZcHDwQ/e5v3S5Wq0/n4SrqytRUYUTtqampnLjRuGcKTVq1CAyMpK7dwuXHz969OhDH8vPzw9TU1OOHSu8Wp2UlMTVq4XzltSrVw+1Wk1sbCx+fn56t7+zMqWra0FA48E2PThR/33e3t6MGTOGdevW8fbbbzNv3jwA6tevz8WLF/Hx8SlWL2vr4l8uLl++THx8PF999RWtWrUiICBAb7EAQ570MczNzbGzs9O7PemwTZWJCS6VKnDnkv7cNbdDw3Cv+vjzDiXcisLKvuCHtYOHCwM+fYX+H4/R3SrX9qeCvy/9Px5jdOWmv8rU1JRq/tU5E6w/d96Z4FME1gwyuE9gzaBi5U+fOEm1AH9MTAp+NOXkZBf7oXE/qHz/farVavlx+ncc3n+QabOn41HB8LwuJSFfoyYs4S51isyJUruCH1fibhrcx9zEtFjQWqMtCGAUDXW38KmFqUrF/nDDEwSXmJwcNHHxhbeoGDQpqZgEPpBZqVJhUq0q+eERjzycWcO6YGJC3vFTxgvl5qJNTUNhZYlpjQDyzj69lUT/DdQaDRFJUQR56A9HruHuS9gjshT9XSvhbuvEwQcWCigqX6MmOSsNlUJJg4oBnLlTOgtuPMjU1JSq1asRclL/fA85eZqAmiWb0aLVaotdrCkNJiYmePp6E3Zefz6esPOXqVT9CYaSa7Woi8wVCAXznNk5OaBRa7h0IoSABqWfSWdiakIlP19Cz+gvYBJ65gJVAw0Px4KCjLPFM39i9LuvUqtxvWL/r1qjOnFRMbqLjwAxd6Kwd3Io9cAZgImpKb7Vq3L+VIje9gunQqheI+AvHzcnJ6fY0L+Czy0enlJeAtRaDbdTYqnuqr+6dnXXSnoLADzoRmIU9ubWmKkKLwy72Tii0WpIKTIEL1+jJiU7A6VCSe0KfpyPDi/5RhSRr1FzJS6SRt768wg28g7kwiMeX63REJeRjEarpWO1hhyOOF8s2JKrzic+IxmVUknbqvUe2k+WFpWJCidvD6IuR+htj7ocgYuv4d8frlW8yEpJJy+nMOiZGpuEQqHAyuHhgfrSUPD96A51K+h/P6pbwY/LsUa+H6nMDHw/0hYpY+g7VEHa61MYNVxuKBWKcnn7r5LgmfhPSUlJISQkRO8WGRnJ2LFjuXXrFuPGjePy5cts3LiRTz/9lPHjxz/2lcJOnTpRtWpVRowYwblz5zh8+LBuwQBjWWGVK1dGoVCwadMm4uLiSE8vuELTvn17li5dysGDB7lw4QIjRozQywTr2LEj/v7+DB8+nLNnz3Lw4MGHLk4ABZljo0aN4t1332X37t1cuHCBkSNH6rWvevXqPPfccwwfPpx169Zx48YNgoODmTZtmt7Kl0/Kz88Pb29vJk2axNWrV9m8eXOxlT7ffPNNtm/fzo0bNzh9+jR79uwhMLDgC9Orr75KYmIigwcP5sSJE4SHh7Njxw5eeOGFYoFHgEqVKmFmZsb3339PeHg4f/zxB5MnT35oHZ/0MUpK7U7NuHzoNJcPnSYpKo4jK7eRnphCYJuCrIMT63axd0FhNtv5XUeJOBNKSkwCiXdjObFuFzdOhxLUrmCIsImpKU5e7no3cysLTM3NcPJyR2VS8j9Mnhn0LNv+3ML2TVuJjLjJz9/9SGxMDD369QJgwdx5fDN5qq58j769iImO4efZc4iMuMn2TVvZvmkrAwYXLs7QpEUzNq//g3279hB9N4rTJ06yZN5CmrZsrnsv/Dj9O/bs2MWESR9haWVFYkIiiQmJxeaiKyl/XjpEh2oNae/XAC97V0Y26o6LtT07rpwA4Ln6nRnXcoCu/Mlbl2lSOYgu/k1wt3HE37USoxr34mpcwapmD2pfrSEnIkNJz8kqlbo/TM7uA1h07Yhp3VooK3hgNWIw2txcck8Uro5nNXIwFn17FNvXrHkT8kIuoM0ontlgUsMfkxoBKJ2dMAmsjs1bY1HHxJJ75ESptudJWJqaU82lom5C4gp2LlRzqYi7TemuUPukdlw5TmvfurTyrYOnrTP/q9sRZyt79oYVvEYDarVldJNexfZrXaUuYQl3uJMSV+x/VZwq0MDLH1drB6q5eDO+zf9QKBRsufzwCzElpc/AfuzavJ1dm7dzKyKS+T/8THxsHF16F8zZs/SXhcz68lu9fcKvhRF+LYzsrGxSk1MIvxbGrYjCH2drfltJSPBpou9GcfvmLTauXMe+7btp26n9U2lT8x7tOb3nCKf3HiXuTjRbF68lJT6RRh1bAbBz+UbW/rhEV/749v1cPnWehKhYEqJiOb3vKIc37aZ2q0a6MreuRXDpRAiJMfFEhF5nydQf0Wq1tOz9dFYG7NivO4d27OXwjn1ERd5h1S9LSYyLp3X3DgCsX7SChdPn6Mqf2HeEhTPmMmDUUHz9q5GSmExKYjJZD/QRbbp3Ij0tnZU/LyHmThTnT5xh66qNtO3R+am0CaB7/z7s3bqLfVt3cefmLZbOmU98bDwdehWsyrji16XM+WqW3j4R18OJuB5OdnY2qcmpRFwP5/bNwsV96jdtxK4/t3Fk70Fio2I4fyqE1Yt+p0GzRiiLZPOXhn1hp2laOYjG3jVws3Gkb1BrHC1tORJREPzsEdicIfUKn+NTt6+QkZfN4HqdcLdxoopTBXrVaMnxyEvk3VskoJKDO7U8q+JsZUcVpwq83LQvShTsuf7wFcFLysqQXfSq0YIegc2p7OjB6y2exd3WkfX3MpbGNO3LRx1G6sp727vRuXpjKtq7Eejmw2edR1HFuQI/H9uoK1PD3Yc2VepSwc6FOp5+zOj1OgoULDu946m0qaiA9o0IO3KWsKPnSImO59Ta3WQmplKtVV0Azmzcz5Elm3TlfRrVwNzakmO/bSElKp6Y67c4s34vVZrVKrMhmxsvHqJjtUZ08GtIRXtXXmjUExdrB7ZfOQ7A0PpdeKNl4Xe84NuhNK1ck67+TXC3cSLArTKjm/Tialyk7vtR8O3LdPVvSkvf2rjZOFLH048h9ToRfOtSsUCbEE+LDNsU/yn79u2jXj39q6AjRoxg0aJFbNmyhXfffZc6derg5OTEqFGj+Oijjx772CqVig0bNjB69GgaNWpElSpV+Oabb+jVqxcWFhYG9/Hy8uKzzz7j/fff5/nnn2f48OEsWrSIiRMnEh4eTs+ePbG3t2fy5Ml6mWdKpZL169czatQoGjdujI+PD7Nnz6Zr164PreM333xDeno6vXv3xtbWlrfffpuUFP3hGAsXLuSLL77g7bff5s6dOzg7O9OsWTO6d//rk5CampqyfPlyXnnlFerUqUOjRo344osvePbZwrmX1Go1r776Krdv38bOzo6uXbsyc+ZMACpUqMDhw4eZMGECXbp0IScnh8qVK9O1a1eDwU1XV1cWLVrEBx98wOzZs6lfvz7ffvstvXv3NlrHJ32MklK1UU2yMzI5vXk/mSnpOFVwo9u457B1dgAgMyWN9MTC10idr+bYmh1kJKdhYmqCYwU3uo4bQqVaxrMASlubju1ITU1l2cIlJCUkUrmKD5O/nYr7vWzFxIREYmMKM/88Kngy+dup/Dz7Rzat24iTizOvvPkaLdu11pUZMmIYCoWCxb8sICEuHntHB5q0aMbIl0bpymxaXzCx7HuvvaVXn/EfvEfnHg9/L/wVRyLOY2tuxbN12uNoaUtkcgxTdi8m7t4wN0dLW1z+z959R0VxvQ0c/y69914UEAUEe+9d7JrE3o2JMRqNMZpE/RljjJqiiSaxx967xl6xN+wNG4pYQJDe675/oIsrLGoEIXmfzzl7Djt77+y9u8PM7DPPvWNsoSofEHweA119WnvXpl/11iSlp3IlLJgV5/eordfRzJry9m5M3Luo0Nv8OtL2HkShp4thjw9QGBmSdS+UxN/nwQtBSC0ryzyZE1p2tuiU9SBx5tx816swNMCgU1u0LCxQJieTceEyKVt2wkvDB4uTt21p/nwvd/j18Po5+6SdQSeZfHBpcTUrjzMPgjDWN6KDb33MDUx4FBfJb0fXqO6eaW5okmdok6GuPtVcvFl1If8fhLraOrxXoRF2JpakZqZzOewOC079TUpG0QSfX1a/aSPi4xJYu2wVMVHRlHJ3Y/xP32PnYA/k7DciX8oYHvnRZ6q/g2/e5sj+Q9g62LFgbc53lZaayrzfZhEV+RQ9fT2cS7nyxf9GU79po3fSpwp1q5GSmMShjbtIiI3HztWR3t8MwcI2ZxhVQkw8cS8MW1Yqlexf/TcxkVFoaWlhZW9Dix4dqd68nqpMZkYGB9ZuJybiKXoG+pSt7MsHQ/tiaGyU5/2LQo2GdUiKT2TH6k3ERcfiVNqFzyZ+hfWzIfJx0bFER0apyh/dfYDsrCxWz1nM6jmLVcvrNGtI/5E5N7GxsrXm80nfsH7BCr4f+g0W1pY07dCKVp01H6MLW50m9UmMj2fTirXERsfg4laKr6aMx9beDoDY6GiiItSDzmMH5+4r7t0K5sTBI9jY2/L7ypws+fd6d0WhULB+8Uqin0ZjZm5G1To16Pphr3fSp4uPb2OsZ4i/Vy3M9I0IS4hi/qmtqmCEmb4xli8MrUvPymDuyc28X6ExIxt2JykjlYuPb7Mr6ISqjK62Dm2862BtZE5aZgZBESGsPL9HbahnUTpw5xxmBiYMqN4Wa2Mz7kY9ZtS2P3mSkPN/ZG1kjr1p7jBFLS0telRuTikLBzKzszj/6CaDN/5CeELuNqqnrcvHtTriZGZDSkYaJ+9fZdK+xSSmv/uLVwBu1XxIT0rhyq7jpMQnYeFoQ+MhXTCxytmnp8YnkhT9wp2S9fVo+lk3zq7fx66fl6JvbEipqt5UategWNoPcDzkMmb6RnSr3Czn/CgmnEn7l6jOj6yMzLA1sVCVP3jnHIY6+rTxrsuAGm1JSk/lclgwy87tUpVZd+kgSqWSXlVaYmVkTnxqEoEPglh5YQ9CFBeF8nUmQhJC/CPHjx+nfv363LlzhzJlyhR3c8QLbt68ibe3N7dv38bTs/BuTz798OpCW1dJ8GWjHtx7mv/t0v/N3G2c+WDp2OJuRqHa2G8KsYNHvrrgv4jF3F+pN6vo7xr7Lh0fOpcBaycXdzMK1eJu4wh6B8O43iUfBw/WXthX3M0oVN2qtODQnQKGWP9LNfasxrkHQcXdjEJVzdWHL/6eWdzNKFS/dfj8P7k//35f8Vz4KirftviQTku+Ke5mFKot/X8s7ib8Y1uvHinuJuSro1/DVxf6D5LMMyEK0ebNmzExMaFs2bLcuXOHzz//nHr16kngrISJjo5mw4YNmJmZ4er6+nOLCSGEEEIIIYT4/0eCZ0IUooSEBL766isePHiAjY0NzZs3zzO3lyh+AwcO5Ny5c8yZM+eNJ/kXQgghhBBCCPH/iwTPhChEffv2pW/fvsXdDPEKmzdvLu4mCCGEEEIIIYRGijz3ZxfFSe62KYQQQgghhBBCCCGEBhI8E0IIIYQQQgghhBBCAxm2KYQQQgghhBBCCFGCaClk2GZJIplnQgghhBBCCCGEEEJoIMEzIYQQQgghhBBCCCE0kGGbQgghhBBCCCGEECWIQoZtliiSeSaEEEIIIYQQQgghhAYSPBNCCCGEEEIIIYQQQgMZtimEEEIIIYQQQghRgsiwzZJFMs+EEEIIIYQQQgghhNBAgmdCCCGEEEIIIYQQQmggwzaFEEIIIYQQQgghShAtZNhmSSKZZ0IIIYQQQgghhBBCaCDBMyGEEEIIIYQQQgghNJBhm0IIIYQQQgghhBAliNxts2SRzDMhhBBCCCGEEEIIITSQ4JkQQgghhBBCCCGEEBrIsE0hhBBCCCGEEEKIEkRLhm2WKJJ5JoQQQgghhBBCCCGEBgqlUqks7kYIIYQQQgghhBBCiBz7bp4u7ibkq4VXreJuQrGQYZtCCFGIvt4xu7ibUKh+ajuEa2HBxd2MQufrWIaGs4cUdzMK1ZEhs4kdOKy4m1GoLBb+wYC1k4u7GYVqcbdx1Js1uLibUaiOD51LQkREcTejUJna2bEkcEdxN6NQ9a/RllHb/ijuZhS6ae2HMf3w6uJuRqH6slEP+q2ZVNzNKFRLu4+n87Jxxd2MQrWh72Q6LfmmuJtRqLb0/5Fm84YXdzMK1YFPfi/uJvxjcrfNkkWGbQohhBBCCCGEEEIIoYEEz4QQQgghhBBCCCGE0ECGbQohhBBCCCGEEEKUIApk2GZJIplnQgghhBBCCCGEEEJoIMEzIYQQQgghhBBCCCE0kGGbQgghhBBCCCGEECWIltxts0SRzDMhhBBCCCGEEEIIITSQ4JkQQgghhBBCCCGEEBpI8EwIIYQQQgghhBCiBFEoFCXy8U/Mnj0bd3d3DAwMqFatGkePHtVYdtOmTbRo0QJbW1vMzMyoU6cOe/bsUSuzZMmSfNuWmpr6j9r3OiR4JoQQQgghhBBCCCEK3dq1axkxYgTjxo3jwoULNGjQgNatWxMaGppv+SNHjtCiRQt27tzJuXPnaNKkCe3bt+fChQtq5czMzAgLC1N7GBgYFFk/5IYBQgghhBBCCCGEEOKV0tLSSEtLU1umr6+Pvr5+vuV//fVXBg4cyEcffQTAjBkz2LNnD3PmzGHq1Kl5ys+YMUPt+ZQpU9i6dSvbtm2jSpUqquUKhQIHB4e37M3rk8wzIYQQQgghhBBCiBJES6EokY+pU6dibm6u9sgvCAaQnp7OuXPnaNmypdryli1bcuLEidf6HLKzs0lISMDKykpteWJiIqVLl8bFxYV27drlyUwrbJJ5JoQQQgghhBBCCCFeacyYMYwcOVJtmaass6dPn5KVlYW9vb3acnt7e8LDw1/r/aZPn05SUhJdu3ZVLfP29mbJkiVUqFCB+Ph4Zs6cSb169bh06RJly5Z9wx69HgmeCSGEEEIIIYQQQohXKmiIpiYv32hAqVS+1s0HVq9ezXfffcfWrVuxs7NTLa9duza1a9dWPa9Xrx5Vq1bljz/+4Pfff3+jtr0uCZ4JIYQQQgghhBBClCAK/tmdLUsSGxsbtLW182SZRURE5MlGe9natWsZOHAg69evp3nz5gWW1dLSokaNGty+ffut26zxPYpszUIIIYQQQgghhBDi/yU9PT2qVavGvn371Jbv27ePunXraqy3evVq+vfvz6pVq2jbtu0r30epVHLx4kUcHR3fus2aSOaZEEIIIYQQQgghhCh0I0eOpE+fPlSvXp06deowf/58QkNDGTx4MJAzh9qjR49YtmwZkBM469u3LzNnzqR27dqqrDVDQ0PMzc0BmDhxIrVr16Zs2bLEx8fz+++/c/HiRWbNmlVk/ZDgmRBCCCGEEEIIIUQJolD8NwYKduvWjaioKL7//nvCwsLw8/Nj586dlC5dGoCwsDBCQ0NV5efNm0dmZiZDhw5l6NChquX9+vVjyZIlAMTGxjJo0CDCw8MxNzenSpUqHDlyhJo1axZZPyR4Jv6RJ0+eMG/ePIYNG4alpWVxN0cIIYQQQgghhBAl0JAhQxgyZEi+rz0PiD136NChV67vt99+47fffiuElr2+/0YoU7xTSqWSPn36oK+v/8aBMzc3N2bMmFE0DXtLCoWCLVu2vFGdxo0bM2LEiCJpz79Vnz59mDJlSnE3462MGjWK4cOHF3czhBBCCCGEEEKUAJJ59g7079+fpUuXAqCjo4Orqyvvv/8+EydOxNjYuJhb9+amTp2Kh4cHX3/99RvXDQwM/Ff2Wbyey5cvs2PHDmbPnq1a1rhxYw4fPszUqVP55ptv1Mq3adOGXbt2MWHCBPr374+7u3uB658wYQLfffcdAEuXLmXWrFlcu3YNLS0tqlSpwldffUW7du0ASExMxNLSkhUrVtCtWzfVOrp168a6deu4c+cOZcqUUS0vU6YM3bp1Y8qUKXz11VeUKVOGL7744pVtKgy1S/vSyKMKpvpGPEmMZtu144TEhGksr62lRfOyNajiVA5TfSPiUhM5eOccZx/eUJUx0NHD36sWfg4eGOrqE5OSwPbrx7kZGapxvYVp15btbF2zkZioaFzdS/PhZ4MoX9Ev37LRUdEsnb2A4Ft3CHv4mDbvd2DgsE80rvvYgcP8OuknatarzTeTvy2qLuSrk29DelRpjpWROSHRYfxxfD2Xw4I1ln/PryHvV2iMg6kVTxJiWH5+N3tunlYr06ViEzr6NsTe1JK41CQOBZ9n/qmtpGdlFnV3VAw6tEavUT0URoZk3b1P8sp1ZD8OL7COfvPG6DWpj5aVJcrEJNLPXiR149+QmdNu/TYt0K1aCW1He5TpGWQF3yNl/Vayn0QUeX+aeFajtVdtLAxNeBQXyaoL+7j99EG+ZQfWbEd990p5lj+Ki+R/u+cDoK3Qoq1PXeq5V8TS0JSwhCjWXzrI1fC7RdqPf6KSoyc9q7TE264UNsYWfLNzDkfvXSruZgE5F9/mL17M5r//JiEhAd/y5fl65EjKvGI/e+DQIeb+9RcPHz/GxcmJIYMG0aRhQ9Xr8xYtYsHixWp1rK2s2LN1q+r5wcOH2bR1K0G3bhEXF8fKRYvwKlu2cDsInNt3nNM7A0iMjcfW2YHmvTvh6u3xynoPb91jxQ+zsHVxYOCUUarlNwMvc+Lv/cQ8eUp2VjaW9jbUbNOYCvWrF3rbNalbugKNPatgqm/Mk4Rotl47yr3oxxrLa2tp0aJcTao5e2Gqb0xsaiIHbgcS+CBIVaaBeyXquFXA0tCUpPQULofdYWfQSTKzs95FlwC4dugMl/ecIDkuAUsnO+p0a4Vj2dL5ln188x7bpy/Ns7zrxKFYONrmWX7nzBUO/rWR0pW88B/ao9DbrklTz2q08a6DuaEpj+MiWXlhD7ci89/3fVSrAw007PvG7poL5Oz72pWvR333ilgYmhEeH8W6Swe4Eq75uFfY/L1q0aF8fSyNTHkQG8GSwB0ERdzXWL6BeyU6+jbA0cya5PQ0Ljy+xbJzu0hMS1GVMdI1oGeVFtQq5YuxvgERCTEsPbeLC49uvYsu0dqrNp38Gub0KeYJC89s53pEiMbyDT0q855fI5zMrElKT+XCo1ssObuThLRkVZn25evRyqs2NsYWJKQlcSLkKsvP7ybjHZ1LdChfn66VmmFtZEZITDizT2zkSgHHyI6+Dejo2wAHUysiEmNYeX4v+24Hql73L1eTr5r0zlOv1V8j31mfSgItxb//bpv/JRI8e0datWrF4sWLycjI4OjRo3z00UckJSUxZ86c4m7aGxs7duw/rmtrm/cEQ/x3/Pnnn3Tp0gVTU1O15a6urixevFgtePb48WMOHjyouiOKq6srYWG5AaNp06axe/du9u/fr1pmYmIC5GSG/fnnn/zwww906tSJjIwMVqxYQceOHZk5cyafffYZJiYmVK9enYCAALXg2eHDh3F1dSUgIEAVPHv48CF3796lSZMmANjZ2dGyZUvmzp3LTz/9VMifkrqKjp60L1+fLVePcD8mnFqlyvNhzXb8eng1samJ+dbpVcUfU31DNlwOICo5DmM9Q7S1chOJtRVafFSrA4npKaw4v4e41EQsDExIy8wo0r48d+zgYRb/OZ+PRwzBp0J59vy9ix+++paZS+dia2+Xp3xmegZmFuZ80Ls729dvLnDdEeFPWDLnL8pX9C2q5mvU1LMaw+p35tcja7gafpcO5evzc7uh9F09iYjEmDzlO/o2YFDtjvxyaBVBESH42LnxVeNeJKQmc+L+FQBalK3BoNqd+ClgOVfD7+JqYc+Ypn0A+PP4xnfSL/3WzdFv2YTkRSvJehKBQTt/TL78jPhxkyA1Ld86urWqY9C5A8mLV5J15x5aDnYYfZhzkpu6dhMAOuU8SQ84Sua9+6CljeH77TD5cijx/5sM6elF1p+arj70rNyC5ed3czvyAY09qzKyYXfG7Z5HdHJ8nvKrLuxj/eUA1XNthRbf+3+k9kP//QqNqFO6AkvO7iAsPgo/Bw+G1evM5ANLCY19UmR9+ScMdfW5E/WQnTdOMKX14OJujpqlq1axau1aJowdSylXVxYuXcrQL75g46pVGBsZ5Vvn8tWrjP3uOwYPHEiThg0JOHKEb779loWzZuHnm7sf8HB3Z/YLwzde3CcCpKSkUKlCBZo3acIPP/9cJP27fuoC+1dswb//B7iUc+fCwROs/WU+H//0NeY2mjP1U5NT2DZ3FW6+ZUmKS1B7zcDYiLodmmPtZI+2jjZ3Llxnx/w1GJuZ4FHRu0j68aJKTmXp4NeATVcOERIdRu3SfnxUqz2/HFpJbEr+x6g+1Vpjqm/EuksHeZoUi4m+kdoPwCrO5WjjU5d1lw4QEh2GrYkF3So3B+Dva8eKvE8AwYFXObl2N/V7tsXesxRBR86y6/cVdP1uKCbWFhrrdZ30GXoG+qrnBqZ5LwgnRMVyesNeHMqWKoqma1TTtTy9qviz7NxObj19SJMyVfmyYU/G7JqT775v5fk9rL90QPVcS6HFD60GcebBddWyDyo2oW5pPxYF7iAs/ikVHMswvH4XJu1fQmhswRdYCkNdtwr0r96Gv05v40bkfVqUrcHYZv344u+ZPE2Ky1Pe2640n9XrzNKzOzn78AZWRmYMqtWRT+u8zy+HVgKgo6XNty0GEJeaxLTDq4hKjsfG2JyUjPyPd4WtnltFPqzZjnmntnIjIgR/r1qMbzGAYVt+zbdPPnal+bx+VxYFbifwQRDWRmYMrvMeQ+t+wI8By4Gc4Fqfaq3489gGbkSG4mRmw/D6XQBYFLi9yPvUuEwVhtR9n9+Predq+F3ala/H1Daf8uG6KfmeH7UvX5+BNdvz65HV3IwIxduuNCMbdicxPYWT96+qyiWmpdB/7Q9qdf8/Bc5EySPDNt8RfX19HBwccHV1pWfPnvTq1Us1RFCpVPLzzz/j4eGBoaEhlSpVYsOGDaq6MTEx9OrVC1tbWwwNDSlbtiyLX7jCeuXKFZo2bYqhoSHW1tYMGjSIxMT8T2ggZwyxQqFgz549VKlSBUNDQ5o2bUpERAS7du3Cx8cHMzMzevToQXJy7hWN3bt3U79+fSwsLLC2tqZdu3YEB+deeVq2bBkmJibcvn1btWzYsGGUK1eOpKQkIO+wTYVCwbx582jXrh1GRkb4+Phw8uRJ7ty5Q+PGjTE2NqZOnTpq7wMwZ84cypQpg56eHl5eXixfvrzAzz8wMJAWLVpgY2ODubk5jRo14vz58wXWeVlSUhJ9+/bFxMQER0dHpk+fnqdMeno6X331Fc7OzhgbG1OrVi21Mdv379+nffv2WFpaYmxsjK+vLzt37gRyv5cdO3ZQqVIlDAwMqFWrFleuXFF7jxMnTtCwYUMMDQ1xdXVl+PDhqs8XciZcbNu2LYaGhri7u7Nq1Sq1zz0kJASFQsHFixdVdWJjY1EoFGptvX79Om3atMHExAR7e3v69OnD06dPNX4+2dnZrF+/ng4dOuR5rV27dkRFRXH8+HHVsiVLltCyZUvs7HKCKdra2jg4OKgeJiYm6Ojo5Fl26tQppk+fzi+//MKoUaPw9PTEx8eHyZMnM2LECEaOHMmDBzlXWZs0aaLWp6CgIFJSUhgyZIja8oCAAHR1dalXr55qWYcOHVi9erXG/haWBu6VCHwQROCDICISY9h2/ThxqYnULp1/llY5W1c8rJ1YFLiDO1EPiUlJ4GFcBPdjck9iq7v6YKSrz7Kzu7gfE05sSiIhMeGEJUQVeX8Atq3fTLM2LWnRrhUupUsxcNgnWNvZsmfrjnzL2znaM3DYYJr4N8OogMzUrKwsZvzwC90H9Ma+CG9DrUnXSk3ZEXSCHUEnuB8Tzh/HNxCZGEsnv4b5lvf3qsXf145x8M45wuKjOHjnHDuCTtCzagtVGV8Hd66GB7P/9lnCE6IJfBDEgdtn8bLNP/uhKOg3b0zqjr1knL9E9qMwkheuQKGni14tzZktOmXcybxzl4zT58iOiibz2g3ST59Dxy33x2LSjDmkHz9N9uNwsh8+InnRSrSsrdB2cy3S/rT0qsWRexc5cvciYQlRrL6wj+iUeJqWqZpv+ZSMNOJTk1QPNytHjPQMOfZCtlYdtwpsDzrO5bBgIpNiCQg+z9Xwu7TyqlWkffknToVeY8Hpvzl892JxN0WNUqlk9bp1DOjbl6aNGuHp4cHEceNITUtj90u3r3/R6vXrqVW9OgP69MGtdGkG9OlDzWrVWLV+vVo5HW1tbKytVY+Xp5Vo26oVHw8YQM3qRZexdWbXYSo1rkXlJrWxcbanRZ/3MLO24MKB4wXW271oPeXrVMXZM+//fenynnjVqIiNsz2W9jbUaNUQO1dHHty8V1TdUNPIozJnQq9zJvQ6EYkx/H3tKLEpidQpXSHf8l62pShj7cxfp//m9tMHxKQk8CD2idoxys3SkZDoMC48ukVMSgK3Ih9w8dFtXMzt30mfAC7vO4lX/ap4N6iGpaMtdbu1xsTSnOuHzxZYz9DUGCNzU9VD66UgbXZ2Ngf/2ki1Dk0wKyBgWhRaedfmyN0LHL57kbD4p6y6sJfo5Hiaeea/zadkpBGXmqR6uFs5YaRnyNG7ufu+um4V2Hb9OJfD7hCZFMvBO+e4En6X1t6130mf2vvU4+Cdcxy4c5ZHcZEsObuTqKQ4WpbLf99bzsaVyKQYdt44SURiDDci7rPv9hnKWDupyjT1rIaJviE/B6zgZmQoT5NiuRFxX20bLUodfeuz//ZZ9t8O5GFcJAvPbOdpUhytvPL/TMvZliIyMYYdQSeISIwhKOI+e2+ewdPGWVXGy7YUN57c58i9S0QkxnDx8W2O3r2kVqYoda7QhF03TrHzxklCY58w+8QmIhJjaF++fr7lW5Stwfag4xwKvkBYQhQBwefZdfMU3So1e6mkkpiUBLWHEMVJgmfFxNDQkIyMnCyQ//3vfyxevJg5c+Zw7do1vvjiC3r37s3hw4cBGD9+PNevX2fXrl0EBQUxZ84cbGxsAEhOTqZVq1ZYWloSGBjI+vXr2b9/P5999tkr2/Ddd9/x559/cuLECR48eEDXrl2ZMWMGq1atYseOHezbt48//vhDVT4hIYEvvviCwMBA9u/fj1Kp5L333iM7OxuAvn370qZNG3r16kVmZia7d+9m3rx5rFy5ssChmpMmTaJv375cvHgRb29vevbsySeffMKYMWM4ezbnJObF/mzevJnPP/+cL7/8kqtXr/LJJ58wYMAAAgICNL0FCQkJ9OvXj6NHj3Lq1CnKli1LmzZtSEh4/Z3w6NGjCQgIYPPmzezdu5dDhw5x7tw5tTIDBgzg+PHjrFmzhsuXL9OlSxdatWqlCigOHTqUtLQ0jhw5wpUrV/jpp59U2VQvvs+0adMIDAzEzs6ODh06qLaVK1eu4O/vz/vvv8/ly5dZu3Ytx44dU/t8+vbty+PHjzl06BAbN25k/vz5RES82TCpsLAwGjVqROXKlTl79iy7d+/myZMndO3aVWOdy5cvExsbS/V8fpjo6enRq1cvtaDvkiVL+PDDD9+oXZBz62ITExM++STvsL4vv/ySjIwMNm7Mydhp0qQJN2/eVGW0BQQE0KBBA5o2bZoneFarVi2MXsh8qFmzJg8ePOD+fc1DA96WtkILZ3Nbbr80pOJW5ANKW+b/I6K8vTsP4yJo5FGFsc36MqpRT9r61EVHS/uFMm7cj31CJ78G/K95f75o2I0mZaqioOhTvzMyMgi+eYdKNdQDFZVrVOHGtSANtV7P+mWrMbMwp3lb/7dazz+ho6VNOdtSatlIAIEPgvCzz39Ilq6WDulZ6tl+aVkZ+Ni5qbJiLocFU862FD52OT+aHc2sqV3aj1MvXHktSlo21mhZmJN5LXfIL5mZZN68g04ZzUPpMu8Eo1PaFW330qr16FYoT8blaxrrKIwMAFAmJWss87a0tbRws3TkWrh6YOFa+F3K2Li81joaulfm+pN7RL2QqaGrpZ3nand6ViZlbYs2EPhf8igsjKjoaGrXqKFapqenR9XKlbl8VfP2fvnqVWq9UAegds2aeeqEPnxIq06d6NC1K2MmTODhY83DCotCVmYm4fce4u5XTm25u58XD2+HaKx3+fAZYp5E0eD9lq98D6VSScjVW0SHR1LqNYaCvq2cY5Qdt14a7n8rMhQ3q/wvYPg6uPMgNoImZaoxvvkAvm7Sm3bl66kdo+5FP8bFwg5Xi5zjnJWRGd52pQkqYNhaYcrKzORp6GNcypdRW+5SvgxPgvMf4vjcpknzWD5qGtt/XcrjG3kDmOe3H8bQ1Bjv+vkH64vK833fy0PJr4YH4/m6+z6Pylx/cpeo5NzsJ10tbTKy1fd9GVkZ72Tfp6OljYe1E5ce31FbfinsDl62+Wf13YwMxdrInCrOOf+H5gbG1C7lx/mHucMxq7t4cyvyAR/V6sBfXcbwa/vhvO/X6J0Mj9PR0qaMtTMXH99WW37x8W287fK/aHYj4j7WxuZUc/YCwNzAhDpufmpTdQRFhFDGxpmyz75rexMrqrp4qZUpKjnnR6553uvcwxv42ud/HqGrrUP6S6Mh0jIz8LYrrZY1bKirz6qe37Gm1/dMbjUIT+vX25b/SxQKRYl8/H8lwzaLwZkzZ1i1ahXNmjUjKSmJX3/9lYMHD1KnTh0APDw8OHbsGPPmzaNRo0aEhoZSpUoVVVDCzc1Nta6VK1eSkpLCsmXLVAGqP//8k/bt2/PTTz9hb6/5Kt4PP/ygyrQZOHAgY8aMITg4GA+PnBOyzp07ExAQoJrbrEuXLmr1Fy1ahIODA9evX8fPLydLZt68eVSsWJHhw4ezadMmJkyYQI2XTnpfNmDAAFVQ5uuvv6ZOnTqMHz8ef/+cH8iff/45AwYMUJWfNm0a/fv3V92tY+TIkZw6dYpp06apht29rGnTpmrP582bh6WlJYcPH1bNkVWQxMREFi5cyLJly2jRIidrZOnSpbi45O7Eg4ODWb16NQ8fPsTJKecK16hRo9i9ezeLFy9mypQphIaG8sEHH1ChQs4V2+ef9YsmTJiQ5z02b95M165d+eWXX+jZs6fqJgVly5bl999/p1GjRsyZM4eQkBD2799PYGCganv566+/KPuG87rMmTOHqlWrqk38v2jRIlxdXbl16xblypXLUyckJARtbW1VJtnLBg4cSP369Zk5cybnzp0jLi6Otm3bquYwe123bt1SZR2+zMnJCXNzc27dyjlJqlevHrq6uhw6dIgePXpw6NAhGjVqRNWqVYmLi+P27duULVuWQ4cO0bu3+rwKzs7Oqn49v43yi9LS0khLU0/x19fXz1OuIEZ6BmhraZGYnqK2PDEtGVP9/E9MrQzNcLN0JDMri2Vnd2OsZ0Anv4YY6uqz4dnQMysjM8oYmnLx8W0Wn9mBjbE5Hf0aoqXQ4sCdgq+qv62EuHiys7OxsLRQW25uaUlsdN7U/dcVdOUa+3fs4de//nzLFv4z5gYm6Ghp57nqGZ0cj5WrWb51zjy4Tjufehy9d4lbkQ/wsi1FG+866GrrYGFgQlRyPAfvnMPC0JQ/3/sSBQp0tLXZfPUIKy/sfRfdQmGe0/bsePUhPdnxCWhZW2msl3HmPCkmJph8MwJQoNDRJi3gKGm7NGcQGXZ7n8xbwWQ/0jyf39sy1TNCW0uL+JeGPMelJuFnYKKhVi5zAxMqOJZh3qktasuvht/F36sWtyJDiUiMwcfenSrO5WQukjcQFZWT+Wptpb5dWVtaEhauOeMjKjo6bx0rK6Kio1XP/cqXZ+K4cZR2dSUqJoaFS5cy8NNPWbtsGRbm5oXYC82SE5JQZmdjbK4+bYGxuSlJsflfqIsOjyRg7XZ6j/8MLW3tfMtAzrDOP4dNJCszE4WWFv79P8C9glehtj8/z6cEeHFeJYCEtBRM9fMfZmtlZI67lSOZ2ZksObsTYz0D3q/QGCNdA9Y9GyJ48fFtjPUMGVrvAxSAtpY2J0IuE3DnXL7rLGypickos5UYmqlf2DU0MyY5Pv+RG0bmpjTo0x7bUo5kZWZx+9Qltv+2lPZf9sexnBsA4XdCuXnsPB+Mf/fDpZ/v++JSk9SWx6UlYf6a+76Kjp7MPak+dcKV8Lu08qrNzYhQIhKjKW/vThVnr3ey7zPVN0JbS5u4l/fnKYlYOOXfp5uRocw8uo6RDbujq62DjpY2gQ+CWHhmm6qMvakVfiYWHL17iSkHluJoZs1HtTqgpaWlOo8q6j7FvnQuEZeSgKVh3nPr53369cgaRjXuqerT6dDrLDj1t6rMsXuXMdc3YUrrwSgUCnS0tNl14ySbrhwu0v5AToBSO5/zo5iUBKyMTPOtc/ZhEG2863A85Aq3nz6gnI0rrb1qoautg7mBCdHJ8YTGRvDzoZXcjXqs2o/M7DiCQRt+4lF8ZJH3S4j8SPDsHdm+fTsmJiZkZmaSkZFBx44d+eOPP7h+/TqpqamqYMlz6enpVKlSBYBPP/2UDz74gPPnz9OyZUs6depE3bp1gZxhaJUqVVLL7KpXrx7Z2dncvHmzwOBZxYoVVX/b29tjZGSkFsyxt7fnzJkzquehoaFMmjSJ06dP8/TpU1XGWWhoqCp4ZmlpycKFC/H396du3bp5Joh/nXYAquDS82WpqanEx8djZmZGUFAQgwYNUltHvXr1mDlzpsb3iIiI4Ntvv+XgwYM8efKErKwskpOTCQ19vcnTg4ODSU9PVwU4AaysrPDyyj15PX/+PEqlMk9gKS0tDWtrawCGDx/Op59+yt69e2nevDkffPCBWv+BfN8jKCgn2+XcuXPcuXOHlStXqsoolUqys7O5d+8et27dQkdHh6pVc694enp6vvFdUc+dO0dAQECerLjnn0V+wbOUlBT09fU1Xo2oWLEiZcuWZcOGDQQEBNCnTx90dXXfqF2vQ6lUqtpgZGREzZo1VcGzw4cPM3r0aHR0dKhXrx6HDh1CX1+fe/fu5QmwGhoaAqgNXX7R1KlTmThxotqyCRMmQI38g4evarMahQJl/kVVfVtzcT+pmTnzRm0POkHvqv5suXqEzOwsFChISk9h4+VDKFHyKD4SMwNjGnpULvLg2cvtVFEq/3HmW0pyMjMnT2PI6OGYWbybH8KavPxdKRQKlBq+raVnd2FlZMbc978CBcQkJ7D7xil6Vm1JljJn/1nZqSx9qvnz65E1BD0JwdncluH1uxBVrTXLzu0q9Pbr1qqOUd/uqueJM+c+69hLBRUKeHm7fIGOlycG7fxJWbGOzLshaNvZYtjjA7Lb+ZO2fU+e8oa9uqDt4kTCjzMKoRevlqc7KPJZmld994okZ6Ry/tFNteWrLuyjf/U2TGk9GCUQkRjDsXuX8r3RgMixa+9epkybpno+49n8kS/vBV7cZ2v00usv16lXO3e4kydQ0deXTt27s33XLnp378479XJbUeZZBjnD+7bOWkGDD1ph7VjwcUPfQJ8PJ39JRlo6Idduc2DlVixsrSld3rNQm/66Cvq6nn8vq87vVR2j/r52jL7VW7PpyiEys7MoY+1Ms7LV2XTlEKExT1QXeJqnJrP/hQnDi9rLx6QCdnlYONhg4WCjem5fxpXEmHgu7T2BYzk30lPTCFi4iQZ9OuQ7D9q78vLxSIHmY9SLGrhXIjkjlXOP1LOHVp7fw4Aa7fixzaeqfd/Rexdp4F65EFtdsPzOkTRxMbflw5rtWH/5IJce3cbCyJS+1VozqHZH5jwLDCoUCuJSk5h3agvZSiV3ox9jaWhGR98GRR4806iAcwkXczs+rtWBtRcPcOHxLSwNTelfvQ2f1nmPP0/kjLTwc/Cgc6UmzDu1lduRoTiY2fBRzfbEVExg3eWD76gTeY+8mv6nlp/bg6WhGX92GolCkRNo23PrDN0rN1f9vgyKCFHLRr0afo+5H4ymk19DZp14N3PCCvEyCZ69I02aNGHOnDno6uri5OSkChrcu5eT8r1jxw5Vpstzz7NYWrduzf3799mxYwf79++nWbNmDB06lGnTphV40vmqk9EXAxcKhSJPIEOhUKh2YJAzb5W7uzsLFizAycmJ7Oxs3NzcSH9p4ucjR46gra3N48ePSUpKwsws/6wMTe3QtOzFtrzct1edfPfv35/IyEhmzJhB6dKl0dfXp06dOnnarkmeA3c+srOz0dbW5ty5c2i/dAX5eRDqo48+wt/fnx07drB3716mTp3K9OnTGTZsWIHrfvEz+OSTTxg+fHieMqVKleLmzZt5lr/c/ufzc7y47Pmw0Bf78jx78WWOGuaasrGxITk5mfT09HyzwgA+/PBDZs2axfXr19UCs2+iXLlyHDt2LN/3efz4MfHx8WqZdk2aNGHt2rVcu3aNlJQUVWCxUaNGBAQEoKenh4GBAbVrq881Ef0sq0HTTS7GjBnDyJEj1Zbp6+vz7f6Fr92X5PRUsrKz81zBN9EzJDEt/6BdQlrO3CTPf5QARCbGoKVQYG5gQlRyHAlpSWQps9VOxCISYzAzMEZboaUK3BQFU3MztLS0iHkpyywuNhZzK4t/tM7wR2FEhD9hypjcYOXz7bdz03b8uXwBDs5FOwdaXGoimdlZWBmp788sDU2JSc4/qyQ9K4OfAlYw7fAqrAzNiEqOo335+iSlpxCXkpMdMLBme/bePMOOoBMA3I1+jIGuPqMb9WT5ud2v9aPnTWRcukLCxJDcBTo5pwFa5mZkxeVmn2mZmqCM1zys3aBTO9JPniH96EmAnGwyfT2M+vYgbcdetV+hhj07o1u5Aok/zUQZE1uo/XlZQnoyWdnZeTItzAyM8mRk5KeBeyVOhFwhK1v9fyQhLZk/jm9AR0sbE30jYlMS6FKxCU+TYguz+f8pDevXx698edXz9GfHmafR0aqpJwCiY2OxstKc5WhtZaXKWlPViYnBqoCLQoaGhpTx8ODBw4f/tPlvzMjUGIWWFkmx6lmcyXGJGJvnvRCVnpJG+L0HPLn/iL1Lc26yoVQqQankx76j6P71J7j55hzLFFpaWDnkHIvsSzsT9egJJ7cdKPLgWVJ6isZj1MvZaM8lpCYRl5qodoyKSIxGS6HAwtCEp0lx+HvV5vzDm5wJzZmYPjwhCj1tXTpXasKB24GFvNfLy8DECIWWIk+WWWpCEkZmr87Ses7O3YU7py8DEB8ZTUJULHtmrVK9/vw4tWDwRLp9PwwzO83b+dt6vu+zeHnfp29E/Ovs+zwqcSLkcr77vt+PrUP32b4vJiWBrpWavZN9X0JaMlnZWVgYqmcvmRsYa7xZxXt+jbgZcV9144n7sU9YkPk3P7QaxOqL+4lNSSAmOYEsZRbZLxynHsVFYmlkio6WdpHe8VVzn0w09qlzxcYERYSw5dqRnD7FhDPv1BamtvmUlRf2EpOSQM8qLTgUfF4VfL4f+wQDHV2G1H2f9ZcDCv1c4kVxqUlkZWdhafjy+ZGJxjnK0rMymHZ4Fb8dXYOloRnRyXG09alHUnqqxmO1EiU3I0NxMf//dfM5rXcw5Yp4fTLn2TtibGyMp6cnpUuXVgsMlS9fHn19fUJDQ/H09FR7uLrmDtuytbWlf//+rFixghkzZjB//nxV/YsXL6pNGH/8+HG0tLTyzQ76p6Kiorhy5QqjR4+mVq1auLq6EhISkqfciRMn+Pnnn9m2bRtmZmavDAr9Ez4+Phw7pn43phMnTuDj46OxztGjRxk+fDht2rTB19cXfX39Aie/f5mnpye6urqcOnVKtSwmJkY1PBCgSpUqZGVlERERkee7dHBwUJVzdXVl8ODBbNq0iS+//JIFCxaovVd+7+HtnXNHrapVq3Lt2rU86/f09ERPTw9vb28yMzO5cOGCah137twhNjZW9fx5MOjFO1u+ePOAF9/Hzc0tz/tomr+ucuXKQM6NBjTp2bMnV65cwc/Pj/Iv/Kh6E927dycxMZF58+bleW3atGno6urywQcfqJY1adKE27dvs2rVKurXr68KbDZq1IhDhw5x6NAh6tSpg4GBgdq6rl69iq6uLr6++d/VUV9fHzMzM7XHmw7bzFJm8yguMs/cIWVtXLgfk/8d/EKiwzEzMEJPO/fah42xOdnKbNXQhpCYcKyNzNUOtzbGFsSnJhVp4AxyAt9lvDy5dPaC2vJLZy/g7av5f7QgzqVc+W3RbKb/9afqUaNuLfyqVGT6X39ibWfz6pW8pczsLG5FhlLdVb0P1V28ufpE863YAbKys4lMiiVbqaSZZ3VOhFxVncga6OjlCc5nZ2ejUBSc3fGPpaaRHfE09/E4nOzYOHTKvzAETFsbHS9PMoMLmJBcTzdvmka2Mk9akWHPLuhWrUTiL3+Q/bTob1iRlZ1NSEwYvg7q86yUt3cn+GnBgRQv21LYm1px9IUbBbwsMzuL2JQEtBVaVHPx5sKjWxrL/n9nbGSEq4uL6uHh5oa1lRWnA3MzizIyMjh/8SIV/fK/QQpART8/Tp9Vz5g9HRhYYJ309HRC7t/H5lnW97ugraODg7sL966qbxP3rt7CpaxbnvL6hvp8NHU0Ayd/qXpUaVoHK0c7Bk7+Eqcymu/UqERJVkbR33Eu5xgVQbmXjlHlbEsREp3/8Ot70WGYGRijp517rmtrbEG2MlsVHNDT1snzYz5bmf0sE6zofyhq6+hgU8qJR9fVb0b1MCgY+zKvP5dX1IMwjJ4FRi0cbOg84VM+GD9Y9Shd0QsnL3c+GD8YY6uCLyS/rdx9n/p0IL4OHtx5xb7P2640DqbWBd5kJCM7i5hn+77qLt55snOLQmZ2FnejHlPRST1IXNHRk5uR+Y8c0dfRJfvlbetZQPD5lnUz8j4OptZqmYeOZtZEJ8cXaeAMcvoUHPWIyi/1qbKTJzci8p9jV187n/OEl57ra+tqKKMomnOJF+ScHz2gmov6UPJqLt5ce1LwjU2ysrN5+uz8qEmZqpy6f7XAQF8Za2e1+UiFeNck86yYmZqaMmrUKL744guys7OpX78+8fHxnDhxAhMTE/r168e3335LtWrV8PX1JS0tje3bt6sCRb169WLChAn069eP7777jsjISIYNG0afPn0KHLL5piwsLLCysmLu3Lk4ODhw//591VxozyUkJNCnTx+GDRtG69atKVWqFNWrV6ddu3Z55kt7G6NHj6Zr165UrVqVZs2asW3bNjZt2sT+/fs11vH09GT58uVUr16d+Ph4Ro8erRqW9zpMTEwYOHAgo0ePxtraGnt7e8aNG6d2l6Vy5crRq1cv+vbty/Tp06lSpQpPnz7l4MGDVKhQgTZt2jBixAhat25NuXLliImJ4eDBg3mCft9//73ae9jY2NCpUycgZ0642rVrM3ToUD7++GOMjY0JCgpS3dzB29ub5s2bM2jQIFWm45dffomhoaEqe83Q0JDatWvz448/4ubmxtOnT/nf//6n1oahQ4eyYMECevTowejRo7GxseHOnTusWbOGBQsW5Mmsg5ygXNWqVTl27JgqkPYyS0tLwsLC3mq4Zp06dfj8888ZPXo06enpdOrUiYyMDFasWMHMmTOZMWOGWuC5bt266Ovr88cffzBu3DjV8ho1ahAXF8fGjRsZPXp0nvc5evQoDRo0eKPt5J84eu8S3So342FsBKGxT6jpWh4LQ1NOheZMht3KqzZmBsYvzBVzi2Zlq9OlUlP23QrEWM+ANt51Ofvghuqk79T9a9Rzq0B73/qcCLmCjbEFTTyrcjzkisZ2FKb2Xd7j9ynT8fQqi5evN3u37ebpk0hadmgDwIr5i4l6GsXnY0ep6ty7nfMjJjUlhfi4OO7dDkZHVxdXt1Lo6etR2sNN7T2Mn2Vzvry8KK27dJBxzfpxM+I+157co335etiZWrL16lEABtXuiI2xBVMOLAVyhlr42LsR9OQepvpGdK3UDHdrR6YcXKpa54n7V+haqSm3nj5QDdscWKsdx0Ou5Dk5Lipp+w9h0LYl2U8iyYqIxKBNS5TpGaSfzg1YGA3sQ3ZMLKmbcuaNybx0Ff2WTcgKfUjW3fto2dlg0KktGRevqoJqhr27olerGol/LECZmorCLOdKuzIlFV7Kdi1Me2+e5uNaHQmJDuPO04c0KlMFayNzAoJz7rDcuUJjLIxM+ev0NrV6DT0qExz1iEdxeedS8bBywtLQlNDYJ1gYmtLJrwEKhYKdN04WWT/+KUNdfbUr805mNpS1cSE+NYknif983sG3pVAo6NG1K4tXrKCUqyuuLi4sXr4cA319Wr0wdcW3P/yAnY0Nnw3OmTeqe+fODBo2jCUrV9K4fn0OHTvG6bNnWThrlqrOjFmzaFC3Lg729sTExLBw2TKSkpJo17q1qkxcfDzhT54Q+ezC2f1n0zZYW1kVWpCtZutGbJuzCkcPV5w93bgYcJL4qBiqNMuZauPQ2u0kxMTTfnBPFFpa2LqqZ8wam5mgo6ujtvzE3/txdHfFwt6G7MxMgi8GcfXYWfz7dy6UNr/K4bsX6VGlBQ9ic+7qXLu0LxaGJqqbmrT2roO5gQlrLubMd3jh0S1alKtBt8rN2HvzNMZ6hrQrX48zoUGqY9T1J/do6FGFR3GRhMY8wdrYnFbetbkWfq9IM2ReVLFFHQIWbcKmtBP2ZVwJOnKOxOg4fBrlzBd7ZtN+kmLjafLh+wBc2X8SU2sLLJ3syMrK4s6py9w7H0SLwTlz9uro6mLlrH7erf/sJikvLy8qu2+c4pPanbgX/Zg7UY9o8mzfd/DZXHJdKjbF0tCU+ae3qtVr6FGZO08fat73GZkRGhOOpZEpnfwa5ez7nmVLF7VtQccZVq8zd6MecTMylBZla2BjbM7eWzmjF3pWaYm1kRl/HN8AwNmHNxhc5z1alqvJxce3sTQ0ZUCNttyOfKDKgtpz8wytveswoGZbdgWdxNHMhvcrNH5n+/Ot144xokFX7jx9xM3I+7QsVwsbYwv23DwNQO+q/lgbmTPz2DoAAh8GMaTu+7TyqsWFR7exNDJlYM123IoMVfUp8OENOpSvz93ox9yKfICjqTU9q7Qg8MH1d3IuseFKAN806cOtyAdcf3KPtj51sTOxZNv1nGSHgTXbY2Nszk8BK4Cc4bXedqUJenIfE31DulRsgruVo+p1gD7VWhH0JIRHcZEY6Rnwnl8jPK1d+P3Y+nzbIMS7IMGzEmDSpEnY2dkxdepU7t69i4WFBVWrVmXs2LFAzt2oxowZQ0hICIaGhjRo0IA1a9YAOXM67dmzh88//5waNWpgZGTEBx98wK+//lqobdTW1mbt2rUMHz4cPz8/vLy8+P3332ncuLGqzOeff46xsbFqknlfX19++uknBg8eTN26dfMMS/2nOnXqxMyZM/nll18YPnw47u7uLF68WK0tL1u0aBGDBg2iSpUqlCpViilTpjBq1CiN5fPzyy+/kJiYSIcOHTA1NeXLL78kLi5OrczixYv54Ycf+PLLL3n06BHW1tbUqVOHNm1yggZZWVkMHTqUhw8fYmZmRqtWrfjtt9/U1vHjjz/y+eefc/v2bSpVqsTff/+tGp5YsWJFDh8+zLhx42jQoAFKpZIyZcrQrVs3Vf1ly5YxcOBAGjZsiIODA1OnTuXatWtqmVWLFi3iww8/pHr16nh5efHzzz/TsmXunb6cnJw4fvw4X3/9Nf7+/qSlpVG6dGlatWqV57bsLxo0aBBLliwp8G6vFhYWr/6wX2HGjBlUrFiROXPmMH78eBQKBVWrVmXLli20b99erezzIZmHDx9W20Z0dXWpU6cOBw4cyPdGE6tXr84zp1lRuBx2ByM9fZqVrY6ZvjHhiVEsDtyuukJvqm+EhWHuMIz0rEz+Ov03HX0bMKx+Z5LT07gcdkd10gU5Qwz/Or2N9uXrMaJBN+JTkzh+7zKHgi/kef+iUL9pIxLiE1i3dBUx0dGUcndj3E8TsXPI+QERExXD0yfqJ+lffpybpRp86w5H9x/C1t6OeWuXvJM2v46Dd85hpm9Mv+ptsDY2415UGF9vn82TxJwhvtZGZtib5A4l09bSolulZpSysCczO4sLj24xZNM0whNyJzpfdnYXSqWSj2q1x9bYgtiURE6EXGHB6b/zvH9RSdu1H4WuLoa9u6IwNiLrbgiJv86C1NwbYmhZWaplmqVu34OSnOGbWpbmKBMSybh0ldRN21Vl9Js0AMD068/V3i950QrSj5+mqJx5EISxvhEdfOtjbmDCo7hIfju6RnW12tzQBGsj9bnzDHX1qebizSoNN2rQ1dbhvQqNsDOxJDUzncthd1hw6m9SMtLyLV+cvG1L8+d7uUPKh9fPuXi1M+gkk18I3BaHfj17kpaWxo/Tp5OQmIifjw9//vorxi/c6Tj8yRO1ycgrVajA5AkTmPPXX8z96y9cnJ2ZOnEifi9kBT+JiGDcxInExsVhaWGBn68vi+fOxfGFrO8jx44xcepU1fOxz25W8/GAAXzyD+78nJ/ytauQkpDM8c17SYyNx9bFka6jP8bcJme4XmJsAvFP3yyAmZGWzp4lG0mIjkVHTxdrJ3vaf9qL8rWrFEqbX+XS49sY6xrQolzNnGNUQhQLT29T/Wg3MzDGUu0YlcG8U1t5z68hnzfsRnJ6Kpce32HXC4GJ/c+GZrbyro25gQmJ6SlcD7+nVqaolanhR2pSMud3HCY5LhErJztaD+uFqbUFAMlxCSRG557jZWVmcWrDXpJiE9DR1cHSyY5Ww3pSqkLhjfJ4W2ceXMdE35COfg2xeLbv+/XIatXdM80NTbAyVs+AM9TVp7qLDyvP552rEnL2fR9UaIytiSVpmelcfnyH+Se3kPyO9n0nQq5gqm9E54pNVBcwphxYpho2amloio1x7v78UPAFDHX1ae1dm37VW5OUnsrV8LusOJfbv6jkOCbtW0z/Gm2Y3mEY0cnx7Aw6oRoWWdSOh1zGTN+IbpWb5fQpJpxJ+5cQ+axPVkZm2JpYqMofvHMOQx192njXZUCNtiSlp3I5LFhtXtR1lw6iVCrpVaUlVkbmxKcmEfggiJUX8v9eC9uh4AuY6RvTp5o/VkbmhESHMWbXXCKeXbCxNjLD7oXzIy2FFp0rNsXV3I7M7CwuPb7NsC2/qc6nIGd4+MiG3bE0MiMpPYU7Tx/yxbaZGrMO/6v+P9/ZsiRSKF9nMichRJE7dOgQTZo0ISYmplACTM89fPgQV1dX1Xx5RSk1NRUvLy/WrFmjduODf5sdO3YwevRoLl++jI7Om11j+HrH7CJqVfH4qe0QroUFv7rgv4yvYxkazh5S3M0oVEeGzCZ2YOEPlS9OFgv/YMDaycXdjEK1uNs46s1693fjK0rHh84lISKiuJtRqEzt7FgSuKO4m1Go+tdoy6htfxR3MwrdtPbDmH54dXE3o1B92agH/dZMKu5mFKql3cfTedm4Vxf8F9nQdzKdlrz65mj/Jlv6/0izeXnnVv43O/DJ78XdhH/szP1rxd2EfNUsnf+0Nv91knkmxH/MwYMHSUxMpEKFCoSFhfHVV1/h5uZGw4YNi/y9DQwMWLZs2RvNJ1cSJSUlsXjx4jcOnAkhhBBCCCGE+O+RX4ZC/MdkZGQwduxY7t69i6mpKXXr1mXlypVvNc/Ym2jUqNE7eZ+i1LVr1+JughBCCCGEEOL/MRm2WbJI8EyIEqJx48Z57pTzT/j7++Pv718ILRJCCCGEEEIIIYTmmb+FEEIIIYQQQgghhPh/TjLPhBBCCCGEEEIIIUoQLRm2WaJI5pkQQgghhBBCCCGEEBpI8EwIIYQQQgghhBBCCA1k2KYQQgghhBBCCCFECaJAhm2WJJJ5JoQQQgghhBBCCCGEBhI8E0IIIYQQQgghhBBCAxm2KYQQQgghhBBCCFGCyN02SxbJPBNCCCGEEEIIIYQQQgMJngkhhBBCCCGEEEIIoYEM2xRCCCGEEEIIIYQoQRQybLNEkcwzIYQQQgghhBBCCCE0kOCZEEIIIYQQQgghhBAayLBNIYQQQgghhBBCiBJEhm2WLJJ5JoQQQgghhBBCCCGEBhI8E0IIIYQQQgghhBBCAwmeCSGEEEIIIYQQQgihgcx5JoQQQgghhBBCCFGCaCFznpUkknkmhBBCCCGEEEIIIYQGCqVSqSzuRgghhBBCCCGEEEKIHFcf3ynuJuTLz8mzuJtQLGTYphBCFKIV53YXdxMKVe9qrVhwamtxN6PQfVy7I/EHjxR3MwqVWdOGxLR4r7ibUags920mKPxucTejUPk4eJAQEVHczShUpnZ21Js1uLibUaiOD53LlANLi7sZhWpss34khD8p7mYUOlMH++JuQpFICLlf3E0oVKZupYn9/Jvibkahspj5I78ErCjuZhSq0U16/+f2E//mfYRCIcM2SxIZtimEEEIIIYQQQgghhAYSPBNCCCGEEEIIIYQQQgMZtimEEEIIIYQQQghRgmjJsM0SRTLPhBBCCCGEEEIIIYTQQIJnQgghhBBCCCGEEEJoIMM2hRBCCCGEEEIIIUoQudtmySKZZ0IIIYQQQgghhBBCaCDBMyGEEEIIIYQQQgghNJBhm0IIIYQQQgghhBAliAIZtlmSSOaZEEIIIYQQQgghhBAaSPBMCCGEEEIIIYQQQggNZNimEEIIIYQQQgghRAmipZBcp5JEvg0hhBBCCCGEEEIIITSQ4JkQQgghhBBCCCGEEBrIsE0hhBBCCCGEEEKIEkShkLttliSSeSaEEEIIIYQQQgghhAYSPBNCCCGEEEIIIYQQQgMZtimEEEIIIYQQQghRgmjJsM0SRTLPhBBCCCGEEEIIIYTQQIJn4j/h0KFDKBQKYmNji7sphc7NzY0ZM2b84/ohISEoFAouXrxYaG0SQgghhBBCCCH+v5Bhm+Jf48SJEzRo0IAWLVqwe/fu4m6OEIXm7L6jnNx+kITYeGydHfDv+z6lvMu8st6Dm3dZOukP7FwdGTT1K9XyiIdhHF6/k7B7D4l7Gk3LPu9Rq3XjIuxBXhcOnCBw52GS4hKwcbKnSa8OuHi5v7Leo1shrJk6FxsXe/pN+kLttdSkFI5t3M3ts1dJTU7B3MaKxj3a4lHJp6i68UpKpZIFO7ax+dgREpKT8XVz56vuPSnj5KyxzsEL51myeycPIiPIzMrC1c6O3s1b0qZWnXfY8n9Gt35t9Nu2RLtsGbTMzYgf/AVZwSHF3aw8dm7ezpY1G4iJjsbVrTQDP/sE30p++ZaNjopm8awFBN+6TdjDx7T9oAMfDRusVubkkeNsWLGWsEePycrMxNHFmY5d36eJf7Mi64NSqWT+4sVs/vtvEhIS8C1fnq9HjqSMe8H/RwcOHWLuX3/x8PFjXJycGDJoEE0aNlS9Pm/RIhYsXqxWx9rKij1bt6qeHzx8mE1btxJ06xZxcXGsXLQIr7JlC7eDb6CSoyc9q7TE264UNsYWfLNzDkfvXSq29hTkxuFzXNt/iuS4RCwcbanZpTn2nqXyLRt+6z57ZqzMs7zTt4Mwd7BRPb9+8Aw3j5wnKSYefWNDSlf1plrHJmjrFs1pvFKpZP6SxWzeti132xvxxau3vcOHmLtwYe6299HHatseQERkJH/Mm8uJ06dJTUujtKsr47/6Gh8vLzIzM5n91wKOnzrFo7AwTIyNqVmtOsM++QRbGxsN7ypepFQqmb9iOZt37iQhMRFfb2++HvoZZdzcNNYJDglh7rJl3Lhzm7AnTxj5yWB6vv++WpnzVy6zfP16gm7f5ml0NNMmTKBx3XpF3JtcBq2ao1e3JgpDQ7LuPyB5wxaywyM0ljf5bBA6ZT3yLM+4doOk+UtU6zRo3Vzt9ez4BOLHTy7Utufn+qGzXN53kpS4BCycbKnTxR+HsvnvJx7fDGHnb8vzLO/83adYPNtP3DpxiSPL/s5Tpv8fY9D5l+0n5i1exIIlS9TqWFtZsWfzFtXzqOho/pg3l1OBgSQkJlK1UiVGf/45pVxcC7OLJYICGbZZkkjwTPxrLFq0iGHDhvHXX38RGhpKqVL5H2TEv0dGRga6urrF3Yxide3kefYs20ybD7vgUs6d8wdOsOqnuXz6yxjMbaw01ktNTmHrnBW4+5YjKT5B7bXMtHQs7WzwqVWFfSs2F3UX8rhx+iIBK7fRvG8nnMu5cSngNBunL2TA1C8xs7bUWC8tOYWd89dQurxnnj5lZWay/pcFGJmZ0OGzPphYmZMQHYuegX5Rd6dAy/buZtWBfXzbdwCl7OxZtGsHn/3+Gxu++wFjA4N865gbGzOgdRvc7B3R1dHm6JXLfL9sCZamptQpn3+Ap6RQGOiTee0G6UdOYDxyaHE3J1/HDh5m0Z/z+OSLoXj7lWfPtp1M+no8fyydh629XZ7yGekZmFuY06V3d/5en///i4mpKV16d8O5lCs6ujqcPXmGP376FQtLC6rUrFYk/Vi6ahWr1q5lwtixlHJ1ZeHSpQz94gs2rlqFsZFRvnUuX73K2O++Y/DAgTRp2JCAI0f45ttvWThrFn6+vqpyHu7uzP7tN9VzbS31gQgpKSlUqlCB5k2a8MPPPxdJ/96Eoa4+d6IesvPGCaa0HvzqCsXk3tnrBG7YR63urbDzcOHWsQvsn7WWjuMHYWJlrrFepwmfqO3L9E1zv9+7Z65ybksA9fq0w87Dmbgn0Rxfvh2Amp1bFEk/lq5exap165gwZgylXFxZuHwZQ78cycYVKwve9iZOZPCHA2nSoAEBR4/yzXcTWPjnLPzKlwcgPiGBgZ8NpXrlKsz8+WesLCx5+PgxpiYmAKSmpnLj1m0+6tuPsp6eJCQkMP3PPxg5dgzL5y8okr7+1yxdt45VmzYx4ctRlHJxZuGqVQwd8w0bFy7S+N2lpqXh4uhA84YN+HXevHzLpKSmUtbDg/Yt/flq0vdF2YU89Js1Qr9JfZJXricr8ikGLZtiMuQj4idPg7T0fOskLVoO2tqq5wpjI0y/+pyMi1fUymWFhZM466/cBdnKIunDi4LPXuPU+j3U7dEG+zIu3Dh6nt1/rqLzhE8L3E90mTgE3Rf2Ewam6t+nroE+XSYOUVtWVIEzKLr9BDw7Rk3/VfVc+4XvUqlUMmrcOHR0tJk+eQrGxsasXLeWISNHsn7pMgwNDYusz0LIsE3xr5CUlMS6dev49NNPadeuHUteuiKRn+PHj9OoUSOMjIywtLTE39+fmJgYAHbv3k39+vWxsLDA2tqadu3aERwcrKr7fKjjpk2baNKkCUZGRlSqVImTJ0++9nsolUp+/vlnPDw8MDQ0pFKlSmzYsKHANkdERNC+fXsMDQ1xd3dn5cq8V6Tj4uIYNGgQdnZ2mJmZ0bRpUy5dev0r8FlZWQwcOBB3d3cMDQ3x8vJi5syZhVJn8eLF+Pj4YGBggLe3N7Nnz1a99vwzXbduHY0bN8bAwIAVK1aQnZ3N999/j4uLC/r6+lSuXFkts/B1vouoqCh69OiBi4sLRkZGVKhQgdWrV6tenzdvHs7OzmRnZ6u1t0OHDvTr10/1fNu2bVSrVg0DAwM8PDyYOHEimZmZr/3Z/hOndh6iSuPaVGlSR5V1ZmZtydn9xwust+OvtfjWrYZLWbc8rzmVKU3zXh3xq1sVbZ13f43k7O6jVGhYg4qNa2HtZE/TXh0wtbLg4oFTBdbbu2QTPnWq4JhPlsaVI4GkJibTaXg/nMu5YW5jiUs5d+xKORVVN15JqVSy+uABBrRqQ9MqVfF0dua7fgNITU9nT+BpjfWqlfOiSeWquDs64mJrR4+mzfF0duHinTvvsPX/TPr+w6SuWEfm+ZKZ9QOwdd1mmrdpSYt2rXB1K8VHwwZjY2vL7q078i1v72jPR8MH06RVc4xMjPMtU6FKRWo3rIerWykcnZ1o37kTbh7uXL9yrUj6oFQqWb1uHQP69qVpo0Z4engwcdw4UtPS2L1vn8Z6q9evp1b16gzo0we30qUZ0KcPNatVY9X69WrldLS1sbG2Vj0sLdWD2m1bteLjAQOoWb16kfTvTZ0KvcaC039z+O7F4m5Kga4fPINn3UqUq1cZC0cbanZpgbGFGTePnC+wnqGpMYbmJqqH1gvBzIh7j7Ar44JHDV9MrC1wLu+Be/XyRN0PK5I+KJVKVq9fz4A+fWja8Nm2N2Zszra3v4Btb8N6alWrzoDevXO2vd6982x7S1etxN7WjgljxuDnUx4nR0dqVquGi3NOpq6JiQmzf/2VFk2b4laqFBV8fRk9/HOCbt4k/MmTIunvf4lSqWT1ls0M6N6DpvXr4+nmzsRRo3O+u4CDGuv5ennx+ceD8G/cBD0NFzTr1ajJkP4DaFq/flE1XyP9RvVI3RtAxuVrZIc9IXnFOhS6uuhVq6yxjjI5BWVCouqh61UWMjJIv3hZvWBWtlo5ZVJS0XYGuLr/FOXqVcG7fhUsHW2p09UfY0szgg6fLbCegakxRuYmqofWSxc9FArUXjcyNymyPhTlfgLyOUZZWKheC334kCvXr/HNyC/x9fHBrVQpvvliJCkpKew5cKCouiwEIMEz8S+xdu1avLy88PLyonfv3ixevBilUvPVoYsXL9KsWTN8fX05efIkx44do3379mRlZQE5wbiRI0cSGBjIgQMH0NLS4r333ssTXBk3bhyjRo3i4sWLlCtXjh49eqiCKa96j//9738sXryYOXPmcO3aNb744gt69+7N4cOHNba7f//+hISEcPDgQTZs2MDs2bOJiMhNS1cqlbRt25bw8HB27tzJuXPnqFq1Ks2aNSM6Ovq1Psvs7GxcXFxYt24d169f59tvv2Xs2LGsW7fureosWLCAcePGMXnyZIKCgpgyZQrjx49n6dKlauv6+uuvGT58OEFBQfj7+zNz5kymT5/OtGnTuHz5Mv7+/nTo0IHbt2+/9neRmppKtWrV2L59O1evXmXQoEH06dOH06dzAhhdunTh6dOnBAQEqNYXExPDnj176NWrFwB79uyhd+/eDB8+nOvXrzNv3jyWLFnC5MlFl76flZlJ2L0HeFT0UltepoIXD2/d01jv4qFTxEQ8pdEHrYqsbf9UVmYmT0Ie4eZXTm25m19ZHt8J0VjvypFAYiOiqNupeb6vB1+4jpNnaQ4s28zsYd+zeOx0Tm07mOd/9l169PQpUfFx1C6fm9Gjp6tL1bLluPxCML4gSqWSMzeCuP8knKply726gihQRkYGwbduU7lGVbXllWtU5cbV64XyHkqlkkvnLvDowUN8KxZNpuCjsDCioqOpXaOGapmenh5VK1fm8tWrGutdvnqVWi/UAahds2aeOqEPH9KqUyc6dO3KmAkTePj4ceF24P+hrMwsokLDcPJRHyrm5ONO5N2HBdbdNnUh676ZyZ6ZKwm7GaL2mn0ZF6JCw4kMyfmOEp7G8OhqMC5+noXa/udU2171l7a9SpUK3vauXcu77dWoyeVruXWOHD+Oj7cXX3/7LS06dqDnwIFs3ratwPYkJiWhUCgwMSm6QMB/xaPw8JzvrlpuNqyenh5VK1Tk8vXC2f+9a1rWVmiZm5F544VzwqwsMoPvoeNe+rXXo1e7BunnL0F6hvr6bW0w+34spt9+hVG/HmhZa874LwxZmVk8DQ3D5aX9hItPGZ68Yj+xefICVn71Gzt/W87jl/YTABlp6awZ+zurvpnBnllreBpaNAF2KNr9BDw7Rr3/Hh26dWXMxO/UjlEZ6TnZhvp6eqpl2tra6OjocPHKS8HR/wCFQlEiH/9fybBN8a+wcOFCevfuDUCrVq1ITEzkwIEDNG+e/w/tn3/+merVq6tlPvm+MGTlgw8+yLN+Ozs7rl+/jp9f7o+hUaNG0bZtWwAmTpyIr68vd+7cwdvbu8D3SEpK4tdff+XgwYPUqZMzj5GHhwfHjh1j3rx5NGrUKE+bb926xa5duzh16hS1atVStcvHJ3c+p4CAAK5cuUJERAT6+jmp29OmTWPLli1s2LCBQYMGveqjRFdXl4kTJ6qeu7u7c+LECdatW0fXrl3/cZ1JkyYxffp03n82T4a7u7sqCPVidteIESNUZZ63/+uvv6Z79+4A/PTTTwQEBDBjxgxmzZqlKlfQd+Hs7MyoUaNUZYcNG8bu3btZv349tWrVwsrKilatWrFq1SqaNcuZo2j9+vVYWVmpnk+ePJlvvvlG1VYPDw8mTZrEV199xYQJE/J8JmlpaaSlpakte/6dvK7khCSU2dkYm5upLTc2NyUxLiHfOlFhERxcs41+Ez5H64U09pIi5VmfXr7iaWRuSpKGPsWER3J0/S66j/tUY5/iIqMJDQrGp04V3h/5IbFPnrJ/2Rays7Ko26lohi69SlR8HABWpurfn5WZGeFRUQXWTUxJps2Yr0jPyERbS8HXPXpRy6d8gXXEqyXExZOdlY2FlXomlbmlBTHRMW+17qTEJAZ27k1GegZa2lp8MmJoniBdYYl6tv1YW6n/kLO2tCQsPFxzvejovHWsrIh64eKKX/nyTBw3jtKurkTFxLBw6VIGfvopa5ctw8Jc85AhUbC0xGSU2UoMTdWzFw3MjEmJzz+bxdDchDo9W2NdypGszEzunr7K3t9X4T+it2r+I/fqvqQmJLN7+jKUSlBmZ+PVoCoV/OsWST+iojVte1aEPXnFtvdSBqO1paXatvcoLIyNW7fSq0tXBvTuzbUbQUz7fSa6urq0a5X3YlBaWhp/zp9Hq+bNMTHOPytU5Hr+Wef9HiwIi9A8P1hJpjDNOZfITlA/f8hOSEDLUvM0EC/SLuWCtpMDyavVR39k3g8la+U6siIi0TI1zRkOOuJTEqb+hjI5uXA68JLU5/sJM/Xt2dDMmJT4xHzrGJmbUL9XW2xKO5KVmcWdU5fZOWM5bUf2xbFsTgDRwsGahv06YOVsR0ZKOlcPnmbbL0t4/3+DMLe3LvR+FOV+ws+nPBPHjqW0y7Nj1PJlDBw6hLVLlmJhbo5b6dI4Ojjw5/z5jB01CkMDA1auW0tUdDRPX3HuJcTbkuCZKPFu3rzJmTNn2LRpEwA6Ojp069aNRYsWaQyeXbx4kS5dumhcZ3BwMOPHj+fUqVM8ffpUlb0SGhqqFjyrWLGi6m9HR0cgZ2ilt7d3ge9x/fp1UlNTadFC/Ud9eno6VapUybdOUFAQOjo6VH9hmIy3tzcWL6Qqnzt3jsTERKyt1Q+EKSkpasNOX2Xu3Ln89ddf3L9/n5SUFNLT06lcufI/rhMZGcmDBw8YOHAgH3/8sapOZmYm5i/9GHuxf/Hx8Tx+/Jh69dQnna1Xr16eoagFfRdZWVn8+OOPrF27lkePHqkCW8YvnGz36tWLQYMGMXv2bPT19Vm5ciXdu3dXzaNw7tw5AgMD1TLNsrKySE1NJTk5GaOX5m+YOnWqWkARYMKECXi2r13g55ifl6/fKPNZBjkZgJtnLaNR59ZYO+adu6kkyXNVSqnM90pVdnY22+eupu57LbBysNW4PmW2EiNTE1oO+AAtLS0c3F1IjI0ncOfhdxY823XmFFNXrVA9/23IMCBnqIRaW5X5LHyJkb4BK8d+S3JaKoE3b/DbhnU429hSrZxXgfXeJb2mDTEakTu/VOLYSWReDSrGFr2JvP9Vb3ul1NDIkN/+mkVKSgqXz19k0ewF2Ds5UqFKxVdXfoVde/cyZdo01fMZP/0E5NMLDf9Hal56/eU69Wrn7qM8gYq+vnTq3p3tu3bR+9lFDPEWXneHDpjbW6v9sLXzcCEpJp5r+0+rgmfht+5zec8JanVvha2bE/GRMQSu38elnceo1Obth9Dt2reXKdOnq57P+PHZtpdnv/YPtr2X/u+ys7Mp7+XF0GcX+rzLlePuvRA2bt2aJ3iWmZnJ2O8nkp2dzddfjHzTbv2/sOvgAaa8MIXGjEk/APntN/49k47rVquMUbf3VM8T5y3RUFJBzj/Xq+nVrkHW43CyQtUzuzKDbqn+zg57QmLIfczGf4VezaqkHTr2hi1/Q/nspzXtKCwcbFQ3BgCw93AhMSaeK/tOqoJndh4u2Hm45JYp48rmKQu4diiQut3efpTCu9xP5HuM6tmD7bt307tbN3R0dPj5+0lM+vknmrZri7a2NjWrVaPus8QDIYqSBM9Eibdw4UIyMzNxds69e51SqURXV5eYmJg8c7UAr5wssn379ri6urJgwQKcnJzIzs7Gz8+P9HT1iUdfnMz++Y79eaCtoPd4XmbHjh1q7QbN2UnPh6EWdNDJzs7G0dGRQ4cO5XntxSBbQdatW8cXX3zB9OnTqVOnDqampvzyyy+qIY7/pM7z/i5YsECVNfec9kuZRMb5XD1+uc/5HXwL+i6mT5/Ob7/9xowZM6hQoQLGxsaMGDFC7fts37492dnZ7Nixgxo1anD06FF+/TV3MtLs7GwmTpyolhX3nEE+E7+PGTOGkSPVT+j19fVZfzUgT1lNjEyNUWhpkRgXr7Y8OS4BY3PTPOXTU1IJu/uA8JBH7FqyEXi23SiV/ND7C3qN+RR33+Id+mf4rE9JsepXiZPjEzEyyzvsJj0ljSf3HhJx/zEHlufc7e95n6YP+IYuoz+iVHlPjC1M0dLWVpvjw8rRjqS4BLIyM9/J3G4NK1bGzy13qEV6Zs7wj6j4eGzMLVTLYxLisX4pG+1lWlpauNrlBEC9XEsREhbGkt07S1TwLP3kGTJvvPDj4unrDQ0vTqbmZmhpaxH70jD2uJg4LCwt3mrdWlpaOLrkzLHnUbYMD+8/YOPKtYUSPGtYv77aZMnpGTnb1tPoaGxeuMtgdGwsVlaahxVZW1mpstZUdWJisCogQ8PQ0JAyHh48eFjwkCFRMH0TIxRaijxZZqkJSXmy0Qpi6+7M3TO5Q5gubDtMmZp+lKtXGQBLZzsy0zI4uWonFVvVQ6H1dkGRhvXq4+eTz7YXFY2N9YvbXsHb0csZjgDRMbFqdWysrXF/6a6P7qVLc/CI+nQWmZmZfDNhAo/Dwpjz2wzJOtOgYe06+Hl5q56rvruYGGxeuMgaHRuL1Vvu/96VjKvXSbj/IHeBTs45pJapKVkv3EhIy9QEZUL+mVpqdHXRq1qJlF2a5+FSSc8gKywcLduiu7OrwfP9RJx621MTkvNkoxXEzt2ZO2euaHxdoaXAtrQT8RGFc9x+l/uJlxkaGlLGXf0Y5ePlxaqFi0hMTCQjMxNLCwv6Df6E8l4l5xyqsGj9Px4iWRJJ8EyUaJmZmSxbtozp06fTsmVLtdc++OADVq5cyWeffZanXsWKFTlw4ECezCDIGQ4TFBTEvHnzaNCgAQDHjr35FaaC3qN8+fLo6+sTGhqa7xDN/Pj4+JCZmcnZs2epWbMmkJN1FxsbqypTtWpVwsPD0dHRwa2A244X5OjRo9StW5chQ3LvyPOqrLVX1bG3t8fZ2Zm7d++q5hB7HWZmZjg5OXHs2DEavnCb6hMnTqg+g9dx9OhROnbsqBram52dze3bt9WGvBoaGvL++++zcuVK7ty5Q7ly5aj2wrwgVatW5ebNm3h6vt48Mvr6+m88TPNl2jo6OLq7cvfKTbxrVFItv3v1JuWqVcj7noYGfPLT12rLzu47Rsi123QeMQAL28JPzX9T2jo62Ls5E3LtNmWr52Zxhly7jWcV3zzl9Q316TdZPQh58cBJHgTdof1nfTC3zQkUOJd1I+jURZTZ2SieBdBinjzF2ML0nd0UwdjAQO0OmkqlEmszc04HXcfLNSdLJCMzk/O3bzHsvQ80rSZfSpSkF/HNKd5YSirZKZqHX5REurq6lClXlotnL1C7YW5G68Wz56lVv06hvpdSqSQjI+PVBV+DsZGR2t3JlEol1lZWnA4MxLtcTkA8IyOD8xcvMmyw5rtNVvTz4/TZs/Tq1k217HRgIBX9NM/Nlp6eTsj9+1Sp+PZBwP/PtHW0sS7lSFjQPUpXzv0B9/jGPVwrvv5FjeiH4Ri+MOw9Mz0zz8Wk5wEzJcq3zijSuO2dPau+7V26xLBPPtG4noq+vpw+G0ivF6Z/OB0YSEXf3G2vkl8F7oc+UKt3/+EDHO3tVc+fB85CHz1k3oyZMpS4ABq/u/Pn8X52LpORkcH5K5cZNnBgcTXzzaSlk52mfgEgOy4eHS9Psh49m/dKWxudMu6kbNv1ytXpVakIOtpkBF549Xtra6Ntb0dmcMg/aPjr0dbRxqaUI4+C7uJWJTfw+SjoLqUrvf5+IupBOEb5XGR9TqlUEvUwHCvnwhml8C73Ey9LT08nJDT/Y9TzuRBDHz4g6OZNPv23bOfiX0uCZ6JE2759OzExMQwcODDP8L/OnTuzcOHCfINnY8aMoUKFCgwZMoTBgwejp6dHQEAAXbp0wcrKCmtra+bPn4+joyOhoaF88803b9y2gt7DxsaGUaNG8cUXX5CdnU39+vWJj4/nxIkTmJiYqM0B9pyXlxetWrXi448/Zv78+ejo6DBixAi1DLfmzZtTp04dOnXqxE8//YSXlxePHz9m586ddOrUSW1IpCaenp4sW7aMPXv24O7uzvLlywkMDMTd3f2t6nz33XcMHz4cMzMzWrduTVpaGmfPniUmJiZPhtaLRo8ezYQJEyhTpgyVK1dm8eLFXLx4Md87jRbUvo0bN3LixAksLS359ddfCQ8PVwueQc7Qzfbt23Pt2jVVoO25b7/9lnbt2uHq6kqXLl3Q0tLi8uXLXLlyhR9++OG12/KmardpzJbZK3DyKIVzWTcuHDxB3NMYqjXL+eF/YM02EqLj6DSkNwotLexc1e8uaWxmio6ertryrMxMIh+Gq/5OiI4jPOQhegb6BQ6NLCzVWzVg57y1OLi74ORZissBp0mIiqVS05xU/CPrdpEYE0ebT7qj0NLC1sVBrb6RmTHaujpqyys1rcP5/cc5uPJvqrSoR0z4U05vO0jVFupDft8lhUJBj6bNWLx7J652drja2rNk904M9PTwr5GbgTlhyUJsLSz5rFNOVuPi3TspX9oNZxtbMrMyOX71CjtOneKbHq8feC4uClMTtOxsUDybVFnLJSezNjs6FmVMbDG2LFfHru8xY/I0PL3K4uXrw97tu3gaEYl/hzYALJ+/mKjIKEaMy50n8e7tnIsBqSmpxMfGcfd2MLq6Ori65QyJ2bBiLZ5eZXFwdiQzI5NzpwI5tOcAg0fmPf4UBoVCQY+uXVm8YgWlXF1xdXFh8fLlGOjr0+qF6QC+/eEH7Gxs+OxZQK17584MGjaMJStX0rh+fQ4dO8bps2dZ+ML8kTNmzaJB3bo42NsTExPDwmXLSEpKol3r1qoycfHxhD95QuTTpwDcDw0FcrIGbKzffZDeUFcfF/PcfZeTmQ1lbVyIT03iSeLbzWVXmMo3rcmxpX9jXdoRW3dnbh2/QFJMPF4NcubGO7clgOTYBBr07wDk3J3TxMocCydbsjKzuHvmKvcv3KTxx7kZ0K4VPLl+8AxWrvbYuDmTEBnDxe1HcK1QNs/d9gqDQqGgR5cuLF65glIuLjnb3ooVOdte8xe2vcmTsbO14bNBOT+Uu3fuzKDhw1myaiWN69Xn0PFjnD53loV/5m57Pbt04cOhQ1i0fDktmjThWlAQm7dtY9yzOUszMzP56tvx3Lx1i99+/ImsrCzVHEbmZmZqGegiL4VCQY9O77F4zWpKOTvh6uzM4tVrcr67Jk1V5b79+WfsbKz57MOcQENGRgZ3n/2PZ2RkEBn1lJvBwRgZGOD6bPREckoKD16YtP1ReDg3g4MxNzXFwa5op5FIO3wcgxZNyH4aRVbkUwxaNEGZkUH6uYuqMka9upIdF0fq9j1qdfVqVyfjyvV85zAz6NiGjKtBKGNiUZiaYNCyKQoDfdLPnCvS/vg1r83hxVuwKe2EnYczN49eIDEmDu+GORd0AzcfICk2gcYDOgFw9cBpTKzNsXS0JTsrizunrxBy4QbNPumsWuf57Yexc3fBzM6KjNQ0rgWcIerBE+p2b51fE95aUe4nZsyeRYO69XCwtyMmJjb3GPXC0O79AQFYWFjgYG/PnbvBTP/jDxrVr0/tGq9/4V2If0KCZ6JEW7hwIc2bN88TOIOczLMpU6Zw/nzeW8CXK1eOvXv3MnbsWGrWrImhoSG1atWiR48eaGlpsWbNGoYPH46fnx9eXl78/vvvNG7c+I3aVtB7QM4E+nZ2dkydOpW7d+9iYWFB1apVGTt2rMZ1Ll68mI8++ohGjRphb2/PDz/8wPjx41WvKxQKdu7cybhx4/jwww+JjIzEwcGBhg0bYv/ClduCDB48mIsXL9KtW7ecg1+PHgwZMoRduzRfwXudOh999BFGRkb88ssvfPXVVxgbG1OhQgVGjBhRYHuGDx9OfHw8X375JREREZQvX56///6bsmXLvlZ/AMaPH8+9e/fw9/fHyMiIQYMG0alTJ+Li4tTKNW3aFCsrK27evEnPnj3VXvP392f79u18//33/Pzzz+jq6uLt7c1HH3302u34J3zrVCUlMYkjm/aQGBuHrYsjPb76BItnGVeJsfHER73Zj8OEmDgWjP1F9fzkjoOc3HGQ0j6e9B0/rFDbnx/vWpVJSUzm5Nb9JMXGY+PswPsjP8TcJictPykunvjo2Ddap5m1BV1Gf0zAqm0s/d9vmFiYUbVlfWq2bVz4HXgDfVu2Ii0jg59WryIhOQlfdw/+GPaFWoZaeHS0WuZIaloaP61eSURsDPq6upR2cOT7AQNpWb1Gfm9RoujWqYHx6OGq5yb/y/nRm7JsDanL1xZXs9TUb9qI+LgE1i5bRUxUNKXc3Rj/0/fYOeTsI6Ojool8afLskR/lBsGCb97myP5D2DrYsWBtzt2C01JTmffbLKIin6Knr4dzKVe++N9o6jd9vczif6Jfz56kpaXx4/TpJCQm4ufjw5+//qp29T/8yRO1IR2VKlRg8oQJzPnrL+b+9Rcuzs5MnTgRvxdumPMkIoJxEycSGxeHpYUFfr6+LJ47F0eH3GD1kWPHmDh1qur52O++A+DjAQP45MMPi6zPmnjblubP93IvwgyvnzPf6M6gk0w+uFRTtXfOvXp50pJSuLTzGCnxiVg42tJsSDdMrHPOYVLiE0mKyR2mn52ZxdnNB0mOTUBbVwcLRxuaDemqdifNiq3rg0LBhW1HSI5NwMDECJcKnlTt0LjI+tGvx7Nt77dfc7e9adPVt72IJ2i9MGS0kl8FJn87gTkL/2LuwoW4ODkx9bvv1IYj+/r4MO2Hyfw5fx5/LVuKk4MDX342jNYtckYWRERGcuT4cQB6DlTfzubOmEl1DXPGilz9unYlLT2NH//8k4SEBPy8vflz6lT17y4yQu27i4yKoteQT1XPl2/YwPING6hasSLzf8mZi/H6rVsM/mq0qsxv8+YB0K5FC74blbu8KKQdOIxCVxfDzh1RGBmSdf8BiXMWQlru1BxalhbPJhzNpWVrg04ZdxJn/5XverUszDHu1wOFsRHKxCQy7z8g4dfZRX4hqEx1X9ISU7iw4wjJ8YlYOtni/1kPTK0tAEiOSyQxOnc/kZWZxZmN+0mKTUBHVwcLJ1v8h3bHtULueXJ6chrHVu4gOT4RPUN9rF0daDeqH3buzi+/faEpqv3Ek8hIxn3/wjGqfHkWz1E/Rj2NiuK3WX8S9WyIclt/fz7qmzcx4b/g//OdLUsihVKpfL3ZFoUQQrzSinO7i7sJhap3tVYsOLW1uJtR6D6u3ZH4g0eKuxmFyqxpQ2JavPfqgv8ilvs2ExR+t7ibUah8HDxI+Jfe+U4TUzs76s3SPJz03+j40LlMOVByAnOFYWyzfiSEPynuZhQ6U4fXu3j4b5MQcr+4m1CoTN1KE/v5m4/0KMksZv7ILwErXl3wX2R0k97/uf3Ev3kf8SCmZE6f4Wrp8OpC/0GFn+8thBBCCCGEEEIIIcR/hAzbFEIIIYQQQgghhChB3vamMKJwSeaZEEIIIYQQQgghhBAaSPBMCCGEEEIIIYQQQggNZNimEEIIIYQQQgghRAmiJXfbLFEk80wIIYQQQgghhBBCCA0keCaEEEIIIYQQQgghhAYybFMIIYQQQgghhBCiBFHIsM0SRTLPhBBCCCGEEEIIIYTQQIJnQgghhBBCCCGEEEJoIMM2hRBCCCGEEEIIIUoQudtmySKZZ0IIIYQQQgghhBBCaCDBMyGEEEIIIYQQQgghNJBhm0IIIYQQQgghhBAliAIZtlmSSOaZEEIIIYQQQgghhBAaSPBMCCGEEEIIIYQQQggNZNimEEIIIYQQQgghRAmikLttliiSeSaEEEIIIYQQQgghhAYSPBNCCCGEEEIIIYQQRWL27Nm4u7tjYGBAtWrVOHr0aIHlDx8+TLVq1TAwMMDDw4O5c+fmKbNx40bKly+Pvr4+5cuXZ/PmzUXVfECCZ0IIIYQQQgghhBAlii7aJfLxptauXcuIESMYN24cFy5coEGDBrRu3ZrQ0NB8y9+7d482bdrQoEEDLly4wNixYxk+fDgbN25UlTl58iTdunWjT58+XLp0iT59+tC1a1dOnz79jz/vV5HgmRBCCCGEEEIIIYQodL/++isDBw7ko48+wsfHhxkzZuDq6sqcOXPyLT937lxKlSrFjBkz8PHx4aOPPuLDDz9k2rRpqjIzZsygRYsWjBkzBm9vb8aMGUOzZs2YMWNGkfVDgmdCCCGEEEIIIYQQ4pXS0tKIj49Xe6SlpeVbNj09nXPnztGyZUu15S1btuTEiRP51jl58mSe8v7+/pw9e5aMjIwCy2haZ2GQu20KIUQh6l2tVXE3odB9XLtjcTehSJg1bVjcTSh0lvuKdq6H4uDj4FHcTSh0pnZ2xd2EQnd8aN65SP7txjbrV9xNKHSmDvbF3QTxmkzdShd3Ewqdxcwfi7sJhW50k97F3YRCJ/uJkkNbWTLvtjl16lQmTpyotmzChAl89913eco+ffqUrKws7O3Vtyt7e3vCw8PzXX94eHi+5TMzM3n69CmOjo4ay2haZ2GQ4JkQQhSiv68WPPnlv00Hvwbce/qouJtR6NxtnP9z/XK3cSb+wOHibkahMmvWiLUX9hV3MwpVtyotWBK4o7ibUaj612jLlANLi7sZhWpss37UmzW4uJtRqI4PncvuoJPF3YxC18qnDglBN4u7GYXK1MeL6YdXF3czCtWXjXqQcPFycTejUJlWrsiCU1uLuxmF6uPaHVkWuLO4m1Go+tZoU9xN+M8ZM2YMI0eOVFumr69fYB2FQj0QqFQq8yx7VfmXl7/pOt+WBM+EEEIIIYQQQgghxCvp6+u/Mlj2nI2NDdra2nkywiIiIvJkjj3n4OCQb3kdHR2sra0LLKNpnYVB5jwTQgghhBBCCCGEKEmys0vm4w3o6elRrVo19u1TH0mwb98+6tatm2+dOnXq5Cm/d+9eqlevjq6uboFlNK2zMEjmmRBCCCGEEEIIIYQodCNHjqRPnz5Ur16dOnXqMH/+fEJDQxk8OGd6hDFjxvDo0SOWLVsGwODBg/nzzz8ZOXIkH3/8MSdPnmThwoWsXp07nP3zzz+nYcOG/PTTT3Ts2JGtW7eyf/9+jh07VmT9kOCZEEIIIYQQQgghhCh03bp1Iyoqiu+//56wsDD8/PzYuXMnpUvn3BglLCyM0NBQVXl3d3d27tzJF198waxZs3BycuL333/ngw8+UJWpW7cua9as4X//+x/jx4+nTJkyrF27llq1ahVZPyR4JoQQQgghhBBCCFGSPJsk/79gyJAhDBkyJN/XlixZkmdZo0aNOH/+fIHr7Ny5M507dy6M5r0WmfNMCCGEEEIIIYQQQggNJHgmhBBCCCGEEEIIIYQGMmxTCCGEEEIIIYQQoiT5Dw3b/C+QzDMhhBBCCCGEEEIIITSQ4JkQQgghhBBCCCGEEBrIsE0hhBBCCCGEEEKIkiRbhm2WJJJ5JoQQQgghhBBCCCGEBhI8E0IIIYQQQgghhBBCAxm2KYQQQgghhBBCCFGSKLOLuwXiBZJ5JoQQQgghhBBCCCGEBhI8E0IIIYQQQgghhBBCAxm2KYQQQgghhBBCCFGSKOVumyWJZJ4JIYQQQgghhBBCCKGBBM+EKEQ3b95k6tSppKWlFXdThBBCCCGEEEIIUQgkeCZEIUlISOC9997D3d0dfX39Qluvm5sbM2bM+Mf1Q0JCUCgUXLx4sdDaJIQQQgghhBCiCGUrS+bj/ymZ80wUiRMnTtCgQQNatGjB7t2737j+d999x5YtW/5VAZ9+/frx0Ucf0b179+JuiviXObE7gENb95AQE4u9qxMdBnTHo3y5fMveC7rNjuUbiHwUTnp6OpY21tRu2ZCG7VvmW/7isTOs/G0+vjUq0/+bz4qsD9s2bWXDqrVER0VR2t2NwcOH4le5osbyly9cYv4fs7l/LwRrGxu69OxG2/c6qF4f/dkXXLlwKU+9GnVqMWnaVACSk5JZtmARJ44cIzYmljLlPBk84jO8fLxLZJ8ANq/dwPbNfxP5JAIzC3MaNG7IgMEfo6evB8CVi5fYsGott2/cJjoqim+nfk/dhvULpT+vS6lUsmDHNjYfP0pCcjK+bu581a0nZZycNNY5eOE8S/bs4kFkBJlZWbja2dG7WQva1KrzDlue68zeIxzbdoDE2DhsXRxp3fcD3Hw88y17/0Ywe1dt5enjcDLSMrCwtaJ6s3rUbdtUVSYrM4sjW/dy8fBpEmJisXa0p2XPjpStXP5ddYlz+45zemcAibHx2Do70Lx3J1y9PV5Z7+Gte6z4YRa2Lg4MnDJKtfxm4GVO/L2fmCdPyc7KxtLehpptGlOhfvWi7IaaG4fPcW3/KZLjErFwtKVml+bYe5bKt2z4rfvsmbEyz/JO3w7C3MFG9fz6wTPcPHKepJh49I0NKV3Vm2odm6CtW7JOeSs5etKzSku87UphY2zBNzvncPRe3n1eSXV05wEObtlFfEwsDq7OvD+wJ2V8vfItG3z9FtuWrePJozAy0tKxtLWmrn8TmnTwf8etVqdUKpm/ZjWb9+4lISkR37Ll+PqTwZQplf82CBAcGsrcVSu5ERxMWGQEIz8cSM8OHfOUi4iK4o9lSzhx/jypaWmUdnJm/GfD8PHMfz9UWK4dOsPlPSdIjkvA0smOOt1a4Vi2dL5lH9+8x/bpS/Ms7zpxKBaOtgDcO3+dC7uOEh8RTXZWNuZ2VlRoUZdydSoVaT9eRalUMn/DejYf2E9CYiK+Zcvy9YcfUcbVVWOdzQf2s+PIYYIfPADAx92DIT164OdZ9l01W+XCgRME7jxMUlwCNk72NOnVARcv91fWe3QrhDVT52LjYk+/SV+ovZaalMKxjbu5ffYqqckpmNtY0bhHWzwq+RRVN9Sc3XeMUy8co1r07kQp7zKvrPfg1l2WPztGfTxltGr5jcDLHP97n9oxqnabxlSoX6MouyFEgUrWmYT4z1i0aBHDhg3jr7/+IjQ0lFIFnIj8V2zatKm4m/Cvk5GRga6ubnE3o1hdPH6Gvxev4b2Pe+Hm7cmpvUdYOHkmo2Z8j6WtdZ7yevr61GvdFMfSLugZ6HMv6DYb5y1HT1+f2i0bqZWNiYhi+9L1uPsU7Ynh4f0BzJs5i6Fffo5vRT92btnG/0Z9w/wVi7FzsM9TPvxxGONHjaF1+zZ89e1Yrl2+yqzpMzG3sKB+k4YAfDtlIhkZmao68XFxDOn/MQ2a5PZxxo/TCLl7j9HfjsHaxoYDe/Yx5vPRzF+5CBtb2xLXp4N79rNo7gJGjvkKnwq+PAp9wPTJPwPwyedDAUhNScXdswwt2rTih3HfvVUf/qll+/aw6uB+vu3Tn1L29izatYPP/viNDRMmYWxgkG8dc2NjBrRqg5u9A7o62hy9coXvly/F0tSMOuV932n7r5w4x66lG2k3sBulvDwI3H+MFT/O5rPp/8PCxipPeT19PWr5N8ShlDO6+nqE3gzm77/WoKevR/XmOYHLA2u3celYIB0H9cTGyZ47l4JYPX0BH38/Ekd3zT/WCsv1UxfYv2IL/v0/wKWcOxcOnmDtL/P5+KevMbex1FgvNTmFbXNX4eZblqS4BLXXDIyNqNuhOdZO9mjraHPnwnV2zF+DsZkJHhULJwBdkHtnrxO4YR+1urfCzsOFW8cusH/WWjqOH4SJlbnGep0mfIKeQW52t76pkervu2eucm5LAPX6tMPOw5m4J9EcX74dgJqdWxRdZ/4BQ1197kQ9ZOeNE0xpPbi4m/NGzh87zeZFq+jySV/cvctyYk8Acyf9ypg/pmCVz3FL30CfBm2a4+Tmip6+HneDbrNuzhL09fWp69/43XfgmaWbN7Hq761MGP45pZycWbh+HUMnfMvG2bMxNjTKt05qWhouDg40r1ePXxctzLdMfGIiA7/5muoVKjBz/ASszM15GB6OqbFxUXaH4MCrnFy7m/o922LvWYqgI2fZ9fsKun43FBNrC431uk76TO1/ysA0t536xoZUadMQCwcbtLW1uX/lFoeXbsHQzBhX36INBBZk6d9bWbVjOxM+HUopR0cWbtrI0MmT2PjbTIwNDfOtc+7aNfzr1qeiVzn0dfVY+vdWPpv8A+um/4qdVd7ttqjcOH2RgJXbaN63E87l3LgUcJqN0xcyYOqXmFlr3p+nJaewc/4aSpf3JClefX+elZnJ+l8WYGRmQofP+mBiZU5CdKza91qUrp+6wL4VW2jVvzOu5dw5f/AEa36Zzyc/ffPKY9Tfc1fh7luWxJeOUYbGRtTr0AKbZ8eo2xeusW3+GozMTCnzDo5RQuRHhm2KQpeUlMS6dev49NNPadeuHUuWLFF7fcmSJVhYWKgt27JlCwqFQvX6xIkTuXTpEgqFAoVCoVpHaGgoHTt2xMTEBDMzM7p27cqTJ09U6/nuu++oXLkyy5cvx83NDXNzc7p3705CQu4OOS0tjeHDh2NnZ4eBgQH169cnMDBQ9fqhQ4dQKBTs2bOHKlWqYGhoSNOmTYmIiGDXrl34+PhgZmZGjx49SE5OVtVr3LgxI0aMUD1fsWIF1atXx9TUFAcHB3r27ElERESBn11ERATt27fH0NAQd3d3Vq7Me5U9Li6OQYMGYWdnh5mZGU2bNuXSpde/Wp2VlcXAgQNxd3fH0NAQLy8vZs6cWSh1Fi9ejI+PDwYGBnh7ezN79mzVa8+Hj65bt47GjRtjYGDAihUryM7O5vvvv8fFxQV9fX0qV66slq34vN6mTZto0qQJRkZGVKpUiZMnT6rKREVF0aNHD1xcXDAyMqJChQqsXr1a9fq8efNwdnYmOztbrb0dOnSgX79+qufbtm2jWrVqGBgY4OHhwcSJE8nMzKQoHdm2jxpN61OreUPsXZzo+GF3LKwtObnnUL7lnT1KUaVBLRxKOWNlZ0O1RnXwquzLvaDbauWys7JZNXMBLbt1wMr+7QJJr7Jp7Xr827WmdYe2lHIrzeARn2FrZ8f2zX/nW37Hlm3Y2dsxeMRnlHIrTesObWnZtjUbVq9TlTE1M8PK2kr1uBB4DgN9Axo2zQmepaWlcezwEQYO/YQKlSvh5OJMn4H9cXB00Pi+xd2noKvX8K3gR5OWzXBwdKBarRo0btGUWzduqcrUqFOL/oMGUr9xw7fuwz+hVCpZfXA/A1q1oWmVqng6OfNd3wGkpqezJ/C0xnrVynnRpHIV3B0dcbG1o0fTZng6O3Mx+M47bH2OEzsOUrVJHao1rYutswNt+nXGzNqSwH1H8y3v6O5KxXrVsXN1xNLOmkoNauJZ0Yf7N4JVZS4dO0PDTi0pV8UXK3sbarZsgGclH47vOPhO+nRm12EqNa5F5Sa1sXG2p0Wf9zCztuDCgeMF1tu9aD3l61TF2TNv5knp8p541aiIjbM9lvY21GjVEDtXRx7cvFdU3VBz/eAZPOtWoly9ylg42lCzSwuMLcy4eeR8gfUMTY0xNDdRPbS0ck9lI+49wq6MCx41fDGxtsC5vAfu1csTdT+sqLvzxk6FXmPB6b85fPdicTfljR3auofazRtSp0UjHFydeP+jXljaWHF8d/7/Dy4epanWsDaOpZyxtrelRuO6eFepQPD1m++45bmUSiWrt/3NgC5daVqnLp6lSzPx8xGkpqWx+8gRjfV8y5bl8/4D8G/QED2d/C/+Ld20EXsbGyYM/xy/cuVwsrenZqVKuDg6FlV3ALi87yRe9avi3aAalo621O3WGhNLc64fPltgPUNTY4zMTVWPF/+nnLzcca/ig6WjLWZ2VlRoVhsrZ3vC74QWaV8KolQqWb1zBwPee5+mtWrhWaoUE4d+lvPdHTumsd4Pwz+ni78/Xm7uuDk7879PPkGpVHLmytV32Ho4u/soFRrWoGLjWlg72dO0VwdMrSy4eOBUgfX2LtmET50qOOaTnXvlSCCpicl0Gt4P53JumNtY4lLOHbtSmjPGC9PpXYeo3LgWVZ4do1o+O0adf8Uxatei9fjWqYqzp1ue10qX98T7hWNUzVaNnh2j7hZRL0oopbJkPv6fkuCZKHRr167Fy8sLLy8vevfuzeLFi1G+wT9Zt27d+PLLL/H19SUsLIywsDC6deuGUqmkU6dOREdHc/jwYfbt20dwcDDdunVTqx8cHMyWLVvYvn0727dv5/Dhw/z444+q17/66is2btzI0qVLOX/+PJ6envj7+xMdHa22nu+++44///yTEydO8ODBA7p27cqMGTNYtWoVO3bsYN++ffzxxx8a+5Gens6kSZO4dOkSW7Zs4d69e/Tv37/Avvfv35+QkBAOHjzIhg0bmD17tlrATalU0rZtW8LDw9m5cyfnzp2jatWqNGvWLE/7NcnOzsbFxYV169Zx/fp1vv32W8aOHcu6deveqs6CBQsYN24ckydPJigoiClTpjB+/HiWLlUfEvD1118zfPhwgoKC8Pf3Z+bMmUyfPp1p06Zx+fJl/P396dChA7dvqweDxo0bx6hRo7h48SLlypWjR48eqsBWamoq1apVY/v27Vy9epVBgwbRp08fTp/O+bHfpUsXnj59SkBAgGp9MTEx7Nmzh169egGwZ88eevfuzfDhw7l+/Trz5s1jyZIlTJ48+bU+138iMyOTR8H3KVdZPTOnXCVf7t8M1lBL3aO7oYTcDMbDV32Y57712zA2M6Vm8waF1t78ZGRkcPvmLarWVB/qVbVmdYKuXsu3TtDVa3nKV6tVnds3bmoMVu7ZvotGzZtg8OyKclZmFtlZ2ejp6amV09PX59rltzsRLqo++VaqwO2bt7h5PQiAsEePCTx5mpp1a71VewvTo6inRMXHU9sndziinq4uVcuW4/Ld1zthVSqVnLkRxP0nT6j6jofDZGZmEnbvAWUqqg9T8azoQ+it1wsKhd17wINbd3Ern9v2zIxMdF7KktXV0yX0xuv9n76NrMxMwu89xN1P/X/c3c+Lh7dDNNa7fPgMMU+iaPB+/kO6X6RUKgm5eovo8EhKvcZQ0LeVlZlFVGgYTj7q7+Xk407k3YcF1t02dSHrvpnJnpkrCbsZovaafRkXokLDiQx5DEDC0xgeXQ3Gxa/4MmT+azIzMnkQHIJXZT+15V6V/bh34/WC5Q/v3ufejdt4+hVf9sijJ0+IiomhduXKqmV6urpU9fPl8o2gt1r3kTNn8PH05Ouff6RFvz70/OJzNu/d85YtLlhWZiZPQx/jUl59mJxL+TI8CX5QYN1Nk+axfNQ0tv+6lMc3NO8nlUolj4LuEvckSuNQ0HfhUUQEUbGx1K6YO3RUT1eXquXLc/nW6wdkU9PSyczMxNzEpCiama+szEyehDzC7aX9uZtfWR7fCdFY78qRQGIjoqjbqXm+rwdfuI6TZ2kOLNvM7GHfs3jsdE5tO5jnonFRyMrMJOzeQ9z91Idte7ziGHXp8Glinjyl4fuvHr6tVCq5pzpGvXooqBBFRYZtikK3cOFCevfuDUCrVq1ITEzkwIEDNG+e/w7/ZYaGhpiYmKCjo4ODg4Nq+b59+7h8+TL37t3D9dmcBsuXL8fX15fAwEBq1MgZA5+dnc2SJUswNTUFoE+fPhw4cIDJkyeTlJTEnDlzWLJkCa1btwZygj779u1j4cKFjB6dO9b+hx9+oF69egAMHDiQMWPGEBwcjIdHzsl+586dCQgI4Ouvv863Hx9++KHqbw8PD37//Xdq1qxJYmIiJvkcqG/dusWuXbs4deoUtWrVUn2WPj65PwIDAgK4cuUKERERqpsSTJs2jS1btrBhwwYGDRr0ys9XV1eXiRMnqp67u7tz4sQJ1q1bR9euXf9xnUmTJjF9+nTef/99VZnnQagXs7tGjBihKvO8/V9//bVqrriffvqJgIAAZsyYwaxZs1TlRo0aRdu2bQGYOHEivr6+3LlzB29vb5ydnRk1Kncun2HDhrF7927Wr19PrVq1sLKyolWrVqxatYpmzZoBsH79eqysrFTPJ0+ezDfffKNqq4eHB5MmTeKrr75iwoQJeT6TtLS0PHdVfdMbRSQlJJKdnY2puZnachMLMxJi4wqs+8PHo0mMTyA7O4sWXTtQq3luptK9G7cJPHCML6Z/+0bt+SfiY+Ny5qKwUk/Lt7S0JDoq/4BuTHQMlpYvlbeyJCsri7jYOKxt1IdP3LweRMjde3wxJvc7NjI2wsevPKuWLKdU6VJYWFlyaP9Bbl4PwsnFuUT2qXHzpsTFxPLlp5+jVCrJysqi3Xsd6Nan51u1tzBFxcUDYGWqvk1amZoRHh1VYN3ElGTajP2a9IwMtLW0+Lp7T2r5vLs5wQCS43P+p0zMTdWWG5ubkhgbX2DdaUP+R1J8ItlZWTTp3IZqTeuqXvOs6MOJnQdx8/HE0t6Gu1dvcuPsZbLfwaS5yQlJKLOzMc6nT0mxCfnWiQ6PJGDtdnqP/wwtbW2N605NTuHPYRPJysxEoaWFf/8PcK+Q/7xVhSktMRllthJDU/VhbAZmxqTEJ+Vbx9DchDo9W2NdypGszEzunr7K3t9X4T+iNw5lczIx3Kv7kvp/7N11dBRX+8Dxb4y4u4cQBYK7hECB4BSneJGW4qUt0PalQGmpUFpeatAWL+7u7u4EC4RgAeKuu78/AptsshugJCRvf8/nnD0nO3tn9t7dyczsM8+9NymV7TMW5d4cVyjwb1yDoNAGGrcpXl1KUhIKhQILK/VjhLmlBUlxRZ+3vhj0IckJueet1j3epn6LJkWWL0kx8XEA2BboCWFracWjp09fa9sPHkexZvs2enfoyLtdu3Hl5k1++OtPDAwMaNe02Ys38A+kP/+fslD/nzK2MCU1MVnjOiaW5jTu2x57D2dysnO4efwCm39aSPuPBuDs56Uql5mazt/jZ5CTlYOurg4Ne7UtFKR7k2Li4wGwtVTv3m1racmjp9EvvZ1fli7B3saGOkFBxVm9IqU9O56bWKr/DjCxNC/Utf65uKinHFq1jZ6ff6D1eJ7wNJbIsHAC61en89iBxD+OZvei9Shycmjwdsl2WX9+jnqV8+7zc1TfiSNfeI6aNXKy6hzVakBXvN/AOUoIbSR4JorV9evXOXnypGr8L319fXr06MG8efNeOnimTVhYGO7u7qrAGUDFihWxsrIiLCxMFTzz8vJSBc4AnJ2dVdlb4eHhZGVlqYJikBsYqlOnDmFh6ncaq1TJGxjc0dERExMTVeDs+bKTJ09qre+5c+eYPHky58+fJzY2VnX3JzIykooVC/+gDAsLQ19fn1q18rJXAgIC1Lq4njlzhuTkZGxt1YMLaWlphIe/fAbE7Nmz+euvv7h79y5paWlkZmZSLd/d11dd5+nTp9y7d49BgwYxZMgQ1TrZ2dlYFri4yd++xMREHj58qPZ9ADRs2LBQV9T834fzs64PT548ISAggJycHL799ltWrFjBgwcPVIEt03zji/Tu3Zv33nuP3377DUNDQ5YsWULPnj3Re3bSPnPmDKdOnVLLNMvJySE9PZ3U1FRMTNTHP/nmm2/UAooAkyZNokbXt4r8HDV61mVZRakEdDQWfW7YV+PISM8g8sZttv69BjsnB6o3rkt6WjrL/juXrh/0w9TCvMhtFKsCbVCiVHXFfqnyyueLC6+zffM2vLzL419RPZvok4mf8tM30+n9dnd09XTx8fMlpMVbhN+4WWgb/0gxt+nC2fMsX7SE4R+NJqBSIA/vP2D2f3/Fev5ier/bt3jq/Iq2nTzBN8v+Vj3/6YMRz+qsXk6JsvDCAkwMjVjy6URSMzI4dT2Mn9aswtXOnpp+pXChW6iqL/jugEGTx5CZnsG9mxHsWrYBGyd7qjTMPV61GdCVDX8sY9bYqejo6GDtaEf1kHqc2190N5tipWF/1PSdKBQKNvz6N427tMLW2aHITRoaGTLw64/Iysgk4spN9izZgJW9LZ4V31CmVsHqF3Hos3S0xdIx79zn4O1GSlwiV3afUAXPom7c5eKOo9Tt2Qp7LxcSn8ZxatUuLmw9TNU2b3bijX8/DV9e0f9ijJ72GRlp6UTcCGfT4lXYOTtSM7heidUwv20H9jPt97yhJGb+J/fmkg4F/69eeKh7IYVSScUKPgzv2w+AAO8K3I6MZM32bSUWPHuuUHuKiO9bOdlhlW+yDccK7iTHJXJh51G14JmBUTm6TBxKVkYmD8PucHzVDizsrXF5iQHui8O2Q4eY9ucc1fOZEz4FCl8vKF98mlJZuGEDO44cZs6kKRgWyGB/Ewqdj5Saz1EKhYLNs5fRoFMLbJy0D8GhVCgxMTej5btd0NXVxam8G8nxiZzaeqDEg2cqhc5Rmq/pFAoF639d/NLnqMFff0xmRiYRV26we8l6rN/kOaosUJZ89qB4eRI8E8Vq7ty5ZGdn4+qal/mhVCoxMDAgLi43M0NXV7dQN86srKwXblup5cRScHnBAeh1dHRUgavn71v4hFt42/m3o6OjU+R2C0pJSaFly5a0bNmSv//+G3t7eyIjIwkNDSUzM1Nr+zTVLT+FQoGzszP79+8v9FrBceS0WblyJR9++CEzZsygfv36mJubM336dFUXx3+yzvPP4c8//1RlzT2nV+COkqmGAXP/yfeR/31nzJjBTz/9xMyZMwkKCsLU1JQxY8aofdbt27dHoVCwZcsWateuzaFDh/jxxx9VrysUCqZMmaKWFfeckYZB0j/99FPGjh2rtszQ0JAdN7UHVAsyNc8dr6dglllyQhLmBe7qF/R8HDNnTzeSEhLZtXIj1RvXJSbqCXFPopn/TV6X4uf71vhu7/HJz19h51T0xcqrsLCyRFdPl7gCGVnxcfGFMrees7axJi62YPk49PT0sCiQhZeens6B3fvoN3hAoe24uLky/deZpKelkZKSiq2dLdMmfomjs1OhsmWhTYv+nE+z0Ba07pCbQVm+gjfp6enM+u5H3unfW22cmTcluEpVKnvl/QDKfNbFNCYxETtLK9XyuKQkbM2L3id1dXVxd8jdt/zd3YmIimLBjm1vNHhmYpH7P5VcICMrJSG5UOZWQdYOuT8iHT1cSU5IYt/qrargmamFOb0+fo+szCzSklMwt7Zk19INWDmU/CDTJuam6OjqklLgDn5qQjKmloWzmDPTMoi6c4/Hdx+wc2HujSzlszFKvu33MT3Hv49XpdwuqTq6uqofZI6ersQ8eMyxTXtK/IeJoZkJOro6hbLM0pNSCmWjFcW+vCu3T+Z10z636QAV6lTGr2E1AKxdHcjOyOLY0q1UadUQHd3XjIoITM1zx8RKLHDeSkpIwtxK+0QPALbPzlsuXu4kxSeyffn6NxY8C65Th8p+eV3lMp9NSBMdH4edTd5EIrEJ8di85PWUNnbW1pQvMOtjeTc39h47+lrbLYrRs/+pgllm6UkpmFi8fLdEh/Ju3DpxUW2Zjq4uls+OdXbuzsRFPeX8tsNvLHgWXKsWlX3zjkl53108dvkyvmMTE7DJd97SZvGmjcxfv5bf/vMFvp5vtvupsep4rn6OSk1M1vg9ZaZl8PjOfZ7cfciexRuAvOP5jHcn0O2TwXhU9MHUyhxdPT216wgbZwdSEpLIyc5GT7/kfvI/P0cVzDJLTUjSeN7NTMvg0Z17RN19wI4C56hp/T6i1/ihGs9RTp6uRD94zNFNu/9/Bc9EmSLBM1FssrOzWbRoETNmzKBlS/UxVrp06cKSJUsYMWIE9vb2JCUlkZKSogqknD9/Xq18uXLlyMnJUVtWsWJFIiMjuXfvnir77OrVqyQkJKh1bSyKj48P5cqV4/Dhw/TqldtVKisri9OnT6sN9v+6rl27RnR0NN9++62qrqdPFz1ga2BgINnZ2Zw+fZo6deoAuZl88c/S0wFq1KhBVFQU+vr6eHl5/aO6HTp0iAYNGjBs2DDVshdlrb1oHUdHR1xdXbl9+7ZqDLGXYWFhgYuLC4cPHyY4OK/b4dGjR1Wfwcs4dOgQHTt2VHUXVigU3Lx5U22/MDY2pnPnzixZsoRbt27h5+dHzZo1Va/XqFGD69ev4/OS08gbGhq+cjfNgvQN9HGt4MnNC1cJqltDtfzGxatUql3tpbejVCrJfnYx6eDqzEc/qWfEbV+6joz0dDoOfAcr28KzDb4OAwMDfP39OHfqDA2b5I2vdu7UGeo10txVKrByJU4cOaa27OzJ0/gG+KNf4ALv4J79ZGVl0ixUe+aqkbExRsbGJCUmcebkKQYNe/81WlRybcrISC8UIHt+M+FVxoUsTqZGRmozaCqVSmwtLDgRdhV/99xsnqzsbM7evMHItwsHlouiVCpVwbg3RV9fH+fy7oRfukbFOnnj4YRfukZArVfomqNUkpNVuO4G5QwwsLEiJzuHqyfPU6leDQ0rFy89fX2cyrtx5/IN/GvnZeDeuXwDv5qFZzI1NDZk8DefqC07s/sId6/eovOo/ljaaz8GKNHc7uKmp6+HrYczj8Lu4FktL7j68Nod3Kv4FbGmutj7URjnCyBmZ2YXuvHyPGCmRFkoK0e8On0DfdwreHH9/BWq1ss7h14/f4WgutVffkNKJdkvceO0uJgam6jNoKlUKrG1tubE+fMEeOd2QczKyuLs5SuMzDfUxD9RNSCQuw8eqC27+/AhzvbFd+OqID19few8XHhwNZzy1fOufe6HheNV9eXHlou596hQl8JClLnjXL0ppsbGajNoKpVKbK2sOHHxIgHlcwN4WdlZnL16lZG9+hS5rUUbNzB37Rp++ew/VKzw5rue6unr4+jlSsSVm/jWyhs3MOLKTXyqaz6e9/9a/Ubt+T3HuBd2i/Yj+qqO566+XoQdP49SoUDn2XVG3ONoTK3MSzRwBrltcn52jgoodI6qXKi8obEhQ74Zp7Ys9xx1k86jBmBVxDkKUF3vClEaJHgmis3mzZuJi4tj0KBBhbrqde3alblz5zJixAjq1q2LiYkJn332GSNHjuTkyZOFZuT08vLizp07nD9/Hjc3N8zNzWnevDlVqlShd+/ezJw5k+zsbIYNG0aTJk3UugIWxdTUlA8++IBPPvkEGxsbPDw8+P7770lNTWXQoEHF9VHg4eFBuXLl+Pnnnxk6dCiXL19m6tSpRa7j7+9Pq1atGDJkCH/88Qf6+vqMGTMG43wXDM2bN6d+/fq8/fbbfPfdd/j7+/Pw4UO2bt3K22+//VKfg4+PD4sWLWLHjh2UL1+exYsXc+rUKcqX134H8WXWmTx5MqNGjcLCwoLWrVuTkZHB6dOniYuLK5Shld8nn3zCpEmTqFChAtWqVWP+/PmcP39e40yjRdVvzZo1HD16FGtra3788UeioqIKBVV79+5N+/btuXLliirQ9twXX3xBu3btcHd3p1u3bujq6nLx4kUuXbrEV1999dJ1eVXB7VuwfNZc3Cp44envzYldB4mPjqV+yxAAtv69hoTYeN4Zlbt/Htm2F2s7G+xdc7uuRly7ycGNO2nYOrcriEE5A5w81Mf8MjLN/cFQcHlx6dyjG9OnfoNvgD+BlSuybcNmnjx+TNtO7QGY9/ufxERH88nE3K4Wbd9uz8Y165kz6zdad2hL2OWr7Ni8jQmT/1No2zs2b6NB40ZYWBbOaDh94hQolbh5uPPw/gP++nUObh7utGzbqky2qW7D+qxbvpoKfj4EVMzttrnoz/nUa9RAlaGZlprGw/t5P7qiHj4i/MYtzC3McXByfO12vYiOjg7vNGvO/B3bcHdwxN3BgQXbt2FUrhyhtfOySictmIe9lRUjngXU5m/fRkVPT1zt7cnOzubIlctsOXGMCe+8fDC9uDRo24y1vy7C1dsDd7/ynN59hIToWGo/mzxj17INJMYm0GV4bpeqEzsOYGlng71L7ud793o4RzbvoW6rvPGY7t2MICkuHidPNxJj49m3eitKpZJGHV5vOIKXVad1Ezb9vhRnb3dcfbw4v+8YiTFxVH8rN5i7f8VmkuISaT+0Fzq6uti7q8/qZ2phhr6Bvtryoxt341zeHStHOxTZ2YSfD+Py4dOEDuj6RtpUsVkdDi/ciK2nM/blXblx5BwpcYn4N84NSJ5Zv4/U+CQaD+gA5M7OaWZjiZWLPTnZOdw+eZm7564TMiQvqOse5MPVvSexcXfEzsuVpKdxnN98EPcg31LJ7CyKsYEhbpZ53bBcLOzwtXMjMT2Fx8lxpVizFwvpGMrfM//Aw8cLL38fju7cT1x0DA1DmwKwafEqEmLi6DMmdxzWQ1t3Y21ni4Nb7v53O+wmezdsJ7jtm/n/0URHR4d32ndg/urVeLi44O7swvzVqzAyNKRVvpt5X8z8CQdbG0b0zQ2oZWVlcfte7gD8WdnZPI2N5frt25gYG+HunDuzYa8OHRk4YRzzVq2kRaNGXLlxk3U7d/D5sOEl2qYqLeqzb95a7DxdcKzgTtjBMyTHJhDYJPfa8OTa3aTEJ9J0YO7/zKXdxzC3tcLaxYGcnBxuHb/InbNhtBiaN/7tuW2HsPd0wcLeGkV2DpGXb3Lj2AUa925bom0pio6ODu+0acv89WvxcHbC3cmZ+evX5n53jfK6Z3/xy8842NgwolfueWjhhg3MXrmcr0aNxtnBnuhn496ZGBlhYmSs8b1KQq1Wjdk6ZwVO5d1w8fHg4r4TJMXEU7VZbhbmwZXbSI5LoM37PXOP527q2fQmFqboGeirLa/arD5ndx9h75KNVG/RkLioaE5s2kuNFurDopSUuq1D2PD7Epy93XHz8eLcvqMkxMRR49k5at+KzSTFJdBhaG90dHVx0HCO0jPQV1t+5Nk5ytrRlpzsHMLPh3Hp8ClaDej2RtpUZryBsVXFy5PgmSg2c+fOpXnz5oUCZ5CbeTZt2jTOnj1LjRo1+Pvvv/nkk0/4448/aN68OZMnT1Yb7L5Lly6sXbuWpk2bEh8fz/z58xkwYADr169n5MiRBAcHo6urS6tWrYqc8VKTb7/9FoVCQd++fUlKSqJWrVrs2LGj0GDfr8Pe3p4FCxbw2WefMWvWLGrUqMEPP/xAhw4dilxv/vz5DB48mCZNmuDo6MhXX33FxIkTVa/r6OiwdetWPv/8cwYOHMjTp09xcnIiODgYR8eX+2E9dOhQzp8/T48ePXIvQN55h2HDhrFt27bXWmfw4MGYmJgwffp0xo0bh6mpKUFBQS/M6Bs1ahSJiYl89NFHPHnyhIoVK7Jx40Z8fV9+lr6JEydy584dQkNDMTEx4b333uPtt98mIUG9W0mzZs2wsbHh+vXrqszD50JDQ9m8eTNffvkl33//PQYGBgQEBDB48OCXrsc/Ua1hHVKTUti9ahOJcQk4ebgw6LPRWD/rIpEYl0B8dN5A7Uqlkq1L1hL7JBo9PT1sHe1p3bsz9VqW3sDLTZo3JTExkSXzFxEXE4untxdTf/gGx2cTfsTGxPLkcd6ssU4uzkz94RvmzPqVzWs3YGNnywdjRtCoabDadu9H3uPKxUtM++l7je+bmpzC/Nl/Ev00GjMLcxo1acyA9wcVyl4rK23q1b8vOjo6LPxjHjFPo7G0tqJuw/oMeC8vcH/j2nXGj8wLNv/x8+8ANG8dysf/0Tw5SXHr1yKUjMxMvlu+hKTUVCp5lefnkWPUMtSi4mLVusClZ2bw3fKlPImPw9DAAE9HJ74cMIiWtWq/kTrnF9SgJmnJKexfs42k+EQc3J3pM2GY6m52UlwiCdF5XWyVSiW7l20k7mkMurq62Dja0eKdjtRqnvejIzsriz0rNhP3JJpyRob4VqtEl+H9MDY1KfT+JaFiveqkJaVyZN1OkuMTsXdzpvsnQ7C0y21TcnwSidGvFnDJyshkx4I1JMXGo1/OAFsXR9p/0JuK9V4he+g1lK9VkYyUNC5sPUxaYjJWzva8NawHZra51xBpicmkxOV1A1Jk53B63V5S45PQM9DHytmOt4Z1V5tJs0rrRqCjw7lNB0mNT8LIzAS3IB9qdAh5I216FQH2nvzSKe9/fVSj3B+EW8OO8fXehdpWKxNqNKpLSmIyO1ZsICEuAWcPV96fOBabZ12fE2PjiXua77ylULLp79XEPn6Krp4edk4OtO/bjQahIaXUglz9O3UmIyODb+fMJik5mcp+fvwyeYpahlrU06fo5stmfBobS++xY1TPF69fx+L166hRqTJ/fD0NgEq+vvww4TN+WbyIv1auwMXRkY8GDaZ1k5ASbU+F2pVJT0nl7JYDpCYkY+PiQOuRvTG3tQJyu9Elx+ZdF+Vk53B89U5S4pPQN9DH2sWBViN74RGUl/2ZnZHJ4aVbSIlLRN9AHysnO5oN6kyF2oUzit6k/h06kpGZybdz/yIpJYXKPj788tl/1DLUomKi0c13nlq9awdZ2dmM/3GG2raGdO3G+900T5hVEgLqViMtOZVjG3KDmXauTnQeOxBLu9zfISkJiSTGxr/SNi1srej2yRD2Ld3Ewv/8hJmVBTVaNqJO25Dib4AGFetVJzUphcPrdqjOUT0/eS/fOSqRhH9wjtq+YDVJsQnPzlEOdPygzxs7RwmhiY6ytPqKCCHEv9DGy4dKuwrFqkPlxtyJfvDigv9jytu5/uvaVd7OlcQ9B0q7GsXK4q0mrDi3q7SrUax6VG/BglNbSrsaxWpA7bZM21O2Az6v6rO3+tPw16GlXY1idWT4bLaHHXtxwf8xrQLrkxR2vbSrUazMA/2ZcWBZaVejWH3U5B2Szl98ccH/IebVqvDn8Q2lXY1iNaReRxad2lra1ShW/Wq3Ke0q/GNJ98vmtar5a85s/79KMs+EEEIIIYQQQgghyhLJcypTytYAEEIIIYQQQgghhBBClCESPBNCCCGEEEIIIYQQQgvptimEEEIIIYQQQghRlki3zTJFMs+EEEIIIYQQQgghhNBCgmdCCCGEEEIIIYQQQmgh3TaFEEIIIYQQQgghyhKForRrIPKRzDMhhBBCCCGEEEIIIbSQ4JkQQgghhBBCCCGEEFpIt00hhBBCCCGEEEKIskRm2yxTJPNMCCGEEEIIIYQQQggtJHgmhBBCCCGEEEIIIYQW0m1TCCGEEEIIIYQQoixRSLfNskQyz4QQQgghhBBCCCGE0EKCZ0IIIYQQQgghhBBCaCHdNoUQQgghhBBCCCHKEplts0yRzDMhhBBCCCGEEEIIIbSQ4JkQQgghhBBCCCGEEFpIt00hhBBCCCGEEEKIskSpKO0aiHwk80wIIYQQQgghhBBCCC0keCaEEEIIIYQQQgghhBbSbVMIIYQQQgghhBCiLFHIbJtliY5SKfOfCiGEEEIIIYQQQpQVSddvlnYVNDL39y3tKpQKyTwTQohi1Hvp5NKuQrFa0msySXciSrsaxc68vBcfrJle2tUoVr93+YT4fkNLuxrFymrRbPbfOlPa1ShWIT41+XjTz6VdjWL1Q/uRJEU9Lu1qFCtzJ0e2hx0r7WoUq1aB9Wn467/rGAFwZPhsfti/pLSrUaw+DunN0rM7SrsaxapXjVBmHVpZ2tUoVqMad2fkuh9LuxrF6udOY5l5cEVpV6NYjQnuUdpVEP8SEjwTQgghhBBCCCGEKEukk2CZIhMGCCGEEEIIIYQQQgihhQTPhBBCCCGEEEIIIYTQQrptCiGEEEIIIYQQQpQl0m2zTJHMMyGEEEIIIYQQQgghtJDgmRBCCCGEEEIIIYQQWki3TSGEEEIIIYQQQoiyRKEo7RqIfCTzTAghhBBCCCGEEEIILSR4JoQQQgghhBBCCCGEFtJtUwghhBBCCCGEEKIskdk2yxTJPBNCCCGEEEIIIYQQQgsJngkhhBBCCCGEEEIIoYV02xRCCCGEEEIIIYQoS6TbZpkimWdCCCGEEEIIIYQQQmghwTMhhBBCCCGEEEIIIbSQbptCCCGEEEIIIYQQZYlCum2WJZJ5JoQQQgghhBBCCCGEFhI8E+Jf5vHjx3z55ZfExcWVdlWEEEIIIYQQQoj/eRI8E/+TvLy8mDlz5hvdno6ODuvXr3+t95k8eTLVqlV7rW0URalU0rdvXwwNDbG2tn6ldYv7M31VwcHBLF26tNTeP7+PP/6YUaNGlXY1hBBCCCGEEP9fKRVl8/H/lATPxBvVvn17mjdvrvG1Y8eOoaOjw9mzZ99wreDUqVO89957b/x9i9s333yDt7c348ePf+V1S/Mz2Lx5M1FRUfTs2VO1rGAwT6lU8tFHH2Fubs7evXsBCAkJYcyYMYW2t3TpUvT09Bg6dKjG95szZw5Vq1bF1NQUKysrqlevznfffad6fdy4ccyfP587d+4UTwOFEEIIIYQQQvzPkgkDxBs1aNAgOnfuzN27d/H09FR7bd68eVSrVo0aNWq88XrZ29u/8fcsCZ999tk/Xrc0P4NZs2bx7rvvoqurOZ6fk5PDkCFD2LRpE3v37qV27dpFbm/evHmMGzeO33//nR9//BETExPVa3PnzmXs2LHMmjWLJk2akJGRwcWLF7l69aqqjIODAy1btmT27NlqQbWS0ty3Nm0DG2BlbM6DhCcsPrOd608jtZZv4BVEu8CGOJnbkpqVzsWHt1h6bifJmWkANK1Qg0blq+Ju5QDAndhHrLiwh9sxD0q8LfkplUr++Ptv1m3bSlJyMpX8Axg/fDgVvLy0rhMeEcHsxYu4dvMWj548Zuz779OrU2e1MmcvXWLx6lWE3bxJdGwsP3wxiZAGDUq4NbmCvavRwq82lkZmPEqMZtWFvdzS8rn2q9ma+l6VCy1/mBjN1F3zCy2v5RbAoLrtOf/wJnOOrS/uqhfJqFM7yoU0QsfUhJzwCFIXLUPx4FGR6xiGNqNcs2B0bW1QJiWTeeoc6avWQVa2qoyOtRXG3TuhX7USOgblUEQ9JnXuYnIitO/fxWH/5l3sXLuZhNh4XDxc6f5eP3wrB2gse/bISQ5u3c2923fJzsrG2dOV9r26UKlmVbVyqckprF+0knNHT5GanIKdoz1dB/cmqHb1Em3Lcw08gwjxqY65oSmPk2LZcOUQd2Ifai2vp6tLC7861HT1x9zQlPj0ZPbcPMWpe2GqMo3LV6W+VxDWxuakZKZx8dEttoYdI1uRUyJtUCqV/LFgPus2bSIpKYlKFSsyfsyHVChfvsj19hzYz+y5c7n/8CFuLi4MGzyEpsHBamWePH3Kz3Nmc/TECdIzMvB0d2fiuPEE+vuTnZ3Nb3/9yZHjx3nw6BFmpqbUqVmLke+/j72dXYm0Nb9DW/ewd/02EuPicXJ3pfOgXlSo5K+xbPjVG2xatJLHDx6RlZGJtb0tDUKb0rRDaInX83VVdfahV/WWBDh4YGdqxYStv3PozoXSrpZWV/ef4sLOY6QlJGHt4kC97i1x9vXUWPbh9Qi2/Lio0PJuU4Zh5VR4Hwo/dZm9f63Fs6o/LYf1KPa6v6xTOw9xdPMekuITcXBzIrRfFzwDKrxwvcjrt1nw5Swc3J0Z+u2r35AtTpf2neDcjsOkxidj4+JAo56tcfHz0lj2wbU7rP9hXqHlvaaOwto59zo35sFjTm7Yy9O7D0mKiadRj9ZUbfFmriGea1y+Km/51sLCyJRHiTGsvbSfcC3XEn1qhFLXs1Kh5Y8So5m2J3efdDK3pW1gA9ytHLA1tWTNxX3sDz9Xkk0o5PK+k5zfcZjUhGSsXexp2KOI7+n6HTb+UPg6qOeXI1Xf09WDp7l+7DyxD58AYO/pQt1OzXEs71ZibRDiRSTzTLxR7dq1w8HBgQULFqgtT01NZcWKFQwaNAiAo0ePEhwcjLGxMe7u7owaNYqUlBSt242MjKRjx46YmZlhYWFB9+7defz4sVqZjRs3UqtWLYyMjLCzs6Nz57wf5AWznG7evElwcDBGRkZUrFiRXbt2FXrP8ePH4+fnh4mJCd7e3kycOJGsrCy1Mt9++y2Ojo6Ym5szaNAg0tPTi/x89u/fj46ODjt27KB69eoYGxvTrFkznjx5wrZt2wgMDMTCwoJ33nmH1NRU1Xrbt2+nUaNGWFlZYWtrS7t27QgPD1e9vmjRIszMzLh586Zq2ciRI/Hz81N9rgU/Ax0dHebMmUO7du0wMTEhMDCQY8eOcevWLUJCQjA1NaV+/fpq7xMeHk7Hjh1xdHTEzMyM2rVrs3v37iLbHB0dze7du+nQoYPG1zMyMujWrRu7du3i4MGDLwycRUREcPToUSZMmEBAQACrV69We33Tpk10796dQYMG4ePjQ6VKlXjnnXeYOnWqWrkOHTqwbNmyIt+rONTzqETfGq3YcOUQn2+bzbUnkYwL6YOtiaXG8n72HnxQrxMHws8xfsuvzDq8Cm9bVwbXzfv8Ah29OHb3Ml/vXsiknXOJTklgQtO+WBubl3h78lu4aiVL161l3LDhLJz1M7Y21gz/7FNS8u27BaVnZODm5MyIgQOxtbbRWCYtPR3f8t6MGza8pKquUU03f7pVbcb2a8eZtmcht6LvM7xRV62f68oLexi/+TfV49Otv5OckcbZ+9cLlbUxsaBzUAg3n94r6WYUYti2JYat3iJt8XKSJn2LIiEBs3GjwchQ6zoG9etg1K0T6eu3kDRhCqlzF1Oubk2MunVSldExMcH8P5+gzMkh5YdfSPp0CmnLVqMs4vsvDqcOHmPln4to0+Nt/jNrGj6VA/h50nfEPonWWP7mlWsEVg9i5JRxfPbfr/CvUpFfv/yByPAIVZnsrGxm/ucbYh4/5f3PRvPlHz/Qd9RgrGw176PFraqLLx0qN2b3zdP8dHA5t2MfMrhue6yMzbSu07dma3zt3Fl5YS/f7VvMkrM7eJKcNxZmdVc/2gQ2YNeNk3y/729WXthDVRdf2gTWL7F2LFy2lKUrVzJuzBgWzvkDWxsbhn80tshjwsXLl/lsyhTatAxl2dx5tGkZyoTJk7ic74ZHYlISg0YMR19Pn/9+/z2rFi5izLDhmJvlfj7p6elcu3GTwf368/effzF96ldE3r/H2M8+LbG2Pnf28AnWzVtKy27t+eTHL6lQ0Y/ZU38k9mmMxvKGRoY0btOcUV9/xqc/T6Nltw5sXbKGozv2l3hdX5exgSG3Yu7z48HlpV2VFwo/dYVjK3dQvU0jOv3nPZx8PNj+81KSYxOKXK/bl8Pp/f1Y1cPCofAxICkmnhOrd+Hk41FS1X8pl4+dZfuitTR+uyXvfzMOD/8KLPn2dxKiY4tcLz01jfW/Lca7st8bqql2N09e4vDybdRq04TuX3yAs58nm/67mKSY+CLX6/3VaAbMGKd6WDraql7LzszCwt6a+l1aYGKp/RhaUmq4+tG5Sgg7rp/gu31/Ex7zgA8adNJ6LbH64j4+2zpb9Zi47Q9SMtM49yDvmr6cnj7RqQlsvHKYhPTkN9UUlVunLnFkxTZqtG1Cty8+wNnXky2z/n7h9/TO1FH0/+ET1SP/9/TwegS+darQ8aN36TxhCOY2lmz+aRHJcYkl3JoyRqksm4//pyR4Jt4ofX19+vXrx4IFC1Dm+8dbtWoVmZmZ9O7dm0uXLhEaGkrnzp25ePEiK1as4PDhw4wYMULjNpVKJW+//TaxsbEcOHCAXbt2ER4eTo8eeXf6tmzZQufOnWnbti3nzp1jz5491KpVS+P2FAoFnTt3Rk9Pj+PHjzN79myN3SDNzc1ZsGABV69e5b///S9//vknP/30k+r1lStXMmnSJL7++mtOnz6Ns7Mzv/3220t9TpMnT+aXX37h6NGj3Lt3j+7duzNz5kyWLl3Kli1b2LVrFz///LOqfFJSEh9++CGnTp1i9+7dKJVKOnXqhEKR2ye9X79+tGnTht69e5Odnc327duZM2cOS5YswdTUVGs9pk6dSr9+/Th//jwBAQH06tWL999/n08//ZTTp08DqH0vycnJtGnTht27d3Pu3DlCQ0Np3749kZHas0wOHz6sCs4VlJycTNu2bbly5QpHjhzRWKagefPm0bZtWywtLenTpw9z585Ve93JyYnjx49z9+7dIrdTp04d7t2798Jyr6t1QH323z7L/vCzPEyM5u+z24lJTaC5r+b908fWjacp8ey4cYKnKfHceBrJ3lun8bZxUZX57ehadt88xd34KB4lRvPXyY3o6uhQycm7RNuSn1KpZNm69bzbsyfNGjXCx8uLKR99THpGBtv37dO6XiV/f0YPGUJoSAjlDAw0lmlYuzbDBgygWaNGJVV9jd7yrcXRiEscibhEVFIsqy7uIy41iWDvahrLp2dnkpiRonp4WjthUs6IY3cvq5XTQYd3a7dlc9gRolOK/uFWEgxD3yJ94zayTp9H8eAhqX8sRKdcOcrVr6N1HX0fb7JvhpN17BSK6BiyL4eRefwU+uXzfiwatmuJIjaWtL8WkXM7Irfc1esotASxisvudVtp2DKERqFNcfZwpcd7/bC2s+XAVs2B/B7v9SO0a3u8/Crg6OpMp/49cXBx4uKJvCEEjuzaT0pSMsMmjsWnoj+2Dvb4VArA3Vtzlkpxa+JdjZORVzkZeZUnyXFsvHKI+LRk6nsGaSzvb+9BBVtX/jqxkZvR94hLS+Je/GPuxkWpynhZOxMR+4hzD24Ql5bEjaf3OP/gJm6WjiXSBqVSybJVq3i3b1+aBTfBx9ubKZ9+lntM2F34BtVzy1avom7NWrzbpw9enp6826cPdWrWZOmqVaoyC5cuwdHegUmffkrlwIq4ODtTp2ZN3FxdATAzM+O3H3+kRbNmeHl4EFSpEp+MGk3Y9etEFbjRVtz2b9hBvebB1G/RBCd3FzoP7o21nQ1Htu/VWN7N25OawfVw9nDF1tGe2iENCKgeRPjVwkH3suZ45BX+PLGRA7fPl3ZVXujS7mP4N6xOQKMaWDvbU79HKGbWllw9cLrI9YzNTTGxNFM9CmbMKxQK9s1dR432IZjbv9rYs8Xt+JZ9VG9ajxrNGmDv6kSr/l2wtLXm1K7DRa63+a8VVG5YCzdfrzdT0SKc33WUwEY1qBhcCxsXBxr3bIO5tQWX958scj1jC1NMLc1Vj/zfk2N5Nxp2a4VvnSro6b/5TlhNfWpyLOIyx+5e5nFSLGsv7ScuLYlG5atqLJ+enUlSRqrq4WHtiLGBEcfzXUtExj9mw+WDnH1wneyckskcLsqFXUcJaFSDio1rYu1sT6OebTCztuDKgVNFrmdsYYqJpbnqkf97aj6kK5Wb1sHOwxlrZ3ua9OuIUqnkQdjtkm6OEFpJ8Ey8cQMHDiQiIoL9+/erls2bN4/OnTtjbW3N9OnT6dWrF2PGjMHX15cGDRowa9YsFi1apDFza/fu3Vy8eJGlS5dSs2ZN6taty+LFizlw4ACnTuUetL/++mt69uzJlClTCAwMpGrVqlq7OO7evZuwsDAWL15MtWrVCA4OZtq0aYXK/ec//6FBgwZ4eXnRvn17PvroI1auXKl6febMmQwcOJDBgwfj7+/PV199RcWKFV/qM/rqq69o2LAh1atXZ9CgQRw4cIDff/+d6tWr07hxY7p27cq+fAGIbt260aVLF3x9falevTrz5s3j0qVLal0R58yZw6NHjxg1ahQDBgxg0qRJL8zievfdd+nevTt+fn6MHz+eiIgIevfuTWhoKIGBgYwePVrte6xatSrvv/8+QUFB+Pr68tVXX+Ht7c3GjRu1vkdERASOjo4au2xOnTqV8+fPc+jQITw8XnwHV6FQsGDBAvr06QNAz549Vdlyz02aNAkrKyu8vLzw9/dnwIABrFy5UhVofM712Y+uiIiIF77vP6Wnq0d5GxcuPQpXW34pKhxfO3eN69yMvoeNiQVVXXwBsDAypY57Rc4/vKmxPIChngF6OrqkZKQVX+Vf4EFUFDFxsdSrUVO1rFy5ctQICuJi2NUi1iyb9HR08bBy4urjCLXlYU8i8LZ1faltNPAK4tqTu8Smqt81bRvYgOSMNI5GXCqu6r40XXs7dK0syb6c15WP7Gyyr99E31d7sDX7xi30vTzQ8/ZSbcegamWyLuRdzBtUr0r2nUhMRgzB4pfvMZv6GeVCSjbgmZ2VTeStO1SsXkVtecUaQYSH3XipbSgUCtLT0jE1z7uxcPHEGbwDfFn623w+7j2UKcPGsXXFehQ5JT9orp6OLq6WDtwo0JX7xtNIvGycNa5Tyak89+Kf0LRCTSY2f5fxTfvQrmJD9HX1VGXuxD7EzcoBd6vcYJmNiQUBDp6EPYkokXY8ePSImNhY6tXKO++UK1eOGlWrcvHyZa3rXbxyhboFzlX1atfh4pW8dQ4eOUJggD/jv/iCFh070GvQINZt2lRkfZJTUtDR0cHMrOQyT7KzsrkXHoF/NfXu2/7VKnPn2i0ta6m7f/sud67dxEdLt2Px6nKyc4iOfIRrRfXui64VvXkcXnT279qv/uDvT35ky4+LeHi98Lio5zYfxMjchIBGb6Y7tzY52dk8vHOPClXU9xvvKgHcv6F9PNdz+48T9ziakC6tSrqKL5STnc3Tuw/xqOSjtty9kg9RL/ieVnz5G/M/+o71P8zn/rWyE2zR09HF3cqRa0/Ub8xee3yX8rYuWtZSV8+zMtef3CUuLakkqvjKcr+nR7gX+H/K/Z6KHqJh1Ze/s/Dj79k4Yz4PXvA9ZWdmocjJwdDU+LXrLMQ/JWOeiTcuICCABg0aMG/ePJo2bUp4eDiHDh1i586dAJw5c4Zbt26xZMkS1TpKpRKFQsGdO3cKZR+FhYXh7u6Ou3tesKFixYpYWVkRFhZG7dq1OX/+PEOGDHmp+oWFheHh4YGbW16f+vr1C3djWb16NTNnzuTWrVskJyeTnZ2NhYWF2nYKDlhfv359taCXNlWq5P3wc3R0VHUNzb/s5Mm8u26RkZFMnTqVEydOEB0drQoERUZGUrly7kW7tbU1c+fOJTQ0lAYNGjBhwoRXrgdAUFCQ2rL09HQSExOxsLAgJSWFKVOmsHnzZh4+fEh2djZpaWlFZp6lpaVhZGSk8bWWLVuye/dupk2b9lIzge7cuZOUlBRat24NgJ2dHS1btmTevHmqAKizszPHjh3j8uXLHDhwgKNHj9K/f3/++usvtm/frgriGRvnnpxTtXQnysjIICMjQ22ZoaH2bm6amBuaoKerS0K6epfkhLQULJ01/6C7GX2P346uZWTDrhjo6aOvq8eZ+9dYeHqr1vfpWa05sWlJXI56cxeQMXG53UJsC8z6amttzaPHT95YPYqLmaExerq6JBX4rpLSU7B01J69+ZyFkSmVHL2Zd3Kz2nJvW1caeAXx9Z6FxVrfl6VjmXvMUiSoB/QUCYno2mnvkph14jRpFmaY/edjQAcdfT0y9hwgY/MOVRldezsMmwWTsX03KZu2o+fthXGf7iizssg6cqJE2pOcmIRCocDCSr3bs7mVJYlxL5fVt2vdFjLTM6jZuJ5q2dOoJ8Q8vkrdkIaMnDyOJw+jWPb7AhQ5Ctr16lzE1l6fabln+16G+rEoKSMNc0MTjevYmFhS3saZbEU2C05vxbScEZ2DQjAxMGLlhT0AnH94E9Nyxgxv2AUdcoP5RyMusu/WmRJpR0xsbjdFWxv1/crW2oZHj6M0rfJsvViNx5GY2LyuZw8ePWLNhg307tadd/v04cq1MH6Y9V8MDAxo16pwECAjI4Nf/phDq+bNMSsi+/p1pSQ93x8t1JabW1qQ9IL98YtBH5KckIRCkUPrHm9Tv0WTEqvn/zfpyakoFUpMLNS/e2NzU9ISNQ8RYmJpRuM+7bDzdCYnK5ubJy6x5afFtBvbH2e/3AzUqFuRXD9yjs4T3y/xNrxIamIKSoUCM0v1roBmluaEJ2gOusQ8esKeZZt4d/JodPX0NJZ5k3K/JwXGFurXQ8YWZqRqaYOJlRkh/Tri4OlCTnY2149dYMOMBXT6ZKDW8bfeJNPn1xIZBa4lMlKx0HI8z8/C0JSKjuWLvOZ7055/TyYFvydzU1ITNHchNbE0p0nfDtg/+55uHL/Axh8X0vHjd7V+T8fX7MLUygK3im+uF0VZoFT8/+0iWRZJ8EyUikGDBjFixAh+/fVX5s+fj6enJ2+99RaQe9f//fffZ9SoUYXW05R9pFQq0dHRKXL580DIy1Bq6MddcPvHjx9XZbKFhoZiaWnJ8uXLmTFjxku/T1EM8nVX09HRUXv+fFn+TKl27dpRvnx5/vzzT1xcXFAoFHh5eZGZmam23sGDB9HT0+Phw4ekpKSoBfteph7alj2vyyeffMKOHTv44Ycf8PHxwdjYmK5duxaqR352dnbExcVpfO2tt95i1KhRdOzYkZycHLWuqprMmzeP2NhYtQkCFAoF586dY+rUqejluxisXLkylStXZvjw4Rw+fJjGjRtz4MABmjZtCkDssx9m2iZS+Oabb5gyZYraskmTJsE/GCJEifo+p6MDaDlXulrY069ma9ZdPsDFR+FYGZvRq1pLBtZpx58nCmf4tQtsSH3PIL7as4AsRbaGLRaPbXv3Mm3Wf1XPZ36ZO4Zcwf/M3P/LEqtGidNwdCj0/WlS37MyaVnpXMiXIWiob8C7tduw5OwOUjLfTFagQf06mLzbS/U8ecavuX8UPO7p6GjdBwH0A/wwat+atIXLyA6/g56jA8Z9uqOITyBjw7OLel0dcu7cJX31BgBy7t5Dz9UZw7ealFjwLK/+BZ4rNSzT4OT+o2xespZhE8eqBeCUCiXmVhb0GTkYXT1dPH29iY+NY+eaLSUePNOmqP+j58fmpWd3kp6de/zdeOUw/Wq1Zu2l/WQrcqhg68pbvrVYe2k/kXGPsTO1pGPlYJqnp7L7ZtFdbV7Gtl07mZbvnDjz2+801lvbObxAg9TXQX0dhUJBRX9/hj+bMTrAz4/bdyJYs2FDoeBZdnY2n305BYVCwfgPx75qs/4hDTvkC5o8etpnZKSlE3EjnE2LV2Hn7EjN4HpFryRKjJWTndrEAI4V3EmJTeDirmM4+3mSmZ7Bvnnrady3HUZmLw6CvDkF/neUmvc9hULB2l8WEdK1NbbODm+obi+n0OFBqdR6ALR2ssfaKe+6zamCB8mxCZzbcbhMBM+e+6fhkLqeFUnLyuDiw5fLXH2jNHwl2o7t1k52WOf7f3r+PZ3feUTj93Ru+yFunbxEx0/eRV/LkB5CvAkSPBOlonv37owePZqlS5eycOFChgwZojrA1qhRgytXruDj4/OCreSqWLEikZGR3Lt3T5V9dvXqVRISElRZalWqVGHPnj28++67L729hw8f4uKSm0J97NgxtTJHjhzB09OTzz//XLWs4NhYgYGBHD9+nH79+qmWHT9+/KXa9CpiYmK4dOkSv/32G3Xr1gXgwIEDhcodPXqU77//nk2bNjFhwgRGjhzJwoXFm+1y6NAhBgwYQKdOuYOGJycnv7DbY/Xq1YmKiiIuLg7rAtkFAC1atGDz5s20b98ehULBL7/8ovFkHBMTw4YNG1i+fDmVKuXNSqRQKGjcuDHbtm2jXbt2GuvwvDtt/kkpLl++jIGBgdq28vv0008ZO1b9h5ehoSED13xTZHvzS8pIJUehwMpI/W6dhZGp1gFfO1RqxI3oSLaEHQXgXvxj5mdvYVKLgay6sJf4fOu1CWhAh0qN+WbvIu7Fl+y4PsH16lE5IG/2uMzM3MkzouPisLPNGwA2Nj4eGw3fc1mXnJFGjkKBhZF6poK5kQmJ6S8eAL+BVxAnIq+So8wLetubWmNnasUHDfKCL8/37V86fcTknXOJTokvngY8k3XuAknh+brsGOReBuhaWZKTL/tM18IcZaL2QXmNurQn8+gJMg8cAUBx/yEYlsPk3T5kbNwGSiXK+ARyCszYmfMwCoNaJTejsplF7pgpBbPMkhISCmWjFXTq4DEWzfqD9yeMJrC6+lhiljZW6OnpoauX173c2d2VxLh4srOy0TcoucuplMzcfa9glplZOeNC2WjPJaWnkJCerAqcATxJjkVXRwcrYzOiUxII9a/H2fvXORmZ2406KimGcnoGdK3alD03T/3jH3fPBTdsROXAvKEKMp9NqBMdE4udbd6Pptj4uCKPCbY2NmpZZgCxcerHETtbW8oXmMW3vKcnew+qnwuzs7OZMGkSDx894vefZpZo1hmAqfmz/TG+4P6YhPkL9kdbx9wAgIuXO0nxiWxfvl6CZ8XEyMwEHV0dUgtkmaUlpWBs8fL7hIO3G7dO5Ha3T3oaR3JMPDt+zZss4fnN2L8+mEr3L4djYf9mJhgBMLEwRUdXl+QCWcUpicmYWRQemD4zLZ2HtyN5FHGfrQtyJ1pSPhsY/MveY+j76TDKv+EJBHK/J91C2UtpSSmFspyK4ujtxo3jZWPW15Tn1xKGBa4lDE1I1HI8z6+eZ2VO3VO/lihtRX1Pr/L/5OjtrvF7Or/jMGe3HqL92P7Yujm9dn2FeB0SPBOlwszMjB49evDZZ5+RkJDAgAEDVK+NHz+eevXqMXz4cIYMGYKpqSlhYWGFBsl/rnnz5lSpUoXevXszc+ZMsrOzGTZsGE2aNFFNCjBp0iTeeustKlSoQM+ePcnOzmbbtm2MGzdO4/b8/f3p168fM2bMIDExUS1IBuDj40NkZCTLly+ndu3abNmyhXXr1qmVGT16NP3796dWrVo0atSIJUuWcOXKFbXul8XBysoKGxsbZs+ejZOTE3fv3i00wUFSUhJ9+/Zl5MiRtG7dGg8PD2rVqkW7du3o1q1bsdXFx8eHtWvX0r59e3R0dJg4cWKhscQKql69Ovb29hw5ckRrcKtZs2Zs2bKFdu3aoVQq+fXXXwsF0BYvXoytrS3dunUrNH5au3btmDt3Lu3ateODDz7AxcWFZs2a4ebmxqNHj/jqq6+wt7dX65576NAhGjdurDVr0dDQ8JW7aRaUo8jhTuxDKjtV4PT9a6rlQU4VOJPveX7l9AxQFLhoUj3P95m0DWzA25WC+W7f39yJffha9XwZpiYmmObL+FMqldha23Di3FkCngXCs7KyOHvpEiMHDirx+hS3HKWCyPgoAh081bLHcp8XfQfY184dBzPrQmOaRSXFMHWX+lTt7Ss1wki/HKsu7CUuVXvw6h9Lz0CR/lRtkSI+Af1KgeTcfTaGjJ4e+v6+pK1cp2EDz5QrBwW7EigUanees2+Go+esPvi8rpMjihjNswwWB30DfTx8yhN27hLVG+SNkxV27jJV69XUut7J/UdZ9N85DB43gqA6hccqqlDRj1P7j6JQKFTHl8cPHmFpY1WigTPI3fceJDzBz95dreu1n72H1q7Yd2IfUcXFh3J6BmTm5Aat7E2tUCgVxKfl/sApp6dfKGtSoVSggw65X+Trhc80HhNsbDhx+jQBfrk/wrOysjh74QIj39feza1KpUqcOH2K3t27q5adOHWKKpXyxhGrWjmIu5HqYyDdvX8PZ8e8/e954CzywX3mzPwvVpZFB6+Kg76BPu4VvLh+/ora/nf9/BWC6r7CmFhKJdkFZvMW/5yevh52Hs48CLtN+ep5Y4I9CLuNZ1X/ItZUF3MvCuNnszVaOtnR5Qv1oTpOb9hHVnoG9Xu0wtS65Pe3/PT09XEp787ti9cJrJ03EP3tS9fwr1l4ohFDYyM++F59OI9TOw9z5+oNuo8ZiJW9baF1Spqevj72ni7cuxqOd428QPy9q+GUr/byYwBG33uEidWbnW1cmxylgnvxjwlw8ODio7xrB38Hz0Lj3xbkY+eGg5k1f0VoHyOyNOR+T87cD1P/nu5fDcfrVb6nyEeYFOhmfG7HYc5uOUDb0f1w8Hq58WX/df4fz2xZFsmEAaLUDBo0iLi4OJo3b67WHbNKlSocOHCAmzdv0rhxY6pXr87EiRNxdtY8MLKOjg7r16/H2tqa4OBgmjdvjre3NytWrFCVCQkJYdWqVWzcuJFq1arRrFkzTpzQ3G1IV1eXdevWkZGRQZ06dRg8eDBff/21WpmOHTvy4YcfMmLECKpVq8bRo0eZOHGiWpkePXrwxRdfMH78eGrWrMndu3f54IMP/unHpZWenh4rVqzg7NmzVK5cmbFjxxbqPjp69GhMTU1V435VqlSJ7777jqFDh/LgwYNiq8tPP/2EtbU1DRo0oH379oSGhlKjRtFZJnp6egwcOFBtjDtNQkJC2Lp1K4sXL+aDDz4o1L123rx5dOrUSePEA126dGHz5s08fvyY5s2bc/z4cbp164afnx9dunTByMiIPXv2YJsvQ2rZsmUvPU7e69h27RhNK9SgiXd1XCzs6FMjFFsTS/bczJ3xq0fVtxhav5Oq/LkHN6jlHshbPrWwN7XGz86dfjVbcyv6PvHPBo9tF9iQblWa8ceJDTxNicfSyAxLIzMM9cuVeHue09HR4Z1ObzN/+XL2HTnCrYgIJs/4ASNDQ1o96xoL8MX07/ll3jzV86ysLK6Hh3M9PJys7CyeRsdwPTycew/z9tPUtDRVGcidnOB6eDhRT0p2LLU9N0/TsHwV6ntWxsnchq5VmmJtYsGhO7l3SjtWakz/Wm0KrdfQK4g7MQ95mKg+y2S2IoeHidFqj7TMDNKzM3mYGP3G7ixn7NiDUftWGNSshq6rCybv9UeZmUnmsbxxFU3eG4BRt7fz6n7+EoZvBWNQtxa6drboVwrEqEsHss5dVF3oZWzfg14Fbwzbt0LXwR6D+rUxbNqIjN2FM2OLU/NObTi8cx9Hdu7nUeQDVv6xmNin0QS3yR0aYN2C5cyfkTfz8cn9R5n/4+90HdSH8v6+JMTGkxAbT1pKXhZAkzYtSE5KZsWcRTx+8IhLJ8+xbeUGQtq2LNG2PHfg9nnqeFSitnsgDmbWdKjUCCtjM9Vsa60D6tOzWgtV+XMPbpCamU6Pam/haGaNt40L7So25GRkGNmK3JnYrj6+Q33PIKq5+GJjbIGvnTutAupxJerOS3VFflU6Ojq8060b85f8zb6DB7l1+zaTv/km95jQPK/uX3z9Nb/8MUf1vGfXrpw4fZoFS5cQcfcuC5Yu4cSZ0/TKd+OnV7duXLp6hXmLF3Pv/n2279rFuk2b6PYsCzo7O5txX0wk7Po1vvrPRHJycoiOiSE6JoasEg5KhXQM5fjuAxzffZCoew9ZO3cpcdExNAzNPQ5uWryKv2f+oSp/aOtuLp88x5OHUTx5GMXxPYfYu2E7tUIalGg9i4OxgSG+dm742uWOGetiYYevnRuOZmUv2zioeX2uHz7L9SPniHv0lGMrd5Acm0BgcG6Q8+S6Peybv15V/tLu40Scv0bC4xhiHz7h5Lo93DkbRqWQ3CC9voE+Nq4Oao9yJkYYGBli4+qAnv6bH0OsXtumnN13jHP7jvH0QRTbF60lITqOWs1zJ27ZvWwj635bDICOri4O7i5qD1NLM/QNDHBwd6Gc0evdLPynqrVowNVDZ7h6+AyxD59wePlWkmITqBSSOxv0sTU72T13tar8hV1HuX3uKvGPY4h58Jhja3YSfuYqQU3rqsrkZGfzNPIRTyMfkZOdQ3J8Ik8jHxH/uORu7OS379YZ6nsFUc+zEo7mNnQOaoKNiTmHn11LtK/YiL41C4/VWN+zMndiH/EoqXA9cyeWscfV0h59XT0sjc1xtbTHztSqpJsDQNUWDQg7dJaww2eJe/SUIyu25X5PTXL/P46v3cWeuWtU5S/sPsqdc2HEP44h9sETjq/dxe2zVwlqlvc9ndt+iJPr9xDS/20s7KxITUgiNSGJrPSMQu8vxJsimWei1NSvX1/j+GIAtWvXVk0goEnBroAeHh5s2LChyPfr3LkznTtrHpum4Pb8/Pw4dOiQ2rKCdf3+++/5/vvv1ZaNGTNG7flnn31WaFbP7777TmsdQ0JCCr3PgAED1DLzACZPnszkyZNVz5s3b642s2bB+s7LF5x4btSoUWrjyhX8DArWw8vLq9CygvX18vJi7969amWGDx9e6L0LGjNmDJUqVeLu3bt4enpqrA9AcHAwSUl5g8Tmn+nz4sWLWrffuXNn1Q+kLl260KVLlyLrs2XLFvT09OjatesL6/66jkdewczQhE6Vm2BlbMb9hCdM37+E6NTcbj5WxubYmuTdsT545zxGBuVo6VeH3jVCSc1M58rjOyw/v0tVprlvbQz09BnTuIfae625tJ+1l/aXeJue69+tOxkZmXz7yy8kJSdROSCAX6Z9o5aNEvXkKbo6eQHPpzEx9B4+TPV88ZrVLF6zmhpBVfhj+nQArt64wdDxeVmjPz37od2ueQsmf/xxibXnzP3rmJYzpm1gAyyMTHmUGM2vR9aoZs+0NDLDxkT9rqmRfjmqu/qx8sJeTZssEzK27ESnXDmM+7+DjokJObfvkPz9LMh3gapra6N29zN9w1aUSiVGXTuga22FMimZrHMXVeObAeTcuUvKrNkYd3sbo45tUURHk7ZkFVn5gnIloXZwfVISk9mybC0JsfG4eLoxYso4bB1yu8ElxMYT+zTvx8eh7XtQ5OSw7Pf5LPs9LxOw/lvBDBibm0liY2/L6KkTWPXn33w5fAJWttY069CKVl07lGhbnrvw8CamBka08KuDhaEpUUkxzD2xSTXbmoWRKdbGed2YMnOymHN8A50qBzM6uAepmelceHiLbdfyhiDY/axrZquAelgamZGcmcbVqDtqZYpb/3d6kZGRwbc//UhScjKVAwP55YcZBY4Jj9HVzUthrFo5iK+/mMTvc/9i9ty5uLm48M3kyVTON3t1pcBAfvgqN+j216KFuDg58dGIkbRukRvcfPL0KQeP5HYx7jVooFqdZs/8L7Wql9zMiDUa1SUlMZkdKzaQEJeAs4cr708ci41DbtfVxNh44vLtj0qFkk1/ryb28VN09fSwc3Kgfd9uNAgNKbE6FpcAe09+6ZQ3nMGoRrkBzq1hx/h6b+lMiqJNhdqVyEhJ5eyWg6QmJGPj4kCrEb0wt7UCIDUhmZTYvO62ipwcTqzeRUp8EvoG+li52BM64h08gnxLqQUvVrl+DdKSUjiwdgfJ8Qk4uDvTe/xQrJ51H02OTyQhWvOYs2WFb50g0lNSOb1pPykJSdi6ONJ+dF8s8n1PSTF531NOdg5HVu4gJT4RfQMDbFwdaDuqL15V8rqcpsQnsfLLvBso53cc4fyO3LG2Oo0r+cz4sw9uYFrOmFb+9Z5dS8Tw+9F1quO5pZEp1saFryWqufiy5tJ+jdu0NDZjQrO+qufNfWvR3LcWN5/eY9bhVSXVFBWf2kGkJ6dxZnPu92Tj4kDbUX3y/p/ik0jO//+UncPRVXnfk7WLPW1G9cEzKO97urL/FIrsHHbOXqH2XrXah1C7Q7MSb5MQmugotUUvhBDiDdqwYQM2NjY0bty4tKvCypUr8fT0VI0h9yp6L51c/BUqRUt6TSbpTkRpV6PYmZf34oM100u7GsXq9y6fEN9v6IsL/g+xWjSb/SU0+2NpCfGpycebip785H/ND+1HkhRVsuMqvmnmTo5sDyu5QGJpaBVYn4a//ruOEQBHhs/mh/1FZ6//r/k4pDdLz+54ccH/Ib1qhDLr0MrSrkaxGtW4OyPX/Vja1ShWP3cay8yDK15c8H/ImOAeLy5URiWeKpvXQBa1tQ+F8W8mmWdCiDKhY8eOpV0Fle75xtYRQgghhBBCCPH/m4x5JoQQQgghhBBCCCGEFpJ5JoQQQgghhBBCCFGWFJzZXJQqyTwTQgghhBBCCCGEEEILCZ4JIYQQQgghhBBCiFIVFxdH3759sbS0xNLSkr59+xIfH6+1fFZWFuPHjycoKAhTU1NcXFzo168fDx8+VCsXEhKCjo6O2qNnz56vVDcJngkhhBBCCCGEEEKUJUpl2XyUoF69enH+/Hm2b9/O9u3bOX/+PH379tVaPjU1lbNnzzJx4kTOnj3L2rVruXHjBh06dChUdsiQITx69Ej1mDNnzivVTcY8E0IIIYQQQgghhBClJiwsjO3bt3P8+HHq1q0LwJ9//kn9+vW5fv06/v7+hdaxtLRk165dast+/vln6tSpQ2RkJB4eHqrlJiYmODk5/eP6SeaZEEIIIYQQQgghhHihjIwMEhMT1R4ZGRmvvd1jx45haWmpCpwB1KtXD0tLS44ePfrS20lISEBHRwcrKyu15UuWLMHOzo5KlSrx8ccfk5SU9Er1k+CZEEIIIYQQQgghRFmiUJbJxzfffKMak+z545tvvnnt5kZFReHg4FBouYODA1FRUS+1jfT0dCZMmECvXr2wsLBQLe/duzfLli1j//79TJw4kTVr1tC5c+dXqp902xRCCCGEEEIIIYQQL/Tpp58yduxYtWWGhoZay0+ePJkpU6YUuc1Tp04BoKOjU+g1pVKpcXlBWVlZ9OzZE4VCwW+//ab22pAhQ1R/V65cGV9fX2rVqsXZs2epUaPGC7cNEjwTQgghhBBCCCGEEC/B0NCwyGBZQSNGjHjhzJZeXl5cvHiRx48fF3rt6dOnODo6Frl+VlYW3bt3586dO+zdu1ct60yTGjVqYGBgwM2bNyV4JoQQQgghhBBCCPE/Sako7RoUCzs7O+zs7F5Yrn79+iQkJHDy5Enq1KkDwIkTJ0hISKBBgwZa13seOLt58yb79u3D1tb2he915coVsrKycHZ2ful2yJhnQgghhBBCCCGEEKLUBAYG0qpVK4YMGcLx48c5fvw4Q4YMoV27dmozbQYEBLBu3ToAsrOz6dq1K6dPn2bJkiXk5OQQFRVFVFQUmZmZAISHh/Pll19y+vRpIiIi2Lp1K926daN69eo0bNjwpesnwTMhhBBCCCGEEEIIUaqWLFlCUFAQLVu2pGXLllSpUoXFixerlbl+/ToJCQkA3L9/n40bN3L//n2qVauGs7Oz6vF8hs5y5cqxZ88eQkND8ff3Z9SoUbRs2ZLdu3ejp6f30nWTbptCCCGEEEIIIYQQZYlSWdo1eONsbGz4+++/iyyjzPe5eHl5qT3XxN3dnQMHDrx23STzTAghhBBCCCGEEEIILSR4JoQQQgghhBBCCCGEFtJtUwghhBBCCCGEEKIsUfz/67ZZlknmmRBCCCGEEEIIIYQQWkjwTAghhBBCCCGEEEIILaTbphBCCCGEEEIIIURZolSUdg1EPpJ5JoQQQgghhBBCCCGEFjpKpVJGoRNCCCGEEEIIIYQoIxIPHC7tKmhk0aRRaVehVEi3TSGEKEb7b50p7SoUqxCfmiQ9fFja1Sh25i4uLDq1tbSrUaz61W5T2lUoEWfuhZV2FYpVTfdAZhxYVtrVKFYfNXmntKtQIpLCrpd2FYqVeaA/P+xfUtrVKHYfh/Sm4a9DS7saxerI8NksPbujtKtRrHrVCC3tKpSIidv/KO0qFKuprd5j85WyGbD5p9pV+h8O9EieU5ki3TaFEEIIIYQQQgghhNBCgmdCCCGEEEIIIYQQQmgh3TaFEEIIIYQQQgghyhKFdNssSyTzTAghhBBCCCGEEEIILSR4JoQQQgghhBBCCCGEFtJtUwghhBBCCCGEEKIskdk2yxTJPBNCCCGEEEIIIYQQQgsJngkhhBBCCCGEEEIIoYV02xRCCCGEEEIIIYQoS5SK0q6ByEcyz4QQQgghhBBCCCGE0EKCZ0IIIYQQQgghhBBCaCHdNoUQQgghhBBCCCHKEoXMtlmWSOaZEEIIIYQQQgghhBBaSPBMCCGEEEIIIYQQQggtpNumEEIIIYQQQgghRFmilG6bZYlkngkhhBBCCCGEEEIIoYUEz4QQQgghhBBCCCGE0EK6bQohhBBCCCGEEEKUJdJts0yRzDMh/octWLCAbdu2lXY1hBBCCCGEEEKIfy0JnokS4+XlxcyZM9/o9nR0dFi/fv1rvc/kyZOpVq3aK62zf/9+dHR0iI+Pf633fhVr167l+++/p169ev9o/QEDBvD222+rnoeEhDBmzJgi1ymu73Tv3r0EBASgUChee1sl4cmTJ9jb2/PgwYPSrooQQgghhBBCiFIm3TZFIe3btyctLY3du3cXeu3YsWM0aNCAM2fOUKNGjTdar1OnTmFqavpG37Osun37Nv/5z3/Ytm0b1tbWxbLNtWvXYmBgUCzbepFx48bx+eefo6ubG79fsGABY8aMUQs+hoWF0aJFC+rUqcOyZcswNDRk3759TJ8+nRMnTpCWloaXlxetW7dm7NixuLq6qr2Hv78/d+7c4c6dO4Veu337Np9//jkHDhwgNjYWOzs7atasyfTp0/Hz88PBwYG+ffsyadIk/vrrrxL/PPZv3sXOtZtJiI3HxcOV7u/1w7dygMayZ4+c5ODW3dy7fZfsrGycPV1p36sLlWpWVSuXmpzC+kUrOXf0FKnJKdg52tN1cG+CalcvkTYolUr+WLiQdZs3k5SURKXAQMaPHk2F8uWLXG/PgQPMnj+f+w8f4ubiwrBBg2jauLHq9fY9e/Lo8eNC63Xr2JHxz4K9ew8eZO2mTYTduEFCYiJL/vwTfx+fYm3fc6d3Heb41n0kxydi7+pEiz5v4xFQ4YXr3btxm8Vf/Yq9mxNDpn2iWn5u3zEuHTrF0/tRADiVdyOke1tcK3iWSP3/v9i1YSubV60nPiYOVy93+g0bREBQJY1l42JiWTJ7PnduhhP14BGhndrSb9jgQuW2rdnI7k3biX4SjbmlOXUbN6DH4L6UK1eupJsDwJX9J7m44yipCUlYuzhQv0crnH017ycPr99h84yFhZZ3nzIcK2f7QstvnbzE3r/W4FnVn9Dh7xR73f+/USqV/LF8Get27iQpJZlKvn6Mf38oFTw8tK4THhnJ7KVLuBYezqOnTxg7cBC9OnQsVO5JTAw/L1rA0bNnSc/IwNPFlYkjRhJYQsc8gKv7T3Fh5zHSnu179bq3LGLfi2DLj4sKLe82ZRhWTnaFloefuszev9biWdWflsN6FHvdX1dVZx96VW9JgIMHdqZWTNj6O4fuXCjtar20UzsPcXTzHpLiE3FwcyK0Xxc8X+KcFXn9Ngu+nIWDuzNDvx3/Bmr6/0sd94o0Kl8FM0MTniTHse3aMe7GRWktr6ejS1OfmlR18cHM0ITE9BQOhJ/j7IPrAFR09CLYuzo2Jhbo6egSk5rAkYhLXHh48001iSPb9rJ/ww4S4+Jxcnel48CeeFf001j2dthNtixazZMHj8jMzMTa3pb6LZvQpH1LVZnjuw5wev8xoiJzb2S7VfCkTe/OePh6v5H2lBllNNHg/ysJnolCBg0aROfOnbl79y6enuoXR/PmzaNatWpvPHAGYG9f+IL//ytvb2+uXr36UmWzsrJeKihmY2PzutV6KUePHuXmzZt069ZNa5lTp07RunVrOnbsyB9//IGenh5z5sxh2LBh9O/fnzVr1uDl5UVkZCSLFi1ixowZ/Pjjj6r1Dx8+THp6Ot26dWPBggV8/vnnqtcyMzNp0aIFAQEBrF27FmdnZ+7fv8/WrVtJSEhQlXv33XepU6cO06dPL7YApca2HjzGyj8X0WvYQCoE+nFw+x5+nvQdk3+fjo1D4R8aN69cI7B6EG/374GxqQlHdx/g1y9/YMKPU/Go4AVAdlY2M//zDeaWFrz/2Wis7WyIexqDobFxibVj4fLlLF21iknjx+Ph7s7cxYsZ/sknrFm0CFMTE43rXLxyhc++/JKhAwfStHFj9h06xIQpU5g7axaVK1YEYNHs2eTku3AIv3OH4R9/zFshIaplaenpVK1cmeYhIXz1ww8l1sarx8+x6+/1tBrQFXe/8pzde5Tl0//g/e8mYGmnfR9JT01j4+yllK/kS3JCktprd8NuUbF+Ddz8yqNvoM+xzXtZ9t1s3vt2PBY2ViXWln+zY/sOs+j3eQwc9T5+lQLYs2UH3306lelzf8bOsfB5JDsrC3MrSzr26sa2NRs1bvPwngMs/2sx7308Ar9KATy6/5DZ02cB0HfYoBJtD+QGGI6t2E6jXm1x9PEg7OBpts36m+6Th2Nma6V1ve5TR1DOyFD13Mi88A2opJh4TqzeiZOv9sCOeDUL161l6cYNTBo1Gg8XV+auWsnwSV+w5rffMDXWfDxMz8jAzcmJ5g0b8uO8uRrLJCYnM2jCeGoFBfHfiZOwsbTkflQU5iV4YzH81BWOrdxBw15tcKzgzrWDZ9n+81K6TR6GmY2l1vW6fTm8wL5XuN25+94unHzK7r5nbGDIrZj7bL12lGmth5Z2dV7J5WNn2b5oLW0HdsPd35szu4+w5NvfGf7DZ1jaab/mS09NY/1vi/Gu7FfonCVeX2Unb1oH1mfz1cNExj2mlnsgfWu25ufDK0lIT9G4To9qzTEzNGbd5YPEpiZgWs4YXZ28DmSpWRkcCD9HdEo82Yoc/B086VS5CSmZadyKvl/ibTp3+CQb5i+n85A+lA/04diOA/z51UzG/Xcq1va2hcqXMyxHwzbNcPF0o5yRIXfCbrJ69iLKGRpSv2UTAG5dvk71RnXwCvBB38CAfeu3MWfKj4z771QsbUvuulyIoki3TVFIu3btcHBwYMGCBWrLU1NTWbFiBYMG5f5QOHr0KMHBwRgbG+Pu7s6oUaNISdF80AeIjIykY8eOmJmZYWFhQffu3XlcIKNk48aN1KpVCyMjI+zs7OjcubPqtYJdBm/evElwcDBGRkZUrFiRXbt2FXrP8ePH4+fnh4mJCd7e3kycOJGsrCy1Mt9++y2Ojo6Ym5szaNAg0tPTX/gZbd26FT8/P4yNjWnatCkRERGFyrzq5/O8u+icOXNwd3fHxMSEbt26FeoKOn/+fAIDAzEyMiIgIIDffvtN9VpERAQ6OjqsXLmSkJAQjIyM+Pvvv8nJyWHs2LFYWVlha2vLuHHjUBYYgLJgt80nT57Qvn17jI2NKV++PEuWLClU5x9//JGgoCBMTU1xd3dn2LBhJCcnF/nZLV++nJYtW2JkZKTx9b1799KsWTPeffdd5s6di56eHvfv32fUqFGMGjWKefPmERISgpeXF8HBwfz111988cUXatuYO3cuvXr1om/fvsybN0+trVevXuX27dv89ttv1KtXD09PTxo2bMjXX39N7dq1VeWCgoJwcnJi3bp1Rbbnde1et5WGLUNoFNoUZw9XerzXD2s7Ww5sLZz5CdDjvX6Edm2Pl18FHF2d6dS/Jw4uTlw8cVZV5siu/aQkJTNs4lh8Kvpj62CPT6UA3L1LJptJqVSybPVq3u3Th2bBwfiUL8+UCRNIT09nu4YM1ueWrV5N3Vq1eLd3b7w8PHi3d2/q1KjB0jVrVGWsrayws7FRPQ4fO4abiws1q+Zl2rVt2ZIh/ftTp2bNEmnfcye27adaSF2qN62HnasjLft2wsLWirN7jhS53rZ5q6hUvwauPl6FXnt7WF9qtWiEk6crdi6OtB3cA6VCScSVN3e3+N9m65oNhLRqTtM2LXD1dKffsMHYOtixe9N2jeXtnRzpP3wwwS2bYmKqObBx8+p1/CoH0PCtJtg7OVKlVnUaNG3M7Ru3SrIpKhd3HcO/UQ0CGtfE2tmeBj1aY2ZtydUDp4tcz9jcFBNLc9XjebbvcwqFgr1/raFmh6ZYFBEAFi9PqVSybNNG3u3WnWb1G+Dj6cmU0WNIz8hg+8GDWter5OvL6AHvEto4mHL6mm94LVy7Bkc7OyaNGk1lPz9cHB2pU7Uqbs7OJdUcLu0+hn/D6gQ0qoG1sz31e4S+wr5npnpo2vf2zV1HjfYhmNuX3X3veOQV/jyxkQO3z5d2VV7Z8S37qN60HjWaNcDe1YlW/btgaWvNqV2Hi1xv818rqNywFm6+Xm+mov/PNPCqwtn71zlz/zpPU+LZdu0YienJ1PGoqLG8j50bXjbOLD6zndsxD4hPS+ZBwlPuxef9hoqIfUTYkwiepsQTl5bE8buXeZwUi6eV0xtp08FNO6nzVmPqtQjG0c2Ftwe9g5WtDUd37NdY3s3bkxqN6+Lk4YqNgx01m9THv1pl7oTdUJXp8+F7NGzdDNfyHji6OdP9gwEolUpuXgx7I20SQhMJnolC9PX16devHwsWLFALOqxatYrMzEx69+7NpUuXCA0NpXPnzly8eJEVK1Zw+PBhRowYoXGbSqWSt99+m9jYWA4cOMCuXbsIDw+nR4+8FP0tW7bQuXNn2rZty7lz59izZw+1atXSuD2FQkHnzp3R09Pj+PHjzJ49m/HjC6eVm5ubs2DBAq5evcp///tf/vzzT3766SfV6ytXrmTSpEl8/fXXnD59GmdnZ7VglCb37t2jc+fOtGnThvPnzzN48GAmTJigVuZVP5/nbt26xcqVK9m0aRPbt2/n/PnzDB8+XPX6n3/+yeeff87XX39NWFgY06ZNY+LEiSxcqN49Z/z48YwaNYqwsDBCQ0OZMWMG8+bNY+7cuRw+fJjY2NgXBoUGDBhAREQEe/fuZfXq1fz22288efJErYyuri6zZs3i8uXLLFy4kL179zJu3Lgit3vw4EGt3+u6deto27Ytn3/+OdOnT1ctf77vadu2lZWV6u+kpCRWrVpFnz59aNGiBSkpKezfv1/1ur29Pbq6uqxevZqcnJwi61qnTh0OHTpUZJnXkZ2VTeStO1SsXkVtecUaQYTnu4AoikKhID0tHdN8GSUXT5zBO8CXpb/N5+PeQ5kybBxbV6xHkVMyqd8PHj0iJjaWevm+13LlylGjalUuXrmidb2LV69St8C+UK92ba3rZGVlsXXXLjq0bo2Ojk7xVP4l5WRn8+jOfcpX9ldb7l3Zn/s3I7Sud+HACeIeRxPcOfSl3icrIxNFjgJjM81BHFG07Kws7twIp0qtamrLg2pW48bVa/94u/6VA7lzI5xb13L/Lx8/jOL8ybNUr6v5WFaccrKziY58iFtF9a5WbhUr8Dj8XpHrrp06h8Uf/8DmHxfy8NqdQq+f3XwAY3NTAhq9+Wzyf6sHjx8TExdHvXxjp5YzMKBG5UpcvPZ6P/oOnjxJoI8P47//lhb9+9Lrw9Gs27njNWusXU52DtGRj3AtsO+5VvR+8b731R/8/cmPbPlxEQ+vF973zm0+iJG5CQGNSmYogf/vcrKzeXjnHhWqqA8B4V0lgPs3Cn8fz53bf5y4x9GEdGlV0lX8f0lPRxcXC7tC2WC3ou/jbuWocZ0AB08eJjylUfmqfBLSm9GNuxPqXxd9XT2t7+Nt44KdqSURcY+Ktf6aZGdlcz/8Lv5V1YdG8K9WkYhrL3eD6f7tu0Rcv4V3RX+tZTIzM8jJycFEQwb1v5pSWTYf/09Jt02h0cCBA5k+fTr79++nadOmQG6Xzc6dO2Ntbc3o0aPp1auXKlPJ19eXWbNm0aRJE37//fdCWUW7d+/m4sWL3LlzB3d3dwAWL15MpUqVOHXqFLVr1+brr7+mZ8+eTJkyRbVe1XzZJQW3FxYWRkREBG5ubgBMmzaN1q1bq5X7z3/+o/rby8uLjz76iBUrVqiCMDNnzmTgwIEMHpw7vs1XX33F7t27i8w++/333/H29uann35CR0cHf39/Ll26xHfffacqM3369Ff6fJ5LT09n4cKFqjb9/PPPtG3blhkzZuDk5MTUqVOZMWOGKiOvfPnyXL16lTlz5tC/f3/VdsaMGaOWtTdz5kw+/fRTunTpAsDs2bPZsUP7BfeNGzfYtm0bx48fp27dukBuNldgYKBaufyZauXLl2fq1Kl88MEHRQYgIyIicHFxKbQ8OTmZbt268dlnnxUKRt68eRMLCwucX+Lu+vLly/H19aVSpdyTeM+ePZk7d65qP3Z1dWXWrFmMGzeOKVOmUKtWLZo2bUrv3r3x9lYfR8HV1ZVz585pfJ+MjAwyMjLUlhkaGmosq01yYhIKhQILK/WuL+ZWliTGJWhZS92udVvITM+gZuO8iSOeRj0h5vFV6oY0ZOTkcTx5GMWy3xegyFHQrlfnIrb2z8TExgJgW6B7q621tcbxyvKvp2md59sraP/hwyQnJ9O+1Zu/qE9NSkGpUGBmaa623NTSnOT4RI3rxEY9Zd+KzfSdOBJdPe0XufntW7EZc2tLylfSPE6IKFpSQu7/lKW1ldpyS2tLEmLj/vF2GzRtTFJ8AlPGfAZKJTk5OTRv34oO73R5zRq/WHpyKkqFEmML9R8MxhampCZqzvQ1sTSncd/22Hs4k5Odw83jF9j800LafzQAZz8vAKJuRXL98Fm6TPzf6opW1sXE5+5ntvlu6gDYWlrx6OnT19r2g8dRrNm+jd4dOvJu125cuXmTH/76EwMDA9o1bfZa29bk+b5nUnDfMzclLVFzJr2JpRmN+7TDztOZnKxsbp64xJafFtNubH+c/XKzn6NuRXL9yDk6T3y/2OsscqUmaj5nmVmaE66lK2bMoyfsWbaJdyePfulzlng1JuWM0NPVJTkzTW15cmYa5oaab5rZGFvgYe1EtiKHped2YmJgRPtKjTA2MGL95QOqcob6BnwS0gd9XT0USgWbrx4hPKbkJ75KSco975pZWagtN7O0JCn+cpHrfjn442fXwjmEdu9IvRbBWstuWbwGSxtrfKtoztAT4k2Q4JnQKCAggAYNGjBv3jyaNm1KeHg4hw4dYufOnQCcOXOGW7duqXXlUyqVKBQK7ty5UyjIEhYWhru7uypwBlCxYkWsrKwICwujdu3anD9/niFDhrxU/cLCwvDw8FAFmQDq169fqNzq1auZOXMmt27dIjk5mezsbCwsLNS2M3So+g+H+vXrs2/fviLfu169emqZLwXf+1U/n+c0tUmhUHD9+nX09PS4d+8egwYNUvucsrOzsbRUD77kz+xKSEjg0aNHanXU19enVq1ahbpu5m/j8zLPBQQEqGV4Aezbt49p06Zx9epVEhMTyc7OJj09nZSUFK2TO6SlpWkMHhobG9OoUSP+/PNP3nnnHbXPSKlUvnSm0dy5c+nTp4/qeZ8+fQgODiY+Pl5V/+HDh9OvXz/27dvHiRMnWLVqFdOmTWPjxo20aNFCrU6pqaka3+ebb75RC/QCTJo0iZA+7V+qnmoKNk2pYZkGJ/cfZfOStQybOFYtAKdUKDG3sqDPyMHo6uni6etNfGwcO9dsKZbg2bZdu5iWb4y5md98k9uMAt+RUsOyQgquo1RqbfqGrVtpULcu9naFx4J7Y16yjQqFgvW/LqZxl1bYOju81KaPbd7DlWPn6PP5cPTLvZnJO/61NP5P/fNsxavnL7F+6WoGjnqfCgG+PH4YxaJf/2Kt7Qo693kzg5zrUPB/RXtZKyc7tcHZHSu4kxyXyIWdR3H28yIzPYN9c9fSuG8HjeOgiZe37cB+pv2ed8No5n9yhxEo9H3xWrsgAAqlkooVfBjetx8AAd4VuB0ZyZrt20okePZPaNr3UmITuLjrGM5+nrn73rz1NO7bDiPJsH0DCp9jNZ1kFQoFa39ZREjX1i99zhKvQ/0AroOO1mP682uMVRf3kpGdO/TM9mvH6FGtBZuvHiZbkduLIjM7i9+OrqGcngHeti60CqhHbFoiEbEln32WW8+CS5QvPOgN/3o8mekZ3L0RzpbFa7B1dqBG47qFyu1dt41zh08w7MtxGMj1kShFEjwTWg0aNIgRI0bw66+/Mn/+fDw9PXnrrbeA3JPs+++/z6hRowqt56FhNiltwY/8y41fYTBzTUGfgts/fvy4KpMtNDQUS0tLli9fzowZM176fV72vQt61c9Hm+dt0tHRQfFs0PQ///xTlQ32nF6BO4SvOyvp8zYWFfi4e/cubdq0YejQoUydOhUbGxsOHz7MoEGDCo0rl5+dnR1xcYUzQPT09Fi/fj1dunShadOm7N27l4rPBo338/NTBQGLyj67evUqJ06c4NSpU2rdeHNycli2bBkffPCBapm5uTkdOnSgQ4cOfPXVV4SGhvLVV1+pBc9iY2O1TlTx6aefMnbsWLVlhoaGHLtX9F22/MwscscgKphllpSQUCgbraBTB4+xaNYfvD9hNIHVg9Res7SxQk9PD129vJ75zu6uJMbFk52Vjb7B6x36gxs2VA3oD7mTMABEx8ZiZ5s3MGxsXBw2RUy2YGtjUyjLLDY+XuPkFY+iojh59izfFwhYvikm5qbo6OoWyjJLTUjCtMCdfYDMtAwe3blH1N0H7Fi4Fnj2f6VUMq3fR/QaPxSvSr6q8se37OPIxt30mvABjh6FMzPFyzF/Nq5XQmy82vKE+IRC2WivYtWCpTRqHkLTNrnHBw9vLzLS0/nrp994u1e3QuM5FScjMxN0dHUKZZmlJ6VgYmH20ttxKO/GrRMXAUh8GktSTDw7fl2qev35cf/PoVPo8eVILBzezCQy/+uC69Shsl9epmhmVjYA0fFx2OU7lsUmxGNT4AbUq7KztqZ8vpuQAOXd3Nh77OhrbVebvH1PPcssLSmlUCZkURy83bh14hIASU/jSI6JZ8evy1WvP9/3/vpgKt2/HI6Fvex7r8vE4tk5K0H9nJWSmIyZhaZzVjoPb0fyKOI+WxesBvLOWV/2HkPfT4dRvrJkRL+u1Mx0chQKzMqpB45NyxmRnKn5Zm1SRiqJ6SmqwBnA0+R4dHV0sDAyJTY19ztWgurvqKQY7E2tCfauVuLBM1Pz3PNuUpz6vpackIi5pYWWtXLZPpvEx9nTjaT4RHau2FAoeLZv/Xb2rNnC0Mkf4+Llrmkz/27/j7tIlkUSPBNade/endGjR7N06VIWLlzIkCFDVMGUGjVqcOXKFXxecmr0ihUrEhkZyb1791TZZ1evXiUhIUGVYVSlShX27NnDu++++9Lbe/jwoaoL4LFjx9TKHDlyBE9PT7WZFu/evatWJjAwkOPHj9OvXz/VsuPHj7/wvdevX6+2rOA6r/r5PKepTbq6uvj5+eHo6Iirqyu3b9+md+/eL71NS0tLnJ2dOX78OMHBuenQ2dnZnDlzRuusqYGBgWRnZ3P69Gnq1KkDwPXr19UmLzh9+jTZ2dnMmDFD9cNx5cqVL6xP9erVtc4UamhoyNq1a+natStNmzZlz549VK5cma5duzJhwgS+//57tTHrnnueVTZ37lyCg4P59ddf1V5fvHgxc+fOVQue5aejo0NAQABHj6r/ALl8+TIh+WZ1LFjXV+2mWZC+gT4ePuUJO3eJ6g3yJisIO3eZqvW0D35/cv9RFv13DoPHjSCoTuHxYipU9OPU/qMoFArVd/P4wSMsbaxeO3AGYGpiojaDplKpxNbGhhOnTxPgmxsQysrK4uyFC4x87z2t26lSsSInzpyhd76ZV0+cPk2VSpUKld24fTvWVlY00pBh+ibo6evjXN6NO5dvEFA7b4y6O5dv4FezcqHyhsaGDPlGfYy+M7uPcPfqTTqPGoBVvh+Hxzbv5ciGXbwz/n1cvMvurHP/C/QNDCjvV4FLZ85Tu1FeV+bLZ85Ts0Hhu9kvKyMjA11d9ZsJurq6ude0JXxhq6evj52HCw+uhlO+el5G7v2wcLyqBhSxprqYe48wscwNtlk52dF1kvrx8NT6vWRlZNKgRytMbYr+wSPymBqbqM2gqVQqsbW25sT58wR4544VlpWVxdnLVxiZb3iFf6JqQCB3H6h3w7r78CHO9iWTKaSnr4edhzMPwm5TvnrevvYg7DaeVbWPTVRQzL0ojJ/te5ZOdnT5Qj3j//SGfWSlZ1C/RytMrYu+cSRejp6+Pi7l3bl98TqBtfOGQLl96Rr+NYMKlTc0NuKD79WHzDi18zB3rt6g+5iBWGmYMVG8uhylgoeJ0VSwcyXsSYRqeQU7N67le55fZFwUlZy8KaenT2ZObnDe1tQShVJBopbZOSE36auocdGKi76BPm4VPLlx4QpB9fJ+V9y4cJVKGq5RtVLmjp+W377129m9ejPvTfwQdw2TLgnxpknwTGhlZmZGjx49+Oyzz0hISGDAgAGq18aPH0+9evUYPnw4Q4YMwdTUlLCwMHbt2sXPP/9caFvNmzenSpUq9O7dm5kzZ5Kdnc2wYcNo0qSJqmvgpEmTeOutt6hQoQI9e/YkOzubbdu2aRwkvnnz5vj7+9OvXz9mzJhBYmKiWpAMwMfHh8jISJYvX07t2rXZsmVLoUHyR48eTf/+/alVqxaNGjViyZIlXLlypdDYV/kNHTqUGTNmMHbsWN5//33OnDlTaGbSV/18njMyMqJ///788MMPJCYmMmrUKLp3746TU+5sOZMnT2bUqFFYWFjQunVrMjIyOH36NHFxcYWyoAq289tvv8XX15fAwEB+/PHHQrN45ufv70+rVq0YMmQIf/zxB/r6+owZM0YtO7BChQpkZ2fz888/0759e44cOcLs2bO1bvO50NDQQhMc5FeuXDnWrFlD9+7dadasGXv27CEoKIiffvqJESNGkJiYSL9+/fDy8uL+/fssWrQIMzMzvv32WxYvXsyXX35J5crqwYzBgwfz/fffc+HCBZRKJZMmTaJv375UrFiRcuXKceDAAebNm6eWrZaamsqZM2eYNm3aC9v0Opp3asP8Gb/h6euNd4Avh7bvJfZpNMFtcrM81y1YTnxMLO9+NAzIDZzN//F3erzXj/L+vqoMm3KG5TB+NlNgkzYt2LdpJyvmLKJZh1CePIhi28oNNGtfMmOF6ejo8E7XrsxfsgQPNzfc3dyY//ffGBkZ0ap5c1W5L6ZNw8HenhHPuh337NKF90aPZsGyZYQ0bMj+I0c4ceYMc2fNUtu+QqFg0/bttAsNRV/DOCwJiYlEPXnC0+hoAO5GRgK5mW12GrLY/qm6rUPY8PsSnL3dcfPx4ty+oyTExFHjrQZA7nhlSXEJdBjaGx1dXRzc1bMkTS3M0DPQV1t+bPMeDqzextvD+mJpZ6PKbCtnZEg5o9cLzv5/1aZLR377bibefj74VvRn75adRD+J5q32uZM2LP9rMbHRMQybMEa1TsSt20DuuJOJ8YlE3LqNvoEBbp65N3tq1KvNtjUb8fTxxifAj8cPH7FqwVJq1q/9RsYGqtKiPvvmrcXO0wXHCu6EHTxDcmwCgU1yz58n1+4mJT6RpgNzu2Vf2n0Mc1srrF0cyMnJ4dbxi9w5G0aLod2B3CCjjav64NSGJrnd6QsuF69GR0eHd9p3YP7q1Xi4uODu7ML81aswMjSkVXDeeD5fzPwJB1sbRvTNDahlZWVx+17uIPxZ2dk8jY3l+u3bmBgb4e6ce0OtV4eODJwwjnmrVtKiUSOu3LjJup07+HzY8MIVKSZBzeuzf/467D2dcfB249qhs7n7XnDuDZ6T6/aQEp9E03ffBuDS7uOY21lh7Wyfu++duMSds2E0fz/3Jom+gT42rurBvnKqfa/sdRc0NjDEzTIvA93Fwg5fOzcS01N4nPzPx1F8E+q1bcq6Xxfj4u2Om195zuw5SkJ0HLWaNwJg97KNJMUl0GlY32fnLPWsZ1NLM/QNDAotF6/naMRFulRpysOEaO7FP6aWeyCWRmacjMydUKSFX20sDE1Zc2k/ABcf3SKkQg06BYWw9+ZpTMoZEepfl7P3r6u6bAZ7V+NBwlNiU5gnmD4AAQAASURBVBPR09XFz96Dai5+bLpacpNe5RfcviXLZv2Fm48XXv4VOL7zIHHRsdRv2QSALX+vISEmjl6jc8eYPrxtL9Z2Nji45l4P3Qm7yf6NO2jUJq/7+d5129i+bD19PhyCtYOdqpeGoZEhhsaax44WoqRJ8EwUadCgQcydO5eWLVuqdTesUqUKBw4c4PPPP6dx48YolUoqVKigNntmfjo6Oqxfv56RI0cSHByMrq4urVq1UgskhYSEsGrVKqZOncq3336LhYWFKlOqIF1dXdatW8egQYOoU6cOXl5ezJo1i1b5BhLv2LEjH374ISNGjCAjI4O2bdsyceJEJk+erCrTo0cPwsPDGT9+POnp6XTp0oUPPvigyMH0PTw8WLNmDR9++CG//fYbderUYdq0aQwcOPAffz7P+fj4qGbyjI2NpU2bNmqD7w8ePBgTExOmT5/OuHHjMDU1JSgoSG3gfk0++ugjHj16xIABA9DV1WXgwIF06tSJhATtg9LPnz+fwYMH06RJExwdHfnqq6+YOHGi6vVq1arx448/8t133/Hpp58SHBzMN998o5bFp0mfPn0YP348169fx99f851rAwMDVq5cyTvvvKMKoA0bNgw/Pz9++OEHOnXqRFpaGl5eXrRr146xY8eyceNGYmJi6NSpU6Ht+fr6EhQUxNy5c/niiy/w8vJiypQpREREoKOjo3r+4YcfqtbZsGEDHh4eNG7cuMj2vK7awfVJSUxmy7K1JMTG4+Lpxogp47B1yL1YT4iNJ/ZpjKr8oe17UOTksOz3+Sz7fb5qef23ghkwNvduvo29LaOnTmDVn3/z5fAJWNla06xDK1p17VBi7ejfsycZGRl8O3MmSUlJVA4M5Jfp09Uy1KKePFHr3la1cmW+/uILfp87l9nz5uHm4sI3X3yh1iUU4OSZM0Q9fkyHAhOCPHfw6FGm5Juw47OpUwEY0r8/7+cL+r+uivWqk5qUwuF1O0iOT8TezZmen7yHpV1ugC45PpGE6Ff7MXVm9xFysnNYM2uB2vLGnUIJltnO/pH6TRuRnJjI2r9XEB8bh5uXB+OmTcTeMfeHeXxsLDFP1Adu/2xo3s2HOzfCObr3IHaO9sxa8icAnfp0R0dHh1XzlxAbHYuFpQU16tem+8CXzwJ+HRVqVyY9JZWzWw6QmpCMjYsDrUf2xtzWCsjtPpwcm3c8z8nO4fjqnaTEJ6FvoI+1iwOtRvbCI0i6Xb0J/Tt1zj0ezplNUnIylf38+GXyFLUMtainT9HNNzTC09hYeo8do3q+eP06Fq9fR41Klfnj69ybOJV8fflhwmf8sngRf61cgYujIx8NGkzrJiEl1pYKtSuRkZLK2S0HVfteqxG98u17yaTk2/cUOTmcWL1Lte9ZudgTOuIdPIJ8tbxD2RZg78kvnfKOD6Ma5QYBt4Yd4+u92m8ElgWV69cgLSmFA2t3kByfgIO7M73HD1VlPv+Tc5Z4fZejbmNiYESITw3MDU14nBTL4jPbSEjP7ZpvZmiCpXFel/zMnGwWnN5C28CGDG3QmbTMdC5H3Wb3zVOqMgZ6+rSv2AgLI1OycrKJToln9cW9XI66/UbaVL1RHVKTktm1chOJcQk4e7gy+PPR2Djkjn+YGBdPfHTeMB1KhZKtf68h9kk0unp62Dra07ZPF+o9C7YBHN2+j5zsbBZO/13tvVp270Boz45vpF1lgkK6bZYlOsqXGcBJCFHiJk+ezPr16zl//nxpV6XEjRs3joSEBObMmVPaVdGqTp06jBkzhl69er3SevtvnSmhGpWOEJ+aJD18WNrVKHbmLi4sOrW1tKtRrPrVblPaVSgRZ+6FlXYVilVN90BmHFhW2tUoVh81eae0q1AiksKul3YVipV5oD8/7F/y4oL/Yz4O6U3DX/9ds8YeGT6bpWe138j9X9SrRmhpV6FETNz+R2lXoVhNbfUem68cLu1qFKt2lRqVdhX+scSN20q7ChpZdNB8Q/vfruRGuBVCCC0+//xzPD09ycnJKe2qaPTkyRO6du3KO+/8O38QCiGEEEIIIYR4edJtUwjxxllaWvLZZ5+VdjW0cnBw0DjWnhBCCCGEEEK8EUpFaddA5COZZ0KUEZMnT/5/0WVTCCGEEEIIIYT4XyLBMyGEEEIIIYQQQgghtJBum0IIIYQQQgghhBBlicy2WaZI5pkQQgghhBBCCCGEEFpI8EwIIYQQQgghhBBCCC2k26YQQgghhBBCCCFEWaKUbptliWSeCSGEEEIIIYQQQgihhQTPhBBCCCGEEEIIIYTQQrptCiGEEEIIIYQQQpQl0m2zTJHMMyGEEEIIIYQQQgghtJDgmRBCCCGEEEIIIYQQWki3TSGEEEIIIYQQQoiyRKEo7RqIfCTzTAghhBBCCCGEEEIILSR4JoQQQgghhBBCCCGEFtJtUwghhBBCCCGEEKIskdk2yxTJPBNCCCGEEEIIIYQQQgsJngkhhBBCCCGEEEIIoYV02xRCCCGEEEIIIYQoS6TbZpkimWdCCCGEEEIIIYQQQmghwTMhhBBCCCGEEEIIIbTQUSolF1AIIYQQQgghhBCirEhctqa0q6CRxTtdSrsKpULGPBNCiGI059j60q5CsXq//tvci4sq7WoUO3drJ3r8PbG0q1GsVvSZSuLWnaVdjWJl0aYlH278b2lXo1j91GE0/ZdPLe1qFKuFPSeSFHG3tKtRrMy9PJlxYFlpV6NYfdTkHZae3VHa1Sh2vWqE/uva1atGKA1/HVra1ShWR4bPpuP88aVdjWK14d3v+GDN9NKuRrH6vcsntPrzw9KuRrHaPuSn0q6C+JeQbptCCCGEEEIIIYQQQmghmWdCCCGEEEIIIYQQZYhSqSjtKoh8JPNMCCGEEEIIIYQQQggtJHgmhBBCCCGEEEIIIYQW0m1TCCGEEEIIIYQQoixRKku7BiIfyTwTQgghhBBCCCGEEEILCZ4JIYQQQgghhBBCCKGFdNsUQgghhBBCCCGEKEsU0m2zLJHMMyGEEEIIIYQQQgghtJDgmRBCCCGEEEIIIYQQWki3TSGEEEIIIYQQQoiyRGbbLFMk80wIIYQQQgghhBBCCC0keCaEEEIIIYQQQgghhBbSbVMIIYQQQgghhBCiLFEqSrsGIh/JPBNCCCGEEEIIIYQQQgsJngkhhBBCCCGEEEIIoYV02xRCCCGEEEIIIYQoSxQy22ZZIplnQgghhBBCCCGEEEJoIcEzIYQQQgghhBBCCCG0kOCZEOJ/VkhICGPGjCntagghhBBCCCFE8VIqy+bj/ykZ80wIIUrZ+T3HOL3tACnxSdi6OhLSqz1u/uVfuN6DmxGs/GYOdq6O9J06RrV85TdzuH/9dqHy5asE0Gnsu8VZdZUNq9exaslyYmJi8SrvxbAPRxBUrarW8hfOnmf2f38l4k4Etna29OjzDu07d1S9vmPzNqZ/9W2h9bYe2Ek5Q0MAer/dg8dRUYXKdOjyNqM++bAYWlVYS786tK/YCCtjM+7HP2Hh6W1ce3pXa/lGXlXoUKkxTuY2pGZlcOHhTRaf2U5yZlqhsg08gxjduDun7oXxw4GlJVJ/TZRKJX/u2Ma6Y0dISkujkocn47p0p4Kzs9Z19l48z4JdO7kXHU22Igd3O3v6hDSjTe06qjJ/bN/Knzu2qa1nY27Oji+nlVhbnmvoVYWmFWpgYWRKVFIM6y8f5HbsQ63l9XT1CPWrQ023ACwMTYhPT2bXjVOcvHcVAF0dXZr71qK2eyCWRmY8SY5j89UjRX73xa2ZT03aBNTH0tichwlPWXJuBzee3tNYdnDdDjQuX/j/70HCUz7bNhsAPR1d2lVsSKPyVbAytiAqMYaVF/ZwKSq8RNuRn1Kp5I+/F7Nu61aSkpOpFBDA+OEjqODlpXWd8IgIZi9axLVbN3n0+DFj3x9Kr86d1cqcvXSRxatWEXbzJtGxsfwwaRIhDRqWcGtyXdl/kos7jpKakIS1iwP1e7TC2ddTY9mH1++wecbCQsu7TxmOlbM9AHfOXuXctkMkPolFkaPA0sGGoBYN8Kuv/fj6JpzaeYijm/eQFJ+Ig5sTof264BlQ4YXrRV6/zYIvZ+Hg7szQb8e/gZq+vH9jmzSp6uxDr+otCXDwwM7Uiglbf+fQnQulXS2tWgfUo1PlJlgbmxMZ/5i5Jzdx9XGE1vJNvKvRKagJLhZ2pGSmc+7BDeaf2kJSRiqQe+zrWqUpTX1qYmtiwYPEpyw8vY1zD268oRZBsHc1WvjVxtLIjEeJ0ay6sJdbMQ80lu1XszX1vSoXWv4wMZqpu+YDUM3Fl1YB9bA3tUJPV5cnyfHsvnmKk5FXS7Qd+bULbEjXqk2xMbbgblwUs4+v50pU4WvR59pXbEj7io1xNLfmaXI8y87vYs/N06rXW/nXo7lfbTytnQC4FX2f+ae2cONpZIm3RQhtJHgmhCgxmZmZlCtXrrSrUaSsrCwMDAxK7f2vn7jA/qWbeKvf27j4enJx3wnW/TiP/tPGYmFrrXW9jNQ0tv+xAo+KFUhNSFZ7rf3Iviiyc1TP01JSWDzxv/jVDiqRNuzbtZffZ/7CqE8+pFKVymxZv4lPPxzP3GULcXRyLFT+0cNHfD52PG06tmPC5M+5cvEys6b/hKWVFcHNmqjKmZiasmDlYrV1nwfOAH6dPweFIq+dd8LvMH7URwQ3Cyn+RgL1PSvTv2Zr5p7azPUnkTT3rcWnzfoydtPPxKQmFCrvb+/B8AZdWHhmG2fuX8PGxIIhdTvwfr23mXFwmVpZO1NL+tQIJayIHwQlZdHe3Szdv48vevXGw96Bebt2MGL2L6z+dCKmRkYa17E0MeXdFqF4OTpioKfHoStX+HL5EqzNzakfEKgq5+3kzK8fjFA919PVKfH2VHPx5e3Kway+uI87sQ9p4BnEe/U68u2+v4lPS9K4Tv+arTE3NGHF+d08TYnH3NAEXZ285Pw2AfWp6RbAygt7eJIci7+DJ+/WacesQyt5kPi0xNtUx70ivauHsujMVm5E36dphRp8FNyLT7f9TmxqYqHyS87uYNWFParnujq6fNXqPVUwEKBLlaY08KzMvFNbeJQYTZBzBUY16sbU3QuIjC8clC4JC1euZOnatUz66GM83FyZu3Qpwz+dwJq58zA1MdG4TnpGBm7OTjQPbsyPc+ZoLJOWno6vtzftW4YybuqXJdkENeGnLnNsxXYa9WqLo48HYQdPs23W33SfPBwzWyut63WfOoJyRnnHNiNzU9XfhqbGVG8TjJWTHXp6ety9dIMDC9djbGGKeyWfkmyOVpePnWX7orW0HdgNd39vzuw+wpJvf2f4D59haWejdb301DTW/7YY78p+JCdo/l8sLf/GNmljbGDIrZj7bL12lGmth5Z2dYrUqHwVBtVpz5xj6wl7cpdQ/7p80WIgI9b9SHRKfKHygQ5ejG7cg3knN3HyXhi2JpZ80KATIxp24Zu9udcTvWuGEuJdnV+PruF+/FOqu/rxabN+jN/yG3eKuMlSXGq6+dOtajOWn9tFeMwDGpevyvBGXfly5zziNJyjVl7Yw/rLB1XPdXV1+PytAZy9f121LCUznW3XjvM4KYZshYIgZ2/61WxNUkbqG7muCPauxvv13+bXI6u58vgObQIa8FWr93hv1bc81fA9tQ1swIDa7fjvoRXceHoPf3sPRjfuTnJGGicirwBQxcWH/bfOcvXxHTJzsulWtRnTWg/l/dXfabzmEv8ecXFxjBo1io0bNwLQoUMHfv75Z6ysrLSuM2DAABYuVL8ZVbduXY4fP656npGRwccff8yyZctIS0vjrbfe4rfffsPNze2l6ybdNoX4l1AqlXz//fd4e3tjbGxM1apVWb16NQD79+9HR0eHPXv2UKtWLUxMTGjQoAHXr+eeeK9fv46Ojg7Xrl1T2+aPP/6Il5cXymfpuVevXqVNmzaYmZnh6OhI3759iY6OVpUPCQlhxIgRjB07Fjs7O1q0aAHAxo0b8fX1xdjYmKZNm7Jw4UJ0dHSIj48HICYmhnfeeQc3NzdMTEwICgpi2TL14EJKSgr9+vXDzMwMZ2dnZsyYUegz0NHRYf369WrLrKysWLBgAQARERHo6OiwcuVKQkJCMDIy4u+//36p9y8pZ3YconJwbYKa1MHWxZGmvTtgbmPJhb3Hi1xv94K1BNSrhnOFwhkNxmYmmFqZqx6Rl29iUM4AvzpVSqQNa5atpFX7NrTp2A7P8l4M+3AkDg72bFq7QWP5zWs34ODowLAPR+JZ3os2HdvRqn0bVi1drlZOR0cHG1tbtUd+VtZWaq+dOHIMFzdXqtaoViLtbBvYgL3hZ9l760zuneoz24hJTaSlXx2N5X3t3HmSEs/268d5mhLP9aeR7L55Cm9b10LtHNmwG6su7uVxcmyJ1F0bpVLJsgP7ebdFS5pVqYaPswuTe/UhPTOLHWdPa12vpo8vTatUpbyjE2529rzTJAQfZxfO31bPWtLT1cXOwkL1sDYzL+kmEVKhBicir3Ai8gpPkuNYf+Ug8WnJNPTSHDwOsPfEx86NP09s4Eb0PeLSkoiMf0xE3CNVmVruAey+eYqwJxHEpCZyNOIS15/cJcSnRom3B6BVQD0O3j7HgdvneZQYzdJzO4lNTeQtn1oay6dlZZCQnqJ6lLdxwaScMYdu52WXNPAKYtPVI1x8dIunKfHsvXWG/2PvvsOjKtoGDv92N733RkiBQEIIvSX03jsIAqIIigVsqK/9tX32gl2xgCgCUqQ3QXoPJdQkJIGQ3ntPdvf7I7Bhk11ATeHV576uvXRnZ87OcE7OOTvnmZlzaZcZERTaKG3SarWsWL+O+++exsDevQnw8+f1Z56lrLyc7Xt2Gy3XNjCQJx6cy7D+AzAz8uCjV7fuPDrrfgb27t1Q1Tfo7M4jBPbuTFCfLjh6utJz6ghsHO25uM/43xKApa01Vva2updSWXN77hXoj3+nNjh6umLn5kS7QaE4NXMnLbbpoi+ObtlDpwGhdB7YE9dmHgy/bxL2zo6E7zx403Kbv/+VkF5d8W7l1zgV/RP+iW0y5mjCBb47tpF9lyOauiq3NK5tH3bFhLMzJpyk/Ax+OL6JrOJ8o+epQDef6sjgyMNkFOUSmRHPjuhjBLjU/Dge0LIza87u4WRSNOlFOWyPPsrp5EuMD+nTKG0a1Korh+PPcSj+HGmFOaw+u4fckkL6tuhoMH9ZVQUF5cW6l6+jB1ZmFhy5el6XJyYrkTMpMaQV5pBVnMee2FMk52cSUOt+o6FMbNefHdHH2B59jMS8DBYdXU9mUR6jgw1H/A5q1ZVtkYfZfzmCtMJs9l0+zY7oY0zpMFCX5/09y9gceYjLOSkk5Wfw6YFfUSgUdGzWqlHadMfQaO/MVwOaPn06ERERbN++ne3btxMREcHMmTNvWW748OGkpqbqXlu3btX7/Mknn2TdunWsXLmSgwcPUlRUxOjRo1Gr1Ua2WJd0ngnxD/Hyyy+zZMkSvv76ay5cuMBTTz3FPffcw759+3R5XnrpJT766CNOnDiBiYkJs2fPBiAwMJAuXbrwyy+/6G1z+fLlTJ8+HYVCQWpqKv369aNjx46cOHGC7du3k56ezpQpU/TKLF26FBMTEw4dOsSiRYuIj49n8uTJjB8/noiICB566CFeeuklvTJlZWV06dKFzZs3c/78eebOncvMmTM5duyYLs+zzz7Lnj17WLduHb///jt79+7l5MmTf+nf6rnnnuPxxx8nMjKSYcOG3db3NwR1VRXp8cn4hujfCPiGtCYl1vhwsPMHwsnLzCFs/ODb+p5zB04Q2KMDpub1HwVYWVnJpehLdO3RTS+9S49uXDx33mCZi+cv0KVW/q49unEpMpqqqipdWmlpKdPHT+HuMZN56enniYk2PqSisrKSXdt3Mnz0CBSK+o9uUilVtHDy4mxqrF76mdRYWrs2N1jmUmYCzlZ2dPSq3r/2Ftb08GnL6eRovXyT2w2goKyYPXGn6r3et5KcnU12YQGhgUG6NDMTUzoHBHD2ypXb2oZWq+X4pWiuZmbQuaV+JExiViYjXn2JcW++yos/LSHphs72hqBSKPG2dyM6Q79jITrzKn6OhoehtvVoQWJeOgMDuvLqkDm8MPBexgb3xlSp0uUxUaqo0ujfXFWqq2jh5FX/jahFpVTi5+jJ+VrDX86nxen9ILyZvi06cjH9st7TelOlikpNlV6+SnUlrYwcz/UtOS2N7JwcQrt00aWZmZnRuV17zl5svKFG9UVdVUVWQgrewfrD/LyDW5IeZ3h47XW/vbmIn5/5kM0fLyUlyvjfnVarJTnyMvnp2UaHgjY0dVUVKVcSadk+SC+9Rfsgki4Zr/vpvUfJTc+i/6ThDV3FP+2f2KZ/AhOlipbOzYhIjtFLj0i5RJCb4eM/KuMqLtb2dPEOBMDewoaefu04kVjzcNhEqaJCrX/uq1BX0sbNr34bYIBKocTHwaPOsNPIjPg6D9aM6enXjqiMqwajjq8LdPXB3daRmKykv1Pd22KiVNHKxZtTte5tTiVH08bdz2AZU6WJwX3Q2tUHlcJw94S5iRkmSqVu+K34Z4qMjGT79u18//33hIWFERYWxnfffcfmzZt1QR/GmJub4+HhoXs5OdVEDefn5/PDDz/w0UcfMXjwYDp16sSyZcs4d+4cu3btuu36ybBNIf4BiouL+fjjj9m9ezdhYWEAtGjRgoMHD7Jo0SLmzp0LwFtvvUW/ftXD4p5//nlGjRpFWVkZFhYWzJgxgy+++II333wTgEuXLnHy5El++uknAL7++ms6d+7M22/XzFe0ePFimjdvzqVLl2jdujUAAQEBvP/++7o8zz//PIGBgXzwwQdAdUfd+fPneeutt3R5mjVrxjPPPKN7/9hjj7F9+3ZWr15Njx49KCoq4ocffuCnn37SRbMtXbr0T4XZ3ujJJ59kYq05cm72/YaUl5dTXl6ul2Z+w5DC21FaWIJWo8HazkYv3crOhhIjwz9y07I4uHo7U198GKVKZTDPjVIvJ5KdlMbQ2ZP/VN1uV35ePhq1Gkcn/WEtjk6O5GQbjqLKyc7B0cmxVn4n1Go1+Xn5OLs409zPh/+8/Dz+AS0oKS7mt1/X8uTc+Sz6eTHePnX3+6F9BygqKmLoqBH117gb2JlboVKqyC/VHyKbX1qEg5fhaKpLWYl8fmgNT/aZiqnKBBOlivDESJaEb9HlCXT1YUDLzjy39asGqfetZBdW33w72drppTvZ2JKWe/MouKLSUka+9jIVVVWolEqemzyFHjd0wrX19eX16TPxcXUju7CAxTt3MOezj/n1uZdwsLa+yZb/OmszS1QGbq4Ly0uxszD8nc7Wdvg7eVGpVrMkfDPWZpZMbj8AKzMLVkZU31BFZSTQv0Un4rKTyS7Oo5WrDyEeLVA2QEdtbbZmVqiUSvLLivXS88uLsbewMVKqhr2FDe09A/jmyDq99HNplxkeGEp0RgIZRTkEu/vTqVlgo7QJIDun+vhydtQ/Fzg7OpCakdEodahPZUUlaDVaLO30jzNLO2tKCooMlrGyt6XPzDG4+niirlITc/QMmxcuZczTs/Bs7afLV1FSxrLnPkJdqUapVNBr+qg6nXSNpaSgGK1Gg429/nnPxt6WOCPXrezUDP5YsYn7X3vitq5bje2f2KZ/guvX3bwy/b+fvNIiHC0NX3ejMq7y8b6VPNt/hu66eyzhAt8erYmEP518iXFt+3Ah/TJpBTm09wqgh0+w3lD9hmJjfu0aVet8XlhWjL37ra+LdhbWtHVvweLjm+t8ZmFixjujHsFUqUKj1bLi9E6iMhp+Xk47C2tUShW5Jfp/K7mlhThZ2hksczIpiuFBoRy+eo7YrCRauTRnaOsemKpMsLewIae0bsfg7G6jyS7Ob9S56YRxxn4H/dnfQrUdOXIEe3t7vd9foaGh2Nvbc/jwYQIDA42W3bt3L25ubjg4ONCvXz/eeust3NzcADh58iSVlZUMHTpUl9/Ly4uQkBAOHz7MsGHDbqt+0nkmxD/AxYsXKSsr03UsXVdRUUGnTp1079u3rxm253ltMvCMjAx8fHy4++67efbZZzl69CihoaH88ssvdOzYkeDgYKD6pLNnzx5sbOr+WIuLi9N1nnXtqj+MKDo6mm7d9KOMunfXH+amVqt59913+fXXX0lOTtadkK2v/cCOi4ujoqJC1zEI4OTkdNMT6M3UruOtvt+Qd955h9dff10v7dVXX8VzWMc/X6HaP1a1AHV/wGo0GrYuWkHY+CE4erje1qbP7z+Os7cHni0aNpqkThO03DwCrNZn14cGX08ODmlLcEhb3edt27fjkfseZP3qtcx/+ok6m9u2aSvdQ7vj4ury1xpwm2oHqisUCl3da2tm78qsriNZe24PZ1JicbS0ZUbnYTzQYyyLjq7HwsSM+b0m8+2xDY32JHXbyXDeWVUzPHbhg9Xz3dTeU1q0dXdqLVbm5vzyzPOUVJQTfimahevX0czZhS4B1ZF2vdrU7L8AvGjv58/4t15nS/gxZvQfaGyz9UJbZ09hdD8pUaAFlp3aTllVBQDrL+xnVtdRrD27h0qNmnXn9zG1wyBeGDgTrRayS/I5nniR7s2DG7IZ+vWv1SYFCoPtrK2PfwdKKss4maw/LP+XUzu4v9to3h35CFogoyiXA1ci6OPfsR5rXWPb7j94+9NPde8/efP/AAPHnra6bf+ratf9ZouSOXi44OBRc85yb9mcotwCzvx+WK/zzNTCjEmvPExleQUpkVc4unoHdq6OeN3GwjINx8A53MBu02g0/PbFT/SfPAJnT7dGqttf9U9s0/++2uduhYG065rbu/Fg6Fh+jdjFqeRLOFnaMavbSB7pOZEvDlVPZfL9sU3M6zWJLyc8A2hJK8zhj5gTDGpleBh8Q6hb+9s7n4f5hlBaWcaZlJg6n5VXVfD2rqWYm5gR6ObD5PYDyCrOJybr5pGv9cfAfjLSpuWnd+JoZccn455EQXVH286Y40zpMAi1VlMn/+T2A+nfshP/2fIllbUi1v7xDPx73AmM/Q567bXX/tZ209LSdB1eN3JzcyPNwCJh140YMYK77roLX19frly5wiuvvMLAgQM5efIk5ubmpKWlYWZmhmOtB3bu7u433W5t0nkmxD+ARlN9Yt2yZQvNmumHfZubmxMXVz0P0Y0T41/v2Lhe1tPTkwEDBrB8+XJCQ0NZsWIFDz30kN53jBkzhvfee6/O93vesCpf7Q4nrVZbpxOl9k3PRx99xMKFC/nkk09o164d1tbWPPnkk1RUVBjMb4yhjozKyso6+WrX8Vbfb8gLL7zAggUL9NLMzc358dQ2IyXqsrS1QqFUUlzryXZJYRFW9nU7KStKy0m/kkTG1RR2L6t+iqq9tmT0wtkvMOmZOfgE1wybqyyvIPrYGXpOGFpnW/XF3sEepUpVJ8osLze3TnTZdU7OTuQayK9SqbCztzdYRqlU0rpNIMmJdYcgpKemcTr8JK++++ZfbMWtFZSXoNaocbDU3y92FtbklxmOKhnfti+XMhPYdPEQAAl56ZQdr+CNYQ/y65ld2FvY4GbjyH/6z9CVuf63snz6azy18VPSi3LrtR1927Yj5Bk/3fuKa8NkswsLcLnh3z63qAjnW8xPplQqae5a3Ykb2Myb+PR0ftz1u67zrDZLc3MCPL1IzGy4CfaLK0pRazTYmev/jduaWxrtoCwoLya/rEjXcQaQXpiDUqHA3tKWrOI8iitKWRy+GROlCmszC/LLihndptdNh83Ul8KKEtQaDQ61oszszK0oqBW9YEifFh04HH8WtUb/BrywvITPDq7CVKnCxtyK3NJCpnQYZHAS7vrQNzSMkBsiEyuunZuzcnNxuWE+w5y8PJwcHRqkDg3JwsYKhVJRJ8qsrLAYK7tbRwhe5+bvTeyxs3ppCqUSe7fqfyOX5p7kpmUSse1gk3SeWdlZo1AqKcrXP/aLC4qwsat7zqgoLSPlcgKp8Uls/bG68+L6deuNGU8y84VH8Q9p3Sh1N+af2KZ/guvX3dpRZvaWNnWi0a6b1H4AkenxrLs2wf7V3DTKjlTw7qhH+OXUDnJLCykoL+ad3T9hqjLB1tyKnJIC7u06gvTC+r3eGlJUfu0aVSsS2tbCioKyWz9E6+nXjmMJFw12MGlBNzl/Un4GnrbODA/qQczBhu08Kygrrt5PVvpRZg6WtgYXQIDqIZoL96/kswOrcLSyJaekgBFBYRRXlNW5rk1q15+7Ow7mha1fcyUn1eD2ROMz9jvImNdee61OZ1tt4eHhgOGH74Z+T95o6tSpuv8PCQmha9eu+Pr6smXLljqjjf7MdmuTzjMh/gGCg4MxNzcnISFBNyzzRtc7z25lxowZPPfcc0ybNo24uDjuvvtu3WedO3dm7dq1+Pn5YWJy+6eOoKCgOhM2njihP3nygQMHGDduHPfccw9Q3VEXExNDmzbVq/YFBARgamrK0aNH8fHxAapXYrl06ZJee11dXUlNrbmwxsTEUFJy65uRW32/IfURmqwyMcHdrxkJF2Jo1aVmGfKrF2Jo2aluRIu5pTn3/t9Temlndh8h4WIcY+bfg72r/tDJS8fPoq5U06ZnJxqKqakprQNbc/L4CXr376tLP3n8BD37Gp6sOzikLUcOHtZLO3EsnNZtAo0eW1qtlriYWPxbtqjz2fbN23BwdCC0Z8NNdK7WqLmck0J7j5aEJ0bq0tt7tOREUpTBMuYmpnU6LDTXI+xQkJKfxTObPtf7fGrHwViYmLH0xFayGqBjxtrCQm8FTa1Wi7OtHceiown0ro5OrKyq4lRsLI+NGfuntq1Fq+uMM6SiqpL49HQ6tmi44WZqrYak/Axau/pwLq3mvNfa1afOnGHXXclJpYNnK8xUplSoqzt03Gwc0Wg15Ne68a/SqMkvK0apUNLeK6DOXDwNQa3REJ+bSluPFpy8YU6Zth4tbjl8JcjNFw9bZz67vNponkqNmtzSQlQKJV29g/RW5KxP1lZWeitoarVanJ2cOHbqFEEB1Z3+lZWVnDp3lsfmzGmQOjQklYkJLj5eJF+Mw79TzbUjKTIOvw5BNympLzsx1eDDEz3a6nm6moLKxAQv/+ZcPhtNm24ddOmXz0UR2KXuohzmlhY88v7zemnhvx/kysVLTHlyNg6uznXKNLZ/Ypv+Cao0auKyk+ng1Yqj11ZghOoVlY8lGD5PmZuYotHWvu5Wv6/987hSXUVOSQEqhZKeviEcvHKWhqbWakjIS6ONm69e9Fj1+9iblKxehMjNxpHD8edu+/tMlA0/pLhKoyYmK4lOzVrr1a1Ts9YcvWp47tvr1FoNWcXVc3H2a9mJ4wkX9KLVJrcfwLROQ3hp26JGjKATt+PP/g6aP3++3u9KQ/z8/Dh79izp6el1PsvMzMTd3f22v8/T0xNfX19iYqr/zjw8PKioqCA3N1cv+iwjI4OePXve9nal80yIfwBbW1ueeeYZnnrqKTQaDb1796agoIDDhw9jY2ODr+/tTSw8ceJEHnnkER555BEGDBigF8U2b948vvvuO6ZNm8azzz6Li4sLsbGxrFy5ku+++w6VkTk/HnroIT7++GOee+455syZQ0REhG71y+s9/QEBAaxdu5bDhw/j6OjIxx9/TFpamq7zysbGhjlz5vDss8/i7OyMu7s7L730kt6KZAADBw7kiy++IDQ0FI1Gw3PPPacXbWfMrb6/IXUZ1odt3/6Ku583ngE+nNt7nMLsPDoMqO4IOrB6G0W5BYyYOxWFUomLt4deeUtbG0xMTeqkQ/XCAgGdg7G0aZj5pa6bNG0K773+Fq3bBBIc0pYtGzaTkZ7BmAnVnS/ff/UtWZmZPP9q9UIRoyeOY8OadXz9yReMHDeai+cvsH3TVl5847+6bf70/Y+0CQmmWXNvSoqLWbdqLXGXYnn8Gf3OQ41Gw44t2xgycjiqP9Gp+1dsiTzM/J6TiMtJISYzkUGtuuJibc/OmOMATOs4BCcrO748vBaAk0nRzA0dx5BW3TiTWj1s876uI4i5tqIjQGK+/txOxRWlBtMbikKhYFq//izZ9TvNXV1p7urKj7t+x8LMlGGda4awvPrLT7jaOzB/dPU+XbLrd4Kb+9DM2YUqdRWHIi+yJfw4z99V8+Tvkw3r6NM2BA9HR3KLivjh9x0Ul5UxupvheQTry964U8zoPIzEaytm9vRth6Olre6mflSbnthb2LD89O9A9X4a0ro70zoNYXvUUazNLBgT3JtjCRepvLZIgI+DO/aWNqTkZ2JvYcOwwFCUKNgde/NVFOvL9qijPBQ6nis5KcRmJzOgZSecrezZHVu9aMpd7QfiaGnLt8f0V7jt26IjsVlJJOfXjfZr4eSFo5UdCblpOFrZMj6kHwqFgq2Rh+vkbQgKhYJp4yewZOUKfJp50bxZM5asWImFuTnDB9QM6/3v++/j5uLM/NnVHWqVlZVcTkjQ/X9mdhbRcXFYWVjQ/No1q6S0lMSUFN02ktPSiI6Lw97WFg8Dw0HqS/shYexZ/Bsuvl64t2xO5P6TFOXk06Zf9d/S8d92UZxXwIDZ1U/Bz+06gq2zA45ebqjVamKPnuXKqUiGPFyzEM/pbQdw9fXCztURTZWahPMxXDpyhj4zRjVYO24ldNQA1n35M14tmuPd2p+TfxwmPyuXroOrH5jsWrGRwtx8Jjw6E4VSiVtz/YU1rO1tMDE1rZPelP6JbTLG0tQcb/uaqR+87Fxo5eJNQVlxvUc7/10bLhzgyT5Tic1OIjojgWGB3XGxdmB7VPWK5DO7DMfZyo5PDqwCIDwxknm9JjE8MJTTyZdwtLLlge5juJSZQM61625rl+Y4WdtxJTsVZ2s77u44BIVCwbrz+4zWoz79EXOCWd1GcTU3jSs5KfT274CjlR0HrlSvhjyubR8cLG1ZekL/wXMvv3ZcyU4hpaDuwjvDAntwNTeNrOI8VEoVIR4tCPVty4rTOxulTb+d28uz/WcQk5lIZEY8I4J64mbjyJZr15P7u43C2dqeD/cuB6qntQh09SEq4yo25lZMbNcPP0dPPrr2OVQP1by36wje2/0z6YU5ugjE0spyvUjxfzzNnTls889ycXHBxeXWU6uEhYWRn5/P8ePHddP8HDt2jPz8/D/VyZWdnU1iYqJudFSXLl0wNTVl586dusXuUlNTOX/+vN5c3bcinWdC/EO8+eabuLm58c4773D58mUcHBzo3LkzL774om5o5q3Y2dkxZswYVq9ezeLFi/U+8/Ly4tChQzz33HMMGzaM8vJyfH19GT58eJ1OrBv5+/uzZs0ann76aT799FPCwsJ46aWXeOSRR3RPLF555RWuXLnCsGHDsLKyYu7cuYwfP578/JqV4T744AOKiooYO3Ystra2PP3003qfQ/Xwy/vvv5++ffvi5eXFp59+elsrct7O9zeUwB4dKC0q4eiGPyjOL8C5mQcTFtyPnUv1U5HivEIKs/P+9HZz0zJJvhTPpGcaPoJjwJCBFOTns+yHn8jJzsavhT9vf/we7p7VHXo5WdlkpNV0Bnl6efLWx+/x9SdfsHHtepxdnJm34HH6DqyJIiwqKmLhux+Sm52DtY01LVu3YuE3nxHUVr9D81T4STLS0hkxZmSDt/PI1fPYmlsxqV1/HC1tScxL5909P+uemjpY2uBsXTP0cd/l01iamjEsMJSZXYZTXFHGhfQr/HJqR4PX9c+4d+BgyisreW/NKgpLS2jr68fnD8/Ti1BLy83VC2svq6jgvTWryMjPw9zUFF83d964516GdqpZOTEjP4+Xf/6RvOJiHG1sCPH1Y/GTC/CstbhEfYtIicHazJJhgT2wM7citTCbb49u0HVY2plb6w0DqlBX8s2RdUxs158Ffe+muLKMiJQYtt3QiWSqMmFkUBjOVvaUV1USmRHPL6d2NNoN/PHEi9iYWzIupC8OFjYk52fy8f4VutUz7S1tcLLWHzJjaWpOV+82Ro83U5UJk9r1x9XGkfKqCs6mxPLtkfWUVJYbzN8Q7psyhfKKct794gsKCwsJCQrii3fe0YtQS8vMQKmsOfYys7OZ8egjuvc/r1nDz2vW0Ll9e7794EMALl66xMP/eVaXZ+GiRQCMHjKE156pSa9vLbuFUFZcwqkt+yjJL8LJy40Rj83A1tkBgJL8Qopyaq4r6io1R9f8TnFeISamJjh6uTH8sen4tKsZ8ldVXsHB5Vsozi3AxNQEBw8XBs6ZSMtuIbW/vtGEhHWmtLCYfb/toCgvH7fmnsx47mEcrkU/F+UVkJ91Z3XC3Mo/sU3GBLn68sWEmuFWj/e+C4CtkUd4a/fSpqqWQQevnMXW3IqpHQbhZGXH1dw03ti5RDc80dHSFhdrB13+3bEnsTQ1Z1SbnszuPoriijLOpsay9ETNlBqmKhPu6TwMdxsnyqoqOJkUxSf7V1JcUdYobTqZFI21mSWj2vTEzsKa1IIsvjy0VjcNgL2FDU5W+kNVLUzM6NSsNavO7Da4TXOVKdM6DcHB0oZKdRVphTksCd/CyaSbr05YX/ZfjsDO3JoZnYfhaGXH1ZxUXtn+LRnXOmOdrOxws66J9lEqFExs1x9vBzfUGjVnUmJZUGuqijHBvTBTmfDKkPv1vmvZye0su8Puo0T9adOmDcOHD+fBBx9k0bVr99y5cxk9erTeXNdBQUG88847TJgwgaKiIl577TUmTZqEp6cn8fHxvPjii7i4uDBhwgQA7O3tmTNnDk8//TTOzs44OTnxzDPP0K5dOwYPHnzb9VNob3cyISGEqCdvvfUW33zzDYmJ/7wQ7EVH1jd1FerVQ2HjScy9/Yk0/1c0d/Rg6rJXmroa9erXe96kYOvvTV2NemU3cihPbfz01hn/hywc+wT3rWy4+fmawtK7X6EwvuFXdWtMtn6+fLRvRVNXo1493W8ay/+BPzqndx72j2vX9M7D6PXlw01djXp1aN43jFvyXFNXo15tuP89Hln7QVNXo159PelZhn/31K0z/g/Z/uDCpq7CX5a/aElTV8Eg+4fuv3WmvygnJ4fHH3+cjRs3AjB27Fi++OILHBwcdHkUCgVLlixh1qxZlJaWMn78eE6fPk1eXp5uHu8333yT5s1rFkwrKyvj2WefZfny5ZSWljJo0CC++uorvTy3IpFnQogG99VXX9GtWzecnZ05dOgQH3zwAfPnz2/qagkhhBBCCCHEnelfGOfk5OTEsmXLbprnxvgvS0tLduy49QMUCwsLPv/8cz7//PNb5jVGOs+EEA0uJiaG//u//yMnJwcfHx+efvppXnjhhaaulhBCCCGEEEIIcUvSeSaEaHALFy5k4cL/3ZBpIYQQQgghhBD/XtJ5JoQQQgghhBBCCHEn0fz7hm3eyYwvkSeEEEIIIYQQQgghxL+cdJ4JIYQQQgghhBBCCGGEDNsUQgghhBBCCCGEuJP8C1fbvJNJ5JkQQgghhBBCCCGEEEZI55kQQgghhBBCCCGEEEbIsE0hhBBCCCGEEEKIO4lW09Q1EDeQyDMhhBBCCCGEEEIIIYyQzjMhhBBCCCGEEEIIIYyQYZtCCCGEEEIIIYQQdxKNrLZ5J5HIMyGEEEIIIYQQQgghjJDOMyGEEEIIIYQQQgghjJBhm0IIIYQQQgghhBB3Eq0M27yTSOSZEEIIIYQQQgghhBBGSOeZEEIIIYQQQgghhBBGyLBNIYQQQgghhBBCiDuJDNu8o0jkmRBCCCGEEEIIIYQQRkjnmRBCCCGEEEIIIYQQRsiwTSGEEEIIIYQQQog7iUbT1DUQN1BotTKQVgghhBBCCCGEEOJOkf/xl01dBYPsF8xr6io0CYk8E0KIejTl55ebugr1atXM/yOjILupq1Hv3Oyc6fvVo01djXq1/9GvyHvqxaauRr1yWPg2vb58uKmrUa8OzfuGyT+91NTVqFdr7n2LvCeeb+pq1CuHT9+lMOJsU1ejXtl2bM9nB1Y1dTXq3eN9pjR1FRrEuCXPNXUV6tWG+9/7R57PZ696u6mrUa8WT3mRfl//szpG9j1yZ3ZAif890nkmhBBCCCGEEEIIcSeRQYJ3FFkwQAghhBBCCCGEEEIII6TzTAghhBBCCCGEEEIII2TYphBCCCGEEEIIIcSdRIZt3lEk8kwIIYQQQgghhBBCCCOk80wIIYQQQgghhBBCCCNk2KYQQgghhBBCCCHEnUQjwzbvJBJ5JoQQQgghhBBCCCGEEdJ5JoQQQgghhBBCCCGEETJsUwghhBBCCCGEEOJOotU0dQ3EDSTyTAghhBBCCCGEEEIII6TzTAghhBBCCCGEEEIII2TYphBCCCGEEEIIIcSdRCurbd5JJPJMCCGEEEIIIYQQQggjpPNMCCGEEEIIIYQQQggjZNimEEIIIYQQQgghxJ1EI8M27yQSeSaEEEIIIYQQQgghhBHSeSaEEEIIIYQQQgghhBEybFMIIYQQQgghhBDiTiKrbd5RJPJMCPGvs3fvXhQKBXl5eU1dFSGEEEIIIYQQdziJPBNCNIq0tDTeeusttmzZQnJyMm5ubnTs2JEnn3ySQYMGNXX1mtTQ1t0Z27YPDpY2JOVl8OOJrURlXDWav7d/B8YG98bTzpmSinIiUmL4+eQ2iipKAejXohPzek2qU27GL69RqalqkDasW72WFcuWk52VjV8Lfx5f8AQdOnU0mv/0ydN88clnxF++grOLC9PvncH4SRN0n1+Ju8wPi74nOiqKtNQ0HnvqCaZMn6q3jbvGTiQtNa3OtidMnsiC556pt7bdaHzbvkzrNBgnK3vic1L5/NBqzqbGGc0/IaQvE9v1x8PWifTCXH4+tZ0d0cf08tzVfgDj2vbF3daR/LJi9sad4tujG6hQN8y+MsRi2CDMwrqhsLREnZBIydqNaNIyjOa3mfcAJgEt6qRXXoyi+Luf6qSbD+qH5ehhlO87ROn6LfVad0MmhPRjeqchOFvZcyUnhc8OruZMaqzR/BND+jGpXX887ZxJL8xh6cltbL9hP6mUSu7tPJwRQWG4WDuQkJfO10d+41jCxQZvy3XDAnswNrg3jla2JOZl8GP4FiJvcp7o49+BcW376M4Tp1Mu8dPJbRSVl+ryWJlaML3TEHr4tMXa3IKMwlyWntzG6eRLjdEkACyGD8asZ/fqY+9qIiVr1t/82Js/F5NWBo69C1EUf/ujbpsWIwbrfa4pKKTglbfqte63S6vV8u2a1az7YxeFRUW0bdWK52Y/QMvmzY2WWffHLrbs30dcYiIAbfxb8Oi0aYQEtGqsauuc23OM0zsOUpJXhJOXG73vHoFXaz+DeZOjrrD+w8V10qe/+TiOnq4AZCenc3zDbjKvplCYnUfvqSPoMKRnQzbhX2NEUCgTQvrhaGlLQl46PxzfxMX0eKP5+7XoyIR2/fCyc6G4oozTyZdYEr6FwvISAFQKJZPbD2BAQBecrexILshk6YnGPUfcrg6eAUzvNJQgNx9crB14fuvXHLhypqmrZdCAlp0ZHhiKg6UNyfmZrIjYRUxWosG8s7uNprd/+zrpyfmZvLLjO937Ia26MaBlZ5ys7CiqKOVEUhRrzu6hSqNusHbcaHzbPtzd8dr9UW4qXxxac9P7o/Ft+zKxXb/q+6OiXJad3M6OS8d1n38y9gk6NWtdp9yRq+d5fuvXDdIGIW5FOs+EEA0uPj6eXr164eDgwPvvv0/79u2prKxkx44dzJs3j6ioqAb53oqKCszMzBpk2/UlzDeEWV1H8v3xTURnJDC4dTdeHHgvT238jOyS/Dr5A119md9zEktPbuVEUjROlnY8GDqWh8Mm8OG+5bp8JRVlPLHhE72yDdVx9sfvu/js409Z8NwztOvQno2/refZJ57m51W/4O7hUSd/SnIK/3nyacaMH8srb7zKuTNn+fi9D3FwdKD/wAEAlJWV4dnMi/6DB/D5x58Z/N5vl/6ARq3Rvb8Sd5mn5j/BgMEDG6SdAwO68FjvyXy8fyXn0y4zNrg374+ex70r3iSjKLdO/nFt+zA3dBwf7F1OZEY8bdz8+E//GRSWlXD46jmg+mZ3buh43tvzM+fTLtPcwZ0XBs4E4ItDaxukHbWZD+yLef9elCxfizozC4shA7B5eDYF73wM5RUGyxQv+QVUKt17hbUVts88RmXE+Tp5Vc2bYRbWDXVyaoO14UaDArrwRO+7+GjfCs6mxTG+bR8+HDOfe5a/TrqB/TS+bV8eDhvPe3uWEZlxlTZufjw/4B4Ky0s4FF+9n+b2GMew1j14b+8yruam0b15MO+MeJiH1n5g9AdPferp1676PHFsE1GZVxnSqhsvDrqPpzZ+SlZx3fNEkJsv83tNZumJrZxIisLJyo65PcbxSNhEPtj7CwAmShX/HXI/+WXFfLhvOdklBbhY21NaWd7g7bnOfFA/zAf0puSX1dXH3tCB2Dz6AAVvfWj82Fv8c91j7z9PUBlxTi+fOjWNoi+/r0lowhXLlm7cwPItm3n1kXn4eHryw29rmffWm6xd+CnWlpYGy5y8cIFhPXvTPrA15qZmLN24gflv/R+rPvoYNyfnRqt7zPFzHFy5jX4zRuMR4MOF/SfY9OnPTH/jMWydHYyWm/F/T2Bqaa57b2lrrfv/qopK7FwdCejaloO/bmvI6v+r9PZvz5zuY1h0ZD2RGVcZFtiD/w6Zzfx1H5NVnFcnfxs3P57oM5XFxzdxPDESZyt7Huk5gfm9JvHO7p8BmNFlGP1bdOLLw2tJysukU7PWvDDwXp7b8hVXclIauYU3Z2lqTmx2ElujDvP2iIebujpGdWvehmkdh/Dzqe3EZiXRv2UnnuozlZd3fEtOSUGd/CsidrLm3B7de5VCyetD53AiqebeOdSnLZPbD2Bx+GZis5LxsHViTvfRAKyM2NXgbRrQsjPze01m4YFfOZ8ax5i2vXlv1DzuW3mz+6OxfLB3OVEZV2nj7sez/aZTWF7C4avV9xGv7PgOU2VNV4WdhTU/THmBvXGnG7w9dxSN5tZ5RKORYZtCiAb36KOPolAoOH78OJMnT6Z169a0bduWBQsWcPToUeLj41EoFEREROjK5OXloVAo2Lt3ry7t4sWLjBw5EhsbG9zd3Zk5cyZZWVm6z/v378/8+fNZsGABLi4uDBkyBICtW7fSunVrLC0tGTBgAPHx8Xr1y87OZtq0aXh7e2NlZUW7du1YsWJFQ/6T6IwO7sXu2JPsjj157YnuVrJK8hka2N1g/tau3mQU57Et6iiZRblEZ15l16VwWjg308unRUt+WZHeq6H8unwlo8aNYcz4sfj5+/H400/i5u7GujXrDObf8Ns63D3cefzpJ/Hz92PM+LGMGjualctqOv/atA1m3hPzGTx0CGZmpga34+joiLOLs+51+OAhmnk3o2PnTg3SzikdBrIl8jBbIg9zNTeNzw+tIbMoj/EhfQ3mHxbYg40XDrI79iSpBdnsjj3JlsjDTO88RJenrYc/59Pi2BVzgrTCHMITI/kj5gSBrr4N0gZDzPv1pGznXirPXUCTlk7J8tUozEwx69zRaBltSSnawiLdy7R1AFRWUnFGvwMDMzOs7plK6ap1aEtLDW+snk3tOJjNkYfYFHmIq7lpfHpwNRmFuUwI6Wcw//DAHmy4cIA/Yk+SUpDFH7En2Bx5iBmdhunl+enkNo5cPU9KQRbrL+znWMJFpnUcbHCb9W1Mm+rzxB+xJ0jOz+THE1vJLs5naOseBvO3dmlOZnEuW6OOkFGUS1TGVXbGHKels5cuz8CALtiYW/L+nmVEZyaQVZxHVMZVrubWjeZsKOb9elH2+x4qz15Ak5pOybJVKExNMevS0WiZOsdeYKvqYy/irH5GtUYvn7a4uGEbY6y+Wi0rtm7h/gkTGdijBwE+Prw+bz5l5eVsP3jQaLn/e/wJ7ho2jEA/f/yaNePlhx5Cq9Vy/FzdDuqGFLHzMG16dya4b1ecvNzoc/dIbB3tOL/3+E3LWdpZY21vq3splTU/Odz9vel113BadW+PykSe49eXcW37sCsmnJ0x4STlZ/DD8U1kFeczIijUYP5ANx8yinLZHHmYjKJcIjPi2RF9jAAXb12eAS07s+bsHk4mRZNelMP26KOcTr7E+JA+jdWs23Y04QLfHdvIvssRTV2VmxrWujsHrpzhwJUzpBZmsyJiFzmlBQxo2dlg/tLKcgrKinUvP0dPrMwsOXhDVF1L52bEZCVxLOEi2SX5XEi/wrGEi/g5ejZKm6Z0GMTWqCPV90d56XxxaC2ZRbmMa2v4OBnaujsbLx5iT9wpUguv3R9FHWZap6G6PIXlJeSUFuheXZsHUV5Vwd64U43SJiEMkc4zIUSDysnJYfv27cybNw9ra+s6nzs4ONzWdlJTU+nXrx8dO3bkxIkTbN++nfT0dKZMmaKXb+nSpZiYmHDo0CEWLVpEYmIiEydOZOTIkURERPDAAw/w/PPP65UpKyujS5cubN68mfPnzzN37lxmzpzJsWP6w+vqm0qpooWTV53hZGdTYgl09TFYJjozAWcrOzp5VYey21tYE+rbltPJ0Xr5LEzM+HLCM3w98VmeG3BPg91AVVZWcikqmu499Dv7uvXozvmz5wyWuXDuPN1q5e8e2oOoi1FUVf216LjKykp+37aDkWNHo1Ao/tI2bsZEqaK1qw/hiZF66eGJkYS41x1CBmCqNKFCXamXVq6upI2bH6prPyTPpsbR2tWHNm7VnWWeds6E+oZw9Grj/EBWOjuitLOjKjqmJlGtpir2Cib+ho9BQ8x6dKXi9Fmo0G+v1eSxVEZGUXXJ+NCN+mSiVBHo6sPxBP39dDwxkhAPI/tJZUJFVa39VFVJsHvNfjJVGdiXVZW09wyox9obZqJU0cLZizMp+ueJM6m3Ok/Y64a82FtYE+oTwqmkmqFWXb2DuJSZyAM9xvL9XS/w8ZjHmRjSD2UD/P0YonR2QmlvR1VUrWMv7gom/rffeWwW2o2KU2fqHHtKVxfs3ngR2//+B6v7pqF0dqqvqv8pyRkZZOflEdq+gy7NzNSUzsHBnL0UfZOS+srKK6iqqsLexqYhqmmQuqqKzKsp+LTVP86btw0gLe7mEZe/vvEVS55+j/UfLiEp6nJDVlNQfZ5o6dyMiOQYvfSIlEsEuRn+e4rKuIqLtT1dvAMBsLewoadfO04k1kQ0mShVdaYQqLh2HRN/nkqpxNfRkwvp+n8TF9KuEODsbaSUvj4tOnAx/QrZN0SpxWQl4efogb9T9b2eq7UD7TxbcvYm0xXUl+r7o+aG74/+5HW3jZuv7rpb26igMHbHnqSsynBUshCNQR73CCEaVGxsLFqtlqCgoL+1na+//prOnTvz9ttv69IWL15M8+bNuXTpEq1bV/9IDAgI4P3339flefHFF2nRogULFy5EoVAQGBjIuXPneO+993R5mjVrxjPP1MyR9dhjj7F9+3ZWr15Njx6GIzvKy8spL9cf3mRubm4wrzF25laolKo6UWH5ZcU4WBj+gXQpM5HPDq7myb5TMVWZYKJUEZ4YyeLjm3V5Ugoy+erwbyTkpWNpas7IoDDeHP4gz27+krTC7D9Vx1vJz8tDrVbj6KT/w9TR2Ymc7ByDZbKzc+he64eso5MTarWavLw8XFxc/nQ9DuzdT1FRESNHj/zTZW+HvYUNJkoVuaWFeuk5JQU4NbczWOZ44kVGt+nFgStnuJSZSKCrDyODwjBVmeBgYUN2SQG7Y0/iYGnLFxOeRoECE5WKdef388vp3xukHbUpbG0B0BTqH4OaoiKUjg63tQ2VjzcqLw9Kfv1NL920U3tUzbwoWfhVvdT1djhc2085pfpDX3JLC3C2usl+Cu7N/itniM5MIMjVh1Fteurtp2MJF7m742AiUmJJzs+kq3cQffw7oFQ2fEeTrbHzRGkRDl6GzxPRmQl8emAVC/rerXee+OH4Jl0ed1snQmwcOHD5DG//sRRPO2ce6DEWpVLJmrN7DG63Pilsq+uuKdT/m9IUFqJ0dLytbeiOvRVr9NKrriag/mUV6oxMlLa21cNBn3yEwncWoi0pqZ8G3KbsawvTONvb66U729uTmplloIRhXyz/BVcnJ7q3a1ef1bupsqIStBoNlnb6x5mlnQ0l+YUGy1g52ND/3nG4+Xqhrqoi+sgZNnz0IxOenW10njTx912/n8irdZ7IKy3C0dLWYJmojKt8vG8lz/afoTtPHEu4wLdHN+jynE6+xLi2fbiQfpm0ghzaewXQwycYpULiL/4KWzMrVEol+WX6kbAF5cXYW9R9wFybvYU17Txa6u0jqL6O2Zpb8cKAe0FR3aG1O/YkW6OO1Gv9Ddfp2nW3pPZ1txAnI9fd8MRIRrfpycErZ7iUpX9/ZG9hU2dbQW6+tHBuxnvXph34V5HVNu8o0nkmhGhQ2msn/b8bDXTy5En27NmDjYGn7nFxcbrOs65du+p9FhkZSWhoqN73h4WF6eVRq9W8++67/PrrryQnJ+s6xgxFyl33zjvv8Prrr+ulvfrqq9DyTzfN4HXR2KWymb0r93cbxZqzeziTEoOjpS33dBnOg6Hj+OZI9TDJmKwkYrKSdGWiMxJ4b9SjjAgKZUl4w0zWXmf3arV1027Mj/6H2mstrp1+uzZv3ESPsFBcXF3/Uvnbpa21sxQKha7utS09sQ0nKzu+mfgfUEBuSSHbo44yvfNQ1NrqOSw6erViZpdhfLx/JZHp8TSzd+Xx3neR3WUEP52s/7mATDt3wGrKeN37IgOT+1dTGD8IazHr0RV1ShrqhJpjTuFgj+WE0RR9sxj+YjTh31F7P4HCaHOWhG/FycqObyc9p9tPW6OOcE/nYaivbefTA6t4bsA9LJ/+Glq0pORnsiXqMKOCGm+S8zptuskfmLe9K7O7j2b12d2cSY7BwcqWe7uMYG7oOL6+dp5QKBTklxWz6Oh6NFotl3NScLS0Y1zbPg3SeWbapSNWU2sWBSla9KORnApu9+AzC+1W59gDqIqsibDTpKZTFH8Vu1f+g1n3zpTvNT5Usj5sO3CAt79bpHv/yfMvAHWvgVrtTXehnqUbNrDj0EEWvfo65k0wj6eh87uxyjt6uOLoUXMe9mjpQ1FOPqd3HJTOs0ZQ5xplIO265vZuPBg6ll8jdnEq+RJOlnbM6jaSR3pO5ItD1R3S3x/bxLxek/hywjOAlrTCHP6IOcGgVl0NblP8Nbd71uvl156SyjJOpehHrQa6+jC6TU9+PrWdyzkpuNs4Mq3jEPKDi9h08VCD1Pl2GDv2lp7YhpOlHV9PfLbm/ij6KNM7DUWjrTvH16ignlzOTr7pYlpCNAbpPBNCNKhWrVqhUCiIjIxk/PjxBvNcnwvlxotsZaV+OLdGo2HMmDF6EWPXeXrWDEms3eFl7MJ9o48++oiFCxfyySef0K5dO6ytrXnyySepqDAeGv7CCy+wYMECvTRzc3Nmrnrzlt93XUF5CWqNGgdL/Q5Bewtro3OUTQjpR3RmApsuVv8ATMhLp+zYJt4c/iArI3aSV1q3nBYtcdnJeNjW/yTT9g4OqFSqOlFmuTm5daLRrnN2diInWz8CLi8nF5VKhb2DvcEyN5OWmsrJ4yf4v/ffvnXmvyi/rIgqjbrOU1RHS1tySwxHYFSoK3lvzzI+3LccJ0s7skvyGRPcm+KKUvJLq586z+k+ht+jj7Ml8jAAl3NSsDA159l+0/n55HajHXN/VeWFSAo/vGG41bX5hpS2NqgLatqhtLFGW3Qb8+SZmmLWqT2l2/UnJDbx9kJpa4Ptgnm6NIVKhaqFH2a9Q8l/9r8N8jQ179p+crbSP44cLW0NTsQM1fvpnd0/8/7eX3T7aWxwn2v7qUi33Re2fYOZygQ7CxuyivN4JGwCqYW3Hzn0VxXqzhP60SP2FtYG/97h2nki4yobL1SfJ67mpfNd1Ub+b/hcVkTsIq+0kNySQtRaNZob9kNyfiaOVraYKFX1vkJb5fmLFF698dirnvRfaWurf+zZ2qAtvM1jr3MHSrftvHXeikrUqWkoXf98VOuf1bdrV0Ja1QxzrKis7jzOysvD5YaIupyCfJzsHW65vZ83bWTJ+t/46uX/0sq38eZCBLCwsUKhVFKSr78/SguLsbK7/eGj7i28uXT0zlz18J/i+v1E7Sgze0ubOtFo101qP4DI9HjWnd8PwNXcNMqOVPDuqEf45dQOcksLKSgv5p3dP2GqMsHW3IqckgLu7TqC9MK6k8CLWyusKEGt0dSJMrM1t6ag7NbzMvbx78CRq+dR15pEfkJIPw5fPa9bXTQ5PxMzlSn3dR3J5ouH6vlOQt9N749Kb3J/tHcZH+43fn90nbmJKQMDurA4fLPBbQnRmCTmVgjRoJycnBg2bBhffvklxQYmbM7Ly8P1WrRQamrNanw3Lh4A0LlzZy5cuICfnx8BAQF6r5tFiAUHB3P06FG9tNrvDxw4wLhx47jnnnvo0KEDLVq0ICZGf96Q2szNzbGzs9N7/dlhm2qNmss5KXXmTWrvGUB0ZoLh71WZ1ukQvP6U7mZRW76OHuQZuYn5O0xNTWkdFEj4Mf3Jo8OPhxPS3vDworbtQgg/Hq6XdvzYcYKCgzD5C5NHb920BQdHR8J6NVwUUJVGzaXMBLo2b6OX3tU7iPPpN5/PR63RkFmch0arZVBAVw7Hn9d1ilmYmNXdnxoNCsXtR6X8KeUVaLJyal5pGWgKCjAJvOEYVKkwCfCn6orhY/BGZh3bgYmKyhP6q19VxsRR8N6nFH74he5VlZBE5akzFH74RYMNQ6jSqInOTKBbrf3UrXkbzqfd/n4a3Korh+LP1em8rFBXkVWch0qppH/LTrofKg2pSqPmcnYK7b3+xHnCxBQNdY8rQHeWiM68ioets955w9POmZySgnrvOAOuHXvZNa+0DDT5Bo69lv5UXbl1dIFZp/bVx174bay8plKhcndDU1D/58DarC0tae7hqXu18PbG2cGBY2drFjSorKrk1MWLtG8deNNt/bRxA9+vXcPnL7xEcMu/ENb8N6lMTHD19SLxov6chYkX4/Bo2fy2t5OVmIqVg+Ghg6J+VGnUxGUn08GrlV56R69WRqN1zE1M65zjau4n9FWqq8gpKUClUNLTN4RjCRfqre7/JmqNhqu5qQS7++ult3X3JzY7yUipaoGuPrjbOnHgct3rjpnKpM6+1Gq11fuxgeexrL4/SqSrt/70LF29g/7UdXdgQBeOXD1fpx0DWnbBVGXCzkvhRrbyD6fV3pmvfynpPBNCNLivvvoKtVpN9+7dWbt2LTExMURGRvLZZ58RFhaGpaUloaGhvPvuu1y8eJH9+/fz8ssv621j3rx55OTkMG3aNI4fP87ly5f5/fffmT17Nmq18R96Dz/8MHFxcSxYsIDo6GiWL1/Ojz/+qJcnICCAnTt3cvjwYSIjI3nooYdIS2ucFec2XzzEoIAuDGjZmWZ2rtzXdQQu1va6m4RpnYYwr+ckXf4TSVF09wlmSOvuuNk4Eujqw/3dRhGTlah7wje5/QA6eAbgZuOIr6MHj4RNwM/Jk98v3Xx1tL9q6vS72bxhE1s2bib+SjyfffwpGWnpjJ80HoBvvvia/3v1DV3+cRMnkJ6axucLPyX+SjxbNm5my4ZN3H3PdF2eyspKYqIvERN9icrKKjIzM4mJvkRSov7NpUajYeumLYwYNeIvdbz9GavO7GZ0m56MDArD19GD+b0m4WbryIbzBwCYGzqOFwfdp8vvbe/GkNbd8bZ3pY2bL68OmY2/syffHauZq+Tw1XOMC+nDwIAueNo609U7iDk9RnMo/pxeRFBDKt93GIvB/TFtF4zSwx2raZPRVlRScSpCl8dq+mQsRg2tU9YstCuV5yLRltRaSbO8Ak1aut6Ligq0xSXV/9+Afo3YxZjgXoxq0xNfRw8e73UX7raOrLtQHV3xcOh4Xh40S5e/ub0bQ1t3x9vejTZufrw+dA4tnL1YdMOcMsHufvRr0REvOxc6eAbw8ZjHUaDgl1ONMzfdpsjq88TAgC40s3dlVteRuFjb6/6mp3caymO9Juvyn0iKoodPW4becJ6Y3X00MZk154kd0cexNbfi/u6j8LR1pnOzQCa268/26IZdKOVG5fsOYTFkAKbt26L0dMdqxl1oKyupOBmhy2M1YwoWo4fVKVt97F00OIeZxbiRqFr6o3RyROXbHOvZ96CwMKfi+MmGbI5BCoWCaSNHsWT9b+w5fozYhARe++pLLMzNGd67ty7ff7/4nC+W18zns3TDBr7+dSX/feRRPN1cycrLJSsvl5Kyxlm19rqOQ3py8cBJLh48SU5KBgdXbqUwJ5+2/asXfTmy9nd2/VAz59yZnYe5fPoieenZZCenc2Tt78SdvEi7ATXzh6qrqshMSCUzIRV1lZqivAIyE1LJS6/fOTn/bTZcOMCQ1t0Y1Kor3vZuzOk+GhdrB7ZHVT8wnNllOE/2qVlkKTwxklDfEIYHhuJu40SQmy8P9hjLpcwEcq6dJ1q7NCfUty3uNk4Eu/vx6tA5KBQK1p3f1yRtvBlLU3NauXjT6tpqoV52LrRy8cbd5vbmUGwsOy4dp69/R3r7t8fT1pm7Ow7GycpOt4rkpHb9eaD7mDrl+vh3IC47meSCzDqfnUmNZUDLznRvHoyLtT3B7n6MD+lLRErMbY3A+LtWnfmDUdfvjxzcmddzEm62Trro5wd7jOXFgffq8nvbuzGkVTea2bsS5ObLfwffj7+TJ98d21hn26PahHHwyhkKyptmxWQhbiTDNoUQDc7f359Tp07x1ltv8fTTT5OamoqrqytdunTh66+/Bqon/589ezZdu3YlMDCQ999/n6FDa36se3l5cejQIZ577jmGDRtGeXk5vr6+DB8+XDfs0xAfHx/Wrl3LU089xVdffUX37t15++23mT17ti7PK6+8wpUrVxg2bBhWVlbMnTuX8ePHk5+f33D/KNccuXoeW3MrJrUfgKOlLYl56byz+2eyivOA6rB3F2sHXf59l09jaWrO8MAe3NtlOMUVZVxIu8yyUzt0eazNLJgbOh4HSxtKKsu4kpPKqzu+Jy47uUHaMGjoYAry8/nx+8VkZ2Xj37IF73/yIR7XhtNmZ2WTfkOHiVczL97/5CM+X/gp61b/hourC0888xT9Bw7Q5cnKzGL2PbN071cuW87KZcvp2LkTny/6Upd+4ng46WnpjBw7ukHadqPdsSexM7fmvq4jcba240p2Ks9t/or0ouohq85Wdno36SqlkqkdBuHj4E6VRs3p5Es8+tuHpBXWDHH96cQ2tFotD/QYg6u1A3mlRRyOP2fwBrKhlO/ej8LUFMvJY1FYWqK+mkTRN0ugvGbYstLRoc6TRqWrMyYt/Cj6enGj1fV2/BF7EjsLG+7vOgpnazsuZ6fwzKYvSC+8vp/scbetGVKsVCqZ1nEwPg4eVGnUnEqO5uG1H+gtrmGmMuXBHuPwsnOhtLKcI1fP8+bOJRRVNE5HxuH4c9iaWzH52nkiIS+dt//4qdZ5omao6t646vPEiKBQ7us6guKKMs6nXWbZyZrzRHZJPm/uXMKsbiP5aOxj5JQUsDXyMOuvdTI2hvI/9l079sahsLJEfTWRoq9/uI1jzwWTlv4UffW9we0qHeyxvm8aCmsrtEXFVF1NpPDjr9Dm5jVga4y7b+w4yisqePeH7yksLiYkIIAvXnwZa0tLXZ607Cy9BSjW7NxBZVUVz338kd62Hpx8Fw/dpb/KdENq1b0dZcUlnNi0l+L8Qpy93BnzxEzsnB0AKMkvojC75lqprlJzaNUOivMKMDE1xamZG6Men4lf+9a6PMV5hax6o2YhkYgdh4jYcQiv1n5M+M+cxmraP87BK2exNbdiaodBOFnZcTU3jTd2LiHTyP3E7tiTWJqaM6pNT2Z3H0VxRRlnU2NZeqJmvk1TlQn3dB6Gu40TZVUVnEyK4pP9KymuKGvk1t1akKsvX0yomU7j8d53AbA18ghv7V7aVNWqIzwxEhszS8YG98bewobk/Ew+OfCrbvVMewubOkMgLU3N6eIdxIoIw8PUN108iFarZUJIXxwtbSksL+FMaixrz+1t4NZU2xN3CnsLa+7tMqL6/ignlee23Hh/ZI/bjfdHCgVTOwyi+fX7o5RLzFv3kd79EVR3srX3DODpTZ83SjuEuBWFtjG6o4UQ4l9iys8v3zrT/5BVM/+PjIJ/XjSAm50zfb96tKmrUa/2P/oVeU+92NTVqFcOC9+m15cPN3U16tWhed8w+aeXmroa9WrNvW+R98TzTV2NeuXw6bsURpy9dcb/IbYd2/PZgVVNXY1693ifxutMbEzjljzX1FWoVxvuf+8feT6fvarh5lttCounvEi/r+fdOuP/kH2PfHnrTHeo/FffaeoqGGT/+gtNXYUmIcM2hRBCCCGEEEIIIYQwQjrPhBBCCCGEEEIIIYQwQuY8E0IIIYQQQgghhLiDyAxbdxaJPBNCCCGEEEIIIYQQwgjpPBNCCCGEEEIIIYQQwggZtimEEEIIIYQQQghxJ5Fhm3cUiTwTQgghhBBCCCGEEMII6TwTQgghhBBCCCGEEMIIGbYphBBCCCGEEEIIcSfRyLDNO4lEngkhhBBCCCGEEEIIYYR0ngkhhBBCCCGEEEIIYYQM2xRCCCGEEEIIIYS4k2g1TV0DcQOJPBNCCCGEEEIIIYQQwgjpPBNCCCGEEEIIIYQQwggZtimEEEIIIYQQQghxJ9HKapt3Eok8E0IIIYQQQgghhBDCCOk8E0IIIYQQQgghhBDCCBm2KYQQQgghhBBCCHEn0ciwzTuJRJ4JIYQQQgghhBBCCGGEdJ4JIYQQQgghhBBCCGGEDNsUQgghhBBCCCGEuJPIapt3FIk8E0IIIYQQQgghhBDCCIVWK92ZQgghhBBCCCGEEHeKvP+82tRVMMjh/debugpNQoZtCiFEPXpywydNXYV69cm4J7mcldTU1ah3LVy8Gb342aauRr3aPPsD8u6e09TVqFcOK3/gjZ2Lm7oa9eq/Q2Yz/sfnm7oa9Wr9rHf5YM+ypq5GvXp2wD18d3RDU1ejXj0YOo7H1n3c1NWod59PWMAr279t6mrUqzeHz+WRtR80dTXq1deTnmX2qrebuhr1avGUF+n15cNNXY16dWjeN//I/fQ/S6tp6hqIG8iwTSGEEEIIIYQQQgghjJDOMyGEEEIIIYQQQgghjJBhm0IIIYQQQgghhBB3Eo1MT38nkcgzIYQQQgghhBBCCCGMkM4zIYQQQgghhBBCCCGMkGGbQgghhBBCCCGEEHcSrQzbvJNI5JkQQgghhBBCCCGEEEZI55kQQgghhBBCCCGEEEZI55kQQgghhBBCCCHEnUSrvTNfDSg3N5eZM2dib2+Pvb09M2fOJC8v76ZlFAqFwdcHH3ygy9O/f/86n999991/qm4y55kQQgghhBBCCCGEaFLTp08nKSmJ7du3AzB37lxmzpzJpk2bjJZJTU3Ve79t2zbmzJnDpEmT9NIffPBB3njjDd17S0vLP1U36TwTQgghhBBCCCGEELdUXl5OeXm5Xpq5uTnm5uZ/a7uRkZFs376do0eP0qNHDwC+++47wsLCiI6OJjAw0GA5Dw8PvfcbNmxgwIABtGjRQi/dysqqTt4/Q4ZtCiGEEEIIIYQQQtxJNJo78vXOO+/ohlVef73zzjt/u7lHjhzB3t5e13EGEBoair29PYcPH76tbaSnp7NlyxbmzJlT57NffvkFFxcX2rZtyzPPPENhYeGfqp9EngkhhBBCCCGEEEKIW3rhhRdYsGCBXtrfjToDSEtLw83NrU66m5sbaWlpt7WNpUuXYmtry8SJE/XSZ8yYgb+/Px4eHpw/f54XXniBM2fOsHPnztuun3SeCSGEEEIIIYQQQohb+rNDNF977TVef/31m+YJDw8Hqif/r02r1RpMN2Tx4sXMmDEDCwsLvfQHH3xQ9/8hISG0atWKrl27curUKTp37nxb25bOMyGEEEIIIYQQQog7SQOvbNlY5s+ff8uVLf38/Dh79izp6el1PsvMzMTd3f2W33PgwAGio6P59ddfb5m3c+fOmJqaEhMTI51nQgghhBBCCCGEEKLpuLi44OLicst8YWFh5Ofnc/z4cbp37w7AsWPHyM/Pp2fPnrcs/8MPP9ClSxc6dOhwy7wXLlygsrIST0/PWzfgGlkwQAghhBBCCCGEEEI0mTZt2jB8+HAefPBBjh49ytGjR3nwwQcZPXq03kqbQUFBrFu3Tq9sQUEBq1ev5oEHHqiz3bi4ON544w1OnDhBfHw8W7du5a677qJTp0706tXrtusnnWdCCCGEEEIIIYQQdxKN9s58NaBffvmFdu3aMXToUIYOHUr79u35+eef9fJER0eTn5+vl7Zy5Uq0Wi3Tpk2rs00zMzP++OMPhg0bRmBgII8//jhDhw5l165dqFSq266bDNsUQgghhBBCCCGEEE3KycmJZcuW3TSP1sBccHPnzmXu3LkG8zdv3px9+/b97bpJ5JkQ4n+KQqFg/fr1/5jvEUIIIYQQQghxZ5PIMyGEuAP18mvPwIAu2FlYk1aYzbpz+7ick2Iw7/ROQ+nuE1wnPbUgm/f2/GygRP3b/NsG1ixfRU52Nr7+fjz0+KOEdGxvNP/Z02f47vOvuXolHmcXFyZPn8qoCWP08qz7dS1b1m0kMz0DOwd7evfvy/0PP4CZuRkA902aTkZa3RV5Rk8cy7ynn6jfBhoxMiiMie3642RpS0JeOt8d28iF9CtG849q05PRbXriZuNEZnEuq87sZnfsyUap681YTB6L2cB+KGysUMdepmTxL2iSDB9v15mPGIzZkAEoXZzQFhZRcewEZSvWQmXVtY1aYDllPKbdOqOwt0Udn0DpjytQX45v+AbVcmn/KS7+cZzS/CIcPF3oMmkQbgHNjeZXV1ZxbtthroRfoKywGCsHW0KGhdEyzPgx3dBGBIYyPqQvjla2JOam88PxzVzMiDeav2+LjkwI6YeXnTPFFWWcTr7Ejye2UlheosszJrgXwwNDcbF2oLC8mMPx5/n51HYq1VWN0CK4uPcEZ3ceoTS/EAcvV8LuGoZHKx+DeVOi49m6sO75bPJrj+DgUT0J8aXDZ9j/08Y6eWZ9/gImpo1zy3v6j8OEb91HcX4hLl7uDJgxFu9A/1uWS74Uz8p3vsHF25373nxK77Oy4lIOrt1OzInzlJWUYu/iRP9po2jRoU1DNUNPH/8ODGrVFTsLa1ILsvnt3F7ispMN5r2n8zB6+Latk55akMXbf/wEgIetM6Pa9KS5gxvO1vasPbuHvXGnG7IJBnVvHkxv//bYmFuRUZTLtqgjXM1NM5pfpVAyIKALHbwCsDG3oqCsmH1xpzmVHA1AsLsffVt0wsnKDpVCSXZJPofiz3EmJaaxmkTfFh0Z0rob9hY2pBZksfrMbmKN7Kt7u4wgzC+kTnpKQRZv7lwCQEevVgwPCsXV2gGVUklGUR67YsI5nnCxQdtxowEtOzM8MBQHSxuS8zNZEbGLmKxEg3lndxtNb/+65+nk/Exe2fGd7v2QVt0Y0LIzTlZ2FFWUciIpijVn91ClUTdYO/6KDp4BTO80lCA3H1ysHXh+69ccuHKmqatl0L95PzWof8hqm/8U0nkmhBB3mE5erZnQrh9rzuzmSk4KPf3a81DYeN7Z/TN5pYV18v92bi+bLh7UvVcqlPxnwIxGu2Hft2sPiz79inlPP05w+xC2rt/MK8+8wKJli3HzqLusdFpKKv995kWGjxnJs/99gYtnz/PlR59h72BP7wF9Adi9YxdLvvmOp154luB2bUlKSOLjt94H4KEnHgXg0++/QqPR6LZ79fIVXnzyP/QZ0K8RWl39g/LBHmP5+sg6LqbHMyIolNeGzuHR3z4kszivTv4RQWHc12UEnx9aw6WsRAJdmjO/92SKyks4nhjZKHU2xHzsCMxHDqXk68WoU9OxmDgamxefpmDBS1BWZrCMaa8eWEybTMmiJagvxaL09MDq4dkAlP1UvTy41UP3ofJuRvGX36PNzcOsTyg2Lz9NwdOvoM3Na6zmEX8ykpNr/6Db1KG4tmhGzMEI9ny1mtEvP4C1k53BMgcXb6C0sJjQGSOwdXWkrLAY7Q3HWmPr5dee2d1Hs+joBqIy4hkW2INXhtzPY+s/Jqs4v07+Nm6+PNF7CovDNxOeGImzlR0Ph01gXs9JvHutQ71vi47M7DKcLw6uISozAS87Fx7vfRcAi8M3N3ib4k5c4OjqHfScNhL3lt5EHTjF9i+WM/nVR7Bxsjda7q7XH8XUwlz33sLWSu9zUwtz7nr9Ub20xuo4izoWwZ5fNjH43vE0a+3HmT3HWPvRD9z/ztPYOTsaLVdeUsrWb1fiGxxAcYH+OV5dVcXqD77Dys6GsfNnYuNkT2FOHmY3/Bs0pM7NWjOxfX9WRfzB5ZwUevm155GeE3hr11JyDVyP1pzdw4YLB3TvVQolzw+ayenkmuuRmcqErJJ8TidfYmL7xjlf1xbi0YIRbcLYfPEgCbnpdG3ehpldRvD5wVXklxUbLDO142BszC1Zd34/OSX5WJtZolTUDOIpqSxnX9xpsorzqNKoCXTzZUJIP4orSonNSmrwNnXxDuSuDgNZeXoncdnJ9PHvwLzek3nj98UG99WqM3+w/vx+3XulUsFLg2ZxKilal1ZcUca2qKOkF2ZTpdHQzrMF93YZQWF5CZHp8Q3epm7N2zCt4xB+PrWd2Kwk+rfsxFN9pvLyjm/JKSmok39FxE7WnNuje69SKHl96BxOJEXp0kJ92jK5/QAWh28mNisZD1sn5nQfDcDKiF0N3qY/w9LUnNjsJLZGHebtEQ83dXWM+rfvJ/HvIcM2hRD1pn///syfP5/58+fj4OCAs7MzL7/8sm5c+rJly+jatSu2trZ4eHgwffp0MjIygOqx6wEBAXz44Yd62zx//jxKpZK4uDiD33nu3DkGDhyIpaUlzs7OzJ07l6KiIt3n4eHhDBkyBBcXF+zt7enXrx+nTp3S20ZMTAx9+/bFwsKC4OBgdu7cWZ//LH9a/4DOHLt6gaMJF0gvymXd+X3klRbR289w1EtZVQWF5SW6l4+DO5amFhxLuNAo9V336xqGjh7B8LGj8PHz5eEn5+Hq5saWdZsM5t+yfhNu7m48/OQ8fPx8GT52FENHDWftilW6PFHnLxLcLoQBQwfh7ulBlx5d6T9kADFRNTf1Do4OODk76V7HDh3Fs5kX7Trdennq+jA+pC87L4Xz+6XjJOVn8N2xjWQV5zEyKMxg/oEtO7Mt+igHrpwhvTCH/VfOsPNSOJPaD2iU+hpjPmIwZeu3UBl+Ck1SMiVf/YDC3AyzXj2MljFp3ZKqS7FUHjqGJjObqrMXqDh8DJMWftUZTE0x7d6F0uVrUEddQpOeQdmajWgysjAf0rjtjdodTsuw9gT07IC9hwtdJw/GytGWSwcMR7qkXLxMemwiAx65C88gP2yc7XHx88K1hXej1vtG49r2ZlfMCXbFhJOUn8kPxzeTVZzP8MBQg/lbu/qQWZTLlsjDZBTlEplxld+jjxPg0kyXJ9DVh6j0q+y/coaMolwiUmI4cPmMXp6GdH7XUVr36kRQ7044eroSNmUY1o52RO47cdNyFrbWWNnb6F5Kpf6trEKB3udW9jYN2Qw9J7YfoF3fbrTv3wNnL3cGzhiLrZMDEX8cvWm533/8jTZhnfAMqBt1d25/OGVFJYx//D6atfbD3sUR79b+uPl4NVQz9AwI6MKR+PMcuXqe9MIcfju3l9zSQnr7Gz7P1rkeOVZfj45ePa/Lk5CXzobz+zmVHE2VumkiSHr6tedUUjQnk6LJLM5jW9QRCsqKDEZxAwS4eOPn5MnPJ7dzOTuZvNIikvMzScyriX6Oz0klMiOezOI8cksLOXrt38zXwaNR2jSoVVcOx5/jUPw50gpzWH12D7klhfRt0dFg/rKqCgrKi3UvX0cPrMwsOHLDvorJSuRMSgxphTlkFeexJ/YUyfmZBDg3znliWOvuHLhyhgNXzpBamM2KiF3klBYwoGVng/lLK8spKCvWvfwcPbEys+TgDdFaLZ2bEZOVxLGEi2SX5HMh/QrHEi7i5+jZKG36M44mXOC7YxvZdzmiqatyU//2/ST+PaTzTAhRr5YuXYqJiQnHjh3js88+Y+HChXz//fcAVFRU8Oabb3LmzBnWr1/PlStXmDVrFlA9x9js2bNZsmSJ3vYWL15Mnz59aNmyZZ3vKikpYfjw4Tg6OhIeHs7q1avZtWsX8+fP1+UpLCzkvvvu48CBAxw9epRWrVoxcuRICgurn8JqNBomTpyISqXi6NGjfPPNNzz33HMN9K9zayqFEm97N6Iyr+qlR2Vcxc/p9m4Yevi25VJmgsEnzfWtsrKSmOhLdO7eVS+9c/cuXDxvuPMu6vxFOnfvop+/Rzdioi5RVVU9XCy4Qwix0ZeIvlj9FDI1OYXwI8fp3tNwZ0FlZSV7ft/F0FHDUSgUf7dZt2SiVBHg3IzTKZf00k8nXyLIzddgGVOVSZ3hcBVVlbR2aY5K0TSXY6WbC0pHB6rO3rCvqqqoiozGpHXdvzldlqhYTPx9UbX0123HtFM7Kk+drc6gUqFQqaCyUq+ctqISk6CAem+HMeoqNTmJaXi20R8259nGn6wrhocyJZ2LxdnHg4u7jvHbS1+y8fVvOfXbbqoqKg3mb2gmShUtnZsRUSuSNCIlxuixFpVxFWdre7o0q17W3d7ChjC/EL2n+pEZ8bR0aUYrl+pOQXcbJzp7B+rlaSjqKjVZCal4t2mhl+7dpiXpl28eobPure/45T8L2brwZ1Ki4+t8XllewcoXP2P585+w48uVZCWk1mfVjVJXVZEen4xfSGu9dL+QVqTE1q3ndef2h5OXkU3P8YMNfh53+iJeAb788dM6vnrsDZa8+BFHN+3Wi7ptKCqFkuYO7kRl1LoepV/F3/n2Ou9CfUOIzrjaKNej26VSKPGyc6kTDRablURzh7rR0gBBbr6k5GfS278Dz/afwRN9pjAssAcmSuOrtLVw8sLF2p743IY/BlUKJT4OHlysFQ0WmRFPi9vs6Orp146ojKsGI4WuC3T1wd3WkZhGiKRTKZX4OnpyIf2yXvqFtCsEON/ew4w+LTpwMf0K2Te0KSYrCT9HD/yv3VO5WjvQzrMlZ1Nj66/y/yKynxqYVnNnvv6lZNimEKJeNW/enIULF6JQKAgMDOTcuXMsXLiQBx98kNmzZ+vytWjRgs8++4zu3btTVFSEjY0N999/P//97385fvw43bt3p7KykmXLlvHBBx8Y/K5ffvmF0tJSfvrpJ6ytrQH44osvGDNmDO+99x7u7u4MHDhQr8yiRYtwdHRk3759jB49ml27dhEZGUl8fDze3tUX+bfffpsRI0bctJ3l5eWUl5frpZmb//1hNNbmlqiUSgrLSvTSC8tLsLOwMlKqhp25FW3c/Pj55La/XZfbUZCXj0atwdFJf0iSg6Mjudk5Bsvk5uTg4Kif39HJEbVaTUFePk4uzvQfPJD83HyeeeQJtFotarWaURPGMmVm3eWnAY7sP0RRURFDRg6rn4bdgp25NSqlqs4PwtzSIjpb2Roscyo5mqGtu3Pk6nnispMJcPZmcOtumKpMsLOwbpIflwqH6uFxmnz9H0ua/AKULs5Gy1UeOU6pnQ02rz9fvR0TE8p/30P5xmvHXVkZVZdisZg4muLkVLR5+Zj26oEqwB9NWkbDNMaA8qIStBptnaF9FrbWlBYYHppVlJVHRlwSShMT+j44gfLiUsJ//Z3ykjLC7hnZGNXWY2tuhUqpqjNkO7+0EEfL1gbLRGcm8PH+lTzTfzqmKhNMlCqOJVzku6M184EdvHIWe3Mb3h7xMAqFAhOlim1RR/jt3N9fjepWyq7tF0s7a710SztrSguKDJaxsreh94xRuPh6oq5SE3v0LFs/+ZlRC+7Fs1V1J6KDhzN97xuLUzM3KksrOL/7GJs++JGJL8/F3t348VwfSq8N7a0d6WZlb0txvuG/7dy0TA6s3sbdLz2CUmW4EyY/M4eEyDjahHVi4oLZ5KVnseun9WjUanqOH1Lv7biR7npUrv+3Ulhegp357VyPrAl292fpia0NVcW/xMrMApVSSVFFqV56UUUptkba5WRph4+jB1UaNctP/46VqQVj2vbG0tSC9edr/mbMTUx5tv89mChVaLQaNl88ZHR+uPpko7t3qLWvyoqxd7c2UqqGnYU1bd1bsPh43SHbFiZmvDPqEUyVKjRaLStO76zTodoQbM2sUCmVdYbRFpQXY29x6zbZW1jTzqMl3x7doJd+PPEituZWvDDgXlBUP6DYHXuSrVFH6rX+/xayn8S/iXSeCSHqVWhoqF7kT1hYGB999BFqtZqzZ8/y2muvERERQU5Oju7JeUJCAsHBwXh6ejJq1CgWL15M9+7d2bx5M2VlZdx1110GvysyMpIOHTroOs4AevXqhUajITo6Gnd3dzIyMvjvf//L7t27SU9PR61WU1JSQkJCgm4bPj4+uo6z63W+lXfeeYfXX39dL+3VV1+FTg63/W/1ZygUtzdnaHeftpRWlnMu1fAw14ZSO9hLCzeNAKv9mW7J6WvpZ09F8OtPvzDv6ccJbNuGlKQUFn36JcuXODH9/pl1trdj8za6hnbH2dXlb7XjT6u1T262n1ZG7MLR0paPxjyGAsgrLeKPmBNMbj8ATSM9xTPt1QOrB+/VvS9679Pq/6lT6ZsfcCbBgVhMGE3pD8uoir2MysMNy/umockbTflv1T++Sr78HquH7sf+64/QqtWor1yl8tAxVP6Go6UaVu0DVFvnmK35SItCoaDXrDGYWVZ3iKsnDuTAD+vpNmUIJmamDVzX26RQoK19AF7jbe/Ggz3G8mvEH5xOuYSjpS2zuo7kkbAJfHF4LVA959PkDgNYdHQDMZkJeNi58ED3MeS2L2TV2d2N1oYbVZ8HDO8YBw8X3cIAAO4tvCnKLeDcziO6zjO3Ft643TC81r1lc9a9/R0X9obTc+rw+q+/AXXOe9eOp9o0Gg2bv1lBzwlDcPJwNbo9rUaLla0NQ++fhFKpxMPfm6K8AsK37mvwzjNdHf5iuR6+wZRWlnM25U6NFNFvmQKF0dPe9X24+uxuyquqo1C3Rx1haschbL54UDeBeUVVJV8dXouZypQWzl4MDwolp7SA+JzGiYCsW33j54kbhfmGUFpZZnCu1PKqCt7etRRzEzMC3XyY3H4AWcX5RieDb2gKbu+Y7OXXnpLKMk6lROulB7r6MLpNT34+tZ3LOSm42zgyreMQ8oOL2HTxUIPU+d9I9pP4J5LOMyFEoygrK2Po0KEMHTqUZcuW4erqSkJCAsOGDaOiokKX74EHHmDmzJksXLiQJUuWMHXqVKysDD8J1hr5UQI1N7qzZs0iMzOTTz75BF9fX8zNzQkLC9N9p9bAnfLtDPt74YUXWLBggV6aubk5z23/+pZlb6a4vBS1RoNtrSgzGzMrvVXyjOnhE8yJpEjUjdQZY+dgj1KlJCc7Vy89PzcXh1rRaNc5OjmRm6MflZaXm4dKpcLOvnoC95++W8LAYUMYPnYUAP4tW1BeVspn7y3k7vtm6M1zlJ6WTsSJU7z89mv12LKbKygvRq1R41gryszBwsbgog4AFeoqPj24mi8OrcXB0pbc0gKGBYZSUlFGQdmt9219qDx5hsLYGzp9r02krnSwR51XM/G80t4Wbb7xoTsWU8ZTceAIFXuqJwbXJCaDuTlWD95L+botoNWiSc+k6I33wdwMhaUl2rx8rJ54CE1GVsM0zgBzGysUSgVlhfpPxMuKSrCwNfxE3NLOBkt7G13HGYC9hzNooSSvEDs3pwatc22F5SWoNWocLPWPNXsLG/JKDUdpTW7fn8iMeNZfqJ4M/GpuGouOruedkY/wy+nfyS0tZHqnIeyNO8WumPDqPHnpWJiY8mjPiaw+u+e2fnD/VRbX9ktpvn79ywpL6kSj3YybfzNij58z+rlCqcDV14uCDMNRsPXJ0tYahVJJcZ7+339JQRFWdnXnXasoLSf9ShIZV1P44+fqiAutVgtaLR/d/zx3PfsAPsEBWDvYolSp9M55Tp5uFOcXoq6qQmXScLfy169Hdub6+8TW3IqC27gehfqGEJ54sdGuR7erpKIMtUaDjZn+ddbazIKiCsPtKiwvoaCsWNdxBpBZlIdSocDOwlo31FELuv9PK8zG1dqRvi06NnjnWdH1fVUr0sfWwuq2ri89/dpxLMHwvtKCbhGcpPwMPG2dGR7Ug5iDDdt5VlhRglqjqRO9ZGtuTYGRRR1u1Me/A0eunkdda4jzhJB+HL56XrdqZXJ+JmYqU+7rOpLNFw814Jnvn0n2UwPT/Gta+j9B5jwTQtSro0eP1nnfqlUroqKiyMrK4t1336VPnz4EBQXpFgu40ciRI7G2tubrr79m27ZtekM9awsODiYiIoLi4pqL86FDh1AqlbRuXT2c6cCBAzz++OOMHDmStm3bYm5uTlZWlt42EhISSElJ0aUdOXLrkHBzc3Ps7Oz0XvUxbFOt1ZCUn0Ggq/7E0YFuPre8+Q5w9sbVxpGjVxtnoQAAU1NTWgW25nT4Sb30U+EnCQ5pa7BMUEgwp2rnP36CVkGtMbn2Q7C8vByFUr8TU6lUodVq63R47tyyHXtHB7qHGZ4PrSFUadTEZifT0auVXnpHr9a3HM6i1mrILslHo9XS178DxxMjG7SjQk9ZGZr0jJpXUgqa3DxM2t0wSbZKhUmbQKou3SR60cysbmSaRlM3BBGgvAJtXj4KaytM24dQedLwRP0NQWWiwqm5B6lR8XrpqVHxuPgbngfItUUzSvOLqCyv6dQvyMhFoVBg5WB4SG5DqtKoictOpqOX/lxxHb0CjB5r5iqzOn8nmlrvzVWmRvIojEbl1ReViQoXH0+SI/XnyEmOvIz7n1iYITsxDSt74/tEq9WSnZTWKIsGqExMcPdrRvwF/cid+AsxeAX41clvbmnOfW8t4N43n9S9OgwIxcnTlXvffBKPltXXgGat/MjLyNZb7TU3PQtrB9sG7TiD6nNVYl46QW61r0e+XMlOMVKqWoCLN242jhyJP3/TfE1BrdWQUpBFy1qLY7R08dZbAOBGCblp2FpYY6aq+Td3trZHo9XctINAcW24WUNTazUk5KXRptY8iG3cfLl8i2GjrVya42bjyOF44x3RtTVKmzQaruamEuyuP2dlW3d/YrNvPuda9dxsThy4fKbOZ2YqkzrXXK1WWx3z2ghzpv7TyH4S/ybSeSaEqFeJiYksWLCA6OhoVqxYweeff84TTzyBj48PZmZmfP7551y+fJmNGzfy5ptv1imvUqmYNWsWL7zwAgEBATcdQjljxgwsLCy47777OH/+PHv27OGxxx5j5syZuLtXT/obEBDAzz//TGRkJMeOHWPGjBlYWlrqtjF48GACAwO59957OXPmDAcOHOCll16q/3+YP2Fv7ClCfUPo4ROMu40j40P64mhpy6H46snYR7fpxYzOQ+uU6+HblvicVNIKsxu1vhOmTmbHpq3s2LyNhPirLPr0KzLTMxg5YQwAS77+ng/ffFeXf9T4MWSkZfDtZ1+REH+VHZu38fvmbUyaNqWmLb3C2LJuE3t37SYtJZVTx0/w03dLCO3dE9UNcwNpNBp2btnO4BFDUZk0/M38jdaf38/Q1t0Z0qob3vZuPNB9DK42Drr5OO7rMoIFfe/W5feyc6F/y8542bnQ2qU5/+k/A19HD35qpPnpjCnftguL8aMw7dYJpXczrB6djba8gopDx3R5rB6dg8XdE3Xvq06dwXxwf0zDuqN0dcGkXTAWU8ZTeTJC16lm0r4tJh1CdJ/bvPIs6tQ0KvY27nCLoIHdiDt8hrgjZ8lPy+Lk2j8oySmgVZ+OAJzesI/DP9XM8+PXLRhza0uOLttKfmoW6bGJnF63hxZh7ZpsyOaGCwcZ3KobgwK64m3vyuxuo3GxdmBHdPU+uqfzMJ7oXfP3E54USahvCMMDe+Bu40SQmy8P9Bijt5BIeFIUwwND6e3fHjcbRzp4BjC90xDCEy/W6WhrCCGDQ4k+dJroQxHkpmZydNXvFOXmE9S3ejGR8HV/sHfJel3+838cIz4iivz0bHJTMghf9wfxp6MI7l+zWMmpzftIuhBHQWYu2YlpHPh5E9mJ6QT16VL76xtE1+F9OLfvOOf2h5Odks6eXzZSmJ1Hh4HVHfv7V21j66KVACiUSly9PfReVnbWqExNcPX2wMzcDIAOA8MoLSpm9y8byUnLJC4ikmObdtNpUM9GadOe2JOE+bUj1Lct7rZOTGzXDycrW92qeGOCezOzS90hsWG+IVzJSSXVwPVIpVDSzN6VZvaumChV2Fva0szeFRdrh4Zujs7h+LN08Q6ic7NAXK0dGBEUhr2FDccTIgEY0robk9r11+U/mxpLaUUZE9r1x9XaAV9HD4YF9uBUUrRuyGbfFh1p6dwMR0tbXKzt6enXjo5erQ0OhWwIf8ScoJd/e8J8Q/CwdWJy+wE4WtnpInfGte3DfV3rztvYy68dV7JTSCmoGxU8LLAHQW6+uFjb427rxKBWXQn1bcvxhIsN3h6AHZeO09e/I7392+Np68zdHQfjZGXH3rjqVdMntevPA93H1CnXx78DcdnJJBdk1vnsTGosA1p2pnvzYFys7Ql292N8SF8iUmIMjkZoSpam5rRy8dYt7OJl50IrF2/cbQxH9jeVf/t+Ev8eMmxTCFGv7r33XkpLS+nevTsqlYrHHnuMuXPnolAo+PHHH3nxxRf57LPP6Ny5Mx9++CFjx46ts405c+bw9ttv3zTqDMDKyoodO3bwxBNP0K1bN6ysrJg0aRIff/yxLs/ixYuZO3cunTp1wsfHh7fffptnnnlG97lSqWTdunXMmTOH7t274+fnx2effcbw4Y0zP44hp1MuYWVmwbDAUOzMrUgtzGbR0Q26H712FtY4WtrplbEwMaODZwC/3TBxcWPpN3gAhQUFLF/yMznZOfi18OOND9/B3aO6AzMnO5uM9JooQw8vT9748G2+/ewrNv22EWcXZx5+cj69B/TV5Zl23z0oFAp++nYJ2ZlZ2Ds60KNXKPfNnaP33afDT5GRnsHQUY2/vw5cOYOtuZXuJvFqbhqv/f6DbniLo5Udrjf8GFQqlEwI6Usze1fUGjVnU+N4dvOXZBTlGv6CRlK+cRsKM1MsZ9+Dwtoadexlit7+GMrKdHmULk56kWZlv21GqwWLqeNROjmiLSik8uQZyn79TZdHYWWJxbRJ1Z8XFVN5/CSlK9eBWt2o7fPr0oaK4lLObTtEaUExDp4u9H/0LmycqhdLKCsoojinZoiqqbkZA+dP5cTqnWx7fynm1pb4dA6iw+g+jVrvGx2KP4uduRVTOw7C0dKWhNw03tz1o+5Yc7Kyw9XGQZd/d+xJLE3MGRnUk/u7jaK4ooyzqXF6HbWrzuxGq9Uyo9NQnKzsKSgrJjwxkl9O72iUNrXs2pbyolJOb9lPSUERjl6uDJs/DVvn6naU5BdRdMN+UVepOb52F8V5hZiYmuDg5cqweXfTvF1N9GdFSTkHf9lCSUERZpbmODf3YPQz9+FmJMqwvgX16EhpUQlHNuyiOK8Al2YeTFwwG3uX6h+6xfkFFOTk/alt2jk7cNezD7Jn+SaWvrwQGwc7Og/tTfdR/eu/AQacSr6EtZklwwNDsbOwJrUgm68Pr9Ndj+wtrHGsNaTYwsSMjl6tWHtur8Ft2lva8PzAmrkrB7fqyuBWXYnJTOSzg6sbqil6zqddxsrUgv4BnbE1tyK9MIefT24jv6x6KLGNuRX2ljURixXqKn48sYVRbXrxcM+JlFaUcT7tsm7YM1SvqDwmuDd2FtZUqqvIKs5jzdndnE+7XOf7G8LJpGiszSwZ1abntX2VxZeH1uqGkdpb2OBkVXdfdWrWmlVnDM9zaK4yZVqnIThY2lCpriKtMIcl4Vs4mRRtMH99C0+MxMbMkrHBvbG3sCE5P5NPDvyqW5Wxuk3690OWpuZ08Q5iRcROg9vcdPEgWq2WCdceTBaWl3AmNdbo8dqUglx9+WJCzRQhj/eungN4a+QR3tq9tKmqVce/fT81KOkovKMotNJ1K4SoJ/3796djx4588sknf2s7hw4don///iQlJekiyP5XPLnhk6auQr36ZNyTXG6EJekbWwsXb0Yvfrapq1GvNs/+gLy759w64/8Qh5U/8MbOxU1djXr13yGzGf/j801djXq1fta7fLBnWVNXo149O+Aevqu1+tv/ugdDx/HYuo9vnfF/zOcTFvDK9m+buhr16s3hc3lkreGVxv9XfT3pWWaverupq1GvFk95kV5fPtzU1ahXh+Z984/cT/+r8h56qqmrYJDDooVNXYUmIZFnQog7Rnl5OYmJibzyyitMmTLlf67jTAghhBBCCCHEP4/MeSaEuGOsWLGCwMBA8vPzef/995u6OkIIIYQQQgjRNK6twnzHvf6lJPJMCFFv9u7d+7fKz5o1i1mzZtVLXYQQQgghhBBCiPogkWdCCCGEEEIIIYQQQhghkWdCCCGEEEIIIYQQdxKNpqlrIG4gkWdCCCGEEEIIIYQQQhghnWdCCCGEEEIIIYQQQhghwzaFEEIIIYQQQggh7iT/4pUt70QSeSaEEEIIIYQQQgghhBHSeSaEEEIIIYQQQgghhBEybFMIIYQQQgghhBDiTiLDNu8oEnkmhBBCCCGEEEIIIYQR0nkmhBBCCCGEEEIIIYQRMmxTCCGEEEIIIYQQ4k6ikWGbdxKJPBNCCCGEEEIIIYQQwgjpPBNCCCGEEEIIIYQQwggZtimEEEIIIYQQQghxJ9FqmroG4gYSeSaEEEIIIYQQQgghhBHSeSaEEEIIIYQQQgghhBHSeSaEEEIIIYQQQgghhBEy55kQQgghhBBCCCHEnUSrbeoaiBtI5JkQQgghhBBCCCGEEEYotFrpzhRCCCGEEEIIIYS4U+Td+3BTV8Egh5++aeoqNAkZtimEEPXop/CtTV2FenVvt5EUxsQ2dTXqnW2rAIZ8+0RTV6Ne7Zz7Kf/d/l1TV6NevTH8Qcb/+HxTV6NerZ/1LoMWPd7U1ahXfzz0GYVp6U1djXpl6+H+jzyff7L/16auRr17su9UNl842NTVqFej2/Zm+HdPNXU16tX2BxfS7+t5TV2NerXvkS+Zvertpq5GvVo85UV6fXlndtj8VYfm/Q939GgkzulOIsM2hRBCCCGEEEIIIYQwQjrPhBBCCCGEEEIIIYQwQoZtCiGEEEIIIYQQQtxJZHr6O4pEngkhhBBCCCGEEEIIYYR0ngkhhBBCCCGEEEIIYYQM2xRCCCGEEEIIIYS4k2g0TV0DcQOJPBNCCCGEEEIIIYQQwgjpPBNCCCGEEEIIIYQQwggZtimEEEIIIYQQQghxJ5HVNu8oEnkmhBBCCCGEEEIIIYQR0nkmhBBCCCGEEEIIIYQRMmxTCCGEEEIIIYQQ4k4iwzbvKBJ5JoQQQgghhBBCCCGEEdJ5JoQQQgghhBBCCCGEETJsUwghhBBCCCGEEOIOotXIsM07iUSeCSGEEEIIIYQQQghhhHSeCSGEEEIIIYQQQghhhAzbFEIIIYQQQgghhLiTaDVNXQNxA4k8E0IIIYQQQgghhBDCCOk8E0L8T5s1axbjx4//U2X8/Pz45JNPGqQ+QgghhBBCCCH+WWTYphD/Q2bNmsXSpUvrpA8bNozt27c3+Hfn5eWxfv36Bv2eP+vTTz9Fq63flWji4+Px9/fn9OnTdOzYsV63bciJnQc5unUPRXkFuDbzYMg94/EJannLcomXLvPz/32Jq7cHD779rC49KvwshzbuJDc9C41ag6O7C6Ej+9Oud7eGbEYdWq2Wb5cvZ92O7RQWFdG2dSDPPfIILX19jZaJu3qVb35ZRlRsLKkZGSx48EGmjxtvNP+SVav48qelTBs7jqfnzm2AVugbE9ybu9oPxNnKjvjcNL4+8hvn0y4bzT82uDfj2vbB3daJjKJclp/eya6YcIN5+7fsxEuDZnEo/iyv/f5DQzWhjm7N29DbvwM25pZkFuWyLeooV3PTjOZXKZT0D+hMB68AbMytKCgrZl/caU4nXwKgi3cgHb1a42brCEBKfha7YsJJzs9slPYAjAgMZXxIXxytbEnMTeeH45u5mBFvNH/fFh2ZENIPLztniivKOJ18iR9PbKWwvESXZ0xwL4YHhuJi7UBheTGH48/z86ntVKqrGqFF1cfSlA6DdMfeV4fXcu4mx964tn0Y17YPHteOvV9O/c7OG469Ya27858B99QpN/z7BQ3WJq1Wy7c/LmHdpk0UFhbSNjiY5558ipb+/jct98e+vXzzww8kpaTg7eXFow88yIC+fXWfL1qymO9+/FGvjLOTEzvWrde9z87J4fNF33A0PJzCoiI6d+jAs088gY938/ps4j/yfH5+z3EidhykJL8IRy9Xek0dgVdrP4N5k6OvsPHDJXXS737jMRw9XQG4uP8E0UciyEnJAMDV14seEwbj7u/dYG0w5NC23ezdsIOC3Dw8mjdj3Oy7aRHc2mDey5ExbPlpDRnJqVRUVODo6kzY0H70GzNUl+fozn2c2HuEtIRkALxb+jJyxkR8WrVolPYAjG7Ti8kdBuBkacfV3DS+ObqeCzc5T4wJ7sWY4D642zqSWZTHioid/BFzQvf58MBQBrfuhq+jBwCxWUksCd/CpcyEBm/LdePb9uHujoNxsrInPjeVLw6t4Wxq3E3y92Viu3542DqRXpTLspPb2XHpuO7zT8Y+QadmdffzkavneX7r1w3ShtoGtOzM8MBQHCxtSM7PZEXELmKyEg3mnd1tNL3929dJT87P5JUd3+neD2nVjQEtO+NkZUdRRSknkqJYc3YPVRp1g7Xjr+jgGcD0TkMJcvPBxdqB57d+zYErZ5q6Wneuev6NI/4e6TwT4n/M8OHDWbJE/8bU3Ny8iWrT9Ozt7Zu6Cn/LxaOn2blsPcNnTaZ5a39O7T7Myg++5aH3nsfexdFoubKSUjZ+sxz/tq0oyi/U+8zS2opeY4fg4uWOykRFzOkLbPp2JVZ2trRsH9TQTdJZunYNy9ev49WnnsLHqxk//Por8155mbXfLMLayspgmbLycrw9PBjcqzcff/+dwTzXXbh0iXU7ttPK7+Y/vOtLvxadeCRsAp8fXM2F9CuMatOTt0c8zJxV75BZnFsn/+g2vZjdfQwL968kOjOBIDcfnupzN0XlJRxNuKCX183Gkbk9xnM2NbZR2nJdiEcLRrQJY/PFQyTkptOteRD3dBnOFwdXk19WbLDMlI6DsDG3ZP35/eSUFGBtZolSodB97ufkxdnUWBIj06nSqOnt34F7u47gi4Nr9DqjGkovv/bM7j6aRUc3EJURz7DAHrwy5H4eW/8xWcX5dfK3cfPlid5TWBy+mfDESJyt7Hg4bALzek7i3T0/A9WdazO7DOeLg2uIykzAy86Fx3vfBcDi8M0N3qb+LTvxaM+JfHZwNefTLjM6uBfvjHyE2aveJqOo7rE3Jrg3c7qP4eP9K4jOSCDIzZcFfe+mqKKUI1fP6/IVlZcy69f/0yvbkJ2BS1csZ/mqVbz6wgv4eDfnh59/Yt7TC1i77Bej54Sz58/z4uuv8/DsOQzo04c9Bw7w/Guv8sMXXxISHKzL18Lfn68++lj3XqVS6f5fq9XyzEsvYWKi4qO33sba2ppfVv3KowsWsHrpT1haWtZL+/6J5/PY8HMc+nUbfWaMxjPAhwv7wtny2TLufn0+ts4ORstNe/NxzCxr7k0sbK11/58SHU+r7u3xaNkclakJETsOsnnhT0x9fT42jnYN2Ryd0wePs2HJSiY+eA/+bQI4smMf3/3fJ/zn0zdxdHWuk9/M3IxeIwfi5euNmYU5VyJjWPPNT5iZmxM2tB8Aseej6dS7O35BAZiYmrJn/TYWvf4x//n0Teydje//+tK3RUceChvPl4fWcCH9CiODevJ/w+cyd/W7ZBbn1ck/qk1PZnUbzacHfuVSZiKBrj480WcKReWlHLt2jWrvFcDe2FNcTL9ChbqKuzoM5O0RD/PQmvfILql7Pq1vA1p2Zn6vySw88CvnU+MY07Y3742ax30r3zR47hvXtg9zQ8fywd7lRGVcpY27H8/2m05heQmHr537XtnxHabKmp/AdhbW/DDlBfbGnW7w9kD1A6tpHYfw86ntxGYl0b9lJ57qM5WXd3xLTklBnfwrInay5twe3XuVQsnrQ+dwIilKlxbq05bJ7QewOHwzsVnJeNg6Maf7aABWRuxq+Eb9CZam5sRmJ7E16jBvj3i4qasjxJ8iwzaF+B9jbm6Oh4eH3svRseamTKFQsGjRIkaPHo2VlRVt2rThyJEjxMbG0r9/f6ytrQkLCyMuruap3WuvvUbHjh1ZtGgRzZs3x8rKirvuuou8vDzd50uXLmXDhg0oFAoUCgV79+5l4MCBzJ8/X69+2dnZmJubs3v37jp1z8/PR6VScfLkSaD6B42TkxPdutU8QV+xYgWenp6698nJyUydOhVHR0ecnZ0ZN24c8fHxus9rD9ssLCxkxowZWFtb4+npycKFC+nfvz9PPvmkXl1KSkqYPXs2tra2+Pj48O233+o+878WBdGpUycUCgX9+/e/+U75G45t20vH/j3oNCAUl2buDJ05ATtnB079ceim5bYtXk3bsM40C/Cr85lvcABB3drj0swdR3cXug/vh1tzTxKjjT99rm9arZYVGzZw/9SpDOzZiwA/P15fsICy8nK279tntFzb1q15YvYchvXrh5mpqdF8JaWlvPLhB7z02GPY2tg0RBPqmNS+P9ujj7It+igJeel8fWQdmUW5jAnuZTD/4Fbd2BJ5iH2XT5NWmM3euNNsjz7K1I6D9fIpFQpeGHgvP53cRlpBdmM0RaenXztOJUVzKimarOI8tkUdpaCsiG4+wQbzB7h44+fkybKTO7icnUJeaRHJ+Zkk5mXo8qw9u4fwxEjSCnPIKs5nw/kDKBQKWjg3a5Q2jWvbm10xJ9gVE05SfiY/HN9MVnE+wwNDDeZv7epDZlEuWyIPk1GUS2TGVX6PPk6AS019A119iEq/yv4rZ8goyiUiJYYDl8/o5WlIk9sNYFvUUbZGHSEhL52vDv9GRlEuY4J7G8w/pFU3NkceYm/caVILs9kTd4pt0UeZ2mFQrZxacksL9V4NRavVsmL1au6fOZOBffsR0KIFr7/wYvU5YddOo+VWrFlNjy5duf+ee/Dz9eX+e+6he5cuLF+9Wi+fiUqFi7Oz7uXo4KD7LCEpiXMXL/D8gqdp26YNfj4+PP/UAkpLS9nxxx/11sZ/4vn8zM7DBPXuTHCfLjh6utL77pHYONpxYZ/hCNrrLO2ssbK31b2UypqfHIMfnEzIgO64+Hji6OlKv3vHodVqSY5svGvU/k2/031QH0KH9MXd24vxc6bh4OzE4R17Deb3buFL5z498PBphpObC136hRHYMYQrkZd0ee55ai69Rgykmb8P7t6eTHlkFlqtlpizkY3Spont+rMj+hjbo4+RmJfBoqPrySzKY7SRa9SgVl3ZFnmY/ZcjSCvMZt/l0+yIPsaUDgN1ed7fs4zNkYe4nJNCUn4Gnx74FYVCQcdmrRqlTVM6DGJr1BG2RB7mal46XxxaS2ZRLuPa9jGYf2jr7my8eIg9cadILcxmd+xJtkQdZlqnmgjBwvISckoLdK+uzYMor6pgb9ypRmnTsNbdOXDlDAeunCG1MJsVEbvIKS1gQMvOBvOXVpZTUFase/k5emJlZsnBG6K1Wjo3IyYriWMJF8kuyedC+hWOJVzEz9HT4Dab0tGEC3x3bCP7Lkc0dVWE+NOk80yIf6A333yTe++9l4iICIKCgpg+fToPPfQQL7zwAidOVIfj1+70io2NZdWqVWzatInt27cTERHBvHnzAHjmmWeYMmUKw4cPJzU1ldTUVHr27MkDDzzA8uXLKS8v123nl19+wcvLiwEDBtSpl729PR07dmTv3r0AnD17VvffgoLqp2179+6lX7/qp7glJSUMGDAAGxsb9u/fz8GDB7GxsWH48OFUVFQYbPuCBQs4dOgQGzduZOfOnRw4cIBTp+reEH300Ud07dqV06dP8+ijj/LII48QFVX9FO/48erw/l27dpGamspvv/12e//wf5K6qorUK0n4hwTqpbcICSQpJt5ouTP7jpGbnkXficNu+R1arZYr5y+Rk5Z5W0OH6ktyehrZubmEdqq5GTQzNaVzSAhnI//+D4n3vv6aXt260aNjp7+9rdtholTR2qU5J5Oi9dJPJkXT1t1w5JupyoSKWlE85VWVBLr6oFLUXH7v6TycvNIitkcfrf+K34RKocTTzoW4rGS99NisZHwc3A2WCXLzJSU/i97+7Xmm/3Qe7zOFYYE9MFGqDOaH6n8HlUJJaWW50Tz1xUSpoqVzMyJSYvTSI1JiCHIzPFw4KuMqztb2dGlW/Xdob2FDmF+I3lP9yIx4Wro0o5VL9bAydxsnOnsH6uVpKCZKFa1dm9f5rpNJUTc/9qoq9dLKqyoJcvNFdUMnhqWpOcunv8bKGW/w1vC5BDg33LC55NRUsnNyCO1a87DEzMyMzh06cPb8eaPlzl64QI9u+kMUQ7t15+wF/TIJSUkMnziBsVOn8MLrr5GUkqL7rPLa9cLczEyXplKpMDExIeLc2b/Vruv+iedzdVUVmVdTaR6s/13N2waQFnfzYXur3/iapc+8z8aPlpAcdfNOsaqKSjRqNebW9RMBeCtVlVUkxV0lsENbvfTAjsHER91e9G/S5avER8fSIjjQaJ6KinLUajVWN0TdNRQTpYpWLt6cSta/Rp1KjqaNu5/BMqbKuteoCnUlrWtdo25kbmKGiVLZKFHE18994Yn69wzhiZGEeBgeCmvs3Nem1rnvRqOCwtgde5KyKsP3lfVJpVTi6+jJhXT9v4kLaVdu+/zbp0UHLqZfIfuGKLWYrCT8HD3wd6ruLHO1dqCdZ8tGj2YXDUCjvTNf/1IybFOI/zGbN2/GplakzXPPPccrr7yie3///fczZcoU3WdhYWG88sorDBtWfXP+xBNPcP/99+tto6ysjKVLl+LtXX3x/vzzzxk1ahQfffQRHh4eWFpaUl5ejoeHh67MpEmTeOyxx9iwYYPu+5YsWcKsWbNQ3DCM60b9+/dn7969PP300+zdu5dBgwZx+fJlDh48yMiRI9m7dy9PPfUUACtXrkSpVPL999/rtrdkyRIcHBzYu3cvQ4cO1dt2YWEhS5cuZfny5QwaNEiX38vLq049Ro4cyaOPPqr7N1q4cCF79+4lKCgIV9fqeVmcnZ312nuj8vJyvU5D+PPDZ0sKi9FqNNjY2+qlW9vbUpRXN3QfICctkz2/bmbmK4+hVBnvsCgrKeWzx15DXVWFQqlk+KzJtGhn/Ca/vmXnVg+ncL4h+uP6+9SMvzf31Y59+4iKi+WnhZ/8re38GfYW1qiUKnJL9fdLbmkhjla2BsucTIpiRFAoh+PPEpOVRGuX5gwPDMVUZYK9hQ05pQW0dfdneGAoD699vzGaocfKzAKVUklRhf6PoOKKUmzMDf+IdbS0xcfRnSqNmhWn/5+9+46K4mrDAP4svTcBCyJVEBQVBLsCKmrQ2EsUBYm9gdjzGUvsxt6xYW+IxqixK6AoNqoCAlLEAoogvbPz/UHYuIIKCzLM+v7O4Rz2zhCfG5bZ2bv3vvcGFKTl0L9lF8hLy+L8szuV/oyDiQ2yCnIRn/am0uO1SVlWAZISksj4bAZVZn421OUrr2UUnZqETXdOYa7daEhLSkFKQhIPkyKx78EFwTkBCeFQlVXC6p+mgMfjQUpCEleeB+Lc0y/Poqwt/z33hPv0MT8bGl947j15HQXHFp1wL/EpYj+8gommLn4y7fDfcy8vC0kZ7/Gn33HEp72FoowchljYYevAWZjksw5vsmq/Pl1aetmsygYaGkLtDdQ1kPzuyzX20tLT0UBdeMlbA3V1pKWnCx63MjPHH//7H/Sa6iLt40ccOHoE46dPw+lDh6Gmqgp9PT00btQIO/buxf/mzoW8nByOe59GWno6PqTVzmxPcbyeF+TkgeHzoaAifM8hr6yIvMycSn9GQVUZtmMHQEuvCUpLShDzIAwXNh3GwLmuX6yT9uDsDSiqqaCped3UBsvNzgafz4eSmvASUSVVVWRnfHkgFwCWT5iLnKxs8Pml6DNiIDo6dP/iuf8cPQtVDXU0b135TN7apFJ+ncir5DohX/lS2KDXz9G3RUfcf/kULz68RnNNXfQ26SD0GvW5X236Iy03U1Dj8ntSlVOClIRkhaWMZde+yvv0+FUU+pt1RkBCGGI+lC1FdWzRSeja96kW2nowbKCDdX7Hv1s/PqUsowBJCYkKZRGyCnOhKvftQVZVOUVYNDLC3gd/C7U/ehUJZVkF/GbvDPDKBh5vvwjC5eeBtZqfkB8dDZ4RwjH29vbYvVu4oKnGZ29GWrf+r7Bow4ZlM0gsLCyE2goKCpCVlQUVlbIbkGbNmgkGzgCgU6dO4PP5iI6O/uIAkqysLMaMGQMvLy+MGDECoaGhCAsL++qmAnZ2djhw4AD4fD78/f3Rs2dPNGvWDP7+/rCyskJMTIxg5llQUBBevHgBZWXhNyMFBQVCy07LxcfHo7i4GO3btxe0qaqqwtS04puMT/8f8Xg8NGrUCO/fv69w3pesWbMGf/zxh1Db0qVLYdiv/Rd+4is+G2hk/s30OT6fj/M7j6Lb0L5o0Fj7q/9JWTlZTFg1F0WFRUiMiMHN4+ehrtUAeubG1c9XBVd8fbF65w7B4y1LlwGo2A+GqdDdaklJTcXGfXuxY/kKoVkkdeXzuq08XtnvqzLHgq9BXUEZ2wbNBg9lN/zXYx5iZNte4DN8yEvLYoH9WGy+ewpZhZXXF2PLl+rTlv8+fcJvo/DfT/evPi9binop8l6FwsRdDVrDorERDj76h92ixTwemC/8ppqqamNihwE4HXoLIW9joC6vjHHWjpjaaTB23D8LoKw23LA29tjz4G/EpiahkYomJrT/GR9bZ8M7vOIS9e/j8/y8L/6ejgZdg7q8CnYMmg0er+y5dy3mEX5p2wt8Ph9A2Wy6qE82UXiWkgDPofMwqFV37Py33zVx5cZ1rN64UfB4y9p1Zak/+/tnGOaLH7YIVLhGCv9Ml47/Lck1BtC6ZUsMGj0Kl65exZiRIyElJYU/l6/Aij/XoUf/fpCUlET7du3QuUMH0TpXrazcu55XUMmv50u/M/VGmlBvpCl43MioGXLSMxF6/V6lg2chV+/ixaOnGDjPFVJfWar/PVTswrdfoKavWoCigkK8jInDP0fPokFjbVh1q/g8uv3XFYQEPMS05fMhLVOX/RK+KPCAL177ToTcgLqCCrYMnCV4jboR+wgj2vREKcOvcP6w1j1gZ2SJ+f/srLONUr7kSxtFHX5yBRryKtg9ZB7AAz7mZeNq9AOMtuwNfiV96teiM+LT3uD5+5ffO/JXlf2evq2LfmvkFRcg+K3wDENTrWbob9YZR4OvIj79LRoqqWNUWwdkmufgYuTXl40TQqqOBs8I4RhFRUUYG3/9hln6kxvQ8hvcytrK30RVpvycb72pmTBhAtq2bYvXr1/Dy8sLPXv2hN5XdlPs3r07srOzERwcjLt372LFihXQ1dXF6tWr0bZtW2hra8PMzEyQr127djh+vOInguWzwz5VfjNVccCm4i2J9Gc36Twe76v/Pz7322+/Yfbs2UJtsrKyOB1e9fo5CsqK4ElIVJiVkJeZDUXVijNKivILkZzwCikv3+Da4bKlpAzDAAyD1c5zMHrBFOi3LKtDwpOQgEajsv9HjfR08OHNO9y/ePO7vdnq3qEDWn0ySFlUXDaw8uHjR2h+MribnpkBDTXRCyc/f/EC6RkZGDvLXdBWyucjJOIZvC9dxP2/zgsVCq8tmQW5KOWXVvi0W01OGRl5ldeJKiotxkb/k9hy5zTUFZSRnpcFxxadkVtUgMyCXBg2aILGKg2wos9Ewc+UP3evTtgE19OrkJz9/Wqg5RUVoJTPh5KMcKF2RRl55BblV/oz2YV5yCrIFQycAUBqTgYkeDyoyCkKfarfRd8C3Qzb4vDjy3iXk17Zf67WZRfmoZRfCjV54b8fVTklZORXPlNmWGs7RL1PxPmIsplzLz+mYM+D81jjOBXHQ67jY342Rls6wC8uWLBT6suMd5CTksa0zkNwJtz3i29Oa0P5c0/9s9kj6vJKX6xRVlRajA3+J7D57imoy6sgPS8T/cy6CJ57lWHAIDo1CU1VK15bRdG9S1e0Mvtvxo3gmpCWDs0G/w2upGd8hIb6l68JDTQ0hGaZAUD6x4yv/oy8vDyMDAzx6vVrQZuZqSlOHPBCTk4OiktKoK6mBpcpk2FeyYcrohCn63k5OSUF8CQkKswyy8/OhbxK1ZciNjTURcyDirvphV4LQPDlu/h5tgsaNK38Q7rvQVG5rAZb9kfh31VOZhaUVb++YUGDhmW/h8Z6TZGdkYXrp/+uMHjme/4qbp39B1OWzUUT/drdzfVLssqvE5+/Rskrf/U6sfnOKWy76y14jfqpRSfkFhUg67PrxFALO/zSthd+u7wbCenJ360fn8osyEFJJa+76t/o0zq/Y9hw5wQ05FWQlpeJn827IrcoH5n5wn2SlZJGD+N2dbLpS7nsojyU8vkVZpkpyypW+H9emW4GbRD48hlKP7tfHdzKFvdfPhPsWvkmMxUyktJwsXbEpch73/EVinx3lQz6EvbQ4BkhBACQlJSEt2/fCpY4BgYGQkJCAiYmZUudZGRkUFpaceaIhYUFrK2tsW/fPpw4cQLbt2//6r9TXvdsx44d4PF4MDc3R5MmTRASEoJLly4JZp0BgJWVFU6fPg1tbW3BDLmvMTIygrS0NB49egRd3bIb1qysLMTGxgr9d79F5t8ZTZX1t5ysrGyNdzmVlJJCY4OmSHgWgxY2/82ES3gWA5N2rSr+m/KymLhmvlBb0M17eBkZiyFu46CmpVHhZz5VUvz9PilWVFAQ2i2PYRg0UFfHw5AQtDAqq5dTXFyM4GfPMHOc65f+M99k06YNTu3YKdS2fOsW6DVtCpehw77LwBkAlPBLEfPhFax0THEv8b8aSVZNTXE/8elXf7aU4Qt2ebQ3ssLDpAgwYJCU8Q4Tz6wVOnecjSMUpOWw6/65SndHq02lDB/JWR9gpKkjNAPJSFPni5/CJ318h5aNDCHzST03TUVV8Bm+0I1/F/3WsDWyxJEnV/A268N37cenSviliEt7g7ZNjAW7xQH493FkpT8jKymDUkb4b53/2YC7rKR0hUH4snN4ZbMPv+M7kxJ+KWJSX6FdU+HnXrumLXDvW889Ph8f/n0e2RtZ4cHLZ18d6DNqoFNrb4wrvSZoaODhkydo8e/rSnFxMYLDwjBz8uQv/ndat2yJh08ew+nf0gAA8PDxY7RuWfEaWa6oqAiJSS9h+ckM43LlZQ+SXr9CVHQ0po4fX+2+VUacruflJKWkoKXXGK+j4mBo9d9A6OvIOOi3rfpOnx+SkqHw2QBiyLUABP/jj37uztDWr5uNN8pJSUuhqZEeYsIiYNHxv7qcMWGRaNm+GnU0mYq/B9/zV3HT5xImLfaAbiUbQHwvJfxSxH54DUsdE6HXJEsdEzx4+fWlqJ++RtkaWeLRv69R5Ya1tscoSwcsurIHsR9efZ8OVKL82mfdtIVgUAgArJu2QEDi12sVlvL5gtfQHsbtEFjJtc/eqB2kJaVwI+brm1/UplI+Hy8/JsO8oQGCP1n62rKhAULefn0prKlWMzRU1sDdexUHomUkpSr0j2GYskmj3/tFipAfCA2eEcIxhYWFSEkRrg8jJSUFTU3NL/xE1cjJycHFxQUbNmxAVlYW3NzcMGLECMGSTX19fVy7dg3R0dFo0KABVFVVBbO3JkyYgBkzZkBBQQGDBw/+5r9lZ2eHrVu3YvDgweDxeFBXV4e5uTlOnz6Nbdu2Cc5zcnLC+vXrMXDgQCxfvhxNmzZFUlISzp07h3nz5gktMwUAZWVluLi4YN68edDQ0IC2tjaWLl0KCQmJby8L+oS2tjbk5eVx9epVNG3aFHJyclBVVa3yz1dHh5/s8Pfu42hsqIumxvoI8b2PzLSPsOrZGQDge/oSsj9mYsAUJ/AkJKCtK7xzkqKKEiSlpYTa7124icYGulBv2AClJaWIC43C04DH6Dtu+HfpQ2V4PB5GDRyIg2e80axJE+g2aYKDZ7whJyuLvp8MZC7ZuBHaDRpgxrhxAMreTMe/KitEXVxSgtS0NETHx0FBTh66TZpAUUEBxvr6Qv+WnKwc1JRVKrTXtrPhflhgPwYxH5IQ9S4Rjmadoa2kjktRZUsifrXpD01FVfz5b+0UHVUttNDSw/P3L6EkK4+hre2hr9FYcLy4tASJH4UHKnILy2Z8fd7+vdxPfIohre0EO2Za67aAqpwSHieVFWjuZWIDFVlFnHvqBwB4mvwCdkaWGGRhC9/YICjIyKG3aQcEv44RLMvsatAaPZpbwyfsNjLys6EkU1Y/rai0uEJx6u/h74gAzOo2Ai8+vEF06kv0NukATUU1XIt+CAAYY9UHDRRUsTXAGwDw+HUUpnUegr6mHRDyJhbqCsoY374/YlKTBLMbHr9+jgHmXRGf/hYxqa/QWLkBRls64PGryAoDbd+Dz1NfLLQfi5jUV4h8l4B+/z73LkYGAADGt/8ZmoqqWOd7DADQVFULLbT1EPWu7Lk3vLU9DDQaC44DwNh2fRH1LhFvMlOhICOHwa1sYdygKbYFnKk0Q03xeDyMGj4cB48fQ7OmTaHbtCkOHjtWdk3o5SA4b8mqVdDW0sSMSWUDar8MG4ZJbm44dOI47Lp0hd+9ADwMeoIDnwyib9m1E906d0Gjhtr4+DEDB44cQW5uLvr37Ss456avL9TU1NCoYUO8iI/Dxu3bYdu1KzraiLDU/gvE8XrexqEzbh04By09HTQy0kXknSfITs9ES9uyTRwenLuB3I9Z6Dl+KAAg7OZ9qDRQh3oTbfBLShHzMAzxwZHoM/UXwX8z5OpdPPr7NnpNGAYVTTXkZZb9nUnLykBarmYfSlVV95974+S2/WhqrA99UyM8uH4HHz+ko1Pvstenf46dRWbaR4x2nwAACLhyG+qaGtDWKfvdJETFwu/CNXR1/G9nytt/XcHVk+cxxmMi1LU1kfWxbEBKVk4WsvJy371P5576YZ6dE2JTXyHqfSJ+alF2nfgn6j4AwNWmHxooqmKD3wkAZa9RplrN/n2NUsAQC1voqzfGxn+PA2VLNZ2tf8K620fxLjsd6v/O6s0vLqyTAvveYbewqKcLolOTEJESj/7mXaGtrIELEWXXvokdBkBLUQ2rbx8BULYM30xbD5HvE6Esq4ARrXvAQKMx1vx7/FP9zDohICGszssmXIt5hIntByDxYzLiPryBrZElNBRUBLt9DrWwg7q8MvY/uij0c90M2iAu7U2lNSnDkl+gt0l7JH18h/j0N9BWUsegVt0R+jb2i0tc2SIvLSs0w7mJiiaaazZFVkEu3uV8ZDEZId9Gg2eEcMzVq1fRuLHwDbepqalgp0hRGRsbY8iQIXB0dER6ejocHR2xa9cuwfGJEyfCz88P1tbWyMnJga+vL+zs7AAAo0aNwqxZszB69GjIyX37BtHe3h6bNm0S/DwA2NraIjQ0VGiGmIKCAu7cuYMFCxZgyJAhyM7Oho6ODnr27PnFmWibNm3ClClT0L9/f6ioqGD+/Pl49epVlXKVk5KSwrZt27B8+XIsWbIE3bp1E+wQWtvMO1oiLzsXAX9dQ05GFrSaNsYv8yZBVbNs1kFORhYyP1TvZqK4sAhXD/kgOz0TUjLSaNBEGwOnjoF5x7rZmbKcy9BhKCwswtrdu5Cdk4NWpqbYsXyF0GyUlNRUSEj8N7CZmp4OJzc3weOj587h6LlzsGplgb1rhWdp1TX/+BCoyClijFUfaCioIjE9GYuu7MH7f2/2GiioQFvpv6VkkjwJDGttj6Zq2ijllyL0bSzc/95SZ0sYq+JZSjzkpWVhZ2wFZVkFvM9Ox7Ggq8gsKFumpSyrAFX5/5aXFJWW4PCTy+hn1hmTOw9GflEBnqXE41bsE8E5Ns3MISUhiV8sHYT+Ld8XQfB9UXHn29p2LzEcKrIKGNm2J9TllZH0MQUrbh4SzELQUFCBlpKa4PzbL4IgLyULxxad4WrTD7lFBQhPjsORoCuCc7zDboNhGDhZ9oaGgiqyCnLx+FUUjodc++79AQC/uBCoyCpibLv/nnu/XfH84nNPgieBYa17QFdVGyX8UoS9jcXM85uFnntKMvKY3f0XqCuoILcoHy8+vIbHxa2ITv36Loo14TJqNAoLC7F286aya4KZGXZs2Ch8TXj/Tuia0KaVBVYtWYrdB/bD88ABNG3SBGuWLUMr8/9mQr1LTcWi5X8gIzMT6mpqaGVujoO7PdH4k3qdH9LSsHnnDqR9/AjNBg3Qr08fTHB2qdX+ieP13NjGAgU5+Qi65IfczGxoNNFGP7cxUG6gBgDIy8hGTnqm4Hx+SSnun7mG3IwsSElLQ72JFhzdxkDP4r8NOyL8HoNfUorrnqeF/i3rn+1gM6AH6oJl1/bIy87BDe+LyPqYicbNdDBhkTs0tMs+iMz6mIGMD//9vTB8BpePnUX6+w+QkJREg4Za6DdmKDr2/u+e5f5VX5SWlODweuG6tL1HDECfXwZ+9z7diQ+FiqwinKz6QF1BBS/Tk7H46l7BdUJDQQXaip9eJ3gYYmEneI0Ke/sCsy9sFRrA+Nm8C2QkpbDYQXjG+LGgqzgW/P2vf75xwVCVU4Rzu5/QQFEFCenJWPDPLsG1rIGC6mevuzyMbNMTumplG9uEvI3B9L82IiVb+HW3qao2Wjc2xpyLX18t8T08fhUFJRl5DDDvClU5JbzJTMWWu6cFu2eqyilVWKoqLy2Ldk1b4GTojUr/mxcjA8AwDAa36g51eWVkF+YhLPkFzj71+869qb4WWnrYMfi/siduXcs+CLgcFYhVtw+zFav+qmeDnz86HlPfhqMJIXVu2bJlOH/+PEJDQ0X6+VevXkFfXx+PHz+GlZXVt3+gDuXm5kJHRwcbN27E+FpanvM1Rx5f/u7/Rl1ytnFEdqz4bXWu3NwYDnvdv30ih9yYtBVLru5jO0atWt53IgYdWsh2jFp1ftxa9Nzj9u0TOeTW5G3ITnnHdoxapdyooVhez7fcOf3tEzlmVveRuPTvTCRx0b9lV/Td58F2jFp1deJm2O6eznaMWuU/dSd+9V7Ndoxa5TXif+iycwrbMWrVvemebEcQ2ceBo9mOUCn1v098+yQxRDPPCCEiKy4uRnJyMhYuXIiOHTvWi4GzkJAQPH/+HO3bt0dmZiaWL18OABg48Pt/6ksIIYQQQgghRPzQ4BkhRGT37t2Dvb09TExM4OPjw3YcgQ0bNiA6OhoyMjJo164d7t69W+OacIQQQgghhBBSZ/i0SLA+ocEzQgiWLVuGZcuWVfvn7Ozs6l0hUktLSwQFBbEdgxBCCCGEEEKImJBgOwAhhBBCCCGEEEIIIfUVzTwjhBBCCCGEEEIIqU/q2QqfHx3NPCOEEEIIIYQQQggh5Ato8IwQQgghhBBCCCGEkC+gZZuEEEIIIYQQQggh9QnDZzsB+QTNPCOEEEIIIYQQQggh5Ato8IwQQgghhBBCCCGEkC+gZZuEEEIIIYQQQggh9QmfdtusT2jmGSGEEEIIIYQQQgghX0CDZ4QQQgghhBBCCCGEfAEt2ySEEEIIIYQQQgipTxhatlmf0MwzQgghhBBCCCGEEEK+gAbPCCGEEEIIIYQQQgj5Alq2SQghhBBCCCGEEFKf0LLNeoVmnhFCCCGEEEIIIYQQ8gU0eEYIIYQQQgghhBBCWLVq1Sp07twZCgoKUFNTq9LPMAyDZcuWoUmTJpCXl4ednR0iIiKEziksLMTMmTOhqakJRUVFDBgwAK9fv65WNho8I4QQQgghhBBCCKlP+Pz6+fUdFRUVYfjw4Zg6dWqVf+bPP//Epk2bsGPHDjx+/BiNGjWCg4MDsrOzBefMmjULf/31F06dOoWAgADk5OSgf//+KC0trfK/QzXPCCGEEEIIIYQQQgir/vjjDwDAoUOHqnQ+wzDYsmULFi1ahCFDhgAADh8+jIYNG+LEiROYPHkyMjMzceDAARw9ehS9evUCABw7dgy6urq4efMm+vTpU6V/i2aeEUIIIYQQQgghhJBvKiwsRFZWltBXYWEhK1kSEhKQkpKC3r17C9pkZWVha2uL+/fvAwCCgoJQXFwsdE6TJk3QqlUrwTlVwhBCCOGUgoICZunSpUxBQQHbUWoN9YkbqE/cIY79oj5xA/WJG6hP3CGO/RLHPv1Ili5dygAQ+lq6dGmt/hsHDx5kVFVVv3nevXv3GADMmzdvhNonTpzI9O7dm2EYhjl+/DgjIyNT4WcdHByYSZMmVTkTj2Fo/1NCCOGSrKwsqKqqIjMzEyoqKmzHqRXUJ26gPnGHOPaL+sQN1CduoD5xhzj2Sxz79CMpLCysMNNMVlYWsrKylZ6/bNkywXLML3n8+DGsra0Fjw8dOoRZs2YhIyPjqz93//59dOnSBW/fvkXjxo0F7RMnTsSrV69w9epVnDhxAq6urhUyOzg4wMjICJ6enl/9N8pRzTNCCCGEEEIIIYQQ8k1fGyirzIwZM/DLL7989Rx9fX2RsjRq1AgAkJKSIjR49v79ezRs2FBwTlFRET5+/Ah1dXWhczp37lzlf4sGzwghhBBCCCGEEEJIrdPU1ISmpuZ3+W8bGBigUaNGuHHjBiwtLQGU7djp7++PdevWAQDatWsHaWlp3LhxAyNGjAAAJCcn49mzZ/jzzz+r/G/R4BkhhBBCCCGEEEIIYVVSUhLS09ORlJSE0tJShIaGAgCMjY2hpKQEAGjRogXWrFmDwYMHg8fjYdasWVi9ejWaN2+O5s2bY/Xq1VBQUMDo0aMBAKqqqhg/fjzmzJmDBg0aQENDA3PnzoWFhYVg982qoMEzQgjhGFlZWSxdurRa06XrO+oTN1CfuEMc+0V94gbqEzdQn7hDHPsljn0itWPJkiU4fPiw4HH5bDJfX1/Y2dkBAKKjo5GZmSk4Z/78+cjPz8e0adPw8eNHdOjQAdevX4eysrLgnM2bN0NKSgojRoxAfn4+evbsiUOHDkFSUrLK2WjDAEIIIYQQQgghhBBCvkCC7QCEEEIIIYQQQgghhNRXNHhGCCGEEEIIIYQQQsgX0OAZIYQQQgghhBBCCCFfQINnhBBCCCGEEEIIIYR8AQ2eEUIIYRXDMKC9a0hde/HiBa5du4b8/HwAoOdgPVdQUMB2hFonjn0ihJDK9OjRAxkZGRXas7Ky0KNHj7oPRIgIaLdNQgip5woLC/Ho0SMkJiYiLy8PWlpasLS0hIGBAdvRauTIkSNYv349YmNjAQAmJiaYN28exo4dy3Iy0ZSWlmLz5s3w9vZGUlISioqKhI6np6ezlKzmXr9+jQsXLlTar02bNrGUSjRpaWkYOXIkbt++DR6Ph9jYWBgaGmL8+PFQU1PDxo0b2Y4oslevXgldJ1q2bAlZWVm2Y4mMz+dj1apV8PT0xLt37xATEwNDQ0MsXrwY+vr6GD9+PNsRq00c+wSI13MvMzMTf/31F+7evVvhdbdPnz7o3Lkz2xEJ4RwJCQmkpKRAW1tbqP39+/fQ0dFBcXExS8kIqToptgMQQgip3P3797F9+3acP38eRUVFUFNTg7y8PNLT01FYWAhDQ0NMmjQJU6ZMgbKyMttxq2XTpk1YvHgxZsyYgS5duoBhGNy7dw9TpkzBhw8f4OHhwXbEavvjjz+wf/9+zJ49G4sXL8aiRYuQmJiI8+fPY8mSJWzHE9mtW7cwYMAAGBgYIDo6Gq1atUJiYiIYhoGVlRXb8arNw8MDUlJSSEpKgpmZmaB95MiR8PDw4Nzg2cuXL+Hp6YmTJ0/i1atXQjPoZGRk0K1bN0yaNAlDhw6FhAS3FhysXLkShw8fxp9//omJEycK2i0sLLB582ZODjSJU5/E7bmXnJyMJUuW4Pjx42jUqBHat2+Ptm3bCl53fX19sWHDBujp6WHp0qUYOXIk25FF8vjxY5w5c6bSD0POnTvHUqqaEcc+iYvw8HDB95GRkUhJSRE8Li0txdWrV6Gjo8NGNEKqjyGEEFLvDBgwgGncuDEzZ84cxt/fn8nNzRU6HhcXxxw6dIjp06cP06hRI+b69essJRWNvr4+c/jw4Qrthw4dYvT19VlIVHOGhobMpUuXGIZhGCUlJebFixcMwzDM1q1bmVGjRrEZrUZsbGyYxYsXMwxT1q+4uDgmOzubGTBgALNr1y6W01Vfw4YNmdDQUIZh/usPwzBMfHw8o6ioyGa0anNzc2OUlZWZoUOHMocPH2aioqKYrKwspri4mHn37h1z69YtZtmyZYypqSnTsmVL5tGjR2xHrhYjIyPm5s2bDMMI/66ioqIYNTU1NqOJTFz6JI7PPS0tLWbOnDnM06dPv3hOXl4ec+LECaZ9+/bM+vXr6zBd7Th58iQjLS3N9OvXj5GRkWH69+/PmJqaMqqqqsy4cePYjicScerT33//XeUvruDxeIyEhAQjISHB8Hi8Cl8KCgrMgQMH2I5JSJXQzDNCCKmHevfujTNnzkBGRqbS44aGhjA0NISLiwsiIiLw9u3bOk5YM8nJyZUufencuTOSk5NZSFRzKSkpsLCwAAAoKSkhMzMTANC/f38sXryYzWg1EhUVhZMnTwIApKSkkJ+fDyUlJSxfvhwDBw7E1KlTWU5YPbm5uVBQUKjQ/uHDB84tM5ORkUFcXBy0tLQqHNPW1kaPHj3Qo0cPLF26FJcvX8bLly9hY2PDQlLRvHnzBsbGxhXa+Xw+Z5f4iEufxPG5FxERUWl/PiUvL49Ro0Zh1KhRSE1NraNktWf16tXYvHkzpk+fDmVlZWzduhUGBgaYPHkyGjduzHY8kYhTnwYNGiT0mMfjCc3o5PF4gu9LS0vrKlaNJCQkgGEYGBoa4tGjR0J/YzIyMtDW1oakpCSLCQmpuvo/h5oQQn5A06dP/+LA2edatmwJBweH75yodhkbG8Pb27tC++nTp9G8eXMWEtVc06ZNBQN/xsbGuH79OoCy5SRcG5T5lKKiIgoLCwEATZo0QVxcnODYhw8f2Iolsu7du+PIkSOCxzweD3w+H+vXr4e9vT2Lyapv/fr133yzX87R0RHDhg37zolqV8uWLXH37t0K7WfOnIGlpSULiWpOXPokjs+9qvZH1PPrg7i4OPTr1w8AICsri9zcXPB4PHh4eGDv3r0spxONOPWJz+cLvq5fv462bdviypUryMjIQGZmJi5fvgwrKytcvXqV7ahVpqenB319ffD5fFhbW0NPT0/w1bhxYxo4I5xCM88IIYRDCgoKcPr0aeTm5sLBwYGzA01//PEHRo4ciTt37qBLly7g8XgICAjArVu3Kh1U44LBgwfj1q1b6NChA9zd3TFq1CgcOHAASUlJnKzhVq5jx464d+8ezM3N0a9fP8yZMwdPnz7FuXPn0LFjR7bjVdv69ethZ2eHJ0+eoKioCPPnz0dERATS09Nx7949tuNV2++//44ePXqgc+fOkJOTYztOrVq6dCnGjh2LN2/egM/n49y5c4iOjsaRI0dw6dIltuOJRBz79Dl/f3/k5uaiU6dOUFdXZztOtbx48QKZmZlo166doO3WrVtYuXIlcnNzMWjQIPzvf/9jMWHNaGhoIDs7GwCgo6ODZ8+ewcLCAhkZGcjLy2M5nWjEsU8AMGvWLHh6eqJr166Ctj59+kBBQQGTJk1CVFQUi+lEExMTAz8/P7x//x58Pl/oGJdrw5IfB+22SQgh9dS8efNQVFSErVu3AgCKiorQoUMHREREQEFBASUlJbhx4wY6derEclLRBAUFYfPmzYiKigLDMDA3N8ecOXM4Nfviax48eID79+/D2NgYAwYMYDuOyOLj45GTk4PWrVsjLy8Pc+fORUBAAIyNjbF582bo6emxHbHaUlJSsHv3bgQFBYHP58PKygrTp0/n3BIfADAyMkJCQgJkZGTQoUMH2Nvbo0ePHujYsWOVZ6/WZ9euXcPq1auFfldLlixB79692Y4mMnHp0/r165GTk4M//vgDAMAwDH766SfBrFttbW3cunULLVu2ZDNmtQwePBitWrXCihUrAJQtOWvZsiW6deuGFi1awMvLCytWrMCsWbPYDSqi0aNHw9raGrNnz8aqVauwdetWDBw4EDdu3ICVlRUni+uLY5+AsiXCjx49EpSDKBceHo4OHTogPz+fpWSi2bdvH6ZOnQpNTU00atRIaAkqj8dDcHAwi+kIqRoaPCOEkHqqVatWWL16tWDg5eDBg5gzZw5CQkLQrFkz/Prrr3j//j3++ecflpMSQtj05s0b3L59G35+fvDz80NCQgLk5eXRqVMn2Nvbw97evtIag4TUhJWVFRYsWCDYdfLMmTNwcXHBjRs3YGZmBmdnZygoKHBqNrGuri68vb0FH0qtXLkSPj4+CA0NBQAcOHAA27dvFzzmmvT0dBQUFKBJkybg8/nYsGGD4MOQxYsXc26mICCefQLKSgxIS0vj2LFjgg92UlJSMHbsWBQVFcHf35/lhNWjp6eHadOmYcGCBWxHIURkNHhGCCH1lIqKCoKDgwXFpUeNGgVlZWVBDY/Q0FA4OjpybrOAT71//77S6futW7dmKVH1XLhwocrncnn2GdeFh4ejVatWkJCQQHh4+FfPVVJSgq6uLqSlpesoXe179eoVfH194efnh7NnzyI3NxclJSVsx6o2V1dXjBkzBj169BCapcBl4tQndXV13L9/H2ZmZgDK+lZSUoKjR48CKJt9O3z4cLx69YrNmNUiLy+PmJgY6OrqAgB69uyJzp07C2aixcXFoV27dsjIyGAxpWhKSkpw/Phx9OnTB40aNWI7DvmGFy9eYPDgwYiOjkazZs0AAElJSTAxMcH58+cr3XikPlNRUUFoaCgMDQ3ZjkKIyKjmGSGE1FMSEhJCuyw9ePBAaNdGNTU1fPz4kY1oNRYUFAQXFxfBks1P8Xg8zuwi9fnOWF/CpT4BZW+Kq/rGPj09/Tunqbm2bdsiJSUF2traaNu2bYUdzD6nqqoKT09PwYwaLomLi4Ofn59gJlppaSnnNkIol5aWhn79+qFBgwb45ZdfMGbMGM4v6xanPhUXFwtthhIYGAh3d3fB4yZNmnBuUxENDQ0kJydDV1cXfD4fT548EapZWVRU9NVrR30mJSWFqVOncrJW1ueysrKqfK6Kisp3TPL9GBsbIzw8HDdu3MDz588F5S169erFyYH34cOH4/r165gyZQrbUQgRGQ2eEUJIPdWiRQtcvHgRs2fPRkREBJKSkoTeBL98+RINGzZkMaHoXF1dYWJiggMHDqBhw4acvBEEUGHGnLjYsmUL2xFqVUJCgmBnvISEhK+eW1hYiDNnzggtR6vPEhIS4OvrK5hplpmZiS5dusDW1hYzZsyAjY0NpKS4ebt34cIFZGRkwNvbGydOnMCWLVtgamqKMWPGYPTo0dDX12c7YrWJU5+MjY1x584dGBoaIikpCTExMbC1tRUcf/36NRo0aMBiwuqztbXFihUrsGvXLpw5cwZ8Pl/odTcyMpJTv6PPdejQASEhIZysVfkpNTW1Kt83cOmDq8/xeDz07t2bc/UQy23btk3wffky2gcPHsDCwqLC7G43N7e6jkdItdGyTUIIqafOnj2LUaNGoVu3boiIiICNjQ0uXrwoOL5gwQIkJCRwqp5MOWVlZYSEhHBu2QH5MXz8+BHjx4/nRKFpCQkJNGvWDNOmTYO9vT2srKwgKSnJdqzv4vXr1zh58iS8vLwQGxvLyaWon+Nyn/bs2YM5c+Zg5MiRePDgAdTU1IR2rF25ciUePnwo9LpV3yUkJMDBwQEJCQmQkJDAtm3bMHXqVMHxQYMGwcDAAJs3b2YxpejOnDmDhQsXwsPDA+3atYOioqLQca6UTPi03ldiYiIWLlyIcePGCWrVBQYG4vDhw1izZg1cXFzYilljubm58Pf3R1JSEoqKioSOcWGwycDAoErn8Xg8xMfHf+c0hNQcDZ4RQkg9dvPmTfzzzz9o1KgRZs6cCQUFBcGxP/74A3Z2dkKf9HPFoEGDMHbsWAwdOpTtKLWK6ze635Kfn4/i4mKhNq4uicnLy6v098SVN4/lRo4ciTt37qCgoADdunWDra0t7O3tYWlpydkZnZUpLi7GP//8g2PHjuGff/6BhoYG3rx5w3asGhGHPh04cACXLl1Co0aNsHTpUqFaWtOmTUOvXr0wZMgQFhNWX3FxMSIjI6GlpYUmTZoIHQsLC4Ouri40NDRYSlczEhISFdrKl7FzrbxAuZ49e2LChAkYNWqUUPuJEyewd+9e+Pn5sROshkJCQuDo6Ii8vDzk5uZCQ0MDHz58gIKCArS1tWmwiRAW0OAZIYRwWGhoKNq2bct2jGr78OEDXFxc0L59e7Rq1arC9H0uFtcX1xvd3NxcLFiwAN7e3khLS6twnGtvtlJTU+Hq6oorV65Uepxr/Sn3/PlzwdJNf39/FBQUoGvXrrC1tYWdnR1sbGzYjigSX19fnDhxAmfPnkVpaSmGDBkCJycn9OjRo9KBAC4Qxz59SWpqqmDJtDh4+vQpDhw4wNml7S9fvvzqcS4u51RQUEBYWBiaN28u1B4TE4O2bdsiLy+PpWQ1Y2dnBxMTE+zevRtqamoICwuDtLQ0xowZA3d3d84NShMiDmjwjBBCOCYzMxPHjx/HgQMHEBoaysk3+xcuXMDYsWORnZ1d4RhXP/0W1xvd6dOnw9fXF8uXL4ezszN27tyJN2/eYM+ePVi7di2cnJzYjlgtTk5OSExMxJYtW2Bvb4+//voL7969w8qVK7Fx40b069eP7Yi1IjIyEidOnMD27ds5u9tm06ZNkZaWhj59+sDJyQk///wz5OTk2I5VI+LYp88xDIMrV64IZqUVFhayHalGsrKycPLkSRw4cABPnjxB69atERoaynYs8i9TU1P0798fGzduFGqfM2cOLl26hOjoaJaS1YyamhoePnwIU1NTqKmpITAwEGZmZnj48CFcXFzw/PlztiNWy+zZsytt5/F4kJOTg7GxMQYOHMjZWZ3kx0CDZ4QQwhG3b9+Gl5cXzp07Bz09PQwdOhRDhw7l5E5t+vr66N+/PxYvXszZTQ8+J243uuWaNWuGI0eOwM7ODioqKggODoaxsTGOHj2KkydP4vLly2xHrJbGjRvj77//Rvv27aGiooInT57AxMQEFy5cwJ9//omAgAC2I4rs3bt38PPzg5+fH3x9fRETEwNZWVl07NgRvr6+bMertr1792L48OFQV1dnO0qtEcc+lYuPj4eXlxcOHz6MnJwc9OvXD0OHDsXgwYPZjiYSf39/HDhwAGfPnkVBQQHmzZuHCRMmcLpW55EjR7563NnZuY6S1J7Lly9j6NChMDIyQseOHQGU7U4eFxeHs2fPwtHRkeWEotHS0sK9e/dgYmICU1NTbNu2DX369MHz589hZWXFuRl19vb2CA4ORmlpKUxNTcEwDGJjYyEpKYkWLVogOjoaPB4PAQEBMDc3ZzsuIZXi5vZLhBDyg3j9+jUOHToELy8v5ObmYsSIESguLsbZs2c5fXORlpYGDw8PsRk4AwBpaWlBjamGDRsiKSkJZmZmUFVVRVJSEsvpRJeeni4o+quiooL09HQAQNeuXYUKaXNFbm4utLW1AQAaGhpITU2FiYkJLCwsEBwczHK66jtz5oxguWZ0dDSkpKTQvn17jBgxAvb29ujcuTNkZWXZjimSSZMmsR2h1olbnwoKCuDj44P9+/fjwYMHcHBwQHJyMkJDQ9GqVSu241VbcnIyDh48KHjNHTVqFPz9/dGpUyc4OztzeuAMANzd3YUeFxcXIy8vDzIyMlBQUODk4JmjoyNiY2Oxe/duREVFgWEYDBw4EFOmTIGuri7b8URmaWkp+HDH3t4eS5YswYcPH3D06FFYWFiwHa/aymeVHTx4UFArNSsrC+PHj0fXrl0xceJEjB49Gh4eHrh27RrLaQmpHA2eEUJIPeXo6IiAgAD0798f27dvR9++fSEpKQlPT0+2o9XYkCFD4OvrCyMjI7aj1Bpxu9EtZ2hoiMTEROjp6cHc3Bze3t5o3749Ll68CDU1NbbjVZupqSmio6Ohr6+Ptm3bYs+ePdDX14enpycaN27Mdrxqc3JygrW1NQYPHgx7e3t06dIF8vLybMeqNY8fP8aZM2cq3dyBC7uhVkZc+jRt2jScOnUKpqamGDNmDM6ePYsGDRpAWlqas7XbDAwMMHz4cOzcuRMODg6c7ceXfPz4sUJbbGwspk6dinnz5rGQqGaKi4vRu3dv7NmzB6tWrWI7Tq1avXq1oLTFihUr4OLigqlTp8LY2BgHDx5kOV31rV+/Hjdu3BDaZEhFRQXLli1D79694e7ujiVLlqB3794spiTk62jwjBBC6qnr16/Dzc0NU6dOrVAIl+tMTEzw22+/ISAgABYWFhU2DODizpTidqNbztXVFWFhYbC1tcVvv/2Gfv36Yfv27SgpKcGmTZvYjldts2bNQnJyMgBg6dKl6NOnD44fPw4ZGRkcOnSI3XAi+PjxIxQVFdmO8V2cOnUKzs7O6N27N27cuIHevXsjNjYWKSkpnF0KKE592rt3LxYsWICFCxdCWVmZ7Ti1Qk9PDwEBAWjWrBn09PTQokULtiN9d82bN8fatWsxZswYzpUXkJaWxrNnz8RqZ+Fy1tbWgu+1tLQ4VyLhc5mZmXj//n2FVROpqanIysoCUFb+4vMPFAipT6jmGSGE1FOBgYHw8vKCt7c3WrRogbFjx2LkyJFo0qQJwsLCOL1ss3wZYGV4PB5nd6b8ESQlJeHJkycwMjJCmzZt2I5TY3l5eXj+/DmaNWsGTU1NtuOI7M2bNzh79ixiYmLA4/HQvHlzDB06FDo6OmxHE1nr1q0xefJkTJ8+HcrKyggLC4OBgQEmT56Mxo0b448//mA7YrWJU59OnDiBgwcPIjAwEP369cPYsWPRt29fyMvLc/o16t69ezhw4ADOnDkDExMTjBkzBvPnz0d4eDjMzMzYjvddhISEwNbWVjCIwSVz5syBtLQ01q5dy3aU7yI1NVVQD8zU1JSzr1NOTk4IDAzExo0bYWNjAx6Ph0ePHmHu3Lno3Lkzjh49ilOnTmHDhg148uQJ23EJqRQNnhFCSD2Xl5eHU6dOwcvLC48ePUJpaSk2bdqEX3/9VWw+7SeEiG7Xrl2YPXs2ioqKoKqqCoZhkJWVBRkZGWzatAnTpk1jO6JIFBUVERERAX19fWhqasLX1xcWFhaIiopCjx49BDMIuUQc+5SYmIiDBw/i0KFDyMvLQ3p6Ok6fPo1hw4axHa1GcnJycPLkSXh5eeHhw4ewtbXF6NGjMWjQIGhpabEdTyQXLlwQeswwDJKTk7Fjxw7o6uriypUrLCUT3cyZM3HkyBEYGxvD2tq6wkxcLs6QBsrqc86cORNHjx4V7EAuKSkJZ2dnbN++HQoKCiwnrJ6cnBx4eHjgyJEjgt2fpaSk4OLigs2bN0NRUVGwi23btm3ZC0rIV9DgGSGE1FNJSUnQ1dUVWo4QHR2NAwcO4OjRo8jIyICDg0OFm2FCiLDZs2dX+VyuvdH6559/MHDgQMyaNQtz5swR1G1LTk7G+vXrsX37dvz999+c3HFOV1cXly9fhoWFBdq0aYOFCxdi1KhRCAwMRN++fZGZmcl2xGoTxz6VYxgG165dg5eXFy5cuABNTU0MGTIE27ZtYztajUVFRQlee9PT01FcXMx2JJF8XsONx+NBS0sLPXr0wMaNGzlZ99He3v6Lx3g8Hm7fvl2HaWrP5MmTcfPmTezYsQNdunQBAAQEBMDNzQ0ODg7YvXs3ywlFk5OTg/j4eDAMAyMjIygpKbEdiZAqo8EzQgippyQlJZGcnCzYGfBTpaWluHjxouBNChfMnj0bK1asgKKi4jcHM7g2gEHqt6+9ufoUF99o2draolu3bli5cmWlx3///XfcvXsX/v7+dZys5kaPHg1ra2vMnj0bq1atwtatWzFw4EDcuHEDVlZWnCquX04c+1SZ9PR0HDlyBAcPHkRYWBjbcWpNSUkJLly4gCFDhrAdhYg5TU1N+Pj4wM7OTqjd19cXI0aMQGpqKjvBCPmB0eAZIYTUUxISEkhJSal08IyL7O3t8ddff0FNTU1sPykmpK6pqKjg8ePHMDU1rfR4dHQ0rK2tBZtZcEl6ejoKCgrQpEkT8Pl8bNiwAQEBATA2NsbixYuhrq7OdsRqE8c+iZO3b99i06ZNWLJkidCugEBZwfOVK1di7ty5aNiwIUsJyZe8ePECcXFx6N69O+Tl5cEwDKc3ElBQUEBQUFCFOnsRERFo3749cnNzWUpWdUOGDMGhQ4egoqLyzQFncfnggIg3GjwjhJB6StwGzwipT8TljZaSkhLCw8NhaGhY6fH4+Hi0bt0aOTk5dZyMiLuqLIfm8XjYuHFjHaSpHXPnzkVWVhb27t1b6fEpU6ZAVVUV69atq+NkohPnZesAkJaWhhEjRsDX1xc8Hg+xsbEwNDTE+PHjoaamxqnn36d69uyJBg0a4MiRI5CTkwMA5Ofnw8XFBenp6bh58ybLCb/N1dUV27Ztg7KyMlxdXb96Lpd3JSc/Dim2AxBCCPmy/fv3f7MehJubWx2lqT3v3r374if34eHhaN26dR0nEk11avlw6fdUnR3XPp+dUd996Y3WhAkTOPlGq2XLlvj777/h4eFR6fHz58+jZcuWdZyq9vD5fLx48QLv378Hn88XOta9e3eWUtWMuPQpJCSE7Qi17urVq/D09PzicWdnZ0ycOJFTg2ef/56CgoJQWloqmK0aExMDSUlJtGvXjo14Nebh4QFpaWkkJSUJzdIaOXIkPDw8OHdNL7d161b07dsXTZs2RZs2bcDj8RAaGgo5OTlcu3aN7XhV8umAGA2OEXFAM88IIaSekpCQQNOmTSEpKfnFc3g8HuLj4+swVe3Q1tbG/v37MWDAAKH2DRs2YPHixcjPz2cpWfUYGBhU6Tyu/Z4kJCS+OQurfKZW+S5gXOHs7Iz3799j//79MDMzQ1hYGAwNDXH9+nV4eHggIiKC7YjVcvjwYUydOhUbNmzApEmTICVV9rloSUkJ9uzZg3nz5mHXrl0YN24cu0FF8ODBA4wePRovX77E57erXHzuAeLZJ3GiqKiIqKgoNGvWrNLj5QM0XFgyV5lNmzbBz88Phw8fFiwR/vjxI1xdXdGtWzfMmTOH5YTV16hRI1y7dg1t2rSBsrKy4JqekJAACwsLTs+6zc/Px7Fjx/D8+XMwDANzc3M4OTlBXl6e7WgiKSkpgZ+fH+Li4jB69GgoKyvj7du3UFFRoY0DCCfQzDNCCKnHnjx5IpbLNhcsWICRI0cKtihPT0/H2LFjERERgdOnT7Mdr8oSEhLYjvBd+Pr6sh3hu7l+/TquXbuGpk2bCrU3b94cL1++ZCmV6FxcXPD06VPMmDEDv/32G4yMjAAAcXFxyMnJgZubGycHzoCyJXLW1tb4559/0LhxY04uq/2cOPXJz8+vQjHzz02bNg27du2qm0C1QF5eHomJiV8cPEtMTOTswAUAbNy4EdevXxeqraeuro6VK1eid+/enBw8y83NhYKCQoX2Dx8+QFZWloVEtUdeXh4TJ05kO0atePnyJfr27YukpCQUFhbCwcEBysrK+PPPP1FQUPDVGZ+E1Bc0eEYIIaTOzZkzB7169cKYMWPQunVrpKeno2PHjggPD+d8IeaioiIkJCTAyMhIMAuIa2xtbdmO8N2I4xutDRs2YNiwYTh58iRiY2MBlC3/++WXX9CxY0eW04kuNjYWPj4+MDY2ZjtKrRGnPg0cOBC+vr6wsrKq9Pj06dNx/PhxTg2edejQAUePHv3i8tkjR46gffv2dZyq9mRlZeHdu3cVlnK/f/+ek5uKAGXXuiNHjmDFihUAymZw8vl8rF+/vso7LdcX1dk9/fOZ+/Wdu7s7rK2tERYWhgYNGgjaBw8ejAkTJrCYjJCq4+ZdPSGEEADA8ePH4eTkxHYMkRgaGqJly5Y4e/YsAGDEiBGcHjjLy8vDzJkzcfjwYQBldWQMDQ3h5uaGJk2aYOHChSwnFN3du3exZ88exMfH48yZM9DR0cHRo0dhYGCArl27sh2vWsTpjRYALF++HHPnzkXHjh05PVBWmQ4dOuDFixdiMdBUTpz6NGHCBPz000+4e/cuTExMhI7NmDEDR44cweXLl1lKJ5q5c+fCwcEBqqqqmDdvnuA16d27d/jzzz9x6NAhXL9+neWUohs8eDBcXV2xceNGwfXiwYMHmDdv3jd3Q6yv1q9fDzs7Ozx58gRFRUWYP38+IiIikJ6ejnv37rEdr1oGDRpUpfO4uMQ7ICAA9+7dg4yMjFC7np4e3rx5w1IqQqqJIYQQUi+1aNGCefv27RePHz9+nJGWlq7DRLUnICCA0dfXZ9q1a8dERkYy+/btY5SVlZnhw4cz6enpbMcTiZubG9OuXTvm7t27jKKiIhMXF8cwDMP8/fffTNu2bVlOJzofHx9GXl6emTBhAiMrKyvo186dO5mffvqJ5XTVFxERwWhpaTF9+/ZlZGRkmGHDhjFmZmZMw4YNmRcvXrAdr9okJCSYd+/esR3juzh37hxjbm7OHDx4kHny5AkTFhYm9MVF4tYnV1dXplmzZszr168FbTNnzmQUFRUZPz8/FpOJztPTk5GVlWUkJCQYNTU1Rl1dnZGQkGBkZWWZXbt2sR2vRnJzc5mpU6cK+ichIcHIyMgwU6dOZXJyctiOJ7Lk5GRmyZIlTL9+/ZiffvqJWbRo0Vfvn0jdU1dXZyIiIhiGYRglJSXBvcTdu3cZbW1tNqMRUmW0YQAhhNRTdnZ2yM/Px+3bt6GoqCh07NSpU3B2dsa6deu+uMtefSYrKwsPDw+sWLEC0tLSAMpqNI0dOxZJSUl4/fo1ywmrT09PD6dPn0bHjh2Fiha/ePECVlZW1drBsj6xtLSEh4cHnJ2dhfoVGhqKvn37IiUlhe2I1ZaSkoLdu3cjKCgIfD4fVlZWmD59Oho3bsx2tGqTkJBASkqKWNZGlJCQqNDG4/E4u1kFIH594vP5GDZsGKKionD37l2sWrUKe/fuxaVLlzg5k7O4uBjS0tJ48+YNvL298eLFCzAMAxMTEwwbNgxNmzbFs2fP0KpVK7aj1khubi7i4uLAMAyMjY0r3GMQ9iUkJFR5UyIuGDlyJFRVVbF3714oKysjPDwcWlpaGDhwIJo1a0a7cRJOoMEzQgipp3JycmBnZwc1NTVcuXJFMMjk7e2NMWPGYPXq1Zg7dy7LKUXj7+9faV0tPp+PVatWYfHixSykqhkFBQU8e/YMhoaGQoNMYWFh6N69OzIzM9mOKBIFBQVERkZCX19fqF/x8fEwNzdHQUEB2xF/aBISEnj37h20tLTYjlLrvrWBg56eXh0lqT3i2KeioiL069cPYWFhyM3NxYULF9CzZ0+2Y4lk2LBhOHPmzBc3cnj27Bl69uyJd+/e1XEy8iV37tz56vEv1a+r7yQlJdG9e3eMHz8ew4YNg5ycHNuRauTt27ewt7eHpKQkYmNjYW1tjdjYWGhqauLOnTti+QEQET80eEYIIfVYamoqunfvDnNzc/j4+MDHxwdOTk5YsWIFFixYwHY88glbW1sMGzYMM2fOFHyqamBggBkzZuDFixe4evUq2xFFYmRkhD179qBXr15Cg2dHjhzB2rVrERkZyXbEarl69SqUlJQEtdp27tyJffv2wdzcHDt37hTahY4LJCQk0KpVq29uThEcHFxHiciPYtu2bYLvs7OzsWLFCvTp06fCwJmbm1tdRxOZrq4ufvrpJ+zdu7fCsYiICPTo0QPdu3fHmTNnWEhXOx4/fowzZ84gKSkJRUVFQsfOnTvHUirRfWk2ZzmuzeYs9+zZM3h5eeH48eMoLCzEyJEjMX78eE5vWJGfn4+TJ08iODhYMOvbycmJ0zvYkh8LDZ4RQkg99+rVK3Tt2hXGxsYICAjAkiVLsGjRIrZj1Vhubi78/f0rvYHn0putcvfv30ffvn3h5OSEQ4cOYfLkyYiIiEBgYCD8/f3Rrl07tiOK5M8//8Thw4fh5eUFBwcHXL58GS9fvoSHhweWLFmCGTNmsB2xWiwsLLBu3To4Ojri6dOnsLa2xpw5c3D79m2YmZlxbumIhIQE5syZAyUlpa+et3Tp0jpKVLvi4uKwZcsWREVFgcfjwczMDO7u7jAyMmI7msjEpU9VWVLG4/EQHx9fB2lqR1RUlGC2z9q1a4Xa7e3t0blzZ5w5cwaSkpIsphRdecmH3r1748aNG+jduzdiY2ORkpKCwYMHc+76B6DCrO7i4mKEhIRg8eLFWLVqFWdnQZYrKSnBxYsXcejQIVy5cgXNmzfH+PHjMXbsWLGccUxIfUaDZ4QQUk+Fh4cLvn/+/DmcnZ0xaNAg/O9//xM6r3Xr1nUdrdpevXoFXV1dweOQkBA4OjoiNzcX+fn5UFdXx4cPH6CgoABtbW1Ovdn61NOnT7FhwwahWloLFiyAhYUF29FqZNGiRdi8ebNgiaasrCzmzp0r2LGSS5SUlPDs2TPo6+tj2bJlePbsGXx8fBAcHAxHR0fO1XAT55pn165dw4ABA9C2bVt06dIFDMPg/v37CAsLw8WLF+Hg4MB2xGoTxz6Jm8ePH6Nnz55YvHgx5s2bh+fPn8Pe3h7t27fHuXPnODtwBpTdL0yePBnTp08XzCQ2MDDA5MmT0bhxY/zxxx9sR6w1d+7cgYeHB4KCgtiOUisKCwuxa9cu/PbbbygqKoK0tDRGjhyJdevWcaJeZ5MmTWBnZyf4+nyHXkK4gAbPCCGknpKQkBAqJF1+uf78ey4sSVi5ciWSk5OxY8cO8Hg82NnZwcjICHv37kXTpk0RFhaG/Px8jBkzBh4eHhgyZAjbkcln8vLyEBkZCT6fD3Nz82/OdKqvNDQ0EBAQAHNzc3Tt2hXOzs6YNGkSEhMTYW5ujry8PLYjVoukpCSSk5PFcvDM0tISffr0EZoBBAALFy7E9evXObkUVRz7JI5u376N/v37Y/78+di3bx+srKxw7tw5Qe1RrlJUVERERAT09fWhqakJX19fWFhYICoqCj169EBycjLbEWtNVFQUbGxskJOTw3aUGnny5Am8vLxw6tQpKCoqwsXFBePHj8fbt2+xZMkSZGdn49GjR2zH/KaTJ0/C398ffn5+iImJQcOGDWFraws7OzvY2trCzMyM7YiEfBMNnhFCSD31rcLS5bhQYDo/Px8zZsxAamoqLly4ADU1NTx48AAtWrRA06ZNERgYCF1dXTx48ADjxo3D8+fP2Y5cJdXZQVNFReU7JiFVNWDAABQVFaFLly5YsWIFEhISoKOjg+vXr2PGjBmIiYlhO2K1iPPMMzk5OTx9+hTNmzcXao+JiUHr1q05uVmFuPTp1KlT+OWXX6p07qtXr5CUlIQuXbp851S16/z58xg+fDh69+6N8+fPc37gDCir6Xb58mVYWFigTZs2WLhwIUaNGoXAwED07duXkxvbfDpLHwAYhkFycjLWrl2L4uJi3Lt3j6VkNbNp0yYcPHgQ0dHRcHR0xIQJE+Do6ChU4+3Fixdo0aIFSkpKWExafe/evYOvry8uXbqE06dPg8/nc+KDYEK+Xl2WEEIIa7gwKFZV8vLyOHDgAE6dOgUAkJaWFtwAamtr4+XLl9DV1YWamhqSkpLYjFotampqX9yV7XNcujGszsw/rhWY3rFjB6ZNmwYfHx/s3r0bOjo6AIArV66gb9++LKervoSEBLGte6OlpYXQ0NAKA02hoaGcHSwUlz7t3r0by5Ytg6urKwYMGFBh1khmZibu3buHY8eO4ebNmzhw4ABLSatHXV29wjX97t27aNiwoVBbenp6XcaqNd26dcONGzdgYWGBESNGwN3dHbdv38aNGzc4Wxusbdu2QjPyy3Xs2BFeXl4spaq53bt349dff4WrqysaNWpU6TnNmjXjzN8WULaLfEBAgGAGWkhICCwsLCrdfZ2Q+ogGzwghpB5KSkpCs2bNqnz+mzdvBIMA9Vn5TAVLS0s8fvwYJiYmsLW1xW+//YapU6fi8OHDnKoP5uvrK/g+MTERCxcuxLhx49CpUycAQGBgIA4fPow1a9awFVEkqqqqgu8ZhsFff/0FVVVVWFtbAwCCgoKQkZHByeW1zZo1w6VLlyq0b968mYU0NXf48OEqnbdkyZLvnKT2TZw4EZMmTUJ8fDw6d+4MHo+HgIAArFu3DnPmzGE7nkjEpU/+/v64dOkStm/fjv/9739QVFREw4YNIScnh48fPyIlJQVaWlpwdXXFs2fPODMwuGXLFrYjfFc7duwQzG787bffIC0tjYCAAAwZMgSLFy9mOZ1oEhIShB5LSEhAS0sLcnJyLCWqHbGxsd88R0ZGBi4uLnWQpuY6dOiA8PBwtGrVCnZ2dvjf//6Hbt26QU1Nje1ohFQZLdskhJB6qGHDhhgwYAAmTpz4xW3JMzMz4e3tja1bt2Ly5MmYOXNmHacU3ZMnT5CVlYUePXogPT0d48aNg5+fH4yMjHDo0CG0adOG7YjV1rNnT0yYMAGjRo0Saj9x4gT27t0LPz8/doLV0IIFC5Ceng5PT09BoezS0lJMmzYNKioqWL9+PcsJq+dbMxurM2hdH0hISKBJkybQ1tauMPOiHI/H42QtLYZhsGXLFmzcuBFv374FUFZ0et68eXBzc6vyrM/6RBz7lJaWhoCAACQmJiI/Px+ampqwtLSEpaWl0BIzQmrT7du3MWPGDDx48KBCWYTMzEx07twZnp6e6NatG0sJa0deXl6lu5JzYbOoT2loaIDH46FXr16CTQOozhnhGho8I4SQeig9PR2rV6+Gl5cXpKWlYW1tjSZNmgg+1Y+MjERERASsra3x+++/46effmI78g9PQUEBYWFhldYyatu2LecK0ZfT0tJCQEAATE1Nhdqjo6PRuXNnpKWlsZRMNOUbcXwJl5bXAoCjoyN8fX3Rp08f/Prrr+jXrx+ndwMsV1JSguPHj6NPnz5o1KgRsrOzAQDKysosJxOdOPbpR1a+mQ+XfKlOJ4/Hg6ysLGRkZOo4kegGDBgAe3t7eHh4VHp827Zt8PX1xV9//VXHyWpHamoqxo0bh6tXr1Z6nGuvVUBZfTo/Pz/4+/vj7t27kJCQgK2tLezt7TFlyhS24xHyTfRxECGE1EMaGhrYsGED3r59i927d8PExAQfPnwQTON3cnJCUFAQ7t27RwNn9YSuri48PT0rtO/Zswe6urosJKodJSUliIqKqtAeFRUFPp/PQqKaCQkJQXBwsODr4cOH8PT0hImJCc6cOcN2vGq7fPky4uPj0aFDB8ybNw9NmzbFggULEB0dzXa0GpGSksLUqVNRWFgIoGyAieuDTOLYJ3FiZmaGEydOVJjh87nY2FhMnToV69atq6NktUdNTQ3q6uoVvtTU1CAvLw89PT0sXbqUE9f2sLCwr9ap7N27N4KCguowUe2aNWsWMjIy8ODBA8jLy+Pq1as4fPgwmjdvjgsXLrAdTyStW7eGm5sbzp49iytXruCnn37CuXPnMH36dLajEVIlVPOMEELqMTk5OQwZMoSTtaW+Ji0tDUuWLIGvry/ev39f4Uadi8WYN2/ejKFDh+LatWvo2LEjAODBgweIi4vD2bNnWU4nOldXV/z666948eKFUL/Wrl0LV1dXltNVX2VLgstndq5fv56Tf2uNGzfGb7/9ht9++w137tzBwYMHYWNjAwsLC9y8eRPy8vJsRxRJhw4dEBISIlabp4hjn8TFzp07sWDBAkyfPh29e/eudMZ3QEAAIiMjMWPGDEybNo3tyNV26NAhLFq0COPGjUP79u3BMAweP36Mw4cP4/fff0dqaio2bNgAWVlZ/O9//2M77le9e/fuqzugSklJITU1tQ4T1a7bt2/j77//ho2NDSQkJKCnpwcHBweoqKhgzZo16NevH9sRqyUkJAR+fn7w8/PD3bt3kZ2djTZt2sDd3R329vZsxyOkSmjwjBBCSJ0bM2YM4uLiMH78eDRs2JBzS18q4+joiNjYWOzevRtRUVFgGAYDBw7ElClTOD3zbMOGDWjUqBE2b96M5ORkAGWDNfPnz+dUgfNvMTExwePHj9mOUWM2NjZITExEZGQkQkJCUFxczNnBs2nTpmHOnDl4/fo12rVrB0VFRaHjXKv5A4hnn8RFjx498PjxY9y/fx+nT5/GiRMnKtRxc3Z2xpgxYzhb5Pzw4cPYuHEjRowYIWgbMGAALCwssGfPHty6dQvNmjXDqlWr6v3gmY6ODp4+fQpjY+NKj4eHh6Nx48Z1nKr25ObmCjba0NDQQGpqKkxMTGBhYcHJGpY2NjawtLSEra0tJk6ciO7du1eoVUdIfUc1zwghhNQ5ZWVlBAQEcHJjgB9Zeb0cLt/wfl7zh2EYJCcnY9myZXj+/DlCQ0PZCVZDgYGB8PLygre3N0xMTODq6orRo0dz9k0+gEqLzfN4PEGtKS7W/BHHPhHu+FJtztjYWLRp0wZ5eXlISEhAy5Yt632dzpkzZ8LPzw+PHz+usLNmfn4+2rdvD3t7e2zbto2lhDVjY2ODlStXok+fPhg0aJBgxtm2bdvg4+ODuLg4tiNWS1ZWFqfvHQgBaOYZIYQQFrRo0QL5+flsxyDVJA43vmpqahVmOjIMA11dXZw6dYqlVKL7888/cfDgQaSlpcHJyQkBAQGwsLBgO1atSEhIYDtCrRPHPhHuaNq0KQ4cOIC1a9cKtR84cEAwQzotLQ3q6upsxKuW33//HefOnYOJiQlmzJgBU1NT8Hg8REVFYefOnSgtLcWiRYvYjimyWbNmCWZ7L126FH369MGxY8cgIyODw4cPs5yu+sTh/oEQmnlGCCGkzj1+/BgLFy7EkiVL0KpVqwp1S+gmi3wv/v7+Qo8lJCSgpaUFY2NjSElx7zNFCQkJNGvWDP379//qTnmbNm2qw1TkR/P69WtcuHABSUlJFQru03Ov/rhw4QKGDx+OFi1awMbGBjweD48fP8bz58/h4+OD/v37Y/fu3YiNjeXE7+3ly5eYOnUqrl27hvK3tDweD3369MGuXbugr6/PbsBawjAM8vPz8fz5czRr1gyamppsRyLkh0SDZ4QQQupcbGwsRo0ahZCQEKF2WrpEvgcrKyvcunUL6urqWL58OebOnQsFBQW2Y9UKOzu7b9YM5PF4uH37dh0lql1Hjx6Fp6cnEhISEBgYCD09PWzZsgUGBgYYOHAg2/FEIm59unXrFgYMGAADAwNER0ejVatWSExMBMMwsLKy4uxzT1y9fPkSnp6eiI6OBsMwaNGiBSZPnszpgaaPHz/ixYsXYBgGzZs358TMuao4cOAANm/eLNhpvXnz5pg1axYmTJjAcjJCfkw0eEYIIRwQFxeHLVu2ICoqCjweD2ZmZnB3d4eRkRHb0UTSvn17SElJwd3dvdINA2xtbVlKRsSRvLw8YmNj0bRpU0hKSiIlJQVaWlpsxyLfsHv3bixZsgSzZs3CqlWr8OzZMxgaGuLQoUM4fPgwfH192Y5YbeLYp/bt26Nv375Yvnw5lJWVERYWBm1tbTg5OaFv376YOnUq2xEJgOLiYvTu3Rt79uyBiYkJ23HINyxevBibN2/GzJkz0alTJwBltS137NgBd3d3rFy5kuWEhPx4aPCMEELquWvXrmHAgAFo27YtunTpAoZhcP/+fYSFheHixYtwcHBgO2K1KSgoICQkBKampmxHIT+ATp06QUlJCV27dsUff/yBuXPnQklJqdJzlyxZUsfpyJeYm5tj9erVGDRokGBQxtDQEM+ePYOdnR0+fPjAdsRqE8c+KSsrIzQ0FEZGRlBXV0dAQABatmyJsLAwDBw4EImJiWxHJP/S0tLC/fv3K2wYQOofTU1NbN++HaNGjRJqP3nyJGbOnMm5a4Wfnx/s7OzYjkFIjXCvuAchhPxgFi5cCA8PjwoFfhcuXIgFCxZwcvDM2toar169ErvBMx8fH3h7e1da94eLW8uX8/f3x4YNG4RmPs6bNw/dunVjO1qVHDp0CEuXLsWlS5fA4/Fw5cqVSuub8Xg8Tg2erV27FjNnzoSiouI3z3348CE+fPiAfv361UGy2pGQkABLS8sK7bKyssjNzWUhUc2JY58UFRVRWFgIAGjSpAni4uLQsmVLAODcG/zK5Ofno7i4WKiNq3U5nZ2dK90wgNQ/paWlsLa2rtDerl07lJSUsJCoZvr27QsdHR24urrCxcVFsEEFIVxCg2eEEFLPRUVFwdvbu0L7r7/+ii1bttR9oFowc+ZMuLu7Y968ebCwsKiwYUDr1q1ZSia6bdu2YdGiRXBxccHff/8NV1dXxMXF4fHjx5g+fTrb8UR27NgxuLq6YsiQIXBzcxPMfOzZsycOHTqE0aNHsx3xm0xNTQU7aUpISODWrVvQ1tZmOVXNRUZGQk9PD8OHD8eAAQNgbW0tWI5aUlKCyMhIBAQE4NixY0hOTsaRI0dYTlw9BgYGCA0NhZ6enlD7lStXYG5uzlKqmhHHPnXs2BH37t2Dubk5+vXrhzlz5uDp06c4d+4cOnbsyHY8keTl5WH+/Pnw9vZGWlpaheNcrctZVFSE/fv348aNG7C2tq4w8M6FTQJ+FGPGjMHu3bsr/E727t0LJycnllKJ7u3btzh27BgOHTqEZcuWoWfPnhg/fjwGDRr01c1uCKlPaNkmIYTUc7q6uti0aROGDx8u1O7t7Y25c+ciKSmJpWSik5CQqNDG4/E4vWFAixYtsHTpUowaNUpoOdaSJUuQnp6OHTt2sB1RJGZmZpg0aRI8PDyE2jdt2oR9+/YhKiqKpWQEAMLDw7Fz506cOXMGmZmZkJSUhKysLPLy8gAAlpaWmDRpElxcXCArK8ty2uo5ePAgFi9ejI0bN2L8+PHYv38/4uLisGbNGuzfvx+//PIL2xGrTRz7FB8fj5ycHLRu3Rp5eXmYO3cuAgICYGxsjM2bN1cYKOSC6dOnw9fXF8uXL4ezszN27tyJN2/eYM+ePVi7di0nBy8AwN7e/ovHuLyxiDiaOXMmjhw5Al1dXcEg9IMHD/Dq1Ss4OzsLfejItUHP0NBQeHl54eTJk+Dz+XBycsL48ePRpk0btqMR8lU0eEYIIfXc8uXLsXnzZixcuBCdO3cGj8dDQEAA1q1bhzlz5uD3339nO2K1vXz58qvHufhmS0FBAVFRUdDT04O2tjZu3LiBNm3aIDY2Fh07dqx09gIXyMrKIiIiAsbGxkLtL168QKtWrVBQUMBSMvIphmEQHh6OxMRE5OfnQ1NTE23btoWmpibb0Wpk3759WLlyJV69egUA0NHRwbJlyzB+/HiWk4lOHPskbpo1a4YjR47Azs4OKioqCA4OhrGxMY4ePYqTJ0/i8uXLbEckYu5rA52f4uqg59u3b7F3716sXbsWUlJSKCgoQKdOneDp6SlY9k1IfUODZ4QQUs8xDIMtW7Zg48aNePv2LYCyujLz5s2Dm5tbhZ0qCTsMDQ3h4+MDKysr2NjYYMKECZg8eTKuX7+OX375Benp6WxHFImxsTHmzZuHyZMnC7Xv2bMHGzZsQGxsLEvJyI/kw4cP4PP5YrHctpw49iknJwd8Pl+ojYv1wZSUlBAREQE9PT00bdoU586dQ/v27ZGQkAALCwvk5OSwHbFGXrx4gbi4OHTv3h3y8vKCWd+EfE/FxcX4+++/4eXlJVg6PH78eIwaNQrp6elYsGABQkNDERkZyXZUQipFNc8IIaQeKykpwfHjxzFq1Ch4eHggOzsbQNnuZqR+6dGjBy5evAgrKyuMHz8eHh4e8PHxwZMnTzBkyBC244lszpw5cHNzQ2hoqNDMx0OHDmHr1q1sxyM/CK7PoKuMuPQpISEBM2bMgJ+fn9BMVC4vwzc0NERiYiL09PRgbm4Ob29vtG/fHhcvXoSamhrb8USWlpaGESNGwNfXFzweD7GxsTA0NMSECROgpqaGjRs3sh2RiKmZM2fi5MmTAMrquf35559o1aqV4LiioiLWrl0LfX19lhIS8m0084wQQuq5T5cDkvqLz+eDz+cLdnH09vYW1P2ZMmUKpwvi/vXXX9i4caOgvln5bpsDBw5kORkRd+K4g6249alz584AAHd3dzRs2LDCDCZbW1s2YtXI5s2bISkpCTc3N/j6+qJfv34oLS1FSUkJNm3aBHd3d7YjisTZ2Rnv37/H/v37YWZmJqjNef36dXh4eCAiIoLtiERM9ezZExMmTMDQoUO/eD9UUlKCe/fucfKaQX4MNHhGCCH1nL29Pdzd3TFo0CC2oxBCSJ35dAfbffv2VdjBdtWqVWxHrDZx7JOSkhKCgoJgamrKdpTvJikpCU+ePIGRkRGni5o3atQI165dQ5s2bYQ2thGX5aik/rpz5w46d+4s+ICxXElJCe7fv4/u3buzlIyQqqNlm4QQUs9NmzYNc+bMwevXr9GuXbsKW8u3bt2apWTkc3fv3sWePXsQFxcHHx8f6Ojo4OjRozAwMEDXrl3ZjicSQ0NDPH78GA0aNBBqz8jIgJWVFeLj41lKVnXq6upVrufD1dp04mjXrl3Yu3cvRo0ahcOHD2P+/PlCO9hykTj2ycbGBq9evRLrwbNmzZqhWbNmbMeosdzcXCgoKFRo//DhA+d24yXcYm9vj+Tk5Ao1HjMzM2Fvb8/J5d3kx0ODZ4QQUs+NHDkSAODm5iZo4/F4nK4nUy4oKAhRUVHg8XgwMzODlZUV25FEdvbsWYwdOxZOTk4ICQlBYWEhACA7OxurV6/m7O5siYmJlT7HCgsL8ebNGxYSVd+WLVvYjlBnxKkQeFJSkmBJoLy8vKDm49ixY9GxY0fs2LGDzXgiEcc+7d+/H1OmTMGbN2/QqlUrSEtLCx3nygc827Ztq/K5n74ec0n37t1x5MgRrFixAkDZvQSfz8f69eurvLsjIaL40mtRWlpahQ+FCamvaPCMEELquYSEBLYj1Lr379/jl19+gZ+fH9TU1MAwjODTx1OnTkFLS4vtiNW2cuVKeHp6wtnZGadOnRK0d+7cGcuXL2cxmWguXLgg+P7atWtQVVUVPC4tLcWtW7c4U9jXxcWF7QjfXVpaGkaOHInbt2+LTSHwRo0aIS0tDXp6etDT08ODBw/Qpk0bJCQkgKtVR8SxT6mpqYiLi4Orq6ugjYsf8GzevFnocWpqKvLy8gQbBGRkZEBBQQHa2tqcHTxbv3497Ozs8OTJExQVFWH+/PmIiIhAeno67t27x3Y8IobKN0zi8XgYN26c0AzH0tJShIeHCz5QIKS+o8EzQgip58Rxo4CZM2ciKysLERERMDMzAwBERkbCxcUFbm5ugh2ZuCQ6OrrSmh0qKirIyMio+0A1VF5jj8fjVRh8kpaWhr6+PicHZAAgLi4OBw8eRFxcHLZu3QptbW1cvXoVurq6aNmyJdvxROLh4QEpKSkkJSUJ/qaAspmrHh4enPxdieMOtuLYp19//RWWlpY4efJkpRsGcMWnH1SdOHECu3btwoEDBwTLUaOjozFx4kRMnjyZrYg1Zm5ujvDwcOzevRuSkpLIzc3FkCFDMH36dDRu3JjteEQMlX/wxjAMlJWVIS8vLzgmIyODjh07YuLEiWzFI6RaaMMAQgjhgKNHj8LT0xMJCQkIDAyEnp4etmzZAgMDA07ueKiqqoqbN2/CxsZGqP3Ro0fo3bs3JwebjIyMsGfPHvTq1UuoEPORI0ewdu1aREZGsh1RJAYGBnj8+DE0NTXZjlIr/P398dNPP6FLly64c+cOoqKiYGhoiD///BOPHj2Cj48P2xFFIo6FwMVxB1tx7JOioiLCwsJgbGzMdpRaY2RkBB8fH1haWgq1BwUFYdiwYWI5I5yQ7+mPP/7A3LlzaYkm4TQJtgMQQgj5ut27d2P27NlwdHRERkaGYAmMmpoaZ2s58fn8CnVxgLIZTXw+n4VENTd58mS4u7vj4cOH4PF4ePv2LY4fP465c+di2rRpbMcTWUJCgtgMnAHAwoULsXLlSty4cUNooMLe3h6BgYEsJqsZcSwELiEhIbQz24gRI7Bt2za4ublxcpAJEM8+9ejRA2FhYWzHqFXJyckoLi6u0F5aWop3796xkKh2GBgYYPHixYiOjmY7CvnBLF26FIqKinj//j3u3r2LgIAAvH//nu1YhFQLDZ4RQkg9t337duzbtw+LFi2CpKSkoN3a2hpPnz5lMZnoevToAXd3d7x9+1bQ9ubNG3h4eKBnz54sJhPd/PnzMWjQINjb2yMnJwfdu3fHhAkTMHnyZMyYMYPteCJzc3OrtJD2jh07MGvWrLoPVENPnz7F4MGDK7RraWkhLS2NhUS1o7wQeDkqBE7qys8//wwPDw8sW7YMZ8+exYULF4S+uKhnz56YOHEinjx5IqhF9+TJE0yePBm9evViOZ3oZs6ciatXr8LMzAzt2rXDli1bkJyczHYs8gPIysrC2LFjoaOjA1tbW3Tv3h06OjoYM2YMMjMz2Y5HSJXQsk1CCKnn5OXl8fz5c+jp6Qktx4qNjUXr1q2Rn5/PdsRqe/XqFQYOHIhnz55BV1cXPB4PSUlJsLCwwN9//42mTZuyHbFaSktLERAQAAsLC8jJySEyMhJ8Ph/m5uZQUlJiO16N6Ojo4MKFC2jXrp1Qe3BwMAYMGIDXr1+zlEw0TZs2hbe3Nzp37iz09/TXX39h7ty5iIuLYzuiSCIjI2FnZ4d27drh9u3bGDBggFAhcCMjI7YjEjElIfHlz+K5tGHAp1JTU+Hi4oKrV68KZkmXlJSgT58+OHToELS1tVlOWDMxMTE4fvw4Tp06hfj4eNjb22PMmDFwdnZmOxoRUyNGjEBoaCi2b9+OTp06gcfj4f79+3B3d0fr1q3h7e3NdkRCvokGzwghpJ4zNzfHmjVrMHDgQKE3+9u2bcPhw4cRFBTEdkSR3bhxA8+fPwfDMDA3N+f0J/pycnKIioqCgYEB21FqlZycHJ49e1ahntGLFy/QqlUrFBQUsJRMNPPnz0dgYCDOnDkDExMTBAcH4927d3B2doazszOWLl3KdkSRpaSkYPfu3QgKCgKfz4eVlRUVAiekBmJiYgSvUWZmZjAxMWE7Uq178OABpk6divDwcE4OdBJuUFRUxLVr19C1a1eh9rt376Jv377Izc1lKRkhVUe7bRJCSD03b948TJ8+HQUFBWAYBo8ePcLJkyexZs0a7N+/n+14NeLg4AAHBwe2Y9QKCwsLxMfHi93gmbGxMa5evVph6emVK1dgaGjIUirRrVq1CuPGjYOOjo5g0La0tBSjR4/G77//zna8GmnUqBH++OMPtmMQIjZMTEzEcsAMKNug58SJEzh9+jQyMzMxbNgwtiMRMdagQQPBzpufUlVVhbq6OguJCKk+mnlGCCEcsG/fPqxcuRKvXr0CULaUbtmyZRg/fjzLyapu27ZtmDRpEuTk5CqtofUpNze3OkpVe65fv44FCxZgxYoVaNeuXYUdpVRUVFhKVjNeXl6YMWMG5s2bhx49egAAbt26hY0bN2LLli2c3WI+Li4OISEh4PP5sLS0RPPmzdmOVCN37tz56vHu3bvXURLyIxDH6/ns2bOxYsUKKCoqYvbs2V89d9OmTXWUqnaVL9c8ceIEEhMTYW9vDycnJwwZMgTKyspsxyNibO/evThz5gyOHDkimA2dkpICFxcXDBkyBJMnT2Y5ISHfRoNnhBDCIR8+fACfz+dkvRUDAwM8efIEDRo0+OrsLB6Ph/j4+DpMVjs+rfvD4/EE3zMMw9m6P+V2796NVatWCTZ40NfXx7Jly6g+Tj1SWd2pT5+HXH3++fj4wNvbG0lJSSgqKhI6FhwczFKqmhGHPonj9dze3h5//fUX1NTUvrrJBo/Hw+3bt+swWe2RkJCAtbU1Ro8ejV9++QWNGjViOxL5QVhaWuLFixcoLCxEs2bNAABJSUmQlZWt8OEVV66D5MdDyzYJIaSeS0hIQElJCZo3bw5NTU1Be2xsLKSlpaGvr89euGpISEio9Htx4evry3aE72bq1KmYOnUqUlNTIS8vz7lNEL41i+RTXJ1R8vHjR6HHxcXFCAkJweLFi7Fq1SqWUtXMtm3bsGjRIri4uODvv/+Gq6sr4uLi8PjxY0yfPp3teCIRlz6J4/X802u4uF7Pnz9/LrbLUEn9NmjQILYjEFJjNPOMEELqOVtbW/z6669wcXERaj927Bj2798PPz8/doKRKgsNDUXbtm3ZjiGykpIS+Pn5IS4uDqNHj4aysjLevn0LFRUVTgykfT6LJCgoCKWlpTA1NQVQtpRJUlJSsFOlOLlz5w48PDw4ubFIixYtsHTpUowaNUpos5QlS5YgPT0dO3bsYDtitYljn/Lz8yEvL1/pseTkZNqwoh4qKirC+/fvwefzhdrLZwQRQgipiAbPCCGknlNRUUFwcHClux1aW1sjIyODnWA1MGzYMFhbW2PhwoVC7evXr8ejR49w5swZlpLVnszMTBw/fhz79+9HWFgYZ5fNvXz5En379kVSUhIKCwsRExMDQ0NDzJo1CwUFBfD09GQ7YrVs2rQJfn5+OHz4sKBI8cePH+Hq6opu3bphzpw5LCesXVFRUbCxsUFOTg7bUapNQUEBUVFR0NPTg7a2Nm7cuIE2bdogNjYWHTt2RFpaGtsRq00c+9SiRQucOHECVlZWQu0+Pj6CGatcMGTIkCqfe+7cue+Y5PuJjY3Fr7/+ivv37wu1i0N5AcIdOTk5FQZuuVoXlvxYKhbIIIQQUq/weDxkZ2dXaM/MzOTsja6/vz/69etXob1v377fLHxe392+fRtjxoxB48aNsX37djg6OuLJkydsxxKZu7s7rK2t8fHjR6HZJYMHD8atW7dYTCaajRs3Ys2aNUK7e6mrq2PlypXYuHEji8lqJjw8XOgrLCwMV69exdSpU9GmTRu244mkUaNGgsEkPT09PHjwAEDZMkGufvYrjn1ycHBA586dsXbtWjAMg5ycHIwbNw4uLi5YsmQJ2/GqTFVVVfCloqKCW7duCV27g4KCcOvWrUp3DOSKcePGQUJCApcuXUJQUBCCg4MRHByMkJAQqjNFvquEhAT069cPioqKgh021dXVoaamRrttEs6gmmeEEFLPdevWDWvWrMHJkychKSkJoKz495o1a9C1a1eW04kmJycHMjIyFdqlpaWRlZXFQqKqS01NhZaWllDb69evcejQIXh5eSE3NxcjRoxAcXExzp49C3Nzc5aS1o6AgADcu3evwu9LT08Pb968YSmV6LKysvDu3Tu0bNnVrS96AABheElEQVRSqP39+/eVDlJzRdu2bcHj8SoMwHTs2BFeXl4spaqZHj164OLFi7CyssL48ePh4eEBHx8fPHnypFqzhOoTcezT9u3b0a9fP7i6uuKff/4RLOl+/Pgxp65/Bw8eFHy/YMECjBgxAp6enkKvu9OmTeP0DJnQ0FAEBQWhRYsWbEchPxgnJycAZTt4N2zYUGhDG0K4ggbPCCGknvvzzz/RvXt3mJqaolu3bgCAu3fvIisri7P1mVq1aoXTp09XmJVw6tSpev9ma+/evXj9+jW2bdsGaWlpODo6IiAgAP3798f27dvRt29fSEpKcm4545fw+fxKZzi+fv0aysrKLCSqmcGDB8PV1RUbN25Ex44dAQAPHjzAvHnzODt4AVQs2i4hIQEtLS3IycmxlKjm9u7dK1jaM2XKFGhoaCAgIAA///wzpkyZwnI60YhjnwCgd+/eGDJkCHbv3g0pKSlcvHix3l/Lv8bLywsBAQGCgTMAkJSUxOzZs9G5c2esX7+exXSiMzc3x4cPH9iOQX5A4eHhCAoKEtQaJYSLqOYZIYRwwNu3b7Fjxw6EhYVBXl4erVu3xowZM6ChocF2NJFcuHABQ4cOxejRo9GjRw8AwK1bt3Dy5EmcOXOmXu/KVFhYiMWLF0NTUxPz58+HlJQU3NzcMHXqVKHt1qWlpREWFsbpN5AAMHLkSKiqqmLv3r1QVlZGeHg4tLS0MHDgQDRr1kxotgYX5OXlYe7cufDy8kJxcTEAQEpKCuPHj8f69euhqKjIckJCuKV8I5GUlBTs378f/v7+2LBhA9zc3LBq1SpIS0uzHbHa1NXVcfDgwQqvRefPn4erq2uF3W254vbt2/j999+xevVqWFhYVPjdcHlWHanf7O3tsWjRIvTq1YvtKISIjAbPCCGEsOKff/7B6tWrERoaKhgQXLp0KWxtbdmOViVFRUWQkZFBYGAgvLy84O3tjRYtWmDs2LEYOXIkmjRpIhaDZ2/fvoW9vT0kJSURGxsLa2trxMbGQlNTE3fu3IG2tjbbEUWSm5uLuLg4MAwDY2Njzg+abdu2rdJ2Ho8HOTk5GBsbo3v37kIzaeqj8PDwKp/bunXr75ik9ohjnz6lrKyMfv36wdPTE2pqagCA+/fvw9nZGcrKyggJCWE3oAhmz56NQ4cO4X//+5/QDNW1a9fC2dkZmzZtYjmhaCQk/it3/emyOdowgHxvcXFxmDJlCsaMGYNWrVpVGLjl4rWP/Hho8IwQQuq5bxXQ7969ex0lIV+Tl5eHU6dOwcvLC48ePUJpaSk2bdqEX3/9lZPLGz+Vn5+PkydPIjg4GHw+H1ZWVnBychLaQICwy8DAAKmpqcjLy4O6ujoYhkFGRgYUFBSgpKSE9+/fw9DQEL6+vtDV1WU77hdJSEhUWrvtc1x6oy+OffrU0aNHMXbs2Art2dnZmDVrFg4cOMBCqprh8/nYsGEDtm7diuTkZABA48aN4e7ujjlz5tT7Qegv8ff3/+pxrnx4RbjnwYMHGD16NBITEwVt5ddFrl77yI+HBs8IIaSe+/ST4nKffmJMNxz1T3R0NA4cOICjR48iIyMDDg4OuHDhAtuxiBg7efIk9u7di/3798PIyAgA8OLFC0yePBmTJk1Cly5d8Msvv6BRo0bw8fFhOe2XvXz5ssrn6unpfccktUcc+yTOSkpKcPz4cfTp0weNGjUSbGIjLksa7969iz179iAuLg4+Pj7Q0dHB0aNHYWBgwNlNiEj9Z25uDjMzM8yfP7/SDQPo2ke4gAbPCCGknsvMzBR6XFxcjJCQECxevBirVq1Cz549WUpWPRoaGoiJiYGmpibU1dW/utNSenp6HSb7fkpLS3Hx4kV4eXlxevAsOjoa27dvR1RUFHg8Hlq0aIEZM2bQjm31iJGREc6ePYu2bdsKtYeEhGDo0KGIj4/H/fv3MXToUMFMGkJqU2RkJJKSklBUVCRo4/F4+Pnnn1lMJRoFBQVERUWJ3Rv6s2fPYuzYsXBycsLRo0cRGRkJQ0ND7Nq1C5cuXcLly5fZjkjElKKiIsLCwmBsbMx2FEJERrttEkJIPaeqqlqhzcHBAbKysvDw8EBQUBALqapv8+bNguWLmzdv/iG2KZeUlMSgQYPq9QYI3+Lj44NRo0bB2toanTp1AlC2/MLCwgInTpzA8OHDWU5IACA5ORklJSUV2ktKSpCSkgIAaNKkCbKzs+s6GhEzmZmZQq9L8fHxGDJkCMLDw4WWp5Zf47k4O7pDhw4ICQkRu8GzlStXwtPTE87Ozjh16pSgvXPnzli+fDmLyYi469GjBw2eEc6jwTNCCOEoLS0tREdHsx2jylxcXATfjxs3jr0gpFrmz5+P3377rcIbq6VLl2LBggU0eFZP2NvbY/Lkydi/fz8sLS0BlM06mzp1qmBH26dPn8LAwIDNmEQMbNu2DXJycpg3bx4AwN3dHbq6urh27Rratm2Lhw8fIiEhAXPnzuVsYf1p06Zhzpw5eP36Ndq1a1dhQxGuFjePjo6utE6qiooKMjIy6j4Q+WH8/PPP8PDwwNOnTyvd6XXAgAEsJSOk6mjZJiGE1HOf79TGMAySk5Oxdu1aFBcX4969eywlE529vT3GjBmDYcOGVTqzjtQfCgoKCA8Pr/BpcWxsLNq0aYO8vDyWklVddZbMcvUGPiUlBWPHjsWtW7cEb0pKSkrQs2dPHD16FA0bNoSvry+Ki4vRu3dvltMSLnv//j3Gjh0LY2Nj7Ny5E5qamrh16xbatGkDHR0dPHr0CDo6Orh16xbmzp3Lyd02v1RrlOvFzY2MjLBnzx706tULysrKCAsLg6GhIY4cOYK1a9ciMjKS7YhETFX2N1WOy39T5MdCM88IIaSea9u2baU7tXXs2BFeXl4spaoZCwsL/P7775gxYwYcHR0xduxYODo6QkZGhu1o5DN2dna4e/duhcGzgIAAdOvWjaVU1fP5stnP/57EYQOORo0a4caNG3j+/DliYmLAMAxatGgBU1NTwTn29vYsJiTiQltbG9euXcOaNWsAlP3NlC/J19TUxNu3b6GjowN9fX1OzY7+VEJCAtsRvovJkyfD3d0dXl5e4PF4ePv2LQIDAzF37lwsWbKE7XhEjPH5fLYjEFJjNPOMEELquc93apOQkICWlhbk5ORYSlQ7+Hw+bt68iRMnTuCvv/6CpKQkhg0bBicnJ9ja2rIdj/zL09MTS5YswYgRI9CxY0cAZTXPzpw5gz/++ANNmjQRnMuFWVs3b97EggULsHr1anTq1Ak8Hg/379/H77//jtWrV8PBwYHtiDVSVFSEhIQEGBkZQUqK+5+RZmRkwMfHB3FxcZg3bx40NDQQHByMhg0bQkdHh+14IhG3PnXr1g2zZ8/G4MGDMWnSJKSmpmLevHnw9PREcHAwnj17xnZE8olFixZh8+bNKCgoAADIyspi7ty5WLFiBcvJCCGkfqPBM0IIqcfKl1jt2bMHJiYmbMf5bgoKCnDx4kWsWrUKT58+5ezsH3H0taUWn+LKsotWrVrB09MTXbt2FWq/e/cuJk2ahKioKJaS1UxeXh5mzpyJw4cPAwBiYmJgaGgINzc3NGnSBAsXLmQ5YfWFh4ejV69eUFVVRWJiIqKjo2FoaIjFixfj5cuXOHLkCNsRq00c+3Tt2jVkZ2dj2LBhePXqFfr164dnz55BQ0MD3t7egpp7XHP06FF4enoiISEBgYGB0NPTw5YtW2BgYICBAweyHa9G8vLyEBkZCT6fD3NzcygpKbEdiYgpR0dHnDx5UlCiY9WqVZg+fTrU1NQAAGlpaejWrRstGSacULU7YkIIIayQlpbGs2fPxHpnypSUFHh6emLdunUIDw+HtbU125HIJ/h8fpW+uDBwBgBxcXGV1tkrH8zgqt9++w1hYWHw8/MTmpXaq1cvnD59msVkops9ezbGjRuH2NhYoT799NNPuHPnDovJRCeOferTpw+GDRsGANDV1UV4eDg+fPiA9+/fc2bg7Nq1a8jMzBQ83r17N2bPng1HR0dkZGQIrm9qamrYsmULSylrj4KCAqytrdG+fXsaOCPf1bVr11BYWCh4vG7dOqSnpwsel5SUcHZ5N/nx0OAZIYTUc87Ozjhw4ADbMWpVVlYWDh48CAcHB+jq6mL37t34+eefERMTg4cPH7Idj3wDl3dls7GxwaxZs5CcnCxoS0lJwZw5c9C+fXsWk9XM+fPnsWPHDnTt2lVosN3c3BxxcXEsJhPd48ePMXny5ArtOjo6SElJYSFRzYljnyqjoaFR5Vmr9UFKSgq6dOmC169fAwC2b9+Offv2YdGiRZCUlBScZ21tjadPn7IVkxDO+XyRGy16I1zG/WIYhBAi5oqKirB//37cuHED1tbWUFRUFDq+adMmlpKJrmHDhlBXV8eIESOwevVq2NjYsB2JfMG6deugr6+PkSNHAgCGDx+Os2fPonHjxrh8+TLatGnDcsLq8fLywuDBg6Gnp4dmzZoBAJKSkmBiYoLz58+zG64GUlNToa2tXaE9NzeXszNX5eTkkJWVVaE9OjoaWlpaLCSqOXHsU0FBAbZv3w5fX1+8f/++QmHw4OBglpJVnYuLC5SVldG3b188e/YMCQkJsLS0rHCerKwscnNzWUhICCGEbTR4Rggh9dyzZ89gZWUFoKyO0ae4+KaYYRhs3boVY8aMgYKCAttxyDfs2bMHx44dAwDcuHEDN2/exNWrV+Ht7Y158+bh+vXrLCesHmNjY4SHhwt2pmQYBubm5ujVqxcn/57K2djY4J9//sHMmTMB/Hdt2LdvHzp16sRmNJENHDgQy5cvh7e3N4CyPiUlJWHhwoUYOnQoy+lEI459+vXXX3Hjxg0MGzYM7du35+zf0ZAhQwQDZgYGBggNDYWenp7QOVeuXIG5uTkb8QjhJB6PV+GawNVrBCG0YQAhhJA6xefzIScnh4iICDRv3pztOOQb5OXlERMTA11dXbi7u6OgoAB79uxBTEwMOnTogI8fP7IdkQC4f/8++vbtCycnJxw6dAiTJ09GREQEAgMD4e/vj3bt2rEdsdqysrLg6OiIiIgIZGdno0mTJkhJSUGnTp1w+fLlCrNwuUAc+6SqqorLly+jS5cubEepNQcPHsTixYuxceNGjB8/Hvv370dcXBzWrFmD/fv345dffmE7IiGcICEhgZ9++gmysrIAgIsXL6JHjx6Ca11hYSGuXr3Kmbqp5MdGM88IIaSey8zMRGlpKTQ0NITa09PTISUlBRUVFZaSiUZCQgLNmzdHWloaDZ5xgLq6Ol69egVdXV1cvXoVK1euBFA2g5CLN7vLly//6vElS5bUUZLa1blzZ9y7dw8bNmyAkZERrl+/DisrKwQGBsLCwoLteCJRUVFBQEAAbt++jeDgYPD5fFhZWaFXr15sRxOZOPZJR0cHysrKbMeoVa6urigpKcH8+fORl5eH0aNHQ0dHB1u3bqWBM0KqwcXFRejxmDFjKpzj7OxcV3EIqRGaeUYIIfXcTz/9hJ9//hnTpk0Tavf09MSFCxdw+fJllpKJ7p9//sHatWuxe/dutGrViu045CtmzJiBS5cuoXnz5ggJCUFiYiKUlJRw+vRprFu3jhP1jD71eR2j4uJiJCQkQEpKCkZGRpzrDyFsu3LlCrZt2wZPT88KyxzFwYcPH8Dn8yutKUgIIeTHQYNnhBBSz2loaODevXswMzMTan/+/Dm6dOmCtLQ0lpKJTl1dHXl5eSgpKYGMjAzk5eWFjn+6jTlhV3FxMbZu3YpXr15h3LhxgsGnLVu2QElJCRMmTGA5Yc1lZWVh3LhxGDx4MMaOHct2HJGVlpbir7/+QlRUFHg8HszMzDBw4EBISXFnocG2bduqfK6bm9t3TFJ7xLFPn0pNTcWIESNw584dKCgoQFpaWug4l6/n79+/R3R0NHg8HkxNTTm7qQMhhJCao8EzQgip5xQVFfHgwYMKS6+ePn2KDh06IC8vj6Vkojt8+PBXj38+zZ+Q7+3Zs2fo378/EhMT2Y4ikmfPnmHgwIFISUmBqakpgLINRrS0tHDhwgXOLN00MDAQepyamoq8vDyoqakBADIyMqCgoABtbW3Ex8ezkLD6xLFPn+rVqxeSkpIwfvx4NGzYsEIxcC5ez7OysjB9+nScPHlSsHuopKQkRo4ciZ07d0JVVZXlhIQQQuoaDZ4RQkg9Z2dnBwsLC2zfvl2offr06QgPD8fdu3dZSkZ+JJGRkUhKSkJRUZFQ+4ABA1hKVLsCAgLw888/c3YDhI4dO0JbWxuHDx+Guro6AODjx48YN24c3r9/j8DAQJYTVt+JEyewa9cuHDhwQDAgGB0djYkTJ2Ly5MlwcnJiOWH1iWOfFBQUEBgYiDZt2rAdpdaMGDECoaGh2L59Ozp16gQej4f79+/D3d0drVu3FuyWSggh5MdBg2eEEFLP3bt3D7169YKNjQ169uwJALh16xYeP36M69evo1u3biwnFE1cXBwOHjyIuLg4bN26Fdra2rh69Sp0dXXRsmVLtuORf8XHx2Pw4MF4+vQpeDweym8bymeXcG3TgM+X0DEMg+TkZBw9ehTdu3fHyZMnWUpWM/Ly8njy5EmFv51nz57BxsYG+fn5LCUTnZGREXx8fCrUqQsKCsKwYcOQkJDAUjLRiWOfrKyssGvXLnTs2JHtKLVGUVER165dQ9euXYXa7969i759+yI3N5elZIQQQtgiwXYAQgghX9elSxcEBgZCV1cX3t7euHjxIoyNjREeHs7ZgTN/f39YWFjg4cOHOHfuHHJycgAA4eHhWLp0KcvpyKfc3d1hYGCAd+/eQUFBAREREbhz5w6sra3h5+fHdrxq27x5s9DXtm3b4OfnBxcXF+zdu5fteCIzNTXFu3fvKrS/f/8exsbGLCSqueTkZBQXF1doLy0trbSvXCCOfVq7di3mzJkDPz8/pKWlISsrS+iLixo0aFDp0kxVVVXBzE5CCCE/Fpp5RgghpM516tQJw4cPx+zZs6GsrIywsDAYGhri8ePHGDRoEN68ecN2RPIvTU1N3L59G61bt4aqqioePXoEU1NT3L59G3PmzEFISAjbEQmAy5cvY/78+Vi2bJlgBtCDBw+wfPlyrF27VmgGjYqKClsxq+Xnn39GUlISDhw4gHbt2oHH4+HJkyeYOHEidHV1ceHCBbYjVps49klCouyz+M9rnTEMAx6Px7nZqQCwd+9enDlzBkeOHEHjxo0BACkpKXBxccGQIUMwefJklhMSQgipazR4RgghpM4pKSnh6dOnMDAwEBo8S0xMRIsWLVBQUMB2RPIvdXV1BAUFwdDQEEZGRti/fz/s7e0RFxcHCwsLzm1Y8euvv2Lr1q1QVlYWas/NzcXMmTPh5eXFUrKaKR/AAP4bxPh8iS3XBjNSU1Ph4uKCq1evCnZwLCkpQZ8+fXDo0CFoa2uznLD6xLFP/v7+Xz1ua2tbR0lqj6WlJV68eIHCwkI0a9YMAJCUlARZWVk0b95c6Nzg4GA2IhJCCKlj3Nm7nBBCiNhQU1NDcnJyhV3oQkJCoKOjw1IqUplWrVohPDwchoaG6NChA/7880/IyMhg7969MDQ0ZDtetR0+fBhr166tMHiWn5+PI0eOcHbwzNfXl+0ItU5LSwuXL19GbGwsoqKiwDAMzMzMYGJiwnY0kYljn7g4OPYtgwYNYjsCIYSQeoZmnhFCCKlz8+fPR2BgIM6cOQMTExMEBwfj3bt3cHZ2hrOzM9U9q0euXbuG3NxcDBkyBPHx8ejfvz+eP3+OBg0a4PTp0+jRowfbEaskKysLDMNAXV0dsbGx0NLSEhwrLS3FxYsXsXDhQrx9+5bFlIQQQgghpD6iwTNCCCF1rri4GOPGjcOpU6fAMAykpKRQWlqK0aNH49ChQ5CUlGQ7IvmK9PR0qKurV6hxVJ9JSEh8NS+Px8Mff/yBRYsW1WGq2nP16lUoKSkJapvt3LkT+/btg7m5OXbu3ElFzgkhhBBCaoAGzwghhLAmPj4ewcHB4PP5sLS0rFBLhpDa4u/vD4Zh0KNHD5w9exYaGhqCYzIyMtDT00OTJk1YTFgzFhYWWLduHRwdHfH06VNYW1tjzpw5uH37NszMzHDw4EG2IxJCCCGEcBYNnhFCSD00ZMiQKp977ty575ikbpSWluLp06fQ09OjGTLku3r58iWaNWvGqVlzVaGkpIRnz55BX18fy5Ytw7Nnz+Dj44Pg4GA4OjoiJSWF7YiEEEIIIZxFGwYQQkg9pKqqynaE72rWrFmwsLDA+PHjUVpaCltbW9y/fx8KCgq4dOkS7Ozs2I5IxEh4eDhatWoFCQkJZGZm4unTp188t3Xr1nWYrPbIyMgIdj69efMmnJ2dAQAaGhrIyspiMxohhBBCCOfRzDNCCCF1rmnTpjh//jysra1x/vx5TJs2DX5+fjhy5Ah8fX1x7949tiMSMSIhIYGUlBRoa2sLap9VdvvD4/FQWlrKQsKaGzBgAIqKitClSxesWLECCQkJ0NHRwfXr1zFjxgzExMSwHVEkd+/exZ49exAXFwcfHx/o6Ojg6NGjMDAwENR346K8vDwkJSWhqKhIqJ0rg7eWlpZVnr0ZHBz8ndN8P0VFRUhISICRkRGkpGjOASGE/Mgk2A5ACCHk20pKSnDz5k3s2bMH2dnZAIC3b98iJyeH5WSi+fDhAxo1agQAuHz5MkaMGAETExOMHz/+q7OCCBFFQkKCYHfNhIQExMfHIyEhocJXfHw8y0lFt2PHDkhJScHHxwe7d++Gjo4OAODKlSvo27cvy+lEc/bsWfTp0wfy8vIICQlBYWEhACA7OxurV69mOZ1oUlNT0b9/fygrK6Nly5awtLQU+uKKQYMGYeDAgRg4cCD69OmDuLg4yMrKws7ODnZ2dpCTk0NcXBz69OnDdlSR5OXlYfz48VBQUEDLli2RlJQEAHBzc8PatWtZTkcIIYQNNPOMEELquZcvX6Jv375ISkpCYWEhYmJiYGhoiFmzZqGgoACenp5sR6w2PT097Nu3Dz179oSBgQF27dqF/v37IyIiAl27dsXHjx/Zjkg+cfToUXh6eiIhIQGBgYHQ09PDli1bYGBggIEDB7Idj4gpS0tLeHh4wNnZGcrKyggLC4OhoSFCQ0PRt29fTtZxc3JyQmJiIrZs2QJ7e3v89ddfePfuHVauXImNGzeiX79+bEestgkTJqBx48ZYsWKFUPvSpUvx6tUreHl5sZRMdO7u7rh37x62bNmCvn37Ijw8HIaGhrhw4QKWLl2KkJAQtiMSQgipYzT/mBBC6jl3d3dYW1sjLCwMDRo0ELQPHjwYEyZMYDGZ6FxdXTFixAg0btwYPB4PDg4OAICHDx+iRYsWLKcjn9q9ezeWLFmCWbNmYdWqVYJljWpqatiyZQvnBs8uXLhQaTuPx4OcnByMjY1hYGBQx6lqR1xcHA4ePIi4uDhs3boV2trauHr1KnR1ddGyZUu241VbdHQ0unfvXqFdRUUFGRkZdR+oFty+fRt///03bGxsICEhAT09PTg4OEBFRQVr1qzh5ODZmTNn8OTJkwrtY8aMgbW1NScHz86fP4/Tp0+jY8eOQstTzc3NERcXx2IyQgghbKHBM0IIqecCAgJw7949yMjICLXr6enhzZs3LKWqmWXLlqFVq1Z49eoVhg8fDllZWQCApKQkFi5cyHI68qnt27dj3759GDRokNByJWtra8ydO5fFZKIZNGhQpTXPytt4PB66du2K8+fPc2rnV39/f/z000/o0qUL7ty5g1WrVkFbWxvh4eHYv38/fHx82I5YbY0bN8aLFy+gr68v1B4QEABDQ0N2QtVQbm4utLW1AZRt5pCamgoTExNYWFhwtjaYvLw8AgIC0Lx5c6H2gIAAyMnJsZSqZlJTUwW/p0/l5uaK3U69hBBCqoZqnhFCSD3H5/MrLWL++vVrKCsrs5CodgwbNgweHh5o2rSpoM3FxYVzM5nEXUJCQqW1mGRlZZGbm8tCopq5ceMGbGxscOPGDWRmZiIzMxM3btxA+/btcenSJdy5cwdpaWmcGxhcuHAhVq5ciRs3bggNtNvb2yMwMJDFZKKbPHky3N3d8fDhQ/B4PLx9+xbHjx/H3LlzMW3aNLbjicTU1BTR0dEAgLZt22LPnj148+YNPD090bhxY5bTiWbWrFmYOnUqZsyYgWPHjuHYsWOYMWMGpk+fDg8PD7bjicTGxgb//POP4HH5gNm+ffvQqVMntmIRQghhEc08I4SQes7BwQFbtmzB3r17AZTdxOfk5GDp0qVwdHRkOR0RdwYGBggNDYWenp5Q+5UrV2Bubs5SKtG5u7tj79696Ny5s6CtZ8+ekJOTw6RJkxAREYEtW7bg119/ZTFl9T19+hQnTpyo0K6lpYW0tDQWEtXc/PnzkZmZCXt7exQUFKB79+6QlZXF3LlzMWPGDLbjiWTWrFlITk4GUFYTrE+fPjh+/DhkZGRw6NAhdsOJaOHChTA0NMTWrVsFz0EzMzMcOnQII0aMYDmdaNasWYO+ffsiMjISJSUl2Lp1KyIiIhAYGAh/f3+24xFCCGEBDZ4RQkg9t3nzZtjb28Pc3BwFBQUYPXo0YmNjoampiZMnT7Idj4i5efPmYfr06SgoKADDMHj06BFOnjyJNWvWYP/+/WzHq7a4uDioqKhUaFdRURHsttm8eXN8+PChrqPViJqaGpKTkyvUawsJCRHsvMlFq1atwqJFixAZGQk+nw9zc3MoKSmxHavKiouLIS0tLXjs5OQk+N7S0hKJiYl4/vw5mjVrBk1NTTYi1ooRI0ZwdqCsMp07d8a9e/ewYcMGGBkZ4fr167CyskJgYCAsLCzYjkcIIYQFtNsmIYRwQH5+Pk6dOoWgoCDw+XxYWVnByckJ8vLybEcjP4B9+/Zh5cqVePXqFQBAR0cHy5Ytw/jx41lOVn1du3aFsrIyjhw5Ai0tLQBl9Y2cnZ2Rm5uLO3fu4ObNm5g2bRpiYmJYTlt18+fPR2BgIM6cOQMTExMEBwfj3bt3cHZ2hrOzM5YuXcp2xGrLzMxEaWkpNDQ0hNrT09MhJSVV6SBofbN27VoYGhqK1cASIYQQ8iOiwTNCCCGEVMmHDx/A5/MrLaTNFdHR0Rg4cCASEhKgq6sLHo+HpKQkGBr+v707j6q62v8//jogChjiXM6C5EChgpqpZU44ZDlfKk1N9NpwFXMqu6Vm5fDNGb1KpjjmgJpmV0scSRJSERxREy3MOQ1UkFQ4vz+68ZPQUlP2OYfnYy3Xgn3OH0/XNb3nzf7s7a0vvvhCVatW1erVq3X58mV1797ddO4du379ul555RUtXbpUVqtVBQoUUGZmprp27ap58+bJ2dnZdOJda9OmjZ5//vlc55uFhYVpzZo1WrdunaGyO3fkyBF17NhRffv21YABAzRo0KA/ff+kSZPyqOz+yczM1OTJkxUREaHk5GRdu3Ytx+sXL140VHZ3Ll26dMfvtYfBLQDg/mJ4BgAwIikpSXPnzlVSUpKmTp2q0qVL6+uvv1aFChX02GOPmc6DA7NarVq/fr2OHDkiq9Wq6tWrKzAwUE5O9n+PUlJSkuLj45WVlSV/f/9cNyDak+LFi+vbb79VjRo1cqwfOnRIjRo1spuz3C5fvqxXXnlFK1euVNOmTW/7PovFos2bN+dh2f0xYsQIzZ49W4MGDdLw4cP17rvv6ocfftDq1as1YsQIhYSEmE68I05OTnd8k+atLvEBADg2hmcAgDwXFRWlNm3aqFGjRvrmm2+UmJgob29vffzxx9qxY4dWrFhhOjFf8/f3v+MPkbt3737ANcivChcurNjY2FxnTO3bt0/169dXenq6oTLcrEqVKgoNDVXbtm3l4eGhhISE7LXY2NhbXmRhi26+COCHH37QsGHD9Morr2TfrhkTE6P58+dr7Nix6tmzp6lMAIAhDM8AAHmuQYMG+sc//qFBgwbJw8NDe/bskbe3t3bu3KkOHTro5MmTphPztVGjRmV/nZGRoRkzZsjX1zf7Q2RsbKwOHDigN954Q2PHjjWVec82bdqkTZs26dy5c8rKysrxWnh4uKGqu/dXjwDezB4fB2zSpIn8/Pw0bdq0HOv/+te/tHfvXm3bts1QGW5WuHBhJSYmqmLFiipTpozWrl2rgIAAHTt2TP7+/kpNTTWdeNeaN2+uPn366KWXXsqxvnjxYs2aNUtbt241EwYAMIbbNgEAeW7fvn233I1QqlQpu3kUy5HdfLh8nz59FBISog8//DDXe36/QMCejBo1Sh988IHq1q2rMmXK3PEOO1sUHx+f4/u4uDhlZmaqWrVqkn47b8vZ2Vl16tQxkfe3jR49Wi1atNCePXvUvHlzSb8NPnfu3KnIyEjDdfcmIyND06ZN05YtW245vLXHnZzly5fX6dOnVbFiRfn4+GTfTLlz504VKlTIdN49iYmJUVhYWK71unXrqk+fPgaKAACmMTwDAOS5okWL6vTp0/Ly8sqxHh8fr3Llyhmqwq0sX75cu3btyrX+8ssvq27duna1U0v67bD5efPm2dVlALezZcuW7K8nTZokDw8PzZ8/X8WKFZMk/fLLL+rVq5eefvppU4l/S6NGjRQTE6Px48crIiJCbm5uqlmzpubMmWO3Z7kFBwdrw4YN6tKli5544gm7Ht7+rmPHjtq0aZPq16+vAQMG6KWXXtKcOXOUnJysgQMHms67JxUqVFBYWJgmTpyYY/2TTz5RhQoVDFUBAEzisU0AsHGOcpPZzd566y3FxMRo+fLlqlq1qnbv3q2zZ8+qR48e6tGjR46dTzDrkUce0dixY9WrV68c63PnztWwYcN09uxZQ2X3pkSJEtqxY4eqVKliOuW+KleunCIjI3NdtrF//361bNlSp06dMlSGm3l6emrdunVq1KiR6ZQHJjY2Vtu3b5ePj4/atWtnOueerFu3Tp07d1aVKlX05JNPSvrt95WUlKSVK1fq2WefNVwIAMhr7DwDABs3atSoP73JzB6NHj1ar7zyisqVKyer1SpfX19lZmaqa9eueu+990zn4SZvvvmmXn/9dcXFxeX4EBkeHm6Xf/769OmjxYsXa/jw4aZT7qtLly7p7NmzuYZn586d0+XLlw1V/X1ZWVk6evToLR9xbNy4saGqe1euXDl5eHiYznignnzyyey/K+zVs88+q++//14zZ85UYmKirFar2rdvr9dee42dZwCQT7HzDABsnKPcZHYrSUlJio+PV1ZWlvz9/e32USxHFxERoalTpyoxMVGSVKNGDQ0YMEBBQUGGy+7egAEDtGDBAtWsWVM1a9aUi4tLjtft8WB9SerRo4eioqI0ceLEHEPOoUOHqnHjxpo/f77hwrsXGxurrl276scff9Qf/++qxWJRZmamobJ799VXXyk0NFRhYWGqVKmS6Zx7tmbNmjt+r73uPgMA4GYMzwDAxjniTWaAKU2bNr3taxaLRZs3b87DmvsnPT1dQ4YMUXh4uK5fvy5JKlCggHr37q3x48ercOHChgvvXu3atVW1alWNGjXqlpc7eHp6Giq7d+fPn1dQUJC++eYbubu75xre2stj+E5OTjm+t1gstxxwSrLLIScAAH/EY5sAYOMc5SazQYMG3fF77XX3D2zfzYfsOxJ3d3fNmDFD48ePV1JSkqxWq3x8fOxyaPa777//XitWrJCPj4/plPvmpZde0smTJzVmzBg9/PDDdnthwM2P0G7cuFFvv/22xowZowYNGshisWj79u167733NGbMGIOVAADcPwzPAMDGOcpNZvHx8Tm+j4uLU2ZmpqpVqyZJOnLkiJydnVWnTh0TeciHfvrpJ1ksFoe64bVw4cKqWbOm6Yz7on79+jp69KhDDc+2b9+umJgY1apVy3TKffPmm28qLCxMTz31VPZaq1at5O7urr59+2Y/7g0AgD1jeAYANm7cuHHZX3fp0kUVKlTQt99+a3c3md2842fSpEny8PDQ/PnzVaxYMUnSL7/8ol69eunpp582lYh8ICsrSx999JEmTpyoK1euSJI8PDw0ePBgvfvuu7keR4M5/fv31+DBg3XmzBn5+fnlesTRHoeE1atX19WrV01n3FdJSUm3fITW09NTP/zwQ94HAQDwAHDmGQAgz5UrV06RkZG5bgbcv3+/WrZsqVOnThkqg6N75513NGfOHI0aNUqNGjWS1WrVt99+q/fff1///Oc/NXr0aNOJ+J9bDTJ/P1vLXi8MiIyM1KhRozR69OhbDgSLFCliqOzeNW7cWC4uLlq0aJHKlCkjSTpz5oy6d++ua9euKSoqynAhAAB/H8MzALBxzs7Oaty4sVauXKnixYtnr589e1Zly5a1yw+QHh4e+uKLL9SsWbMc65s3b1b79u11+fJlQ2VwdGXLllVYWFiuXZtffPGF3njjDZ08edJQGf7oxx9//NPX7fG2yt8Hgn8868yeB4JHjx5Vx44ddfjwYVWsWFGSlJycrKpVq2r16tV289itv7//HZ9Bt3v37gdcAwCwNTy2CQA2zmq16tdff1XdunW1Zs0aPf744zles0cdO3ZUr169NHHiRD355JOSpNjYWA0dOlSdOnUyXIebZWZmat68edq0aZPOnTuX46BwSXZ3O+XFixdVvXr1XOvVq1e3m5sO8wt7HI79FUe8sMLHx0d79+7Vhg0bdOjQIVmtVvn6+qpFixZ2dSFChw4dsr/OyMjQjBkz5OvrqwYNGkj67d+oAwcO6I033jBUCAAwiZ1nAGDjnJ2d9dNPP2ncuHGaO3euFi5cqPbt29v1zrP09HQNGTJE4eHhun79uiSpQIEC6t27t8aPH2/XNwQ6mn79+mnevHlq27atypQpk+vD8OTJkw2V3Zv69eurfv36Cg0NzbHev39/7dy5U7GxsYbK7t6aNWvu+L32dD7iHx08eFDJycm6du1ajnV7/j3BtvXp00dlypTRhx9+mGN95MiROnHihMLDww2VAQBMYXgGADbOyclJZ86cUenSpTVr1iyFhITovffeU58+fVSuXDm7HJ79Li0tTUlJSbJarfLx8WFoZoNKliypBQsW6NlnnzWdcl9ERUWpbdu2qlixoho0aCCLxaLt27frxIkTWrdunV1dWHGnlxvY6+OAx44dU8eOHbVv377ss86k///Ioz3+niQpJSVFc+bMUWJioiwWi3x9fRUcHHzLQ/dtVWhoqPr27StXV9dcg+g/CgkJyaOq+8fT01O7du3So48+mmP9+++/V926dZWammqoDABgCsMzALBxNw/PJGnr1q3q0qWL/P39tXnzZrv9AAn7ULZsWW3dulVVq1Y1nXLfnDp1Sv/5z39yPGL2xhtvqGzZsqbTcJPnn39ezs7O+vTTT+Xt7a0dO3bowoULGjx4sCZMmGBXg87f7dq1S61atZKbm5ueeOIJWa1W7dq1S1evXlVkZKQCAgJMJ94RLy8v7dq1SyVKlJCXl9dt32exWHTs2LE8LLs/HnnkEY0dO1a9evXKsT537lwNGzZMZ8+eNVQGADCF4RkA2LibP6T87ujRo3r++ed15MgRhmd4oCZOnKhjx45p+vTpdnV+0d06ceKERo4cyeNYNqRkyZLavHmzatasKU9PT+3YsUPVqlXT5s2bNXjwYMXHx5tOvGtPP/20fHx89Omnn6pAgd+OHr5x44b69OmjY8eO6ZtvvjFcCEkaN26c3n//ffXp0yfHuZzh4eEaMWKEhg0bZrgQAJDXGJ4BgJ3KyMjQ2bNnHfJQbdiOjh07asuWLSpevLgee+wxubi45Hj9888/N1R2f+3Zs0cBAQF2PYxOS0tTVFTULc8Hs8dH54oVK6a4uDh5e3urSpUqmj17tpo2baqkpCT5+fkpPT3ddOJdc3NzU3x8fK5LKw4ePKi6deva5e/JUUVERGjq1KlKTEyUJNWoUUMDBgxQUFCQ4TIAgAnctgkAdsrV1ZXBGR64okWLqmPHjqYz8Bfi4+P17LPPKj09XWlpaSpevLh+/vlnubu7q3Tp0nY5PHv88ce1d+9eeXt7q379+vr4449VsGBBzZo1S97e3qbz7kmRIkWUnJyca3h24sQJeXh4GKr6e7p06aK6devm2o01fvx47dixQ8uXLzdU9vcEBQUxKAMAZGPnGQDYoOLFi+vIkSMqWbKkihUr9qePy128eDEPywDHZO87z5o0aaKqVatq5syZKlq0qPbs2SMXFxe9/PLLGjBggDp16mQ68a6tX79eaWlp6tSpk44dO6bnnntOhw4dUokSJbRs2TI1a9bMdOJdCwkJ0apVqzRhwgQ1bNhQFotF0dHRGjp0qDp37qwpU6aYTrxrpUqV0ubNm+Xn55djfd++fWrRogXngwEAHAI7zwDABk2ePDl7F4I9fpi6E0lJSZoyZUr2jXO/PxJTpUoV02m4hfPnz+vw4cOyWCyqWrWqSpUqZToJN0lISNAnn3wiZ2dnOTs769dff5W3t7c+/vhj9ezZ0y6HZ61atcr+2tvbWwcPHtTFixf/8gcKtmzChAmyWCzq0aOHbty4IUlycXHR66+/rnHjxhmuuzdXrlxRwYIFc627uLjo0qVLBor+vszMTE2ePFkRERG3fAyaH1oBQP7D8AwAbFDPnj1v+bWjWL9+vdq1a6fatWurUaNGslqt2r59ux577DF9+eWXCgwMNJ2I/0lLS1P//v21YMECZWVlSZKcnZ3Vo0cPTZs2Te7u7oYL78xfDY9SUlLyJuQBcXFxyR4oPfzww0pOTlaNGjXk6emp5ORkw3X3JjU1VZmZmSpevHj2WvHixXXx4kUVKFBARYoUMVh3bwoWLKipU6dq7NixSkpKktVqlY+Pj938d3Qrjz/+uJYtW6YRI0bkWF+6dKl8fX0NVf09o0aN0uzZszVo0CANHz5c7777rn744QetXr061+8TAJA/8NgmANigu/lpvT1+gPT391erVq1y7bQYNmyYIiMjtXv3bkNlmDJlivz8/NS8eXNJ0quvvqqNGzdq+vTpatSokSQpOjpaISEhCgwM1MyZM03m3rFevXrd0fvmzp37gEsejJYtW+qVV15R165d9dprryk+Pl4hISFauHChfvnlF3333XemE+9amzZt9Pzzz+uNN97IsR4WFqY1a9Zo3bp1hspwszVr1qhz587q2rVr9qO0mzZt0pIlS7R8+XJ16NDBbOA9qFKlikJDQ9W2bVt5eHgoISEhey02NlaLFy82nQgAyGMMzwDABjk5Of3lY0lWq1UWi8Uuz2hydXXVvn379Oijj+ZYP3LkiGrWrKmMjAxDZYiLi1NQUJDef/99de/eXSVLltSKFSvUpEmTHO/bsmWLgoKCdP78eTOhyGHXrl26fPmymjZtqvPnz6tnz56Kjo6Wj4+P5s6dq1q1aplOvGvFixfXt99+qxo1auRYP3TokBo1aqQLFy4YKrt3aWlpGjdunDZt2qRz585l7+b83bFjxwyV/T1r167VmDFjlJCQIDc3N9WsWVMjR47UM888YzrtnhQuXFiJiYmqWLGiypQpo7Vr1yogIEDHjh2Tv7+/UlNTTScCAPIYj20CgA3asmWL6YQHqlSpUkpISMg1PEtISFDp0qUNVUGS6tSpo++++049e/ZU9+7dlZ6erocffjjX+0qXLq309HQDhbiVunXrZn9dqlQph9iV9euvv2afC3az69ev6+rVqwaK/r4+ffooKipK3bt3V5kyZez27LY/atu2rdq2bWs6474pX768Tp8+rYoVK8rHx0eRkZEKCAjQzp07VahQIdN5AAADGJ4BgA2y15/W36l//vOf6tu3r44dO5bjxrn/+7//0+DBg03n5XslS5bU2rVrJUkNGjTQyJEjtWDBArm6ukqSrl69qlGjRqlBgwYmM+Hg6tWrp1mzZmnatGk51sPCwlSnTh1DVX/PV199pbVr12Y/Au1I4uLisi+A8fX1lb+/v+mke9axY0dt2rRJ9evX14ABA/TSSy9pzpw5Sk5O1sCBA03nAQAM4LFNALAT6enpt7z1q2bNmoaK7p3VatWUKVM0ceJEnTp1SpJUtmxZDR06VCEhIQ6zG8MR7N+/X61bt1ZGRoZq1aoli8WihIQEubq6av369XrsscdMJ8JBffvtt2rRooXq1auXfQbfpk2btHPnTkVGRurpp582XHj3vLy8tG7dulyPotqzc+fO6cUXX9TWrVtVtGhRWa1WpaamqmnTplq6dKlD3MwbGxur7du3y8fHR+3atTOdAwAwgOEZANi48+fPq1evXvrqq69u+bo9nnl2s8uXL0uSPDw8DJfgdq5evapFixbp0KFDslqt8vX1Vbdu3eTm5mY6DQ4uISFB48ePz3GW1jvvvJPrkW97sWjRIn3xxReaP3++Xd+webMXXnhBSUlJWrhwYfZQ8ODBg+rZs6d8fHy0ZMkSw4UAAPx9DM8AwMZ169ZNP/zwg6ZMmaKmTZtq1apVOnv2rD766CNNnDjRoc6ZAQBH5u/vr6SkJFmtVlWuXFkuLi45XrfHm4Y9PT21ceNG1atXL8f6jh071LJlS6WkpJgJu0tr1qy54/ey+wwA8h/OPAMAG7d582Z98cUXqlevnpycnFSpUiUFBgaqSJEiGjt2rF0Oz86ePashQ4Zk3zj3x5/j2PtuOnu3Zs0atWnTRi4uLn/5gZIPkbifLl26dMfvLVKkyAMseTA6dOhgOuG+y8rKyjUElCQXF5dct4nasj/+b2OxWHL92/T7kQL8GwUA+Q87zwDAxhUpUkR79+5V5cqVVblyZX322Wdq1KiRjh8/rscee8wubzxs06aNkpOT1a9fv1veONe+fXtDZZAkJycnnTlzRqVLl5aTk9Nt32exWPgQaUOioqI0YcKE7EPba9SooaFDh9rV2WBOTk5/eeah1Wrlz54Nad++vVJSUrRkyRKVLVtWknTy5El169ZNxYoV06pVqwwX3r2NGzfq7bff1pgxY9SgQQNZLBZt375d7733nsaMGaPAwEDTiQCAPMbOMwCwcdWqVdPhw4dVuXJl1a5dW5988okqV66ssLAwlSlTxnTePYmOjta2bdtUu3Zt0ym4hZt3i9jTzpH8bNGiRerVq5c6deqkkJAQWa1Wbd++Xc2bN9e8efPUtWtX04l3ZMuWLaYT8oQj3Uw5ffp0tW/fXpUrV1aFChVksViUnJwsPz8/LVq0yHTePXnzzTcVFhamp556KnutVatWcnd3V9++fZWYmGiwDgBgAjvPAMDGffbZZ7p+/bpeeeUVxcfHq1WrVrpw4YIKFiyoefPm6YUXXjCdeNd8fX312Wef2fUHRsCW1KhRQ3379tXAgQNzrE+aNEmffvqpw33YT0hIsMvhuyPfTLlhw4Ycl4q0aNHCdNI9c3Nz044dO+Tn55djfe/evapfv76uXr1qqAwAYArDMwCwM+np6Tp06JAqVqyokiVLms65J5GRkZo4cWL2LjrYrpCQEPn4+CgkJCTH+vTp03X06FFNmTLFTBhyKFSokA4cOCAfH58c60ePHtXjjz+ujIwMQ2X3T2pqqj777DPNnj1be/bsscvHNrmZ0j40btxYLi4uWrRoUfYO7zNnzqh79+66du2aoqKiDBcCAPIawzMAsBPXrl3T8ePHVaVKFRUoYH9P3RcrVizHWUZpaWm6ceOG3N3dcx02ffHixbzOw22UK1dOa9asUZ06dXKs7969W+3atdNPP/1kqAw38/Hx0dChQ/Xqq6/mWP/kk080YcIEff/994bK/r7NmzcrPDxcn3/+uSpVqqTOnTurc+fOdrlz1VFupgwNDb3j9/5x8G4Pjh49qo4dO+rw4cOqWLGiJCk5OVlVq1bV6tWrcw2pAQCOz/4+fQFAPpOenq7+/ftr/vz5kqQjR47I29tbISEhKlu2rIYNG2a48M6wQ8k+XbhwQZ6enrnWixQpop9//tlAEW5l8ODBCgkJUUJCgho2bCiLxaLo6GjNmzdPU6dONZ1313766SfNmzdP4eHhSktLU1BQkK5fv66VK1fK19fXdN49c5SbKSdPnnxH77NYLHY5PPPx8dHevXtv+SjqX11oAQBwTOw8AwAbN2DAAH377beaMmWKWrdurb1798rb21tr1qzRyJEjFR8fbzoRDuzxxx/Xa6+9pn79+uVYnzZtmmbOnKmDBw8aKsMfrVq1ShMnTsw+3+z32zbt7fbaZ599VtHR0XruuefUrVs3tW7dWs7OznJxcdGePXvsenjmiDdTAgCQH7DzDABs3OrVq7Vs2TI9+eSTOX7i7evrq6SkJINl927dunVydnZWq1atcqxHRkYqMzNTbdq0MVSGPxo0aJD69eun8+fPq1mzZpKkTZs2aeLEiewmtDEdO3ZUx44dTWf8bZGRkQoJCdHrr7+uRx991HTOfeWIN1P+zt6PFggNDVXfvn3l6ur6l4+l2uNuOgDA32N//7IBQD5z/vx5lS5dOtd6Wlqa3T4+MmzYMI0bNy7XelZWloYNG8bwzIYEBwfr119/1ejRo/Xhhx9KkipXrqyZM2eqR48ehutwK1euXMn1CGCRIkUM1dy9bdu2KTw8XHXr1lX16tXVvXt3u7xV+FYqVKig3bt3O9TNlI5ytMDkyZPVrVs3ubq6/uljqfb6KCoA4O/hsU0AsHHPPPOMunTpov79+8vDw0N79+6Vl5eX+vXrp6NHj+rrr782nXjX3NzclJiYmOumzR9++EGPPfaY0tLSzIThT50/f15ubm566KGHTKfgD44fP65+/fpp69atOW7WtFqtslgsdnkzZXp6upYuXarw8HDt2LFDmZmZmjRpkoKDg+Xh4WE6754sWLBAL7zwggoVKpRj/dq1a1q6dKldDqQ5WgAAkB8wPAMAG7d9+3a1bt1a3bp107x58/Tqq6/qwIEDiomJUVRUVK5bEO3BI488osWLF2c/Bvi7jRs3qmvXrjp37pyhMsA+NWzYUNJvg4yHH344167UZ555xkTWfXP48GHNmTNHCxcuVEpKigIDA7VmzRrTWXfN2dlZp0+fzrWb+MKFCypdurRdDjkrVaqUfbSAh4eH9uzZI29vbx09elQBAQG6dOmS6UQAAP42HtsEABvXsGFDbd++XePHj1eVKlUUGRmpgIAAxcTEyM/Pz3TePWnXrp3efPNNrVq1SlWqVJEkHT16VIMHD1a7du0M1+GPVqxYoYiICCUnJ+vatWs5Xtu9e7ehKtxs7969iouLU7Vq1UynPBDVqlXTxx9/rLFjx+rLL79UeHi46aR78vtOwD/66aefbnmrrT1wxKMFunTporp16+Z65HT8+PHasWOHli9fbqgMAGCKk+kAAMDtXb9+Xb169ZK7u7vmz5+v/fv36+DBg1q0aJHdDs6k3z6AFC5cWNWrV5eXl5e8vLxUo0YNlShRQhMmTDCdh5uEhoaqV69eKl26tOLj4/XEE0+oRIkSOnbsGGfT2ZB69erpxIkTpjMeOGdnZ3Xo0MHudp35+/srICBAFotFzZs3V0BAQPavWrVq6emnn7bbc8/q1auntWvXZn//+8Ds008/VYMGDUxl/S1RUVFq27ZtrvXWrVvrm2++MVAEADCNnWcAYMNcXFy0atUqDR8+3HTKfeXp6ant27drw4YN2rNnj9zc3FSzZk01btzYdBr+YMaMGZo1a5ZeeuklzZ8/X2+99Za8vb01YsQIXbx40XQe/mf27Nl67bXXdPLkST3++ONycXHJ8XrNmjUNlUGSOnToIElKSEhQq1atcpwbWLBgQVWuXFmdO3c2VPf3jB07Vq1bt9bBgwd148YNTZ06NcfRAvboypUrKliwYK51FxcXHkMFgHyKM88AwMb16tVLfn5+GjRokOmUByIjI0OFChWy28d7HJ27u7sSExNVqVIllS5dWhs2bFCtWrX0/fff68knn9SFCxdMJ0JSbGysunbtqh9++CF7zWKx2PWFAY5o/vz5euGFF+Tq6mo65W9LSEhQ7dq1JUn79u3ThAkTFBcXp6ysLAUEBOjtt9+22x3S9erV0/PPP68RI0bkWH///ff15ZdfKi4uzlAZAMAUdp4BgI3z8fHRhx9+qO3bt6tOnToqXLhwjtdDQkIMld27rKwsjR49WmFhYTp79qyOHDkib29vDR8+XJUrV1bv3r1NJ+J/HnnkEV24cEGVKlVSpUqVFBsbq1q1aun48ePi52+2Izg4WP7+/lqyZMktLwyAbejZs6fphPsmICBA/v7+6tOnj7p27ar58+ebTrpvhg8frs6dOyspKSn7YptNmzZpyZIlnHcGAPkUO88AwMZ5eXnd9jWLxaJjx47lYc398cEHH2j+/Pn64IMP9M9//lP79++Xt7e3IiIiNHnyZMXExJhOxP/06dNHFSpU0MiRIxUWFqZBgwapUaNG2rVrlzp16qQ5c+aYToSkwoULa8+ePfLx8TGdgj/h5OT0p4NNe9ohGBMTo/DwcEVEROj69evq3LmzgoOD1bRpU9Np98XatWs1ZswYJSQkZB8tMHLkSLu/uRYAcG8YngEA8pyPj48++eQTNW/eXB4eHtqzZ4+8vb116NAhNWjQQL/88ovpRPxPVlaWsrKyVKDAb5vVIyIiFB0dLR8fH7322mu3PBcIee/555/XK6+8YrfnZuUXq1evzjE8u379uuLj4zV//nyNGjXKLnfdXr16VREREZo7d662bdumypUrKzg4WD179lT58uVN5wEAcF8wPAMA5Dk3NzcdOnRIlSpVyjE8O3jwoJ544glduXLFdCJgV2bNmqWPPvpIwcHB8vPzy3VhQLt27QyV4U4sXrxYy5Yt0xdffGE65W9JSkrS3LlztWDBAp0+fVqBgYFat26d6ax7FhcXp8TERFksFvn6+srf3990EgDAEIZnAIA8V7duXb355pt6+eWXcwzPRo0apY0bN2rbtm2mE/O1vXv33vF7ucXRNjg5Od32NS4MsH1JSUmqWbOm0tLSTKf8bVeuXNFnn32mf//730pJSbHLP3vnzp3Tiy++qK1bt6po0aKyWq1KTU1V06ZNtXTpUpUqVcp0IgAgj3FhAAAgzwQHB2vq1KkaOXKkunfvrpMnTyorK0uff/65Dh8+rAULFui///2v6cx8r3bt2tk3Nf4ZhjK2Iysry3QC7tHVq1c1bdo0u3/EMSoqSuHh4Vq5cqWcnZ0VFBRkl4+hSlL//v116dIlHThwQDVq1JAkHTx4UD179lRISIiWLFliuBAAkNfYeQYAyDPOzs46ffq0SpcurfXr12vMmDGKi4tTVlaWAgICNGLECLVs2dJ0Zr73448/3vF7K1Wq9ABLAMdSrFixHGeeWa1WXb58We7u7lq0aJHdPV574sQJzZs3T/PmzdPx48fVsGFD9e7dW0FBQbluhrYnnp6e2rhxo+rVq5djfceOHWrZsqVSUlLMhAEAjGHnGQAgz9z885pWrVqpVatWBmtwOwzE7ENoaKj69u0rV1dXhYaG/ul7Q0JC8qgKf2bKlCk5vndyclKpUqVUv359FStWzEzUPQoMDNSWLVtUqlQp9ejRQ8HBwapWrZrprPsiKysr17mBkuTi4sIuTwDIp9h5BgB2YNu2bfrkk0+UlJSkFStWqFy5clq4cKG8vLz01FNPmc67Y05OTjp79iznxdiZhQsXKiwsTMePH1dMTIwqVaqkKVOmyMvLS+3btzedl295eXlp165dKlGihLy8vG77PovFomPHjuVhGe5FQkKCateubTrjjrVr1069e/fWc889J2dnZ9M591X79u2VkpKiJUuWqGzZspKkkydPqlu3bipWrJhWrVpluBAAkNduf7osAMAmrFy5Uq1atZKbm5vi4+P166+/SpIuX76sMWPGGK67e1WrVlXx4sX/9Bdsx8yZMzVo0CA9++yzOQ7/Llq0aK5dNMhbx48fV4kSJbK/vt0vBme2KzU1VTNmzFBAQIDq1KljOueurFmzRu3bt3e4wZkkTZ8+XZcvX1blypVVpUoV+fj4yMvLS5cvX9a0adNM5wEADGDnGQDYOH9/fw0cOFA9evTIcTNlQkKCWrdurTNnzphOvGNOTk6aMmWKPD09//R9PXv2zKMi/BVfX1+NGTNGHTp0yPHnb//+/WrSpIl+/vln04nQb4fOu7m53fK106dPq0yZMnlchD+zefNmhYeH6/PPP1elSpXUuXNnde7cWf7+/qbTcJMNGzbo0KFDslqt8vX1VYsWLUwnAQAM4cwzALBxhw8fVuPGjXOtFylSxC4PLX7xxRdVunRp0xm4Q8ePH7/lB/pChQopLS3NQBFuxd/fX4sXL1ZAQECO9RUrVuj111/X+fPnDZXhdz/99JPmzZun8PBwpaWlKSgoSNevX9fKlSvl6+trOg+3EBgYqMDAQNMZAAAbwPAMAGxcmTJldPToUVWuXDnHenR0tLy9vc1E3aObb5mDffDy8lJCQkKuSwS++uorPvDbkMDAQDVs2FDvv/++3n77baWlpalfv35avny5xo0bZzov33v22WcVHR2t5557TtOmTVPr1q3l7OyssLAw02n4n7+6dONmXMABAPkPwzMAsHGvvvqqBgwYoPDwcFksFp06dUoxMTEaMmSIRowYYTrvrnBSgP0ZOnSo/vWvfykjI0NWq1U7duzQkiVLNHbsWM2ePdt0Hv5n2rRpatu2rXr16qW1a9fq1KlTKlKkiHbu3MmQ0wZERkYqJCREr7/+uh599FHTObiFyZMn39H7LBYLwzMAyIcYngGAjXvrrbeUmpqqpk2bKiMjQ40bN1ahQoU0ZMgQ9evXz3TeXcnKyjKdgLvUq1cv3bhxQ2+99ZbS09PVtWtXlStXTlOnTtWLL75oOg83admypTp16qSZM2eqQIEC+vLLLxmc2Yht27YpPDxcdevWVfXq1dW9e3e98MILprNwk+PHj5tOAADYMC4MAAA7kZ6eroMHDyorK0u+vr566KGHTCchn/n555+VlZWVfWbdyZMnVa5cOcNVkKSkpCR17dpVZ86c0ezZsxUVFaUJEyYoJCREo0ePlouLi+lE6Le/x5cuXarw8HDt2LFDmZmZmjRpkoKDg+Xh4WE6D39w7do1HT9+XFWqVFGBAuw5AID8jOEZANiZS5cuafPmzapWrZpq1KhhOgf50JkzZzR69GjNnj1bV69eNZ0DSR4eHmrbtq3CwsJUtGhRSdL27duzb+mNj483G4hcDh8+rDlz5mjhwoVKSUlRYGCg1qxZYzoL+m3I2b9/f82fP1+SdOTIEXl7eyskJERly5bVsGHDDBcCAPKak+kAAMCfCwoK0vTp0yVJV69eVb169RQUFKSaNWtq5cqVhuvgqFJSUtStWzeVKlVKZcuWVWhoqLKysjRixAh5e3srNjZW4eHhpjPxPzNmzNDSpUuzB2eS1LBhQ8XHx+e6gRO2oVq1avr444/1008/acmSJaZzcJN33nlHe/bs0datW+Xq6pq93qJFCy1btsxgGQDAFHaeAYCNe+SRR7R+/XrVqlVLixcv1siRI7Vnzx7Nnz9fs2bNYkcJHog33nhDX375pV544QV9/fXXSkxMVKtWrZSRkaGRI0fqmWeeMZ0IAA9EpUqVtGzZMj355JPy8PDQnj175O3traNHjyogIECXLl0ynQgAyGM8vA8ANi41NVXFixeXJH399dfq3Lmz3N3d1bZtWw0dOtRwHRzV2rVrNXfuXLVo0UJvvPGGfHx8VLVqVU2ZMsV0Gv7EwYMHlZycrGvXrmWvWSwWPf/88warAPty/vz57LMdb5aWliaLxWKgCABgGsMzALBxFSpUUExMjIoXL66vv/5aS5culST98ssvOR4nAe6nU6dOZd/U6O3tLVdXV/Xp08dwFX6XmpoqT0/P7O+PHTumTp06ae/evbJYLPr9wYLfP+hnZmYa6QTsUb169bR27Vr1799f0v//7+jTTz9VgwYNTKYBAAxheAYANu7NN99Ut27d9NBDD6lSpUpq0qSJJOmbb76Rn5+f2Tg4rKysrBw3NDo7O6tw4cIGi3Cz0NBQubq6Zu8+HTBggCpUqKD169erdu3a+u6773T8+HENGTJEkyZNMlwL2JexY8eqdevWOnjwoG7cuKGpU6fqwIEDiomJUVRUlOk8AIABnHkGAHYgLi5OycnJCgwM1EMPPSTpt8fqihYtqkaNGhmugyNycnJSmzZtVKhQIUnSl19+qWbNmuUaoH3++ecm8vK9c+fOqXv37vLx8dF//vMflSxZUps2bVKtWrVUrlw57dixQ+XKldOmTZs0ZMgQzkYE7kBCQoJq164tSdq3b58mTJiguLg4ZWVlKSAgQG+//TY/tAKAfIrhGQAAyKVXr1539L65c+c+4BL8mbFjx+qdd95RsWLFFBcXJ29vb9WqVUuzZ89WvXr1lJSUJD8/P6Wnp5tOBWyek5OT/P391adPH3Xt2jXHo9EAgPyN4RkAAICde/rppzVo0CB17NhRffv21fnz5zV06FCFhYVp9+7d2r9/v+lEwObFxMQoPDxcERERun79ujp37qzg4GA1bdrUdBoAwDCGZwAAAHZu/fr1unz5srp06aITJ06obdu22r9/v4oXL66IiAg1a9bMdCJgN65evaqIiAjNnTtX27ZtU+XKlRUcHKyePXuqfPnypvMAAAYwPAMAAHBAFy9eVNGiReXk5GQ6BbBbSUlJmjt3rhYsWKDTp08rMDBQ69atM50FAMhjDM8AAAAA4DauXLmizz77TP/+97+VkpKizMxM00kAgDxWwHQAAOCvbdu2TZ988omSkpK0YsUKlStXTgsXLpSXl5eeeuop03kADMvIyNC0adO0ZcsWnTt3TllZWTle3717t6EywH5FRUUpPDxcK1eulLOzs4KCgtS7d2/TWQAAAxieAYCNW7lypbp3765u3bopPj5ev/76qyTp8uXLGjNmDI+PAFBwcLA2bNigLl266IknnpDFYjGdBNilEydOaN68eZo3b56OHz+uhg0batq0aQoKClLhwoVN5wEADOGxTQCwcf7+/ho4cKB69OghDw8P7dmzR97e3kpISFDr1q115swZ04kADPP09NS6devUqFEj0ymA3QoMDNSWLVtUqlQp9ejRQ8HBwapWrZrpLACADWDnGQDYuMOHD6tx48a51osUKaKUlJS8DwJgc8qVKycPDw/TGYBdc3Nz08qVK/Xcc8/J2dnZdA4AwIZw/RIA2LgyZcro6NGjudajo6Pl7e1toAiArZk4caLefvtt/fjjj6ZTALu1Zs0atW/fnsEZACAXdp4BgI179dVXNWDAAIWHh8tisejUqVOKiYnRkCFDNGLECNN5AGxA3bp1lZGRIW9vb7m7u8vFxSXH6xcvXjRUBgAAYP8YngGAjXvrrbeUmpqqpk2bKiMjQ40bN1ahQoU0ZMgQ9evXz3QeABvw0ksv6eTJkxozZowefvhhLgwAAAC4j7gwAADsRHp6ug4ePKisrCz5+vrqoYceMp0EwEa4u7srJiZGtWrVMp0CAADgcNh5BgB2wt3dXXXr1jWdAcAGVa9eXVevXjWdAQAA4JDYeQYANq5p06Z/+gjW5s2b87AGgC2KjIzUqFGjNHr0aPn5+eU686xIkSKGygAAAOwfO88AwMbVrl07x/fXr19XQkKC9u/fr549e5qJAmBTWrduLUlq3rx5jnWr1SqLxaLMzEwTWQAAAA6B4RkA2LjJkyffcv3999/XlStX8rgGgC3asmWL6QQAAACHxWObAGCnjh49qieeeEIXL140nQI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", "text/plain": [ "
" ] @@ -21407,7 +23095,7 @@ "

Tabla de correlaciones con filtro de umbral de correlación

\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -21434,6 +23122,47 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21465,6 +23194,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21478,7 +23213,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -21494,6 +23229,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21523,6 +23264,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21552,6 +23299,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21581,6 +23334,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21610,6 +23369,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21639,6 +23404,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21668,6 +23439,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21697,6 +23474,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21726,11 +23509,17 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -21740,11 +23529,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21768,7 +23563,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -21784,6 +23579,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21813,6 +23614,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21842,6 +23649,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21871,6 +23684,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21900,6 +23719,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21913,7 +23738,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -21929,6 +23754,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21958,6 +23789,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21987,6 +23824,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -22016,6 +23859,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -22045,6 +23894,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -22074,6 +23929,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -22103,6 +23964,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -22132,6 +23999,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -22161,6 +24034,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -22190,6 +24069,222 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Capacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Modelo
nannannan0.735nannannannannan
Altitud a la que se realiza el cruceronannannan-0.952-0.955nannannannannannannannannannannannan
Velocidad a la que se realiza el crucero (KTAS)nannannannannannannannannan
Techo de servicio máximonannannannannannannannannan
Velocidad de pérdida limpia (KCAS)nannannannannannannannannan
Área del ala1.000nan0.899nannannannannannan
Relación de aspecto del alanannannannannannannannannan
Longitud del fuselaje0.929nannannannannannannannan
Ancho del fuselajenannannan0.794nannannannannan
Peso máximo al despegue (MTOW)0.9760.758nannannannannannannan
Alcance de la aeronavenan-0.952-0.955nannannan0.982nannannan0.843nannannan-0.7550.804nannannannannannannannannannannannan0.843nannannannan-0.732nannannannannannannan
Velocidad máxima (KIAS)0.7050.910nannannannannannannan
Velocidad de pérdida (KCAS)nannannannannannannannannan
envergaduranannannannannannannannannan
Cuerdanannannannannannannannannan
payloadnan0.8680.875nan0.804nan0.715nan0.7110.846nannannannannannannan
duracion en VTOLnannannannan-0.904nannannannan
Crucero KIASnannannannannannannannannan
RTF (Including fuel & Batteries)nannannannannannannannannan
Empty weight0.995nannannannannannannannan
Maximum Crosswindnannannannan-0.943nannannannan
Rango de comunicaciónnannannannannannannannannan
Capacidad combustiblenannan0.817nannannannannannan
Consumonannan0.998nannannannannannan
Precio0.8170.998nannannannannannannan
Despegue0.735nannannannannannannan0.794nannannannannannannannannannannannannannannannannannannannannannannan
Propulsión horizontalnannannannannannannannannannannannannannannannannan-0.904nannannan-0.943nannannannannannannannannannan
Propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannan
Cantidad de motores propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannannan
Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
" @@ -22210,10 +24305,10 @@ "\n", "=== Imputación para el parámetro: **Velocidad a la que se realiza el crucero (KTAS)** ===\n", "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing']\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5]\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344]\n", "Ecuación de regresión: y = -3.588x + 72.195\n", @@ -22223,7 +24318,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: RTF (Including fuel & Batteries) (r = 0.723) ---\n", - "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [27.344, 27.344, 27.344, 18.091, 21.875, 21.875, 27.344]\n", "Ecuación de regresión: y = 0.104x + 21.164\n", @@ -22237,7 +24332,7 @@ "--- Imputación para aeronave: **AAI Aerosonde** ---\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS']\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5]\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892]\n", "Ecuación de regresión: y = -3.686x + 73.696\n", @@ -22247,7 +24342,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 30.407, 18.266, 30.625, 30.953, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", "Ecuación de regresión: y = 0.607x + 4.971\n", @@ -22257,7 +24352,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing']\n", "Valores para Cuerda: [0.239, 0.318, 0.352]\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344]\n", "Ecuación de regresión: y = 77.731x + -2.949\n", @@ -22271,7 +24366,7 @@ "--- Imputación para aeronave: **Orbiter 4** ---\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 18.266, 30.625, 30.953, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", "Ecuación de regresión: y = 0.625x + 4.123\n", @@ -22285,7 +24380,7 @@ "--- Imputación para aeronave: **Orbiter 3** ---\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", "Ecuación de regresión: y = 0.625x + 4.123\n", @@ -22313,7 +24408,7 @@ "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", "Ecuación de regresión: y = 0.625x + 4.123\n", @@ -22392,7 +24487,7 @@ "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", "\n", "--- Correlación: Ancho del fuselaje (r = 0.936) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 36.094, 30.625]\n", "Ecuación de regresión: y = 93.547x + -1.195\n", @@ -22402,7 +24497,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", "Ecuación de regresión: y = 0.625x + 4.123\n", @@ -22412,7 +24507,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Crucero KIAS (r = 1.0) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 33.0, 24.0, 30.0]\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 30.407, 18.266, 30.625, 30.953, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 36.094, 26.25, 32.813]\n", "Ecuación de regresión: y = 1.094x + 0.0\n", @@ -23091,10 +25186,10 @@ "\n", "=== Imputación para el parámetro: **Área del ala** ===\n", "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57]\n", "Ecuación de regresión: y = -0.219x + 4.203\n", @@ -23104,7 +25199,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.022x + 0.512\n", @@ -23114,7 +25209,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.084x + 0.605\n", @@ -23128,7 +25223,7 @@ "--- Imputación para aeronave: **Fulmar X** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.546x + 0.007\n", @@ -23138,7 +25233,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.022x + 0.514\n", @@ -23148,7 +25243,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.082x + -1.305\n", @@ -23158,7 +25253,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.46x + -0.446\n", @@ -23172,7 +25267,7 @@ "--- Imputación para aeronave: **Orbiter 4** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.521x + 0.078\n", @@ -23182,7 +25277,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.022x + 0.513\n", @@ -23192,7 +25287,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.057x + -0.607\n", @@ -23202,7 +25297,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.46x + -0.445\n", @@ -23212,7 +25307,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.084x + 0.605\n", @@ -23226,7 +25321,7 @@ "--- Imputación para aeronave: **Orbiter 3** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.45x + 0.279\n", @@ -23236,7 +25331,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.512\n", @@ -23246,7 +25341,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.058x + -0.629\n", @@ -23256,7 +25351,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.417x + -0.317\n", @@ -23266,7 +25361,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.084x + 0.605\n", @@ -23280,7 +25375,7 @@ "--- Imputación para aeronave: **Mantis** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.423x + 0.354\n", @@ -23290,7 +25385,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.512\n", @@ -23300,7 +25395,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.057x + -0.594\n", @@ -23310,7 +25405,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.398x + -0.27\n", @@ -23324,7 +25419,7 @@ "--- Imputación para aeronave: **ScanEagle** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.432x + 0.325\n", @@ -23334,7 +25429,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.524\n", @@ -23344,7 +25439,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.058x + -0.649\n", @@ -23354,7 +25449,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.381x + -0.202\n", @@ -23364,7 +25459,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.083x + 0.619\n", @@ -23378,7 +25473,7 @@ "--- Imputación para aeronave: **Integrator** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.432x + 0.325\n", @@ -23388,7 +25483,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.522\n", @@ -23398,7 +25493,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.049x + -0.376\n", @@ -23408,7 +25503,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.38x + -0.191\n", @@ -23418,7 +25513,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.083x + 0.622\n", @@ -23432,7 +25527,7 @@ "--- Imputación para aeronave: **Integrator VTOL** ---\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.53\n", @@ -23442,7 +25537,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.081x + 0.628\n", @@ -23456,7 +25551,7 @@ "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.46x + 0.297\n", @@ -23466,7 +25561,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.53\n", @@ -23476,7 +25571,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.049x + -0.376\n", @@ -23486,7 +25581,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.397x + -0.238\n", @@ -23496,7 +25591,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.081x + 0.628\n", @@ -23510,7 +25605,7 @@ "--- Imputación para aeronave: **ScanEagle 3** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.46x + 0.297\n", @@ -23520,7 +25615,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.53\n", @@ -23530,7 +25625,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.049x + -0.376\n", @@ -23540,7 +25635,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.397x + -0.238\n", @@ -23550,7 +25645,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.081x + 0.628\n", @@ -23561,10 +25656,10 @@ "Valores imputados: ['Longitud del fuselaje: 1.401', 'Peso máximo al despegue (MTOW): 1.286', 'Velocidad máxima (KIAS): 1.625', 'envergadura: 1.349', 'payload: 1.326']\n", "**Mediana calculada:** 1.349\n", "\n", - "--- Imputación para aeronave: **RQNan21A Blackjack** ---\n", + "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.458x + 0.299\n", @@ -23574,7 +25669,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.533\n", @@ -23584,7 +25679,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.046x + -0.31\n", @@ -23594,7 +25689,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.397x + -0.238\n", @@ -23604,7 +25699,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.081x + 0.63\n", @@ -23618,7 +25713,7 @@ "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.476x + 0.281\n", @@ -23628,7 +25723,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.533\n", @@ -23638,7 +25733,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.405x + -0.261\n", @@ -23648,7 +25743,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.08x + 0.635\n", @@ -23662,7 +25757,7 @@ "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.477x + 0.279\n", @@ -23672,7 +25767,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.537\n", @@ -23682,7 +25777,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.405x + -0.259\n", @@ -23692,7 +25787,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.08x + 0.631\n", @@ -23706,7 +25801,7 @@ "--- Imputación para aeronave: **V32** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.477x + 0.279\n", @@ -23716,7 +25811,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.537\n", @@ -23726,7 +25821,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.046x + -0.3\n", @@ -23736,7 +25831,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.405x + -0.259\n", @@ -23746,7 +25841,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.08x + 0.631\n", @@ -23780,7 +25875,7 @@ "--- Imputación para aeronave: **V35** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.459x + 0.325\n", @@ -23790,7 +25885,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.538\n", @@ -23800,7 +25895,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.046x + -0.324\n", @@ -23810,7 +25905,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.405x + -0.26\n", @@ -23820,7 +25915,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.08x + 0.631\n", @@ -23844,7 +25939,7 @@ "--- Imputación para aeronave: **V39** ---\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.538\n", @@ -23854,7 +25949,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.046x + -0.324\n", @@ -23864,7 +25959,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.405x + -0.257\n", @@ -23874,7 +25969,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.08x + 0.622\n", @@ -23898,7 +25993,7 @@ "--- Imputación para aeronave: **Volitation VT370** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.459x + 0.326\n", @@ -23908,7 +26003,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.548\n", @@ -23918,7 +26013,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.046x + -0.324\n", @@ -23928,7 +26023,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.403x + -0.255\n", @@ -23938,7 +26033,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.079x + 0.636\n", @@ -23972,7 +26067,7 @@ "--- Imputación para aeronave: **Volitation VT510** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.461x + 0.329\n", @@ -23982,7 +26077,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.021x + 0.549\n", @@ -23992,7 +26087,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.046x + -0.299\n", @@ -24002,7 +26097,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615]\n", "Ecuación de regresión: y = 0.313x + 0.036\n", @@ -24012,7 +26107,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", "Ecuación de regresión: y = 0.076x + 0.65\n", @@ -24046,7 +26141,7 @@ "--- Imputación para aeronave: **Ascend** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993]\n", "Ecuación de regresión: y = 0.485x + 0.296\n", @@ -24056,7 +26151,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993]\n", "Ecuación de regresión: y = 0.019x + 0.589\n", @@ -24066,7 +26161,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993]\n", "Ecuación de regresión: y = 0.046x + -0.299\n", @@ -24076,7 +26171,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993]\n", "Ecuación de regresión: y = 0.329x + -0.009\n", @@ -24086,7 +26181,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993]\n", "Ecuación de regresión: y = 0.071x + 0.685\n", @@ -24096,7 +26191,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: RTF (Including fuel & Batteries) (r = 0.923) ---\n", - "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo']\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo']\n", "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 6.8]\n", "Valores para Área del ala: [1.55, 1.55, 1.55, 2.503, 0.84]\n", "Ecuación de regresión: y = 0.025x + 0.682\n", @@ -24120,7 +26215,7 @@ "--- Imputación para aeronave: **Transition** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771]\n", "Ecuación de regresión: y = 0.492x + 0.273\n", @@ -24130,7 +26225,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771]\n", "Ecuación de regresión: y = 0.019x + 0.589\n", @@ -24140,7 +26235,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771]\n", "Ecuación de regresión: y = 0.047x + -0.36\n", @@ -24150,7 +26245,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771]\n", "Ecuación de regresión: y = 0.323x + 0.017\n", @@ -24160,7 +26255,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771]\n", "Ecuación de regresión: y = 0.07x + 0.689\n", @@ -24170,7 +26265,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: RTF (Including fuel & Batteries) (r = 0.923) ---\n", - "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend']\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend']\n", "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 6.8, 8.9]\n", "Valores para Área del ala: [1.55, 1.55, 1.55, 2.503, 0.84, 0.771]\n", "Ecuación de regresión: y = 0.026x + 0.624\n", @@ -24194,7 +26289,7 @@ "--- Imputación para aeronave: **Reach** ---\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986]\n", "Ecuación de regresión: y = 0.48x + 0.28\n", @@ -24204,7 +26299,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986]\n", "Ecuación de regresión: y = 0.019x + 0.592\n", @@ -24214,7 +26309,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986]\n", "Ecuación de regresión: y = 0.047x + -0.373\n", @@ -24224,7 +26319,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0, 3.0]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986]\n", "Ecuación de regresión: y = 0.323x + 0.017\n", @@ -24234,7 +26329,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5]\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986]\n", "Ecuación de regresión: y = 0.069x + 0.705\n", @@ -24244,7 +26339,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: RTF (Including fuel & Batteries) (r = 0.923) ---\n", - "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend', 'Transition']\n", "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 6.8, 8.9, 16.5]\n", "Valores para Área del ala: [1.55, 1.55, 1.55, 2.503, 0.84, 0.771, 0.986]\n", "Ecuación de regresión: y = 0.026x + 0.605\n", @@ -24270,7 +26365,7 @@ "--- Imputación para aeronave: **Fulmar X** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754]\n", "Ecuación de regresión: y = -0.27x + 19.967\n", @@ -24280,7 +26375,7 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754]\n", "Ecuación de regresión: y = -1.556x + 16.146\n", @@ -24290,7 +26385,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", "Ecuación de regresión: y = -1.519x + 18.038\n", @@ -24300,7 +26395,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754]\n", "Ecuación de regresión: y = -0.04x + 15.296\n", @@ -24310,7 +26405,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", "Ecuación de regresión: y = -0.176x + 19.272\n", @@ -24320,7 +26415,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5]\n", "Ecuación de regresión: y = -0.303x + 20.091\n", @@ -24334,7 +26429,7 @@ "--- Imputación para aeronave: **Orbiter 4** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218]\n", "Ecuación de regresión: y = -0.211x + 18.836\n", @@ -24344,7 +26439,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218]\n", "Ecuación de regresión: y = -1.355x + 15.647\n", @@ -24354,7 +26449,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218]\n", "Ecuación de regresión: y = -0.157x + 14.552\n", @@ -24364,7 +26459,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218]\n", "Ecuación de regresión: y = -0.036x + 14.967\n", @@ -24374,7 +26469,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218]\n", "Ecuación de regresión: y = -0.122x + 17.914\n", @@ -24384,7 +26479,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5]\n", "Ecuación de regresión: y = -0.158x + 15.309\n", @@ -24398,7 +26493,7 @@ "--- Imputación para aeronave: **Orbiter 3** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443]\n", "Ecuación de regresión: y = -0.208x + 18.787\n", @@ -24408,7 +26503,7 @@ "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443]\n", "Ecuación de regresión: y = -1.358x + 15.647\n", @@ -24418,7 +26513,7 @@ "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443]\n", "Ecuación de regresión: y = 0.102x + 13.89\n", @@ -24428,7 +26523,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443]\n", "Ecuación de regresión: y = -0.035x + 14.97\n", @@ -24437,18 +26532,8 @@ "\tR²: 0.5601880164889009, Desviación Estándar: 0.7722572050409231, Varianza: 0.5963811907376184, Incertidumbre: 0.25741906834697437\n", "\tNivel de confianza: Confianza Baja\n", "\n", - "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443]\n", - "Ecuación de regresión: y = 0.0x + 14.293\n", - "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 14.299\n", - "\tR²: 0.021248458324993225, Desviación Estándar: 0.896822690408026, Varianza: 0.8042909380306901, Incertidumbre: 0.4010712999033189\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443]\n", "Ecuación de regresión: y = -0.123x + 17.936\n", @@ -24458,7 +26543,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443]\n", "Ecuación de regresión: y = -0.158x + 15.313\n", @@ -24466,73 +26551,73 @@ "Predicción obtenida: 14.446\n", "\tR²: 0.787811738102038, Desviación Estándar: 0.565704012619775, Varianza: 0.3200210298941146, Incertidumbre: 0.21381601900903846\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.263', 'Área del ala: 14.018', 'Longitud del fuselaje: 14.012', 'Peso máximo al despegue (MTOW): 13.857', 'Alcance de la aeronave: 14.299', 'Velocidad máxima (KIAS): 13.495', 'payload: 14.446']\n", - "**Mediana calculada:** 14.012\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.263', 'Área del ala: 14.018', 'Longitud del fuselaje: 14.012', 'Peso máximo al despegue (MTOW): 13.857', 'Velocidad máxima (KIAS): 13.495', 'payload: 14.446']\n", + "**Mediana calculada:** 13.934\n", "\n", "--- Imputación para aeronave: **Mantis** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012]\n", - "Ecuación de regresión: y = -0.198x + 18.646\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934]\n", + "Ecuación de regresión: y = -0.199x + 18.661\n", "Valor del parámetro correlacionado para la aeronave: 18.266\n", - "Predicción obtenida: 15.033\n", - "\tR²: 0.8162307529020697, Desviación Estándar: 0.463001126334729, Varianza: 0.2143700429872277, Incertidumbre: 0.16369561806414812\n", + "Predicción obtenida: 15.029\n", + "\tR²: 0.8259424603654436, Desviación Estándar: 0.45023923501123037, Varianza: 0.2027153687434979, Incertidumbre: 0.1591836081163423\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012]\n", - "Ecuación de regresión: y = -1.357x + 15.646\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934]\n", + "Ecuación de regresión: y = -1.354x + 15.633\n", "Valor del parámetro correlacionado para la aeronave: 0.754\n", - "Predicción obtenida: 14.623\n", - "\tR²: 0.4900269781864812, Desviación Estándar: 0.7712927169798122, Varianza: 0.5948924552661006, Incertidumbre: 0.2726931552281109\n", + "Predicción obtenida: 14.611\n", + "\tR²: 0.48854653028077, Desviación Estándar: 0.7717914841458512, Varianza: 0.5956620950000557, Incertidumbre: 0.27286949605078054\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012]\n", - "Ecuación de regresión: y = 0.102x + 13.89\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934]\n", + "Ecuación de regresión: y = 0.118x + 13.849\n", "Valor del parámetro correlacionado para la aeronave: 1.48\n", - "Predicción obtenida: 14.041\n", - "\tR²: 0.0046993987852630426, Desviación Estándar: 1.008260850059533, Varianza: 1.0165899417627717, Incertidumbre: 0.3810867808485868\n", + "Predicción obtenida: 14.024\n", + "\tR²: 0.006260092700906439, Desviación Estándar: 1.0085765160538325, Varianza: 1.0172265887352867, Incertidumbre: 0.3812060913797692\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012]\n", - "Ecuación de regresión: y = -0.035x + 14.994\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934]\n", + "Ecuación de regresión: y = -0.035x + 14.982\n", "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 14.767\n", - "\tR²: 0.5649822868921843, Desviación Estándar: 0.7340836887623863, Varianza: 0.538878862106992, Incertidumbre: 0.23213764496672915\n", + "Predicción obtenida: 14.755\n", + "\tR²: 0.5642432263469788, Desviación Estándar: 0.732986855728399, Varianza: 0.5372697306706048, Incertidumbre: 0.23179079590669788\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012]\n", - "Ecuación de regresión: y = 0.0x + 14.219\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934]\n", + "Ecuación de regresión: y = 0.0x + 14.449\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 14.223\n", - "\tR²: 0.03697239299019073, Desviación Estándar: 0.8249333692250005, Varianza: 0.680515063660911, Incertidumbre: 0.3367776377327012\n", + "Predicción obtenida: 14.451\n", + "\tR²: 0.00695796488843492, Desviación Estándar: 0.8160949284662801, Varianza: 0.6660109322683828, Incertidumbre: 0.36496874722868605\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012]\n", - "Ecuación de regresión: y = -0.117x + 17.798\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934]\n", + "Ecuación de regresión: y = -0.118x + 17.819\n", "Valor del parámetro correlacionado para la aeronave: 25.6\n", - "Predicción obtenida: 14.81\n", - "\tR²: 0.6133897598737705, Desviación Estándar: 0.6283946045669089, Varianza: 0.3948797790488017, Incertidumbre: 0.23751083555697344\n", + "Predicción obtenida: 14.805\n", + "\tR²: 0.6226111062384435, Desviación Estándar: 0.6215370810067415, Varianza: 0.38630834306638073, Incertidumbre: 0.23491893527840643\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218]\n", "Ecuación de regresión: y = -0.214x + 18.598\n", @@ -24540,1389 +26625,1379 @@ "Predicción obtenida: 15.024\n", "\tR²: 0.8546770471425846, Desviación Estándar: 0.4778243263623709, Varianza: 0.2283160868636535, Incertidumbre: 0.23891216318118544\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 15.033', 'Área del ala: 14.623', 'Longitud del fuselaje: 14.041', 'Peso máximo al despegue (MTOW): 14.767', 'Alcance de la aeronave: 14.223', 'Velocidad máxima (KIAS): 14.81', 'Crucero KIAS: 15.024']\n", - "**Mediana calculada:** 14.767\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 15.029', 'Área del ala: 14.611', 'Longitud del fuselaje: 14.024', 'Peso máximo al despegue (MTOW): 14.755', 'Alcance de la aeronave: 14.451', 'Velocidad máxima (KIAS): 14.805', 'Crucero KIAS: 15.024']\n", + "**Mediana calculada:** 14.755\n", "\n", "--- Imputación para aeronave: **ScanEagle** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767]\n", - "Ecuación de regresión: y = -0.192x + 18.474\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755]\n", + "Ecuación de regresión: y = -0.193x + 18.483\n", "Valor del parámetro correlacionado para la aeronave: 30.625\n", - "Predicción obtenida: 12.602\n", - "\tR²: 0.8235797866498076, Desviación Estándar: 0.4433751181910776, Varianza: 0.19658149543095202, Incertidumbre: 0.14779170606369255\n", + "Predicción obtenida: 12.586\n", + "\tR²: 0.8322301664609455, Desviación Estándar: 0.43196932859791437, Varianza: 0.18659750084933294, Incertidumbre: 0.14398977619930478\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767]\n", - "Ecuación de regresión: y = -1.383x + 15.694\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755]\n", + "Ecuación de regresión: y = -1.38x + 15.68\n", "Valor del parámetro correlacionado para la aeronave: 1.063\n", - "Predicción obtenida: 14.224\n", - "\tR²: 0.5237760149013296, Desviación Estándar: 0.7284550833069877, Varianza: 0.5306468083957905, Incertidumbre: 0.24281836110232924\n", + "Predicción obtenida: 14.213\n", + "\tR²: 0.5222926726573038, Desviación Estándar: 0.7289150646703415, Varianza: 0.5313171715033681, Incertidumbre: 0.24297168822344717\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767]\n", - "Ecuación de regresión: y = 0.031x + 14.11\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755]\n", + "Ecuación de regresión: y = 0.046x + 14.071\n", "Valor del parámetro correlacionado para la aeronave: 1.71\n", - "Predicción obtenida: 14.162\n", - "\tR²: 0.00041867860015110114, Desviación Estándar: 0.9721354662815678, Varianza: 0.9450473648024812, Incertidumbre: 0.34370179021982145\n", + "Predicción obtenida: 14.149\n", + "\tR²: 0.0009397134116212458, Desviación Estándar: 0.9728249313313656, Varianza: 0.9463883470198762, Incertidumbre: 0.34394555292587303\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767]\n", - "Ecuación de regresión: y = -0.035x + 14.994\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755]\n", + "Ecuación de regresión: y = -0.035x + 14.982\n", "Valor del parámetro correlacionado para la aeronave: 26.5\n", - "Predicción obtenida: 14.067\n", - "\tR²: 0.6041711076219263, Desviación Estándar: 0.6999213418164504, Varianza: 0.48988988473014045, Incertidumbre: 0.21103422486975046\n", + "Predicción obtenida: 14.057\n", + "\tR²: 0.6034073830513715, Desviación Estándar: 0.698875551331174, Varianza: 0.48842703624845235, Incertidumbre: 0.21071890717438813\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767]\n", - "Ecuación de regresión: y = -0.116x + 17.772\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755]\n", + "Ecuación de regresión: y = -0.117x + 17.789\n", "Valor del parámetro correlacionado para la aeronave: 41.2\n", - "Predicción obtenida: 12.99\n", - "\tR²: 0.6343488080766746, Desviación Estándar: 0.5879646533380922, Varianza: 0.3457024335749829, Incertidumbre: 0.2078768967366813\n", + "Predicción obtenida: 12.97\n", + "\tR²: 0.6429053256386628, Desviación Estándar: 0.5816082132170911, Varianza: 0.3382681136815773, Incertidumbre: 0.20562955577979825\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012]\n", - "Ecuación de regresión: y = -0.151x + 15.186\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934]\n", + "Ecuación de regresión: y = -0.15x + 15.164\n", "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 14.43\n", - "\tR²: 0.779730301455002, Desviación Estándar: 0.5464452282253737, Varianza: 0.29860238745028067, Incertidumbre: 0.19319756321259615\n", + "Predicción obtenida: 14.414\n", + "\tR²: 0.7728271432710303, Desviación Estándar: 0.5530726265073179, Varianza: 0.30588933019170317, Incertidumbre: 0.19554070234598958\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767]\n", - "Ecuación de regresión: y = -0.207x + 18.409\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755]\n", + "Ecuación de regresión: y = -0.207x + 18.401\n", "Valor del parámetro correlacionado para la aeronave: 28.0\n", "Predicción obtenida: 12.607\n", - "\tR²: 0.8557406648350283, Desviación Estándar: 0.43818596791937064, Varianza: 0.19200694248143574, Incertidumbre: 0.19596272221085095\n", + "\tR²: 0.8547867905694756, Desviación Estándar: 0.43920425690338755, Varianza: 0.19290037928205683, Incertidumbre: 0.19641811488865116\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.602', 'Área del ala: 14.224', 'Longitud del fuselaje: 14.162', 'Peso máximo al despegue (MTOW): 14.067', 'Velocidad máxima (KIAS): 12.99', 'payload: 14.43', 'Crucero KIAS: 12.607']\n", - "**Mediana calculada:** 14.067\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.586', 'Área del ala: 14.213', 'Longitud del fuselaje: 14.149', 'Peso máximo al despegue (MTOW): 14.057', 'Velocidad máxima (KIAS): 12.97', 'payload: 14.414', 'Crucero KIAS: 12.607']\n", + "**Mediana calculada:** 14.057\n", "\n", "--- Imputación para aeronave: **Integrator** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067]\n", - "Ecuación de regresión: y = -0.157x + 17.776\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057]\n", + "Ecuación de regresión: y = -0.157x + 17.782\n", "Valor del parámetro correlacionado para la aeronave: 30.953\n", - "Predicción obtenida: 12.923\n", - "\tR²: 0.6641740677951478, Desviación Estándar: 0.5805266763901159, Varianza: 0.3370112220005544, Incertidumbre: 0.18357865398802614\n", + "Predicción obtenida: 12.908\n", + "\tR²: 0.6712253814763909, Desviación Estándar: 0.5738697173551367, Varianza: 0.32932645249726444, Incertidumbre: 0.18147353870392907\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067]\n", - "Ecuación de regresión: y = -1.374x + 15.667\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057]\n", + "Ecuación de regresión: y = -1.371x + 15.653\n", "Valor del parámetro correlacionado para la aeronave: 1.872\n", - "Predicción obtenida: 13.095\n", - "\tR²: 0.5219103690121566, Desviación Estándar: 0.6926593947898028, Varianza: 0.47977703719057585, Incertidumbre: 0.2190381330249543\n", + "Predicción obtenida: 13.087\n", + "\tR²: 0.5204404709133397, Desviación Estándar: 0.6930832915381803, Varianza: 0.4803644490093982, Incertidumbre: 0.21917218094671553\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067]\n", - "Ecuación de regresión: y = 0.033x + 14.095\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057]\n", + "Ecuación de regresión: y = 0.049x + 14.056\n", "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 14.178\n", - "\tR²: 0.000490016744001398, Desviación Estándar: 0.9170252395244427, Varianza: 0.8409352899248614, Incertidumbre: 0.3056750798414809\n", + "Predicción obtenida: 14.177\n", + "\tR²: 0.001042484555474843, Desviación Estándar: 0.917647035048045, Varianza: 0.8420760809324679, Incertidumbre: 0.305882345016015\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067]\n", - "Ecuación de regresión: y = -0.035x + 14.994\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057]\n", + "Ecuación de regresión: y = -0.035x + 14.982\n", "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 12.376\n", - "\tR²: 0.6075184827366995, Desviación Estándar: 0.6701236710904337, Varianza: 0.4490657345557197, Incertidumbre: 0.19344804094720108\n", + "Predicción obtenida: 12.372\n", + "\tR²: 0.6067435478664236, Desviación Estándar: 0.669122407636399, Varianza: 0.4477247964011312, Incertidumbre: 0.19315900108484274\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067]\n", - "Ecuación de regresión: y = -0.094x + 17.194\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057]\n", + "Ecuación de regresión: y = -0.095x + 17.205\n", "Valor del parámetro correlacionado para la aeronave: 46.3\n", - "Predicción obtenida: 12.823\n", - "\tR²: 0.526405227591237, Desviación Estándar: 0.6312351449660243, Varianza: 0.39845780824027766, Incertidumbre: 0.21041171498867475\n", + "Predicción obtenida: 12.803\n", + "\tR²: 0.5330694834785845, Desviación Estándar: 0.6273765557788624, Varianza: 0.39360134274094805, Incertidumbre: 0.20912551859295414\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067]\n", - "Ecuación de regresión: y = -0.146x + 15.095\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057]\n", + "Ecuación de regresión: y = -0.145x + 15.074\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 12.462\n", - "\tR²: 0.7754905637643377, Desviación Estándar: 0.5266918070184679, Varianza: 0.27740425958037895, Incertidumbre: 0.1755639356728226\n", + "Predicción obtenida: 12.46\n", + "\tR²: 0.7690943364673855, Desviación Estándar: 0.53238130110139, Varianza: 0.28342984976240887, Incertidumbre: 0.17746043370046335\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067]\n", - "Ecuación de regresión: y = -0.155x + 17.54\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057]\n", + "Ecuación de regresión: y = -0.156x + 17.537\n", "Valor del parámetro correlacionado para la aeronave: 28.3\n", - "Predicción obtenida: 13.14\n", - "\tR²: 0.663687056459504, Desviación Estándar: 0.6116803288548116, Varianza: 0.3741528247079305, Incertidumbre: 0.24971744856535041\n", + "Predicción obtenida: 13.136\n", + "\tR²: 0.6651166916698918, Desviación Estándar: 0.6098787538174729, Varianza: 0.3719520943579537, Incertidumbre: 0.2489819586362145\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.923', 'Área del ala: 13.095', 'Longitud del fuselaje: 14.178', 'Peso máximo al despegue (MTOW): 12.376', 'Velocidad máxima (KIAS): 12.823', 'payload: 12.462', 'Crucero KIAS: 13.14']\n", - "**Mediana calculada:** 12.923\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.908', 'Área del ala: 13.087', 'Longitud del fuselaje: 14.177', 'Peso máximo al despegue (MTOW): 12.372', 'Velocidad máxima (KIAS): 12.803', 'payload: 12.46', 'Crucero KIAS: 13.136']\n", + "**Mediana calculada:** 12.908\n", "\n", "--- Imputación para aeronave: **Integrator VTOL** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923]\n", - "Ecuación de regresión: y = -0.157x + 17.776\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908]\n", + "Ecuación de regresión: y = -0.157x + 17.782\n", "Valor del parámetro correlacionado para la aeronave: 21.463\n", - "Predicción obtenida: 14.411\n", - "\tR²: 0.6955131309495535, Desviación Estándar: 0.5535104680562337, Varianza: 0.3063738382478309, Incertidumbre: 0.16688968546150232\n", + "Predicción obtenida: 14.403\n", + "\tR²: 0.7022195601444223, Desviación Estándar: 0.5471633158350149, Varianza: 0.2993876941955682, Incertidumbre: 0.16497594706104288\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923]\n", - "Ecuación de regresión: y = -1.406x + 15.693\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908]\n", + "Ecuación de regresión: y = -1.404x + 15.68\n", "Valor del parámetro correlacionado para la aeronave: 2.09\n", - "Predicción obtenida: 12.754\n", - "\tR²: 0.5643813137066285, Desviación Estándar: 0.6620561206988881, Varianza: 0.4383183069548606, Incertidumbre: 0.1996174311378599\n", + "Predicción obtenida: 12.745\n", + "\tR²: 0.5633237239923707, Desviación Estándar: 0.6625958144708088, Varianza: 0.43903321335423456, Incertidumbre: 0.199780154932692\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923]\n", - "Ecuación de regresión: y = -0.033x + 14.943\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908]\n", + "Ecuación de regresión: y = -0.033x + 14.932\n", "Valor del parámetro correlacionado para la aeronave: 75.0\n", - "Predicción obtenida: 12.498\n", - "\tR²: 0.608454130768421, Desviación Estándar: 0.6573629374382771, Varianza: 0.4321260315174802, Incertidumbre: 0.18231967519410266\n", + "Predicción obtenida: 12.491\n", + "\tR²: 0.6086276801287058, Desviación Estándar: 0.6558904966751431, Varianza: 0.4301923436287659, Incertidumbre: 0.18191129360398245\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923]\n", - "Ecuación de regresión: y = -0.139x + 15.064\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908]\n", + "Ecuación de regresión: y = -0.138x + 15.044\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 12.555\n", - "\tR²: 0.7681447903007532, Desviación Estándar: 0.516359328939539, Varianza: 0.266626956582891, Incertidumbre: 0.16328715705250396\n", + "Predicción obtenida: 12.551\n", + "\tR²: 0.7628945280960475, Desviación Estándar: 0.5206483180033946, Varianza: 0.2710746710397639, Incertidumbre: 0.1646434544826377\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.411', 'Área del ala: 12.754', 'Peso máximo al despegue (MTOW): 12.498', 'payload: 12.555']\n", - "**Mediana calculada:** 12.654\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.403', 'Área del ala: 12.745', 'Peso máximo al despegue (MTOW): 12.491', 'payload: 12.551']\n", + "**Mediana calculada:** 12.648\n", "\n", "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654]\n", - "Ecuación de regresión: y = -0.14x + 17.225\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648]\n", + "Ecuación de regresión: y = -0.141x + 17.233\n", "Valor del parámetro correlacionado para la aeronave: 33.045\n", - "Predicción obtenida: 12.59\n", - "\tR²: 0.510023026717956, Desviación Estándar: 0.7136489244448807, Varianza: 0.509294787361335, Incertidumbre: 0.2060126993175694\n", + "Predicción obtenida: 12.574\n", + "\tR²: 0.5159927919290995, Desviación Estándar: 0.7087508493711017, Varianza: 0.502327766484258, Incertidumbre: 0.20459874683639073\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654]\n", - "Ecuación de regresión: y = -1.426x + 15.711\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648]\n", + "Ecuación de regresión: y = -1.423x + 15.698\n", "Valor del parámetro correlacionado para la aeronave: 1.872\n", - "Predicción obtenida: 13.042\n", - "\tR²: 0.6128293293992375, Desviación Estándar: 0.6343778425999941, Varianza: 0.40243524718182283, Incertidumbre: 0.18312910909652033\n", + "Predicción obtenida: 13.033\n", + "\tR²: 0.6116464984342147, Desviación Estándar: 0.6348648613448064, Varianza: 0.4030533921703601, Incertidumbre: 0.1832696992982292\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923]\n", - "Ecuación de regresión: y = -0.175x + 14.359\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908]\n", + "Ecuación de regresión: y = -0.163x + 14.323\n", "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 13.92\n", - "\tR²: 0.013083522010913673, Desviación Estándar: 0.93913737855047, Varianza: 0.8819790157906487, Incertidumbre: 0.29698131520192456\n", + "Predicción obtenida: 13.917\n", + "\tR²: 0.011206334911924909, Desviación Estándar: 0.9412441639775184, Varianza: 0.8859405762217376, Incertidumbre: 0.2976475392509969\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654]\n", - "Ecuación de regresión: y = -0.032x + 14.931\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648]\n", + "Ecuación de regresión: y = -0.032x + 14.92\n", "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 12.534\n", - "\tR²: 0.6320455971220006, Desviación Estándar: 0.6345600288026622, Varianza: 0.40266643015403547, Incertidumbre: 0.16959330136578327\n", + "Predicción obtenida: 12.528\n", + "\tR²: 0.6321568018371059, Desviación Estándar: 0.6331526544068619, Varianza: 0.4008822837824551, Incertidumbre: 0.16921716473692594\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767]\n", - "Ecuación de regresión: y = 0.0x + 14.332\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755]\n", + "Ecuación de regresión: y = 0.0x + 14.526\n", "Valor del parámetro correlacionado para la aeronave: 500.0\n", - "Predicción obtenida: 14.381\n", - "\tR²: 0.017282284063206976, Desviación Estándar: 0.7854344289958513, Varianza: 0.6169072422520389, Incertidumbre: 0.2968663100387202\n", + "Predicción obtenida: 14.54\n", + "\tR²: 0.0015535116931560955, Desviación Estándar: 0.752709188227462, Varianza: 0.5665711220420449, Incertidumbre: 0.30729223931030347\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923]\n", - "Ecuación de regresión: y = -0.092x + 17.137\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908]\n", + "Ecuación de regresión: y = -0.093x + 17.144\n", "Valor del parámetro correlacionado para la aeronave: 46.3\n", - "Predicción obtenida: 12.859\n", - "\tR²: 0.5980072707351919, Desviación Estándar: 0.5993741728944746, Varianza: 0.3592493991329355, Incertidumbre: 0.18953875570260967\n", + "Predicción obtenida: 12.84\n", + "\tR²: 0.6038367583191466, Desviación Estándar: 0.5957808601774445, Varianza: 0.35495483335377565, Incertidumbre: 0.18840245044950332\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654]\n", - "Ecuación de regresión: y = -0.138x + 15.058\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648]\n", + "Ecuación de regresión: y = -0.137x + 15.038\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 12.572\n", - "\tR²: 0.7800961130119188, Desviación Estándar: 0.4930778276036819, Varianza: 0.24312574407436624, Incertidumbre: 0.14866855878226887\n", + "Predicción obtenida: 12.567\n", + "\tR²: 0.7750989860654982, Desviación Estándar: 0.4971353116989971, Varianza: 0.24714351813805902, Incertidumbre: 0.14989193626745023\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.59', 'Área del ala: 13.042', 'Longitud del fuselaje: 13.92', 'Peso máximo al despegue (MTOW): 12.534', 'Alcance de la aeronave: 14.381', 'Velocidad máxima (KIAS): 12.859', 'payload: 12.572']\n", - "**Mediana calculada:** 12.859\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.574', 'Área del ala: 13.033', 'Longitud del fuselaje: 13.917', 'Peso máximo al despegue (MTOW): 12.528', 'Alcance de la aeronave: 14.54', 'Velocidad máxima (KIAS): 12.84', 'payload: 12.567']\n", + "**Mediana calculada:** 12.84\n", "\n", "--- Imputación para aeronave: **ScanEagle 3** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859]\n", - "Ecuación de regresión: y = -0.135x + 17.109\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84]\n", + "Ecuación de regresión: y = -0.136x + 17.118\n", "Valor del parámetro correlacionado para la aeronave: 25.703\n", - "Predicción obtenida: 13.643\n", - "\tR²: 0.535314561075031, Desviación Estándar: 0.6887523223637181, Varianza: 0.4743797615614151, Incertidumbre: 0.19102552418286836\n", + "Predicción obtenida: 13.632\n", + "\tR²: 0.5415803492274942, Desviación Estándar: 0.6840025560016795, Varianza: 0.4678594966168307, Incertidumbre: 0.18970817601634496\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859]\n", - "Ecuación de regresión: y = -1.448x + 15.728\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84]\n", + "Ecuación de regresión: y = -1.447x + 15.715\n", "Valor del parámetro correlacionado para la aeronave: 1.349\n", - "Predicción obtenida: 13.774\n", - "\tR²: 0.6339313870790875, Desviación Estándar: 0.6113146322958206, Varianza: 0.37370557965897433, Incertidumbre: 0.16954817324492275\n", + "Predicción obtenida: 13.764\n", + "\tR²: 0.6330253270433377, Desviación Estándar: 0.6119897631287073, Varianza: 0.37453147017433136, Incertidumbre: 0.16973542084766274\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859]\n", - "Ecuación de regresión: y = -0.322x + 14.544\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84]\n", + "Ecuación de regresión: y = -0.311x + 14.512\n", "Valor del parámetro correlacionado para la aeronave: 2.4\n", - "Predicción obtenida: 13.772\n", - "\tR²: 0.042345280621575165, Desviación Estándar: 0.9416884437522549, Varianza: 0.8867771250965437, Incertidumbre: 0.2839297488490841\n", + "Predicción obtenida: 13.765\n", + "\tR²: 0.039424169255703134, Desviación Estándar: 0.9449335704173759, Varianza: 0.8928994525017299, Incertidumbre: 0.2849081913532099\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859]\n", - "Ecuación de regresión: y = -0.031x + 14.911\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84]\n", + "Ecuación de regresión: y = -0.031x + 14.9\n", "Valor del parámetro correlacionado para la aeronave: 36.3\n", - "Predicción obtenida: 13.783\n", - "\tR²: 0.6385049587577492, Desviación Estándar: 0.6178453553338438, Varianza: 0.3817328831076036, Incertidumbre: 0.15952698478263871\n", + "Predicción obtenida: 13.773\n", + "\tR²: 0.6393936966590913, Desviación Estándar: 0.6161349689692996, Varianza: 0.3796222999867998, Incertidumbre: 0.15908536492227474\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859]\n", - "Ecuación de regresión: y = -0.092x + 17.137\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84]\n", + "Ecuación de regresión: y = -0.093x + 17.144\n", "Valor del parámetro correlacionado para la aeronave: 41.2\n", - "Predicción obtenida: 13.33\n", - "\tR²: 0.6473061906505011, Desviación Estándar: 0.5714808508233791, Varianza: 0.32659036285781323, Incertidumbre: 0.17230795973220137\n", + "Predicción obtenida: 13.314\n", + "\tR²: 0.6528561194484994, Desviación Estándar: 0.5680547657716968, Varianza: 0.32268621691593735, Incertidumbre: 0.1712749562216309\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859]\n", - "Ecuación de regresión: y = -0.135x + 15.044\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84]\n", + "Ecuación de regresión: y = -0.134x + 15.025\n", "Valor del parámetro correlacionado para la aeronave: 8.6\n", - "Predicción obtenida: 13.883\n", - "\tR²: 0.7797023232836269, Desviación Estándar: 0.4782732644237212, Varianza: 0.22874531546252275, Incertidumbre: 0.13806559898061826\n", - "\tNivel de confianza: Confianza Alta\n", + "Predicción obtenida: 13.87\n", + "\tR²: 0.7755555066361247, Desviación Estándar: 0.4815099276447935, Varianza: 0.2318518104204943, Incertidumbre: 0.1389999431715994\n", + "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923]\n", - "Ecuación de regresión: y = -0.161x + 17.637\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908]\n", + "Ecuación de regresión: y = -0.161x + 17.639\n", "Valor del parámetro correlacionado para la aeronave: 23.5\n", - "Predicción obtenida: 13.85\n", - "\tR²: 0.7175450779766854, Desviación Estándar: 0.5704794589847608, Varianza: 0.3254468131235454, Incertidumbre: 0.21562096807776418\n", + "Predicción obtenida: 13.844\n", + "\tR²: 0.7192364194160978, Desviación Estándar: 0.5692595198003065, Varianza: 0.32405640088327564, Incertidumbre: 0.2151598744068086\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.643', 'Área del ala: 13.774', 'Longitud del fuselaje: 13.772', 'Peso máximo al despegue (MTOW): 13.783', 'Velocidad máxima (KIAS): 13.33', 'payload: 13.883', 'Crucero KIAS: 13.85']\n", - "**Mediana calculada:** 13.774\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.632', 'Área del ala: 13.764', 'Longitud del fuselaje: 13.765', 'Peso máximo al despegue (MTOW): 13.773', 'Velocidad máxima (KIAS): 13.314', 'payload: 13.87', 'Crucero KIAS: 13.844']\n", + "**Mediana calculada:** 13.765\n", "\n", - "--- Imputación para aeronave: **RQNan21A Blackjack** ---\n", + "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774]\n", - "Ecuación de regresión: y = -0.135x + 17.114\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765]\n", + "Ecuación de regresión: y = -0.135x + 17.123\n", "Valor del parámetro correlacionado para la aeronave: 33.797\n", - "Predicción obtenida: 12.563\n", - "\tR²: 0.5342243863228833, Desviación Estándar: 0.6645511147608912, Varianza: 0.44162818412994326, Incertidumbre: 0.17760875624528186\n", + "Predicción obtenida: 12.545\n", + "\tR²: 0.5404546291978183, Desviación Estándar: 0.6600090502158572, Varianza: 0.43561194636683787, Incertidumbre: 0.17639483843412967\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774]\n", - "Ecuación de regresión: y = -1.448x + 15.728\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765]\n", + "Ecuación de regresión: y = -1.447x + 15.715\n", "Valor del parámetro correlacionado para la aeronave: 1.802\n", - "Predicción obtenida: 13.119\n", - "\tR²: 0.6340136541211301, Desviación Estándar: 0.5890775326049247, Varianza: 0.3470123394199061, Incertidumbre: 0.15743759294669885\n", + "Predicción obtenida: 13.108\n", + "\tR²: 0.6331129407716816, Desviación Estándar: 0.5897281920140293, Varianza: 0.3477793404561358, Incertidumbre: 0.15761148898843813\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774]\n", - "Ecuación de regresión: y = -0.322x + 14.544\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765]\n", + "Ecuación de regresión: y = -0.311x + 14.512\n", "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 13.74\n", - "\tR²: 0.04429674165169528, Desviación Estándar: 0.9015982192102218, Varianza: 0.8128793488830431, Incertidumbre: 0.2602689872809544\n", + "Predicción obtenida: 13.734\n", + "\tR²: 0.04129311851250839, Desviación Estándar: 0.9047050168185119, Varianza: 0.8184911674565839, Incertidumbre: 0.2611658424986864\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774]\n", - "Ecuación de regresión: y = -0.031x + 14.91\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765]\n", + "Ecuación de regresión: y = -0.031x + 14.9\n", "Valor del parámetro correlacionado para la aeronave: 61.0\n", - "Predicción obtenida: 13.014\n", - "\tR²: 0.6395237688067317, Desviación Estándar: 0.598229831780838, Varianza: 0.35787893163252976, Incertidumbre: 0.1495574579452095\n", + "Predicción obtenida: 13.006\n", + "\tR²: 0.6404167494362927, Desviación Estándar: 0.5965733368182722, Varianza: 0.35589974620248765, Incertidumbre: 0.14914333420456805\n", "\tNivel de confianza: Confianza Media\n", "\n", - "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859]\n", - "Ecuación de regresión: y = 0.0x + 14.115\n", - "Valor del parámetro correlacionado para la aeronave: 92.6\n", - "Predicción obtenida: 14.128\n", - "\tR²: 0.022722235303102134, Desviación Estándar: 0.889761474449699, Varianza: 0.7916754814149023, Incertidumbre: 0.31457818611096156\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774]\n", - "Ecuación de regresión: y = -0.089x + 17.059\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765]\n", + "Ecuación de regresión: y = -0.09x + 17.066\n", "Valor del parámetro correlacionado para la aeronave: 46.3\n", - "Predicción obtenida: 12.932\n", - "\tR²: 0.6311618765895944, Desviación Estándar: 0.5601049538041426, Varianza: 0.3137175592759407, Incertidumbre: 0.16168837292663232\n", + "Predicción obtenida: 12.914\n", + "\tR²: 0.6362072062495043, Desviación Estándar: 0.5573026681001448, Varianza: 0.31058626387154015, Incertidumbre: 0.16087942272385766\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774]\n", - "Ecuación de regresión: y = -0.134x + 15.028\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765]\n", + "Ecuación de regresión: y = -0.134x + 15.009\n", "Valor del parámetro correlacionado para la aeronave: 17.7\n", - "Predicción obtenida: 12.649\n", - "\tR²: 0.7813316684515454, Desviación Estándar: 0.4604041822404213, Varianza: 0.21197201102447108, Incertidumbre: 0.12769314511583893\n", + "Predicción obtenida: 12.643\n", + "\tR²: 0.7773273437346053, Desviación Estándar: 0.46343938731299195, Varianza: 0.21477606571304134, Incertidumbre: 0.1285349595405083\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774]\n", - "Ecuación de regresión: y = -0.161x + 17.634\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765]\n", + "Ecuación de regresión: y = -0.162x + 17.636\n", "Valor del parámetro correlacionado para la aeronave: 30.9\n", - "Predicción obtenida: 12.645\n", - "\tR²: 0.7186963836121465, Desviación Estándar: 0.5342166106536912, Varianza: 0.28538738709831746, Incertidumbre: 0.18887409400785932\n", + "Predicción obtenida: 12.636\n", + "\tR²: 0.7203735267257434, Desviación Estándar: 0.533129992286115, Varianza: 0.2842275886749931, Incertidumbre: 0.18848991639972185\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.563', 'Área del ala: 13.119', 'Longitud del fuselaje: 13.74', 'Peso máximo al despegue (MTOW): 13.014', 'Alcance de la aeronave: 14.128', 'Velocidad máxima (KIAS): 12.932', 'payload: 12.649', 'Crucero KIAS: 12.645']\n", - "**Mediana calculada:** 12.973\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.545', 'Área del ala: 13.108', 'Longitud del fuselaje: 13.734', 'Peso máximo al despegue (MTOW): 13.006', 'Velocidad máxima (KIAS): 12.914', 'payload: 12.643', 'Crucero KIAS: 12.636']\n", + "**Mediana calculada:** 12.914\n", "\n", "--- Imputación para aeronave: **DeltaQuad Evo** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973]\n", - "Ecuación de regresión: y = -0.128x + 16.957\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914]\n", + "Ecuación de regresión: y = -0.129x + 16.982\n", "Valor del parámetro correlacionado para la aeronave: 18.091\n", - "Predicción obtenida: 14.65\n", - "\tR²: 0.5421915391164227, Desviación Estándar: 0.6489139037011813, Varianza: 0.42108925441670597, Incertidumbre: 0.16754884947714124\n", - "\tNivel de confianza: Confianza Baja\n", + "Predicción obtenida: 14.648\n", + "\tR²: 0.5523215209559584, Desviación Estándar: 0.6432370596243068, Varianza: 0.41375391487412394, Incertidumbre: 0.1660830946392245\n", + "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973]\n", - "Ecuación de regresión: y = -1.461x + 15.737\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914]\n", + "Ecuación de regresión: y = -1.465x + 15.728\n", "Valor del parámetro correlacionado para la aeronave: 0.84\n", - "Predicción obtenida: 14.509\n", - "\tR²: 0.6465018935699594, Desviación Estándar: 0.5702151055687861, Varianza: 0.3251452666188218, Incertidumbre: 0.14722890717492082\n", + "Predicción obtenida: 14.498\n", + "\tR²: 0.6463458428923345, Desviación Estándar: 0.5717120330519926, Varianza: 0.3268546487364427, Incertidumbre: 0.14761541218911678\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973]\n", - "Ecuación de regresión: y = -0.403x + 14.649\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914]\n", + "Ecuación de regresión: y = -0.399x + 14.624\n", "Valor del parámetro correlacionado para la aeronave: 0.75\n", - "Predicción obtenida: 14.347\n", - "\tR²: 0.06842483962479096, Desviación Estándar: 0.8886738964344558, Varianza: 0.7897412942039979, Incertidumbre: 0.2464737923662158\n", + "Predicción obtenida: 14.325\n", + "\tR²: 0.06604254768080076, Desviación Estándar: 0.8947186094782155, Varianza: 0.8005213901466314, Incertidumbre: 0.24815029412196515\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973]\n", - "Ecuación de regresión: y = -0.031x + 14.911\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914]\n", + "Ecuación de regresión: y = -0.031x + 14.901\n", "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 14.599\n", - "\tR²: 0.6468495695110492, Desviación Estándar: 0.5804480353649806, Varianza: 0.3369199217590657, Incertidumbre: 0.14077932705835708\n", + "Predicción obtenida: 14.589\n", + "\tR²: 0.64862940600494, Desviación Estándar: 0.5791516164367155, Varianza: 0.33541659482126046, Incertidumbre: 0.14046489928328137\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973]\n", - "Ecuación de regresión: y = 0.0x + 13.94\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755, 12.84]\n", + "Ecuación de regresión: y = 0.0x + 14.236\n", "Valor del parámetro correlacionado para la aeronave: 270.0\n", - "Predicción obtenida: 13.997\n", - "\tR²: 0.04703203479609108, Desviación Estándar: 0.9111851551421984, Varianza: 0.8302583869515122, Incertidumbre: 0.30372838504739946\n", + "Predicción obtenida: 14.259\n", + "\tR²: 0.010075858581318875, Desviación Estándar: 0.9138813895344626, Varianza: 0.8351791941374402, Incertidumbre: 0.34541469778833345\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973]\n", - "Ecuación de regresión: y = -0.131x + 15.014\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914]\n", + "Ecuación de regresión: y = -0.131x + 14.998\n", "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 14.62\n", - "\tR²: 0.7771803589437556, Desviación Estándar: 0.45102082932177623, Varianza: 0.20341978848210282, Incertidumbre: 0.1205403869729095\n", + "Predicción obtenida: 14.604\n", + "\tR²: 0.7761013348288335, Desviación Estándar: 0.45173648062543886, Varianza: 0.2040658479278575, Incertidumbre: 0.12073165282910263\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973]\n", - "Ecuación de regresión: y = -0.153x + 17.471\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914]\n", + "Ecuación de regresión: y = -0.155x + 17.498\n", "Valor del parámetro correlacionado para la aeronave: 16.54\n", "Predicción obtenida: 14.941\n", - "\tR²: 0.7387286606060905, Desviación Estándar: 0.5118777159980322, Varianza: 0.26201879613536216, Incertidumbre: 0.1706259053326774\n", + "\tR²: 0.7453275443355423, Desviación Estándar: 0.5085476473721651, Varianza: 0.25862070964776396, Incertidumbre: 0.16951588245738836\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.65', 'Área del ala: 14.509', 'Longitud del fuselaje: 14.347', 'Peso máximo al despegue (MTOW): 14.599', 'Alcance de la aeronave: 13.997', 'payload: 14.62', 'Crucero KIAS: 14.941']\n", - "**Mediana calculada:** 14.599\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.648', 'Área del ala: 14.498', 'Longitud del fuselaje: 14.325', 'Peso máximo al despegue (MTOW): 14.589', 'Alcance de la aeronave: 14.259', 'payload: 14.604', 'Crucero KIAS: 14.941']\n", + "**Mediana calculada:** 14.589\n", "\n", "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599]\n", - "Ecuación de regresión: y = -0.127x + 16.936\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589]\n", + "Ecuación de regresión: y = -0.128x + 16.957\n", "Valor del parámetro correlacionado para la aeronave: 17.5\n", - "Predicción obtenida: 14.717\n", - "\tR²: 0.5673252249031574, Desviación Estándar: 0.6284176728117805, Varianza: 0.39490877150217396, Incertidumbre: 0.15710441820294513\n", + "Predicción obtenida: 14.714\n", + "\tR²: 0.5769309788482372, Desviación Estándar: 0.6229572635807227, Varianza: 0.38807575224798196, Incertidumbre: 0.15573931589518067\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599]\n", - "Ecuación de regresión: y = -1.472x + 15.757\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589]\n", + "Ecuación de regresión: y = -1.476x + 15.749\n", "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 14.727\n", - "\tR²: 0.6655463732035445, Desviación Estándar: 0.5525041655219294, Varianza: 0.3052608529190835, Incertidumbre: 0.13812604138048235\n", + "Predicción obtenida: 14.716\n", + "\tR²: 0.6654453208012552, Desviación Estándar: 0.5539699350076821, Varianza: 0.3068826888924155, Incertidumbre: 0.13849248375192053\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599]\n", - "Ecuación de regresión: y = -0.452x + 14.76\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589]\n", + "Ecuación de regresión: y = -0.449x + 14.74\n", "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 14.353\n", - "\tR²: 0.10767788693788138, Desviación Estándar: 0.8582189379484234, Varianza: 0.7365397454533199, Incertidumbre: 0.22936865918885752\n", + "Predicción obtenida: 14.335\n", + "\tR²: 0.10523063156350132, Desviación Estándar: 0.8642071856334788, Varianza: 0.746854059700538, Incertidumbre: 0.2309690857020446\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599]\n", - "Ecuación de regresión: y = -0.031x + 14.91\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589]\n", + "Ecuación de regresión: y = -0.031x + 14.901\n", "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 14.717\n", - "\tR²: 0.668742738945738, Desviación Estándar: 0.5640940957956807, Varianza: 0.3182021489115466, Incertidumbre: 0.1329582534548066\n", + "Predicción obtenida: 14.707\n", + "\tR²: 0.6704861354330247, Desviación Estándar: 0.5628342077229468, Varianza: 0.3167823453831172, Incertidumbre: 0.13266129498821788\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599]\n", - "Ecuación de regresión: y = 0.0x + 14.014\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589]\n", + "Ecuación de regresión: y = 0.0x + 14.29\n", "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 14.033\n", - "\tR²: 0.03658191201096572, Desviación Estándar: 0.8828624043127778, Varianza: 0.7794460249489389, Incertidumbre: 0.2791856058160841\n", + "Predicción obtenida: 14.297\n", + "\tR²: 0.006384217652188662, Desviación Estándar: 0.861582649338378, Varianza: 0.7423246616409385, Incertidumbre: 0.30461546694991914\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599]\n", - "Ecuación de regresión: y = -0.131x + 15.009\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589]\n", + "Ecuación de regresión: y = -0.131x + 14.994\n", "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 14.852\n", - "\tR²: 0.7989376635956842, Desviación Estándar: 0.43575473105263046, Varianza: 0.1898821856347503, Incertidumbre: 0.11251138775986198\n", + "Predicción obtenida: 14.837\n", + "\tR²: 0.7981390800156483, Desviación Estándar: 0.43643291254269034, Varianza: 0.19047368715049562, Incertidumbre: 0.11268649346764252\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599]\n", - "Ecuación de regresión: y = -0.146x + 17.287\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589]\n", + "Ecuación de regresión: y = -0.148x + 17.309\n", "Valor del parámetro correlacionado para la aeronave: 16.0\n", - "Predicción obtenida: 14.945\n", - "\tR²: 0.7422196460742506, Desviación Estándar: 0.4948955953255141, Varianza: 0.244921650272595, Incertidumbre: 0.15649972852136038\n", + "Predicción obtenida: 14.943\n", + "\tR²: 0.7479549673253836, Desviación Estándar: 0.49233652426651103, Varianza: 0.24239525312682883, Incertidumbre: 0.15569047919729348\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.717', 'Área del ala: 14.727', 'Longitud del fuselaje: 14.353', 'Peso máximo al despegue (MTOW): 14.717', 'Alcance de la aeronave: 14.033', 'payload: 14.852', 'Crucero KIAS: 14.945']\n", - "**Mediana calculada:** 14.717\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.714', 'Área del ala: 14.716', 'Longitud del fuselaje: 14.335', 'Peso máximo al despegue (MTOW): 14.707', 'Alcance de la aeronave: 14.297', 'payload: 14.837', 'Crucero KIAS: 14.943']\n", + "**Mediana calculada:** 14.714\n", "\n", "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = -0.127x + 16.936\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", + "Ecuación de regresión: y = -0.128x + 16.957\n", "Valor del parámetro correlacionado para la aeronave: 17.5\n", - "Predicción obtenida: 14.717\n", - "\tR²: 0.5929127107679053, Desviación Estándar: 0.6096546936267196, Varianza: 0.37167884546108937, Incertidumbre: 0.14786298217509053\n", + "Predicción obtenida: 14.714\n", + "\tR²: 0.6023394020952446, Desviación Estándar: 0.60435731717502, Varianza: 0.3652477668229877, Incertidumbre: 0.1465781796663248\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = -1.471x + 15.755\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", + "Ecuación de regresión: y = -1.476x + 15.748\n", "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 14.726\n", - "\tR²: 0.685319780926233, Desviación Estándar: 0.5360124441675181, Varianza: 0.28730934030243677, Incertidumbre: 0.13000211317342153\n", + "Predicción obtenida: 14.716\n", + "\tR²: 0.6855376342976433, Desviación Estándar: 0.5374299221408195, Varianza: 0.2888309212122873, Incertidumbre: 0.13034590207965144\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = -0.501x + 14.874\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714]\n", + "Ecuación de regresión: y = -0.5x + 14.859\n", "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 14.424\n", - "\tR²: 0.14520304942233875, Desviación Estándar: 0.8333946066823736, Varianza: 0.6945465704472683, Incertidumbre: 0.2151815621666609\n", + "Predicción obtenida: 14.409\n", + "\tR²: 0.14307201753173704, Desviación Estándar: 0.8395031403094158, Varianza: 0.7047655225893706, Incertidumbre: 0.21675877876714636\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = -0.031x + 14.91\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", + "Ecuación de regresión: y = -0.031x + 14.902\n", "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 14.717\n", - "\tR²: 0.6901941411051212, Desviación Estándar: 0.5490488826768337, Varianza: 0.3014546755686795, Incertidumbre: 0.12596045235011777\n", + "Predicción obtenida: 14.708\n", + "\tR²: 0.692130699174681, Desviación Estándar: 0.54782447874912, Varianza: 0.300111659516745, Incertidumbre: 0.1256795548244873\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = 0.0x + 14.096\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714]\n", + "Ecuación de regresión: y = 0.0x + 14.354\n", "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 14.111\n", - "\tR²: 0.0237018252029767, Desviación Estándar: 0.8638914663706774, Varianza: 0.7463084656680793, Incertidumbre: 0.2604730775946788\n", + "Predicción obtenida: 14.358\n", + "\tR²: 0.002361606473490707, Desviación Estándar: 0.8223839641550523, Varianza: 0.6763153844993783, Incertidumbre: 0.2741279880516841\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = -0.129x + 14.981\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", + "Ecuación de regresión: y = -0.129x + 14.969\n", "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 14.826\n", - "\tR²: 0.8169712264348787, Desviación Estándar: 0.4230085386335259, Varianza: 0.17893622375687118, Incertidumbre: 0.10575213465838147\n", + "Predicción obtenida: 14.813\n", + "\tR²: 0.81671987260067, Desviación Estándar: 0.4234784703530429, Varianza: 0.17933401485255304, Incertidumbre: 0.10586961758826073\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = -0.143x + 17.182\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714]\n", + "Ecuación de regresión: y = -0.144x + 17.203\n", "Valor del parámetro correlacionado para la aeronave: 16.0\n", - "Predicción obtenida: 14.9\n", - "\tR²: 0.7519794290280211, Desviación Estándar: 0.4758638703382313, Varianza: 0.22644642309328103, Incertidumbre: 0.14347835538165868\n", + "Predicción obtenida: 14.898\n", + "\tR²: 0.7575308845016266, Desviación Estándar: 0.4734960530805051, Varianza: 0.22419851228281648, Incertidumbre: 0.1427644316165672\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.717', 'Área del ala: 14.726', 'Longitud del fuselaje: 14.424', 'Peso máximo al despegue (MTOW): 14.717', 'Alcance de la aeronave: 14.111', 'payload: 14.826', 'Crucero KIAS: 14.9']\n", - "**Mediana calculada:** 14.717\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.714', 'Área del ala: 14.716', 'Longitud del fuselaje: 14.409', 'Peso máximo al despegue (MTOW): 14.708', 'Alcance de la aeronave: 14.358', 'payload: 14.813', 'Crucero KIAS: 14.898']\n", + "**Mediana calculada:** 14.714\n", "\n", "--- Imputación para aeronave: **V21** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = -0.127x + 16.936\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", + "Ecuación de regresión: y = -0.128x + 16.957\n", "Valor del parámetro correlacionado para la aeronave: 19.688\n", - "Predicción obtenida: 14.439\n", - "\tR²: 0.5929127107679053, Desviación Estándar: 0.6096546936267196, Varianza: 0.37167884546108937, Incertidumbre: 0.14786298217509053\n", + "Predicción obtenida: 14.434\n", + "\tR²: 0.6023394020952446, Desviación Estándar: 0.60435731717502, Varianza: 0.3652477668229877, Incertidumbre: 0.1465781796663248\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = -1.471x + 15.755\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", + "Ecuación de regresión: y = -1.476x + 15.748\n", "Valor del parámetro correlacionado para la aeronave: 0.8\n", - "Predicción obtenida: 14.578\n", - "\tR²: 0.685319780926233, Desviación Estándar: 0.5360124441675181, Varianza: 0.28730934030243677, Incertidumbre: 0.13000211317342153\n", + "Predicción obtenida: 14.568\n", + "\tR²: 0.6855376342976433, Desviación Estándar: 0.5374299221408195, Varianza: 0.2888309212122873, Incertidumbre: 0.13034590207965144\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = -0.501x + 14.874\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714]\n", + "Ecuación de regresión: y = -0.5x + 14.859\n", "Valor del parámetro correlacionado para la aeronave: 0.93\n", - "Predicción obtenida: 14.409\n", - "\tR²: 0.14520304942233875, Desviación Estándar: 0.8333946066823736, Varianza: 0.6945465704472683, Incertidumbre: 0.2151815621666609\n", + "Predicción obtenida: 14.394\n", + "\tR²: 0.14307201753173704, Desviación Estándar: 0.8395031403094158, Varianza: 0.7047655225893706, Incertidumbre: 0.21675877876714636\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = -0.031x + 14.91\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", + "Ecuación de regresión: y = -0.031x + 14.902\n", "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 14.599\n", - "\tR²: 0.6901941411051212, Desviación Estándar: 0.5490488826768337, Varianza: 0.3014546755686795, Incertidumbre: 0.12596045235011777\n", + "Predicción obtenida: 14.59\n", + "\tR²: 0.692130699174681, Desviación Estándar: 0.54782447874912, Varianza: 0.300111659516745, Incertidumbre: 0.1256795548244873\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973]\n", - "Ecuación de regresión: y = -0.089x + 17.046\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914]\n", + "Ecuación de regresión: y = -0.09x + 17.066\n", "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.119\n", - "\tR²: 0.6582796109191538, Desviación Estándar: 0.5382312625669818, Varianza: 0.2896928920044473, Incertidumbre: 0.14927849348022884\n", + "Predicción obtenida: 14.107\n", + "\tR²: 0.6655168135921319, Desviación Estándar: 0.535439085733295, Varianza: 0.28669501453090684, Incertidumbre: 0.148504082961458\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = -0.129x + 14.981\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", + "Ecuación de regresión: y = -0.129x + 14.969\n", "Valor del parámetro correlacionado para la aeronave: 1.5\n", - "Predicción obtenida: 14.787\n", - "\tR²: 0.8169712264348787, Desviación Estándar: 0.4230085386335259, Varianza: 0.17893622375687118, Incertidumbre: 0.10575213465838147\n", + "Predicción obtenida: 14.775\n", + "\tR²: 0.81671987260067, Desviación Estándar: 0.4234784703530429, Varianza: 0.17933401485255304, Incertidumbre: 0.10586961758826073\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = -0.143x + 17.182\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714]\n", + "Ecuación de regresión: y = -0.144x + 17.203\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 14.615\n", - "\tR²: 0.7519794290280211, Desviación Estándar: 0.4758638703382313, Varianza: 0.22644642309328103, Incertidumbre: 0.14347835538165868\n", + "Predicción obtenida: 14.61\n", + "\tR²: 0.7575308845016266, Desviación Estándar: 0.4734960530805051, Varianza: 0.22419851228281648, Incertidumbre: 0.1427644316165672\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.439', 'Área del ala: 14.578', 'Longitud del fuselaje: 14.409', 'Peso máximo al despegue (MTOW): 14.599', 'Velocidad máxima (KIAS): 14.119', 'payload: 14.787', 'Crucero KIAS: 14.615']\n", - "**Mediana calculada:** 14.578\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.434', 'Área del ala: 14.568', 'Longitud del fuselaje: 14.394', 'Peso máximo al despegue (MTOW): 14.59', 'Velocidad máxima (KIAS): 14.107', 'payload: 14.775', 'Crucero KIAS: 14.61']\n", + "**Mediana calculada:** 14.568\n", "\n", "--- Imputación para aeronave: **V25** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578]\n", - "Ecuación de regresión: y = -0.128x + 16.971\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568]\n", + "Ecuación de regresión: y = -0.129x + 16.992\n", "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 14.173\n", - "\tR²: 0.606745390877248, Desviación Estándar: 0.5932915224926394, Varianza: 0.351994830661634, Incertidumbre: 0.13984015292501215\n", + "Predicción obtenida: 14.164\n", + "\tR²: 0.6159186819254363, Desviación Estándar: 0.5881023640765761, Varianza: 0.34586439063245766, Incertidumbre: 0.13861705655679563\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578]\n", - "Ecuación de regresión: y = -1.471x + 15.755\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568]\n", + "Ecuación de regresión: y = -1.476x + 15.748\n", "Valor del parámetro correlacionado para la aeronave: 0.52\n", - "Predicción obtenida: 14.99\n", - "\tR²: 0.6968457130521275, Desviación Estándar: 0.5209104729685736, Varianza: 0.2713477208483431, Incertidumbre: 0.12277977594239006\n", + "Predicción obtenida: 14.981\n", + "\tR²: 0.6970733829876015, Desviación Estándar: 0.5222880039127576, Varianza: 0.2727847590311727, Incertidumbre: 0.12310446309969901\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578]\n", - "Ecuación de regresión: y = -0.519x + 14.918\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568]\n", + "Ecuación de regresión: y = -0.519x + 14.904\n", "Valor del parámetro correlacionado para la aeronave: 0.93\n", - "Predicción obtenida: 14.435\n", - "\tR²: 0.16853217384488628, Desviación Estándar: 0.807864743048595, Varianza: 0.6526454430609725, Incertidumbre: 0.20196618576214875\n", + "Predicción obtenida: 14.421\n", + "\tR²: 0.16641438018273524, Desviación Estándar: 0.8138274929561254, Varianza: 0.6623151882912524, Incertidumbre: 0.20345687323903136\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578]\n", - "Ecuación de regresión: y = -0.031x + 14.908\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568]\n", + "Ecuación de regresión: y = -0.031x + 14.899\n", "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 14.519\n", - "\tR²: 0.703248032143867, Desviación Estándar: 0.5351650687571093, Varianza: 0.2864016508178016, Incertidumbre: 0.11966654729242453\n", + "Predicción obtenida: 14.509\n", + "\tR²: 0.7051231818210476, Desviación Estándar: 0.5339726907463312, Varianza: 0.2851268344628771, Incertidumbre: 0.11939992346372695\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578]\n", - "Ecuación de regresión: y = -0.09x + 17.131\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568]\n", + "Ecuación de regresión: y = -0.091x + 17.151\n", "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.156\n", - "\tR²: 0.6564776133589556, Desviación Estándar: 0.5318209559369983, Varianza: 0.28283352917374266, Incertidumbre: 0.14213512915877488\n", + "Predicción obtenida: 14.144\n", + "\tR²: 0.6634608751199247, Desviación Estándar: 0.5293377198828594, Varianza: 0.2801984216907845, Incertidumbre: 0.14147145640698403\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578]\n", - "Ecuación de regresión: y = -0.127x + 14.946\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568]\n", + "Ecuación de regresión: y = -0.127x + 14.933\n", "Valor del parámetro correlacionado para la aeronave: 2.2\n", - "Predicción obtenida: 14.666\n", - "\tR²: 0.8258810341686761, Desviación Estándar: 0.41302279972648664, Varianza: 0.1705878330939055, Incertidumbre: 0.10017274288591961\n", + "Predicción obtenida: 14.653\n", + "\tR²: 0.8257579107606323, Desviación Estándar: 0.41340767338372525, Varianza: 0.17090590441254486, Incertidumbre: 0.100266088458938\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578]\n", - "Ecuación de regresión: y = -0.142x + 17.171\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568]\n", + "Ecuación de regresión: y = -0.144x + 17.191\n", "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 14.326\n", - "\tR²: 0.7588352909196386, Desviación Estándar: 0.4557148035481193, Varianza: 0.20767598217290095, Incertidumbre: 0.13155353225110206\n", + "Predicción obtenida: 14.318\n", + "\tR²: 0.7641303564183091, Desviación Estándar: 0.45348228917663347, Varianza: 0.20564618659687983, Incertidumbre: 0.13090906086442852\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.173', 'Área del ala: 14.99', 'Longitud del fuselaje: 14.435', 'Peso máximo al despegue (MTOW): 14.519', 'Velocidad máxima (KIAS): 14.156', 'payload: 14.666', 'Crucero KIAS: 14.326']\n", - "**Mediana calculada:** 14.435\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.164', 'Área del ala: 14.981', 'Longitud del fuselaje: 14.421', 'Peso máximo al despegue (MTOW): 14.509', 'Velocidad máxima (KIAS): 14.144', 'payload: 14.653', 'Crucero KIAS: 14.318']\n", + "**Mediana calculada:** 14.421\n", "\n", "--- Imputación para aeronave: **V32** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", - "Ecuación de regresión: y = -0.129x + 17.012\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", + "Ecuación de regresión: y = -0.13x + 17.031\n", "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 14.189\n", - "\tR²: 0.6110785668236963, Desviación Estándar: 0.5803950706423994, Varianza: 0.3368584380259958, Incertidumbre: 0.13315176106628257\n", + "Predicción obtenida: 14.18\n", + "\tR²: 0.6201449706987955, Desviación Estándar: 0.5752636617985344, Varianza: 0.3309282805858586, Incertidumbre: 0.13197453514057741\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", - "Ecuación de regresión: y = -1.399x + 15.635\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", + "Ecuación de regresión: y = -1.403x + 15.627\n", "Valor del parámetro correlacionado para la aeronave: 1.03\n", - "Predicción obtenida: 14.194\n", - "\tR²: 0.6872612258181625, Desviación Estándar: 0.5204555466744641, Varianza: 0.2708739760642153, Incertidumbre: 0.11940069118732659\n", + "Predicción obtenida: 14.182\n", + "\tR²: 0.6872369206933386, Desviación Estándar: 0.5219942765247394, Varianza: 0.27247802472458604, Incertidumbre: 0.11975370002515603\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", - "Ecuación de regresión: y = -0.519x + 14.918\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", + "Ecuación de regresión: y = -0.519x + 14.904\n", "Valor del parámetro correlacionado para la aeronave: 1.0\n", - "Predicción obtenida: 14.399\n", - "\tR²: 0.18078496325464366, Desviación Estándar: 0.7837439261959693, Varianza: 0.614254541849073, Incertidumbre: 0.19008582300836904\n", + "Predicción obtenida: 14.385\n", + "\tR²: 0.17855538005879423, Desviación Estándar: 0.7895286400719489, Varianza: 0.623355473493861, Incertidumbre: 0.1914888222039361\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", - "Ecuación de regresión: y = -0.031x + 14.898\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", + "Ecuación de regresión: y = -0.031x + 14.889\n", "Valor del parámetro correlacionado para la aeronave: 23.5\n", - "Predicción obtenida: 14.171\n", - "\tR²: 0.7106634123093627, Desviación Estándar: 0.5225600649328873, Varianza: 0.27306902146266343, Incertidumbre: 0.11403195489123787\n", + "Predicción obtenida: 14.16\n", + "\tR²: 0.7123924260887753, Desviación Estándar: 0.5214279546204159, Varianza: 0.2718871118596305, Incertidumbre: 0.11378490816733594\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435]\n", - "Ecuación de regresión: y = -0.091x + 17.178\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421]\n", + "Ecuación de regresión: y = -0.092x + 17.198\n", "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.177\n", - "\tR²: 0.6583687729951575, Desviación Estándar: 0.5184390004821812, Varianza: 0.26877899722096305, Incertidumbre: 0.1338603743261271\n", + "Predicción obtenida: 14.164\n", + "\tR²: 0.6652782895416913, Desviación Estándar: 0.5160037700308047, Varianza: 0.2662598906860036, Incertidumbre: 0.1332316005273032\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", - "Ecuación de regresión: y = -0.125x + 14.913\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", + "Ecuación de regresión: y = -0.125x + 14.9\n", "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 14.287\n", - "\tR²: 0.8297829718090136, Desviación Estándar: 0.4046033990551174, Varianza: 0.16370391052695454, Incertidumbre: 0.09536593572100009\n", + "Predicción obtenida: 14.274\n", + "\tR²: 0.8296041524851132, Desviación Estándar: 0.4050204961761552, Varianza: 0.16404160232277895, Incertidumbre: 0.09546424645523317\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", - "Ecuación de regresión: y = -0.143x + 17.19\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", + "Ecuación de regresión: y = -0.144x + 17.209\n", "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 14.335\n", - "\tR²: 0.760755459593193, Desviación Estándar: 0.43879195181776326, Varianza: 0.19253837698004228, Incertidumbre: 0.12169899088768228\n", + "Predicción obtenida: 14.327\n", + "\tR²: 0.7660206652821594, Desviación Estándar: 0.43655078246489853, Varianza: 0.19057658567071517, Incertidumbre: 0.12107740234777827\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.189', 'Área del ala: 14.194', 'Longitud del fuselaje: 14.399', 'Peso máximo al despegue (MTOW): 14.171', 'Velocidad máxima (KIAS): 14.177', 'payload: 14.287', 'Crucero KIAS: 14.335']\n", - "**Mediana calculada:** 14.194\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.18', 'Área del ala: 14.182', 'Longitud del fuselaje: 14.385', 'Peso máximo al despegue (MTOW): 14.16', 'Velocidad máxima (KIAS): 14.164', 'payload: 14.274', 'Crucero KIAS: 14.327']\n", + "**Mediana calculada:** 14.182\n", "\n", "--- Imputación para aeronave: **V35** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", - "Ecuación de regresión: y = -0.129x + 17.012\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", + "Ecuación de regresión: y = -0.13x + 17.032\n", "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 13.484\n", - "\tR²: 0.6135185471717993, Desviación Estándar: 0.5657000289670463, Varianza: 0.320016522773317, Incertidumbre: 0.12649437196439156\n", + "Predicción obtenida: 13.467\n", + "\tR²: 0.6225279923162458, Desviación Estándar: 0.5606978547880688, Varianza: 0.31438208436394227, Incertidumbre: 0.12537585181444277\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", - "Ecuación de regresión: y = -1.399x + 15.635\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", + "Ecuación de regresión: y = -1.403x + 15.627\n", "Valor del parámetro correlacionado para la aeronave: 1.202\n", - "Predicción obtenida: 13.953\n", - "\tR²: 0.6892242356314006, Desviación Estándar: 0.5072773180656356, Varianza: 0.25733027742386405, Incertidumbre: 0.11343065666385432\n", + "Predicción obtenida: 13.941\n", + "\tR²: 0.6891992560944142, Desviación Estándar: 0.5087770862467862, Varianza: 0.25885412348976977, Incertidumbre: 0.11376601502420872\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", - "Ecuación de regresión: y = -0.503x + 14.88\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", + "Ecuación de regresión: y = -0.503x + 14.866\n", "Valor del parámetro correlacionado para la aeronave: 1.88\n", - "Predicción obtenida: 13.934\n", - "\tR²: 0.17980664787964395, Desviación Estándar: 0.7630256503950571, Varianza: 0.5822081431607998, Incertidumbre: 0.1798468705378736\n", + "Predicción obtenida: 13.92\n", + "\tR²: 0.17769866949605284, Desviación Estándar: 0.768609327920775, Varianza: 0.5907602989668254, Incertidumbre: 0.18116295595200493\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", - "Ecuación de regresión: y = -0.031x + 14.9\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", + "Ecuación de regresión: y = -0.031x + 14.891\n", "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 13.909\n", - "\tR²: 0.7135650005961087, Desviación Estándar: 0.5105679278629256, Varianza: 0.26067960896224157, Incertidumbre: 0.10885344796857536\n", + "Predicción obtenida: 13.898\n", + "\tR²: 0.7152702339309572, Desviación Estándar: 0.5094595753554049, Varianza: 0.2595490589213094, Incertidumbre: 0.10861714641999741\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194]\n", - "Ecuación de regresión: y = -0.091x + 17.181\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182]\n", + "Ecuación de regresión: y = -0.092x + 17.201\n", "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.178\n", - "\tR²: 0.6602446946044866, Desviación Estándar: 0.5019934524987022, Varianza: 0.25199742635156674, Incertidumbre: 0.12549836312467555\n", + "Predicción obtenida: 14.166\n", + "\tR²: 0.6671372923996113, Desviación Estándar: 0.4996364202272749, Varianza: 0.24963655241752605, Incertidumbre: 0.12490910505681872\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", - "Ecuación de regresión: y = -0.125x + 14.903\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", + "Ecuación de regresión: y = -0.125x + 14.891\n", "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 13.656\n", - "\tR²: 0.8320650835497453, Desviación Estándar: 0.394342588657032, Varianza: 0.1555060772287291, Incertidumbre: 0.09046839437317004\n", + "Predicción obtenida: 13.643\n", + "\tR²: 0.8319028879241045, Desviación Estándar: 0.3947401644934259, Varianza: 0.15581979746429697, Incertidumbre: 0.09055960452544556\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", - "Ecuación de regresión: y = -0.142x + 17.168\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", + "Ecuación de regresión: y = -0.144x + 17.186\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 13.613\n", - "\tR²: 0.7592272998904299, Desviación Estándar: 0.4243796984927565, Varianza: 0.18009812849280288, Incertidumbre: 0.11342024526159432\n", + "Predicción obtenida: 13.598\n", + "\tR²: 0.7644167301146686, Desviación Estándar: 0.42230373151011547, Varianza: 0.1783404416473677, Incertidumbre: 0.11286541974764448\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.484', 'Área del ala: 13.953', 'Longitud del fuselaje: 13.934', 'Peso máximo al despegue (MTOW): 13.909', 'Velocidad máxima (KIAS): 14.178', 'payload: 13.656', 'Crucero KIAS: 13.613']\n", - "**Mediana calculada:** 13.909\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.467', 'Área del ala: 13.941', 'Longitud del fuselaje: 13.92', 'Peso máximo al despegue (MTOW): 13.898', 'Velocidad máxima (KIAS): 14.166', 'payload: 13.643', 'Crucero KIAS: 13.598']\n", + "**Mediana calculada:** 13.898\n", "\n", "--- Imputación para aeronave: **V39** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", - "Ecuación de regresión: y = -0.127x + 16.984\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", + "Ecuación de regresión: y = -0.128x + 17.003\n", "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 13.51\n", - "\tR²: 0.6032988200454845, Desviación Estándar: 0.5593319783823425, Varianza: 0.3128522620411053, Incertidumbre: 0.12205624426411849\n", + "Predicción obtenida: 13.494\n", + "\tR²: 0.6120846873528347, Desviación Estándar: 0.5547180985616944, Varianza: 0.30771216887190167, Incertidumbre: 0.12104941314385424\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", - "Ecuación de regresión: y = -1.399x + 15.632\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", + "Ecuación de regresión: y = -1.403x + 15.625\n", "Valor del parámetro correlacionado para la aeronave: 1.203\n", - "Predicción obtenida: 13.95\n", - "\tR²: 0.6891267288117411, Desviación Estándar: 0.4951418045804114, Varianza: 0.24516540664314632, Incertidumbre: 0.10804879996317941\n", + "Predicción obtenida: 13.937\n", + "\tR²: 0.6891124350538647, Desviación Estándar: 0.49659866715385637, Varianza: 0.24661023621898662, Incertidumbre: 0.10836671344032035\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", - "Ecuación de regresión: y = -0.031x + 14.9\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", + "Ecuación de regresión: y = -0.031x + 14.891\n", "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 14.157\n", - "\tR²: 0.713892022390023, Desviación Estándar: 0.4993452842769774, Varianza: 0.24934571292965538, Incertidumbre: 0.10412068936317738\n", + "Predicción obtenida: 14.146\n", + "\tR²: 0.7155976388717264, Desviación Estándar: 0.4982613003458266, Varianza: 0.24826432342231405, Incertidumbre: 0.10389466308892618\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909]\n", - "Ecuación de regresión: y = -0.09x + 17.141\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898]\n", + "Ecuación de regresión: y = -0.091x + 17.161\n", "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.161\n", - "\tR²: 0.6545775391356355, Desviación Estándar: 0.49107571880076345, Varianza: 0.2411553615956865, Incertidumbre: 0.11910335639950966\n", + "Predicción obtenida: 14.148\n", + "\tR²: 0.6615818300119434, Desviación Estándar: 0.488766926306414, Varianza: 0.2388931082510195, Incertidumbre: 0.11854339196881342\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", - "Ecuación de regresión: y = -0.125x + 14.915\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", + "Ecuación de regresión: y = -0.125x + 14.903\n", "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 14.292\n", - "\tR²: 0.8291128061127491, Desviación Estándar: 0.38828624580477394, Varianza: 0.15076620868116533, Incertidumbre: 0.0868234440347667\n", + "Predicción obtenida: 14.28\n", + "\tR²: 0.8289103542494105, Desviación Estándar: 0.38872886000472096, Varianza: 0.15111012660056994, Incertidumbre: 0.08692241557865553\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", - "Ecuación de regresión: y = -0.14x + 17.137\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", + "Ecuación de regresión: y = -0.141x + 17.154\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 13.64\n", - "\tR²: 0.7524328660064801, Desviación Estándar: 0.4163959134902486, Varianza: 0.1733855567713786, Incertidumbre: 0.10751296255843715\n", + "Predicción obtenida: 13.625\n", + "\tR²: 0.7574536879812513, Desviación Estándar: 0.4146205350250817, Varianza: 0.17191018806448502, Incertidumbre: 0.10705456180985003\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.51', 'Área del ala: 13.95', 'Peso máximo al despegue (MTOW): 14.157', 'Velocidad máxima (KIAS): 14.161', 'payload: 14.292', 'Crucero KIAS: 13.64']\n", - "**Mediana calculada:** 14.054\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.494', 'Área del ala: 13.937', 'Peso máximo al despegue (MTOW): 14.146', 'Velocidad máxima (KIAS): 14.148', 'payload: 14.28', 'Crucero KIAS: 13.625']\n", + "**Mediana calculada:** 14.042\n", "\n", "--- Imputación para aeronave: **Volitation VT370** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054]\n", - "Ecuación de regresión: y = -0.125x + 16.949\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042]\n", + "Ecuación de regresión: y = -0.126x + 16.968\n", "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 13.542\n", - "\tR²: 0.5871778945845514, Desviación Estándar: 0.5579448935987363, Varianza: 0.3113025042929052, Incertidumbre: 0.11895425100220543\n", + "Predicción obtenida: 13.525\n", + "\tR²: 0.5957680406681929, Desviación Estándar: 0.5537225849540539, Varianza: 0.3066087010881995, Incertidumbre: 0.11805405177448465\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054]\n", - "Ecuación de regresión: y = -1.4x + 15.638\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042]\n", + "Ecuación de regresión: y = -1.404x + 15.631\n", "Valor del parámetro correlacionado para la aeronave: 1.424\n", - "Predicción obtenida: 13.645\n", - "\tR²: 0.6890363238184001, Desviación Estándar: 0.4842444656090505, Varianza: 0.2344927024729949, Incertidumbre: 0.10324126695908932\n", + "Predicción obtenida: 13.632\n", + "\tR²: 0.6890205544749124, Desviación Estándar: 0.48567165393621425, Varianza: 0.23587695543713788, Incertidumbre: 0.103545544532815\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", - "Ecuación de regresión: y = -0.504x + 14.879\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", + "Ecuación de regresión: y = -0.504x + 14.866\n", "Valor del parámetro correlacionado para la aeronave: 2.02\n", - "Predicción obtenida: 13.862\n", - "\tR²: 0.18057230208723418, Desviación Estándar: 0.7426949723818971, Varianza: 0.5515958220013469, Incertidumbre: 0.1703859121309695\n", + "Predicción obtenida: 13.848\n", + "\tR²: 0.17842685862395946, Desviación Estándar: 0.7481250281419246, Varianza: 0.5596910577323555, Incertidumbre: 0.17163165235811428\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054]\n", - "Ecuación de regresión: y = -0.031x + 14.893\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042]\n", + "Ecuación de regresión: y = -0.031x + 14.883\n", "Valor del parámetro correlacionado para la aeronave: 40.0\n", - "Predicción obtenida: 13.657\n", - "\tR²: 0.7145635232521227, Desviación Estándar: 0.4892582472099815, Varianza: 0.2393736324629834, Incertidumbre: 0.09986942150941053\n", + "Predicción obtenida: 13.645\n", + "\tR²: 0.7162472212244984, Desviación Estándar: 0.48820758180424606, Varianza: 0.2383466429311496, Incertidumbre: 0.09965495533154001\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054]\n", - "Ecuación de regresión: y = -0.09x + 17.126\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042]\n", + "Ecuación de regresión: y = -0.091x + 17.146\n", "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.154\n", - "\tR²: 0.6540169951205086, Desviación Estándar: 0.47786020803476664, Varianza: 0.22835037842303046, Incertidumbre: 0.11263273118686595\n", + "Predicción obtenida: 14.142\n", + "\tR²: 0.6610410820046247, Desviación Estándar: 0.47561618075428896, Varianza: 0.22621075139529648, Incertidumbre: 0.11210380888446815\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054]\n", - "Ecuación de regresión: y = -0.124x + 14.893\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042]\n", + "Ecuación de regresión: y = -0.124x + 14.881\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 12.67\n", - "\tR²: 0.8273716345066311, Desviación Estándar: 0.38222671429420746, Varianza: 0.1460972611201457, Incertidumbre: 0.08340870718511739\n", + "Predicción obtenida: 12.657\n", + "\tR²: 0.8271914508346105, Desviación Estándar: 0.3826399844398456, Varianza: 0.1464133576921253, Incertidumbre: 0.08349889012439606\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054]\n", - "Ecuación de regresión: y = -0.137x + 17.097\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042]\n", + "Ecuación de regresión: y = -0.138x + 17.114\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 13.675\n", - "\tR²: 0.7375431593293715, Desviación Estándar: 0.41513917051541654, Varianza: 0.1723405308962281, Incertidumbre: 0.10378479262885414\n", + "Predicción obtenida: 13.66\n", + "\tR²: 0.7425159710448364, Desviación Estándar: 0.41365132002637955, Varianza: 0.17110741455956627, Incertidumbre: 0.10341283000659489\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.542', 'Área del ala: 13.645', 'Longitud del fuselaje: 13.862', 'Peso máximo al despegue (MTOW): 13.657', 'Velocidad máxima (KIAS): 14.154', 'payload: 12.67', 'Crucero KIAS: 13.675']\n", - "**Mediana calculada:** 13.657\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.525', 'Área del ala: 13.632', 'Longitud del fuselaje: 13.848', 'Peso máximo al despegue (MTOW): 13.645', 'Velocidad máxima (KIAS): 14.142', 'payload: 12.657', 'Crucero KIAS: 13.66']\n", + "**Mediana calculada:** 13.645\n", "\n", "--- Imputación para aeronave: **Skyeye 2600** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", - "Ecuación de regresión: y = -0.124x + 16.942\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", + "Ecuación de regresión: y = -0.125x + 16.96\n", "Valor del parámetro correlacionado para la aeronave: 36.094\n", - "Predicción obtenida: 12.462\n", - "\tR²: 0.5876976210919462, Desviación Estándar: 0.5461828235331814, Varianza: 0.29831567672267834, Incertidumbre: 0.11388699141705999\n", + "Predicción obtenida: 12.435\n", + "\tR²: 0.5961936628210269, Desviación Estándar: 0.5420953383497691, Varianza: 0.2938673558605506, Incertidumbre: 0.1130346918390738\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", - "Ecuación de regresión: y = -1.399x + 15.638\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", + "Ecuación de regresión: y = -1.403x + 15.631\n", "Valor del parámetro correlacionado para la aeronave: 0.88\n", - "Predicción obtenida: 14.407\n", - "\tR²: 0.6899904903283296, Desviación Estándar: 0.4736064692670731, Varianza: 0.22430308773162308, Incertidumbre: 0.09875377543286375\n", + "Predicción obtenida: 14.396\n", + "\tR²: 0.6899612272653157, Desviación Estándar: 0.4750038307207795, Varianza: 0.22562863919941495, Incertidumbre: 0.09904514543760928\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657]\n", - "Ecuación de regresión: y = -0.51x + 14.88\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645]\n", + "Ecuación de regresión: y = -0.51x + 14.867\n", "Valor del parámetro correlacionado para la aeronave: 2.05\n", - "Predicción obtenida: 13.834\n", - "\tR²: 0.18539329015630057, Desviación Estándar: 0.725250008930158, Varianza: 0.5259875754531943, Incertidumbre: 0.16217083206501628\n", + "Predicción obtenida: 13.821\n", + "\tR²: 0.18318737073904479, Desviación Estándar: 0.7305092307354947, Varianza: 0.5336437361897641, Incertidumbre: 0.16334682981156448\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", - "Ecuación de regresión: y = -0.031x + 14.893\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", + "Ecuación de regresión: y = -0.031x + 14.883\n", "Valor del parámetro correlacionado para la aeronave: 15.0\n", - "Predicción obtenida: 14.429\n", - "\tR²: 0.714747401512647, Desviación Estándar: 0.4793732254945338, Varianza: 0.22979868932103314, Incertidumbre: 0.09587464509890677\n", + "Predicción obtenida: 14.419\n", + "\tR²: 0.7164307643262642, Desviación Estándar: 0.4783437902243881, Varianza: 0.2288127816462334, Incertidumbre: 0.09566875804487762\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", - "Ecuación de regresión: y = -0.116x + 14.861\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", + "Ecuación de regresión: y = -0.116x + 14.849\n", "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 14.398\n", - "\tR²: 0.7784792227330228, Desviación Estándar: 0.42305623788558205, Varianza: 0.17897658041390221, Incertidumbre: 0.09019589297591563\n", + "Predicción obtenida: 14.385\n", + "\tR²: 0.7782311439723943, Desviación Estándar: 0.42352889042939196, Varianza: 0.17937672102835192, Incertidumbre: 0.0902966628368432\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", - "Ecuación de regresión: y = -0.137x + 17.098\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", + "Ecuación de regresión: y = -0.138x + 17.115\n", "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 12.578\n", - "\tR²: 0.7417224511187792, Desviación Estándar: 0.40276642319311073, Varianza: 0.16222079165177197, Incertidumbre: 0.09768520619278931\n", + "Predicción obtenida: 12.553\n", + "\tR²: 0.7465935530165513, Desviación Estándar: 0.4013165330809285, Varianza: 0.16105495972409598, Incertidumbre: 0.0973335561881972\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.462', 'Área del ala: 14.407', 'Longitud del fuselaje: 13.834', 'Peso máximo al despegue (MTOW): 14.429', 'payload: 14.398', 'Crucero KIAS: 12.578']\n", - "**Mediana calculada:** 14.116\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.435', 'Área del ala: 14.396', 'Longitud del fuselaje: 13.821', 'Peso máximo al despegue (MTOW): 14.419', 'payload: 14.385', 'Crucero KIAS: 12.553']\n", + "**Mediana calculada:** 14.103\n", "\n", "--- Imputación para aeronave: **Skyeye 2930 VTOL** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116]\n", - "Ecuación de regresión: y = -0.1x + 16.412\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103]\n", + "Ecuación de regresión: y = -0.101x + 16.425\n", "Valor del parámetro correlacionado para la aeronave: 26.25\n", - "Predicción obtenida: 13.779\n", - "\tR²: 0.45776737032064685, Desviación Estándar: 0.6141620658053559, Varianza: 0.37719504307430235, Incertidumbre: 0.12536530671639556\n", + "Predicción obtenida: 13.765\n", + "\tR²: 0.4647548108442666, Desviación Estándar: 0.6119553027249928, Varianza: 0.37448929253323754, Incertidumbre: 0.12491485308888704\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116]\n", - "Ecuación de regresión: y = -1.382x + 15.604\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103]\n", + "Ecuación de regresión: y = -1.386x + 15.597\n", "Valor del parámetro correlacionado para la aeronave: 1.0\n", - "Predicción obtenida: 14.223\n", - "\tR²: 0.686240688916129, Desviación Estándar: 0.4671845538323762, Varianza: 0.21826140733955643, Incertidumbre: 0.0953636477165951\n", + "Predicción obtenida: 14.211\n", + "\tR²: 0.6861710827108773, Desviación Estándar: 0.4685865248148631, Varianza: 0.21957333123807035, Incertidumbre: 0.0956498238450352\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116]\n", - "Ecuación de regresión: y = -0.501x + 14.877\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103]\n", + "Ecuación de regresión: y = -0.501x + 14.864\n", "Valor del parámetro correlacionado para la aeronave: 2.03\n", - "Predicción obtenida: 13.861\n", - "\tR²: 0.1804114233108679, Desviación Estándar: 0.7102825050039291, Varianza: 0.5045012369146565, Incertidumbre: 0.1549963497134931\n", + "Predicción obtenida: 13.847\n", + "\tR²: 0.17824780030191256, Desviación Estándar: 0.7154090188937324, Varianza: 0.5118100643144928, Incertidumbre: 0.1561150467587915\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116]\n", - "Ecuación de regresión: y = -0.03x + 14.866\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103]\n", + "Ecuación de regresión: y = -0.031x + 14.856\n", "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 14.013\n", - "\tR²: 0.7118496958649173, Desviación Estándar: 0.47380475199318356, Varianza: 0.2244909430113222, Incertidumbre: 0.09292075677100896\n", + "Predicción obtenida: 14.001\n", + "\tR²: 0.7134393100886465, Desviación Estándar: 0.4728662953123409, Varianza: 0.22360253324241802, Incertidumbre: 0.0927367102737675\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", - "Ecuación de regresión: y = -0.089x + 17.061\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", + "Ecuación de regresión: y = -0.09x + 17.081\n", "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 14.392\n", - "\tR²: 0.6368881023118692, Desviación Estándar: 0.47811022561640054, Varianza: 0.2285893878389654, Incertidumbre: 0.10968600828080638\n", + "Predicción obtenida: 14.383\n", + "\tR²: 0.6440323429773995, Desviación Estándar: 0.4759723084801662, Varianza: 0.2265496384399385, Incertidumbre: 0.10919553645204273\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116]\n", - "Ecuación de regresión: y = -0.114x + 14.834\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103]\n", + "Ecuación de regresión: y = -0.114x + 14.822\n", "Valor del parámetro correlacionado para la aeronave: 6.0\n", - "Predicción obtenida: 14.148\n", - "\tR²: 0.776389170737342, Desviación Estándar: 0.41760392510378513, Varianza: 0.17439303826208777, Incertidumbre: 0.0870764377509503\n", + "Predicción obtenida: 14.136\n", + "\tR²: 0.7761269966964691, Desviación Estándar: 0.4180768449775009, Varianza: 0.17478824830634132, Incertidumbre: 0.0871750483613141\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116]\n", - "Ecuación de regresión: y = -0.107x + 16.512\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103]\n", + "Ecuación de regresión: y = -0.109x + 16.524\n", "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 13.932\n", - "\tR²: 0.5759571817973563, Desviación Estándar: 0.5016106192563546, Varianza: 0.25161321335074355, Incertidumbre: 0.11823075679711727\n", + "Predicción obtenida: 13.919\n", + "\tR²: 0.5802472498582693, Desviación Estándar: 0.5020212903739392, Varianza: 0.25202537598871494, Incertidumbre: 0.11832755290781109\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.779', 'Área del ala: 14.223', 'Longitud del fuselaje: 13.861', 'Peso máximo al despegue (MTOW): 14.013', 'Velocidad máxima (KIAS): 14.392', 'payload: 14.148', 'Crucero KIAS: 13.932']\n", - "**Mediana calculada:** 14.013\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.765', 'Área del ala: 14.211', 'Longitud del fuselaje: 13.847', 'Peso máximo al despegue (MTOW): 14.001', 'Velocidad máxima (KIAS): 14.383', 'payload: 14.136', 'Crucero KIAS: 13.919']\n", + "**Mediana calculada:** 14.001\n", "\n", "--- Imputación para aeronave: **Skyeye 3600** ---\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013]\n", - "Ecuación de regresión: y = -1.374x + 15.586\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001]\n", + "Ecuación de regresión: y = -1.378x + 15.578\n", "Valor del parámetro correlacionado para la aeronave: 1.33\n", - "Predicción obtenida: 13.759\n", - "\tR²: 0.684015300708086, Desviación Estándar: 0.45956965580498865, Varianza: 0.21120426853671576, Incertidumbre: 0.09191393116099773\n", + "Predicción obtenida: 13.746\n", + "\tR²: 0.6839560448092026, Desviación Estándar: 0.46094201511946703, Varianza: 0.21246754130239498, Incertidumbre: 0.0921884030238934\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013]\n", - "Ecuación de regresión: y = -0.496x + 14.877\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001]\n", + "Ecuación de regresión: y = -0.496x + 14.863\n", "Valor del parámetro correlacionado para la aeronave: 2.488\n", - "Predicción obtenida: 13.641\n", - "\tR²: 0.17871439167546932, Desviación Estándar: 0.6946713690287201, Varianza: 0.4825683109482361, Incertidumbre: 0.14810443350865574\n", + "Predicción obtenida: 13.628\n", + "\tR²: 0.17653575217536233, Desviación Estándar: 0.6996903479356846, Varianza: 0.48956658299435946, Incertidumbre: 0.14917448340699427\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013]\n", - "Ecuación de regresión: y = -0.03x + 14.866\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001]\n", + "Ecuación de regresión: y = -0.031x + 14.856\n", "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 14.013\n", - "\tR²: 0.7125790322691798, Desviación Estándar: 0.4649478109590719, Varianza: 0.21617646691563286, Incertidumbre: 0.08947924793878247\n", + "Predicción obtenida: 14.001\n", + "\tR²: 0.7141618045464115, Desviación Estándar: 0.46402689980399064, Varianza: 0.21532096374170276, Incertidumbre: 0.08930201850435383\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013]\n", - "Ecuación de regresión: y = -0.114x + 14.824\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001]\n", + "Ecuación de regresión: y = -0.114x + 14.812\n", "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 13.686\n", - "\tR²: 0.7764391254855179, Desviación Estándar: 0.4096931753354857, Varianza: 0.16784849791647305, Incertidumbre: 0.08362826922271187\n", + "Predicción obtenida: 13.673\n", + "\tR²: 0.7761914041781102, Desviación Estándar: 0.41014763806155546, Varianza: 0.16822108500747268, Incertidumbre: 0.08372103603821063\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Área del ala: 13.759', 'Longitud del fuselaje: 13.641', 'Peso máximo al despegue (MTOW): 14.013', 'payload: 13.686']\n", - "**Mediana calculada:** 13.722\n", + "Valores imputados: ['Área del ala: 13.746', 'Longitud del fuselaje: 13.628', 'Peso máximo al despegue (MTOW): 14.001', 'payload: 13.673']\n", + "**Mediana calculada:** 13.71\n", "\n", "--- Imputación para aeronave: **Skyeye 3600 VTOL** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013]\n", - "Ecuación de regresión: y = -0.1x + 16.413\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001]\n", + "Ecuación de regresión: y = -0.101x + 16.427\n", "Valor del parámetro correlacionado para la aeronave: 32.813\n", - "Predicción obtenida: 13.132\n", - "\tR²: 0.45509872448190547, Desviación Estándar: 0.6034999397625862, Varianza: 0.36421217729344524, Incertidumbre: 0.12069998795251724\n", + "Predicción obtenida: 13.112\n", + "\tR²: 0.46205070143923843, Desviación Estándar: 0.6013720296168469, Varianza: 0.36164831800548575, Incertidumbre: 0.12027440592336938\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722]\n", - "Ecuación de regresión: y = -1.374x + 15.585\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71]\n", + "Ecuación de regresión: y = -1.378x + 15.578\n", "Valor del parámetro correlacionado para la aeronave: 1.32\n", - "Predicción obtenida: 13.771\n", - "\tR²: 0.6844716239481952, Desviación Estándar: 0.45070169488409495, Varianza: 0.2031320177713958, Incertidumbre: 0.08838987450089776\n", + "Predicción obtenida: 13.758\n", + "\tR²: 0.6844100847233376, Desviación Estándar: 0.4520440536186202, Varianza: 0.20434382641195398, Incertidumbre: 0.08865313270788183\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722]\n", - "Ecuación de regresión: y = -0.491x + 14.87\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71]\n", + "Ecuación de regresión: y = -0.49x + 14.856\n", "Valor del parámetro correlacionado para la aeronave: 2.42\n", - "Predicción obtenida: 13.682\n", - "\tR²: 0.18314058790644772, Desviación Estándar: 0.6795903299866143, Varianza: 0.4618430166113153, Incertidumbre: 0.14170437945601297\n", + "Predicción obtenida: 13.669\n", + "\tR²: 0.18085287153910534, Desviación Estándar: 0.6845046469228117, Varianza: 0.4685466116589231, Incertidumbre: 0.14272908537245493\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722]\n", - "Ecuación de regresión: y = -0.03x + 14.851\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71]\n", + "Ecuación de regresión: y = -0.03x + 14.841\n", "Valor del parámetro correlacionado para la aeronave: 40.0\n", - "Predicción obtenida: 13.637\n", - "\tR²: 0.7086441586316434, Desviación Estándar: 0.4597355870860578, Varianza: 0.2113568100333622, Incertidumbre: 0.08688185944828475\n", + "Predicción obtenida: 13.625\n", + "\tR²: 0.7102145115795546, Desviación Estándar: 0.45885229575829384, Varianza: 0.21054542932265677, Incertidumbre: 0.0867149330776788\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717]\n", - "Ecuación de regresión: y = 0.0x + 14.096\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714]\n", + "Ecuación de regresión: y = 0.0x + 14.354\n", "Valor del parámetro correlacionado para la aeronave: 300.0\n", - "Predicción obtenida: 14.141\n", - "\tR²: 0.0237018252029767, Desviación Estándar: 0.8638914663706774, Varianza: 0.7463084656680793, Incertidumbre: 0.2604730775946788\n", + "Predicción obtenida: 14.367\n", + "\tR²: 0.002361606473490707, Desviación Estándar: 0.8223839641550523, Varianza: 0.6763153844993783, Incertidumbre: 0.2741279880516841\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013]\n", - "Ecuación de regresión: y = -0.087x + 16.976\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001]\n", + "Ecuación de regresión: y = -0.088x + 16.996\n", "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.103\n", - "\tR²: 0.6259343281303104, Desviación Estándar: 0.47309816529550813, Varianza: 0.22382187400597592, Incertidumbre: 0.1057879657631188\n", + "Predicción obtenida: 14.091\n", + "\tR²: 0.6330464973659589, Desviación Estándar: 0.4711473913359555, Varianza: 0.22197986436267603, Incertidumbre: 0.1053517594448892\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722]\n", - "Ecuación de regresión: y = -0.114x + 14.826\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71]\n", + "Ecuación de regresión: y = -0.114x + 14.813\n", "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 13.687\n", - "\tR²: 0.7763699681724744, Desviación Estándar: 0.40147919692479167, Varianza: 0.16118554556337567, Incertidumbre: 0.08029583938495834\n", + "Predicción obtenida: 13.674\n", + "\tR²: 0.7761192880267639, Desviación Estándar: 0.4019269643743591, Varianza: 0.16154528469118734, Incertidumbre: 0.08038539287487181\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013]\n", - "Ecuación de regresión: y = -0.107x + 16.512\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001]\n", + "Ecuación de regresión: y = -0.108x + 16.524\n", "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 13.292\n", - "\tR²: 0.5754628990444985, Desviación Estándar: 0.48856755387819806, Varianza: 0.23869825470252598, Incertidumbre: 0.11208508391831203\n", + "Predicción obtenida: 13.274\n", + "\tR²: 0.579753910323783, Desviación Estándar: 0.488970938859074, Varianza: 0.2390925790487243, Incertidumbre: 0.11217762677973205\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.132', 'Área del ala: 13.771', 'Longitud del fuselaje: 13.682', 'Peso máximo al despegue (MTOW): 13.637', 'Alcance de la aeronave: 14.141', 'Velocidad máxima (KIAS): 14.103', 'payload: 13.687', 'Crucero KIAS: 13.292']\n", - "**Mediana calculada:** 13.684\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.112', 'Área del ala: 13.758', 'Longitud del fuselaje: 13.669', 'Peso máximo al despegue (MTOW): 13.625', 'Alcance de la aeronave: 14.367', 'Velocidad máxima (KIAS): 14.091', 'payload: 13.674', 'Crucero KIAS: 13.274']\n", + "**Mediana calculada:** 13.672\n", "\n", "--- Imputación para aeronave: **Skyeye 5000** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684]\n", - "Ecuación de regresión: y = -0.095x + 16.308\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672]\n", + "Ecuación de regresión: y = -0.096x + 16.32\n", "Valor del parámetro correlacionado para la aeronave: 36.094\n", - "Predicción obtenida: 12.878\n", - "\tR²: 0.4401977193966343, Desviación Estándar: 0.6005761559399635, Varianza: 0.36069171908362335, Incertidumbre: 0.11778267455909297\n", + "Predicción obtenida: 12.855\n", + "\tR²: 0.44669483333289495, Desviación Estándar: 0.5987984426089217, Varianza: 0.35855957487087003, Incertidumbre: 0.11743403629122542\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684]\n", - "Ecuación de regresión: y = -1.375x + 15.584\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672]\n", + "Ecuación de regresión: y = -1.379x + 15.576\n", "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 11.987\n", - "\tR²: 0.6847809364305605, Desviación Estándar: 0.4425842972237917, Varianza: 0.19588086014907763, Incertidumbre: 0.08517538771375249\n", + "Predicción obtenida: 11.969\n", + "\tR²: 0.6847227318034814, Desviación Estándar: 0.4438937988851134, Varianza: 0.19704170468865748, Incertidumbre: 0.08542740142597527\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684]\n", - "Ecuación de regresión: y = -0.491x + 14.87\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672]\n", + "Ecuación de regresión: y = -0.49x + 14.856\n", "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 13.153\n", - "\tR²: 0.18887857116834583, Desviación Estándar: 0.6652816445821408, Varianza: 0.44259966661791794, Incertidumbre: 0.13580004703882315\n", + "Predicción obtenida: 13.14\n", + "\tR²: 0.18648513764579755, Desviación Estándar: 0.6700926766915704, Varianza: 0.4490241953556735, Incertidumbre: 0.13678209485584383\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Ancho del fuselaje (r = -0.996) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277]\n", "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5]\n", "Ecuación de regresión: y = -38.708x + 23.253\n", @@ -25932,527 +28007,527 @@ "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684]\n", - "Ecuación de regresión: y = -0.03x + 14.852\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672]\n", + "Ecuación de regresión: y = -0.03x + 14.842\n", "Valor del parámetro correlacionado para la aeronave: 90.0\n", - "Predicción obtenida: 12.123\n", - "\tR²: 0.7086905843429328, Desviación Estándar: 0.4518198530718025, Varianza: 0.20414117962982523, Incertidumbre: 0.08390084041127426\n", + "Predicción obtenida: 12.108\n", + "\tR²: 0.710260620996489, Desviación Estándar: 0.45095219827945476, Varianza: 0.20335788513307268, Incertidumbre: 0.0837397209611883\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684]\n", - "Ecuación de regresión: y = -0.086x + 16.93\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672]\n", + "Ecuación de regresión: y = -0.087x + 16.95\n", "Valor del parámetro correlacionado para la aeronave: 42.0\n", - "Predicción obtenida: 13.305\n", - "\tR²: 0.6139919598950958, Desviación Estándar: 0.4702102654821254, Varianza: 0.22109769376477087, Incertidumbre: 0.10260829210081529\n", + "Predicción obtenida: 13.284\n", + "\tR²: 0.6211901244543757, Desviación Estándar: 0.4683247546231655, Varianza: 0.21932807579284816, Incertidumbre: 0.102196839899156\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684]\n", - "Ecuación de regresión: y = -0.114x + 14.826\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672]\n", + "Ecuación de regresión: y = -0.114x + 14.813\n", "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 12.548\n", - "\tR²: 0.7763982091486223, Desviación Estándar: 0.39368317088871674, Varianza: 0.15498643904099454, Incertidumbre: 0.07720762194364038\n", + "Predicción obtenida: 12.535\n", + "\tR²: 0.7761469380573378, Desviación Estándar: 0.39412206749545337, Varianza: 0.15533220408689072, Incertidumbre: 0.07729369664987883\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684]\n", - "Ecuación de regresión: y = -0.103x + 16.426\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672]\n", + "Ecuación de regresión: y = -0.104x + 16.437\n", "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 13.035\n", - "\tR²: 0.5683584083709616, Desviación Estándar: 0.4831583112495382, Varianza: 0.2334419537295056, Incertidumbre: 0.10803748278479687\n", + "Predicción obtenida: 13.014\n", + "\tR²: 0.5722804312940908, Desviación Estándar: 0.4837831260054291, Varianza: 0.23404611300758488, Incertidumbre: 0.10817719561154858\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.878', 'Área del ala: 11.987', 'Longitud del fuselaje: 13.153', 'Ancho del fuselaje: 8.737', 'Peso máximo al despegue (MTOW): 12.123', 'Velocidad máxima (KIAS): 13.305', 'payload: 12.548', 'Crucero KIAS: 13.035']\n", - "**Mediana calculada:** 12.713\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.855', 'Área del ala: 11.969', 'Longitud del fuselaje: 13.14', 'Ancho del fuselaje: 8.737', 'Peso máximo al despegue (MTOW): 12.108', 'Velocidad máxima (KIAS): 13.284', 'payload: 12.535', 'Crucero KIAS: 13.014']\n", + "**Mediana calculada:** 12.695\n", "\n", "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713]\n", - "Ecuación de regresión: y = -0.097x + 16.349\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695]\n", + "Ecuación de regresión: y = -0.098x + 16.36\n", "Valor del parámetro correlacionado para la aeronave: 30.625\n", - "Predicción obtenida: 13.383\n", - "\tR²: 0.47992913317978514, Desviación Estándar: 0.590075692205931, Varianza: 0.34818932253230866, Incertidumbre: 0.11356011991244967\n", + "Predicción obtenida: 13.366\n", + "\tR²: 0.4861597087141194, Desviación Estándar: 0.5882900301706695, Varianza: 0.3460851595982073, Incertidumbre: 0.11321646909353636\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713]\n", - "Ecuación de regresión: y = -1.253x + 15.452\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695]\n", + "Ecuación de regresión: y = -1.257x + 15.445\n", "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 12.175\n", - "\tR²: 0.6860448617033243, Desviación Estándar: 0.4503815543594209, Varianza: 0.202843544507208, Incertidumbre: 0.08511411342326757\n", + "Predicción obtenida: 12.157\n", + "\tR²: 0.6861946820655023, Desviación Estándar: 0.4516214364570259, Varianza: 0.2039619218675075, Incertidumbre: 0.085348429115075\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713]\n", - "Ecuación de regresión: y = -0.545x + 14.955\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695]\n", + "Ecuación de regresión: y = -0.546x + 14.942\n", "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 13.046\n", - "\tR²: 0.2643820653371962, Desviación Estándar: 0.6563198967362653, Varianza: 0.43075580685190196, Incertidumbre: 0.13126397934725306\n", + "Predicción obtenida: 13.032\n", + "\tR²: 0.2617903079866821, Desviación Estándar: 0.6611047129721297, Varianza: 0.437059441513962, Incertidumbre: 0.13222094259442593\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Ancho del fuselaje (r = -0.996) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.713]\n", - "Ecuación de regresión: y = -16.3x + 18.292\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.695]\n", + "Ecuación de regresión: y = -16.401x + 18.314\n", "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 12.179\n", - "\tR²: 0.700384799125958, Desviación Estándar: 0.742124944233944, Varianza: 0.5507494328542346, Incertidumbre: 0.371062472116972\n", + "Predicción obtenida: 12.164\n", + "\tR²: 0.7048037347079352, Desviación Estándar: 0.7388896951434575, Varianza: 0.5459579815891915, Incertidumbre: 0.36944484757172874\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713]\n", - "Ecuación de regresión: y = -0.029x + 14.809\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695]\n", + "Ecuación de regresión: y = -0.029x + 14.8\n", "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 11.945\n", - "\tR²: 0.7105088370955781, Desviación Estándar: 0.45476821765148717, Varianza: 0.20681413178591043, Incertidumbre: 0.08302893708137953\n", + "Predicción obtenida: 11.929\n", + "\tR²: 0.7123076858370674, Desviación Estándar: 0.45382550140258615, Varianza: 0.20595758572330874, Incertidumbre: 0.08285682142976294\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717, 13.684]\n", - "Ecuación de regresión: y = 0.0x + 14.051\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672]\n", + "Ecuación de regresión: y = 0.0x + 14.269\n", "Valor del parámetro correlacionado para la aeronave: 800.0\n", - "Predicción obtenida: 14.182\n", - "\tR²: 0.027074923206067303, Desviación Estándar: 0.8366422137567701, Varianza: 0.699970193839829, Incertidumbre: 0.24151780366393782\n", + "Predicción obtenida: 14.323\n", + "\tR²: 0.005851871754970817, Desviación Estándar: 0.8072016250304093, Varianza: 0.6515744634517334, Incertidumbre: 0.2552595666085276\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713]\n", - "Ecuación de regresión: y = -0.09x + 17.04\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695]\n", + "Ecuación de regresión: y = -0.091x + 17.059\n", "Valor del parámetro correlacionado para la aeronave: 42.0\n", - "Predicción obtenida: 13.251\n", - "\tR²: 0.6306274945350463, Desviación Estándar: 0.47489583792466356, Varianza: 0.22552605687816832, Incertidumbre: 0.10124813283983614\n", + "Predicción obtenida: 13.23\n", + "\tR²: 0.6374089155434113, Desviación Estándar: 0.4729515606700395, Varianza: 0.22368317874022606, Incertidumbre: 0.10083361153635637\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713]\n", - "Ecuación de regresión: y = -0.112x + 14.818\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695]\n", + "Ecuación de regresión: y = -0.113x + 14.806\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 12.006\n", - "\tR²: 0.7868817244212251, Desviación Estándar: 0.3874643871417889, Varianza: 0.15012865130316205, Incertidumbre: 0.07456755607256839\n", + "Predicción obtenida: 11.992\n", + "\tR²: 0.7868234537491392, Desviación Estándar: 0.3878273476620516, Varianza: 0.15041005159458187, Incertidumbre: 0.07463740785726136\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713]\n", - "Ecuación de regresión: y = -0.107x + 16.516\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695]\n", + "Ecuación de regresión: y = -0.108x + 16.526\n", "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 13.514\n", - "\tR²: 0.6195025656212503, Desviación Estándar: 0.4757969201229366, Varianza: 0.22638270919847214, Incertidumbre: 0.10382740009001022\n", + "Predicción obtenida: 13.498\n", + "\tR²: 0.6230065242149951, Desviación Estándar: 0.47633025955044683, Varianza: 0.22689051616339603, Incertidumbre: 0.10394378429466117\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.383', 'Área del ala: 12.175', 'Longitud del fuselaje: 13.046', 'Ancho del fuselaje: 12.179', 'Peso máximo al despegue (MTOW): 11.945', 'Alcance de la aeronave: 14.182', 'Velocidad máxima (KIAS): 13.251', 'payload: 12.006', 'Crucero KIAS: 13.514']\n", - "**Mediana calculada:** 13.046\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.366', 'Área del ala: 12.157', 'Longitud del fuselaje: 13.032', 'Ancho del fuselaje: 12.164', 'Peso máximo al despegue (MTOW): 11.929', 'Alcance de la aeronave: 14.323', 'Velocidad máxima (KIAS): 13.23', 'payload: 11.992', 'Crucero KIAS: 13.498']\n", + "**Mediana calculada:** 13.032\n", "\n", "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046]\n", - "Ecuación de regresión: y = -0.098x + 16.379\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032]\n", + "Ecuación de regresión: y = -0.099x + 16.39\n", "Valor del parámetro correlacionado para la aeronave: 33.885\n", - "Predicción obtenida: 13.042\n", - "\tR²: 0.4912217506212547, Desviación Estándar: 0.5827288503285376, Varianza: 0.33957291300521925, Incertidumbre: 0.11012540141084928\n", + "Predicción obtenida: 13.022\n", + "\tR²: 0.49733952651439894, Desviación Estándar: 0.5809229222603671, Varianza: 0.33747144160752457, Incertidumbre: 0.10978411308555996\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046]\n", - "Ecuación de regresión: y = -1.137x + 15.327\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032]\n", + "Ecuación de regresión: y = -1.141x + 15.319\n", "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 12.354\n", - "\tR²: 0.6639527907853231, Desviación Estándar: 0.4654542390243011, Varianza: 0.21664764862569122, Incertidumbre: 0.08643268232155993\n", + "Predicción obtenida: 12.337\n", + "\tR²: 0.6639636558875286, Desviación Estándar: 0.4668168117669745, Varianza: 0.21791793574828286, Incertidumbre: 0.0866857057278015\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046]\n", - "Ecuación de regresión: y = -0.545x + 14.955\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032]\n", + "Ecuación de regresión: y = -0.546x + 14.942\n", "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 13.046\n", - "\tR²: 0.30031202594013184, Desviación Estándar: 0.643574609531079, Varianza: 0.4141882780330809, Incertidumbre: 0.12621536509430112\n", + "Predicción obtenida: 13.032\n", + "\tR²: 0.2974987662982014, Desviación Estándar: 0.6482665064635713, Varianza: 0.4202494634024835, Incertidumbre: 0.12713552178716822\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Ancho del fuselaje (r = -0.996) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.713, 13.046]\n", - "Ecuación de regresión: y = -13.682x + 17.712\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.695, 13.032]\n", + "Ecuación de regresión: y = -13.779x + 17.734\n", "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 12.582\n", - "\tR²: 0.6751786720121111, Desviación Estándar: 0.7218724853677485, Varianza: 0.5210998851310104, Incertidumbre: 0.3228311896738016\n", + "Predicción obtenida: 12.567\n", + "\tR²: 0.6797443662647868, Desviación Estándar: 0.7194173301196193, Varianza: 0.5175612948764412, Incertidumbre: 0.3217332108677751\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046]\n", - "Ecuación de regresión: y = -0.026x + 14.728\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032]\n", + "Ecuación de regresión: y = -0.026x + 14.718\n", "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 12.163\n", - "\tR²: 0.672459148806959, Desviación Estándar: 0.48114314921165147, Varianza: 0.2314987300333055, Incertidumbre: 0.08641586063227229\n", + "Predicción obtenida: 12.147\n", + "\tR²: 0.6741658765761376, Desviación Estándar: 0.48040741723701913, Varianza: 0.2307912865363434, Incertidumbre: 0.08628371926875769\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046]\n", - "Ecuación de regresión: y = -0.091x + 17.075\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032]\n", + "Ecuación de regresión: y = -0.092x + 17.093\n", "Valor del parámetro correlacionado para la aeronave: 38.0\n", - "Predicción obtenida: 13.6\n", - "\tR²: 0.6449500428703554, Desviación Estándar: 0.4662598472902855, Varianza: 0.21739824519516038, Incertidumbre: 0.09722189885607506\n", + "Predicción obtenida: 13.583\n", + "\tR²: 0.6514471014044674, Desviación Estándar: 0.4642468173732389, Varianza: 0.21552510744118147, Incertidumbre: 0.09680215310244272\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046]\n", - "Ecuación de regresión: y = -0.102x + 14.742\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032]\n", + "Ecuación de regresión: y = -0.102x + 14.73\n", "Valor del parámetro correlacionado para la aeronave: 15.0\n", - "Predicción obtenida: 13.216\n", - "\tR²: 0.7459460023089699, Desviación Estándar: 0.41981136981986106, Varianza: 0.17624158623002814, Incertidumbre: 0.0793368915786228\n", + "Predicción obtenida: 13.203\n", + "\tR²: 0.74595960714623, Desviación Estándar: 0.42014445536437306, Varianza: 0.17652136337342567, Incertidumbre: 0.07939983882977203\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046]\n", - "Ecuación de regresión: y = -0.11x + 16.561\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032]\n", + "Ecuación de regresión: y = -0.111x + 16.571\n", "Valor del parámetro correlacionado para la aeronave: 35.0\n", - "Predicción obtenida: 12.711\n", - "\tR²: 0.6278560933803895, Desviación Estándar: 0.47470648197778126, Varianza: 0.22534624403172154, Incertidumbre: 0.10120776201631464\n", + "Predicción obtenida: 12.689\n", + "\tR²: 0.6312746472246312, Desviación Estándar: 0.47515011887040703, Varianza: 0.22576763546256193, Incertidumbre: 0.10130234571962422\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.042', 'Área del ala: 12.354', 'Longitud del fuselaje: 13.046', 'Ancho del fuselaje: 12.582', 'Peso máximo al despegue (MTOW): 12.163', 'Velocidad máxima (KIAS): 13.6', 'payload: 13.216', 'Crucero KIAS: 12.711']\n", - "**Mediana calculada:** 12.876\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.022', 'Área del ala: 12.337', 'Longitud del fuselaje: 13.032', 'Ancho del fuselaje: 12.567', 'Peso máximo al despegue (MTOW): 12.147', 'Velocidad máxima (KIAS): 13.583', 'payload: 13.203', 'Crucero KIAS: 12.689']\n", + "**Mediana calculada:** 12.856\n", "\n", "--- Imputación para aeronave: **Volitation VT510** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876]\n", - "Ecuación de regresión: y = -0.1x + 16.407\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856]\n", + "Ecuación de regresión: y = -0.101x + 16.417\n", "Valor del parámetro correlacionado para la aeronave: 32.813\n", - "Predicción obtenida: 13.134\n", - "\tR²: 0.5120481249262255, Desviación Estándar: 0.5733507354534118, Varianza: 0.32873106584496814, Incertidumbre: 0.1064685587140827\n", + "Predicción obtenida: 13.115\n", + "\tR²: 0.5181009063264552, Desviación Estándar: 0.5715788333906897, Varianza: 0.3267023627802618, Incertidumbre: 0.10613952476132892\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876]\n", - "Ecuación de regresión: y = -1.079x + 15.265\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856]\n", + "Ecuación de regresión: y = -1.083x + 15.257\n", "Valor del parámetro correlacionado para la aeronave: 1.993\n", - "Predicción obtenida: 13.114\n", - "\tR²: 0.6668961846597724, Desviación Estándar: 0.46579848190753764, Varianza: 0.21696822574736668, Incertidumbre: 0.08504277859747346\n", + "Predicción obtenida: 13.099\n", + "\tR²: 0.6672340951247093, Desviación Estándar: 0.4670272390002237, Varianza: 0.21811444196817206, Incertidumbre: 0.08526711792325965\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876]\n", - "Ecuación de regresión: y = -0.56x + 14.977\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856]\n", + "Ecuación de regresión: y = -0.56x + 14.965\n", "Valor del parámetro correlacionado para la aeronave: 2.905\n", - "Predicción obtenida: 13.351\n", - "\tR²: 0.3416103143456418, Desviación Estándar: 0.632254792757014, Varianza: 0.3997461229642147, Incertidumbre: 0.12167749159823103\n", + "Predicción obtenida: 13.337\n", + "\tR²: 0.3389580451352642, Desviación Estándar: 0.6369033781917752, Varianza: 0.40564591315209547, Incertidumbre: 0.12257211228226782\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876]\n", - "Ecuación de regresión: y = -0.024x + 14.684\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856]\n", + "Ecuación de regresión: y = -0.024x + 14.674\n", "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 12.281\n", - "\tR²: 0.6638393025961682, Desviación Estándar: 0.4873610039590521, Varianza: 0.23752074817997518, Incertidumbre: 0.08615406769633238\n", + "Predicción obtenida: 12.265\n", + "\tR²: 0.6659304816788436, Desviación Estándar: 0.4864938763937024, Varianza: 0.236676291768571, Incertidumbre: 0.08600077975092925\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876]\n", - "Ecuación de regresión: y = -0.093x + 17.103\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856]\n", + "Ecuación de regresión: y = -0.094x + 17.121\n", "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 12.449\n", - "\tR²: 0.632488199457406, Desviación Estándar: 0.4786898436308466, Varianza: 0.2291439663953244, Incertidumbre: 0.09771215516235353\n", + "Predicción obtenida: 12.421\n", + "\tR²: 0.6386649948497098, Desviación Estándar: 0.4770137198562853, Varianza: 0.22754208893113068, Incertidumbre: 0.09737001782956828\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876]\n", - "Ecuación de regresión: y = -0.103x + 14.741\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856]\n", + "Ecuación de regresión: y = -0.103x + 14.729\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 12.172\n", - "\tR²: 0.7481817504133035, Desviación Estándar: 0.41708148056338223, Varianza: 0.173956961428943, Incertidumbre: 0.07745008658060275\n", + "Predicción obtenida: 12.157\n", + "\tR²: 0.7481095898704668, Desviación Estándar: 0.41758688156853935, Varianza: 0.1743788036581373, Incertidumbre: 0.07754393719117042\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876]\n", - "Ecuación de regresión: y = -0.108x + 16.515\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856]\n", + "Ecuación de regresión: y = -0.109x + 16.525\n", "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 13.281\n", - "\tR²: 0.6540354444609318, Desviación Estándar: 0.4653103999726643, Varianza: 0.21651376832272087, Incertidumbre: 0.09702392540496321\n", + "Predicción obtenida: 13.263\n", + "\tR²: 0.6572957320511896, Desviación Estándar: 0.4657670897559235, Varianza: 0.2169389818997025, Incertidumbre: 0.09711915180752535\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.134', 'Área del ala: 13.114', 'Longitud del fuselaje: 13.351', 'Peso máximo al despegue (MTOW): 12.281', 'Velocidad máxima (KIAS): 12.449', 'payload: 12.172', 'Crucero KIAS: 13.281']\n", - "**Mediana calculada:** 13.114\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.115', 'Área del ala: 13.099', 'Longitud del fuselaje: 13.337', 'Peso máximo al despegue (MTOW): 12.265', 'Velocidad máxima (KIAS): 12.421', 'payload: 12.157', 'Crucero KIAS: 13.263']\n", + "**Mediana calculada:** 13.099\n", "\n", "--- Imputación para aeronave: **Ascend** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114]\n", - "Ecuación de regresión: y = -0.1x + 16.409\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099]\n", + "Ecuación de regresión: y = -0.101x + 16.419\n", "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 14.225\n", - "\tR²: 0.5225539026607045, Desviación Estándar: 0.5637250248651999, Varianza: 0.31778590365927023, Incertidumbre: 0.10292163744961022\n", + "Predicción obtenida: 14.216\n", + "\tR²: 0.5284756398961317, Desviación Estándar: 0.5619789405509674, Varianza: 0.3158203296227878, Incertidumbre: 0.10260284752753986\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114]\n", - "Ecuación de regresión: y = -1.079x + 15.265\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099]\n", + "Ecuación de regresión: y = -1.083x + 15.257\n", "Valor del parámetro correlacionado para la aeronave: 0.771\n", - "Predicción obtenida: 14.433\n", - "\tR²: 0.6740457304881754, Desviación Estándar: 0.4582240184158411, Varianza: 0.20996925105316108, Incertidumbre: 0.08229946322349961\n", + "Predicción obtenida: 14.422\n", + "\tR²: 0.6743723185855064, Desviación Estándar: 0.4594327962255891, Varianza: 0.21107849424766364, Incertidumbre: 0.08251656612710266\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114]\n", - "Ecuación de regresión: y = -0.571x + 14.991\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099]\n", + "Ecuación de regresión: y = -0.572x + 14.979\n", "Valor del parámetro correlacionado para la aeronave: 1.562\n", - "Predicción obtenida: 14.1\n", - "\tR²: 0.3593175354516496, Desviación Estándar: 0.6223578212520778, Varianza: 0.3873292576736333, Incertidumbre: 0.11761457296635622\n", + "Predicción obtenida: 14.086\n", + "\tR²: 0.3566167414715793, Desviación Estándar: 0.6269166459626101, Varianza: 0.39302448098500864, Incertidumbre: 0.1184761098559851\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114]\n", - "Ecuación de regresión: y = -0.022x + 14.64\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099]\n", + "Ecuación de regresión: y = -0.022x + 14.63\n", "Valor del parámetro correlacionado para la aeronave: 9.5\n", - "Predicción obtenida: 14.427\n", - "\tR²: 0.6427542977157497, Desviación Estándar: 0.4983661335008928, Varianza: 0.24836880302062972, Incertidumbre: 0.08675440832753979\n", + "Predicción obtenida: 14.417\n", + "\tR²: 0.6448304134719163, Desviación Estándar: 0.49760568948205036, Varianza: 0.24761142220490673, Incertidumbre: 0.08662203201525417\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114]\n", - "Ecuación de regresión: y = -0.086x + 16.872\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099]\n", + "Ecuación de regresión: y = -0.087x + 16.885\n", "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 14.293\n", - "\tR²: 0.6198077286916189, Desviación Estándar: 0.4840831085935595, Varianza: 0.23433645602560393, Incertidumbre: 0.0968166217187119\n", + "Predicción obtenida: 14.284\n", + "\tR²: 0.6250452670025234, Desviación Estándar: 0.4830714233111504, Varianza: 0.23335800001986062, Incertidumbre: 0.09661428466223007\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114]\n", - "Ecuación de regresión: y = -0.095x + 14.682\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099]\n", + "Ecuación de regresión: y = -0.095x + 14.67\n", "Valor del parámetro correlacionado para la aeronave: 0.6\n", - "Predicción obtenida: 14.626\n", - "\tR²: 0.7144699709998399, Desviación Estándar: 0.4395959045047289, Varianza: 0.19324455925733075, Incertidumbre: 0.08025886436137562\n", + "Predicción obtenida: 14.614\n", + "\tR²: 0.7145248980811096, Desviación Estándar: 0.44003758459985504, Varianza: 0.19363307586047457, Incertidumbre: 0.08033950374514283\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114]\n", - "Ecuación de regresión: y = -0.109x + 16.535\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099]\n", + "Ecuación de regresión: y = -0.11x + 16.544\n", "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 14.357\n", - "\tR²: 0.6656187134909644, Desviación Estándar: 0.45668281214871304, Varianza: 0.20855919091205674, Incertidumbre: 0.0932199886719708\n", + "Predicción obtenida: 14.349\n", + "\tR²: 0.6687656157013713, Desviación Estándar: 0.45709955119724055, Varianza: 0.20893999970471874, Incertidumbre: 0.09330505517403626\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.225', 'Área del ala: 14.433', 'Longitud del fuselaje: 14.1', 'Peso máximo al despegue (MTOW): 14.427', 'Velocidad máxima (KIAS): 14.293', 'payload: 14.626', 'Crucero KIAS: 14.357']\n", - "**Mediana calculada:** 14.357\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.216', 'Área del ala: 14.422', 'Longitud del fuselaje: 14.086', 'Peso máximo al despegue (MTOW): 14.417', 'Velocidad máxima (KIAS): 14.284', 'payload: 14.614', 'Crucero KIAS: 14.349']\n", + "**Mediana calculada:** 14.349\n", "\n", "--- Imputación para aeronave: **Transition** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", - "Ecuación de regresión: y = -0.1x + 16.428\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", + "Ecuación de regresión: y = -0.101x + 16.438\n", "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 14.232\n", - "\tR²: 0.5298654936239107, Desviación Estándar: 0.5550399694090079, Varianza: 0.30806936764155246, Incertidumbre: 0.09968812134263677\n", + "Predicción obtenida: 14.223\n", + "\tR²: 0.5357657902510582, Desviación Estándar: 0.5533318320555499, Varianza: 0.30617611636595127, Incertidumbre: 0.09938133081736573\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", - "Ecuación de regresión: y = -1.075x + 15.258\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", + "Ecuación de regresión: y = -1.079x + 15.25\n", "Valor del parámetro correlacionado para la aeronave: 0.986\n", - "Predicción obtenida: 14.197\n", - "\tR²: 0.6793546401299544, Desviación Estándar: 0.45119534421783186, Varianza: 0.2035772386438478, Incertidumbre: 0.07976082188405685\n", + "Predicción obtenida: 14.186\n", + "\tR²: 0.6797569865020562, Desviación Estándar: 0.4523706806342405, Varianza: 0.20463923269748602, Incertidumbre: 0.07996859397161137\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", - "Ecuación de regresión: y = -0.577x + 15.013\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", + "Ecuación de regresión: y = -0.578x + 15.001\n", "Valor del parámetro correlacionado para la aeronave: 2.3\n", - "Predicción obtenida: 13.686\n", - "\tR²: 0.3655687526307442, Desviación Estándar: 0.6133106646584037, Varianza: 0.37614997138373285, Incertidumbre: 0.11388893128133473\n", + "Predicción obtenida: 13.672\n", + "\tR²: 0.36290901642874207, Desviación Estándar: 0.6178541362486212, Varianza: 0.3817437336795298, Incertidumbre: 0.11473263277477799\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0, 9.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", - "Ecuación de regresión: y = -0.022x + 14.635\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", + "Ecuación de regresión: y = -0.022x + 14.625\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 14.233\n", - "\tR²: 0.6493187979493635, Desviación Estándar: 0.4911193487224634, Varianza: 0.2411982146895766, Incertidumbre: 0.08422627344201288\n", + "Predicción obtenida: 14.222\n", + "\tR²: 0.6514348621062943, Desviación Estándar: 0.4903621688911602, Varianza: 0.2404550566796427, Incertidumbre: 0.08409641817224593\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", - "Ecuación de regresión: y = -0.086x + 16.884\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", + "Ecuación de regresión: y = -0.087x + 16.898\n", "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 14.297\n", - "\tR²: 0.6276324914013882, Desviación Estándar: 0.4748361245225159, Varianza: 0.22546934515156222, Incertidumbre: 0.09312302556534931\n", + "Predicción obtenida: 14.288\n", + "\tR²: 0.6328453004599623, Desviación Estándar: 0.47385313335474916, Varianza: 0.22453679199011367, Incertidumbre: 0.09293024513665193\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", - "Ecuación de regresión: y = -0.093x + 14.657\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", + "Ecuación de regresión: y = -0.093x + 14.645\n", "Valor del parámetro correlacionado para la aeronave: 1.5\n", - "Predicción obtenida: 14.517\n", - "\tR²: 0.7184307080462207, Desviación Estándar: 0.43488206123514683, Varianza: 0.18912240718412998, Incertidumbre: 0.07810712395416497\n", + "Predicción obtenida: 14.505\n", + "\tR²: 0.7186695306905169, Desviación Estándar: 0.43524135975547956, Varianza: 0.18943504124179877, Incertidumbre: 0.0781716558734271\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", - "Ecuación de regresión: y = -0.109x + 16.535\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", + "Ecuación de regresión: y = -0.11x + 16.544\n", "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 14.357\n", - "\tR²: 0.670906735462538, Desviación Estándar: 0.44745594752331586, Varianza: 0.20021682497398838, Incertidumbre: 0.08949118950466317\n", + "Predicción obtenida: 14.349\n", + "\tR²: 0.6740321107300629, Desviación Estándar: 0.4478642716421813, Varianza: 0.20058240581358153, Incertidumbre: 0.08957285432843626\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.232', 'Área del ala: 14.197', 'Longitud del fuselaje: 13.686', 'Peso máximo al despegue (MTOW): 14.233', 'Velocidad máxima (KIAS): 14.297', 'payload: 14.517', 'Crucero KIAS: 14.357']\n", - "**Mediana calculada:** 14.233\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.223', 'Área del ala: 14.186', 'Longitud del fuselaje: 13.672', 'Peso máximo al despegue (MTOW): 14.222', 'Velocidad máxima (KIAS): 14.288', 'payload: 14.505', 'Crucero KIAS: 14.349']\n", + "**Mediana calculada:** 14.223\n", "\n", "--- Imputación para aeronave: **Reach** ---\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 21.875]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", - "Ecuación de regresión: y = -0.1x + 16.428\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", + "Ecuación de regresión: y = -0.101x + 16.438\n", "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 13.683\n", - "\tR²: 0.5344512568573817, Desviación Estándar: 0.5462986887260068, Varianza: 0.2984422573037545, Incertidumbre: 0.09657287683786958\n", + "Predicción obtenida: 13.669\n", + "\tR²: 0.5403248300026882, Desviación Estándar: 0.544617405388324, Varianza: 0.29660811825191014, Incertidumbre: 0.09627566512557671\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", - "Ecuación de regresión: y = -1.077x + 15.26\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", + "Ecuación de regresión: y = -1.08x + 15.252\n", "Valor del parámetro correlacionado para la aeronave: 2.329\n", - "Predicción obtenida: 12.753\n", - "\tR²: 0.6824488751728104, Desviación Estándar: 0.444348541369244, Varianza: 0.19744562621697476, Incertidumbre: 0.07735115250889142\n", + "Predicción obtenida: 12.736\n", + "\tR²: 0.6828633114546836, Desviación Estándar: 0.44550944206432913, Varianza: 0.19847866296846983, Incertidumbre: 0.07755323938068918\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", - "Ecuación de regresión: y = -0.569x + 15.015\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", + "Ecuación de regresión: y = -0.57x + 15.003\n", "Valor del parámetro correlacionado para la aeronave: 4.712\n", - "Predicción obtenida: 12.334\n", - "\tR²: 0.3541273256305937, Desviación Estándar: 0.6109197380467731, Varianza: 0.3732229263351379, Incertidumbre: 0.11153817378445491\n", + "Predicción obtenida: 12.318\n", + "\tR²: 0.35147007195166013, Desviación Estándar: 0.615443744042397, Varianza: 0.3787710020809235, Incertidumbre: 0.11236414049581885\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0, 9.5, 18.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", - "Ecuación de regresión: y = -0.022x + 14.635\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", + "Ecuación de regresión: y = -0.022x + 14.625\n", "Valor del parámetro correlacionado para la aeronave: 91.0\n", - "Predicción obtenida: 12.602\n", - "\tR²: 0.6533839808972084, Desviación Estándar: 0.4840525196077455, Varianza: 0.2343068417386068, Incertidumbre: 0.08181980929170896\n", + "Predicción obtenida: 12.587\n", + "\tR²: 0.6554972240005492, Desviación Estándar: 0.4833062580516597, Varianza: 0.23358493907189748, Incertidumbre: 0.08169366806585572\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", - "Ecuación de regresión: y = -0.086x + 16.872\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", + "Ecuación de regresión: y = -0.087x + 16.886\n", "Valor del parámetro correlacionado para la aeronave: 35.0\n", - "Predicción obtenida: 13.864\n", - "\tR²: 0.6317983869828936, Desviación Estándar: 0.46611492225485973, Varianza: 0.21726312074865395, Incertidumbre: 0.08970385861238159\n", + "Predicción obtenida: 13.85\n", + "\tR²: 0.6369900213647028, Desviación Estándar: 0.46515224472263894, Varianza: 0.2163666107705098, Incertidumbre: 0.08951859123492475\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", - "Ecuación de regresión: y = -0.092x + 14.633\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", + "Ecuación de regresión: y = -0.092x + 14.621\n", "Valor del parámetro correlacionado para la aeronave: 7.0\n", - "Predicción obtenida: 13.991\n", - "\tR²: 0.7193105569936625, Desviación Estándar: 0.4307419939368637, Varianza: 0.18553866534070512, Incertidumbre: 0.07614514621364275\n", + "Predicción obtenida: 13.978\n", + "\tR²: 0.7196334122958243, Desviación Estándar: 0.4310592638988115, Varianza: 0.1858120889929852, Incertidumbre: 0.07620123214903278\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 20.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.767, 14.067, 12.923, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", - "Ecuación de regresión: y = -0.108x + 16.516\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", + "Ecuación de regresión: y = -0.109x + 16.525\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 13.808\n", - "\tR²: 0.6725895092170291, Desviación Estándar: 0.4393983706690954, Varianza: 0.19307092814665575, Incertidumbre: 0.08617311024163819\n", + "Predicción obtenida: 13.795\n", + "\tR²: 0.67567314504279, Desviación Estándar: 0.43981688532599306, Varianza: 0.1934388926178577, Incertidumbre: 0.08625518771864765\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.683', 'Área del ala: 12.753', 'Longitud del fuselaje: 12.334', 'Peso máximo al despegue (MTOW): 12.602', 'Velocidad máxima (KIAS): 13.864', 'payload: 13.991', 'Crucero KIAS: 13.808']\n", - "**Mediana calculada:** 13.683\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.669', 'Área del ala: 12.736', 'Longitud del fuselaje: 12.318', 'Peso máximo al despegue (MTOW): 12.587', 'Velocidad máxima (KIAS): 13.85', 'payload: 13.978', 'Crucero KIAS: 13.795']\n", + "**Mediana calculada:** 13.669\n", "\n", "=== Imputación para el parámetro: **Longitud del fuselaje** ===\n", "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", "\n", "--- Correlación: Área del ala (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3, 4.712]\n", "Ecuación de regresión: y = 1.343x + 0.345\n", @@ -26462,17 +28537,17 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = -0.648x + 11.093\n", + "Ecuación de regresión: y = -0.642x + 11.003\n", "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 2.991\n", - "\tR²: 0.281158048672818, Desviación Estándar: 0.7756999486858921, Varianza: 0.6017104103912956, Incertidumbre: 0.13931982356588346\n", + "Predicción obtenida: 2.974\n", + "\tR²: 0.27906683530984955, Desviación Estándar: 0.7768274399509152, Varianza: 0.6034608714606927, Incertidumbre: 0.13952232697507047\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.786) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", "Ecuación de regresión: y = 0.025x + 1.186\n", @@ -26482,7 +28557,7 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: RTF (Including fuel & Batteries) (r = 0.94) ---\n", - "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", "Valores para Longitud del fuselaje: [3.0, 3.0, 3.0, 0.75, 1.562, 2.3, 4.712]\n", "Ecuación de regresión: y = 0.044x + 1.202\n", @@ -26490,13 +28565,13 @@ "Predicción obtenida: 4.329\n", "\tR²: 0.8836944979649586, Desviación Estándar: 0.39788597040864104, Varianza: 0.15831324544802597, Incertidumbre: 0.150386761123267\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Área del ala: 3.707', 'Relación de aspecto del ala: 2.991', 'Peso máximo al despegue (MTOW): 3.482', 'RTF (Including fuel & Batteries): 4.329']\n", + "Valores imputados: ['Área del ala: 3.707', 'Relación de aspecto del ala: 2.974', 'Peso máximo al despegue (MTOW): 3.482', 'RTF (Including fuel & Batteries): 4.329']\n", "**Mediana calculada:** 3.594\n", "\n", "--- Imputación para aeronave: **Integrator VTOL** ---\n", "\n", "--- Correlación: Área del ala (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3, 4.712]\n", "Ecuación de regresión: y = 1.328x + 0.361\n", @@ -26506,17 +28581,17 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = -0.69x + 11.683\n", - "Valor del parámetro correlacionado para la aeronave: 12.654\n", - "Predicción obtenida: 2.958\n", - "\tR²: 0.3242880012094823, Desviación Estándar: 0.7699928935334471, Varianza: 0.5928890560920104, Incertidumbre: 0.13611679912073793\n", + "Ecuación de regresión: y = -0.684x + 11.599\n", + "Valor del parámetro correlacionado para la aeronave: 12.648\n", + "Predicción obtenida: 2.947\n", + "\tR²: 0.3216836388295986, Desviación Estándar: 0.7714753386968983, Varianza: 0.595174198217494, Incertidumbre: 0.13637886087769133\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.786) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", "Ecuación de regresión: y = 0.025x + 1.181\n", @@ -26534,642 +28609,1566 @@ "Predicción obtenida: 1.922\n", "\tR²: 0.46781484146630226, Desviación Estándar: 1.0520768032800647, Varianza: 1.1068656, Incertidumbre: 0.47050304993697967\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 3.135', 'Relación de aspecto del ala: 2.958', 'Peso máximo al despegue (MTOW): 3.049', 'Maximum Crosswind: 1.922']\n", - "**Mediana calculada:** 3.004\n", + "Valores imputados: ['Área del ala: 3.135', 'Relación de aspecto del ala: 2.947', 'Peso máximo al despegue (MTOW): 3.049', 'Maximum Crosswind: 1.922']\n", + "**Mediana calculada:** 2.998\n", "\n", "--- Imputación para aeronave: **V39** ---\n", "\n", "--- Correlación: Área del ala (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = 1.317x + 0.37\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = 1.316x + 0.371\n", "Valor del parámetro correlacionado para la aeronave: 1.203\n", "Predicción obtenida: 1.954\n", - "\tR²: 0.6599199989051069, Desviación Estándar: 0.5314530046626257, Varianza: 0.2824422961649329, Incertidumbre: 0.09702959963587277\n", + "\tR²: 0.6597115958770696, Desviación Estándar: 0.5314985507982902, Varianza: 0.28249070950068267, Incertidumbre: 0.09703791518450966\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = -0.692x + 11.72\n", - "Valor del parámetro correlacionado para la aeronave: 14.054\n", - "Predicción obtenida: 1.993\n", - "\tR²: 0.3405258074673375, Desviación Estándar: 0.758274848598365, Varianza: 0.5749807460168733, Incertidumbre: 0.13199870821416507\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", + "Ecuación de regresión: y = -0.687x + 11.639\n", + "Valor del parámetro correlacionado para la aeronave: 14.042\n", + "Predicción obtenida: 1.994\n", + "\tR²: 0.3377422956329197, Desviación Estándar: 0.759743862528399, Varianza: 0.5772107366495708, Incertidumbre: 0.13225443071567164\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.786) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", "Ecuación de regresión: y = 0.025x + 1.181\n", "Valor del parámetro correlacionado para la aeronave: 24.0\n", "Predicción obtenida: 1.778\n", - "\tR²: 0.6242739779894806, Desviación Estándar: 0.5673572653887533, Varianza: 0.32189426658940423, Incertidumbre: 0.09876422284830895\n", + "\tR²: 0.6241247541602877, Desviación Estándar: 0.5673714496579856, Varianza: 0.32191036188700406, Incertidumbre: 0.09876669201264836\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Área del ala: 1.954', 'Relación de aspecto del ala: 1.993', 'Peso máximo al despegue (MTOW): 1.778']\n", + "Valores imputados: ['Área del ala: 1.954', 'Relación de aspecto del ala: 1.994', 'Peso máximo al despegue (MTOW): 1.778']\n", "**Mediana calculada:** 1.954\n", "\n", "=== Peso máximo al despegue (MTOW): No hay valores faltantes para imputar. ===\n", "\n", "=== Imputación para el parámetro: **Alcance de la aeronave** ===\n", "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 Fixed Wing** ---\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.7 Fixed Wing** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.978x + 36149.4\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.907x + 35759.111\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 280.964\n", - "\tR²: 0.936732753505972, Desviación Estándar: 214.69795555899483, Varianza: 46095.21212121212, Incertidumbre: 61.97796121822399\n", + "Predicción obtenida: 316.444\n", + "\tR²: 0.941347558801303, Desviación Estándar: 221.17418977408332, Varianza: 48918.02222222222, Incertidumbre: 69.94141993284252\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 118.915x + -1120.395\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 43.142x + 16.54\n", "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 366.039\n", - "\tR²: 0.01562311222731727, Desviación Estándar: 816.7749459018694, Varianza: 667121.3122530016, Incertidumbre: 226.53261138180895\n", + "Predicción obtenida: 555.818\n", + "\tR²: 0.0017873010996798389, Desviación Estándar: 871.652385533205, Varianza: 759777.8812057271, Incertidumbre: 262.81308276653186\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 26.0, 8.0, 6.0, 2.0, 19.0, 4.53, 1.83, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = 106.024x + -335.63\n", + "Valor del parámetro correlacionado para la aeronave: 19.8\n", + "Predicción obtenida: 1763.644\n", + "\tR²: 0.7148030071984908, Desviación Estándar: 487.71248080374903, Varianza: 237863.46393174725, Incertidumbre: 154.2282282630995\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Orbiter 3', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 5.5, 18.0, 3.0, 1.2, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 50.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 21.387x + 171.916\n", + "Valor del parámetro correlacionado para la aeronave: 14.5\n", + "Predicción obtenida: 482.027\n", + "\tR²: 0.6151852469174406, Desviación Estándar: 137.60082187743836, Varianza: 18933.986181346518, Incertidumbre: 48.64923712318945\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 280.964', 'Relación de aspecto del ala: 366.039']\n", - "**Mediana calculada:** 323.501\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 316.444', 'Relación de aspecto del ala: 555.818', 'Autonomía de la aeronave: 1763.644', 'payload: 482.027']\n", + "**Mediana calculada:** 518.922\n", "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 VTOL** ---\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.7 VTOL** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.971x + 36110.407\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.867x + 35536.386\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 284.508\n", - "\tR²: 0.9368275882693853, Desviación Estándar: 206.58415814438524, Varianza: 42677.01439622437, Incertidumbre: 57.29613652985713\n", + "Predicción obtenida: 336.692\n", + "\tR²: 0.9369828282191571, Desviación Estándar: 218.68983155385746, Varianza: 47825.242425054545, Incertidumbre: 65.9374651572882\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 124.04x + -1194.892\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 48.512x + -61.995\n", "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 355.606\n", - "\tR²: 0.020800876574606608, Desviación Estándar: 787.1259997898155, Varianza: 619567.3395451166, Incertidumbre: 210.3682722453848\n", + "Predicción obtenida: 544.404\n", + "\tR²: 0.002995498991272716, Desviación Estándar: 834.5905621601189, Varianza: 696541.4064467433, Incertidumbre: 240.92554286313288\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 26.0, 8.0, 6.0, 2.0, 19.0, 4.53, 1.83, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = 86.745x + -257.466\n", + "Valor del parámetro correlacionado para la aeronave: 12.0\n", + "Predicción obtenida: 783.47\n", + "\tR²: 0.5747433288658067, Desviación Estándar: 568.0998879939582, Varianza: 322737.4827387478, Incertidumbre: 171.28856108353526\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Orbiter 3', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 5.5, 18.0, 3.0, 1.2, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 50.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 21.739x + 172.796\n", + "Valor del parámetro correlacionado para la aeronave: 11.3\n", + "Predicción obtenida: 418.452\n", + "\tR²: 0.6350137109462142, Desviación Estándar: 130.21864982605558, Varianza: 16956.896762520882, Incertidumbre: 43.40621660868519\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 284.508', 'Relación de aspecto del ala: 355.606']\n", - "**Mediana calculada:** 320.057\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 336.692', 'Relación de aspecto del ala: 544.404', 'Autonomía de la aeronave: 783.47', 'payload: 418.452']\n", + "**Mediana calculada:** 481.428\n", "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 Fixed wing** ---\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 Fixed wing** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.966x + 36080.328\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.84x + 35391.65\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 287.243\n", - "\tR²: 0.9369459484148608, Desviación Estándar: 199.27864216054778, Varianza: 39711.97722135165, Incertidumbre: 53.25945739044961\n", + "Predicción obtenida: 349.85\n", + "\tR²: 0.934808022871879, Desviación Estándar: 213.13565980536123, Varianza: 45426.80948066667, Incertidumbre: 61.526965281266904\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 127.479x + -1244.887\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 55.512x + -164.373\n", "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 348.604\n", - "\tR²: 0.02541188671800909, Desviación Estándar: 760.4804439476538, Varianza: 578330.5056268207, Incertidumbre: 196.3552063017124\n", + "Predicción obtenida: 529.525\n", + "\tR²: 0.004730674917466038, Desviación Estándar: 801.9939500096699, Varianza: 643194.2958521128, Incertidumbre: 222.43310072090526\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Ancho del fuselaje (r = 0.982) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Skyeye 5000 VTOL']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.277, 0.375]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 800.0]\n", - "Ecuación de regresión: y = 2097.004x + -112.685\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 800.0]\n", + "Ecuación de regresión: y = 2261.812x + -85.496\n", "Valor del parámetro correlacionado para la aeronave: 0.277\n", - "Predicción obtenida: 468.185\n", - "\tR²: 0.5286444916758515, Desviación Estándar: 123.60775336921141, Varianza: 15278.876692983793, Incertidumbre: 55.279067815917074\n", + "Predicción obtenida: 541.026\n", + "\tR²: 0.9065116987148334, Desviación Estándar: 45.342194867988525, Varianza: 2055.914635446645, Incertidumbre: 20.27764599477289\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 26.0, 8.0, 6.0, 2.0, 19.0, 4.53, 1.83, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = 85.849x + -273.589\n", + "Valor del parámetro correlacionado para la aeronave: 19.8\n", + "Predicción obtenida: 1426.214\n", + "\tR²: 0.56549691959356, Desviación Estándar: 550.2441974157556, Varianza: 302768.67678970896, Incertidumbre: 158.84181774900804\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'AAI Aerosonde']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde']\n", "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.197]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 3270.0]\n", - "Ecuación de regresión: y = -14120.22x + 5060.052\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 3270.0]\n", + "Ecuación de regresión: y = -13031.161x + 4813.896\n", "Valor del parámetro correlacionado para la aeronave: 0.352\n", - "Predicción obtenida: 89.735\n", - "\tR²: 0.582808463771481, Desviación Estándar: 751.8715055818252, Varianza: 565310.7609058806, Incertidumbre: 336.2471593652147\n", + "Predicción obtenida: 226.928\n", + "\tR²: 0.5279015132670961, Desviación Estándar: 775.5687627719914, Varianza: 601506.9057876774, Incertidumbre: 346.8448949567162\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 287.243', 'Relación de aspecto del ala: 348.604', 'Ancho del fuselaje: 468.185', 'Cuerda: 89.735']\n", - "**Mediana calculada:** 317.924\n", "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Orbiter 3', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 5.5, 18.0, 3.0, 1.2, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 50.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 21.957x + 177.064\n", + "Valor del parámetro correlacionado para la aeronave: 17.7\n", + "Predicción obtenida: 565.695\n", + "\tR²: 0.6360380648248155, Desviación Estándar: 124.96189515821898, Varianza: 15615.475241533713, Incertidumbre: 39.5164209431139\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 349.85', 'Relación de aspecto del ala: 529.525', 'Ancho del fuselaje: 541.026', 'Autonomía de la aeronave: 1426.214', 'Cuerda: 226.928', 'payload: 565.695']\n", + "**Mediana calculada:** 535.276\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.961x + 36056.221\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.809x + 35221.676\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 289.434\n", - "\tR²: 0.9370683744644833, Desviación Estándar: 192.6727401288566, Varianza: 37122.7847887619, Incertidumbre: 49.74788758581407\n", + "Predicción obtenida: 365.302\n", + "\tR²: 0.9310643157348193, Desviación Estándar: 210.61070207807703, Varianza: 44356.86782982052, Incertidumbre: 58.412898884921304\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 129.959x + -1280.934\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 54.995x + -156.81\n", "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 343.556\n", - "\tR²: 0.029508936760294757, Desviación Estándar: 736.3653975548689, Varianza: 542233.99871614, Incertidumbre: 184.09134938871722\n", + "Predicción obtenida: 530.624\n", + "\tR²: 0.005326717468706943, Desviación Estándar: 772.8219439192726, Varianza: 597253.7570031633, Incertidumbre: 206.5453525090388\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 26.0, 8.0, 6.0, 2.0, 19.0, 4.53, 1.83, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = 74.895x + -223.348\n", + "Valor del parámetro correlacionado para la aeronave: 14.0\n", + "Predicción obtenida: 825.177\n", + "\tR²: 0.48851416333887676, Desviación Estándar: 573.687248269721, Varianza: 329117.05882728443, Incertidumbre: 159.11221459356298\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 289.434', 'Relación de aspecto del ala: 343.556']\n", - "**Mediana calculada:** 316.495\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Orbiter 3', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 5.5, 18.0, 3.0, 1.2, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 50.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 21.59x + 178.007\n", + "Valor del parámetro correlacionado para la aeronave: 22.7\n", + "Predicción obtenida: 668.093\n", + "\tR²: 0.6515452684663989, Desviación Estándar: 119.43474251813149, Varianza: 14264.657720372366, Incertidumbre: 36.01092980594082\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 365.302', 'Relación de aspecto del ala: 530.624', 'Autonomía de la aeronave: 825.177', 'payload: 668.093']\n", + "**Mediana calculada:** 599.358\n", + "\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.773x + 35023.629\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 383.306\n", + "\tR²: 0.9250199701732349, Desviación Estándar: 211.66135430129737, Varianza: 44800.52890465933, Incertidumbre: 56.5688764154304\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 49.807x + -80.929\n", + "Valor del parámetro correlacionado para la aeronave: 13.443\n", + "Predicción obtenida: 588.62\n", + "\tR²: 0.004857101152760834, Desviación Estándar: 746.7939888417249, Varianza: 557701.2617701343, Incertidumbre: 192.82137878878717\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 6.0, 2.0, 19.0, 4.53, 1.83, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = 73.997x + -229.547\n", + "Valor del parámetro correlacionado para la aeronave: 24.0\n", + "Predicción obtenida: 1546.391\n", + "\tR²: 0.48293085237829836, Desviación Estándar: 555.8314018926295, Varianza: 308948.5473299259, Incertidumbre: 148.55219076374803\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 3', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 5.5, 18.0, 3.0, 1.2, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 50.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 20.566x + 183.705\n", + "Valor del parámetro correlacionado para la aeronave: 12.0\n", + "Predicción obtenida: 430.493\n", + "\tR²: 0.6713351729638166, Desviación Estándar: 115.62496967809123, Varianza: 13369.133613059515, Incertidumbre: 33.378053684344145\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 383.306', 'Relación de aspecto del ala: 588.62', 'Autonomía de la aeronave: 1546.391', 'payload: 430.493']\n", + "**Mediana calculada:** 509.556\n", "\n", "--- Imputación para aeronave: **ScanEagle** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.958x + 36036.377\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.755x + 34924.433\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 291.238\n", - "\tR²: 0.9371872042106679, Desviación Estándar: 186.6690299794889, Varianza: 34845.32675348333, Incertidumbre: 46.667257494872224\n", + "Predicción obtenida: 392.324\n", + "\tR²: 0.9233053265797887, Desviación Estándar: 206.88287418840918, Varianza: 42800.523632457145, Incertidumbre: 53.41692842314887\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 131.837x + -1308.237\n", - "Valor del parámetro correlacionado para la aeronave: 14.067\n", - "Predicción obtenida: 546.322\n", - "\tR²: 0.03315730870908751, Desviación Estándar: 714.4052565199124, Varianza: 510374.87054328184, Incertidumbre: 173.26872541929876\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 51.148x + -104.281\n", + "Valor del parámetro correlacionado para la aeronave: 14.057\n", + "Predicción obtenida: 614.707\n", + "\tR²: 0.0051433547185812944, Desviación Estándar: 723.3321143254109, Varianza: 523209.34761446924, Incertidumbre: 180.83302858135272\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 19.0, 4.53, 1.83, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = 59.997x + -131.637\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 948.312\n", + "\tR²: 0.38436660533416545, Desviación Estándar: 586.1422469494127, Varianza: 343562.73365890625, Incertidumbre: 151.3412773962447\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 18.0, 3.0, 1.2, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 20.645x + 188.896\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 292.121\n", + "\tR²: 0.6654030506878479, Desviación Estándar: 113.06733122345928, Varianza: 12784.22138999545, Incertidumbre: 31.359235408157918\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 291.238', 'Relación de aspecto del ala: 546.322']\n", - "**Mediana calculada:** 418.78\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 392.324', 'Relación de aspecto del ala: 614.707', 'Autonomía de la aeronave: 948.312', 'payload: 292.121']\n", + "**Mediana calculada:** 503.516\n", "\n", "--- Imputación para aeronave: **Integrator** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 500.0, 92.6, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.942x + 35948.692\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.741x + 34842.892\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 299.21\n", - "\tR²: 0.9354925794506871, Desviación Estándar: 183.555632392381, Varianza: 33692.67018296691, Incertidumbre: 44.51878003122549\n", + "Predicción obtenida: 399.737\n", + "\tR²: 0.9219836638407033, Desviación Estándar: 202.10566728677685, Varianza: 40846.70074943334, Incertidumbre: 50.52641682169421\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 129.193x + -1279.073\n", - "Valor del parámetro correlacionado para la aeronave: 12.923\n", - "Predicción obtenida: 390.493\n", - "\tR²: 0.03207230824276419, Desviación Estándar: 694.8866935211308, Varianza: 482867.5168327301, Incertidumbre: 163.78636438169661\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.84, 14.589, 14.714, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 49.053x + -82.026\n", + "Valor del parámetro correlacionado para la aeronave: 12.908\n", + "Predicción obtenida: 551.147\n", + "\tR²: 0.004755791997559489, Desviación Estándar: 702.2196991834805, Varianza: 493112.5059213379, Incertidumbre: 170.31329365429116\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 19.0, 4.53, 1.83, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = 57.271x + -125.878\n", + "Valor del parámetro correlacionado para la aeronave: 24.0\n", + "Predicción obtenida: 1248.627\n", + "\tR²: 0.3634971486006614, Desviación Estándar: 577.2791780254325, Varianza: 333251.2493817191, Incertidumbre: 144.31979450635814\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 3.0, 1.2, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 19.14x + 220.22\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 564.734\n", + "\tR²: 0.5908236954277724, Desviación Estándar: 121.25887543058778, Varianza: 14703.714870690805, Incertidumbre: 32.407797640482855\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 299.21', 'Relación de aspecto del ala: 390.493']\n", - "**Mediana calculada:** 344.852\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 399.737', 'Relación de aspecto del ala: 551.147', 'Autonomía de la aeronave: 1248.627', 'payload: 564.734']\n", + "**Mediana calculada:** 557.94\n", "\n", "--- Imputación para aeronave: **Integrator VTOL** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 500.0, 92.6, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.936x + 35919.159\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -5.721x + 34734.128\n", "Valor del parámetro correlacionado para la aeronave: 5000.0\n", - "Predicción obtenida: 6238.105\n", - "\tR²: 0.9353863278088558, Desviación Estándar: 178.6890627548751, Varianza: 31929.78114821569, Incertidumbre: 42.11741599928025\n", + "Predicción obtenida: 6130.375\n", + "\tR²: 0.9191867670939571, Desviación Estándar: 199.55997470891685, Varianza: 39824.18350582354, Incertidumbre: 48.40040319826195\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 131.099x + -1307.522\n", - "Valor del parámetro correlacionado para la aeronave: 12.654\n", - "Predicción obtenida: 351.406\n", - "\tR²: 0.034068577542197054, Desviación Estándar: 676.4273006220689, Varianza: 457553.8930268587, Incertidumbre: 155.183065582295\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 14.589, 14.714, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 48.743x + -77.408\n", + "Valor del parámetro correlacionado para la aeronave: 12.648\n", + "Predicción obtenida: 539.096\n", + "\tR²: 0.004881178077821846, Desviación Estándar: 682.436587225711, Varianza: 465719.69558427547, Incertidumbre: 160.8518461857017\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 19.0, 4.53, 1.83, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = 50.082x + -73.061\n", + "Valor del parámetro correlacionado para la aeronave: 16.0\n", + "Predicción obtenida: 728.245\n", + "\tR²: 0.31645626386358894, Desviación Estándar: 580.3838962714162, Varianza: 336845.4670511901, Incertidumbre: 140.76377104321017\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 6238.105', 'Relación de aspecto del ala: 351.406']\n", - "**Mediana calculada:** 3294.755\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 3.0, 1.2, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 19.086x + 220.367\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 563.922\n", + "\tR²: 0.6034520565797792, Desviación Estándar: 117.1587675681931, Varianza: 13726.176818097896, Incertidumbre: 30.250263710253165\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 6130.375', 'Relación de aspecto del ala: 539.096', 'Autonomía de la aeronave: 728.245', 'payload: 563.922']\n", + "**Mediana calculada:** 646.084\n", "\n", "--- Imputación para aeronave: **ScanEagle 3** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -1.252x + 7989.744\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 477.332\n", + "\tR²: 0.21041072214297474, Desviación Estándar: 606.3660387299609, Varianza: 367679.7729250645, Incertidumbre: 142.9218459557267\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 14.589, 14.714, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 42.949x + 7.261\n", + "Valor del parámetro correlacionado para la aeronave: 13.765\n", + "Predicción obtenida: 598.458\n", + "\tR²: 0.004016554225731106, Desviación Estándar: 664.640093521826, Varianza: 441746.45391670155, Incertidumbre: 152.4788948151085\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 4.53, 1.83, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = 49.863x + -74.747\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 822.786\n", + "\tR²: 0.31605998443356176, Desviación Estándar: 564.3431464817085, Varianza: 318483.18698087503, Incertidumbre: 133.01695526445639\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 3.0, 1.2, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 19.662x + 218.783\n", + "Valor del parámetro correlacionado para la aeronave: 8.6\n", + "Predicción obtenida: 387.873\n", + "\tR²: 0.6221596490324965, Desviación Estándar: 115.08764274935331, Varianza: 13245.165513602775, Incertidumbre: 28.771910687338327\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 477.332', 'Relación de aspecto del ala: 598.458', 'Autonomía de la aeronave: 822.786', 'payload: 387.873']\n", + "**Mediana calculada:** 537.895\n", + "\n", + "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 92.6, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -3.54x + 21578.882\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -1.248x + 7967.914\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 336.12\n", - "\tR²: 0.8611383209194157, Desviación Estándar: 346.9605684495537, Varianza: 120381.63605883742, Incertidumbre: 79.59821343500164\n", + "Predicción obtenida: 480.853\n", + "\tR²: 0.21016964286508355, Desviación Estándar: 590.3473792395638, Varianza: 348510.02817502135, Incertidumbre: 135.43497724680736\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 12.973, 14.599, 14.717, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 92.6, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = -19.565x + 891.271\n", - "Valor del parámetro correlacionado para la aeronave: 13.774\n", - "Predicción obtenida: 621.78\n", - "\tR²: 0.0004364512856569469, Desviación Estándar: 908.1994401400673, Varianza: 824826.2230707316, Incertidumbre: 203.07956852804415\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 14.589, 14.714, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 42.563x + 9.506\n", + "Valor del parámetro correlacionado para la aeronave: 12.914\n", + "Predicción obtenida: 559.164\n", + "\tR²: 0.003946364727999141, Desviación Estándar: 647.945383208715, Varianza: 419833.21962148865, Incertidumbre: 144.88499225618375\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 4.53, 1.83, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = 48.663x + -73.639\n", + "Valor del parámetro correlacionado para la aeronave: 16.0\n", + "Predicción obtenida: 704.969\n", + "\tR²: 0.30722335636758213, Desviación Estándar: 552.8882334214711, Varianza: 305685.3986559151, Incertidumbre: 126.84125981878523\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 336.12', 'Relación de aspecto del ala: 621.78']\n", - "**Mediana calculada:** 478.95\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 3.0, 1.2, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 19.184x + 233.095\n", + "Valor del parámetro correlacionado para la aeronave: 17.7\n", + "Predicción obtenida: 572.66\n", + "\tR²: 0.5902643693999969, Desviación Estándar: 117.04644073196869, Varianza: 13699.869288022259, Incertidumbre: 28.387931661206128\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 480.853', 'Relación de aspecto del ala: 559.164', 'Autonomía de la aeronave: 704.969', 'payload: 572.66']\n", + "**Mediana calculada:** 565.912\n", "\n", "--- Imputación para aeronave: **V21** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -3.531x + 21530.226\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = -1.242x + 7938.938\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 343.967\n", - "\tR²: 0.8601185501470221, Desviación Estándar: 339.5976064847731, Varianza: 115326.53433018681, Incertidumbre: 75.93633330961761\n", + "Predicción obtenida: 485.527\n", + "\tR²: 0.20937029275663177, Desviación Estándar: 575.6964484514262, Varianza: 331426.4007595857, Incertidumbre: 128.72963931425926\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = -20.697x + 899.894\n", - "Valor del parámetro correlacionado para la aeronave: 14.578\n", - "Predicción obtenida: 598.169\n", - "\tR²: 0.0004884279001442504, Desviación Estándar: 886.8330331916383, Varianza: 786472.8287598814, Incertidumbre: 193.52283349466512\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 42.313x + 13.229\n", + "Valor del parámetro correlacionado para la aeronave: 14.568\n", + "Predicción obtenida: 629.646\n", + "\tR²: 0.004008828742622317, Desviación Estándar: 632.3315354155332, Varianza: 399843.1706809657, Incertidumbre: 137.98605358806336\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 300.0]\n", + "Ecuación de regresión: y = 48.351x + -76.37\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 68.684\n", + "\tR²: 0.3050591302831972, Desviación Estándar: 539.7354115699455, Varianza: 291314.3145025785, Incertidumbre: 120.68850701342247\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 343.967', 'Relación de aspecto del ala: 598.169']\n", - "**Mediana calculada:** 471.068\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 300.0, 800.0]\n", + "Ecuación de regresión: y = 19.143x + 233.214\n", + "Valor del parámetro correlacionado para la aeronave: 1.5\n", + "Predicción obtenida: 261.928\n", + "\tR²: 0.5985890782746399, Desviación Estándar: 113.75879351787289, Varianza: 12941.063102642038, Incertidumbre: 26.813204772029398\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 485.527', 'Relación de aspecto del ala: 629.646', 'Autonomía de la aeronave: 68.684', 'payload: 261.928']\n", + "**Mediana calculada:** 373.727\n", "\n", "--- Imputación para aeronave: **V25** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'Skyeye 3600 VTOL']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'Skyeye 3600 VTOL']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 300.0]\n", - "Ecuación de regresión: y = -3.523x + 21489.183\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 300.0]\n", + "Ecuación de regresión: y = -1.249x + 7975.04\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 350.587\n", - "\tR²: 0.8593439743794986, Desviación Estándar: 332.51164306024987, Varianza: 110563.99277062701, Incertidumbre: 72.55998922751108\n", + "Predicción obtenida: 479.704\n", + "\tR²: 0.21171906549957753, Desviación Estándar: 562.3240970485209, Varianza: 316208.3901214344, Incertidumbre: 122.70917808678767\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 300.0, 800.0]\n", - "Ecuación de regresión: y = -26.566x + 974.331\n", - "Valor del parámetro correlacionado para la aeronave: 14.435\n", - "Predicción obtenida: 590.85\n", - "\tR²: 0.0008406961271735236, Desviación Estándar: 866.829936711758, Varianza: 751394.1391797104, Incertidumbre: 184.80876346543525\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 300.0, 800.0]\n", + "Ecuación de regresión: y = 30.498x + 162.947\n", + "Valor del parámetro correlacionado para la aeronave: 14.421\n", + "Predicción obtenida: 602.758\n", + "\tR²: 0.0021672955940218452, Desviación Estándar: 619.98780177568, Varianza: 384384.87435064, Incertidumbre: 132.1818434703152\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 300.0]\n", + "Ecuación de regresión: y = 45.798x + -28.528\n", + "Valor del parámetro correlacionado para la aeronave: 4.0\n", + "Predicción obtenida: 154.662\n", + "\tR²: 0.2987687873424638, Desviación Estándar: 530.3673768842218, Varianza: 281289.5544630502, Incertidumbre: 115.73565003367233\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 300.0, 800.0]\n", + "Ecuación de regresión: y = 18.099x + 250.896\n", + "Valor del parámetro correlacionado para la aeronave: 2.2\n", + "Predicción obtenida: 290.713\n", + "\tR²: 0.5851882087506547, Desviación Estándar: 113.23855604202713, Varianza: 12822.970574483317, Incertidumbre: 25.978706436824385\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 350.587', 'Relación de aspecto del ala: 590.85']\n", - "**Mediana calculada:** 470.718\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 479.704', 'Relación de aspecto del ala: 602.758', 'Autonomía de la aeronave: 154.662', 'payload: 290.713']\n", + "**Mediana calculada:** 385.208\n", "\n", "--- Imputación para aeronave: **V32** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 3600 VTOL']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 3600 VTOL']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 300.0]\n", - "Ecuación de regresión: y = -3.516x + 21452.311\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 300.0]\n", + "Ecuación de regresión: y = -1.255x + 8004.043\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 356.534\n", - "\tR²: 0.8586543866843019, Desviación Estándar: 325.82488395990157, Varianza: 106161.85500748333, Incertidumbre: 69.46609866673465\n", + "Predicción obtenida: 475.026\n", + "\tR²: 0.21371901155960438, Desviación Estándar: 549.7463712197905, Varianza: 302221.07266932767, Incertidumbre: 117.20632015795667\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 300.0, 800.0]\n", - "Ecuación de regresión: y = -30.914x + 1028.68\n", - "Valor del parámetro correlacionado para la aeronave: 14.194\n", - "Predicción obtenida: 589.888\n", - "\tR²: 0.0011695676261578303, Desviación Estándar: 848.1204843005861, Varianza: 719308.3558902608, Incertidumbre: 176.8453458337392\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 300.0, 800.0]\n", + "Ecuación de regresión: y = 22.662x + 260.76\n", + "Valor del parámetro correlacionado para la aeronave: 14.182\n", + "Predicción obtenida: 582.152\n", + "\tR²: 0.0012253357519550478, Desviación Estándar: 607.9363047663803, Varianza: 369586.55065300124, Incertidumbre: 126.76348237238503\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 300.0]\n", + "Ecuación de regresión: y = 44.24x + 1.63\n", + "Valor del parámetro correlacionado para la aeronave: 4.5\n", + "Predicción obtenida: 200.71\n", + "\tR²: 0.2958075456148329, Desviación Estándar: 520.2584796289456, Varianza: 270668.88562582195, Incertidumbre: 110.91948054697195\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 300.0, 800.0]\n", + "Ecuación de regresión: y = 17.374x + 263.474\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 350.346\n", + "\tR²: 0.5751384226627532, Desviación Estándar: 112.14449166524236, Varianza: 12576.387010855611, Incertidumbre: 25.07627066656405\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 356.534', 'Relación de aspecto del ala: 589.888']\n", - "**Mediana calculada:** 473.211\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 475.026', 'Relación de aspecto del ala: 582.152', 'Autonomía de la aeronave: 200.71', 'payload: 350.346']\n", + "**Mediana calculada:** 412.686\n", "\n", "--- Imputación para aeronave: **V35** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 3600 VTOL']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 3600 VTOL']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 300.0]\n", - "Ecuación de regresión: y = -3.509x + 21418.189\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 300.0]\n", + "Ecuación de regresión: y = -1.258x + 8022.275\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 362.038\n", - "\tR²: 0.8579938416630444, Desviación Estándar: 319.5466969999151, Varianza: 102110.09156355557, Incertidumbre: 66.63009228881091\n", + "Predicción obtenida: 472.085\n", + "\tR²: 0.21522284145385784, Desviación Estándar: 537.8122565548367, Varianza: 289242.0233006055, Incertidumbre: 112.14160754824495\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 300.0, 800.0]\n", - "Ecuación de regresión: y = -33.601x + 1060.685\n", - "Valor del parámetro correlacionado para la aeronave: 13.909\n", - "Predicción obtenida: 593.334\n", - "\tR²: 0.0013961398337015707, Desviación Estándar: 830.5869011959195, Varianza: 689874.6004382401, Incertidumbre: 169.54284124745584\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 300.0, 800.0]\n", + "Ecuación de regresión: y = 18.768x + 307.082\n", + "Valor del parámetro correlacionado para la aeronave: 13.898\n", + "Predicción obtenida: 567.923\n", + "\tR²: 0.0008476663567767995, Desviación Estándar: 596.0879860214709, Varianza: 355320.88707913324, Incertidumbre: 121.6759506296563\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 300.0]\n", + "Ecuación de regresión: y = 43.01x + 25.948\n", + "Valor del parámetro correlacionado para la aeronave: 2.8\n", + "Predicción obtenida: 146.377\n", + "\tR²: 0.29271465280738995, Desviación Estándar: 510.56947779344574, Varianza: 260681.19165427188, Incertidumbre: 106.461095497527\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 300.0, 800.0]\n", + "Ecuación de regresión: y = 17.075x + 269.602\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 440.357\n", + "\tR²: 0.5701248711886335, Desviación Estándar: 110.22167028805497, Varianza: 12148.8166010887, Incertidumbre: 24.052340348546522\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 362.038', 'Relación de aspecto del ala: 593.334']\n", - "**Mediana calculada:** 477.686\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 472.085', 'Relación de aspecto del ala: 567.923', 'Autonomía de la aeronave: 146.377', 'payload: 440.357']\n", + "**Mediana calculada:** 456.221\n", "\n", "--- Imputación para aeronave: **V39** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 3600 VTOL']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 3600 VTOL']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 300.0]\n", - "Ecuación de regresión: y = -3.503x + 21385.89\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 300.0]\n", + "Ecuación de regresión: y = -1.259x + 8026.705\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 367.247\n", - "\tR²: 0.857327553569526, Desviación Estándar: 313.6680914974852, Varianza: 98387.67162367476, Incertidumbre: 64.02723106345546\n", + "Predicción obtenida: 471.371\n", + "\tR²: 0.21604447260306026, Desviación Estándar: 526.4981352259283, Varianza: 277200.2863963799, Incertidumbre: 107.47098181919851\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 300.0, 800.0]\n", - "Ecuación de regresión: y = -34.612x + 1069.947\n", - "Valor del parámetro correlacionado para la aeronave: 14.054\n", - "Predicción obtenida: 583.507\n", - "\tR²: 0.001482621639041004, Desviación Estándar: 814.1206017478742, Varianza: 662792.3541903207, Incertidumbre: 162.82412034957483\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 300.0, 800.0]\n", + "Ecuación de regresión: y = 17.789x + 316.05\n", + "Valor del parámetro correlacionado para la aeronave: 14.042\n", + "Predicción obtenida: 565.84\n", + "\tR²: 0.0007617532043836528, Desviación Estándar: 584.4539259103746, Varianza: 341586.3915120496, Incertidumbre: 116.89078518207491\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 300.0]\n", + "Ecuación de regresión: y = 41.038x + 62.298\n", + "Valor del parámetro correlacionado para la aeronave: 4.5\n", + "Predicción obtenida: 246.97\n", + "\tR²: 0.28329338577170626, Desviación Estándar: 503.409990644059, Varianza: 253421.61868025156, Incertidumbre: 102.7581340414332\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 300.0, 800.0]\n", + "Ecuación de regresión: y = 17.068x + 270.401\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 355.741\n", + "\tR²: 0.5697467595123673, Desviación Estándar: 107.73817669253191, Varianza: 11607.514717031225, Incertidumbre: 22.969856449695133\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 367.247', 'Relación de aspecto del ala: 583.507']\n", - "**Mediana calculada:** 475.377\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 471.371', 'Relación de aspecto del ala: 565.84', 'Autonomía de la aeronave: 246.97', 'payload: 355.741']\n", + "**Mediana calculada:** 413.556\n", "\n", "--- Imputación para aeronave: **Volitation VT370** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 3600 VTOL']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 3600 VTOL']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 300.0]\n", - "Ecuación de regresión: y = -3.498x + 21356.994\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 300.0]\n", + "Ecuación de regresión: y = -1.262x + 8042.156\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 371.908\n", - "\tR²: 0.8567535916512802, Desviación Estándar: 308.05792801553093, Varianza: 94899.68701322205, Incertidumbre: 61.611585603106185\n", + "Predicción obtenida: 468.879\n", + "\tR²: 0.21731767317513662, Desviación Estándar: 515.9847025403219, Varianza: 266240.21325562446, Incertidumbre: 103.19694050806439\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 300.0, 800.0]\n", - "Ecuación de regresión: y = -36.248x + 1088.263\n", - "Valor del parámetro correlacionado para la aeronave: 13.657\n", - "Predicción obtenida: 593.228\n", - "\tR²: 0.0016327181254005563, Desviación Estándar: 798.5803788241736, Varianza: 637730.6214429607, Incertidumbre: 156.61449749218386\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 300.0, 800.0]\n", + "Ecuación de regresión: y = 15.491x + 341.739\n", + "Valor del parámetro correlacionado para la aeronave: 13.645\n", + "Predicción obtenida: 553.116\n", + "\tR²: 0.0005791285523265577, Desviación Estándar: 573.8483135876736, Varianza: 329301.8870074169, Incertidumbre: 112.54091341637921\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 300.0]\n", + "Ecuación de regresión: y = 40.24x + 78.213\n", + "Valor del parámetro correlacionado para la aeronave: 15.0\n", + "Predicción obtenida: 681.814\n", + "\tR²: 0.2817793757671705, Desviación Estándar: 494.2799550877504, Varianza: 244312.67400154856, Incertidumbre: 98.85599101755008\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 300.0, 800.0]\n", + "Ecuación de regresión: y = 16.811x + 275.566\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 578.158\n", + "\tR²: 0.5654386769863811, Desviación Estándar: 106.0114127800309, Varianza: 11238.419639618098, Incertidumbre: 22.104907619190833\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 371.908', 'Relación de aspecto del ala: 593.228']\n", - "**Mediana calculada:** 482.568\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 468.879', 'Relación de aspecto del ala: 553.116', 'Autonomía de la aeronave: 681.814', 'payload: 578.158']\n", + "**Mediana calculada:** 565.637\n", "\n", "--- Imputación para aeronave: **Skyeye 2600** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600 VTOL']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600 VTOL']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 300.0]\n", - "Ecuación de regresión: y = -3.492x + 21328.643\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 300.0]\n", + "Ecuación de regresión: y = -1.257x + 8017.366\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 376.481\n", - "\tR²: 0.8561269585544401, Desviación Estándar: 302.82209622289093, Varianza: 91701.22196082582, Incertidumbre: 59.38829914567665\n", + "Predicción obtenida: 472.877\n", + "\tR²: 0.2163014350450687, Desviación Estándar: 506.30564828630486, Varianza: 256345.40948661542, Incertidumbre: 99.29470694054284\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 300.0, 800.0]\n", - "Ecuación de regresión: y = -35.81x + 1078.147\n", - "Valor del parámetro correlacionado para la aeronave: 14.116\n", - "Predicción obtenida: 572.657\n", - "\tR²: 0.0015929682850825966, Desviación Estándar: 783.9308450787514, Varianza: 614547.5698658854, Incertidumbre: 150.86756147742264\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 300.0, 800.0]\n", + "Ecuación de regresión: y = 15.442x + 342.884\n", + "Valor del parámetro correlacionado para la aeronave: 14.103\n", + "Predicción obtenida: 560.656\n", + "\tR²: 0.0005756116510963194, Desviación Estándar: 563.1261939754822, Varianza: 317111.1103413124, Incertidumbre: 108.37368655982469\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 300.0]\n", + "Ecuación de regresión: y = 39.985x + 76.738\n", + "Valor del parámetro correlacionado para la aeronave: 2.0\n", + "Predicción obtenida: 156.708\n", + "\tR²: 0.28030070432195453, Desviación Estándar: 485.1921720016292, Varianza: 235411.44377165852, Incertidumbre: 95.15401357226055\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 300.0, 800.0]\n", + "Ecuación de regresión: y = 16.737x + 275.831\n", + "Valor del parámetro correlacionado para la aeronave: 4.0\n", + "Predicción obtenida: 342.778\n", + "\tR²: 0.5745690871363021, Desviación Estándar: 103.8081239768202, Varianza: 10776.126603586874, Incertidumbre: 21.1897445748988\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 376.481', 'Relación de aspecto del ala: 572.657']\n", - "**Mediana calculada:** 474.569\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 472.877', 'Relación de aspecto del ala: 560.656', 'Autonomía de la aeronave: 156.708', 'payload: 342.778']\n", + "**Mediana calculada:** 407.828\n", "\n", "--- Imputación para aeronave: **Skyeye 2930 VTOL** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 3600 VTOL']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 3600 VTOL']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 300.0]\n", - "Ecuación de regresión: y = -3.487x + 21304.51\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 300.0]\n", + "Ecuación de regresión: y = -1.261x + 8033.37\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 380.373\n", - "\tR²: 0.8556590461143002, Desviación Estándar: 297.7366014374206, Varianza: 88647.08383550543, Incertidumbre: 57.2994356624997\n", + "Predicción obtenida: 470.296\n", + "\tR²: 0.21750101210992656, Desviación Estándar: 496.99258866565987, Varianza: 247001.63318859378, Incertidumbre: 95.64626828378923\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 300.0, 800.0]\n", - "Ecuación de regresión: y = -37.527x + 1098.259\n", - "Valor del parámetro correlacionado para la aeronave: 14.013\n", - "Predicción obtenida: 572.395\n", - "\tR²: 0.0017597629068930587, Desviación Estándar: 770.0185720471395, Varianza: 592928.6012975158, Incertidumbre: 145.51983189555736\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 300.0, 800.0]\n", + "Ecuación de regresión: y = 12.779x + 374.006\n", + "Valor del parámetro correlacionado para la aeronave: 14.001\n", + "Predicción obtenida: 552.929\n", + "\tR²: 0.0003958766704269534, Desviación Estándar: 553.7010119743468, Varianza: 306584.8106614157, Incertidumbre: 104.63965559777989\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 300.0]\n", + "Ecuación de regresión: y = 38.499x + 102.922\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 218.418\n", + "\tR²: 0.2751249042805085, Desviación Estándar: 478.34320573310276, Varianza: 228812.2224710215, Incertidumbre: 92.0571928650118\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 300.0, 800.0]\n", + "Ecuación de regresión: y = 16.416x + 281.755\n", + "Valor del parámetro correlacionado para la aeronave: 6.0\n", + "Predicción obtenida: 380.25\n", + "\tR²: 0.5695278609951793, Desviación Estándar: 102.48058029141457, Varianza: 10502.269336865067, Incertidumbre: 20.496116058282915\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 380.373', 'Relación de aspecto del ala: 572.395']\n", - "**Mediana calculada:** 476.384\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 470.296', 'Relación de aspecto del ala: 552.929', 'Autonomía de la aeronave: 218.418', 'payload: 380.25']\n", + "**Mediana calculada:** 425.273\n", "\n", "--- Imputación para aeronave: **Skyeye 3600** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 300.0]\n", - "Ecuación de regresión: y = -3.483x + 21281.79\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 300.0]\n", + "Ecuación de regresión: y = -1.263x + 8044.025\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 384.038\n", - "\tR²: 0.8552047822769252, Desviación Estándar: 292.9125600732201, Varianza: 85797.76784864777, Incertidumbre: 55.355270702929126\n", + "Predicción obtenida: 468.577\n", + "\tR²: 0.218442742826649, Desviación Estándar: 488.10836630332125, Varianza: 238249.77725529726, Incertidumbre: 92.24381072061483\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 300.0, 800.0]\n", - "Ecuación de regresión: y = -38.687x + 1110.907\n", - "Valor del parámetro correlacionado para la aeronave: 13.722\n", - "Predicción obtenida: 580.05\n", - "\tR²: 0.0018749021692411327, Desviación Estándar: 756.8280680927438, Varianza: 572788.7246529949, Incertidumbre: 140.5394440463669\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 300.0, 800.0]\n", + "Ecuación de regresión: y = 11.241x + 390.755\n", + "Valor del parámetro correlacionado para la aeronave: 13.71\n", + "Predicción obtenida: 544.869\n", + "\tR²: 0.0003067365701517888, Desviación Estándar: 544.5675096094902, Varianza: 296553.7725222822, Incertidumbre: 101.12364785716923\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 300.0]\n", + "Ecuación de regresión: y = 37.488x + 121.496\n", + "Valor del parámetro correlacionado para la aeronave: 4.5\n", + "Predicción obtenida: 290.19\n", + "\tR²: 0.2715724467015357, Desviación Estándar: 471.2257714097305, Varianza: 222053.72764069558, Incertidumbre: 89.05330017962268\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 300.0, 800.0]\n", + "Ecuación de regresión: y = 16.272x + 284.956\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 447.672\n", + "\tR²: 0.5668554579176148, Desviación Estándar: 100.85760609280737, Varianza: 10172.256706771894, Incertidumbre: 19.779803906210745\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 384.038', 'Relación de aspecto del ala: 580.05']\n", - "**Mediana calculada:** 482.044\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 468.577', 'Relación de aspecto del ala: 544.869', 'Autonomía de la aeronave: 290.19', 'payload: 447.672']\n", + "**Mediana calculada:** 458.124\n", "\n", "--- Imputación para aeronave: **Skyeye 5000** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0]\n", - "Ecuación de regresión: y = -3.479x + 21259.45\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0]\n", + "Ecuación de regresión: y = -1.263x + 8046.407\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 387.641\n", - "\tR²: 0.8547134757322701, Desviación Estándar: 288.37174235566516, Varianza: 83158.26178924213, Incertidumbre: 53.54928821744092\n", + "Predicción obtenida: 468.193\n", + "\tR²: 0.21896917958386974, Desviación Estándar: 479.6226611274876, Varianza: 230037.89706701285, Incertidumbre: 89.06369225544637\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 800.0]\n", - "Ecuación de regresión: y = -38.509x + 1105.191\n", - "Valor del parámetro correlacionado para la aeronave: 12.713\n", - "Predicción obtenida: 615.631\n", - "\tR²: 0.0018568156686025183, Desviación Estándar: 744.3152856692884, Varianza: 554005.2444809544, Incertidumbre: 135.89275728565698\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 800.0]\n", + "Ecuación de regresión: y = 11.399x + 385.692\n", + "Valor del parámetro correlacionado para la aeronave: 12.695\n", + "Predicción obtenida: 530.401\n", + "\tR²: 0.00031517259985813784, Desviación Estándar: 535.6408225987375, Varianza: 286911.0908342521, Incertidumbre: 97.79418708598382\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Ancho del fuselaje (r = 0.982) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Skyeye 5000 VTOL']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.277, 0.277, 0.375]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 800.0]\n", - "Ecuación de regresión: y = 2039.363x + -122.195\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 800.0]\n", + "Ecuación de regresión: y = 2259.606x + -85.86\n", "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 642.567\n", - "\tR²: 0.4608128378817138, Desviación Estándar: 125.92632805453587, Varianza: 15857.440097298588, Incertidumbre: 51.409208152655864\n", + "Predicción obtenida: 761.493\n", + "\tR²: 0.9064132760257113, Desviación Estándar: 41.446819545787584, Varianza: 1717.8388504610796, Incertidumbre: 16.920593224732006\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 387.641', 'Relación de aspecto del ala: 615.631', 'Ancho del fuselaje: 642.567']\n", - "**Mediana calculada:** 615.631\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 4.5, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0]\n", + "Ecuación de regresión: y = 36.858x + 134.106\n", + "Valor del parámetro correlacionado para la aeronave: 8.0\n", + "Predicción obtenida: 428.972\n", + "\tR²: 0.2689650439391573, Desviación Estándar: 464.0178425419368, Varianza: 215312.5581972736, Incertidumbre: 86.16595019101095\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 800.0]\n", + "Ecuación de regresión: y = 16.27x + 285.358\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 610.76\n", + "\tR²: 0.5667205055911326, Desviación Estándar: 98.99193323862133, Varianza: 9799.402846319661, Incertidumbre: 19.051006434306494\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 468.193', 'Relación de aspecto del ala: 530.401', 'Ancho del fuselaje: 761.493', 'Autonomía de la aeronave: 428.972', 'payload: 610.76']\n", + "**Mediana calculada:** 530.401\n", "\n", "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631]\n", - "Ecuación de regresión: y = -3.469x + 21209.325\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401]\n", + "Ecuación de regresión: y = -1.26x + 8032.73\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 395.726\n", - "\tR²: 0.8517129591346262, Desviación Estándar: 286.4568510267121, Varianza: 82057.52750013994, Incertidumbre: 52.29962635307571\n", + "Predicción obtenida: 470.399\n", + "\tR²: 0.21853303784795208, Desviación Estándar: 471.6931077514649, Varianza: 222494.38790023504, Incertidumbre: 86.11898511173075\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0]\n", - "Ecuación de regresión: y = -38.509x + 1105.19\n", - "Valor del parámetro correlacionado para la aeronave: 12.876\n", - "Predicción obtenida: 609.354\n", - "\tR²: 0.0019510827495129446, Desviación Estándar: 732.2117914662525, Varianza: 536134.1075622188, Incertidumbre: 131.5091199538622\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0]\n", + "Ecuación de regresión: y = 11.399x + 385.691\n", + "Valor del parámetro correlacionado para la aeronave: 12.856\n", + "Predicción obtenida: 532.237\n", + "\tR²: 0.0003313628990518902, Desviación Estándar: 526.9306352412358, Varianza: 277655.89435573225, Incertidumbre: 94.63953588966253\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Ancho del fuselaje (r = 0.982) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.277, 0.277, 0.375, 0.375]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 615.631, 800.0]\n", - "Ecuación de regresión: y = 1955.637x + -102.217\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 530.401, 800.0]\n", + "Ecuación de regresión: y = 1541.282x + 85.542\n", "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 631.147\n", - "\tR²: 0.5379529179260039, Desviación Estándar: 116.84090594745173, Varianza: 13651.797302621262, Incertidumbre: 44.16171144234927\n", + "Predicción obtenida: 663.523\n", + "\tR²: 0.6272402531593875, Desviación Estándar: 76.59759898413333, Varianza: 5867.192170134103, Incertidumbre: 28.95117113381007\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0]\n", + "Ecuación de regresión: y = 15.727x + 288.21\n", + "Valor del parámetro correlacionado para la aeronave: 15.0\n", + "Predicción obtenida: 524.112\n", + "\tR²: 0.5615645241082226, Desviación Estándar: 98.27029747742641, Varianza: 9657.05136630188, Incertidumbre: 18.57134059925773\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 395.726', 'Relación de aspecto del ala: 609.354', 'Ancho del fuselaje: 631.147']\n", - "**Mediana calculada:** 609.354\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 470.399', 'Relación de aspecto del ala: 532.237', 'Ancho del fuselaje: 663.523', 'payload: 524.112']\n", + "**Mediana calculada:** 528.174\n", "\n", "--- Imputación para aeronave: **Volitation VT510** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 609.354]\n", - "Ecuación de regresión: y = -3.46x + 21163.965\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 528.174]\n", + "Ecuación de regresión: y = -1.258x + 8020.463\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 403.042\n", - "\tR²: 0.8490722687449495, Desviación Estándar: 284.3101426544714, Varianza: 80832.2572162059, Incertidumbre: 51.0636090407318\n", + "Predicción obtenida: 472.377\n", + "\tR²: 0.21815805928069354, Desviación Estándar: 464.1348278963494, Varianza: 215421.1384663739, Incertidumbre: 83.36107594546903\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354]\n", - "Ecuación de regresión: y = -38.509x + 1105.19\n", - "Valor del parámetro correlacionado para la aeronave: 13.114\n", - "Predicción obtenida: 600.189\n", - "\tR²: 0.002013297608376652, Desviación Estándar: 720.6801764312006, Varianza: 519379.91670090647, Incertidumbre: 127.39945995530485\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174]\n", + "Ecuación de regresión: y = 11.548x + 383.526\n", + "Valor del parámetro correlacionado para la aeronave: 13.099\n", + "Predicción obtenida: 534.792\n", + "\tR²: 0.0003511194348928548, Desviación Estándar: 518.6324629138196, Varianza: 268979.63158805453, Incertidumbre: 91.68213286746061\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 4.5, 6.0, 8.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401]\n", + "Ecuación de regresión: y = 36.695x + 139.245\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 322.718\n", + "\tR²: 0.2678068616286219, Desviación Estándar: 456.58017132018756, Varianza: 208465.45284277183, Incertidumbre: 83.35975304721335\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 403.042', 'Relación de aspecto del ala: 600.189']\n", - "**Mediana calculada:** 501.616\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174]\n", + "Ecuación de regresión: y = 15.739x + 288.219\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 681.696\n", + "\tR²: 0.5652992923691292, Desviación Estándar: 96.5639245470936, Varianza: 9324.591523936788, Incertidumbre: 17.931470624475867\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 472.377', 'Relación de aspecto del ala: 534.792', 'Autonomía de la aeronave: 322.718', 'payload: 681.696']\n", + "**Mediana calculada:** 503.585\n", "\n", "--- Imputación para aeronave: **Ascend** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 609.354, 501.616]\n", - "Ecuación de regresión: y = -3.456x + 21143.728\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 528.174, 503.585]\n", + "Ecuación de regresión: y = -1.257x + 8014.056\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 406.306\n", - "\tR²: 0.848548280437885, Desviación Estándar: 280.35664298808086, Varianza: 78599.84726754623, Incertidumbre: 49.56052085189198\n", + "Predicción obtenida: 473.411\n", + "\tR²: 0.21812584611652552, Desviación Estándar: 456.8573681831264, Varianza: 208718.65486321275, Incertidumbre: 80.76173576933198\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354, 501.616]\n", - "Ecuación de regresión: y = -36.069x + 1068.826\n", - "Valor del parámetro correlacionado para la aeronave: 14.357\n", - "Predicción obtenida: 550.983\n", - "\tR²: 0.00179166089086924, Desviación Estándar: 709.8750011918696, Varianza: 503922.5173171569, Incertidumbre: 123.57337622902887\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585]\n", + "Ecuación de regresión: y = 12.322x + 372.003\n", + "Valor del parámetro correlacionado para la aeronave: 14.349\n", + "Predicción obtenida: 548.807\n", + "\tR²: 0.00040559910055537607, Desviación Estándar: 510.74155237825306, Varianza: 260856.9333257478, Incertidumbre: 88.90869223718056\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 4.5, 6.0, 8.0, 5.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 503.585]\n", + "Ecuación de regresión: y = 36.114x + 151.21\n", + "Valor del parámetro correlacionado para la aeronave: 6.0\n", + "Predicción obtenida: 367.893\n", + "\tR²: 0.2642430210503296, Desviación Estándar: 450.27008149115517, Varianza: 202743.14628605152, Incertidumbre: 80.87089397983948\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 406.306', 'Relación de aspecto del ala: 550.983']\n", - "**Mediana calculada:** 478.644\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585]\n", + "Ecuación de regresión: y = 14.216x + 299.289\n", + "Valor del parámetro correlacionado para la aeronave: 0.6\n", + "Predicción obtenida: 307.818\n", + "\tR²: 0.5236857913768209, Desviación Estándar: 99.5530852483226, Varianza: 9910.816782459788, Incertidumbre: 18.17582348658036\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 473.411', 'Relación de aspecto del ala: 548.807', 'Autonomía de la aeronave: 367.893', 'payload: 307.818']\n", + "**Mediana calculada:** 420.652\n", "\n", "--- Imputación para aeronave: **Transition** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 609.354, 501.616, 478.644]\n", - "Ecuación de regresión: y = -3.453x + 21129.353\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 528.174, 503.585, 420.652]\n", + "Ecuación de regresión: y = -1.259x + 8024.54\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 408.624\n", - "\tR²: 0.8483145072965955, Desviación Estándar: 276.3539789058695, Varianza: 76371.52165710577, Incertidumbre: 48.10705286196413\n", + "Predicción obtenida: 471.72\n", + "\tR²: 0.21891613091083528, Desviación Estándar: 449.9727679396905, Varianza: 202475.4918873066, Incertidumbre: 78.33020468683851\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354, 501.616, 478.644]\n", - "Ecuación de regresión: y = -38.096x + 1094.475\n", - "Valor del parámetro correlacionado para la aeronave: 14.233\n", - "Predicción obtenida: 552.251\n", - "\tR²: 0.0020361613553377955, Desviación Estándar: 699.4625257506076, Varianza: 489247.82492941944, Incertidumbre: 119.95683352643485\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652]\n", + "Ecuación de regresión: y = 8.718x + 417.57\n", + "Valor del parámetro correlacionado para la aeronave: 14.223\n", + "Predicción obtenida: 541.562\n", + "\tR²: 0.00020665375027828503, Desviación Estándar: 503.6313668149641, Varianza: 253644.55363990893, Incertidumbre: 86.37206684215421\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 4.5, 6.0, 8.0, 5.0, 6.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 503.585, 420.652]\n", + "Ecuación de regresión: y = 35.981x + 154.244\n", + "Valor del parámetro correlacionado para la aeronave: 12.0\n", + "Predicción obtenida: 586.014\n", + "\tR²: 0.2649729523763782, Desviación Estándar: 443.27273842243636, Varianza: 196490.7206285257, Incertidumbre: 78.36028981340885\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652]\n", + "Ecuación de regresión: y = 13.547x + 310.162\n", + "Valor del parámetro correlacionado para la aeronave: 1.5\n", + "Predicción obtenida: 330.483\n", + "\tR²: 0.5062493497217644, Desviación Estándar: 99.81827758318798, Varianza: 9963.688539674367, Incertidumbre: 17.927891893120933\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 408.624', 'Relación de aspecto del ala: 552.251']\n", - "**Mediana calculada:** 480.438\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 471.72', 'Relación de aspecto del ala: 541.562', 'Autonomía de la aeronave: 586.014', 'payload: 330.483']\n", + "**Mediana calculada:** 506.641\n", "\n", "--- Imputación para aeronave: **Reach** ---\n", "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.952) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 609.354, 501.616, 478.644, 480.438]\n", - "Ecuación de regresión: y = -3.451x + 21115.526\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 528.174, 503.585, 420.652, 506.641]\n", + "Ecuación de regresión: y = -1.258x + 8017.816\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 410.855\n", - "\tR²: 0.8480781165196773, Desviación Estándar: 272.52939729083585, Varianza: 74272.27238770625, Incertidumbre: 46.73840604511903\n", + "Predicción obtenida: 472.804\n", + "\tR²: 0.2188220660556668, Desviación Estándar: 443.34533244312075, Varianza: 196555.08379910127, Incertidumbre: 76.03309724353042\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354, 501.616, 478.644, 480.438]\n", - "Ecuación de regresión: y = -39.663x + 1113.914\n", - "Valor del parámetro correlacionado para la aeronave: 13.683\n", - "Predicción obtenida: 571.205\n", - "\tR²: 0.002231863535378964, Desviación Estándar: 689.5003938693345, Varianza: 475410.79314596736, Incertidumbre: 116.54683830313121\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652, 506.641]\n", + "Ecuación de regresión: y = 7.955x + 427.025\n", + "Valor del parámetro correlacionado para la aeronave: 13.669\n", + "Predicción obtenida: 535.762\n", + "\tR²: 0.0001740848142732787, Desviación Estándar: 496.4181896725954, Varianza: 246431.01903781694, Incertidumbre: 83.90998902528435\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 4.5, 6.0, 8.0, 5.0, 6.0, 12.0]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 503.585, 420.652, 506.641]\n", + "Ecuación de regresión: y = 35.911x + 152.567\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 870.792\n", + "\tR²: 0.2643013375727117, Desviación Estándar: 436.7165278868632, Varianza: 190721.3257295574, Incertidumbre: 76.02258949165629\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 410.855', 'Relación de aspecto del ala: 571.205']\n", - "**Mediana calculada:** 491.03\n", + "\n", + "--- Correlación: payload (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5]\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652, 506.641]\n", + "Ecuación de regresión: y = 12.668x + 324.924\n", + "Valor del parámetro correlacionado para la aeronave: 7.0\n", + "Predicción obtenida: 413.601\n", + "\tR²: 0.46249934976527396, Desviación Estándar: 102.70373401095995, Varianza: 10548.056979794012, Incertidumbre: 18.155626693082308\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 472.804', 'Relación de aspecto del ala: 535.762', 'Autonomía de la aeronave: 870.792', 'payload: 413.601']\n", + "**Mediana calculada:** 504.283\n", "\n", "=== Imputación para el parámetro: **Autonomía de la aeronave** ===\n", "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n" + "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = 0.843) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 503.585, 420.652, 506.641, 504.283]\n", + "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 4.5, 6.0, 8.0, 5.0, 6.0, 12.0, 20.0]\n", + "Ecuación de regresión: y = 0.007x + 6.874\n", + "Valor del parámetro correlacionado para la aeronave: 528.174\n", + "Predicción obtenida: 10.747\n", + "\tR²: 0.2498408369463081, Desviación Estándar: 6.374288748814379, Varianza: 40.63155705326158, Incertidumbre: 1.093181501711479\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Alcance de la aeronave: 10.747']\n", + "**Mediana calculada:** 10.747\n", + "\n", + "=== Imputación para el parámetro: **Velocidad máxima (KIAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.072x + 7.262\n", + "Valor del parámetro correlacionado para la aeronave: 27.892\n", + "Predicción obtenida: 37.163\n", + "\tR²: 0.6785150773380106, Desviación Estándar: 4.266538520956066, Varianza: 18.20335095080197, Incertidumbre: 0.8896347797490924\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.786x + 25.969\n", + "Valor del parámetro correlacionado para la aeronave: 2.503\n", + "Predicción obtenida: 42.955\n", + "\tR²: 0.32230308247682127, Desviación Estándar: 5.586912076398391, Varianza: 31.213586549406177, Incertidumbre: 1.0956836037800992\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -7.332x + 136.73\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 45.084\n", + "\tR²: 0.6353583924337338, Desviación Estándar: 4.212708115387483, Varianza: 17.746909665451557, Incertidumbre: 0.7961270013870624\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 36.0, 36.0, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.629x + 28.996\n", + "Valor del parámetro correlacionado para la aeronave: 22.7\n", + "Predicción obtenida: 43.264\n", + "\tR²: 0.4900615381264627, Desviación Estándar: 4.799869922083653, Varianza: 23.03875126892334, Incertidumbre: 0.9797693450697835\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.857) ---\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 8.9, 16.5, 84.0]\n", + "Valores para Velocidad máxima (KIAS): [33.439, 33.439, 33.439, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.067x + 30.152\n", + "Valor del parámetro correlacionado para la aeronave: 70.3\n", + "Predicción obtenida: 34.84\n", + "\tR²: 0.733701886636262, Desviación Estándar: 0.9740665033476542, Varianza: 0.9488055529439255, Incertidumbre: 0.39766098478979256\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 37.163', 'Área del ala: 42.955', 'Relación de aspecto del ala: 45.084', 'payload: 43.264', 'RTF (Including fuel & Batteries): 34.84']\n", + "**Mediana calculada:** 42.955\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.078x + 7.331\n", + "Valor del parámetro correlacionado para la aeronave: 21.463\n", + "Predicción obtenida: 30.477\n", + "\tR²: 0.6650554114482713, Desviación Estándar: 4.333938589551439, Varianza: 18.78302369800311, Incertidumbre: 0.8846615100798704\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.786x + 25.969\n", + "Valor del parámetro correlacionado para la aeronave: 2.09\n", + "Predicción obtenida: 40.152\n", + "\tR²: 0.3552344429430916, Desviación Estándar: 5.48247460485476, Varianza: 30.057527792877355, Incertidumbre: 1.0551027296460609\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -7.182x + 134.601\n", + "Valor del parámetro correlacionado para la aeronave: 12.648\n", + "Predicción obtenida: 43.762\n", + "\tR²: 0.6458631582625831, Desviación Estándar: 4.155986642108519, Varianza: 17.272224969384443, Incertidumbre: 0.7717473449656517\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.626x + 29.01\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 40.28\n", + "\tR²: 0.5133079427191782, Desviación Estándar: 4.703245083983008, Varianza: 22.12051432001033, Incertidumbre: 0.9406490167966016\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 30.477', 'Área del ala: 40.152', 'Relación de aspecto del ala: 43.762', 'payload: 40.28']\n", + "**Mediana calculada:** 40.216\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Evo** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.013x + 9.471\n", + "Valor del parámetro correlacionado para la aeronave: 18.091\n", + "Predicción obtenida: 27.799\n", + "\tR²: 0.6038420321629009, Desviación Estándar: 4.640881006314369, Varianza: 21.537776514769465, Incertidumbre: 0.9281762012628738\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.79x + 25.965\n", + "Valor del parámetro correlacionado para la aeronave: 0.84\n", + "Predicción obtenida: 31.669\n", + "\tR²: 0.3670434992715419, Desviación Estándar: 5.3836956297320535, Varianza: 28.98417863359601, Incertidumbre: 1.0174228407668775\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -6.983x + 131.755\n", + "Valor del parámetro correlacionado para la aeronave: 14.589\n", + "Predicción obtenida: 29.879\n", + "\tR²: 0.6423683336309061, Desviación Estándar: 4.132395523343531, Varianza: 17.076692761349655, Incertidumbre: 0.7544687482228727\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.626x + 29.011\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 30.888\n", + "\tR²: 0.5208842290733458, Desviación Estándar: 4.611927331533329, Varianza: 21.26987371134414, Incertidumbre: 0.904473363798475\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.775) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.065x + 9.368\n", + "Valor del parámetro correlacionado para la aeronave: 16.54\n", + "Predicción obtenida: 26.99\n", + "\tR²: 0.6084339451187571, Desviación Estándar: 4.787548453222305, Varianza: 22.920620191951283, Incertidumbre: 1.0705283786979045\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.857) ---\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 8.9, 16.5, 84.0]\n", + "Valores para Velocidad máxima (KIAS): [33.439, 33.439, 33.439, 42.955, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.119x + 29.159\n", + "Valor del parámetro correlacionado para la aeronave: 6.8\n", + "Predicción obtenida: 29.971\n", + "\tR²: 0.5655356424763149, Desviación Estándar: 2.661463952850547, Varianza: 7.0833903723228575, Incertidumbre: 1.0059388203722117\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 27.799', 'Área del ala: 31.669', 'Relación de aspecto del ala: 29.879', 'payload: 30.888', 'Crucero KIAS: 26.99', 'RTF (Including fuel & Batteries): 29.971']\n", + "**Mediana calculada:** 29.925\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.993x + 10.094\n", + "Valor del parámetro correlacionado para la aeronave: 17.5\n", + "Predicción obtenida: 27.466\n", + "\tR²: 0.6132641570429134, Desviación Estándar: 4.567543566658289, Varianza: 20.862454233321525, Incertidumbre: 0.8957689913683001\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.88x + 25.782\n", + "Valor del parámetro correlacionado para la aeronave: 0.7\n", + "Predicción obtenida: 30.598\n", + "\tR²: 0.3791246211706657, Desviación Estándar: 5.299352481306049, Varianza: 28.083136721124575, Incertidumbre: 0.9840650511355258\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -6.981x + 131.729\n", + "Valor del parámetro correlacionado para la aeronave: 14.714\n", + "Predicción obtenida: 29.009\n", + "\tR²: 0.6511684506287918, Desviación Estándar: 4.065205545483665, Varianza: 16.525896127031146, Incertidumbre: 0.7301324697975139\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.631x + 28.923\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 29.68\n", + "\tR²: 0.5347441062393511, Desviación Estándar: 4.529221125565367, Varianza: 20.51384400426761, Incertidumbre: 0.8716490120215014\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.775) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 29.925, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.031x + 10.342\n", + "Valor del parámetro correlacionado para la aeronave: 16.0\n", + "Predicción obtenida: 26.846\n", + "\tR²: 0.6136657296272687, Desviación Estándar: 4.709745503173601, Varianza: 22.181702704663962, Incertidumbre: 1.0277507272509956\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 27.466', 'Área del ala: 30.598', 'Relación de aspecto del ala: 29.009', 'payload: 29.68', 'Crucero KIAS: 26.846']\n", + "**Mediana calculada:** 29.009\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.979x + 10.518\n", + "Valor del parámetro correlacionado para la aeronave: 17.5\n", + "Predicción obtenida: 27.645\n", + "\tR²: 0.6257444320590095, Desviación Estándar: 4.490845524887884, Varianza: 20.167693528405533, Incertidumbre: 0.8642636242276819\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.975x + 25.6\n", + "Valor del parámetro correlacionado para la aeronave: 0.7\n", + "Predicción obtenida: 30.483\n", + "\tR²: 0.3948008000513574, Desviación Estándar: 5.217765466886563, Varianza: 27.225076467433954, Incertidumbre: 0.9526292819950818\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -6.981x + 131.729\n", + "Valor del parámetro correlacionado para la aeronave: 14.714\n", + "Predicción obtenida: 29.009\n", + "\tR²: 0.6615824796144654, Desviación Estándar: 4.001182559565268, Varianza: 16.00946187496927, Incertidumbre: 0.707315830158487\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.634x + 28.859\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 29.621\n", + "\tR²: 0.5513156760951279, Desviación Estándar: 4.4492527659965795, Varianza: 19.795850175728216, Incertidumbre: 0.8408297384923719\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.775) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.008x + 11.001\n", + "Valor del parámetro correlacionado para la aeronave: 16.0\n", + "Predicción obtenida: 27.134\n", + "\tR²: 0.6236400148434393, Desviación Estándar: 4.621454678981261, Varianza: 21.35784334987779, Incertidumbre: 0.9852974481637925\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 27.645', 'Área del ala: 30.483', 'Relación de aspecto del ala: 29.009', 'payload: 29.621', 'Crucero KIAS: 27.134']\n", + "**Mediana calculada:** 29.009\n", + "\n", + "--- Imputación para aeronave: **Skyeye 2600** ---\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.979x + 10.518\n", + "Valor del parámetro correlacionado para la aeronave: 36.094\n", + "Predicción obtenida: 45.843\n", + "\tR²: 0.6257444320590095, Desviación Estándar: 4.490845524887884, Varianza: 20.167693528405533, Incertidumbre: 0.8642636242276819\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.975x + 25.6\n", + "Valor del parámetro correlacionado para la aeronave: 0.88\n", + "Predicción obtenida: 31.738\n", + "\tR²: 0.3948008000513574, Desviación Estándar: 5.217765466886563, Varianza: 27.225076467433954, Incertidumbre: 0.9526292819950818\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -6.981x + 131.729\n", + "Valor del parámetro correlacionado para la aeronave: 14.103\n", + "Predicción obtenida: 33.275\n", + "\tR²: 0.6615824796144654, Desviación Estándar: 4.001182559565268, Varianza: 16.00946187496927, Incertidumbre: 0.707315830158487\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.634x + 28.859\n", + "Valor del parámetro correlacionado para la aeronave: 4.0\n", + "Predicción obtenida: 31.397\n", + "\tR²: 0.5513156760951279, Desviación Estándar: 4.4492527659965795, Varianza: 19.795850175728216, Incertidumbre: 0.8408297384923719\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.775) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 1.008x + 11.001\n", + "Valor del parámetro correlacionado para la aeronave: 33.0\n", + "Predicción obtenida: 44.275\n", + "\tR²: 0.6236400148434393, Desviación Estándar: 4.621454678981261, Varianza: 21.35784334987779, Incertidumbre: 0.9852974481637925\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 45.843', 'Área del ala: 31.738', 'Relación de aspecto del ala: 33.275', 'payload: 31.397', 'Crucero KIAS: 44.275']\n", + "**Mediana calculada:** 33.275\n", + "\n", + "--- Imputación para aeronave: **Skyeye 3600** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.737) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.275, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 6.912x + 25.735\n", + "Valor del parámetro correlacionado para la aeronave: 1.33\n", + "Predicción obtenida: 34.927\n", + "\tR²: 0.394519537415028, Desviación Estándar: 5.139955848128364, Varianza: 26.419146120708966, Incertidumbre: 0.9231633225073828\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.275, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = -6.981x + 131.729\n", + "Valor del parámetro correlacionado para la aeronave: 13.71\n", + "Predicción obtenida: 36.018\n", + "\tR²: 0.6627761679084383, Desviación Estándar: 3.9400922159021547, Varianza: 15.524326669812753, Incertidumbre: 0.6858820171935385\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.715) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.275, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Ecuación de regresión: y = 0.627x + 28.996\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 35.27\n", + "\tR²: 0.550504283331907, Desviación Estándar: 4.3849564059962995, Varianza: 19.227842682487985, Incertidumbre: 0.8142659627031128\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Capacidad combustible (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Capacidad combustible: [13.0, 11.5, 28.0, 28.0, 25.0]\n", + "Valores para Velocidad máxima (KIAS): [33.0, 33.0, 42.0, 38.0, 50.0]\n", + "Ecuación de regresión: y = 0.607x + 26.384\n", + "Valor del parámetro correlacionado para la aeronave: 11.5\n", + "Predicción obtenida: 33.369\n", + "\tR²: 0.487882729041738, Desviación Estándar: 4.5575735331497516, Varianza: 20.77147651006711, Incertidumbre: 2.038208846515347\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 34.927', 'Relación de aspecto del ala: 36.018', 'payload: 35.27', 'Capacidad combustible: 33.369']\n", + "**Mediana calculada:** 35.098\n", + "\n", + "=== Imputación para el parámetro: **Velocidad de pérdida (KCAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Stalker XE** ---\n" ] }, { @@ -27217,7 +30216,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Autonomía de la aeronave' para la aeronave 'Skyeye 5000 VTOL octo'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Stalker XE'.\n", " \n", " \n", "" @@ -27234,379 +30233,7 @@ "output_type": "stream", "text": [ "\n", - "=== Imputación para el parámetro: **Velocidad máxima (KIAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.072x + 7.262\n", - "Valor del parámetro correlacionado para la aeronave: 27.892\n", - "Predicción obtenida: 37.163\n", - "\tR²: 0.6785150773380106, Desviación Estándar: 4.266538520956066, Varianza: 18.20335095080197, Incertidumbre: 0.8896347797490924\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.786x + 25.969\n", - "Valor del parámetro correlacionado para la aeronave: 2.503\n", - "Predicción obtenida: 42.955\n", - "\tR²: 0.32230308247682127, Desviación Estándar: 5.586912076398391, Varianza: 31.213586549406177, Incertidumbre: 1.0956836037800992\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -7.338x + 136.927\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 45.197\n", - "\tR²: 0.6301640775969515, Desviación Estándar: 4.242607000059795, Varianza: 17.99971415695638, Incertidumbre: 0.8017773594814295\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 36.0, 36.0, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.629x + 28.996\n", - "Valor del parámetro correlacionado para la aeronave: 22.7\n", - "Predicción obtenida: 43.264\n", - "\tR²: 0.4900615381264627, Desviación Estándar: 4.799869922083653, Varianza: 23.03875126892334, Incertidumbre: 0.9797693450697835\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.857) ---\n", - "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 8.9, 16.5, 84.0]\n", - "Valores para Velocidad máxima (KIAS): [33.439, 33.439, 33.439, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.067x + 30.152\n", - "Valor del parámetro correlacionado para la aeronave: 70.3\n", - "Predicción obtenida: 34.84\n", - "\tR²: 0.733701886636262, Desviación Estándar: 0.9740665033476542, Varianza: 0.9488055529439255, Incertidumbre: 0.39766098478979256\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 37.163', 'Área del ala: 42.955', 'Relación de aspecto del ala: 45.197', 'payload: 43.264', 'RTF (Including fuel & Batteries): 34.84']\n", - "**Mediana calculada:** 42.955\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.078x + 7.331\n", - "Valor del parámetro correlacionado para la aeronave: 21.463\n", - "Predicción obtenida: 30.477\n", - "\tR²: 0.6650554114482713, Desviación Estándar: 4.333938589551439, Varianza: 18.78302369800311, Incertidumbre: 0.8846615100798704\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.786x + 25.969\n", - "Valor del parámetro correlacionado para la aeronave: 2.09\n", - "Predicción obtenida: 40.152\n", - "\tR²: 0.3552344429430916, Desviación Estándar: 5.48247460485476, Varianza: 30.057527792877355, Incertidumbre: 1.0551027296460609\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -7.178x + 134.644\n", - "Valor del parámetro correlacionado para la aeronave: 12.654\n", - "Predicción obtenida: 43.812\n", - "\tR²: 0.6405623970503131, Desviación Estándar: 4.186974758785706, Varianza: 17.530757630708617, Incertidumbre: 0.7775016937714918\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.626x + 29.01\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 40.28\n", - "\tR²: 0.5133079427191782, Desviación Estándar: 4.703245083983008, Varianza: 22.12051432001033, Incertidumbre: 0.9406490167966016\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 30.477', 'Área del ala: 40.152', 'Relación de aspecto del ala: 43.812', 'payload: 40.28']\n", - "**Mediana calculada:** 40.216\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Evo** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.013x + 9.471\n", - "Valor del parámetro correlacionado para la aeronave: 18.091\n", - "Predicción obtenida: 27.799\n", - "\tR²: 0.6038420321629009, Desviación Estándar: 4.640881006314369, Varianza: 21.537776514769465, Incertidumbre: 0.9281762012628738\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.79x + 25.965\n", - "Valor del parámetro correlacionado para la aeronave: 0.84\n", - "Predicción obtenida: 31.669\n", - "\tR²: 0.3670434992715419, Desviación Estándar: 5.3836956297320535, Varianza: 28.98417863359601, Incertidumbre: 1.0174228407668775\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -6.974x + 131.719\n", - "Valor del parámetro correlacionado para la aeronave: 14.599\n", - "Predicción obtenida: 29.911\n", - "\tR²: 0.6369164293284615, Desviación Estándar: 4.163774471385446, Varianza: 17.337017848561153, Incertidumbre: 0.760197734113986\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.626x + 29.011\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 30.888\n", - "\tR²: 0.5208842290733458, Desviación Estándar: 4.611927331533329, Varianza: 21.26987371134414, Incertidumbre: 0.904473363798475\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.775) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.065x + 9.368\n", - "Valor del parámetro correlacionado para la aeronave: 16.54\n", - "Predicción obtenida: 26.99\n", - "\tR²: 0.6084339451187571, Desviación Estándar: 4.787548453222305, Varianza: 22.920620191951283, Incertidumbre: 1.0705283786979045\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.857) ---\n", - "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 8.9, 16.5, 84.0]\n", - "Valores para Velocidad máxima (KIAS): [33.439, 33.439, 33.439, 42.955, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.119x + 29.159\n", - "Valor del parámetro correlacionado para la aeronave: 6.8\n", - "Predicción obtenida: 29.971\n", - "\tR²: 0.5655356424763149, Desviación Estándar: 2.661463952850547, Varianza: 7.0833903723228575, Incertidumbre: 1.0059388203722117\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 27.799', 'Área del ala: 31.669', 'Relación de aspecto del ala: 29.911', 'payload: 30.888', 'Crucero KIAS: 26.99', 'RTF (Including fuel & Batteries): 29.971']\n", - "**Mediana calculada:** 29.941\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.993x + 10.098\n", - "Valor del parámetro correlacionado para la aeronave: 17.5\n", - "Predicción obtenida: 27.468\n", - "\tR²: 0.61316433334016, Desviación Estándar: 4.56779675046761, Varianza: 20.864767153582456, Incertidumbre: 0.8958186447984638\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.879x + 25.784\n", - "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 30.599\n", - "\tR²: 0.3790835645732169, Desviación Estándar: 5.299182924288052, Varianza: 28.081339665066075, Incertidumbre: 0.9840335651877286\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -6.972x + 131.702\n", - "Valor del parámetro correlacionado para la aeronave: 14.717\n", - "Predicción obtenida: 29.09\n", - "\tR²: 0.6458059150589619, Desviación Estándar: 4.096069672218646, Varianza: 16.777786759669365, Incertidumbre: 0.7356758306015428\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.631x + 28.924\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 29.681\n", - "\tR²: 0.5346940437266139, Desviación Estándar: 4.529105693357664, Varianza: 20.51279838160481, Incertidumbre: 0.8716267970827714\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.775) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 29.941, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.031x + 10.348\n", - "Valor del parámetro correlacionado para la aeronave: 16.0\n", - "Predicción obtenida: 26.848\n", - "\tR²: 0.6135394527933391, Desviación Estándar: 4.710154634609254, Varianza: 22.185556681931036, Incertidumbre: 1.0278400070497131\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 27.468', 'Área del ala: 30.599', 'Relación de aspecto del ala: 29.09', 'payload: 29.681', 'Crucero KIAS: 26.848']\n", - "**Mediana calculada:** 29.09\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.978x + 10.544\n", - "Valor del parámetro correlacionado para la aeronave: 17.5\n", - "Predicción obtenida: 27.656\n", - "\tR²: 0.6252054405389704, Desviación Estándar: 4.492004783483308, Varianza: 20.178106974836926, Incertidumbre: 0.8644867236483915\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.969x + 25.611\n", - "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 30.489\n", - "\tR²: 0.3944990966454992, Desviación Estándar: 5.216865762262041, Varianza: 27.215688381461913, Incertidumbre: 0.9524650191557678\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -6.972x + 131.702\n", - "Valor del parámetro correlacionado para la aeronave: 14.717\n", - "Predicción obtenida: 29.09\n", - "\tR²: 0.6561408230541541, Desviación Estándar: 4.031560606462846, Varianza: 16.25348092358307, Incertidumbre: 0.712685960898607\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.634x + 28.868\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 29.629\n", - "\tR²: 0.5509617835923953, Desviación Estándar: 4.448771765099154, Varianza: 19.791570217943438, Incertidumbre: 0.8407388378670155\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.775) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.007x + 11.03\n", - "Valor del parámetro correlacionado para la aeronave: 16.0\n", - "Predicción obtenida: 27.147\n", - "\tR²: 0.6229712190050097, Desviación Estándar: 4.623329335321638, Varianza: 21.375174142845616, Incertidumbre: 0.9856971262384814\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 27.656', 'Área del ala: 30.489', 'Relación de aspecto del ala: 29.09', 'payload: 29.629', 'Crucero KIAS: 27.147']\n", - "**Mediana calculada:** 29.09\n", - "\n", - "--- Imputación para aeronave: **Skyeye 2600** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.978x + 10.544\n", - "Valor del parámetro correlacionado para la aeronave: 36.094\n", - "Predicción obtenida: 45.837\n", - "\tR²: 0.6252054405389704, Desviación Estándar: 4.492004783483308, Varianza: 20.178106974836926, Incertidumbre: 0.8644867236483915\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.969x + 25.611\n", - "Valor del parámetro correlacionado para la aeronave: 0.88\n", - "Predicción obtenida: 31.744\n", - "\tR²: 0.3944990966454992, Desviación Estándar: 5.216865762262041, Varianza: 27.215688381461913, Incertidumbre: 0.9524650191557678\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -6.972x + 131.702\n", - "Valor del parámetro correlacionado para la aeronave: 14.116\n", - "Predicción obtenida: 33.28\n", - "\tR²: 0.6561408230541541, Desviación Estándar: 4.031560606462846, Varianza: 16.25348092358307, Incertidumbre: 0.712685960898607\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.634x + 28.868\n", - "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 31.404\n", - "\tR²: 0.5509617835923953, Desviación Estándar: 4.448771765099154, Varianza: 19.791570217943438, Incertidumbre: 0.8407388378670155\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.775) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.007x + 11.03\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 44.27\n", - "\tR²: 0.6229712190050097, Desviación Estándar: 4.623329335321638, Varianza: 21.375174142845616, Incertidumbre: 0.9856971262384814\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 45.837', 'Área del ala: 31.744', 'Relación de aspecto del ala: 33.28', 'payload: 31.404', 'Crucero KIAS: 44.27']\n", - "**Mediana calculada:** 33.28\n", - "\n", - "--- Imputación para aeronave: **Skyeye 3600** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.28, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.906x + 25.745\n", - "Valor del parámetro correlacionado para la aeronave: 1.33\n", - "Predicción obtenida: 34.93\n", - "\tR²: 0.3942174961747027, Desviación Estándar: 5.139063743940044, Varianza: 26.409976164279065, Incertidumbre: 0.9230030958652002\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.28, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -6.972x + 131.702\n", - "Valor del parámetro correlacionado para la aeronave: 13.722\n", - "Predicción obtenida: 36.028\n", - "\tR²: 0.6573526772786173, Desviación Estándar: 3.9700064488669455, Varianza: 15.760951204045135, Incertidumbre: 0.6910894167477619\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.28, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.627x + 29.005\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 35.274\n", - "\tR²: 0.5501540266725506, Desviación Estándar: 4.384458563749019, Varianza: 19.223476897232118, Incertidumbre: 0.8141735157186453\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Capacidad combustible (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Capacidad combustible: [13.0, 11.5, 28.0, 28.0, 25.0]\n", - "Valores para Velocidad máxima (KIAS): [33.0, 33.0, 42.0, 38.0, 50.0]\n", - "Ecuación de regresión: y = 0.607x + 26.384\n", - "Valor del parámetro correlacionado para la aeronave: 11.5\n", - "Predicción obtenida: 33.369\n", - "\tR²: 0.487882729041738, Desviación Estándar: 4.5575735331497516, Varianza: 20.77147651006711, Incertidumbre: 2.038208846515347\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 34.93', 'Relación de aspecto del ala: 36.028', 'payload: 35.274', 'Capacidad combustible: 33.369']\n", - "**Mediana calculada:** 35.102\n", - "\n", - "=== Imputación para el parámetro: **Velocidad de pérdida (KCAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Stalker XE** ---\n" + "--- Imputación para aeronave: **Stalker VXE30** ---\n" ] }, { @@ -27654,7 +30281,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Stalker XE'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Stalker VXE30'.\n", " \n", " \n", "" @@ -27671,7 +30298,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Stalker VXE30** ---\n" + "--- Imputación para aeronave: **Aerosonde Mk. 4.7 Fixed Wing** ---\n" ] }, { @@ -27719,7 +30346,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Stalker VXE30'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde Mk. 4.7 Fixed Wing'.\n", " \n", " \n", "" @@ -27736,7 +30363,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 Fixed Wing** ---\n" + "--- Imputación para aeronave: **Aerosonde Mk. 4.7 VTOL** ---\n" ] }, { @@ -27784,7 +30411,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde® Mk. 4.7 Fixed Wing'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde Mk. 4.7 VTOL'.\n", " \n", " \n", "" @@ -27801,7 +30428,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 VTOL** ---\n" + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 Fixed wing** ---\n" ] }, { @@ -27849,7 +30476,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde® Mk. 4.7 VTOL'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde Mk. 4.8 Fixed wing'.\n", " \n", " \n", "" @@ -27866,7 +30493,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 Fixed wing** ---\n" + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n" ] }, { @@ -27914,7 +30541,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde® Mk. 4.8 Fixed wing'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde Mk. 4.8 VTOL FTUAS'.\n", " \n", " \n", "" @@ -27931,7 +30558,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n" + "--- Imputación para aeronave: **AAI Aerosonde** ---\n" ] }, { @@ -27979,7 +30606,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde® Mk. 4.8 VTOL FTUAS'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'AAI Aerosonde'.\n", " \n", " \n", "" @@ -27996,7 +30623,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **AAI Aerosonde** ---\n" + "--- Imputación para aeronave: **Fulmar X** ---\n" ] }, { @@ -28044,7 +30671,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'AAI Aerosonde'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Fulmar X'.\n", " \n", " \n", "" @@ -28061,7 +30688,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n" + "--- Imputación para aeronave: **Orbiter 4** ---\n" ] }, { @@ -28109,7 +30736,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Fulmar X'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Orbiter 4'.\n", " \n", " \n", "" @@ -28126,7 +30753,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n" + "--- Imputación para aeronave: **Orbiter 3** ---\n" ] }, { @@ -28174,7 +30801,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Orbiter 4'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Orbiter 3'.\n", " \n", " \n", "" @@ -28191,7 +30818,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Orbiter 3** ---\n" + "--- Imputación para aeronave: **Mantis** ---\n" ] }, { @@ -28239,7 +30866,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Orbiter 3'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Mantis'.\n", " \n", " \n", "" @@ -28256,7 +30883,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Mantis** ---\n" + "--- Imputación para aeronave: **ScanEagle** ---\n" ] }, { @@ -28304,7 +30931,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Mantis'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle'.\n", " \n", " \n", "" @@ -28321,7 +30948,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **ScanEagle** ---\n" + "--- Imputación para aeronave: **Integrator** ---\n" ] }, { @@ -28369,7 +30996,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator'.\n", " \n", " \n", "" @@ -28386,7 +31013,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Integrator** ---\n" + "--- Imputación para aeronave: **Integrator VTOL** ---\n" ] }, { @@ -28434,7 +31061,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator VTOL'.\n", " \n", " \n", "" @@ -28451,7 +31078,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n" + "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n" ] }, { @@ -28499,7 +31126,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator VTOL'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator Extended Range (ER)'.\n", " \n", " \n", "" @@ -28516,7 +31143,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n" + "--- Imputación para aeronave: **ScanEagle 3** ---\n" ] }, { @@ -28564,7 +31191,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator Extended Range (ER)'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle 3'.\n", " \n", " \n", "" @@ -28581,7 +31208,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **ScanEagle 3** ---\n" + "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n" ] }, { @@ -28629,7 +31256,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle 3'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'RQ Nan 21A Blackjack'.\n", " \n", " \n", "" @@ -28646,7 +31273,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **RQNan21A Blackjack** ---\n" + "--- Imputación para aeronave: **DeltaQuad Evo** ---\n" ] }, { @@ -28694,7 +31321,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'RQNan21A Blackjack'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Evo'.\n", " \n", " \n", "" @@ -28711,7 +31338,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **DeltaQuad Evo** ---\n" + "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n" ] }, { @@ -28759,7 +31386,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Evo'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Pro #MAP'.\n", " \n", " \n", "" @@ -28776,7 +31403,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n" + "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n" ] }, { @@ -28824,7 +31451,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Pro #MAP'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Pro #CARGO'.\n", " \n", " \n", "" @@ -28841,7 +31468,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n" + "--- Imputación para aeronave: **V35** ---\n" ] }, { @@ -28889,7 +31516,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Pro #CARGO'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'V35'.\n", " \n", " \n", "" @@ -28906,7 +31533,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **V35** ---\n" + "--- Imputación para aeronave: **V39** ---\n" ] }, { @@ -28954,7 +31581,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'V35'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'V39'.\n", " \n", " \n", "" @@ -28971,7 +31598,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **V39** ---\n" + "--- Imputación para aeronave: **Volitation VT370** ---\n" ] }, { @@ -29019,7 +31646,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'V39'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Volitation VT370'.\n", " \n", " \n", "" @@ -29036,7 +31663,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Volitation VT370** ---\n" + "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n" ] }, { @@ -29084,7 +31711,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Volitation VT370'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Skyeye 5000 VTOL'.\n", " \n", " \n", "" @@ -29101,7 +31728,9 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n" + "=== Imputación para el parámetro: **Velocidad de pérdida limpia (KCAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Stalker XE** ---\n" ] }, { @@ -29149,7 +31778,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Skyeye 5000 VTOL'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Stalker XE'.\n", " \n", " \n", "" @@ -29166,1678 +31795,3433 @@ "output_type": "stream", "text": [ "\n", - "=== Imputación para el parámetro: **envergadura** ===\n", - "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 1.904x + 1.375\n", - "Valor del parámetro correlacionado para la aeronave: 2.503\n", - "Predicción obtenida: 6.14\n", - "\tR²: 0.6550202920419386, Desviación Estándar: 0.6922900390325586, Varianza: 0.47926549814370145, Incertidumbre: 0.1285550329147554\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.791) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 0.032x + 2.598\n", - "Valor del parámetro correlacionado para la aeronave: 93.0\n", - "Predicción obtenida: 5.614\n", - "\tR²: 0.6048248274212248, Desviación Estándar: 0.7257069176330723, Varianza: 0.5266505303004948, Incertidumbre: 0.12828807065308317\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = 0.734) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.2, 4.4, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 0.114x + 2.88\n", - "Valor del parámetro correlacionado para la aeronave: 22.7\n", - "Predicción obtenida: 5.476\n", - "\tR²: 0.5142554083484681, Desviación Estándar: 0.8073085344120734, Varianza: 0.6517470697345699, Incertidumbre: 0.1499134313108701\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.936) ---\n", - "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", - "Valores para envergadura: [4.4, 4.4, 4.4, 2.69, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 0.048x + 2.326\n", - "Valor del parámetro correlacionado para la aeronave: 70.3\n", - "Predicción obtenida: 5.674\n", - "\tR²: 0.8762967968830506, Desviación Estándar: 0.44130001156979004, Varianza: 0.1947457002114968, Incertidumbre: 0.16679572631194156\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Área del ala: 6.14', 'Peso máximo al despegue (MTOW): 5.614', 'payload: 5.476', 'RTF (Including fuel & Batteries): 5.674']\n", - "**Mediana calculada:** 5.644\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 1.835x + 1.446\n", - "Valor del parámetro correlacionado para la aeronave: 2.09\n", - "Predicción obtenida: 5.281\n", - "\tR²: 0.6787677993689816, Desviación Estándar: 0.6854337532513997, Varianza: 0.46981943009630067, Incertidumbre: 0.12514250944374053\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.791) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 0.032x + 2.596\n", - "Valor del parámetro correlacionado para la aeronave: 75.0\n", - "Predicción obtenida: 5.033\n", - "\tR²: 0.6331651979245666, Desviación Estándar: 0.7146435397246005, Varianza: 0.5107153888701067, Incertidumbre: 0.12440347223914108\n", - "\tNivel de confianza: Confianza Media\n", + "--- Imputación para aeronave: **Stalker VXE30** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Stalker VXE30'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: payload (r = 0.734) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 5.2, 4.4, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 0.116x + 2.873\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 4.954\n", - "\tR²: 0.5454308932616603, Desviación Estándar: 0.794258196699784, Varianza: 0.6308460830247928, Incertidumbre: 0.14501104360528233\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Área del ala: 5.281', 'Peso máximo al despegue (MTOW): 5.033', 'payload: 4.954']\n", - "**Mediana calculada:** 5.033\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.7 Fixed Wing** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Aerosonde Mk. 4.7 Fixed Wing'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "=== Imputación para el parámetro: **Cuerda** ===\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.7 VTOL** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Aerosonde Mk. 4.7 VTOL'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 Fixed wing** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Aerosonde Mk. 4.8 Fixed wing'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 5500.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", - "Ecuación de regresión: y = 0.0x + -0.975\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.303\n", - "\tR²: 0.5584422980021864, Desviación Estándar: 0.04101181707874288, Varianza: 0.0016819691401002662, Incertidumbre: 0.02050590853937144\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Aerosonde Mk. 4.8 VTOL FTUAS'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", - "Ecuación de regresión: y = 0.009x + 0.094\n", - "Valor del parámetro correlacionado para la aeronave: 27.892\n", - "Predicción obtenida: 0.345\n", - "\tR²: 0.37089288437785595, Desviación Estándar: 0.04895280456579205, Varianza: 0.0023963770748566308, Incertidumbre: 0.024476402282896024\n", - "\tNivel de confianza: Confianza Media\n", + "--- Imputación para aeronave: **AAI Aerosonde** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'AAI Aerosonde'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", - "Ecuación de regresión: y = 0.167x + 0.103\n", - "Valor del parámetro correlacionado para la aeronave: 2.503\n", - "Predicción obtenida: 0.521\n", - "\tR²: 0.9567278474032922, Desviación Estándar: 0.012838655021740699, Varianza: 0.00016483106276726767, Incertidumbre: 0.006419327510870349\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", - "Ecuación de regresión: y = -0.031x + 0.725\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 0.338\n", - "\tR²: 0.33955370790059436, Desviación Estándar: 0.050157286426532284, Varianza: 0.0025157533816731995, Incertidumbre: 0.025078643213266142\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", - "Ecuación de regresión: y = 0.125x + -0.016\n", - "Valor del parámetro correlacionado para la aeronave: 3.594\n", - "Predicción obtenida: 0.432\n", - "\tR²: 0.9863480800941506, Desviación Estándar: 0.007211276455846646, Varianza: 5.2002508122648166e-05, Incertidumbre: 0.003605638227923323\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "--- Imputación para aeronave: **Fulmar X** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Fulmar X'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.197]\n", - "Ecuación de regresión: y = 0.003x + 0.204\n", - "Valor del parámetro correlacionado para la aeronave: 93.0\n", - "Predicción obtenida: 0.481\n", - "\tR²: 0.736966419457739, Desviación Estándar: 0.03164979554992154, Varianza: 0.0010017095583518332, Incertidumbre: 0.012920974926786248\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Orbiter 4'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 3270.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.197]\n", - "Ecuación de regresión: y = -0.0x + 0.338\n", - "Valor del parámetro correlacionado para la aeronave: 316.495\n", - "Predicción obtenida: 0.325\n", - "\tR²: 0.5966603871244396, Desviación Estándar: 0.0391922998443847, Varianza: 0.0015360363670921572, Incertidumbre: 0.01600018941081718\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **Orbiter 3** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Orbiter 3'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", - "Ecuación de regresión: y = 0.074x + -0.018\n", - "Valor del parámetro correlacionado para la aeronave: 5.644\n", - "Predicción obtenida: 0.402\n", - "\tR²: 0.8190779024483189, Desviación Estándar: 0.026251920869893277, Varianza: 0.0006891633493591382, Incertidumbre: 0.013125960434946638\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "--- Imputación para aeronave: **Mantis** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Mantis'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352]\n", - "Ecuación de regresión: y = 0.005x + 0.27\n", - "Valor del parámetro correlacionado para la aeronave: 22.7\n", - "Predicción obtenida: 0.394\n", - "\tR²: 0.6020929401336428, Desviación Estándar: 0.027642283534153083, Varianza: 0.0007640958389825106, Incertidumbre: 0.012362005007137885\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.345', 'Área del ala: 0.521', 'Relación de aspecto del ala: 0.338', 'Longitud del fuselaje: 0.432', 'Peso máximo al despegue (MTOW): 0.481', 'Alcance de la aeronave: 0.325', 'envergadura: 0.402', 'payload: 0.394']\n", - "**Mediana calculada:** 0.394\n", + "--- Imputación para aeronave: **ScanEagle** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'ScanEagle'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n", + "--- Imputación para aeronave: **Integrator** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Integrator'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = 0.0x + -1.225\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.326\n", - "\tR²: 0.5082545132230825, Desviación Estándar: 0.05085224756183586, Varianza: 0.002585951082090241, Incertidumbre: 0.022741816471382584\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Integrator VTOL'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = 0.012x + 0.047\n", - "Valor del parámetro correlacionado para la aeronave: 30.407\n", - "Predicción obtenida: 0.4\n", - "\tR²: 0.5916034080971878, Desviación Estándar: 0.0463426661702313, Varianza: 0.0021476427077655007, Incertidumbre: 0.020725070363043403\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = 0.1x + 0.166\n", - "Valor del parámetro correlacionado para la aeronave: 0.94\n", - "Predicción obtenida: 0.261\n", - "\tR²: 0.8603589273995997, Desviación Estándar: 0.027098580959037145, Varianza: 0.0007343330899934905, Incertidumbre: 0.0121188538236377\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = -0.041x + 0.884\n", - "Valor del parámetro correlacionado para la aeronave: 13.218\n", - "Predicción obtenida: 0.336\n", - "\tR²: 0.5556998392410524, Desviación Estándar: 0.048336833461136076, Varianza: 0.0023364494690496043, Incertidumbre: 0.02161688908723734\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = 0.108x + 0.02\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.15\n", - "\tR²: 0.9729483577129217, Desviación Estándar: 0.011927152694253538, Varianza: 0.00014225697139203943, Incertidumbre: 0.005333984840474135\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = 0.002x + 0.229\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 0.27\n", - "\tR²: 0.7196970118630204, Desviación Estándar: 0.034754348458380345, Varianza: 0.0012078647367665242, Incertidumbre: 0.013135908999850775\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Integrator Extended Range (ER)'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = -0.0x + 0.352\n", - "Valor del parámetro correlacionado para la aeronave: 800.0\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.561672057764841, Desviación Estándar: 0.04346051474042987, Varianza: 0.001888816341503122, Incertidumbre: 0.016426530550576326\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **ScanEagle 3** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'ScanEagle 3'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = 0.072x + -0.01\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 0.207\n", - "\tR²: 0.8941942208767555, Desviación Estándar: 0.023588192102840765, Varianza: 0.0005564028066805194, Incertidumbre: 0.010548960201655131\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'RQ Nan 21A Blackjack'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352]\n", - "Ecuación de regresión: y = 0.008x + 0.145\n", - "Valor del parámetro correlacionado para la aeronave: 27.8\n", - "Predicción obtenida: 0.378\n", - "\tR²: 0.5951449532870101, Desviación Estándar: 0.03013201386753742, Varianza: 0.0009079382597134674, Incertidumbre: 0.017396726317648267\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.326', 'Velocidad a la que se realiza el crucero (KTAS): 0.4', 'Área del ala: 0.261', 'Relación de aspecto del ala: 0.336', 'Longitud del fuselaje: 0.15', 'Peso máximo al despegue (MTOW): 0.27', 'Alcance de la aeronave: 0.313', 'envergadura: 0.207', 'Crucero KIAS: 0.378']\n", - "**Mediana calculada:** 0.313\n", + "--- Imputación para aeronave: **DeltaQuad Evo** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'DeltaQuad Evo'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n", + "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'DeltaQuad Pro #MAP'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313]\n", - "Ecuación de regresión: y = 0.0x + -1.197\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.323\n", - "\tR²: 0.5059374879835691, Desviación Estándar: 0.04665619290169541, Varianza: 0.0021768003360802136, Incertidumbre: 0.019047310991669373\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'DeltaQuad Pro #CARGO'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313]\n", - "Ecuación de regresión: y = 0.008x + 0.114\n", - "Valor del parámetro correlacionado para la aeronave: 26.611\n", - "Predicción obtenida: 0.329\n", - "\tR²: 0.4394712393952719, Desviación Estándar: 0.04969552664305181, Varianza: 0.002469645368330272, Incertidumbre: 0.020288113795727262\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **V35** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'V35'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313]\n", - "Ecuación de regresión: y = 0.093x + 0.184\n", - "Valor del parámetro correlacionado para la aeronave: 1.608\n", - "Predicción obtenida: 0.334\n", - "\tR²: 0.779679530445362, Desviación Estándar: 0.031156292404129815, Varianza: 0.0009707145563716373, Incertidumbre: 0.012719503111178239\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313]\n", - "Ecuación de regresión: y = -0.04x + 0.855\n", - "Valor del parámetro correlacionado para la aeronave: 13.443\n", - "Predicción obtenida: 0.322\n", - "\tR²: 0.5431294809153299, Desviación Estándar: 0.04486574816022439, Varianza: 0.0020129353579766787, Incertidumbre: 0.01831636498679382\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313]\n", - "Ecuación de regresión: y = 0.058x + 0.165\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.235\n", - "\tR²: 0.4883148244734046, Desviación Estándar: 0.047480989850069766, Varianza: 0.002254444397142428, Incertidumbre: 0.019384032935823015\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313]\n", - "Ecuación de regresión: y = 0.002x + 0.24\n", - "Valor del parámetro correlacionado para la aeronave: 55.0\n", - "Predicción obtenida: 0.346\n", - "\tR²: 0.6707432653466199, Desviación Estándar: 0.035236075624649296, Varianza: 0.0012415810254260045, Incertidumbre: 0.012457834008295764\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **V39** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'V39'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313]\n", - "Ecuación de regresión: y = -0.0x + 0.352\n", - "Valor del parámetro correlacionado para la aeronave: 150.0\n", - "Predicción obtenida: 0.344\n", - "\tR²: 0.5617138568209437, Desviación Estándar: 0.04065360565285314, Varianza: 0.0016527156525776928, Incertidumbre: 0.014373220118408109\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **Volitation VT370** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Volitation VT370'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313]\n", - "Ecuación de regresión: y = 0.053x + 0.087\n", - "Valor del parámetro correlacionado para la aeronave: 5.2\n", - "Predicción obtenida: 0.361\n", - "\tR²: 0.6218579555553649, Desviación Estándar: 0.040817446367584644, Varianza: 0.0016660639279706489, Incertidumbre: 0.01666365270066685\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Skyeye 5000 VTOL'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394]\n", - "Ecuación de regresión: y = 0.005x + 0.27\n", - "Valor del parámetro correlacionado para la aeronave: 12.0\n", - "Predicción obtenida: 0.335\n", - "\tR²: 0.7240526671985225, Desviación Estándar: 0.02523396237425335, Varianza: 0.0006367528571052337, Incertidumbre: 0.010301722000918372\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.323', 'Velocidad a la que se realiza el crucero (KTAS): 0.329', 'Área del ala: 0.334', 'Relación de aspecto del ala: 0.322', 'Longitud del fuselaje: 0.235', 'Peso máximo al despegue (MTOW): 0.346', 'Alcance de la aeronave: 0.344', 'envergadura: 0.361', 'payload: 0.335']\n", - "**Mediana calculada:** 0.334\n", + "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Skyeye 5000 VTOL octo'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Imputación para aeronave: **Orbiter 3** ---\n", + "--- Imputación para aeronave: **Ascend** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Ascend'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334]\n", - "Ecuación de regresión: y = 0.0x + -1.217\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.325\n", - "\tR²: 0.518160981799697, Desviación Estándar: 0.04335452050989663, Varianza: 0.001879614448643048, Incertidumbre: 0.016386468497090814\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **Transition** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Transition'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334]\n", - "Ecuación de regresión: y = 0.008x + 0.113\n", - "Valor del parámetro correlacionado para la aeronave: 26.611\n", - "Predicción obtenida: 0.33\n", - "\tR²: 0.45672010492785353, Desviación Estándar: 0.046035749855102136, Varianza: 0.0021192902647215366, Incertidumbre: 0.017399877933568286\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **Reach** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Reach'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334]\n", - "Ecuación de regresión: y = 0.093x + 0.184\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.296\n", - "\tR²: 0.7867060876798443, Desviación Estándar: 0.028845138922881017, Varianza: 0.0008320420394803054, Incertidumbre: 0.010902437731864672\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "=== Imputación para el parámetro: **envergadura** ===\n", "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334]\n", - "Ecuación de regresión: y = -0.04x + 0.865\n", - "Valor del parámetro correlacionado para la aeronave: 14.012\n", - "Predicción obtenida: 0.301\n", - "\tR²: 0.5529769564137459, Desviación Estándar: 0.041758833679074646, Varianza: 0.001743800190236619, Incertidumbre: 0.015783355564991417\n", - "\tNivel de confianza: Confianza Alta\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334]\n", - "Ecuación de regresión: y = 0.038x + 0.223\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.269\n", - "\tR²: 0.2664980716684373, Desviación Estándar: 0.05349140864008716, Varianza: 0.0028613307983007914, Incertidumbre: 0.020217852077171767\n", + "--- Correlación: Área del ala (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 1.904x + 1.375\n", + "Valor del parámetro correlacionado para la aeronave: 2.503\n", + "Predicción obtenida: 6.14\n", + "\tR²: 0.6550202920419386, Desviación Estándar: 0.6922900390325586, Varianza: 0.47926549814370145, Incertidumbre: 0.1285550329147554\n", "\tNivel de confianza: Confianza Media\n", "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334]\n", - "Ecuación de regresión: y = 0.002x + 0.24\n", - "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 0.301\n", - "\tR²: 0.6702878820787533, Desviación Estándar: 0.033427806129933846, Varianza: 0.0011174182226604426, Incertidumbre: 0.011142602043311281\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.791) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 0.032x + 2.598\n", + "Valor del parámetro correlacionado para la aeronave: 93.0\n", + "Predicción obtenida: 5.614\n", + "\tR²: 0.6048248274212248, Desviación Estándar: 0.7257069176330723, Varianza: 0.5266505303004948, Incertidumbre: 0.12828807065308317\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = 0.734) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.2, 4.4, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 0.114x + 2.88\n", + "Valor del parámetro correlacionado para la aeronave: 22.7\n", + "Predicción obtenida: 5.476\n", + "\tR²: 0.5142554083484681, Desviación Estándar: 0.8073085344120734, Varianza: 0.6517470697345699, Incertidumbre: 0.1499134313108701\n", + "\tNivel de confianza: Confianza Baja\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.936) ---\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", + "Valores para envergadura: [4.4, 4.4, 4.4, 2.69, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 0.048x + 2.326\n", + "Valor del parámetro correlacionado para la aeronave: 70.3\n", + "Predicción obtenida: 5.674\n", + "\tR²: 0.8762967968830506, Desviación Estándar: 0.44130001156979004, Varianza: 0.1947457002114968, Incertidumbre: 0.16679572631194156\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Área del ala: 6.14', 'Peso máximo al despegue (MTOW): 5.614', 'payload: 5.476', 'RTF (Including fuel & Batteries): 5.674']\n", + "**Mediana calculada:** 5.644\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.841) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 1.835x + 1.446\n", + "Valor del parámetro correlacionado para la aeronave: 2.09\n", + "Predicción obtenida: 5.281\n", + "\tR²: 0.6787677993689816, Desviación Estándar: 0.6854337532513997, Varianza: 0.46981943009630067, Incertidumbre: 0.12514250944374053\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.791) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 0.032x + 2.596\n", + "Valor del parámetro correlacionado para la aeronave: 75.0\n", + "Predicción obtenida: 5.033\n", + "\tR²: 0.6331651979245666, Desviación Estándar: 0.7146435397246005, Varianza: 0.5107153888701067, Incertidumbre: 0.12440347223914108\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: payload (r = 0.734) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 5.2, 4.4, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Ecuación de regresión: y = 0.116x + 2.873\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 4.954\n", + "\tR²: 0.5454308932616603, Desviación Estándar: 0.794258196699784, Varianza: 0.6308460830247928, Incertidumbre: 0.14501104360528233\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Área del ala: 5.281', 'Peso máximo al despegue (MTOW): 5.033', 'payload: 4.954']\n", + "**Mediana calculada:** 5.033\n", + "\n", + "=== Imputación para el parámetro: **Cuerda** ===\n", + "\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 5500.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", + "Ecuación de regresión: y = 0.0x + -0.975\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.303\n", + "\tR²: 0.5584422980021864, Desviación Estándar: 0.04101181707874288, Varianza: 0.0016819691401002662, Incertidumbre: 0.02050590853937144\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", + "Ecuación de regresión: y = 0.009x + 0.094\n", + "Valor del parámetro correlacionado para la aeronave: 27.892\n", + "Predicción obtenida: 0.345\n", + "\tR²: 0.37089288437785595, Desviación Estándar: 0.04895280456579205, Varianza: 0.0023963770748566308, Incertidumbre: 0.024476402282896024\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", + "Ecuación de regresión: y = 0.167x + 0.103\n", + "Valor del parámetro correlacionado para la aeronave: 2.503\n", + "Predicción obtenida: 0.521\n", + "\tR²: 0.9567278474032922, Desviación Estándar: 0.012838655021740699, Varianza: 0.00016483106276726767, Incertidumbre: 0.006419327510870349\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", + "Ecuación de regresión: y = -0.031x + 0.725\n", + "Valor del parámetro correlacionado para la aeronave: 12.5\n", + "Predicción obtenida: 0.338\n", + "\tR²: 0.33955370790059436, Desviación Estándar: 0.050157286426532284, Varianza: 0.0025157533816731995, Incertidumbre: 0.025078643213266142\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", + "Ecuación de regresión: y = 0.125x + -0.016\n", + "Valor del parámetro correlacionado para la aeronave: 3.594\n", + "Predicción obtenida: 0.432\n", + "\tR²: 0.9863480800941506, Desviación Estándar: 0.007211276455846646, Varianza: 5.2002508122648166e-05, Incertidumbre: 0.003605638227923323\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.197]\n", + "Ecuación de regresión: y = 0.003x + 0.204\n", + "Valor del parámetro correlacionado para la aeronave: 93.0\n", + "Predicción obtenida: 0.481\n", + "\tR²: 0.736966419457739, Desviación Estándar: 0.03164979554992154, Varianza: 0.0010017095583518332, Incertidumbre: 0.012920974926786248\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 3270.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.197]\n", + "Ecuación de regresión: y = -0.0x + 0.342\n", + "Valor del parámetro correlacionado para la aeronave: 599.358\n", + "Predicción obtenida: 0.316\n", + "\tR²: 0.5312147113857546, Desviación Estándar: 0.04225248902560736, Varianza: 0.0017852728288590702, Incertidumbre: 0.01724950641254734\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 2.9]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", + "Ecuación de regresión: y = 0.074x + -0.018\n", + "Valor del parámetro correlacionado para la aeronave: 5.644\n", + "Predicción obtenida: 0.402\n", + "\tR²: 0.8190779024483189, Desviación Estándar: 0.026251920869893277, Varianza: 0.0006891633493591382, Incertidumbre: 0.013125960434946638\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352]\n", + "Ecuación de regresión: y = 0.005x + 0.27\n", + "Valor del parámetro correlacionado para la aeronave: 22.7\n", + "Predicción obtenida: 0.394\n", + "\tR²: 0.6020929401336428, Desviación Estándar: 0.027642283534153083, Varianza: 0.0007640958389825106, Incertidumbre: 0.012362005007137885\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.345', 'Área del ala: 0.521', 'Relación de aspecto del ala: 0.338', 'Longitud del fuselaje: 0.432', 'Peso máximo al despegue (MTOW): 0.481', 'Alcance de la aeronave: 0.316', 'envergadura: 0.402', 'payload: 0.394']\n", + "**Mediana calculada:** 0.394\n", + "\n", + "--- Imputación para aeronave: **Fulmar X** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = 0.0x + -1.225\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.326\n", + "\tR²: 0.5082545132230825, Desviación Estándar: 0.05085224756183586, Varianza: 0.002585951082090241, Incertidumbre: 0.022741816471382584\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = 0.012x + 0.047\n", + "Valor del parámetro correlacionado para la aeronave: 30.407\n", + "Predicción obtenida: 0.4\n", + "\tR²: 0.5916034080971878, Desviación Estándar: 0.0463426661702313, Varianza: 0.0021476427077655007, Incertidumbre: 0.020725070363043403\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = 0.1x + 0.166\n", + "Valor del parámetro correlacionado para la aeronave: 0.94\n", + "Predicción obtenida: 0.261\n", + "\tR²: 0.8603589273995997, Desviación Estándar: 0.027098580959037145, Varianza: 0.0007343330899934905, Incertidumbre: 0.0121188538236377\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = -0.041x + 0.884\n", + "Valor del parámetro correlacionado para la aeronave: 13.218\n", + "Predicción obtenida: 0.336\n", + "\tR²: 0.5556998392410524, Desviación Estándar: 0.048336833461136076, Varianza: 0.0023364494690496043, Incertidumbre: 0.02161688908723734\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = 0.108x + 0.02\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.15\n", + "\tR²: 0.9729483577129217, Desviación Estándar: 0.011927152694253538, Varianza: 0.00014225697139203943, Incertidumbre: 0.005333984840474135\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = 0.002x + 0.229\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 0.27\n", + "\tR²: 0.7196970118630204, Desviación Estándar: 0.034754348458380345, Varianza: 0.0012078647367665242, Incertidumbre: 0.013135908999850775\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = -0.0x + 0.356\n", + "Valor del parámetro correlacionado para la aeronave: 800.0\n", + "Predicción obtenida: 0.319\n", + "\tR²: 0.4747254035414423, Desviación Estándar: 0.04757606143631944, Varianza: 0.002263481621792442, Incertidumbre: 0.017982060988633097\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", + "Ecuación de regresión: y = 0.072x + -0.01\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 0.207\n", + "\tR²: 0.8941942208767555, Desviación Estándar: 0.023588192102840765, Varianza: 0.0005564028066805194, Incertidumbre: 0.010548960201655131\n", + "\tNivel de confianza: Confianza Muy Alta\n", + "\n", + "--- Correlación: Crucero KIAS (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352]\n", + "Ecuación de regresión: y = 0.008x + 0.145\n", + "Valor del parámetro correlacionado para la aeronave: 27.8\n", + "Predicción obtenida: 0.378\n", + "\tR²: 0.5951449532870101, Desviación Estándar: 0.03013201386753742, Varianza: 0.0009079382597134674, Incertidumbre: 0.017396726317648267\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.326', 'Velocidad a la que se realiza el crucero (KTAS): 0.4', 'Área del ala: 0.261', 'Relación de aspecto del ala: 0.336', 'Longitud del fuselaje: 0.15', 'Peso máximo al despegue (MTOW): 0.27', 'Alcance de la aeronave: 0.319', 'envergadura: 0.207', 'Crucero KIAS: 0.378']\n", + "**Mediana calculada:** 0.319\n", + "\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319]\n", + "Ecuación de regresión: y = 0.0x + -1.21\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.324\n", + "\tR²: 0.5124560667085087, Desviación Estándar: 0.04648787282808511, Varianza: 0.0021611223200802133, Incertidumbre: 0.018978594609367214\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319]\n", + "Ecuación de regresión: y = 0.008x + 0.11\n", + "Valor del parámetro correlacionado para la aeronave: 26.611\n", + "Predicción obtenida: 0.331\n", + "\tR²: 0.46337237262389996, Desviación Estándar: 0.048771859040965634, Varianza: 0.0023786942343118215, Incertidumbre: 0.019911028076218723\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319]\n", + "Ecuación de regresión: y = 0.092x + 0.186\n", + "Valor del parámetro correlacionado para la aeronave: 1.608\n", + "Predicción obtenida: 0.335\n", + "\tR²: 0.7613504476982165, Desviación Estándar: 0.032524685665416886, Varianza: 0.0010578551776341746, Incertidumbre: 0.013278147320780956\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319]\n", + "Ecuación de regresión: y = -0.04x + 0.863\n", + "Valor del parámetro correlacionado para la aeronave: 13.443\n", + "Predicción obtenida: 0.323\n", + "\tR²: 0.5527436088338049, Desviación Estándar: 0.044525732581881805, Varianza: 0.001982540861953251, Incertidumbre: 0.018177554208204372\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319]\n", + "Ecuación de regresión: y = 0.056x + 0.17\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.238\n", + "\tR²: 0.45536965068723667, Desviación Estándar: 0.04913418031682804, Varianza: 0.0024141676754065723, Incertidumbre: 0.020058945117688236\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319]\n", + "Ecuación de regresión: y = 0.002x + 0.241\n", + "Valor del parámetro correlacionado para la aeronave: 55.0\n", + "Predicción obtenida: 0.346\n", + "\tR²: 0.6561535955657221, Desviación Estándar: 0.03601567329003582, Varianza: 0.0012971287225345997, Incertidumbre: 0.01273346340619177\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319]\n", + "Ecuación de regresión: y = -0.0x + 0.356\n", + "Valor del parámetro correlacionado para la aeronave: 509.556\n", + "Predicción obtenida: 0.332\n", + "\tR²: 0.4749907143714427, Desviación Estándar: 0.044503358493513535, Varianza: 0.0019805489172021835, Incertidumbre: 0.01573431328816968\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: envergadura (r = 0.885) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319]\n", + "Ecuación de regresión: y = 0.052x + 0.093\n", + "Valor del parámetro correlacionado para la aeronave: 5.2\n", + "Predicción obtenida: 0.361\n", + "\tR²: 0.5927043159163219, Desviación Estándar: 0.04249009480284112, Varianza: 0.0018054081563544256, Incertidumbre: 0.01734650856490736\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: payload (r = 0.776) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394]\n", + "Ecuación de regresión: y = 0.005x + 0.27\n", + "Valor del parámetro correlacionado para la aeronave: 12.0\n", + "Predicción obtenida: 0.335\n", + "\tR²: 0.7240526671985225, Desviación Estándar: 0.02523396237425335, Varianza: 0.0006367528571052337, Incertidumbre: 0.010301722000918372\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.324', 'Velocidad a la que se realiza el crucero (KTAS): 0.331', 'Área del ala: 0.335', 'Relación de aspecto del ala: 0.323', 'Longitud del fuselaje: 0.238', 'Peso máximo al despegue (MTOW): 0.346', 'Alcance de la aeronave: 0.332', 'envergadura: 0.361', 'payload: 0.335']\n", + "**Mediana calculada:** 0.332\n", + "\n", + "--- Imputación para aeronave: **Orbiter 3** ---\n", + "\n", + "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332]\n", + "Ecuación de regresión: y = 0.0x + -1.224\n", + "Valor del parámetro correlacionado para la aeronave: 6000.0\n", + "Predicción obtenida: 0.326\n", + "\tR²: 0.5234700162931528, Desviación Estándar: 0.043118399156113214, Varianza: 0.001859196345785905, Incertidumbre: 0.016297223014041837\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332]\n", + "Ecuación de regresión: y = 0.008x + 0.109\n", + "Valor del parámetro correlacionado para la aeronave: 26.611\n", + "Predicción obtenida: 0.331\n", + "\tR²: 0.4773971261284471, Desviación Estándar: 0.0451547465914678, Varianza: 0.0020389511397396727, Incertidumbre: 0.017066889999309325\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Área del ala (r = 0.984) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332]\n", + "Ecuación de regresión: y = 0.092x + 0.186\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.297\n", + "\tR²: 0.7673579176782551, Desviación Estándar: 0.030127399803337826, Varianza: 0.0009076602189101601, Incertidumbre: 0.011387086789806877\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332]\n", + "Ecuación de regresión: y = -0.041x + 0.87\n", + "Valor del parámetro correlacionado para la aeronave: 13.934\n", + "Predicción obtenida: 0.304\n", + "\tR²: 0.5618645680480575, Desviación Estándar: 0.0413448766429335, Varianza: 0.001709398824619388, Incertidumbre: 0.01562689451197787\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", + "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332]\n", + "Ecuación de regresión: y = 0.037x + 0.225\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 0.27\n", + "\tR²: 0.25541759559597876, Desviación Estándar: 0.053898154753589445, Varianza: 0.0029050110858418765, Incertidumbre: 0.02037158765761021\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332]\n", + "Ecuación de regresión: y = 0.002x + 0.241\n", + "Valor del parámetro correlacionado para la aeronave: 32.0\n", + "Predicción obtenida: 0.301\n", + "\tR²: 0.6530681193804526, Desviación Estándar: 0.034246989275220595, Varianza: 0.0011728562744170745, Incertidumbre: 0.011415663091740198\n", + "\tNivel de confianza: Confianza Alta\n", + "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334]\n", - "Ecuación de regresión: y = -0.0x + 0.35\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332]\n", + "Ecuación de regresión: y = -0.0x + 0.356\n", "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 0.348\n", - "\tR²: 0.5635487874211478, Desviación Estándar: 0.038459910779187215, Varianza: 0.0014791647371430407, Incertidumbre: 0.012819970259729072\n", + "Predicción obtenida: 0.354\n", + "\tR²: 0.47924291008106135, Desviación Estándar: 0.04195830043487889, Varianza: 0.0017604989753835579, Incertidumbre: 0.013986100144959629\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334]\n", - "Ecuación de regresión: y = 0.049x + 0.099\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332]\n", + "Ecuación de regresión: y = 0.048x + 0.105\n", "Valor del parámetro correlacionado para la aeronave: 4.4\n", "Predicción obtenida: 0.315\n", - "\tR²: 0.6143266200258906, Desviación Estándar: 0.03878762933713736, Varianza: 0.0015044801895951585, Incertidumbre: 0.014660345881688361\n", + "\tR²: 0.5811309424136593, Desviación Estándar: 0.04042561655887864, Varianza: 0.0016342304741654829, Incertidumbre: 0.015279446858749655\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332]\n", "Ecuación de regresión: y = 0.005x + 0.269\n", "Valor del parámetro correlacionado para la aeronave: 5.5\n", "Predicción obtenida: 0.299\n", - "\tR²: 0.7239573172494923, Desviación Estándar: 0.02336635054679909, Varianza: 0.0005459863378758981, Incertidumbre: 0.008831650370569787\n", + "\tR²: 0.7234996015016496, Desviación Estándar: 0.02339015181341113, Varianza: 0.0005470992018544201, Incertidumbre: 0.008840646403761759\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.325', 'Velocidad a la que se realiza el crucero (KTAS): 0.33', 'Área del ala: 0.296', 'Relación de aspecto del ala: 0.301', 'Longitud del fuselaje: 0.269', 'Peso máximo al despegue (MTOW): 0.301', 'Alcance de la aeronave: 0.348', 'envergadura: 0.315', 'payload: 0.299']\n", - "**Mediana calculada:** 0.301\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.326', 'Velocidad a la que se realiza el crucero (KTAS): 0.331', 'Área del ala: 0.297', 'Relación de aspecto del ala: 0.304', 'Longitud del fuselaje: 0.27', 'Peso máximo al despegue (MTOW): 0.301', 'Alcance de la aeronave: 0.354', 'envergadura: 0.315', 'payload: 0.299']\n", + "**Mediana calculada:** 0.304\n", "\n", "--- Imputación para aeronave: **Mantis** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", - "Ecuación de regresión: y = 0.0x + -1.179\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", + "Ecuación de regresión: y = 0.0x + -1.19\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.322\n", - "\tR²: 0.5005474478510711, Desviación Estándar: 0.04131034033551268, Varianza: 0.001706544218635886, Incertidumbre: 0.014605410892182586\n", + "Predicción obtenida: 0.323\n", + "\tR²: 0.5088584866234673, Desviación Estándar: 0.04095418731504614, Varianza: 0.0016772454586358862, Incertidumbre: 0.014479491784226604\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", - "Ecuación de regresión: y = 0.008x + 0.119\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", + "Ecuación de regresión: y = 0.008x + 0.114\n", "Valor del parámetro correlacionado para la aeronave: 18.266\n", - "Predicción obtenida: 0.261\n", - "\tR²: 0.43089375042529565, Desviación Estándar: 0.04409692764157503, Varianza: 0.0019445390274263038, Incertidumbre: 0.015590618282425106\n", + "Predicción obtenida: 0.26\n", + "\tR²: 0.4544871001518501, Desviación Estándar: 0.043161596731449474, Varianza: 0.00186292343240827, Incertidumbre: 0.015259928867823522\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", - "Ecuación de regresión: y = 0.093x + 0.185\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", + "Ecuación de regresión: y = 0.092x + 0.188\n", "Valor del parámetro correlacionado para la aeronave: 0.754\n", - "Predicción obtenida: 0.255\n", - "\tR²: 0.7861394448447504, Desviación Estándar: 0.027031926600075157, Varianza: 0.0007307250557118509, Incertidumbre: 0.009557229303725078\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "Predicción obtenida: 0.257\n", + "\tR²: 0.7657531148702028, Desviación Estándar: 0.0282834186887527, Varianza: 0.0007999517727232854, Incertidumbre: 0.009999698574977681\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", - "Ecuación de regresión: y = -0.04x + 0.865\n", - "Valor del parámetro correlacionado para la aeronave: 14.767\n", - "Predicción obtenida: 0.27\n", - "\tR²: 0.553435246087135, Desviación Estándar: 0.03906194604657353, Varianza: 0.0015258356289454217, Incertidumbre: 0.013810483467937597\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", + "Ecuación de regresión: y = -0.041x + 0.87\n", + "Valor del parámetro correlacionado para la aeronave: 14.755\n", + "Predicción obtenida: 0.271\n", + "\tR²: 0.5620081314167932, Desviación Estándar: 0.038674794449433975, Varianza: 0.0014957397257059691, Incertidumbre: 0.013673604708095305\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", - "Ecuación de regresión: y = 0.033x + 0.237\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", + "Ecuación de regresión: y = 0.032x + 0.24\n", "Valor del parámetro correlacionado para la aeronave: 1.48\n", - "Predicción obtenida: 0.286\n", - "\tR²: 0.2386332689505225, Desviación Estándar: 0.05100451268256031, Varianza: 0.002601460313985455, Incertidumbre: 0.01803281839447683\n", + "Predicción obtenida: 0.288\n", + "\tR²: 0.22407617799783763, Desviación Estándar: 0.051475969281914856, Varianza: 0.002649775413512642, Incertidumbre: 0.018199503473696203\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", - "Ecuación de regresión: y = 0.002x + 0.24\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", + "Ecuación de regresión: y = 0.002x + 0.242\n", "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 0.252\n", - "\tR²: 0.6726769802088182, Desviación Estándar: 0.03171272006589414, Varianza: 0.0010056966139777646, Incertidumbre: 0.010028442620755052\n", + "Predicción obtenida: 0.254\n", + "\tR²: 0.654616976883781, Desviación Estándar: 0.0325002987324586, Varianza: 0.00105626941769905, Incertidumbre: 0.01027749686304525\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", - "Ecuación de regresión: y = -0.0x + 0.343\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", + "Ecuación de regresión: y = -0.0x + 0.347\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.342\n", - "\tR²: 0.5063410592901572, Desviación Estándar: 0.03894562904032464, Varianza: 0.001516762021346578, Incertidumbre: 0.012315689267542349\n", + "Predicción obtenida: 0.346\n", + "\tR²: 0.414987640780789, Desviación Estándar: 0.042297955918060494, Varianza: 0.0017891170748461885, Incertidumbre: 0.013375788107046958\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301]\n", - "Ecuación de regresión: y = 0.049x + 0.099\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", + "Ecuación de regresión: y = 0.047x + 0.105\n", "Valor del parámetro correlacionado para la aeronave: 2.1\n", - "Predicción obtenida: 0.201\n", - "\tR²: 0.6088855002504914, Desviación Estándar: 0.036556416184119006, Varianza: 0.0013363715642265182, Incertidumbre: 0.012924644889834101\n", + "Predicción obtenida: 0.204\n", + "\tR²: 0.5774901913231636, Desviación Estándar: 0.037985109973381545, Varianza: 0.0014428685796898902, Incertidumbre: 0.013429764423147424\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313]\n", - "Ecuación de regresión: y = 0.005x + 0.208\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319]\n", + "Ecuación de regresión: y = 0.005x + 0.202\n", "Valor del parámetro correlacionado para la aeronave: 16.7\n", "Predicción obtenida: 0.285\n", - "\tR²: 0.37218727831824217, Desviación Estándar: 0.03267385114997177, Varianza: 0.0010675805489705115, Incertidumbre: 0.016336925574985884\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.322', 'Velocidad a la que se realiza el crucero (KTAS): 0.261', 'Área del ala: 0.255', 'Relación de aspecto del ala: 0.27', 'Longitud del fuselaje: 0.286', 'Peso máximo al despegue (MTOW): 0.252', 'Alcance de la aeronave: 0.342', 'envergadura: 0.201', 'Crucero KIAS: 0.285']\n", - "**Mediana calculada:** 0.27\n", + "\tR²: 0.42206898443002183, Desviación Estándar: 0.031616100972920246, Varianza: 0.0009995778407298887, Incertidumbre: 0.015808050486460123\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.323', 'Velocidad a la que se realiza el crucero (KTAS): 0.26', 'Área del ala: 0.257', 'Relación de aspecto del ala: 0.271', 'Longitud del fuselaje: 0.288', 'Peso máximo al despegue (MTOW): 0.254', 'Alcance de la aeronave: 0.346', 'envergadura: 0.204', 'Crucero KIAS: 0.285']\n", + "**Mediana calculada:** 0.271\n", "\n", "--- Imputación para aeronave: **ScanEagle** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", - "Ecuación de regresión: y = 0.0x + -1.108\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", + "Ecuación de regresión: y = 0.0x + -1.119\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.315\n", - "\tR²: 0.4389240659793703, Desviación Estándar: 0.042140017620528936, Varianza: 0.001775781085058489, Incertidumbre: 0.014046672540176311\n", + "Predicción obtenida: 0.316\n", + "\tR²: 0.4467125550050872, Desviación Estándar: 0.041829866636606275, Varianza: 0.0017497377428362668, Incertidumbre: 0.013943288878868759\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", - "Ecuación de regresión: y = 0.008x + 0.125\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", + "Ecuación de regresión: y = 0.008x + 0.121\n", "Valor del parámetro correlacionado para la aeronave: 30.625\n", - "Predicción obtenida: 0.356\n", - "\tR²: 0.4515433062124382, Desviación Estándar: 0.04166343402845906, Varianza: 0.0017358417350437604, Incertidumbre: 0.013887811342819687\n", + "Predicción obtenida: 0.359\n", + "\tR²: 0.4733288036155826, Desviación Estándar: 0.040811340123434084, Varianza: 0.0016655654826706208, Incertidumbre: 0.013603780041144695\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", - "Ecuación de regresión: y = 0.09x + 0.19\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", + "Ecuación de regresión: y = 0.089x + 0.192\n", "Valor del parámetro correlacionado para la aeronave: 1.063\n", - "Predicción obtenida: 0.286\n", - "\tR²: 0.7885748390015039, Desviación Estándar: 0.025867962774747247, Varianza: 0.0006691514981157094, Incertidumbre: 0.008622654258249082\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "Predicción obtenida: 0.287\n", + "\tR²: 0.7694013001888904, Desviación Estándar: 0.027004708852321632, Varianza: 0.0007292543001986583, Incertidumbre: 0.009001569617440544\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", - "Ecuación de regresión: y = -0.04x + 0.865\n", - "Valor del parámetro correlacionado para la aeronave: 14.067\n", - "Predicción obtenida: 0.298\n", - "\tR²: 0.5714606764166177, Desviación Estándar: 0.03682809308680053, Varianza: 0.0013563084404100449, Incertidumbre: 0.01227603102893351\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", + "Ecuación de regresión: y = -0.041x + 0.87\n", + "Valor del parámetro correlacionado para la aeronave: 14.057\n", + "Predicción obtenida: 0.299\n", + "\tR²: 0.5795819419058201, Desviación Estándar: 0.0364629459060155, Varianza: 0.0013295464241450125, Incertidumbre: 0.012154315302005168\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", - "Ecuación de regresión: y = 0.035x + 0.232\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", + "Ecuación de regresión: y = 0.034x + 0.235\n", "Valor del parámetro correlacionado para la aeronave: 1.71\n", - "Predicción obtenida: 0.292\n", - "\tR²: 0.26160299392487774, Desviación Estándar: 0.04834247216917272, Varianza: 0.0023369946154272393, Incertidumbre: 0.01611415738972424\n", + "Predicción obtenida: 0.293\n", + "\tR²: 0.24695440685743675, Desviación Estándar: 0.04880021191080425, Varianza: 0.0023814606825394006, Incertidumbre: 0.016266737303601415\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", - "Ecuación de regresión: y = 0.002x + 0.244\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", + "Ecuación de regresión: y = 0.002x + 0.246\n", "Valor del parámetro correlacionado para la aeronave: 26.5\n", - "Predicción obtenida: 0.293\n", - "\tR²: 0.683688240941903, Desviación Estándar: 0.03060819392233029, Varianza: 0.000936861535186977, Incertidumbre: 0.00922871770461874\n", + "Predicción obtenida: 0.294\n", + "\tR²: 0.6670417777815097, Desviación Estándar: 0.03132273145396624, Varianza: 0.000981113505737286, Incertidumbre: 0.009444158876533569\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", - "Ecuación de regresión: y = -0.0x + 0.334\n", - "Valor del parámetro correlacionado para la aeronave: 418.78\n", - "Predicción obtenida: 0.317\n", - "\tR²: 0.39569557326663285, Desviación Estándar: 0.042306580420532675, Varianza: 0.0017898467468789986, Incertidumbre: 0.012755913947082096\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", + "Ecuación de regresión: y = -0.0x + 0.336\n", + "Valor del parámetro correlacionado para la aeronave: 503.516\n", + "Predicción obtenida: 0.318\n", + "\tR²: 0.2988770873999306, Desviación Estándar: 0.04545292487076436, Varianza: 0.0020659683793073495, Incertidumbre: 0.013704572492776236\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27]\n", - "Ecuación de regresión: y = 0.037x + 0.154\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", + "Ecuación de regresión: y = 0.036x + 0.158\n", "Valor del parámetro correlacionado para la aeronave: 3.1\n", - "Predicción obtenida: 0.268\n", - "\tR²: 0.5307410102435243, Desviación Estándar: 0.03853809056250838, Varianza: 0.0014851844242040975, Incertidumbre: 0.012846030187502792\n", + "Predicción obtenida: 0.27\n", + "\tR²: 0.5074952168946643, Desviación Estándar: 0.03946538087998614, Varianza: 0.0015575162880023757, Incertidumbre: 0.01315512695999538\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301]\n", - "Ecuación de regresión: y = 0.005x + 0.27\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304]\n", + "Ecuación de regresión: y = 0.005x + 0.271\n", "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 0.297\n", - "\tR²: 0.7420743779557686, Desviación Estándar: 0.02186230804623738, Varianza: 0.0004779605131085757, Incertidumbre: 0.0077294931359418355\n", + "Predicción obtenida: 0.298\n", + "\tR²: 0.7373011137977019, Desviación Estándar: 0.021931062094591367, Varianza: 0.0004809714845968222, Incertidumbre: 0.007753801362854401\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27]\n", - "Ecuación de regresión: y = 0.005x + 0.196\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271]\n", + "Ecuación de regresión: y = 0.005x + 0.191\n", "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 0.338\n", - "\tR²: 0.4325324299944552, Desviación Estándar: 0.02977768882484922, Varianza: 0.0008867107517495501, Incertidumbre: 0.013316987285039737\n", + "Predicción obtenida: 0.342\n", + "\tR²: 0.4796840662294731, Desviación Estándar: 0.028777205287537983, Varianza: 0.0008281275441611041, Incertidumbre: 0.012869557445080261\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.315', 'Velocidad a la que se realiza el crucero (KTAS): 0.356', 'Área del ala: 0.286', 'Relación de aspecto del ala: 0.298', 'Longitud del fuselaje: 0.292', 'Peso máximo al despegue (MTOW): 0.293', 'Alcance de la aeronave: 0.317', 'envergadura: 0.268', 'payload: 0.297', 'Crucero KIAS: 0.338']\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.316', 'Velocidad a la que se realiza el crucero (KTAS): 0.359', 'Área del ala: 0.287', 'Relación de aspecto del ala: 0.299', 'Longitud del fuselaje: 0.293', 'Peso máximo al despegue (MTOW): 0.294', 'Alcance de la aeronave: 0.318', 'envergadura: 0.27', 'payload: 0.298', 'Crucero KIAS: 0.342']\n", "**Mediana calculada:** 0.298\n", "\n", "--- Imputación para aeronave: **Integrator** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", - "Ecuación de regresión: y = 0.0x + -1.087\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", + "Ecuación de regresión: y = 0.0x + -1.097\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.43003046301994163, Desviación Estándar: 0.040303167474752155, Varianza: 0.0016243453084979203, Incertidumbre: 0.012744980613943357\n", + "Predicción obtenida: 0.314\n", + "\tR²: 0.4368458746527305, Desviación Estándar: 0.04005051770019304, Varianza: 0.001604043968053476, Incertidumbre: 0.012665085740149871\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", - "Ecuación de regresión: y = 0.006x + 0.152\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", + "Ecuación de regresión: y = 0.006x + 0.15\n", "Valor del parámetro correlacionado para la aeronave: 30.953\n", - "Predicción obtenida: 0.344\n", - "\tR²: 0.3626732282190418, Desviación Estándar: 0.042618134717395714, Varianza: 0.0018163054067900897, Incertidumbre: 0.013477037533486688\n", + "Predicción obtenida: 0.345\n", + "\tR²: 0.37779542591176063, Desviación Estándar: 0.04209796464341744, Varianza: 0.0017722386271184249, Incertidumbre: 0.013312545313043725\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", - "Ecuación de regresión: y = 0.09x + 0.192\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", + "Ecuación de regresión: y = 0.089x + 0.194\n", "Valor del parámetro correlacionado para la aeronave: 1.872\n", "Predicción obtenida: 0.36\n", - "\tR²: 0.78420367371009, Desviación Estándar: 0.02479906919480939, Varianza: 0.0006149938329289441, Incertidumbre: 0.007842154250771557\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "\tR²: 0.765873381903867, Desviación Estándar: 0.025823784879448163, Varianza: 0.0006668678655000155, Incertidumbre: 0.008166197802527291\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", - "Ecuación de regresión: y = -0.04x + 0.865\n", - "Valor del parámetro correlacionado para la aeronave: 12.923\n", - "Predicción obtenida: 0.345\n", - "\tR²: 0.5716670205992819, Desviación Estándar: 0.03493848859723169, Varianza: 0.0012206979854588887, Incertidumbre: 0.011048520197107344\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", + "Ecuación de regresión: y = -0.041x + 0.87\n", + "Valor del parámetro correlacionado para la aeronave: 12.908\n", + "Predicción obtenida: 0.346\n", + "\tR²: 0.5798392420052842, Desviación Estándar: 0.034594116470138235, Varianza: 0.0011967528943494896, Incertidumbre: 0.010939620168678112\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", - "Ecuación de regresión: y = 0.034x + 0.233\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", + "Ecuación de regresión: y = 0.034x + 0.236\n", "Valor del parámetro correlacionado para la aeronave: 2.5\n", "Predicción obtenida: 0.32\n", - "\tR²: 0.2607143040447977, Desviación Estándar: 0.045900721121043486, Varianza: 0.0021068761994318076, Incertidumbre: 0.014515082498669469\n", + "\tR²: 0.24668522140873417, Desviación Estándar: 0.04632151372017501, Varianza: 0.002145682633328362, Incertidumbre: 0.014648148802249251\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", - "Ecuación de regresión: y = 0.002x + 0.245\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", + "Ecuación de regresión: y = 0.002x + 0.247\n", "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 0.381\n", - "\tR²: 0.6843844359896646, Desviación Estándar: 0.029343086738118862, Varianza: 0.000861016739320767, Incertidumbre: 0.008470619513553731\n", + "Predicción obtenida: 0.38\n", + "\tR²: 0.6682782058720445, Desviación Estándar: 0.030013941218469384, Varianza: 0.0009008366674657355, Incertidumbre: 0.008664278520962451\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", - "Ecuación de regresión: y = -0.0x + 0.332\n", - "Valor del parámetro correlacionado para la aeronave: 344.852\n", - "Predicción obtenida: 0.319\n", - "\tR²: 0.38810758348194185, Desviación Estándar: 0.040856779150680644, Varianza: 0.0016692764025674925, Incertidumbre: 0.01179433622043328\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", + "Ecuación de regresión: y = -0.0x + 0.334\n", + "Valor del parámetro correlacionado para la aeronave: 557.94\n", + "Predicción obtenida: 0.315\n", + "\tR²: 0.29101241634623853, Desviación Estándar: 0.0438788603845218, Varianza: 0.0019253543886443567, Incertidumbre: 0.012666735927368835\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298]\n", - "Ecuación de regresión: y = 0.035x + 0.165\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", + "Ecuación de regresión: y = 0.034x + 0.169\n", "Valor del parámetro correlacionado para la aeronave: 4.8\n", "Predicción obtenida: 0.332\n", - "\tR²: 0.5045938666189362, Desviación Estándar: 0.03757457234940652, Varianza: 0.0014118484872407847, Incertidumbre: 0.011882123073090872\n", + "\tR²: 0.4843054651463291, Desviación Estándar: 0.03832575894901321, Varianza: 0.0014688637990178664, Incertidumbre: 0.012119669133346282\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298]\n", - "Ecuación de regresión: y = 0.005x + 0.27\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298]\n", + "Ecuación de regresión: y = 0.005x + 0.271\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", "Predicción obtenida: 0.368\n", - "\tR²: 0.7571933066563912, Desviación Estándar: 0.02061381414434731, Varianza: 0.0004249293335776932, Incertidumbre: 0.006871271381449103\n", + "\tR²: 0.7530217831084923, Desviación Estándar: 0.020677312991762044, Varianza: 0.00042755127255929146, Incertidumbre: 0.006892437663920681\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298]\n", - "Ecuación de regresión: y = 0.004x + 0.22\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298]\n", + "Ecuación de regresión: y = 0.004x + 0.217\n", "Valor del parámetro correlacionado para la aeronave: 28.3\n", - "Predicción obtenida: 0.323\n", - "\tR²: 0.3103699929495912, Desviación Estándar: 0.029966938700525643, Varianza: 0.0008980174150810615, Incertidumbre: 0.01223395149492497\n", + "Predicción obtenida: 0.325\n", + "\tR²: 0.33548537835814285, Desviación Estándar: 0.02969296652169765, Varianza: 0.0008816722608586575, Incertidumbre: 0.012122102821283785\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.313', 'Velocidad a la que se realiza el crucero (KTAS): 0.344', 'Área del ala: 0.36', 'Relación de aspecto del ala: 0.345', 'Longitud del fuselaje: 0.32', 'Peso máximo al despegue (MTOW): 0.381', 'Alcance de la aeronave: 0.319', 'envergadura: 0.332', 'payload: 0.368', 'Crucero KIAS: 0.323']\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.314', 'Velocidad a la que se realiza el crucero (KTAS): 0.345', 'Área del ala: 0.36', 'Relación de aspecto del ala: 0.346', 'Longitud del fuselaje: 0.32', 'Peso máximo al despegue (MTOW): 0.38', 'Alcance de la aeronave: 0.315', 'envergadura: 0.332', 'payload: 0.368', 'Crucero KIAS: 0.325']\n", "**Mediana calculada:** 0.338\n", "\n", "--- Imputación para aeronave: **Integrator VTOL** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.0x + -1.114\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.0x + -1.123\n", "Valor del parámetro correlacionado para la aeronave: 5000.0\n", "Predicción obtenida: 0.077\n", - "\tR²: 0.4346058124858284, Desviación Estándar: 0.03907455742847307, Varianza: 0.0015268210382310403, Incertidumbre: 0.011781422349039958\n", + "\tR²: 0.44147114715305336, Desviación Estándar: 0.03879212590736499, Varianza: 0.0015048290324128585, Incertidumbre: 0.011696266041359519\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.006x + 0.155\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.006x + 0.153\n", "Valor del parámetro correlacionado para la aeronave: 21.463\n", "Predicción obtenida: 0.285\n", - "\tR²: 0.3877365879710747, Desviación Estándar: 0.040661889117598014, Varianza: 0.0016533892266118358, Incertidumbre: 0.012260020860918912\n", + "\tR²: 0.40064049329349527, Desviación Estándar: 0.040185046321322214, Varianza: 0.0016148379478468121, Incertidumbre: 0.012116247348261574\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.086x + 0.195\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.085x + 0.197\n", "Valor del parámetro correlacionado para la aeronave: 2.09\n", "Predicción obtenida: 0.374\n", - "\tR²: 0.7800260398164971, Desviación Estándar: 0.024372721993982313, Varianza: 0.0005940295773959491, Incertidumbre: 0.007348652179425639\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "\tR²: 0.7619020614155911, Desviación Estándar: 0.025327865532937314, Varianza: 0.0006415007724545539, Incertidumbre: 0.007636638792120725\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", - "Ecuación de regresión: y = -0.04x + 0.857\n", - "Valor del parámetro correlacionado para la aeronave: 12.654\n", - "Predicción obtenida: 0.354\n", - "\tR²: 0.5878792449550165, Desviación Estándar: 0.03336035330709108, Varianza: 0.0011129131727739424, Incertidumbre: 0.010058524981210274\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", + "Ecuación de regresión: y = -0.04x + 0.86\n", + "Valor del parámetro correlacionado para la aeronave: 12.648\n", + "Predicción obtenida: 0.355\n", + "\tR²: 0.5944802102759923, Desviación Estándar: 0.03305421142567004, Varianza: 0.001092580892972896, Incertidumbre: 0.009966219730911451\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.036x + 0.232\n", - "Valor del parámetro correlacionado para la aeronave: 3.004\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.035x + 0.235\n", + "Valor del parámetro correlacionado para la aeronave: 2.998\n", "Predicción obtenida: 0.34\n", - "\tR²: 0.2807426506714811, Desviación Estándar: 0.044071776010299636, Varianza: 0.0019423214407020225, Incertidumbre: 0.01328814044279547\n", + "\tR²: 0.2663264749195765, Desviación Estándar: 0.044460280463140905, Varianza: 0.001976716538861149, Incertidumbre: 0.01340527894274609\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.002x + 0.249\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.002x + 0.251\n", "Valor del parámetro correlacionado para la aeronave: 75.0\n", "Predicción obtenida: 0.371\n", - "\tR²: 0.6495168189859627, Desviación Estándar: 0.030035459570872714, Varianza: 0.0009021288316335293, Incertidumbre: 0.008330337658839016\n", + "\tR²: 0.6338748805446468, Desviación Estándar: 0.030614286662563286, Varianza: 0.0009372345478576003, Incertidumbre: 0.008490875409509635\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", - "Ecuación de regresión: y = -0.0x + 0.334\n", - "Valor del parámetro correlacionado para la aeronave: 3294.755\n", - "Predicción obtenida: 0.204\n", - "\tR²: 0.3910118106632742, Desviación Estándar: 0.03959178893133063, Varianza: 0.0015675097507830348, Incertidumbre: 0.01098078654455848\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", + "Ecuación de regresión: y = -0.0x + 0.336\n", + "Valor del parámetro correlacionado para la aeronave: 646.084\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.2906940367251767, Desviación Estándar: 0.04261143763873455, Varianza: 0.0018157346176397638, Incertidumbre: 0.011818286409822586\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.035x + 0.164\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.034x + 0.168\n", "Valor del parámetro correlacionado para la aeronave: 5.033\n", "Predicción obtenida: 0.341\n", - "\tR²: 0.5236198578383562, Desviación Estándar: 0.03586701404683303, Varianza: 0.0012864426966357181, Incertidumbre: 0.010814311631250163\n", + "\tR²: 0.5033510515754096, Desviación Estándar: 0.036580154402799485, Varianza: 0.0013381076961326505, Incertidumbre: 0.011029331538850273\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.005x + 0.272\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.005x + 0.273\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", "Predicción obtenida: 0.362\n", - "\tR²: 0.713775236159582, Desviación Estándar: 0.02131006366987268, Varianza: 0.00045411881361402743, Incertidumbre: 0.006738833828000416\n", + "\tR²: 0.7098078178906966, Desviación Estándar: 0.021340119345216565, Varianza: 0.00045540069366808625, Incertidumbre: 0.006748338267070541\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.077', 'Velocidad a la que se realiza el crucero (KTAS): 0.285', 'Área del ala: 0.374', 'Relación de aspecto del ala: 0.354', 'Longitud del fuselaje: 0.34', 'Peso máximo al despegue (MTOW): 0.371', 'Alcance de la aeronave: 0.204', 'envergadura: 0.341', 'payload: 0.362']\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.077', 'Velocidad a la que se realiza el crucero (KTAS): 0.285', 'Área del ala: 0.374', 'Relación de aspecto del ala: 0.355', 'Longitud del fuselaje: 0.34', 'Peso máximo al despegue (MTOW): 0.371', 'Alcance de la aeronave: 0.313', 'envergadura: 0.341', 'payload: 0.362']\n", "**Mediana calculada:** 0.341\n", "\n", "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.0x + 0.183\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.0x + 0.178\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", "Predicción obtenida: 0.311\n", - "\tR²: 0.015512130588261508, Desviación Estándar: 0.0503497708973624, Varianza: 0.002535099429416882, Incertidumbre: 0.014534726890614086\n", + "\tR²: 0.01698088208648929, Desviación Estándar: 0.05021794969223678, Varianza: 0.002521842471292024, Incertidumbre: 0.014496673386481996\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.006x + 0.172\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.006x + 0.171\n", "Valor del parámetro correlacionado para la aeronave: 33.045\n", - "Predicción obtenida: 0.355\n", - "\tR²: 0.3206948627028712, Desviación Estándar: 0.041823922738469466, Varianza: 0.0017492405132334633, Incertidumbre: 0.01207352652581073\n", + "Predicción obtenida: 0.357\n", + "\tR²: 0.333272374733811, Desviación Estándar: 0.04135730400423518, Varianza: 0.001710426594498727, Incertidumbre: 0.011938825299901186\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.079x + 0.201\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.078x + 0.203\n", "Valor del parámetro correlacionado para la aeronave: 1.872\n", "Predicción obtenida: 0.349\n", - "\tR²: 0.7610795576292256, Desviación Estándar: 0.02480384274196969, Varianza: 0.0006152306147683624, Incertidumbre: 0.007160252642006673\n", + "\tR²: 0.7434863405092712, Desviación Estándar: 0.025652709521725176, Varianza: 0.0006580615058060095, Incertidumbre: 0.007405299373905654\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = -0.039x + 0.839\n", - "Valor del parámetro correlacionado para la aeronave: 12.859\n", - "Predicción obtenida: 0.344\n", - "\tR²: 0.5993334622949501, Desviación Estándar: 0.03212061490919684, Varianza: 0.0010317339021449184, Incertidumbre: 0.009272422832180553\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = -0.039x + 0.841\n", + "Valor del parámetro correlacionado para la aeronave: 12.84\n", + "Predicción obtenida: 0.345\n", + "\tR²: 0.60452940995621, Desviación Estándar: 0.03185188148615243, Varianza: 0.0010145423542079, Incertidumbre: 0.009194846175113082\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.036x + 0.232\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.035x + 0.235\n", "Valor del parámetro correlacionado para la aeronave: 2.5\n", "Predicción obtenida: 0.322\n", - "\tR²: 0.3085407372763075, Desviación Estándar: 0.04219642069079446, Varianza: 0.001780537919114507, Incertidumbre: 0.012181057422334439\n", + "\tR²: 0.2936236395084689, Desviación Estándar: 0.04256925734185804, Varianza: 0.0018121416706373347, Incertidumbre: 0.012288686092762097\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.002x + 0.251\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.001x + 0.253\n", "Valor del parámetro correlacionado para la aeronave: 74.8\n", "Predicción obtenida: 0.365\n", - "\tR²: 0.6361662614209418, Desviación Estándar: 0.029827965241118803, Varianza: 0.0008897075104253915, Incertidumbre: 0.00797185903406204\n", + "\tR²: 0.6208094760432097, Desviación Estándar: 0.030354867452664958, Varianza: 0.0009214179780688584, Incertidumbre: 0.008112679573486267\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = -0.0x + 0.327\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = -0.0x + 0.338\n", "Valor del parámetro correlacionado para la aeronave: 500.0\n", - "Predicción obtenida: 0.318\n", - "\tR²: 0.12689970642513138, Desviación Estándar: 0.04620662103794288, Varianza: 0.002135051827744066, Incertidumbre: 0.01234923892317737\n", + "Predicción obtenida: 0.321\n", + "\tR²: 0.285409813514665, Desviación Estándar: 0.04167044389712285, Varianza: 0.0017364258945832627, Incertidumbre: 0.01113689458698706\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.035x + 0.164\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.034x + 0.168\n", "Valor del parámetro correlacionado para la aeronave: 4.8\n", "Predicción obtenida: 0.333\n", - "\tR²: 0.5420506031887378, Desviación Estándar: 0.0343400608030557, Varianza: 0.0011792397759575624, Incertidumbre: 0.009913121674316162\n", + "\tR²: 0.5218690773685137, Desviación Estándar: 0.03502284412908208, Varianza: 0.001226599610889979, Incertidumbre: 0.010110224242855922\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.005x + 0.273\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341]\n", + "Ecuación de regresión: y = 0.005x + 0.274\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", "Predicción obtenida: 0.358\n", - "\tR²: 0.6939116284562865, Desviación Estándar: 0.021115874286876224, Varianza: 0.00044588014689916043, Incertidumbre: 0.006366675648171076\n", + "\tR²: 0.6901146819400841, Desviación Estándar: 0.02113001704290527, Varianza: 0.0004464776202334672, Incertidumbre: 0.006370939849557429\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.311', 'Velocidad a la que se realiza el crucero (KTAS): 0.355', 'Área del ala: 0.349', 'Relación de aspecto del ala: 0.344', 'Longitud del fuselaje: 0.322', 'Peso máximo al despegue (MTOW): 0.365', 'Alcance de la aeronave: 0.318', 'envergadura: 0.333', 'payload: 0.358']\n", - "**Mediana calculada:** 0.344\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.311', 'Velocidad a la que se realiza el crucero (KTAS): 0.357', 'Área del ala: 0.349', 'Relación de aspecto del ala: 0.345', 'Longitud del fuselaje: 0.322', 'Peso máximo al despegue (MTOW): 0.365', 'Alcance de la aeronave: 0.321', 'envergadura: 0.333', 'payload: 0.358']\n", + "**Mediana calculada:** 0.345\n", "\n", "--- Imputación para aeronave: **ScanEagle 3** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", - "Ecuación de regresión: y = 0.0x + 0.165\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", + "Ecuación de regresión: y = 0.0x + 0.16\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", "Predicción obtenida: 0.314\n", - "\tR²: 0.02067125389564617, Desviación Estándar: 0.04917798086065571, Varianza: 0.002418473801531019, Incertidumbre: 0.013639517816683143\n", + "\tR²: 0.022373451375729103, Desviación Estándar: 0.049063741225926084, Varianza: 0.002407250703084639, Incertidumbre: 0.013607833442782533\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", - "Ecuación de regresión: y = 0.005x + 0.177\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", + "Ecuación de regresión: y = 0.005x + 0.176\n", "Valor del parámetro correlacionado para la aeronave: 25.703\n", "Predicción obtenida: 0.314\n", - "\tR²: 0.34315816593670323, Desviación Estándar: 0.04027512016870371, Varianza: 0.0016220853046035242, Incertidumbre: 0.011170308530287316\n", + "\tR²: 0.3555220685191681, Desviación Estándar: 0.0398362264992534, Varianza: 0.0015869249416998186, Incertidumbre: 0.011048581328004266\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", - "Ecuación de regresión: y = 0.078x + 0.202\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", + "Ecuación de regresión: y = 0.077x + 0.204\n", "Valor del parámetro correlacionado para la aeronave: 1.349\n", "Predicción obtenida: 0.308\n", - "\tR²: 0.7692709885882784, Desviación Estándar: 0.023870281740101265, Varianza: 0.0005697903503518118, Incertidumbre: 0.0066204249825928384\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "\tR²: 0.7527768409004458, Desviación Estándar: 0.024672817339078838, Varianza: 0.0006087479154475493, Incertidumbre: 0.0068430083097081335\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", - "Ecuación de regresión: y = -0.039x + 0.839\n", - "Valor del parámetro correlacionado para la aeronave: 13.774\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.61434850961859, Desviación Estándar: 0.030860570433120724, Varianza: 0.000952374807457605, Incertidumbre: 0.008559182237437285\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", + "Ecuación de regresión: y = -0.039x + 0.841\n", + "Valor del parámetro correlacionado para la aeronave: 13.765\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.6196701372371902, Desviación Estándar: 0.03060232203692826, Varianza: 0.000936502114051865, Incertidumbre: 0.00848755701941588\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", - "Ecuación de regresión: y = 0.037x + 0.232\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", + "Ecuación de regresión: y = 0.036x + 0.234\n", "Valor del parámetro correlacionado para la aeronave: 2.4\n", - "Predicción obtenida: 0.32\n", - "\tR²: 0.3208426833611422, Desviación Estándar: 0.04095355757307659, Varianza: 0.0016771938778912987, Incertidumbre: 0.011358473210953401\n", + "Predicción obtenida: 0.321\n", + "\tR²: 0.30616062286953594, Desviación Estándar: 0.04133363874625755, Varianza: 0.0017084696920061232, Incertidumbre: 0.011463888761625898\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", - "Ecuación de regresión: y = 0.001x + 0.253\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", + "Ecuación de regresión: y = 0.001x + 0.254\n", "Valor del parámetro correlacionado para la aeronave: 36.3\n", "Predicción obtenida: 0.306\n", - "\tR²: 0.6342327638509584, Desviación Estándar: 0.02924076794795075, Varianza: 0.0008550225101859038, Incertidumbre: 0.007549933819515259\n", + "\tR²: 0.6206727625327728, Desviación Estándar: 0.029696344075245393, Varianza: 0.0008818728514353621, Incertidumbre: 0.007667563069778047\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", - "Ecuación de regresión: y = -0.0x + 0.329\n", - "Valor del parámetro correlacionado para la aeronave: 478.95\n", - "Predicción obtenida: 0.32\n", - "\tR²: 0.1300366441647196, Desviación Estándar: 0.045095878725376695, Varianza: 0.0020336382780138827, Incertidumbre: 0.011643705819064886\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", + "Ecuación de regresión: y = -0.0x + 0.34\n", + "Valor del parámetro correlacionado para la aeronave: 537.895\n", + "Predicción obtenida: 0.321\n", + "\tR²: 0.2869258951677305, Desviación Estándar: 0.04071583119142121, Varianza: 0.0016577789096083083, Incertidumbre: 0.01051278240875779\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344]\n", - "Ecuación de regresión: y = 0.036x + 0.163\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", + "Ecuación de regresión: y = 0.035x + 0.166\n", "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 0.305\n", - "\tR²: 0.5556874366655828, Desviación Estándar: 0.0331246073597043, Varianza: 0.0010972396127345763, Incertidumbre: 0.009187113101155824\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.5360863917912162, Desviación Estándar: 0.033798134019578256, Varianza: 0.001142313863205373, Incertidumbre: 0.009373915786353326\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345]\n", "Ecuación de regresión: y = 0.005x + 0.274\n", "Valor del parámetro correlacionado para la aeronave: 8.6\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.6868798679185576, Desviación Estándar: 0.020578680684602713, Varianza: 0.00042348209871884076, Incertidumbre: 0.005940553416411365\n", + "Predicción obtenida: 0.314\n", + "\tR²: 0.6852525799649505, Desviación Estándar: 0.020536902802633098, Varianza: 0.0004217643767247992, Incertidumbre: 0.0059284931807107\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.004x + 0.214\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338]\n", + "Ecuación de regresión: y = 0.004x + 0.212\n", "Valor del parámetro correlacionado para la aeronave: 23.5\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.39462657346290686, Desviación Estándar: 0.028144814550039556, Varianza: 0.0007921305860561183, Incertidumbre: 0.01063773999934813\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.314', 'Velocidad a la que se realiza el crucero (KTAS): 0.314', 'Área del ala: 0.308', 'Relación de aspecto del ala: 0.308', 'Longitud del fuselaje: 0.32', 'Peso máximo al despegue (MTOW): 0.306', 'Alcance de la aeronave: 0.32', 'envergadura: 0.305', 'payload: 0.313', 'Crucero KIAS: 0.308']\n", - "**Mediana calculada:** 0.31\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.4148261927182897, Desviación Estándar: 0.027776222407102834, Varianza: 0.0007715185312088415, Incertidumbre: 0.01049842526428771\n", + "\tNivel de confianza: Confianza Alta\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.314', 'Velocidad a la que se realiza el crucero (KTAS): 0.314', 'Área del ala: 0.308', 'Relación de aspecto del ala: 0.309', 'Longitud del fuselaje: 0.321', 'Peso máximo al despegue (MTOW): 0.306', 'Alcance de la aeronave: 0.321', 'envergadura: 0.306', 'payload: 0.314', 'Crucero KIAS: 0.309']\n", + "**Mediana calculada:** 0.312\n", "\n", - "--- Imputación para aeronave: **RQNan21A Blackjack** ---\n", + "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", - "Ecuación de regresión: y = 0.0x + 0.166\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", + "Ecuación de regresión: y = 0.0x + 0.161\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.02032631380959693, Desviación Estándar: 0.04739774340606181, Varianza: 0.0022465460799868755, Incertidumbre: 0.012667579766550504\n", + "Predicción obtenida: 0.314\n", + "\tR²: 0.022227755119382575, Desviación Estándar: 0.04728280334178578, Varianza: 0.002235663491857988, Incertidumbre: 0.012636860742226598\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", - "Ecuación de regresión: y = 0.005x + 0.177\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", + "Ecuación de regresión: y = 0.005x + 0.176\n", "Valor del parámetro correlacionado para la aeronave: 33.797\n", - "Predicción obtenida: 0.356\n", - "\tR²: 0.34279456368656225, Desviación Estándar: 0.03882107144365238, Varianza: 0.0015070755880331622, Incertidumbre: 0.010375367766401489\n", + "Predicción obtenida: 0.358\n", + "\tR²: 0.3553724238219026, Desviación Estándar: 0.03839182920470344, Varianza: 0.0014739325496831198, Incertidumbre: 0.010260647952538724\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", - "Ecuación de regresión: y = 0.078x + 0.202\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", + "Ecuación de regresión: y = 0.077x + 0.204\n", "Valor del parámetro correlacionado para la aeronave: 1.802\n", - "Predicción obtenida: 0.343\n", - "\tR²: 0.7691040437079356, Desviación Estándar: 0.023010449764320217, Varianza: 0.0005294807983563044, Incertidumbre: 0.0061498013809756745\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "Predicción obtenida: 0.344\n", + "\tR²: 0.7523839535914149, Desviación Estándar: 0.02379434517349416, Varianza: 0.0005661708622353848, Incertidumbre: 0.00635930624156095\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", - "Ecuación de regresión: y = -0.039x + 0.84\n", - "Valor del parámetro correlacionado para la aeronave: 12.973\n", - "Predicción obtenida: 0.339\n", - "\tR²: 0.6142874133845568, Desviación Estándar: 0.029740539979987123, Varianza: 0.0008844997183012125, Incertidumbre: 0.007948493650197468\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", + "Ecuación de regresión: y = -0.039x + 0.841\n", + "Valor del parámetro correlacionado para la aeronave: 12.914\n", + "Predicción obtenida: 0.342\n", + "\tR²: 0.6194315138686619, Desviación Estándar: 0.02949855729552859, Varianza: 0.000870164882517583, Incertidumbre: 0.007883821057427777\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", - "Ecuación de regresión: y = 0.037x + 0.232\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", + "Ecuación de regresión: y = 0.036x + 0.234\n", "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 0.323\n", - "\tR²: 0.3177456232267585, Desviación Estándar: 0.039553972672793015, Varianza: 0.0015645167542000567, Incertidumbre: 0.010571243859100757\n", + "Predicción obtenida: 0.324\n", + "\tR²: 0.303899170137669, Desviación Estándar: 0.03989518107787113, Varianza: 0.0015916254732361262, Incertidumbre: 0.010662435641192892\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", - "Ecuación de regresión: y = 0.001x + 0.253\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", + "Ecuación de regresión: y = 0.001x + 0.255\n", "Valor del parámetro correlacionado para la aeronave: 61.0\n", "Predicción obtenida: 0.342\n", - "\tR²: 0.6340700925234075, Desviación Estándar: 0.028333016378777862, Varianza: 0.0008027598171200946, Incertidumbre: 0.007083254094694466\n", + "\tR²: 0.6200226373551951, Desviación Estándar: 0.0287868395998115, Varianza: 0.0008286821341452756, Incertidumbre: 0.007196709899952875\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", - "Ecuación de regresión: y = -0.0x + 0.328\n", - "Valor del parámetro correlacionado para la aeronave: 92.6\n", - "Predicción obtenida: 0.326\n", - "\tR²: 0.12800904485497755, Desviación Estándar: 0.04373708652402515, Varianza: 0.0019129327376100622, Incertidumbre: 0.010934271631006288\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", + "Ecuación de regresión: y = -0.0x + 0.339\n", + "Valor del parámetro correlacionado para la aeronave: 565.912\n", + "Predicción obtenida: 0.319\n", + "\tR²: 0.28524296729075405, Desviación Estándar: 0.0394815636598884, Varianza: 0.00155879386902982, Incertidumbre: 0.0098703909149721\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", - "Ecuación de regresión: y = 0.036x + 0.163\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", + "Ecuación de regresión: y = 0.035x + 0.167\n", "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 0.334\n", - "\tR²: 0.5550912173963352, Desviación Estándar: 0.03194128864413451, Varianza: 0.0010202459202479162, Incertidumbre: 0.008536668471314606\n", + "Predicción obtenida: 0.335\n", + "\tR²: 0.5351265760294277, Desviación Estándar: 0.032602561615663286, Varianza: 0.001062927023903121, Incertidumbre: 0.008713401106928507\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31]\n", - "Ecuación de regresión: y = 0.005x + 0.273\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312]\n", + "Ecuación de regresión: y = 0.005x + 0.274\n", "Valor del parámetro correlacionado para la aeronave: 17.7\n", "Predicción obtenida: 0.355\n", - "\tR²: 0.6934350662495181, Desviación Estándar: 0.019790488440900297, Varianza: 0.00039166343272940834, Incertidumbre: 0.005488893910780262\n", + "\tR²: 0.6911388597155106, Desviación Estándar: 0.01973554103206761, Varianza: 0.00038949157982842426, Incertidumbre: 0.005473654241549482\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31]\n", - "Ecuación de regresión: y = 0.004x + 0.214\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312]\n", + "Ecuación de regresión: y = 0.004x + 0.212\n", "Valor del parámetro correlacionado para la aeronave: 30.9\n", - "Predicción obtenida: 0.338\n", - "\tR²: 0.39635942215753095, Desviación Estándar: 0.026334104723749873, Varianza: 0.0006934850716014253, Incertidumbre: 0.009310512013320114\n", + "Predicción obtenida: 0.34\n", + "\tR²: 0.41681296972136983, Desviación Estándar: 0.025997834126762907, Varianza: 0.000675887379282678, Incertidumbre: 0.009191622403598547\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.313', 'Velocidad a la que se realiza el crucero (KTAS): 0.356', 'Área del ala: 0.343', 'Relación de aspecto del ala: 0.339', 'Longitud del fuselaje: 0.323', 'Peso máximo al despegue (MTOW): 0.342', 'Alcance de la aeronave: 0.326', 'envergadura: 0.334', 'payload: 0.355', 'Crucero KIAS: 0.338']\n", - "**Mediana calculada:** 0.338\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.314', 'Velocidad a la que se realiza el crucero (KTAS): 0.358', 'Área del ala: 0.344', 'Relación de aspecto del ala: 0.342', 'Longitud del fuselaje: 0.324', 'Peso máximo al despegue (MTOW): 0.342', 'Alcance de la aeronave: 0.319', 'envergadura: 0.335', 'payload: 0.355', 'Crucero KIAS: 0.34']\n", + "**Mediana calculada:** 0.341\n", "\n", "--- Imputación para aeronave: **DeltaQuad Evo** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", - "Ecuación de regresión: y = 0.0x + 0.166\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", + "Ecuación de regresión: y = 0.0x + 0.148\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.02032631380959693, Desviación Estándar: 0.04739774340606181, Varianza: 0.0022465460799868755, Incertidumbre: 0.012667579766550504\n", + "Predicción obtenida: 0.316\n", + "\tR²: 0.026268244411726593, Desviación Estándar: 0.04616322786282365, Varianza: 0.0021310436067149777, Incertidumbre: 0.011919294181326279\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338]\n", - "Ecuación de regresión: y = 0.005x + 0.184\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", + "Ecuación de regresión: y = 0.005x + 0.182\n", "Valor del parámetro correlacionado para la aeronave: 18.091\n", "Predicción obtenida: 0.274\n", - "\tR²: 0.3487363165953775, Desviación Estándar: 0.037739302980328356, Varianza: 0.0014242549894410209, Incertidumbre: 0.00974424612935251\n", + "\tR²: 0.3645845882344463, Desviación Estándar: 0.03729111540765529, Varianza: 0.0013906272883470658, Incertidumbre: 0.00962852459568985\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338]\n", - "Ecuación de regresión: y = 0.078x + 0.202\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", + "Ecuación de regresión: y = 0.077x + 0.204\n", "Valor del parámetro correlacionado para la aeronave: 0.84\n", - "Predicción obtenida: 0.268\n", - "\tR²: 0.7732658196981622, Desviación Estándar: 0.02226762719794307, Varianza: 0.0004958472210265739, Incertidumbre: 0.005749476619812589\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "Predicción obtenida: 0.269\n", + "\tR²: 0.7583546241989619, Desviación Estándar: 0.02299671197634162, Varianza: 0.0005288487617228141, Incertidumbre: 0.00593772550012665\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338]\n", - "Ecuación de regresión: y = -0.038x + 0.838\n", - "Valor del parámetro correlacionado para la aeronave: 14.599\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", + "Ecuación de regresión: y = -0.039x + 0.84\n", + "Valor del parámetro correlacionado para la aeronave: 14.589\n", "Predicción obtenida: 0.277\n", - "\tR²: 0.622452460235022, Desviación Estándar: 0.02873434266164246, Varianza: 0.0008256624481966859, Incertidumbre: 0.007419175372850569\n", + "\tR²: 0.6288651605214148, Desviación Estándar: 0.028499836195337802, Varianza: 0.0008122406631610867, Incertidumbre: 0.007358626063612177\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", - "Ecuación de regresión: y = 0.037x + 0.232\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", + "Ecuación de regresión: y = 0.037x + 0.234\n", "Valor del parámetro correlacionado para la aeronave: 0.75\n", - "Predicción obtenida: 0.259\n", - "\tR²: 0.3177456232267585, Desviación Estándar: 0.039553972672793015, Varianza: 0.0015645167542000567, Incertidumbre: 0.010571243859100757\n", + "Predicción obtenida: 0.261\n", + "\tR²: 0.31289865533826733, Desviación Estándar: 0.038778133438712926, Varianza: 0.0015037436329906255, Incertidumbre: 0.010012471000342941\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338]\n", - "Ecuación de regresión: y = 0.001x + 0.253\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", + "Ecuación de regresión: y = 0.001x + 0.255\n", "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 0.268\n", - "\tR²: 0.6384849427043453, Desviación Estándar: 0.027500732010490584, Varianza: 0.0007562902611128215, Incertidumbre: 0.0066699072271210235\n", + "Predicción obtenida: 0.269\n", + "\tR²: 0.626055259624176, Desviación Estándar: 0.027928418897225495, Varianza: 0.0007799965820989021, Incertidumbre: 0.006773636533515119\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338]\n", - "Ecuación de regresión: y = -0.0x + 0.329\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", + "Ecuación de regresión: y = -0.0x + 0.341\n", "Valor del parámetro correlacionado para la aeronave: 270.0\n", - "Predicción obtenida: 0.324\n", - "\tR²: 0.135868427381171, Desviación Estándar: 0.04251782061135894, Varianza: 0.001807765069539699, Incertidumbre: 0.01031208619715862\n", + "Predicción obtenida: 0.331\n", + "\tR²: 0.2841904292217742, Desviación Estándar: 0.03864037972786521, Varianza: 0.0014930789455136165, Incertidumbre: 0.009371668648939038\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31]\n", - "Ecuación de regresión: y = 0.036x + 0.163\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", + "Ecuación de regresión: y = 0.035x + 0.166\n", "Valor del parámetro correlacionado para la aeronave: 2.69\n", - "Predicción obtenida: 0.259\n", - "\tR²: 0.5550912173963352, Desviación Estándar: 0.03194128864413451, Varianza: 0.0010202459202479162, Incertidumbre: 0.008536668471314606\n", + "Predicción obtenida: 0.261\n", + "\tR²: 0.5455548123174172, Desviación Estándar: 0.03153677328028606, Varianza: 0.000994568068932165, Incertidumbre: 0.008142759847177798\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338]\n", - "Ecuación de regresión: y = 0.004x + 0.274\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", + "Ecuación de regresión: y = 0.004x + 0.275\n", "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 0.287\n", - "\tR²: 0.6800173021314551, Desviación Estándar: 0.0195304404540393, Varianza: 0.00038143810432877473, Incertidumbre: 0.005219729770843197\n", + "Predicción obtenida: 0.288\n", + "\tR²: 0.6833444264392312, Desviación Estándar: 0.019334288653334628, Varianza: 0.00037381471773046413, Incertidumbre: 0.0051673059969835075\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338]\n", - "Ecuación de regresión: y = 0.004x + 0.214\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341]\n", + "Ecuación de regresión: y = 0.004x + 0.212\n", "Valor del parámetro correlacionado para la aeronave: 16.54\n", - "Predicción obtenida: 0.28\n", - "\tR²: 0.45458778022246527, Desviación Estándar: 0.02482812939297037, Varianza: 0.0006164360091540792, Incertidumbre: 0.008276043130990124\n", + "Predicción obtenida: 0.281\n", + "\tR²: 0.47839540504657707, Desviación Estándar: 0.02451160448637781, Varianza: 0.0006008187544966167, Incertidumbre: 0.008170534828792602\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.313', 'Velocidad a la que se realiza el crucero (KTAS): 0.274', 'Área del ala: 0.268', 'Relación de aspecto del ala: 0.277', 'Longitud del fuselaje: 0.259', 'Peso máximo al despegue (MTOW): 0.268', 'Alcance de la aeronave: 0.324', 'envergadura: 0.259', 'payload: 0.287', 'Crucero KIAS: 0.28']\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.316', 'Velocidad a la que se realiza el crucero (KTAS): 0.274', 'Área del ala: 0.269', 'Relación de aspecto del ala: 0.277', 'Longitud del fuselaje: 0.261', 'Peso máximo al despegue (MTOW): 0.269', 'Alcance de la aeronave: 0.331', 'envergadura: 0.261', 'payload: 0.288', 'Crucero KIAS: 0.281']\n", "**Mediana calculada:** 0.276\n", "\n", "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276]\n", - "Ecuación de regresión: y = 0.0x + 0.184\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", + "Ecuación de regresión: y = 0.0x + 0.166\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.31\n", - "\tR²: 0.0147264484768026, Desviación Estándar: 0.04671466282025224, Varianza: 0.002182259722409857, Incertidumbre: 0.012061674075102113\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.01967074482098219, Desviación Estándar: 0.04573470982394311, Varianza: 0.0020916636826802783, Incertidumbre: 0.011433677455985777\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276]\n", - "Ecuación de regresión: y = 0.005x + 0.185\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", + "Ecuación de regresión: y = 0.005x + 0.183\n", "Valor del parámetro correlacionado para la aeronave: 17.5\n", "Predicción obtenida: 0.272\n", - "\tR²: 0.3724780157591121, Desviación Estándar: 0.03654331382237015, Varianza: 0.0013354137851202294, Incertidumbre: 0.009135828455592538\n", + "\tR²: 0.3888938502611704, Desviación Estándar: 0.03610923381670675, Varianza: 0.0013038767668295981, Incertidumbre: 0.009027308454176687\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276]\n", - "Ecuación de regresión: y = 0.077x + 0.204\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", + "Ecuación de regresión: y = 0.076x + 0.206\n", "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 0.258\n", - "\tR²: 0.7798191830856929, Desviación Estándar: 0.021646276131902738, Varianza: 0.0004685612703785822, Incertidumbre: 0.0054115690329756844\n", + "Predicción obtenida: 0.259\n", + "\tR²: 0.7664145517248199, Desviación Estándar: 0.02232455641736923, Varianza: 0.0004983858192323016, Incertidumbre: 0.005581139104342307\n", "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276]\n", - "Ecuación de regresión: y = -0.039x + 0.839\n", - "Valor del parámetro correlacionado para la aeronave: 14.717\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", + "Ecuación de regresión: y = -0.039x + 0.842\n", + "Valor del parámetro correlacionado para la aeronave: 14.714\n", "Predicción obtenida: 0.272\n", - "\tR²: 0.6362481550089567, Desviación Estándar: 0.02782249399108439, Varianza: 0.000774091171883927, Incertidumbre: 0.006955623497771097\n", + "\tR²: 0.6430546942477204, Desviación Estándar: 0.027596930750302347, Varianza: 0.0007615905868369832, Incertidumbre: 0.006899232687575587\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276]\n", - "Ecuación de regresión: y = 0.034x + 0.238\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", + "Ecuación de regresión: y = 0.034x + 0.239\n", "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 0.268\n", - "\tR²: 0.33427169926483846, Desviación Estándar: 0.03839930074605216, Varianza: 0.0014745062977857622, Incertidumbre: 0.009914656819698003\n", + "Predicción obtenida: 0.27\n", + "\tR²: 0.33445042360589083, Desviación Estándar: 0.037683406226531636, Varianza: 0.0014200391048338034, Incertidumbre: 0.009420851556632909\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276]\n", - "Ecuación de regresión: y = 0.001x + 0.254\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", + "Ecuación de regresión: y = 0.001x + 0.256\n", "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 0.263\n", - "\tR²: 0.6523551364390465, Desviación Estándar: 0.02678966328890024, Varianza: 0.0007176860591326491, Incertidumbre: 0.006314384192428556\n", + "Predicción obtenida: 0.265\n", + "\tR²: 0.6416207643515126, Desviación Estándar: 0.027185535937675302, Varianza: 0.0007390533642186353, Incertidumbre: 0.006407692270573599\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276]\n", - "Ecuación de regresión: y = -0.0x + 0.325\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", + "Ecuación de regresión: y = -0.0x + 0.336\n", "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 0.324\n", - "\tR²: 0.11453431205818987, Desviación Estándar: 0.042754833055918345, Varianza: 0.0018279757496394483, Incertidumbre: 0.010077410794112875\n", + "Predicción obtenida: 0.333\n", + "\tR²: 0.23999230022385276, Desviación Estándar: 0.03958908605237062, Varianza: 0.0015672957344620058, Incertidumbre: 0.009331237069536345\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276]\n", - "Ecuación de regresión: y = 0.034x + 0.17\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", + "Ecuación de regresión: y = 0.034x + 0.172\n", "Valor del parámetro correlacionado para la aeronave: 2.35\n", - "Predicción obtenida: 0.251\n", - "\tR²: 0.5630614192360759, Desviación Estándar: 0.03110892493285136, Varianza: 0.000967765210477781, Incertidumbre: 0.00803228987889\n", + "Predicción obtenida: 0.252\n", + "\tR²: 0.5575576859088149, Desviación Estándar: 0.030724744065250335, Varianza: 0.0009440098978751357, Incertidumbre: 0.007681186016312584\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", "Ecuación de regresión: y = 0.005x + 0.272\n", "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.277\n", - "\tR²: 0.7184184742433418, Desviación Estándar: 0.019057576632793602, Varianza: 0.0003631912271148008, Incertidumbre: 0.004920645127858749\n", + "Predicción obtenida: 0.278\n", + "\tR²: 0.721730846976111, Desviación Estándar: 0.018890410688664185, Varianza: 0.00035684761598639805, Incertidumbre: 0.004877483066680998\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276]\n", - "Ecuación de regresión: y = 0.004x + 0.211\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276]\n", + "Ecuación de regresión: y = 0.004x + 0.209\n", "Valor del parámetro correlacionado para la aeronave: 16.0\n", "Predicción obtenida: 0.277\n", - "\tR²: 0.49980090608872974, Desviación Estándar: 0.023585011510380487, Varianza: 0.0005562527679447801, Incertidumbre: 0.0074582355014090294\n", + "\tR²: 0.5239745729502132, Desviación Estándar: 0.023290105844576407, Varianza: 0.0005424290302515721, Incertidumbre: 0.007364978141526098\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.31', 'Velocidad a la que se realiza el crucero (KTAS): 0.272', 'Área del ala: 0.258', 'Relación de aspecto del ala: 0.272', 'Longitud del fuselaje: 0.268', 'Peso máximo al despegue (MTOW): 0.263', 'Alcance de la aeronave: 0.324', 'envergadura: 0.251', 'payload: 0.277', 'Crucero KIAS: 0.277']\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.313', 'Velocidad a la que se realiza el crucero (KTAS): 0.272', 'Área del ala: 0.259', 'Relación de aspecto del ala: 0.272', 'Longitud del fuselaje: 0.27', 'Peso máximo al despegue (MTOW): 0.265', 'Alcance de la aeronave: 0.333', 'envergadura: 0.252', 'payload: 0.278', 'Crucero KIAS: 0.277']\n", "**Mediana calculada:** 0.272\n", "\n", "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.0x + 0.201\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.0x + 0.183\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.010249079511174597, Desviación Estándar: 0.04616981560043625, Varianza: 0.0021316518725782866, Incertidumbre: 0.011542453900109063\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.014305682652152818, Desviación Estándar: 0.04541585750246479, Varianza: 0.002062600112684187, Incertidumbre: 0.01101496338592133\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.005x + 0.185\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.005x + 0.183\n", "Valor del parámetro correlacionado para la aeronave: 17.5\n", "Predicción obtenida: 0.272\n", - "\tR²: 0.3967685182755928, Desviación Estándar: 0.03545237044618538, Varianza: 0.0012568705702535586, Incertidumbre: 0.008598462825185189\n", - "\tNivel de confianza: Confianza Media\n", + "\tR²: 0.41353937190981327, Desviación Estándar: 0.03503126520298072, Varianza: 0.001227189541721568, Incertidumbre: 0.008496329801818471\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.075x + 0.207\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.075x + 0.209\n", "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 0.26\n", - "\tR²: 0.7836164664637315, Desviación Estándar: 0.021233196730497346, Varianza: 0.0004508486433960032, Incertidumbre: 0.005149806640550596\n", + "Predicción obtenida: 0.261\n", + "\tR²: 0.771939412802745, Desviación Estándar: 0.021845475954437206, Varianza: 0.0004772248196758942, Incertidumbre: 0.005298306164825611\n", "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", - "Ecuación de regresión: y = -0.039x + 0.839\n", - "Valor del parámetro correlacionado para la aeronave: 14.717\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = -0.039x + 0.842\n", + "Valor del parámetro correlacionado para la aeronave: 14.714\n", "Predicción obtenida: 0.272\n", - "\tR²: 0.6503309577441012, Desviación Estándar: 0.026991801495547467, Varianza: 0.0007285573479750386, Incertidumbre: 0.006546473446579232\n", + "\tR²: 0.6574483990804236, Desviación Estándar: 0.0267731451760295, Varianza: 0.0007168013026167517, Incertidumbre: 0.006493441499456798\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.034x + 0.239\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.034x + 0.24\n", "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 0.269\n", - "\tR²: 0.35785791364325414, Desviación Estándar: 0.037188678863664754, Varianza: 0.0013829978356247857, Incertidumbre: 0.009297169715916188\n", + "Predicción obtenida: 0.27\n", + "\tR²: 0.3612099344091373, Desviación Estándar: 0.03656078137464094, Varianza: 0.0013366907347242916, Incertidumbre: 0.008867291962515261\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.001x + 0.256\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.001x + 0.257\n", "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 0.265\n", - "\tR²: 0.6662199673897068, Desviación Estándar: 0.02614021892802073, Varianza: 0.0006833110456048533, Incertidumbre: 0.005996977508909025\n", + "Predicción obtenida: 0.266\n", + "\tR²: 0.6569798349980132, Desviación Estándar: 0.026507694611626824, Varianza: 0.0007026578736232697, Incertidumbre: 0.006081282212543103\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", - "Ecuación de regresión: y = -0.0x + 0.322\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = -0.0x + 0.331\n", "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 0.32\n", - "\tR²: 0.08980696350170814, Desviación Estándar: 0.04316639678565242, Varianza: 0.0018633378114563837, Incertidumbre: 0.009903050597128307\n", + "Predicción obtenida: 0.328\n", + "\tR²: 0.18759571893960836, Desviación Estándar: 0.0407941824996777, Varianza: 0.0016641653258170103, Incertidumbre: 0.009358827315813186\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.032x + 0.179\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.032x + 0.18\n", "Valor del parámetro correlacionado para la aeronave: 2.35\n", - "Predicción obtenida: 0.255\n", - "\tR²: 0.5680260142996714, Desviación Estándar: 0.030501695390844595, Varianza: 0.0009303534217158704, Incertidumbre: 0.007625423847711149\n", + "Predicción obtenida: 0.256\n", + "\tR²: 0.5664966665882962, Desviación Estándar: 0.030118449655234383, Varianza: 0.000907121009634888, Incertidumbre: 0.007304797012257599\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", "Ecuación de regresión: y = 0.005x + 0.271\n", "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.276\n", - "\tR²: 0.7521146796518272, Desviación Estándar: 0.018488012959664887, Varianza: 0.00034180662319673684, Incertidumbre: 0.004622003239916222\n", + "Predicción obtenida: 0.277\n", + "\tR²: 0.7555202520970645, Desviación Estándar: 0.01833377542330803, Varianza: 0.0003361273212722936, Incertidumbre: 0.004583443855827008\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.004x + 0.209\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.004x + 0.207\n", "Valor del parámetro correlacionado para la aeronave: 16.0\n", "Predicción obtenida: 0.276\n", - "\tR²: 0.5394259957381201, Desviación Estándar: 0.02253063800021863, Varianza: 0.0005076296486968957, Incertidumbre: 0.0067932429576407745\n", + "\tR²: 0.5632456052929734, Desviación Estándar: 0.022253056728350776, Varianza: 0.0004951985337551978, Incertidumbre: 0.006709549055130292\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.308', 'Velocidad a la que se realiza el crucero (KTAS): 0.272', 'Área del ala: 0.26', 'Relación de aspecto del ala: 0.272', 'Longitud del fuselaje: 0.269', 'Peso máximo al despegue (MTOW): 0.265', 'Alcance de la aeronave: 0.32', 'envergadura: 0.255', 'payload: 0.276', 'Crucero KIAS: 0.276']\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.311', 'Velocidad a la que se realiza el crucero (KTAS): 0.272', 'Área del ala: 0.261', 'Relación de aspecto del ala: 0.272', 'Longitud del fuselaje: 0.27', 'Peso máximo al despegue (MTOW): 0.266', 'Alcance de la aeronave: 0.328', 'envergadura: 0.256', 'payload: 0.277', 'Crucero KIAS: 0.276']\n", "**Mediana calculada:** 0.272\n", "\n", "--- Imputación para aeronave: **V21** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.0x + 0.201\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.0x + 0.183\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.010249079511174597, Desviación Estándar: 0.04616981560043625, Varianza: 0.0021316518725782866, Incertidumbre: 0.011542453900109063\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.014305682652152818, Desviación Estándar: 0.04541585750246479, Varianza: 0.002062600112684187, Incertidumbre: 0.01101496338592133\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.005x + 0.185\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.005x + 0.183\n", "Valor del parámetro correlacionado para la aeronave: 19.688\n", - "Predicción obtenida: 0.282\n", - "\tR²: 0.3967685182755928, Desviación Estándar: 0.03545237044618538, Varianza: 0.0012568705702535586, Incertidumbre: 0.008598462825185189\n", - "\tNivel de confianza: Confianza Media\n", + "Predicción obtenida: 0.283\n", + "\tR²: 0.41353937190981327, Desviación Estándar: 0.03503126520298072, Varianza: 0.001227189541721568, Incertidumbre: 0.008496329801818471\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.075x + 0.207\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.075x + 0.209\n", "Valor del parámetro correlacionado para la aeronave: 0.8\n", - "Predicción obtenida: 0.268\n", - "\tR²: 0.7836164664637315, Desviación Estándar: 0.021233196730497346, Varianza: 0.0004508486433960032, Incertidumbre: 0.005149806640550596\n", + "Predicción obtenida: 0.269\n", + "\tR²: 0.771939412802745, Desviación Estándar: 0.021845475954437206, Varianza: 0.0004772248196758942, Incertidumbre: 0.005298306164825611\n", "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", - "Ecuación de regresión: y = -0.039x + 0.839\n", - "Valor del parámetro correlacionado para la aeronave: 14.578\n", - "Predicción obtenida: 0.277\n", - "\tR²: 0.6503309577441012, Desviación Estándar: 0.026991801495547467, Varianza: 0.0007285573479750386, Incertidumbre: 0.006546473446579232\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = -0.039x + 0.842\n", + "Valor del parámetro correlacionado para la aeronave: 14.568\n", + "Predicción obtenida: 0.278\n", + "\tR²: 0.6574483990804236, Desviación Estándar: 0.0267731451760295, Varianza: 0.0007168013026167517, Incertidumbre: 0.006493441499456798\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.034x + 0.239\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.034x + 0.24\n", "Valor del parámetro correlacionado para la aeronave: 0.93\n", - "Predicción obtenida: 0.27\n", - "\tR²: 0.35785791364325414, Desviación Estándar: 0.037188678863664754, Varianza: 0.0013829978356247857, Incertidumbre: 0.009297169715916188\n", + "Predicción obtenida: 0.271\n", + "\tR²: 0.3612099344091373, Desviación Estándar: 0.03656078137464094, Varianza: 0.0013366907347242916, Incertidumbre: 0.008867291962515261\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.001x + 0.256\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.001x + 0.257\n", "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 0.27\n", - "\tR²: 0.6662199673897068, Desviación Estándar: 0.02614021892802073, Varianza: 0.0006833110456048533, Incertidumbre: 0.005996977508909025\n", + "Predicción obtenida: 0.271\n", + "\tR²: 0.6569798349980132, Desviación Estándar: 0.026507694611626824, Varianza: 0.0007026578736232697, Incertidumbre: 0.006081282212543103\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", - "Ecuación de regresión: y = -0.0x + 0.322\n", - "Valor del parámetro correlacionado para la aeronave: 471.068\n", - "Predicción obtenida: 0.315\n", - "\tR²: 0.08980696350170814, Desviación Estándar: 0.04316639678565242, Varianza: 0.0018633378114563837, Incertidumbre: 0.009903050597128307\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = -0.0x + 0.331\n", + "Valor del parámetro correlacionado para la aeronave: 373.727\n", + "Predicción obtenida: 0.32\n", + "\tR²: 0.18759571893960836, Desviación Estándar: 0.0407941824996777, Varianza: 0.0016641653258170103, Incertidumbre: 0.009358827315813186\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.032x + 0.179\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.032x + 0.18\n", "Valor del parámetro correlacionado para la aeronave: 2.15\n", - "Predicción obtenida: 0.248\n", - "\tR²: 0.5680260142996714, Desviación Estándar: 0.030501695390844595, Varianza: 0.0009303534217158704, Incertidumbre: 0.007625423847711149\n", + "Predicción obtenida: 0.25\n", + "\tR²: 0.5664966665882962, Desviación Estándar: 0.030118449655234383, Varianza: 0.000907121009634888, Incertidumbre: 0.007304797012257599\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", "Ecuación de regresión: y = 0.005x + 0.271\n", "Valor del parámetro correlacionado para la aeronave: 1.5\n", "Predicción obtenida: 0.278\n", - "\tR²: 0.7521146796518272, Desviación Estándar: 0.018488012959664887, Varianza: 0.00034180662319673684, Incertidumbre: 0.004622003239916222\n", + "\tR²: 0.7555202520970645, Desviación Estándar: 0.01833377542330803, Varianza: 0.0003361273212722936, Incertidumbre: 0.004583443855827008\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.004x + 0.209\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272]\n", + "Ecuación de regresión: y = 0.004x + 0.207\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", "Predicción obtenida: 0.285\n", - "\tR²: 0.5394259957381201, Desviación Estándar: 0.02253063800021863, Varianza: 0.0005076296486968957, Incertidumbre: 0.0067932429576407745\n", + "\tR²: 0.5632456052929734, Desviación Estándar: 0.022253056728350776, Varianza: 0.0004951985337551978, Incertidumbre: 0.006709549055130292\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.308', 'Velocidad a la que se realiza el crucero (KTAS): 0.282', 'Área del ala: 0.268', 'Relación de aspecto del ala: 0.277', 'Longitud del fuselaje: 0.27', 'Peso máximo al despegue (MTOW): 0.27', 'Alcance de la aeronave: 0.315', 'envergadura: 0.248', 'payload: 0.278', 'Crucero KIAS: 0.285']\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.311', 'Velocidad a la que se realiza el crucero (KTAS): 0.283', 'Área del ala: 0.269', 'Relación de aspecto del ala: 0.278', 'Longitud del fuselaje: 0.271', 'Peso máximo al despegue (MTOW): 0.271', 'Alcance de la aeronave: 0.32', 'envergadura: 0.25', 'payload: 0.278', 'Crucero KIAS: 0.285']\n", "**Mediana calculada:** 0.278\n", "\n", "--- Imputación para aeronave: **V25** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.0x + 0.213\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.0x + 0.195\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.007624668273402024, Desviación Estándar: 0.04532987951030284, Varianza: 0.0020547979764185733, Incertidumbre: 0.010994110659852962\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.011044881710893129, Desviación Estándar: 0.044760694317023265, Varianza: 0.0020035197557419985, Incertidumbre: 0.010550196827395105\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.005x + 0.184\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.005x + 0.182\n", "Valor del parámetro correlacionado para la aeronave: 21.875\n", "Predicción obtenida: 0.293\n", - "\tR²: 0.41034339711447, Desviación Estándar: 0.03446795427732797, Varianza: 0.0011880398720639715, Incertidumbre: 0.008124174734375492\n", + "\tR²: 0.42735666066806766, Desviación Estándar: 0.034060469502456774, Varianza: 0.0011601155827277879, Incertidumbre: 0.008028129651861593\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.074x + 0.209\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.074x + 0.211\n", "Valor del parámetro correlacionado para la aeronave: 0.52\n", - "Predicción obtenida: 0.248\n", - "\tR²: 0.7859555597017408, Desviación Estándar: 0.020766719698698297, Varianza: 0.0004312566470443038, Incertidumbre: 0.004894762773983275\n", + "Predicción obtenida: 0.249\n", + "\tR²: 0.7753395291556694, Desviación Estándar: 0.021333978629353077, Varianza: 0.0004551386441576937, Incertidumbre: 0.005028466986168149\n", "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = -0.038x + 0.839\n", - "Valor del parámetro correlacionado para la aeronave: 14.435\n", - "Predicción obtenida: 0.283\n", - "\tR²: 0.6584792308147298, Desviación Estándar: 0.02623158211259663, Varianza: 0.0006880959001298995, Incertidumbre: 0.006182843197689607\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = -0.039x + 0.842\n", + "Valor del parámetro correlacionado para la aeronave: 14.421\n", + "Predicción obtenida: 0.284\n", + "\tR²: 0.6658371155959888, Desviación Estándar: 0.02601882192873693, Varianza: 0.0006769790945593217, Incertidumbre: 0.00613269514143171\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.033x + 0.24\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.034x + 0.241\n", "Valor del parámetro correlacionado para la aeronave: 0.93\n", - "Predicción obtenida: 0.271\n", - "\tR²: 0.3698412259519205, Desviación Estándar: 0.03612198254213046, Varianza: 0.001304797622773978, Incertidumbre: 0.008760867613407119\n", + "Predicción obtenida: 0.272\n", + "\tR²: 0.3758370724436967, Desviación Estándar: 0.035559652505826406, Varianza: 0.0012644888863351262, Incertidumbre: 0.008381490474502354\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.001x + 0.257\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.001x + 0.258\n", "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 0.274\n", - "\tR²: 0.6742199186237361, Desviación Estándar: 0.025535772267198973, Varianza: 0.0006520756652822481, Incertidumbre: 0.005709972264741082\n", + "Predicción obtenida: 0.275\n", + "\tR²: 0.6659897422136262, Desviación Estándar: 0.0258798006020757, Varianza: 0.0006697640792031978, Incertidumbre: 0.005786899339038125\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = -0.0x + 0.32\n", - "Valor del parámetro correlacionado para la aeronave: 470.718\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.08349677643343056, Desviación Estándar: 0.04283055788146815, Varianza: 0.0018344566884377933, Incertidumbre: 0.009577203893720215\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = -0.0x + 0.328\n", + "Valor del parámetro correlacionado para la aeronave: 385.208\n", + "Predicción obtenida: 0.317\n", + "\tR²: 0.17038629659156324, Desviación Estándar: 0.040786738838416804, Varianza: 0.0016635580650732177, Incertidumbre: 0.009120192062323078\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.03x + 0.189\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.03x + 0.19\n", "Valor del parámetro correlacionado para la aeronave: 2.45\n", - "Predicción obtenida: 0.263\n", - "\tR²: 0.5567962058793969, Desviación Estándar: 0.03029342092653269, Varianza: 0.0009176913514320887, Incertidumbre: 0.007347233778905335\n", + "Predicción obtenida: 0.264\n", + "\tR²: 0.5591647654062968, Desviación Estándar: 0.029884546712702416, Varianza: 0.0008930861322236928, Incertidumbre: 0.007043855211079342\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", "Ecuación de regresión: y = 0.005x + 0.271\n", "Valor del parámetro correlacionado para la aeronave: 2.2\n", "Predicción obtenida: 0.281\n", - "\tR²: 0.7713758958351147, Desviación Estándar: 0.017936323591463472, Varianza: 0.00032171170397768915, Incertidumbre: 0.004350197453109518\n", + "\tR²: 0.7749229114992245, Desviación Estándar: 0.017786382883709647, Varianza: 0.00031635541608591945, Incertidumbre: 0.004313831489836053\n", "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.004x + 0.207\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278]\n", + "Ecuación de regresión: y = 0.004x + 0.205\n", "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 0.292\n", - "\tR²: 0.5559821413548826, Desviación Estándar: 0.021644010407351307, Varianza: 0.0004684631865135317, Incertidumbre: 0.00624808761751367\n", + "Predicción obtenida: 0.293\n", + "\tR²: 0.5795655402783035, Desviación Estándar: 0.02138953366069156, Varianza: 0.00045751215022185723, Incertidumbre: 0.006174626508420417\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Empty weight (r = 0.971) ---\n", @@ -30849,109 +35233,109 @@ "Predicción obtenida: 0.253\n", "\tR²: 0.037864541216720005, Desviación Estándar: 0.04025536882356717, Varianza: 0.001620494719121424, Incertidumbre: 0.018002748229764387\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.293', 'Área del ala: 0.248', 'Relación de aspecto del ala: 0.283', 'Longitud del fuselaje: 0.271', 'Peso máximo al despegue (MTOW): 0.274', 'Alcance de la aeronave: 0.313', 'envergadura: 0.263', 'payload: 0.281', 'Crucero KIAS: 0.292', 'Empty weight: 0.253']\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.309', 'Velocidad a la que se realiza el crucero (KTAS): 0.293', 'Área del ala: 0.249', 'Relación de aspecto del ala: 0.284', 'Longitud del fuselaje: 0.272', 'Peso máximo al despegue (MTOW): 0.275', 'Alcance de la aeronave: 0.317', 'envergadura: 0.264', 'payload: 0.281', 'Crucero KIAS: 0.293', 'Empty weight: 0.253']\n", "**Mediana calculada:** 0.281\n", "\n", "--- Imputación para aeronave: **V32** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.0x + 0.222\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.0x + 0.205\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.304\n", - "\tR²: 0.005879006239630669, Desviación Estándar: 0.044414071195000175, Varianza: 0.0019726097201145446, Incertidumbre: 0.010468496974028912\n", + "Predicción obtenida: 0.307\n", + "\tR²: 0.008806840766555024, Desviación Estándar: 0.0439986702137573, Varianza: 0.0019358829805789736, Incertidumbre: 0.010093987216417957\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.005x + 0.182\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.005x + 0.18\n", "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 0.292\n", - "\tR²: 0.41638810258008374, Desviación Estándar: 0.03365333043822183, Varianza: 0.0011325466495841479, Incertidumbre: 0.007720603499673174\n", + "Predicción obtenida: 0.293\n", + "\tR²: 0.4333816032073342, Desviación Estándar: 0.03326639487281157, Varianza: 0.001106653027833824, Incertidumbre: 0.007631834392973959\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", "Ecuación de regresión: y = 0.07x + 0.217\n", "Valor del parámetro correlacionado para la aeronave: 1.03\n", - "Predicción obtenida: 0.288\n", - "\tR²: 0.764143155594072, Desviación Estándar: 0.021393912896673372, Varianza: 0.0004576995090304473, Incertidumbre: 0.004908100227553197\n", + "Predicción obtenida: 0.289\n", + "\tR²: 0.7555854161374564, Desviación Estándar: 0.021848617461412912, Varianza: 0.00047736208497515723, Incertidumbre: 0.005012416608967213\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = -0.039x + 0.84\n", - "Valor del parámetro correlacionado para la aeronave: 14.194\n", - "Predicción obtenida: 0.292\n", - "\tR²: 0.663976576642759, Desviación Estándar: 0.02553587580427515, Varianza: 0.000652080953091365, Incertidumbre: 0.0058583316876653045\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = -0.039x + 0.843\n", + "Valor del parámetro correlacionado para la aeronave: 14.182\n", + "Predicción obtenida: 0.293\n", + "\tR²: 0.6714366085489721, Desviación Estándar: 0.025332029736691172, Varianza: 0.0006417117305806058, Incertidumbre: 0.005811566192473899\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.033x + 0.242\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.033x + 0.243\n", "Valor del parámetro correlacionado para la aeronave: 1.0\n", - "Predicción obtenida: 0.275\n", - "\tR²: 0.3766225687592737, Desviación Estándar: 0.03517033453390517, Varianza: 0.0012369524312268022, Incertidumbre: 0.00828972734850792\n", + "Predicción obtenida: 0.276\n", + "\tR²: 0.3848860095207083, Desviación Estándar: 0.0346607699064813, Varianza: 0.00120136897051004, Incertidumbre: 0.00795172596461409\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.001x + 0.258\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.001x + 0.259\n", "Valor del parámetro correlacionado para la aeronave: 23.5\n", - "Predicción obtenida: 0.29\n", - "\tR²: 0.6799609284870747, Desviación Estándar: 0.02496000967487211, Varianza: 0.0006230020829697093, Incertidumbre: 0.0054467206515205506\n", + "Predicción obtenida: 0.291\n", + "\tR²: 0.6724626600573362, Desviación Estándar: 0.025285590185798745, Varianza: 0.0006393610710441618, Incertidumbre: 0.005517768143716917\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = -0.0x + 0.318\n", - "Valor del parámetro correlacionado para la aeronave: 473.211\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.07878328572710314, Desviación Estándar: 0.04234714665946332, Varianza: 0.0017932808301980953, Incertidumbre: 0.00924090500154224\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = -0.0x + 0.326\n", + "Valor del parámetro correlacionado para la aeronave: 412.686\n", + "Predicción obtenida: 0.315\n", + "\tR²: 0.15808515960741942, Desviación Estándar: 0.04053934883017473, Varianza: 0.0016434388035745891, Incertidumbre: 0.008846411173261676\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.029x + 0.194\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.029x + 0.195\n", "Valor del parámetro correlacionado para la aeronave: 3.2\n", - "Predicción obtenida: 0.287\n", - "\tR²: 0.5552407456896347, Desviación Estándar: 0.02970731891220461, Varianza: 0.0008825247969514297, Incertidumbre: 0.007002082217897084\n", + "Predicción obtenida: 0.288\n", + "\tR²: 0.5599804935133099, Desviación Estándar: 0.029315436669585263, Varianza: 0.0008593948271284642, Incertidumbre: 0.00672542241728888\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", "Ecuación de regresión: y = 0.005x + 0.271\n", "Valor del parámetro correlacionado para la aeronave: 5.0\n", "Predicción obtenida: 0.294\n", - "\tR²: 0.7842231008826372, Desviación Estándar: 0.017431002224719133, Varianza: 0.00030383983855816337, Incertidumbre: 0.004108526625325565\n", + "\tR²: 0.7878383281156356, Desviación Estándar: 0.01728531402736029, Varianza: 0.0002987820810244584, Incertidumbre: 0.004074187587895138\n", "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.004x + 0.205\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", + "Ecuación de regresión: y = 0.004x + 0.203\n", "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 0.291\n", - "\tR²: 0.5586324629674886, Desviación Estándar: 0.021009020466072784, Varianza: 0.00044137894094386517, Incertidumbre: 0.005826853887515214\n", + "Predicción obtenida: 0.292\n", + "\tR²: 0.5815487606672278, Desviación Estándar: 0.020794956088832327, Varianza: 0.0004324301987364647, Incertidumbre: 0.005767483111485157\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Empty weight (r = 0.971) ---\n", @@ -30963,811 +35347,811 @@ "Predicción obtenida: 0.264\n", "\tR²: 0.009256571042910555, Desviación Estándar: 0.03798189451706237, Varianza: 0.0014426243111052524, Incertidumbre: 0.015506043505169485\n", "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.304', 'Velocidad a la que se realiza el crucero (KTAS): 0.292', 'Área del ala: 0.288', 'Relación de aspecto del ala: 0.292', 'Longitud del fuselaje: 0.275', 'Peso máximo al despegue (MTOW): 0.29', 'Alcance de la aeronave: 0.311', 'envergadura: 0.287', 'payload: 0.294', 'Crucero KIAS: 0.291', 'Empty weight: 0.264']\n", - "**Mediana calculada:** 0.291\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.307', 'Velocidad a la que se realiza el crucero (KTAS): 0.293', 'Área del ala: 0.289', 'Relación de aspecto del ala: 0.293', 'Longitud del fuselaje: 0.276', 'Peso máximo al despegue (MTOW): 0.291', 'Alcance de la aeronave: 0.315', 'envergadura: 0.288', 'payload: 0.294', 'Crucero KIAS: 0.292', 'Empty weight: 0.264']\n", + "**Mediana calculada:** 0.292\n", "\n", "--- Imputación para aeronave: **V35** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291]\n", - "Ecuación de regresión: y = 0.0x + 0.227\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", + "Ecuación de regresión: y = 0.0x + 0.21\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.303\n", - "\tR²: 0.00512115327712892, Desviación Estándar: 0.043329805954573745, Varianza: 0.0018774720840610147, Incertidumbre: 0.009940539231537572\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.00781429009993051, Desviación Estándar: 0.0430085862012688, Varianza: 0.001849738487031969, Incertidumbre: 0.00961701223621965\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", - "Ecuación de regresión: y = 0.005x + 0.182\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", + "Ecuación de regresión: y = 0.005x + 0.18\n", "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 0.32\n", - "\tR²: 0.4192571699084816, Desviación Estándar: 0.032802224457642644, Varianza: 0.0010759859293695694, Incertidumbre: 0.007334800370049512\n", + "Predicción obtenida: 0.321\n", + "\tR²: 0.4360697755321421, Desviación Estándar: 0.03242435670008977, Varianza: 0.0010513389074146564, Incertidumbre: 0.007250306570810149\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", - "Ecuación de regresión: y = 0.07x + 0.217\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", + "Ecuación de regresión: y = 0.07x + 0.218\n", "Valor del parámetro correlacionado para la aeronave: 1.202\n", "Predicción obtenida: 0.301\n", - "\tR²: 0.7651489951552293, Desviación Estándar: 0.02085967698747946, Varianza: 0.0004351261240219801, Incertidumbre: 0.00466436557326921\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "\tR²: 0.7565518672120739, Desviación Estándar: 0.021304036943021673, Varianza: 0.0004538619900696323, Incertidumbre: 0.0047637274799763275\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", - "Ecuación de regresión: y = -0.039x + 0.84\n", - "Valor del parámetro correlacionado para la aeronave: 13.909\n", - "Predicción obtenida: 0.303\n", - "\tR²: 0.6656139724811423, Desviación Estándar: 0.02489060361542516, Varianza: 0.000619542148340216, Incertidumbre: 0.005565708168509269\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", + "Ecuación de regresión: y = -0.039x + 0.843\n", + "Valor del parámetro correlacionado para la aeronave: 13.898\n", + "Predicción obtenida: 0.304\n", + "\tR²: 0.6729848491652715, Desviación Estándar: 0.024691224016505753, Varianza: 0.0006096565434332705, Incertidumbre: 0.005521125534858225\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", "Ecuación de regresión: y = 0.032x + 0.245\n", "Valor del parámetro correlacionado para la aeronave: 1.88\n", - "Predicción obtenida: 0.304\n", - "\tR²: 0.37241241226406685, Desviación Estándar: 0.03441429034951141, Varianza: 0.0011843433802604744, Incertidumbre: 0.007895179676167752\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.38147480268763256, Desviación Estándar: 0.03395763069177028, Varianza: 0.001153120682198659, Incertidumbre: 0.007593157058163155\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", - "Ecuación de regresión: y = 0.001x + 0.258\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", + "Ecuación de regresión: y = 0.001x + 0.259\n", "Valor del parámetro correlacionado para la aeronave: 32.0\n", "Predicción obtenida: 0.302\n", - "\tR²: 0.682451786672694, Desviación Estándar: 0.024387054383523732, Varianza: 0.0005947284215049441, Incertidumbre: 0.005199337464370489\n", + "\tR²: 0.6749008669048356, Desviación Estándar: 0.024705639698818668, Varianza: 0.0006103686329278449, Incertidumbre: 0.005267260081811748\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", - "Ecuación de regresión: y = -0.0x + 0.317\n", - "Valor del parámetro correlacionado para la aeronave: 477.686\n", - "Predicción obtenida: 0.31\n", - "\tR²: 0.0764508060932585, Desviación Estándar: 0.04158957925260002, Varianza: 0.0017296931024082978, Incertidumbre: 0.008866928089582757\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", + "Ecuación de regresión: y = -0.0x + 0.325\n", + "Valor del parámetro correlacionado para la aeronave: 456.221\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.15274102701798387, Desviación Estándar: 0.03988377763537778, Varianza: 0.0015907157184682607, Incertidumbre: 0.008503249962830417\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", "Ecuación de regresión: y = 0.029x + 0.195\n", "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 0.296\n", - "\tR²: 0.5565499378750433, Desviación Estándar: 0.028928372807546923, Varianza: 0.0008368507532924203, Incertidumbre: 0.00663662387732458\n", + "Predicción obtenida: 0.297\n", + "\tR²: 0.5616690703122301, Desviación Estándar: 0.028586418069702225, Varianza: 0.0008171832980557979, Incertidumbre: 0.006392117403708249\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", - "Ecuación de regresión: y = 0.005x + 0.27\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", + "Ecuación de regresión: y = 0.005x + 0.271\n", "Valor del parámetro correlacionado para la aeronave: 10.0\n", "Predicción obtenida: 0.317\n", - "\tR²: 0.7895139994440685, Desviación Estándar: 0.0169782050018478, Varianza: 0.0002882594450847696, Incertidumbre: 0.003895067360303755\n", + "\tR²: 0.7929138648348985, Desviación Estándar: 0.016831546785257487, Varianza: 0.0002833009671843116, Incertidumbre: 0.0038614216579165406\n", "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291]\n", - "Ecuación de regresión: y = 0.004x + 0.205\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", + "Ecuación de regresión: y = 0.004x + 0.203\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.5604899238980319, Desviación Estándar: 0.020244967299597776, Varianza: 0.0004098587009617832, Incertidumbre: 0.005410695102966922\n", + "Predicción obtenida: 0.314\n", + "\tR²: 0.5832259382889582, Desviación Estándar: 0.020038522534308566, Varianza: 0.00040154238535799216, Incertidumbre: 0.005355520418609409\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.32', 'Área del ala: 0.301', 'Relación de aspecto del ala: 0.303', 'Longitud del fuselaje: 0.304', 'Peso máximo al despegue (MTOW): 0.302', 'Alcance de la aeronave: 0.31', 'envergadura: 0.296', 'payload: 0.317', 'Crucero KIAS: 0.313']\n", - "**Mediana calculada:** 0.304\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.321', 'Área del ala: 0.301', 'Relación de aspecto del ala: 0.304', 'Longitud del fuselaje: 0.306', 'Peso máximo al despegue (MTOW): 0.302', 'Alcance de la aeronave: 0.312', 'envergadura: 0.297', 'payload: 0.317', 'Crucero KIAS: 0.314']\n", + "**Mediana calculada:** 0.306\n", "\n", "--- Imputación para aeronave: **V39** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", - "Ecuación de regresión: y = 0.0x + 0.227\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", + "Ecuación de regresión: y = 0.0x + 0.21\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.303\n", - "\tR²: 0.005176120015587982, Desviación Estándar: 0.04223283027110972, Varianza: 0.001783611952708361, Incertidumbre: 0.00944354793684122\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.007839382716985033, Desviación Estándar: 0.04197210135142703, Varianza: 0.001761657291854463, Incertidumbre: 0.009159063405679652\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", - "Ecuación de regresión: y = 0.005x + 0.183\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", + "Ecuación de regresión: y = 0.005x + 0.181\n", "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 0.319\n", - "\tR²: 0.4130031365050483, Desviación Estándar: 0.03218362230740495, Varianza: 0.0010357855448256935, Incertidumbre: 0.00702304216007393\n", + "Predicción obtenida: 0.32\n", + "\tR²: 0.4305554307690891, Desviación Estándar: 0.031797681030392426, Varianza: 0.0010110925189105782, Incertidumbre: 0.006938822868849272\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", - "Ecuación de regresión: y = 0.07x + 0.217\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", + "Ecuación de regresión: y = 0.07x + 0.218\n", "Valor del parámetro correlacionado para la aeronave: 1.203\n", - "Predicción obtenida: 0.301\n", - "\tR²: 0.7648474342748249, Desviación Estándar: 0.0203700405430836, Varianza: 0.0004149385517268696, Incertidumbre: 0.004445107271333332\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "Predicción obtenida: 0.302\n", + "\tR²: 0.7560017518320579, Desviación Estándar: 0.02081436063167048, Varianza: 0.000433237608505234, Incertidumbre: 0.00454206586365423\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", - "Ecuación de regresión: y = -0.039x + 0.84\n", - "Valor del parámetro correlacionado para la aeronave: 14.054\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", + "Ecuación de regresión: y = -0.039x + 0.843\n", + "Valor del parámetro correlacionado para la aeronave: 14.042\n", "Predicción obtenida: 0.298\n", - "\tR²: 0.6655940393757499, Desviación Estándar: 0.024291483455634172, Varianza: 0.0005900761684753486, Incertidumbre: 0.005300836270391003\n", + "\tR²: 0.6728675905615071, Desviación Estándar: 0.024100802676844793, Varianza: 0.0005808486896682092, Incertidumbre: 0.005259226313135029\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", "Ecuación de regresión: y = 0.032x + 0.245\n", "Valor del parámetro correlacionado para la aeronave: 1.954\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.3724510383824907, Desviación Estándar: 0.03354292212178016, Varianza: 0.0011251276244678085, Incertidumbre: 0.007500425402828191\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.381487984139919, Desviación Estándar: 0.033139335158115936, Varianza: 0.0010982155347219389, Incertidumbre: 0.007231595802027506\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", - "Ecuación de regresión: y = 0.001x + 0.258\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", + "Ecuación de regresión: y = 0.001x + 0.259\n", "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 0.291\n", - "\tR²: 0.6824856544856703, Desviación Estándar: 0.02385554248252102, Varianza: 0.0005690869071353651, Incertidumbre: 0.004974224462756526\n", + "Predicción obtenida: 0.292\n", + "\tR²: 0.6746862184494904, Desviación Estándar: 0.02417354407482776, Varianza: 0.0005843602331376403, Incertidumbre: 0.0050405323784455825\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", - "Ecuación de regresión: y = -0.0x + 0.317\n", - "Valor del parámetro correlacionado para la aeronave: 475.377\n", - "Predicción obtenida: 0.31\n", - "\tR²: 0.07597157149437128, Desviación Estándar: 0.04069586533624133, Varianza: 0.001656153455465489, Incertidumbre: 0.008485674515131983\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", + "Ecuación de regresión: y = -0.0x + 0.324\n", + "Valor del parámetro correlacionado para la aeronave: 413.556\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.1520436185450239, Desviación Estándar: 0.03902796009378789, Varianza: 0.0015231816690822997, Incertidumbre: 0.008137892230799079\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", - "Ecuación de regresión: y = 0.029x + 0.195\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", + "Ecuación de regresión: y = 0.029x + 0.196\n", "Valor del parámetro correlacionado para la aeronave: 3.9\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.5548768238027968, Desviación Estándar: 0.02824991788942962, Varianza: 0.0007980578607595157, Incertidumbre: 0.006316873675955201\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.5595879139438245, Desviación Estándar: 0.027964002682235915, Varianza: 0.0007819854460120975, Incertidumbre: 0.006102245667871153\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", "Ecuación de regresión: y = 0.005x + 0.27\n", "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 0.293\n", - "\tR²: 0.7844930543930171, Desviación Estándar: 0.016790498176847062, Varianza: 0.0002819208290267045, Incertidumbre: 0.0037544695299516315\n", + "Predicción obtenida: 0.294\n", + "\tR²: 0.7889957205554606, Desviación Estándar: 0.016594408157668158, Varianza: 0.00027537438210328347, Incertidumbre: 0.003710622468692305\n", "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", - "Ecuación de regresión: y = 0.004x + 0.206\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", + "Ecuación de regresión: y = 0.004x + 0.203\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.5561091968076497, Desviación Estándar: 0.019679701564958057, Varianza: 0.00038729065368581255, Incertidumbre: 0.005081277094627639\n", + "Predicción obtenida: 0.314\n", + "\tR²: 0.5799398889644451, Desviación Estándar: 0.01946734334447621, Varianza: 0.0003789774568917221, Incertidumbre: 0.005026446437870543\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.319', 'Área del ala: 0.301', 'Relación de aspecto del ala: 0.298', 'Longitud del fuselaje: 0.306', 'Peso máximo al despegue (MTOW): 0.291', 'Alcance de la aeronave: 0.31', 'envergadura: 0.308', 'payload: 0.293', 'Crucero KIAS: 0.312']\n", - "**Mediana calculada:** 0.304\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.32', 'Área del ala: 0.302', 'Relación de aspecto del ala: 0.298', 'Longitud del fuselaje: 0.308', 'Peso máximo al despegue (MTOW): 0.292', 'Alcance de la aeronave: 0.313', 'envergadura: 0.309', 'payload: 0.294', 'Crucero KIAS: 0.314']\n", + "**Mediana calculada:** 0.307\n", "\n", "--- Imputación para aeronave: **Volitation VT370** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", - "Ecuación de regresión: y = 0.0x + 0.227\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", + "Ecuación de regresión: y = 0.0x + 0.21\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.303\n", - "\tR²: 0.005176120015587982, Desviación Estándar: 0.04223283027110972, Varianza: 0.001783611952708361, Incertidumbre: 0.00944354793684122\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.007920162700700728, Desviación Estándar: 0.04100745249090813, Varianza: 0.0016816111597940874, Incertidumbre: 0.008742818246980276\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", - "Ecuación de regresión: y = 0.005x + 0.183\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", + "Ecuación de regresión: y = 0.005x + 0.181\n", "Valor del parámetro correlacionado para la aeronave: 27.344\n", "Predicción obtenida: 0.319\n", - "\tR²: 0.4130031365050483, Desviación Estándar: 0.03218362230740495, Varianza: 0.0010357855448256935, Incertidumbre: 0.00702304216007393\n", + "\tR²: 0.4264004008303849, Desviación Estándar: 0.031181277073957195, Varianza: 0.0009722720399628886, Incertidumbre: 0.0066478706090504465\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", - "Ecuación de regresión: y = 0.07x + 0.217\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", + "Ecuación de regresión: y = 0.069x + 0.218\n", "Valor del parámetro correlacionado para la aeronave: 1.424\n", - "Predicción obtenida: 0.316\n", - "\tR²: 0.764585109854175, Desviación Estándar: 0.019912811825215336, Varianza: 0.0003965200747864357, Incertidumbre: 0.00424542574579038\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "Predicción obtenida: 0.317\n", + "\tR²: 0.7552866449539557, Desviación Estándar: 0.020366589706083574, Varianza: 0.00041479797625594946, Incertidumbre: 0.004342171515057819\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", - "Ecuación de regresión: y = -0.039x + 0.839\n", - "Valor del parámetro correlacionado para la aeronave: 13.657\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.6645264748338366, Desviación Estándar: 0.023770852184850387, Varianza: 0.0005650534135940064, Incertidumbre: 0.00506796271419343\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", + "Ecuación de regresión: y = -0.039x + 0.842\n", + "Valor del parámetro correlacionado para la aeronave: 13.645\n", + "Predicción obtenida: 0.314\n", + "\tR²: 0.6709647919313728, Desviación Estándar: 0.023616235156659383, Varianza: 0.0005577265629746346, Incertidumbre: 0.005034998253022332\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", "Ecuación de regresión: y = 0.032x + 0.245\n", "Valor del parámetro correlacionado para la aeronave: 2.02\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.37232112301089393, Desviación Estándar: 0.032738858426763454, Varianza: 0.0010718328510876604, Incertidumbre: 0.007144204614623162\n", + "Predicción obtenida: 0.31\n", + "\tR²: 0.3815211319149636, Desviación Estándar: 0.03237814111230407, Varianza: 0.0010483440218882747, Incertidumbre: 0.006903042879406268\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", - "Ecuación de regresión: y = 0.001x + 0.259\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", + "Ecuación de regresión: y = 0.001x + 0.26\n", "Valor del parámetro correlacionado para la aeronave: 40.0\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.6786409596766527, Desviación Estándar: 0.023499467179320575, Varianza: 0.0005522249577119649, Incertidumbre: 0.004796808651384641\n", + "Predicción obtenida: 0.314\n", + "\tR²: 0.6693148854310667, Desviación Estándar: 0.023860379147788803, Varianza: 0.0005693176930762347, Incertidumbre: 0.004870479498452192\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", - "Ecuación de regresión: y = -0.0x + 0.316\n", - "Valor del parámetro correlacionado para la aeronave: 482.568\n", - "Predicción obtenida: 0.31\n", - "\tR²: 0.0755252570158883, Desviación Estándar: 0.03985752064225148, Varianza: 0.0015886219517475032, Incertidumbre: 0.00813588233216365\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", + "Ecuación de regresión: y = -0.0x + 0.324\n", + "Valor del parámetro correlacionado para la aeronave: 565.637\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.15125833221890828, Desviación Estándar: 0.038225905277819265, Varianza: 0.001461219834308811, Incertidumbre: 0.007802830240551638\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", - "Ecuación de regresión: y = 0.029x + 0.195\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", + "Ecuación de regresión: y = 0.029x + 0.196\n", "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 0.383\n", - "\tR²: 0.5544873995856561, Desviación Estándar: 0.027581937552100175, Varianza: 0.0007607632791279538, Incertidumbre: 0.006018872221240193\n", + "Predicción obtenida: 0.384\n", + "\tR²: 0.5595332664978436, Desviación Estándar: 0.027324110665788853, Varianza: 0.0007466070236762762, Incertidumbre: 0.005825519967726159\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", "Ecuación de regresión: y = 0.005x + 0.271\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", "Predicción obtenida: 0.354\n", - "\tR²: 0.7813065689753644, Desviación Estándar: 0.016547337226329974, Varianza: 0.0002738143692818858, Incertidumbre: 0.003610925018553225\n", + "\tR²: 0.7831688836414169, Desviación Estándar: 0.01644167434806251, Varianza: 0.00027032865536773677, Incertidumbre: 0.0035878674881814365\n", "\tNivel de confianza: Confianza Muy Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", - "Ecuación de regresión: y = 0.004x + 0.206\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", + "Ecuación de regresión: y = 0.004x + 0.204\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.5561091968076497, Desviación Estándar: 0.019679701564958057, Varianza: 0.00038729065368581255, Incertidumbre: 0.005081277094627639\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.578641185644784, Desviación Estándar: 0.018914583583003738, Varianza: 0.0003577614721184345, Incertidumbre: 0.0047286458957509344\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.319', 'Área del ala: 0.316', 'Relación de aspecto del ala: 0.313', 'Longitud del fuselaje: 0.308', 'Peso máximo al despegue (MTOW): 0.313', 'Alcance de la aeronave: 0.31', 'envergadura: 0.383', 'payload: 0.354', 'Crucero KIAS: 0.312']\n", - "**Mediana calculada:** 0.313\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.319', 'Área del ala: 0.317', 'Relación de aspecto del ala: 0.314', 'Longitud del fuselaje: 0.31', 'Peso máximo al despegue (MTOW): 0.314', 'Alcance de la aeronave: 0.309', 'envergadura: 0.384', 'payload: 0.354', 'Crucero KIAS: 0.313']\n", + "**Mediana calculada:** 0.314\n", "\n", "--- Imputación para aeronave: **Skyeye 2600** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304]\n", - "Ecuación de regresión: y = 0.0x + 0.227\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", + "Ecuación de regresión: y = 0.0x + 0.208\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.303\n", - "\tR²: 0.005176120015587982, Desviación Estándar: 0.04223283027110972, Varianza: 0.001783611952708361, Incertidumbre: 0.00944354793684122\n", + "Predicción obtenida: 0.307\n", + "\tR²: 0.008376532013914084, Desviación Estándar: 0.04013734006562043, Varianza: 0.001611006067543259, Incertidumbre: 0.008369213945592396\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313]\n", - "Ecuación de regresión: y = 0.005x + 0.183\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", + "Ecuación de regresión: y = 0.005x + 0.182\n", "Valor del parámetro correlacionado para la aeronave: 36.094\n", - "Predicción obtenida: 0.362\n", - "\tR²: 0.41332607845278324, Desviación Estándar: 0.03146617256189972, Varianza: 0.0009901200156952508, Incertidumbre: 0.006708610531166274\n", + "Predicción obtenida: 0.363\n", + "\tR²: 0.4268824074716687, Desviación Estándar: 0.03051385371109099, Varianza: 0.0009310952683018614, Incertidumbre: 0.006362578327191434\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", - "Ecuación de regresión: y = 0.07x + 0.217\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", + "Ecuación de regresión: y = 0.069x + 0.218\n", "Valor del parámetro correlacionado para la aeronave: 0.88\n", - "Predicción obtenida: 0.278\n", - "\tR²: 0.7647651611026156, Desviación Estándar: 0.019487043559427698, Varianza: 0.00037974486668703253, Incertidumbre: 0.004063329469498759\n", - "\tNivel de confianza: Confianza Muy Alta\n", + "Predicción obtenida: 0.279\n", + "\tR²: 0.7555152040915233, Desviación Estándar: 0.019929716367540293, Varianza: 0.0003971935944906035, Incertidumbre: 0.0041556331307013574\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", - "Ecuación de regresión: y = -0.039x + 0.839\n", - "Valor del parámetro correlacionado para la aeronave: 14.116\n", - "Predicción obtenida: 0.295\n", - "\tR²: 0.6651927379024003, Desviación Estándar: 0.023248373246514756, Varianza: 0.000540486858609263, Incertidumbre: 0.004847620925277197\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", + "Ecuación de regresión: y = -0.039x + 0.842\n", + "Valor del parámetro correlacionado para la aeronave: 14.103\n", + "Predicción obtenida: 0.296\n", + "\tR²: 0.6716280796932008, Desviación Estándar: 0.02309713944013369, Varianza: 0.0005334778503169791, Incertidumbre: 0.004816086496754028\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", "Ecuación de regresión: y = 0.032x + 0.245\n", "Valor del parámetro correlacionado para la aeronave: 2.05\n", - "Predicción obtenida: 0.31\n", - "\tR²: 0.3735742379948259, Desviación Estándar: 0.03200010007033279, Varianza: 0.0010240064045113127, Incertidumbre: 0.006822444258446171\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.38238206414682874, Desviación Estándar: 0.03167635049099469, Varianza: 0.0010033911804283393, Incertidumbre: 0.006604975662096291\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", - "Ecuación de regresión: y = 0.001x + 0.259\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", + "Ecuación de regresión: y = 0.001x + 0.26\n", "Valor del parámetro correlacionado para la aeronave: 15.0\n", - "Predicción obtenida: 0.279\n", - "\tR²: 0.6788110572536825, Desviación Estándar: 0.023024764599624735, Varianza: 0.0005301397848681324, Incertidumbre: 0.004604952919924947\n", + "Predicción obtenida: 0.28\n", + "\tR²: 0.6695041439455878, Desviación Estándar: 0.02337831313115393, Varianza: 0.0005465455248582842, Incertidumbre: 0.004675662626230786\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", - "Ecuación de regresión: y = -0.0x + 0.317\n", - "Valor del parámetro correlacionado para la aeronave: 474.569\n", - "Predicción obtenida: 0.31\n", - "\tR²: 0.07577157816533575, Desviación Estándar: 0.039057511178466925, Varianza: 0.001525489179456069, Incertidumbre: 0.007811502235693385\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", + "Ecuación de regresión: y = -0.0x + 0.324\n", + "Valor del parámetro correlacionado para la aeronave: 407.828\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.15110158401290597, Desviación Estándar: 0.03746778536542462, Varianza: 0.0014038349401895272, Incertidumbre: 0.007493557073084923\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", "Ecuación de regresión: y = 0.023x + 0.215\n", "Valor del parámetro correlacionado para la aeronave: 2.6\n", - "Predicción obtenida: 0.274\n", - "\tR²: 0.456917343395016, Desviación Estándar: 0.02979541908647188, Varianza: 0.0008877669985384927, Incertidumbre: 0.006352404693351432\n", + "Predicción obtenida: 0.276\n", + "\tR²: 0.4627661042213257, Desviación Estándar: 0.029543156346368924, Varianza: 0.0008727980869059985, Incertidumbre: 0.006160173934959667\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313]\n", - "Ecuación de regresión: y = 0.004x + 0.272\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", + "Ecuación de regresión: y = 0.004x + 0.273\n", "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 0.289\n", - "\tR²: 0.7248069548381981, Desviación Estándar: 0.018136863954327922, Varianza: 0.0003289458340977995, Incertidumbre: 0.0038667923875069623\n", + "Predicción obtenida: 0.29\n", + "\tR²: 0.7275683615430203, Desviación Estándar: 0.018006770737420567, Varianza: 0.0003242437923900256, Incertidumbre: 0.0038390564204693082\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313]\n", - "Ecuación de regresión: y = 0.004x + 0.206\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", + "Ecuación de regresión: y = 0.004x + 0.204\n", "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 0.346\n", - "\tR²: 0.5625917571382832, Desviación Estándar: 0.01905614616302398, Varianza: 0.00036313670658653355, Incertidumbre: 0.004764036540755995\n", + "Predicción obtenida: 0.348\n", + "\tR²: 0.5842116709677524, Desviación Estándar: 0.018351270832188404, Varianza: 0.0003367691411563289, Incertidumbre: 0.004450836941495841\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Empty weight (r = 0.971) ---\n", "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32']\n", "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292]\n", "Ecuación de regresión: y = 0.0x + 0.265\n", "Valor del parámetro correlacionado para la aeronave: 6.5\n", "Predicción obtenida: 0.268\n", - "\tR²: 0.003649740403420809, Desviación Estándar: 0.03643263340678523, Varianza: 0.0013273367769532033, Incertidumbre: 0.013770241085933945\n", + "\tR²: 0.003486790166087661, Desviación Estándar: 0.03652495038432548, Varianza: 0.001334072000577438, Incertidumbre: 0.013805133623699752\n", "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.362', 'Área del ala: 0.278', 'Relación de aspecto del ala: 0.295', 'Longitud del fuselaje: 0.31', 'Peso máximo al despegue (MTOW): 0.279', 'Alcance de la aeronave: 0.31', 'envergadura: 0.274', 'payload: 0.289', 'Crucero KIAS: 0.346', 'Empty weight: 0.268']\n", - "**Mediana calculada:** 0.295\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.307', 'Velocidad a la que se realiza el crucero (KTAS): 0.363', 'Área del ala: 0.279', 'Relación de aspecto del ala: 0.296', 'Longitud del fuselaje: 0.311', 'Peso máximo al despegue (MTOW): 0.28', 'Alcance de la aeronave: 0.313', 'envergadura: 0.276', 'payload: 0.29', 'Crucero KIAS: 0.348', 'Empty weight: 0.268']\n", + "**Mediana calculada:** 0.296\n", "\n", "--- Imputación para aeronave: **Skyeye 2930 VTOL** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295]\n", - "Ecuación de regresión: y = 0.0x + 0.23\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", + "Ecuación de regresión: y = 0.0x + 0.211\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.303\n", - "\tR²: 0.004769951987118937, Desviación Estándar: 0.04125452439215927, Varianza: 0.0017019357828232645, Incertidumbre: 0.009002475275546283\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.007814506493702633, Desviación Estándar: 0.03934894530801304, Varianza: 0.0015483394968530018, Incertidumbre: 0.008032069826776185\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295]\n", - "Ecuación de regresión: y = 0.004x + 0.205\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", + "Ecuación de regresión: y = 0.004x + 0.203\n", "Valor del parámetro correlacionado para la aeronave: 26.25\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.31968876592912787, Desviación Estándar: 0.03317908352641865, Varianza: 0.0011008515836530652, Incertidumbre: 0.006918317160461927\n", + "Predicción obtenida: 0.31\n", + "\tR²: 0.3329612516575994, Desviación Estándar: 0.03226355293122838, Varianza: 0.0010409368477461755, Incertidumbre: 0.006585770164232172\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", - "Ecuación de regresión: y = 0.069x + 0.219\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", + "Ecuación de regresión: y = 0.068x + 0.22\n", "Valor del parámetro correlacionado para la aeronave: 1.0\n", - "Predicción obtenida: 0.288\n", - "\tR²: 0.7583363925473515, Desviación Estándar: 0.01935863479576612, Varianza: 0.0003747567411558467, Incertidumbre: 0.003951564780542887\n", + "Predicción obtenida: 0.289\n", + "\tR²: 0.7491292552639982, Desviación Estándar: 0.019786166950727165, Varianza: 0.00039149240260204785, Incertidumbre: 0.0040388344162334755\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", - "Ecuación de regresión: y = -0.039x + 0.839\n", - "Valor del parámetro correlacionado para la aeronave: 14.013\n", - "Predicción obtenida: 0.299\n", - "\tR²: 0.6659813449336012, Desviación Estándar: 0.02275906736360552, Varianza: 0.000517975147261134, Incertidumbre: 0.0046456751718719266\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", + "Ecuación de regresión: y = -0.039x + 0.842\n", + "Valor del parámetro correlacionado para la aeronave: 14.001\n", + "Predicción obtenida: 0.3\n", + "\tR²: 0.6723851116250787, Desviación Estándar: 0.022610932560392084, Varianza: 0.000511254271250599, Incertidumbre: 0.004615437281786883\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", - "Ecuación de regresión: y = 0.031x + 0.245\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", + "Ecuación de regresión: y = 0.032x + 0.245\n", "Valor del parámetro correlacionado para la aeronave: 2.03\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.36900063185025456, Desviación Estándar: 0.03143801363195717, Varianza: 0.0009883487011231248, Incertidumbre: 0.006555278991585878\n", + "Predicción obtenida: 0.31\n", + "\tR²: 0.3778731110352854, Desviación Estándar: 0.031158471570466775, Varianza: 0.0009708503506075861, Incertidumbre: 0.0063601963760549695\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", - "Ecuación de regresión: y = 0.001x + 0.26\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", + "Ecuación de regresión: y = 0.001x + 0.261\n", "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 0.298\n", - "\tR²: 0.674546027930947, Desviación Estándar: 0.022774473008881708, Varianza: 0.0005186766208322815, Incertidumbre: 0.00446644162631077\n", + "Predicción obtenida: 0.299\n", + "\tR²: 0.66526195767342, Desviación Estándar: 0.023117889975755625, Varianza: 0.0005344368369311424, Incertidumbre: 0.00453379123459496\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", - "Ecuación de regresión: y = -0.0x + 0.316\n", - "Valor del parámetro correlacionado para la aeronave: 476.384\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.074422965024465, Desviación Estándar: 0.03840695118941138, Varianza: 0.0014750938996658278, Incertidumbre: 0.00753222282970824\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", + "Ecuación de regresión: y = -0.0x + 0.324\n", + "Valor del parámetro correlacionado para la aeronave: 425.273\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.14767582813821534, Desviación Estándar: 0.036889099953161315, Varianza: 0.0013608056953543263, Incertidumbre: 0.007234547711540164\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", "Ecuación de regresión: y = 0.022x + 0.219\n", "Valor del parámetro correlacionado para la aeronave: 2.93\n", - "Predicción obtenida: 0.284\n", - "\tR²: 0.44716444549394097, Desviación Estándar: 0.029426500593546755, Varianza: 0.0008659189371820075, Incertidumbre: 0.006135849529013565\n", + "Predicción obtenida: 0.285\n", + "\tR²: 0.4538529022461858, Desviación Estándar: 0.029193856890482952, Varianza: 0.0008522812801419989, Incertidumbre: 0.005959171083793167\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", "Ecuación de regresión: y = 0.004x + 0.273\n", "Valor del parámetro correlacionado para la aeronave: 6.0\n", - "Predicción obtenida: 0.298\n", - "\tR²: 0.7276037635441446, Desviación Estándar: 0.01777586747096132, Varianza: 0.00031598146434518075, Incertidumbre: 0.0037065245900637154\n", + "Predicción obtenida: 0.299\n", + "\tR²: 0.7300492378182392, Desviación Estándar: 0.0176537442574639, Varianza: 0.0003116546863079396, Incertidumbre: 0.0036810601397585336\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295]\n", - "Ecuación de regresión: y = 0.003x + 0.226\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", + "Ecuación de regresión: y = 0.003x + 0.224\n", "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 0.304\n", - "\tR²: 0.41805913592782484, Desviación Estándar: 0.021338848229013108, Varianza: 0.00045534644374085583, Incertidumbre: 0.005175430892779141\n", + "Predicción obtenida: 0.305\n", + "\tR²: 0.4379735968192333, Desviación Estándar: 0.020751260149570606, Varianza: 0.0004306147977951571, Incertidumbre: 0.0048911189233091824\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Empty weight (r = 0.971) ---\n", "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600']\n", "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296]\n", "Ecuación de regresión: y = 0.0x + 0.27\n", "Valor del parámetro correlacionado para la aeronave: 7.1\n", "Predicción obtenida: 0.272\n", - "\tR²: 0.001017512275129695, Desviación Estándar: 0.03522803474762737, Varianza: 0.001241014432180041, Incertidumbre: 0.012454991128961318\n", + "\tR²: 0.0008846968281794876, Desviación Estándar: 0.03538270380705628, Varianza: 0.0012519357286978749, Incertidumbre: 0.012509674899342281\n", "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.309', 'Área del ala: 0.288', 'Relación de aspecto del ala: 0.299', 'Longitud del fuselaje: 0.308', 'Peso máximo al despegue (MTOW): 0.298', 'Alcance de la aeronave: 0.309', 'envergadura: 0.284', 'payload: 0.298', 'Crucero KIAS: 0.304', 'Empty weight: 0.272']\n", - "**Mediana calculada:** 0.299\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.31', 'Área del ala: 0.289', 'Relación de aspecto del ala: 0.3', 'Longitud del fuselaje: 0.31', 'Peso máximo al despegue (MTOW): 0.299', 'Alcance de la aeronave: 0.312', 'envergadura: 0.285', 'payload: 0.299', 'Crucero KIAS: 0.305', 'Empty weight: 0.272']\n", + "**Mediana calculada:** 0.3\n", "\n", "--- Imputación para aeronave: **Skyeye 3600** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299]\n", - "Ecuación de regresión: y = 0.0x + 0.231\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", + "Ecuación de regresión: y = 0.0x + 0.212\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.303\n", - "\tR²: 0.0046024842532983445, Desviación Estándar: 0.040314820357117226, Varianza: 0.0016252847404266335, Incertidumbre: 0.008595148579885139\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.007526495051969895, Desviación Estándar: 0.03857245496275595, Varianza: 0.001487834281853836, Incertidumbre: 0.00771449099255119\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", - "Ecuación de regresión: y = 0.068x + 0.22\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", + "Ecuación de regresión: y = 0.068x + 0.221\n", "Valor del parámetro correlacionado para la aeronave: 1.33\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.7551997675782928, Desviación Estándar: 0.0190968572628113, Varianza: 0.0003646899573161887, Incertidumbre: 0.0038193714525622596\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.745993216225064, Desviación Estándar: 0.019513738354102667, Varianza: 0.0003807859845523774, Incertidumbre: 0.0039027476708205335\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", - "Ecuación de regresión: y = -0.039x + 0.839\n", - "Valor del parámetro correlacionado para la aeronave: 13.722\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.6662095809690938, Desviación Estándar: 0.022299386888745724, Varianza: 0.0004972626556139646, Incertidumbre: 0.004459877377749145\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", + "Ecuación de regresión: y = -0.039x + 0.842\n", + "Valor del parámetro correlacionado para la aeronave: 13.71\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.6726026581884519, Desviación Estándar: 0.02215416542630856, Varianza: 0.0004908070457362455, Incertidumbre: 0.004430833085261712\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", - "Ecuación de regresión: y = 0.031x + 0.244\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", + "Ecuación de regresión: y = 0.032x + 0.245\n", "Valor del parámetro correlacionado para la aeronave: 2.488\n", - "Predicción obtenida: 0.322\n", - "\tR²: 0.3669311075827396, Desviación Estándar: 0.030832212553008696, Varianza: 0.0009506253309139069, Incertidumbre: 0.006293599032992129\n", + "Predicción obtenida: 0.324\n", + "\tR²: 0.37576047664868784, Desviación Estándar: 0.030590984286507895, Varianza: 0.000935808319617373, Incertidumbre: 0.006118196857301579\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", - "Ecuación de regresión: y = 0.001x + 0.26\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", + "Ecuación de regresión: y = 0.001x + 0.261\n", "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 0.298\n", - "\tR²: 0.6751025711511651, Desviación Estándar: 0.0223500545446565, Varianza: 0.0004995249381491208, Incertidumbre: 0.00430127000258675\n", + "Predicción obtenida: 0.299\n", + "\tR²: 0.6658077862482688, Desviación Estándar: 0.02268722584632889, Varianza: 0.0005147102166023337, Incertidumbre: 0.0043661586498479395\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", - "Ecuación de regresión: y = -0.0x + 0.315\n", - "Valor del parámetro correlacionado para la aeronave: 482.044\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.07362847696573804, Desviación Estándar: 0.03773966564722344, Varianza: 0.001424282363164217, Incertidumbre: 0.007263002040183641\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", + "Ecuación de regresión: y = -0.0x + 0.323\n", + "Valor del parámetro correlacionado para la aeronave: 458.124\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.1458113421289795, Desviación Estándar: 0.03627105024977506, Varianza: 0.0013155890862217073, Incertidumbre: 0.006980366875166025\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", "Ecuación de regresión: y = 0.022x + 0.221\n", "Valor del parámetro correlacionado para la aeronave: 3.6\n", - "Predicción obtenida: 0.299\n", - "\tR²: 0.44117112031756955, Desviación Estándar: 0.02896800757279921, Varianza: 0.0008391454627377525, Incertidumbre: 0.0059130697848697586\n", + "Predicción obtenida: 0.301\n", + "\tR²: 0.4484176985315972, Desviación Estándar: 0.028755636237230698, Varianza: 0.0008268866154079353, Incertidumbre: 0.00575112724744614\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", "Ecuación de regresión: y = 0.004x + 0.273\n", "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 0.315\n", - "\tR²: 0.7298053627667499, Desviación Estándar: 0.01740232203287305, Varianza: 0.0003028408121358188, Incertidumbre: 0.0035522341100110203\n", + "Predicción obtenida: 0.316\n", + "\tR²: 0.7321183484558058, Desviación Estándar: 0.017283518765307298, Varianza: 0.00029872002091072946, Incertidumbre: 0.0035279834945684007\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Empty weight (r = 0.971) ---\n", "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3]\n", "Ecuación de regresión: y = 0.0x + 0.274\n", "Valor del parámetro correlacionado para la aeronave: 11.5\n", - "Predicción obtenida: 0.275\n", - "\tR²: 0.000345064333019951, Desviación Estándar: 0.03430275891623664, Varianza: 0.0011766792692654523, Incertidumbre: 0.01143425297207888\n", + "Predicción obtenida: 0.276\n", + "\tR²: 0.0002618966283592927, Desviación Estándar: 0.03450249234670619, Varianza: 0.0011904219781345188, Incertidumbre: 0.011500830782235396\n", "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Área del ala: 0.311', 'Relación de aspecto del ala: 0.311', 'Longitud del fuselaje: 0.322', 'Peso máximo al despegue (MTOW): 0.298', 'Alcance de la aeronave: 0.309', 'envergadura: 0.299', 'payload: 0.315', 'Empty weight: 0.275']\n", - "**Mediana calculada:** 0.309\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Área del ala: 0.312', 'Relación de aspecto del ala: 0.312', 'Longitud del fuselaje: 0.324', 'Peso máximo al despegue (MTOW): 0.299', 'Alcance de la aeronave: 0.311', 'envergadura: 0.301', 'payload: 0.316', 'Empty weight: 0.276']\n", + "**Mediana calculada:** 0.311\n", "\n", "--- Imputación para aeronave: **Skyeye 3600 VTOL** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309]\n", - "Ecuación de regresión: y = 0.0x + 0.229\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", + "Ecuación de regresión: y = 0.0x + 0.211\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.303\n", - "\tR²: 0.004885808051522722, Desviación Estándar: 0.03944852241805123, Varianza: 0.0015561859209674905, Incertidumbre: 0.008225585537417332\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.0077910339066047385, Desviación Estándar: 0.037836352063430036, Varianza: 0.0014315895374678261, Incertidumbre: 0.0074203191344075535\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299]\n", - "Ecuación de regresión: y = 0.004x + 0.205\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", + "Ecuación de regresión: y = 0.004x + 0.203\n", "Valor del parámetro correlacionado para la aeronave: 32.813\n", - "Predicción obtenida: 0.334\n", - "\tR²: 0.3176237816578882, Desviación Estándar: 0.03254110628346504, Varianza: 0.0010589235981517682, Incertidumbre: 0.006642425505013738\n", + "Predicción obtenida: 0.336\n", + "\tR²: 0.33106974113438137, Desviación Estándar: 0.03166709614118016, Varianza: 0.001002804978014747, Incertidumbre: 0.0063334192282360315\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", - "Ecuación de regresión: y = 0.068x + 0.22\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", + "Ecuación de regresión: y = 0.068x + 0.221\n", "Valor del parámetro correlacionado para la aeronave: 1.32\n", - "Predicción obtenida: 0.31\n", - "\tR²: 0.7552867180720797, Desviación Estándar: 0.018728748952686985, Varianza: 0.0003507660373327738, Incertidumbre: 0.003673009860574284\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.7462264643720709, Desviación Estándar: 0.019135104991392145, Varianza: 0.0003661522430316006, Incertidumbre: 0.003752702836375202\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", - "Ecuación de regresión: y = -0.039x + 0.839\n", - "Valor del parámetro correlacionado para la aeronave: 13.684\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.6663591073897637, Desviación Estándar: 0.021868532210116903, Varianza: 0.0004782327010249204, Incertidumbre: 0.004288772018193017\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", + "Ecuación de regresión: y = -0.039x + 0.842\n", + "Valor del parámetro correlacionado para la aeronave: 13.672\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.6729065655813421, Desviación Estándar: 0.021724190260569178, Varianza: 0.0004719404424774087, Incertidumbre: 0.0042604642329094064\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", "Ecuación de regresión: y = 0.031x + 0.245\n", "Valor del parámetro correlacionado para la aeronave: 2.42\n", - "Predicción obtenida: 0.319\n", - "\tR²: 0.3631026056008778, Desviación Estándar: 0.03031715002312391, Varianza: 0.0009191295855246022, Incertidumbre: 0.006063430004624782\n", + "Predicción obtenida: 0.321\n", + "\tR²: 0.37206338538349726, Desviación Estándar: 0.030099937089659884, Varianza: 0.0009060062128014827, Incertidumbre: 0.005903083329926576\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", - "Ecuación de regresión: y = 0.001x + 0.261\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", + "Ecuación de regresión: y = 0.001x + 0.262\n", "Valor del parámetro correlacionado para la aeronave: 40.0\n", - "Predicción obtenida: 0.314\n", - "\tR²: 0.6721978753808997, Desviación Estándar: 0.02204574905530395, Varianza: 0.00048601505140943494, Incertidumbre: 0.004166254961890813\n", + "Predicción obtenida: 0.315\n", + "\tR²: 0.6623522424362198, Desviación Estándar: 0.02239505431218533, Varianza: 0.0005015384576457308, Incertidumbre: 0.004232267450559075\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", - "Ecuación de regresión: y = -0.0x + 0.315\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", + "Ecuación de regresión: y = -0.0x + 0.323\n", "Valor del parámetro correlacionado para la aeronave: 300.0\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.07367366337637671, Desviación Estándar: 0.037059621839270106, Varianza: 0.0013734155708697057, Incertidumbre: 0.007003610219200486\n", + "Predicción obtenida: 0.315\n", + "\tR²: 0.1459436125708079, Desviación Estándar: 0.03561750202712675, Varianza: 0.0012686064506523779, Incertidumbre: 0.006731075191794021\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", - "Ecuación de regresión: y = 0.022x + 0.221\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", + "Ecuación de regresión: y = 0.022x + 0.222\n", "Valor del parámetro correlacionado para la aeronave: 3.6\n", - "Predicción obtenida: 0.3\n", - "\tR²: 0.43919899422207664, Desviación Estándar: 0.02844841198320688, Varianza: 0.0008093121443662688, Incertidumbre: 0.005689682396641376\n", + "Predicción obtenida: 0.301\n", + "\tR²: 0.4461253083393548, Desviación Estándar: 0.028269195215371138, Varianza: 0.0007991473981247625, Incertidumbre: 0.005544045309105432\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309]\n", - "Ecuación de regresión: y = 0.004x + 0.272\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", + "Ecuación de regresión: y = 0.004x + 0.273\n", "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 0.315\n", - "\tR²: 0.7285836979639178, Desviación Estándar: 0.017095423279435584, Varianza: 0.0002922534971030681, Incertidumbre: 0.003419084655887117\n", + "Predicción obtenida: 0.316\n", + "\tR²: 0.7313295857648042, Desviación Estándar: 0.016962299070521318, Varianza: 0.00028771958975780837, Incertidumbre: 0.0033924598141042636\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299]\n", - "Ecuación de regresión: y = 0.003x + 0.226\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", + "Ecuación de regresión: y = 0.003x + 0.224\n", "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 0.323\n", - "\tR²: 0.41660766587250275, Desviación Estándar: 0.020763504630057248, Varianza: 0.0004311231245224087, Incertidumbre: 0.004894004975037253\n", + "Predicción obtenida: 0.325\n", + "\tR²: 0.4365151702095247, Desviación Estándar: 0.02022420538915309, Varianza: 0.00040901848362264885, Incertidumbre: 0.004639750921301526\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Empty weight (r = 0.971) ---\n", "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311]\n", "Ecuación de regresión: y = 0.001x + 0.272\n", "Valor del parámetro correlacionado para la aeronave: 11.0\n", "Predicción obtenida: 0.281\n", - "\tR²: 0.00958847480002567, Desviación Estándar: 0.03395743305078767, Varianza: 0.0011531072593987265, Incertidumbre: 0.010738283193316921\n", + "\tR²: 0.009760346923073815, Desviación Estándar: 0.03428498479253269, Varianza: 0.0011754601822241977, Incertidumbre: 0.010841864148863874\n", "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.334', 'Área del ala: 0.31', 'Relación de aspecto del ala: 0.312', 'Longitud del fuselaje: 0.319', 'Peso máximo al despegue (MTOW): 0.314', 'Alcance de la aeronave: 0.311', 'envergadura: 0.3', 'payload: 0.315', 'Crucero KIAS: 0.323', 'Empty weight: 0.281']\n", - "**Mediana calculada:** 0.312\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.336', 'Área del ala: 0.311', 'Relación de aspecto del ala: 0.313', 'Longitud del fuselaje: 0.321', 'Peso máximo al despegue (MTOW): 0.315', 'Alcance de la aeronave: 0.315', 'envergadura: 0.301', 'payload: 0.316', 'Crucero KIAS: 0.325', 'Empty weight: 0.281']\n", + "**Mediana calculada:** 0.315\n", "\n", "--- Imputación para aeronave: **Skyeye 5000** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312]\n", - "Ecuación de regresión: y = 0.0x + 0.227\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", + "Ecuación de regresión: y = 0.0x + 0.209\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.304\n", - "\tR²: 0.005277507804714032, Desviación Estándar: 0.03865833333585721, Varianza: 0.0014944667363062491, Incertidumbre: 0.007891099248272937\n", + "Predicción obtenida: 0.306\n", + "\tR²: 0.008217398892604644, Desviación Estándar: 0.03716734216604148, Varianza: 0.001381411323687605, Incertidumbre: 0.007152858334875658\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312]\n", - "Ecuación de regresión: y = 0.004x + 0.209\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315]\n", + "Ecuación de regresión: y = 0.004x + 0.207\n", "Valor del parámetro correlacionado para la aeronave: 36.094\n", - "Predicción obtenida: 0.344\n", - "\tR²: 0.3066672465334307, Desviación Estándar: 0.032165160212105026, Varianza: 0.0010345975314703841, Incertidumbre: 0.006433032042421005\n", + "Predicción obtenida: 0.346\n", + "\tR²: 0.32262835377535615, Desviación Estándar: 0.031288239037149196, Varianza: 0.0009789539020457867, Incertidumbre: 0.006136128515245357\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", - "Ecuación de regresión: y = 0.068x + 0.22\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", + "Ecuación de regresión: y = 0.068x + 0.221\n", "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 0.398\n", - "\tR²: 0.7555609689379138, Desviación Estándar: 0.018382843455420142, Varianza: 0.0003379289335064831, Incertidumbre: 0.003537779872485856\n", + "Predicción obtenida: 0.399\n", + "\tR²: 0.7464203816084714, Desviación Estándar: 0.01879361973555014, Varianza: 0.00035320014276445967, Incertidumbre: 0.0036168338044557788\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", - "Ecuación de regresión: y = -0.039x + 0.839\n", - "Valor del parámetro correlacionado para la aeronave: 12.713\n", - "Predicción obtenida: 0.349\n", - "\tR²: 0.6668851212909376, Desviación Estándar: 0.0214597386033369, Varianza: 0.00046052038092354804, Incertidumbre: 0.004129928619791854\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", + "Ecuación de regresión: y = -0.039x + 0.843\n", + "Valor del parámetro correlacionado para la aeronave: 12.695\n", + "Predicción obtenida: 0.351\n", + "\tR²: 0.6736164896021335, Desviación Estándar: 0.02132148100602976, Varianza: 0.0004546055522904879, Incertidumbre: 0.004103320932784258\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", - "Ecuación de regresión: y = 0.03x + 0.245\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", + "Ecuación de regresión: y = 0.031x + 0.246\n", "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 0.352\n", - "\tR²: 0.36315238206647993, Desviación Estándar: 0.02976054264839433, Varianza: 0.0008856898987268976, Incertidumbre: 0.005836522603818192\n", + "Predicción obtenida: 0.354\n", + "\tR²: 0.3726704559110734, Desviación Estándar: 0.02955977566670113, Varianza: 0.0008737803374656961, Incertidumbre: 0.005688781479451615\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Ancho del fuselaje (r = 0.76) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277]\n", "Valores para Cuerda: [0.239, 0.318, 0.352]\n", "Ecuación de regresión: y = 0.883x + 0.101\n", @@ -31777,757 +36161,757 @@ "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", - "Ecuación de regresión: y = 0.001x + 0.261\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", + "Ecuación de regresión: y = 0.001x + 0.262\n", "Valor del parámetro correlacionado para la aeronave: 90.0\n", "Predicción obtenida: 0.381\n", - "\tR²: 0.67222934329996, Desviación Estándar: 0.021666156030378594, Varianza: 0.00046942231713271076, Incertidumbre: 0.004023304171057925\n", + "\tR²: 0.6626838320176878, Desviación Estándar: 0.022005551176023402, Varianza: 0.000484244282560585, Incertidumbre: 0.004086328267404087\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", - "Ecuación de regresión: y = -0.0x + 0.315\n", - "Valor del parámetro correlacionado para la aeronave: 615.631\n", - "Predicción obtenida: 0.307\n", - "\tR²: 0.07408169434278955, Desviación Estándar: 0.03641523910867908, Varianza: 0.0013260696393422703, Incertidumbre: 0.006762140141084367\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", + "Ecuación de regresión: y = -0.0x + 0.323\n", + "Valor del parámetro correlacionado para la aeronave: 530.401\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.14678275665337615, Desviación Estándar: 0.034998020711235714, Varianza: 0.0012248614537040841, Incertidumbre: 0.006498969291500381\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", "Ecuación de regresión: y = 0.022x + 0.222\n", "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 0.33\n", - "\tR²: 0.4363264574290574, Desviación Estándar: 0.027998638079495427, Varianza: 0.0007839237343065714, Incertidumbre: 0.005490984689283431\n", + "Predicción obtenida: 0.332\n", + "\tR²: 0.4424360357921081, Desviación Estándar: 0.02786766696740896, Varianza: 0.0007766068622064165, Incertidumbre: 0.005363135008440135\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312]\n", - "Ecuación de regresión: y = 0.004x + 0.272\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", + "Ecuación de regresión: y = 0.004x + 0.273\n", "Valor del parámetro correlacionado para la aeronave: 20.0\n", "Predicción obtenida: 0.358\n", - "\tR²: 0.7282681597919514, Desviación Estándar: 0.016773711285579456, Varianza: 0.00028135739029197564, Incertidumbre: 0.0032895954292515833\n", + "\tR²: 0.731317018691535, Desviación Estándar: 0.016633562499201185, Varianza: 0.0002766754014148319, Incertidumbre: 0.00326210999092277\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312]\n", - "Ecuación de regresión: y = 0.003x + 0.228\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315]\n", + "Ecuación de regresión: y = 0.003x + 0.226\n", "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 0.331\n", - "\tR²: 0.4160144196233093, Desviación Estándar: 0.020338026156813578, Varianza: 0.00041363530795523323, Incertidumbre: 0.004665863196244067\n", + "Predicción obtenida: 0.333\n", + "\tR²: 0.43886786937390043, Desviación Estándar: 0.019812140290383477, Varianza: 0.00039252090288583623, Incertidumbre: 0.0044301292469059875\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Empty weight (r = 0.971) ---\n", "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315]\n", "Ecuación de regresión: y = 0.001x + 0.271\n", "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.02251377090831741, Desviación Estándar: 0.033563109667469955, Varianza: 0.0011264823305506154, Incertidumbre: 0.010119658324049803\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.023639785146452374, Desviación Estándar: 0.0340515934789817, Varianza: 0.001159511018457829, Incertidumbre: 0.01026694173486318\n", "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.304', 'Velocidad a la que se realiza el crucero (KTAS): 0.344', 'Área del ala: 0.398', 'Relación de aspecto del ala: 0.349', 'Longitud del fuselaje: 0.352', 'Ancho del fuselaje: 0.432', 'Peso máximo al despegue (MTOW): 0.381', 'Alcance de la aeronave: 0.307', 'envergadura: 0.33', 'payload: 0.358', 'Crucero KIAS: 0.331', 'Empty weight: 0.311']\n", - "**Mediana calculada:** 0.346\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.346', 'Área del ala: 0.399', 'Relación de aspecto del ala: 0.351', 'Longitud del fuselaje: 0.354', 'Ancho del fuselaje: 0.432', 'Peso máximo al despegue (MTOW): 0.381', 'Alcance de la aeronave: 0.309', 'envergadura: 0.332', 'payload: 0.358', 'Crucero KIAS: 0.333', 'Empty weight: 0.312']\n", + "**Mediana calculada:** 0.348\n", "\n", "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346]\n", - "Ecuación de regresión: y = 0.0x + 0.215\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", + "Ecuación de regresión: y = 0.0x + 0.199\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.305\n", - "\tR²: 0.0069178491819321675, Desviación Estándar: 0.03877743810622532, Varianza: 0.0015036897060821354, Incertidumbre: 0.007755487621245063\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.009824679618514076, Desviación Estándar: 0.03730259846752296, Varianza: 0.0013914838524292463, Incertidumbre: 0.00704952848582606\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346]\n", - "Ecuación de regresión: y = 0.004x + 0.209\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348]\n", + "Ecuación de regresión: y = 0.004x + 0.207\n", "Valor del parámetro correlacionado para la aeronave: 30.625\n", - "Predicción obtenida: 0.324\n", - "\tR²: 0.3363378708178395, Desviación Estándar: 0.03154190924564428, Varianza: 0.0009948920388604602, Incertidumbre: 0.006185877336135111\n", + "Predicción obtenida: 0.325\n", + "\tR²: 0.35279566671506324, Desviación Estándar: 0.03070509946045511, Varianza: 0.0009428031328764408, Incertidumbre: 0.00590919914632933\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", - "Ecuación de regresión: y = 0.059x + 0.229\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", + "Ecuación de regresión: y = 0.059x + 0.231\n", "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 0.385\n", - "\tR²: 0.7140844954505441, Desviación Estándar: 0.019953273741700576, Varianza: 0.00039813313301123777, Incertidumbre: 0.003770814297295355\n", + "Predicción obtenida: 0.386\n", + "\tR²: 0.7085123361490802, Desviación Estándar: 0.020239189952971352, Varianza: 0.00040962480995245654, Incertidumbre: 0.0038248473823542318\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", - "Ecuación de regresión: y = -0.038x + 0.836\n", - "Valor del parámetro correlacionado para la aeronave: 13.046\n", - "Predicción obtenida: 0.336\n", - "\tR²: 0.6808265849574946, Desviación Estándar: 0.021081847262580024, Varianza: 0.00044444428400275285, Incertidumbre: 0.003984094645331039\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", + "Ecuación de regresión: y = -0.039x + 0.84\n", + "Valor del parámetro correlacionado para la aeronave: 13.032\n", + "Predicción obtenida: 0.338\n", + "\tR²: 0.6878639211364455, Desviación Estándar: 0.02094377780954981, Varianza: 0.0004386418289357911, Incertidumbre: 0.003958001971304421\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", - "Ecuación de regresión: y = 0.03x + 0.246\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", + "Ecuación de regresión: y = 0.03x + 0.247\n", "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 0.351\n", - "\tR²: 0.3920963576822901, Desviación Estándar: 0.02922109638467694, Varianza: 0.0008538724739225799, Incertidumbre: 0.0056236026212364105\n", - "\tNivel de confianza: Confianza Media\n", + "Predicción obtenida: 0.353\n", + "\tR²: 0.39957074018891947, Desviación Estándar: 0.029047847816911346, Varianza: 0.0008437774627944413, Incertidumbre: 0.0054895272460855635\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Ancho del fuselaje (r = 0.76) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.346]\n", - "Ecuación de regresión: y = 0.4x + 0.207\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.348]\n", + "Ecuación de regresión: y = 0.411x + 0.205\n", "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 0.358\n", - "\tR²: 0.38245795781242664, Desviación Estándar: 0.0353855035263058, Varianza: 0.0012521338598102006, Incertidumbre: 0.0176927517631529\n", + "Predicción obtenida: 0.359\n", + "\tR²: 0.3978508461679193, Desviación Estándar: 0.03522448950237247, Varianza: 0.001240764660702748, Incertidumbre: 0.017612244751186234\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", - "Ecuación de regresión: y = 0.001x + 0.263\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", + "Ecuación de regresión: y = 0.001x + 0.264\n", "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 0.387\n", - "\tR²: 0.6598630268437224, Desviación Estándar: 0.02206649940336974, Varianza: 0.0004869303959189171, Incertidumbre: 0.004028773162799966\n", + "Predicción obtenida: 0.388\n", + "\tR²: 0.6531876768371664, Desviación Estándar: 0.02232772097728044, Varianza: 0.000498527124039289, Incertidumbre: 0.004076465478979263\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", - "Ecuación de regresión: y = -0.0x + 0.317\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", + "Ecuación de regresión: y = -0.0x + 0.324\n", "Valor del parámetro correlacionado para la aeronave: 800.0\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.07051034083815089, Desviación Estándar: 0.03647781384637196, Varianza: 0.0013306309030105662, Incertidumbre: 0.0066599071640440705\n", + "Predicción obtenida: 0.303\n", + "\tR²: 0.14185181764220745, Desviación Estándar: 0.035121922429990356, Varianza: 0.0012335494351782598, Incertidumbre: 0.006412356392617459\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", - "Ecuación de regresión: y = 0.022x + 0.22\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", + "Ecuación de regresión: y = 0.022x + 0.221\n", "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 0.332\n", - "\tR²: 0.4566675873516166, Desviación Estándar: 0.027625614100153503, Varianza: 0.0007631745544106001, Incertidumbre: 0.005316551912417449\n", + "Predicción obtenida: 0.333\n", + "\tR²: 0.4612182728347505, Desviación Estándar: 0.027516263885293008, Varianza: 0.0007571447782050803, Incertidumbre: 0.005200085089293802\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346]\n", - "Ecuación de regresión: y = 0.004x + 0.273\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", + "Ecuación de regresión: y = 0.004x + 0.274\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.377\n", - "\tR²: 0.7340964250736124, Desviación Estándar: 0.01659158262598554, Varianza: 0.0002752806140349053, Incertidumbre: 0.0031930515651315567\n", + "Predicción obtenida: 0.378\n", + "\tR²: 0.7386270567836593, Desviación Estándar: 0.01642870017516188, Varianza: 0.00026990218944536394, Incertidumbre: 0.003161704822855121\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346]\n", - "Ecuación de regresión: y = 0.003x + 0.224\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348]\n", + "Ecuación de regresión: y = 0.003x + 0.222\n", "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 0.317\n", - "\tR²: 0.4797563623056019, Desviación Estándar: 0.02006710184638167, Varianza: 0.0004026885765130546, Incertidumbre: 0.004487140383992096\n", + "Predicción obtenida: 0.318\n", + "\tR²: 0.5000484040159869, Desviación Estándar: 0.019560275570138758, Varianza: 0.0003826043803797671, Incertidumbre: 0.0042684020673503026\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.305', 'Velocidad a la que se realiza el crucero (KTAS): 0.324', 'Área del ala: 0.385', 'Relación de aspecto del ala: 0.336', 'Longitud del fuselaje: 0.351', 'Ancho del fuselaje: 0.358', 'Peso máximo al despegue (MTOW): 0.387', 'Alcance de la aeronave: 0.306', 'envergadura: 0.332', 'payload: 0.377', 'Crucero KIAS: 0.317']\n", - "**Mediana calculada:** 0.336\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.308', 'Velocidad a la que se realiza el crucero (KTAS): 0.325', 'Área del ala: 0.386', 'Relación de aspecto del ala: 0.338', 'Longitud del fuselaje: 0.353', 'Ancho del fuselaje: 0.359', 'Peso máximo al despegue (MTOW): 0.388', 'Alcance de la aeronave: 0.303', 'envergadura: 0.333', 'payload: 0.378', 'Crucero KIAS: 0.318']\n", + "**Mediana calculada:** 0.338\n", "\n", "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", - "Ecuación de regresión: y = 0.0x + 0.207\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", + "Ecuación de regresión: y = 0.0x + 0.199\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.307\n", - "\tR²: 0.008204928681858958, Desviación Estándar: 0.038476561679856074, Varianza: 0.001480445798703769, Incertidumbre: 0.007545874570059327\n", + "Predicción obtenida: 0.308\n", + "\tR²: 0.009824679618514076, Desviación Estándar: 0.03730259846752296, Varianza: 0.0013914838524292463, Incertidumbre: 0.00704952848582606\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336]\n", - "Ecuación de regresión: y = 0.004x + 0.207\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338]\n", + "Ecuación de regresión: y = 0.004x + 0.205\n", "Valor del parámetro correlacionado para la aeronave: 33.885\n", - "Predicción obtenida: 0.337\n", - "\tR²: 0.3474595488240295, Desviación Estándar: 0.03103314635842597, Varianza: 0.000963056172903487, Incertidumbre: 0.005972331801279429\n", + "Predicción obtenida: 0.339\n", + "\tR²: 0.364003579646475, Desviación Estándar: 0.030242361656466255, Varianza: 0.0009146004385605004, Incertidumbre: 0.005715269143020364\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", - "Ecuación de regresión: y = 0.053x + 0.236\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", + "Ecuación de regresión: y = 0.053x + 0.237\n", "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 0.375\n", - "\tR²: 0.6729975157002732, Desviación Estándar: 0.021200760020167443, Varianza: 0.0004494722254327303, Incertidumbre: 0.003936882301555514\n", + "Predicción obtenida: 0.376\n", + "\tR²: 0.6700337636483493, Desviación Estándar: 0.021406727235210983, Varianza: 0.0004582479709227237, Incertidumbre: 0.00397512945320641\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", - "Ecuación de regresión: y = -0.038x + 0.836\n", - "Valor del parámetro correlacionado para la aeronave: 12.876\n", - "Predicción obtenida: 0.343\n", - "\tR²: 0.6878032793029674, Desviación Estándar: 0.020715244926136148, Varianza: 0.00042912137234980943, Incertidumbre: 0.0038467244119793098\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", + "Ecuación de regresión: y = -0.039x + 0.84\n", + "Valor del parámetro correlacionado para la aeronave: 12.856\n", + "Predicción obtenida: 0.344\n", + "\tR²: 0.6950392482679, Desviación Estándar: 0.020579626973971723, Varianza: 0.0004235210463878245, Incertidumbre: 0.003821540790489258\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", "Ecuación de regresión: y = 0.029x + 0.248\n", "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 0.348\n", - "\tR²: 0.4021070843542891, Desviación Estándar: 0.02880802670233707, Varianza: 0.0008299024024825657, Incertidumbre: 0.005444205315492288\n", + "Predicción obtenida: 0.351\n", + "\tR²: 0.40843076343231066, Desviación Estándar: 0.028662780269184288, Varianza: 0.0008215549727595402, Incertidumbre: 0.005322544675180707\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Ancho del fuselaje (r = 0.76) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.346, 0.336]\n", - "Ecuación de regresión: y = 0.335x + 0.222\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.348, 0.338]\n", + "Ecuación de regresión: y = 0.347x + 0.219\n", "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 0.348\n", - "\tR²: 0.3819496237120672, Desviación Estándar: 0.032423341688487206, Varianza: 0.0010512730862483922, Incertidumbre: 0.01450015921463204\n", + "Predicción obtenida: 0.349\n", + "\tR²: 0.401178655632655, Desviación Estándar: 0.03226372501937835, Varianza: 0.001040947952126061, Incertidumbre: 0.014428776470138143\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", - "Ecuación de regresión: y = 0.001x + 0.267\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", + "Ecuación de regresión: y = 0.001x + 0.268\n", "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 0.377\n", - "\tR²: 0.61787092141232, Desviación Estándar: 0.023196525022263995, Varianza: 0.0005380787731085197, Incertidumbre: 0.004166218882595367\n", + "Predicción obtenida: 0.378\n", + "\tR²: 0.6142108778582203, Desviación Estándar: 0.02336858751606866, Varianza: 0.0005460908824961601, Incertidumbre: 0.004197122218762633\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", - "Ecuación de regresión: y = -0.0x + 0.317\n", - "Valor del parámetro correlacionado para la aeronave: 609.354\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.0654907654203285, Desviación Estándar: 0.03627518909145652, Varianza: 0.0013158893436209262, Incertidumbre: 0.006515216292849419\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", + "Ecuación de regresión: y = -0.0x + 0.325\n", + "Valor del parámetro correlacionado para la aeronave: 528.174\n", + "Predicción obtenida: 0.311\n", + "\tR²: 0.12969533614523188, Desviación Estándar: 0.03509886956318445, Varianza: 0.0012319306446134358, Incertidumbre: 0.00630394306869412\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", - "Ecuación de regresión: y = 0.022x + 0.22\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", + "Ecuación de regresión: y = 0.023x + 0.221\n", "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 0.332\n", - "\tR²: 0.46936427126981495, Desviación Estándar: 0.027139388088225875, Varianza: 0.0007365463858033365, Incertidumbre: 0.005128862258279595\n", + "Predicción obtenida: 0.334\n", + "\tR²: 0.47311026916495147, Desviación Estándar: 0.027050505844431656, Varianza: 0.0007317298664396311, Incertidumbre: 0.005023152830642055\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336]\n", - "Ecuación de regresión: y = 0.004x + 0.276\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", + "Ecuación de regresión: y = 0.004x + 0.277\n", "Valor del parámetro correlacionado para la aeronave: 15.0\n", - "Predicción obtenida: 0.332\n", - "\tR²: 0.6901097817245, Desviación Estándar: 0.01772848171904924, Varianza: 0.00031429906406266314, Incertidumbre: 0.0033503681250970823\n", + "Predicción obtenida: 0.333\n", + "\tR²: 0.6973906199473292, Desviación Estándar: 0.017512139839937605, Varianza: 0.00030667504177352985, Incertidumbre: 0.0033094833529329548\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336]\n", - "Ecuación de regresión: y = 0.003x + 0.222\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338]\n", + "Ecuación de regresión: y = 0.004x + 0.22\n", "Valor del parámetro correlacionado para la aeronave: 35.0\n", - "Predicción obtenida: 0.342\n", - "\tR²: 0.4932580403474792, Desviación Estándar: 0.019996932601760886, Varianza: 0.00039987731347936734, Incertidumbre: 0.004363688443547595\n", + "Predicción obtenida: 0.345\n", + "\tR²: 0.5123468231062337, Desviación Estándar: 0.019530346323899706, Varianza: 0.00038143442753146275, Incertidumbre: 0.0041638838269285805\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Empty weight (r = 0.971) ---\n", "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348]\n", "Ecuación de regresión: y = 0.002x + 0.263\n", "Valor del parámetro correlacionado para la aeronave: 35.0\n", - "Predicción obtenida: 0.344\n", - "\tR²: 0.23082193042213683, Desviación Estándar: 0.03253007300095766, Varianza: 0.0010582056494476341, Incertidumbre: 0.00939062320193054\n", + "Predicción obtenida: 0.346\n", + "\tR²: 0.23373450804691387, Desviación Estándar: 0.032994278206234205, Varianza: 0.0010886223943503814, Incertidumbre: 0.009524627702043362\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.307', 'Velocidad a la que se realiza el crucero (KTAS): 0.337', 'Área del ala: 0.375', 'Relación de aspecto del ala: 0.343', 'Longitud del fuselaje: 0.348', 'Ancho del fuselaje: 0.348', 'Peso máximo al despegue (MTOW): 0.377', 'Alcance de la aeronave: 0.309', 'envergadura: 0.332', 'payload: 0.332', 'Crucero KIAS: 0.342', 'Empty weight: 0.344']\n", - "**Mediana calculada:** 0.342\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.308', 'Velocidad a la que se realiza el crucero (KTAS): 0.339', 'Área del ala: 0.376', 'Relación de aspecto del ala: 0.344', 'Longitud del fuselaje: 0.351', 'Ancho del fuselaje: 0.349', 'Peso máximo al despegue (MTOW): 0.378', 'Alcance de la aeronave: 0.311', 'envergadura: 0.334', 'payload: 0.333', 'Crucero KIAS: 0.345', 'Empty weight: 0.346']\n", + "**Mediana calculada:** 0.344\n", "\n", "--- Imputación para aeronave: **Volitation VT510** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", - "Ecuación de regresión: y = 0.0x + 0.199\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344]\n", + "Ecuación de regresión: y = 0.0x + 0.191\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.009671200460906637, Desviación Estándar: 0.038341789857909814, Varianza: 0.0014700928495081159, Incertidumbre: 0.007378880898558765\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.011265848190217476, Desviación Estándar: 0.037236364860275865, Varianza: 0.0013865468680075873, Incertidumbre: 0.006914619365213018\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342]\n", - "Ecuación de regresión: y = 0.004x + 0.207\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344]\n", + "Ecuación de regresión: y = 0.004x + 0.205\n", "Valor del parámetro correlacionado para la aeronave: 32.813\n", - "Predicción obtenida: 0.334\n", - "\tR²: 0.3657078235083421, Desviación Estándar: 0.030486197686657987, Varianza: 0.0009294082493899907, Incertidumbre: 0.005761349821346404\n", + "Predicción obtenida: 0.335\n", + "\tR²: 0.38259337108241265, Desviación Estándar: 0.029730535611440134, Varianza: 0.00088390474774311, Incertidumbre: 0.005520821864551259\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", - "Ecuación de regresión: y = 0.049x + 0.24\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", + "Ecuación de regresión: y = 0.049x + 0.241\n", "Valor del parámetro correlacionado para la aeronave: 1.993\n", - "Predicción obtenida: 0.339\n", - "\tR²: 0.6607991636850806, Desviación Estándar: 0.021542829923793395, Varianza: 0.00046409352112548814, Incertidumbre: 0.003933164633919647\n", + "Predicción obtenida: 0.34\n", + "\tR²: 0.6594487293179985, Desviación Estándar: 0.021710134055956, Varianza: 0.00047132992072758054, Incertidumbre: 0.003963710049636075\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", - "Ecuación de regresión: y = -0.038x + 0.835\n", - "Valor del parámetro correlacionado para la aeronave: 13.114\n", - "Predicción obtenida: 0.334\n", - "\tR²: 0.6968007721860683, Desviación Estándar: 0.020367528893078666, Varianza: 0.00041483623321039427, Incertidumbre: 0.003718585005125804\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", + "Ecuación de regresión: y = -0.039x + 0.84\n", + "Valor del parámetro correlacionado para la aeronave: 13.099\n", + "Predicción obtenida: 0.335\n", + "\tR²: 0.704188353912186, Desviación Estándar: 0.020233866799229377, Varianza: 0.00040940936564895687, Incertidumbre: 0.0036941817571642614\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", - "Ecuación de regresión: y = 0.028x + 0.249\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", + "Ecuación de regresión: y = 0.029x + 0.249\n", "Valor del parámetro correlacionado para la aeronave: 2.905\n", - "Predicción obtenida: 0.331\n", - "\tR²: 0.41997296733972356, Desviación Estándar: 0.028328973372175076, Varianza: 0.0008025307323214044, Incertidumbre: 0.005260558290554748\n", + "Predicción obtenida: 0.333\n", + "\tR²: 0.42514465313889094, Desviación Estándar: 0.028206587035388978, Varianza: 0.0007956115521849735, Incertidumbre: 0.005149794663171771\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", - "Ecuación de regresión: y = 0.001x + 0.269\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", + "Ecuación de regresión: y = 0.001x + 0.27\n", "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 0.371\n", - "\tR²: 0.6038191426117239, Desviación Estándar: 0.02351157571063262, Varianza: 0.0005527941923968098, Incertidumbre: 0.004156298655342311\n", + "Predicción obtenida: 0.372\n", + "\tR²: 0.6021174048921634, Desviación Estándar: 0.023638617780023557, Varianza: 0.0005587842505500459, Incertidumbre: 0.004178756732532887\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", - "Ecuación de regresión: y = -0.0x + 0.318\n", - "Valor del parámetro correlacionado para la aeronave: 501.616\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.06342608491681201, Desviación Estándar: 0.03614981016848977, Varianza: 0.0013068087752178463, Incertidumbre: 0.006390443977186381\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", + "Ecuación de regresión: y = -0.0x + 0.326\n", + "Valor del parámetro correlacionado para la aeronave: 503.585\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.12714259346444579, Desviación Estándar: 0.035011946666754785, Varianza: 0.0012258364093956815, Incertidumbre: 0.006189296227651011\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", - "Ecuación de regresión: y = 0.023x + 0.219\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", + "Ecuación de regresión: y = 0.023x + 0.22\n", "Valor del parámetro correlacionado para la aeronave: 5.1\n", - "Predicción obtenida: 0.335\n", - "\tR²: 0.4836792530977432, Desviación Estándar: 0.026728002730142274, Varianza: 0.0007143861299424928, Incertidumbre: 0.004963265505770935\n", + "Predicción obtenida: 0.337\n", + "\tR²: 0.4865211118670584, Desviación Estándar: 0.026658305995461602, Varianza: 0.0007106652785476641, Incertidumbre: 0.004867118512863177\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", - "Ecuación de regresión: y = 0.004x + 0.276\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", + "Ecuación de regresión: y = 0.004x + 0.277\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.37\n", - "\tR²: 0.6941742799016253, Desviación Estándar: 0.01751465394968648, Varianza: 0.00030676310297726825, Incertidumbre: 0.0032523895882410683\n", + "Predicción obtenida: 0.371\n", + "\tR²: 0.7011767124714305, Desviación Estándar: 0.01732374391424772, Varianza: 0.00030011210320643483, Incertidumbre: 0.0032169384846488743\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342]\n", - "Ecuación de regresión: y = 0.003x + 0.222\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344]\n", + "Ecuación de regresión: y = 0.004x + 0.22\n", "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 0.325\n", - "\tR²: 0.5326016124134147, Desviación Estándar: 0.019537228035800464, Varianza: 0.0003817032793228677, Incertidumbre: 0.004165351012835574\n", + "Predicción obtenida: 0.327\n", + "\tR²: 0.5502375625525282, Desviación Estándar: 0.019101623043785508, Varianza: 0.00036487200290687756, Incertidumbre: 0.003982963736514053\n", "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.308', 'Velocidad a la que se realiza el crucero (KTAS): 0.334', 'Área del ala: 0.339', 'Relación de aspecto del ala: 0.334', 'Longitud del fuselaje: 0.331', 'Peso máximo al despegue (MTOW): 0.371', 'Alcance de la aeronave: 0.312', 'envergadura: 0.335', 'payload: 0.37', 'Crucero KIAS: 0.325']\n", - "**Mediana calculada:** 0.334\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.309', 'Velocidad a la que se realiza el crucero (KTAS): 0.335', 'Área del ala: 0.34', 'Relación de aspecto del ala: 0.335', 'Longitud del fuselaje: 0.333', 'Peso máximo al despegue (MTOW): 0.372', 'Alcance de la aeronave: 0.313', 'envergadura: 0.337', 'payload: 0.371', 'Crucero KIAS: 0.327']\n", + "**Mediana calculada:** 0.335\n", "\n", "--- Imputación para aeronave: **Ascend** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342]\n", - "Ecuación de regresión: y = 0.0x + 0.199\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.335]\n", + "Ecuación de regresión: y = 0.0x + 0.185\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.009671200460906637, Desviación Estándar: 0.038341789857909814, Varianza: 0.0014700928495081159, Incertidumbre: 0.007378880898558765\n", + "Predicción obtenida: 0.31\n", + "\tR²: 0.012363783169715314, Desviación Estándar: 0.0368988404995783, Varianza: 0.0013615244302133194, Incertidumbre: 0.006736775762468075\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342, 0.334]\n", - "Ecuación de regresión: y = 0.004x + 0.207\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344, 0.335]\n", + "Ecuación de regresión: y = 0.004x + 0.205\n", "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 0.291\n", - "\tR²: 0.3754279357663507, Desviación Estándar: 0.02995609491552544, Varianza: 0.0008973676225879692, Incertidumbre: 0.005562707175802361\n", + "Predicción obtenida: 0.292\n", + "\tR²: 0.3919925997911332, Desviación Estándar: 0.029230827578736365, Varianza: 0.0008544412809378144, Incertidumbre: 0.005336794546472675\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", "Ecuación de regresión: y = 0.049x + 0.241\n", "Valor del parámetro correlacionado para la aeronave: 0.771\n", - "Predicción obtenida: 0.278\n", - "\tR²: 0.6655989150459374, Desviación Estándar: 0.021207046925550192, Varianza: 0.00044973883930248784, Incertidumbre: 0.0038088980681594306\n", + "Predicción obtenida: 0.279\n", + "\tR²: 0.6641048421071282, Desviación Estándar: 0.021373342448267822, Varianza: 0.0004568197674109272, Incertidumbre: 0.003838765625742831\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", - "Ecuación de regresión: y = -0.038x + 0.836\n", - "Valor del parámetro correlacionado para la aeronave: 14.357\n", - "Predicción obtenida: 0.286\n", - "\tR²: 0.7014972579081873, Desviación Estándar: 0.02003643818748548, Varianza: 0.0004014588552409264, Incertidumbre: 0.003598650532204136\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", + "Ecuación de regresión: y = -0.039x + 0.84\n", + "Valor del parámetro correlacionado para la aeronave: 14.349\n", + "Predicción obtenida: 0.287\n", + "\tR²: 0.7086760340890808, Desviación Estándar: 0.019904839465063797, Varianza: 0.0003962026341299613, Incertidumbre: 0.0035750147039172522\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", - "Ecuación de regresión: y = 0.028x + 0.248\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", + "Ecuación de regresión: y = 0.029x + 0.249\n", "Valor del parámetro correlacionado para la aeronave: 1.562\n", - "Predicción obtenida: 0.293\n", - "\tR²: 0.4296493321511522, Desviación Estándar: 0.027858702470545647, Varianza: 0.0007761073033423862, Incertidumbre: 0.005086279921980917\n", + "Predicción obtenida: 0.294\n", + "\tR²: 0.43377431731307325, Desviación Estándar: 0.027750151250249728, Varianza: 0.0007700708944117365, Incertidumbre: 0.004984074296589788\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", - "Ecuación de regresión: y = 0.001x + 0.271\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", + "Ecuación de regresión: y = 0.001x + 0.272\n", "Valor del parámetro correlacionado para la aeronave: 9.5\n", - "Predicción obtenida: 0.28\n", - "\tR²: 0.582320619650718, Desviación Estándar: 0.023909852818436363, Varianza: 0.0005716810617992893, Incertidumbre: 0.0041621711328791375\n", + "Predicción obtenida: 0.281\n", + "\tR²: 0.5805077444550542, Desviación Estándar: 0.024037954911906977, Varianza: 0.0005778232763468728, Incertidumbre: 0.00418447084503352\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354, 501.616]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", - "Ecuación de regresión: y = -0.0x + 0.319\n", - "Valor del parámetro correlacionado para la aeronave: 478.644\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.06366520088212502, Desviación Estándar: 0.03579900590384107, Varianza: 0.0012815688237032483, Incertidumbre: 0.006231807033284842\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", + "Ecuación de regresión: y = -0.0x + 0.327\n", + "Valor del parámetro correlacionado para la aeronave: 420.652\n", + "Predicción obtenida: 0.316\n", + "\tR²: 0.1266463739361714, Desviación Estándar: 0.03468411410732085, Varianza: 0.001202987771409653, Incertidumbre: 0.0060377292827023645\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 5.1]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", - "Ecuación de regresión: y = 0.023x + 0.219\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 5.1]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", + "Ecuación de regresión: y = 0.023x + 0.22\n", "Valor del parámetro correlacionado para la aeronave: 2.0\n", - "Predicción obtenida: 0.265\n", - "\tR²: 0.4924880946399838, Desviación Estándar: 0.026279253552868982, Varianza: 0.000690599167295977, Incertidumbre: 0.004797913321768041\n", + "Predicción obtenida: 0.266\n", + "\tR²: 0.494252346723298, Desviación Estándar: 0.026226329343117413, Varianza: 0.0006878203508136613, Incertidumbre: 0.004710387802724272\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334]\n", - "Ecuación de regresión: y = 0.003x + 0.278\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", + "Ecuación de regresión: y = 0.003x + 0.279\n", "Valor del parámetro correlacionado para la aeronave: 0.6\n", - "Predicción obtenida: 0.28\n", - "\tR²: 0.6596231201497137, Desviación Estándar: 0.018262602460999124, Varianza: 0.0003335226486484913, Incertidumbre: 0.0033342797755461936\n", + "Predicción obtenida: 0.281\n", + "\tR²: 0.6661275841307379, Desviación Estándar: 0.01809912723389064, Varianza: 0.00032757840662856185, Incertidumbre: 0.0033044334190526613\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342, 0.334]\n", - "Ecuación de regresión: y = 0.003x + 0.221\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344, 0.335]\n", + "Ecuación de regresión: y = 0.004x + 0.219\n", "Valor del parámetro correlacionado para la aeronave: 20.0\n", "Predicción obtenida: 0.291\n", - "\tR²: 0.5479865342925803, Desviación Estándar: 0.019192420183738294, Varianza: 0.0003683489925091651, Incertidumbre: 0.004001896248949366\n", + "\tR²: 0.5647788073941997, Desviación Estándar: 0.018767936696439058, Varianza: 0.00035223544784154386, Incertidumbre: 0.0038309890359276236\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Empty weight (r = 0.971) ---\n", "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344]\n", "Ecuación de regresión: y = 0.002x + 0.263\n", "Valor del parámetro correlacionado para la aeronave: 3.0\n", "Predicción obtenida: 0.27\n", - "\tR²: 0.3428931453094083, Desviación Estándar: 0.031257301972751485, Varianza: 0.000977018926615774, Incertidumbre: 0.008669215768878245\n", + "\tR²: 0.3462930840767372, Desviación Estándar: 0.031703647782123416, Varianza: 0.0010051212826929388, Incertidumbre: 0.008793009822899704\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.308', 'Velocidad a la que se realiza el crucero (KTAS): 0.291', 'Área del ala: 0.278', 'Relación de aspecto del ala: 0.286', 'Longitud del fuselaje: 0.293', 'Peso máximo al despegue (MTOW): 0.28', 'Alcance de la aeronave: 0.313', 'envergadura: 0.265', 'payload: 0.28', 'Crucero KIAS: 0.291', 'Empty weight: 0.27']\n", - "**Mediana calculada:** 0.286\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.31', 'Velocidad a la que se realiza el crucero (KTAS): 0.292', 'Área del ala: 0.279', 'Relación de aspecto del ala: 0.287', 'Longitud del fuselaje: 0.294', 'Peso máximo al despegue (MTOW): 0.281', 'Alcance de la aeronave: 0.316', 'envergadura: 0.266', 'payload: 0.281', 'Crucero KIAS: 0.291', 'Empty weight: 0.27']\n", + "**Mediana calculada:** 0.287\n", "\n", "--- Imputación para aeronave: **Transition** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.286]\n", - "Ecuación de regresión: y = 0.0x + 0.204\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.335, 0.287]\n", + "Ecuación de regresión: y = 0.0x + 0.19\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.307\n", - "\tR²: 0.008560595514286895, Desviación Estándar: 0.03787184264181916, Varianza: 0.0014342764650867124, Incertidumbre: 0.0071571055230017794\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.011152514691267879, Desviación Estándar: 0.03652974557296492, Varianza: 0.0013344223116255503, Incertidumbre: 0.006560935986593608\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", - "Ecuación de regresión: y = 0.004x + 0.206\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", + "Ecuación de regresión: y = 0.004x + 0.204\n", "Valor del parámetro correlacionado para la aeronave: 21.875\n", "Predicción obtenida: 0.291\n", - "\tR²: 0.38249062220586694, Desviación Estándar: 0.029467308778454444, Varianza: 0.0008683222866447784, Incertidumbre: 0.0053799699089765\n", - "\tNivel de confianza: Confianza Media\n", + "\tR²: 0.39888174922260133, Desviación Estándar: 0.028766571435852473, Varianza: 0.0008275156321740035, Incertidumbre: 0.005166628751011079\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", - "Ecuación de regresión: y = 0.049x + 0.241\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", + "Ecuación de regresión: y = 0.049x + 0.242\n", "Valor del parámetro correlacionado para la aeronave: 0.986\n", - "Predicción obtenida: 0.289\n", - "\tR²: 0.6683533020403162, Desviación Estándar: 0.020914188913517928, Varianza: 0.00043740329791031626, Incertidumbre: 0.00369714120094126\n", + "Predicción obtenida: 0.29\n", + "\tR²: 0.6668892763992251, Desviación Estándar: 0.021077630404536665, Varianza: 0.00044426650347024837, Incertidumbre: 0.003726033847597907\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", - "Ecuación de regresión: y = -0.038x + 0.836\n", - "Valor del parámetro correlacionado para la aeronave: 14.233\n", - "Predicción obtenida: 0.291\n", - "\tR²: 0.7051191690209833, Desviación Estándar: 0.019720887637784245, Varianza: 0.0003889134092221115, Incertidumbre: 0.003486193344923798\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", + "Ecuación de regresión: y = -0.039x + 0.84\n", + "Valor del parámetro correlacionado para la aeronave: 14.223\n", + "Predicción obtenida: 0.292\n", + "\tR²: 0.7122103721028534, Desviación Estándar: 0.01959138083026252, Varianza: 0.0003838222028363777, Incertidumbre: 0.0034632995594716903\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", "Ecuación de regresión: y = 0.029x + 0.248\n", "Valor del parámetro correlacionado para la aeronave: 2.3\n", - "Predicción obtenida: 0.314\n", - "\tR²: 0.4350653976176332, Desviación Estándar: 0.027431632448403815, Varianza: 0.000752494458784321, Incertidumbre: 0.004926866630983046\n", + "Predicción obtenida: 0.315\n", + "\tR²: 0.43946734247407293, Desviación Estándar: 0.027341852629340847, Varianza: 0.000747576905204593, Incertidumbre: 0.0048334023511025365\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0, 9.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", - "Ecuación de regresión: y = 0.001x + 0.272\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", + "Ecuación de regresión: y = 0.001x + 0.273\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 0.288\n", - "\tR²: 0.5874164948558036, Desviación Estándar: 0.023575730786020113, Varianza: 0.0005558150820948966, Incertidumbre: 0.004043204473503137\n", + "Predicción obtenida: 0.29\n", + "\tR²: 0.585626194228399, Desviación Estándar: 0.023701762283004735, Varianza: 0.0005617735353200658, Incertidumbre: 0.004064818696919392\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354, 501.616, 478.644]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", - "Ecuación de regresión: y = -0.0x + 0.318\n", - "Valor del parámetro correlacionado para la aeronave: 480.438\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.06137710301442201, Desviación Estándar: 0.035559431175592925, Varianza: 0.0012644731455317298, Incertidumbre: 0.006098392135086606\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", + "Ecuación de regresión: y = -0.0x + 0.326\n", + "Valor del parámetro correlacionado para la aeronave: 506.641\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.12137030661842563, Desviación Estándar: 0.03451337490962147, Varianza: 0.0011911730476520889, Incertidumbre: 0.005918994965493045\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", "Ecuación de regresión: y = 0.022x + 0.224\n", "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 0.289\n", - "\tR²: 0.4884294242049745, Desviación Estándar: 0.026103896782224575, Varianza: 0.0006814134272170345, Incertidumbre: 0.004688398265647203\n", + "Predicción obtenida: 0.29\n", + "\tR²: 0.49095069370026423, Desviación Estándar: 0.02605597944515681, Varianza: 0.0006789140648464343, Incertidumbre: 0.004606089939031919\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", "Ecuación de regresión: y = 0.003x + 0.279\n", "Valor del parámetro correlacionado para la aeronave: 1.5\n", - "Predicción obtenida: 0.284\n", - "\tR²: 0.6689077573598308, Desviación Estándar: 0.017992987405351088, Varianza: 0.00032374759576912293, Incertidumbre: 0.0032316359373020394\n", + "Predicción obtenida: 0.285\n", + "\tR²: 0.6750655648466569, Desviación Estándar: 0.017835052168221725, Varianza: 0.0003180890858431905, Incertidumbre: 0.00320326993133675\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286]\n", - "Ecuación de regresión: y = 0.004x + 0.22\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", + "Ecuación de regresión: y = 0.004x + 0.218\n", "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 0.29\n", - "\tR²: 0.5566215944971976, Desviación Estándar: 0.018811661715774402, Varianza: 0.00035387861650873226, Incertidumbre: 0.0038399143681247003\n", + "Predicción obtenida: 0.291\n", + "\tR²: 0.5731148377392137, Desviación Estándar: 0.018407788893284933, Varianza: 0.0003388466919397441, Incertidumbre: 0.0036815577786569868\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Empty weight (r = 0.971) ---\n", "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend']\n", "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286]\n", - "Ecuación de regresión: y = 0.002x + 0.265\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287]\n", + "Ecuación de regresión: y = 0.002x + 0.266\n", "Valor del parámetro correlacionado para la aeronave: 5.8\n", - "Predicción obtenida: 0.278\n", - "\tR²: 0.33191540406967635, Desviación Estándar: 0.030388935680947273, Varianza: 0.0009234874118207502, Incertidumbre: 0.008121784690486755\n", + "Predicción obtenida: 0.279\n", + "\tR²: 0.3348753843733273, Desviación Estándar: 0.030833248903070595, Varianza: 0.000950689237918704, Incertidumbre: 0.008240532394029543\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.307', 'Velocidad a la que se realiza el crucero (KTAS): 0.291', 'Área del ala: 0.289', 'Relación de aspecto del ala: 0.291', 'Longitud del fuselaje: 0.314', 'Peso máximo al despegue (MTOW): 0.288', 'Alcance de la aeronave: 0.312', 'envergadura: 0.289', 'payload: 0.284', 'Crucero KIAS: 0.29', 'Empty weight: 0.278']\n", - "**Mediana calculada:** 0.29\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.309', 'Velocidad a la que se realiza el crucero (KTAS): 0.291', 'Área del ala: 0.29', 'Relación de aspecto del ala: 0.292', 'Longitud del fuselaje: 0.315', 'Peso máximo al despegue (MTOW): 0.29', 'Alcance de la aeronave: 0.313', 'envergadura: 0.29', 'payload: 0.285', 'Crucero KIAS: 0.291', 'Empty weight: 0.279']\n", + "**Mediana calculada:** 0.291\n", "\n", "--- Imputación para aeronave: **Reach** ---\n", "\n", "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.286, 0.29]\n", - "Ecuación de regresión: y = 0.0x + 0.208\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.335, 0.287, 0.291]\n", + "Ecuación de regresión: y = 0.0x + 0.194\n", "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.307\n", - "\tR²: 0.0077920393265346055, Desviación Estándar: 0.03734493173285593, Varianza: 0.0013946439261316697, Incertidumbre: 0.006934779727331597\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.010293782952762509, Desviación Estándar: 0.036097016178001054, Varianza: 0.00130299457695487, Incertidumbre: 0.006381111230016264\n", "\tNivel de confianza: Confianza Baja\n", "\n", "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 21.875]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", - "Ecuación de regresión: y = 0.004x + 0.206\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 21.875]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", + "Ecuación de regresión: y = 0.004x + 0.204\n", "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.38722452002086527, Desviación Estándar: 0.028988626049815485, Varianza: 0.0008403404402560409, Incertidumbre: 0.005206510937018345\n", - "\tNivel de confianza: Confianza Media\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.4034376616845361, Desviación Estándar: 0.02831358276093245, Varianza: 0.0008016589687601712, Incertidumbre: 0.005005181592485466\n", + "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", - "Ecuación de regresión: y = 0.049x + 0.241\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", + "Ecuación de regresión: y = 0.049x + 0.242\n", "Valor del parámetro correlacionado para la aeronave: 2.329\n", - "Predicción obtenida: 0.355\n", - "\tR²: 0.6708663750067654, Desviación Estándar: 0.02059524463122736, Varianza: 0.00042416410142009946, Incertidumbre: 0.0035851719092382297\n", + "Predicción obtenida: 0.356\n", + "\tR²: 0.6694211053194665, Desviación Estándar: 0.02075614850551406, Varianza: 0.0004308177007829536, Incertidumbre: 0.0036131816785082483\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.767, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", - "Ecuación de regresión: y = -0.038x + 0.836\n", - "Valor del parámetro correlacionado para la aeronave: 13.683\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.7073494265597657, Desviación Estándar: 0.01942028138024239, Varianza: 0.00037714732888778933, Incertidumbre: 0.0033806370606726797\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", + "Ecuación de regresión: y = -0.039x + 0.84\n", + "Valor del parámetro correlacionado para la aeronave: 13.669\n", + "Predicción obtenida: 0.313\n", + "\tR²: 0.7143961990441017, Desviación Estándar: 0.019292619508205277, Varianza: 0.00037220516748838283, Incertidumbre: 0.0033584139812335425\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", - "Ecuación de regresión: y = 0.028x + 0.248\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", + "Ecuación de regresión: y = 0.029x + 0.248\n", "Valor del parámetro correlacionado para la aeronave: 4.712\n", - "Predicción obtenida: 0.381\n", - "\tR²: 0.4260780462787932, Desviación Estándar: 0.027309060641999183, Varianza: 0.0007457847931483888, Incertidumbre: 0.004827605491948068\n", + "Predicción obtenida: 0.384\n", + "\tR²: 0.43033811917703635, Desviación Estándar: 0.027246943263226592, Varianza: 0.000742395917189489, Incertidumbre: 0.004743084015220378\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0, 9.5, 18.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", - "Ecuación de regresión: y = 0.001x + 0.272\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", + "Ecuación de regresión: y = 0.001x + 0.273\n", "Valor del parámetro correlacionado para la aeronave: 91.0\n", - "Predicción obtenida: 0.357\n", - "\tR²: 0.5911374094922912, Desviación Estándar: 0.02323782088030035, Varianza: 0.0005399963192649228, Incertidumbre: 0.003927908637521\n", + "Predicción obtenida: 0.358\n", + "\tR²: 0.5893624930927692, Desviación Estándar: 0.023361993268515235, Varianza: 0.0005457827294781511, Incertidumbre: 0.003948897601964923\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 323.501, 320.057, 317.924, 316.495, 3270.0, 800.0, 150.0, 50.0, 25.0, 418.78, 344.852, 3294.755, 500.0, 478.95, 92.6, 270.0, 100.0, 471.068, 470.718, 473.211, 477.686, 475.377, 482.568, 474.569, 476.384, 482.044, 300.0, 615.631, 800.0, 609.354, 501.616, 478.644, 480.438]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", - "Ecuación de regresión: y = -0.0x + 0.318\n", - "Valor del parámetro correlacionado para la aeronave: 491.03\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.059744190947153664, Desviación Estándar: 0.03523949892053134, Varianza: 0.0012418222841701293, Incertidumbre: 0.005956562489437605\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652, 506.641]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", + "Ecuación de regresión: y = -0.0x + 0.325\n", + "Valor del parámetro correlacionado para la aeronave: 504.283\n", + "Predicción obtenida: 0.312\n", + "\tR²: 0.11957831865127477, Desviación Estándar: 0.03420786358834695, Varianza: 0.0011701779312789532, Incertidumbre: 0.005782184291372825\n", "\tNivel de confianza: Confianza Media\n", "\n", "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", "Ecuación de regresión: y = 0.022x + 0.224\n", "Valor del parámetro correlacionado para la aeronave: 6.0\n", - "Predicción obtenida: 0.354\n", - "\tR²: 0.491966702458294, Desviación Estándar: 0.025693687153767065, Varianza: 0.0006601655595556547, Incertidumbre: 0.004542045105028593\n", + "Predicción obtenida: 0.356\n", + "\tR²: 0.4948146918700289, Desviación Estándar: 0.025658694953824956, Varianza: 0.0006583686267334423, Incertidumbre: 0.004466605472444141\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", - "Ecuación de regresión: y = 0.003x + 0.279\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", + "Ecuación de regresión: y = 0.003x + 0.28\n", "Valor del parámetro correlacionado para la aeronave: 7.0\n", - "Predicción obtenida: 0.303\n", - "\tR²: 0.6746565418595589, Desviación Estándar: 0.01774066062848476, Varianza: 0.0003147310395350693, Incertidumbre: 0.0031361353582826925\n", + "Predicción obtenida: 0.304\n", + "\tR²: 0.6805607454161662, Desviación Estándar: 0.017587857145488283, Varianza: 0.00030933271897010323, Incertidumbre: 0.0031091232635287597\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 20.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.313, 0.27, 0.298, 0.338, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.313, 0.295, 0.299, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29]\n", - "Ecuación de regresión: y = 0.004x + 0.22\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", + "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 20.0]\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", + "Ecuación de regresión: y = 0.004x + 0.218\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.5621478971519628, Desviación Estándar: 0.018431786174629827, Varianza: 0.0003397307415872752, Incertidumbre: 0.0036863572349259653\n", + "Predicción obtenida: 0.309\n", + "\tR²: 0.5784019179791874, Desviación Estándar: 0.018050324796678542, Varianza: 0.0003258142252655882, Incertidumbre: 0.0035399599371135236\n", "\tNivel de confianza: Confianza Alta\n", "\n", "--- Correlación: Empty weight (r = 0.971) ---\n", "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition']\n", "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291]\n", "Ecuación de regresión: y = 0.002x + 0.267\n", "Valor del parámetro correlacionado para la aeronave: 31.0\n", - "Predicción obtenida: 0.333\n", - "\tR²: 0.32516791212624563, Desviación Estándar: 0.029506663193114138, Varianza: 0.0008706431727918767, Incertidumbre: 0.007618587676605494\n", + "Predicción obtenida: 0.335\n", + "\tR²: 0.3279695994564351, Desviación Estándar: 0.02994220141823091, Varianza: 0.0008965354257699091, Incertidumbre: 0.0077310431627730936\n", "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.307', 'Velocidad a la que se realiza el crucero (KTAS): 0.312', 'Área del ala: 0.355', 'Relación de aspecto del ala: 0.312', 'Longitud del fuselaje: 0.381', 'Peso máximo al despegue (MTOW): 0.357', 'Alcance de la aeronave: 0.311', 'envergadura: 0.354', 'payload: 0.303', 'Crucero KIAS: 0.308', 'Empty weight: 0.333']\n", - "**Mediana calculada:** 0.312\n", + "Valores imputados: ['Altitud a la que se realiza el crucero: 0.309', 'Velocidad a la que se realiza el crucero (KTAS): 0.313', 'Área del ala: 0.356', 'Relación de aspecto del ala: 0.313', 'Longitud del fuselaje: 0.384', 'Peso máximo al despegue (MTOW): 0.358', 'Alcance de la aeronave: 0.312', 'envergadura: 0.356', 'payload: 0.304', 'Crucero KIAS: 0.309', 'Empty weight: 0.335']\n", + "**Mediana calculada:** 0.313\n", "\n", "=== Imputación para el parámetro: **payload** ===\n", "\n", "--- Imputación para aeronave: **AAI Aerosonde** ---\n", "\n", "--- Correlación: Área del ala (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", "Ecuación de regresión: y = 10.406x + -4.881\n", @@ -32537,17 +36921,17 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = -7.847x + 117.681\n", + "Ecuación de regresión: y = -7.839x + 117.46\n", "Valor del parámetro correlacionado para la aeronave: 14.754\n", - "Predicción obtenida: 1.896\n", - "\tR²: 0.7149957845485787, Desviación Estándar: 3.9667001777935313, Varianza: 15.734710300507235, Incertidumbre: 0.6905138688293478\n", + "Predicción obtenida: 1.804\n", + "\tR²: 0.7152857656186882, Desviación Estándar: 3.964681680105561, Varianza: 15.718700824564651, Incertidumbre: 0.6901624934832739\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.875) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 55.0, 32.0, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", "Ecuación de regresión: y = 0.204x + 1.036\n", @@ -32556,18 +36940,28 @@ "\tR²: 0.7186101745846443, Desviación Estándar: 3.7341950529878645, Varianza: 13.944212693759042, Incertidumbre: 0.6706812303187741\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", + "--- Correlación: Alcance de la aeronave (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652, 506.641, 504.283]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.036x + -6.191\n", + "Valor del parámetro correlacionado para la aeronave: 3270.0\n", + "Predicción obtenida: 111.989\n", + "\tR²: 0.4516735209888093, Desviación Estándar: 5.50203492994826, Varianza: 30.272388370370752, Incertidumbre: 0.9577813435917653\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.28, 30.0, 35.102, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.275, 30.0, 35.098, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.878x + -20.785\n", + "Ecuación de regresión: y = 0.877x + -20.776\n", "Valor del parámetro correlacionado para la aeronave: 30.846\n", - "Predicción obtenida: 6.284\n", - "\tR²: 0.5501898082120762, Desviación Estándar: 5.100088235913723, Varianza: 26.01090001410555, Incertidumbre: 0.9311444573588917\n", + "Predicción obtenida: 6.288\n", + "\tR²: 0.5505400445815896, Desviación Estándar: 5.098102304668767, Varianza: 25.99064710886899, Incertidumbre: 0.9307818775787194\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.734) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 5.2, 4.4, 3.1, 4.8, 5.033, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", "Ecuación de regresión: y = 4.794x + -9.245\n", @@ -32577,13 +36971,13 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29, 0.312]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291, 0.313]\n", "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 198.875x + -52.199\n", + "Ecuación de regresión: y = 199.636x + -52.626\n", "Valor del parámetro correlacionado para la aeronave: 0.197\n", - "Predicción obtenida: -13.11\n", - "\tR²: 0.6722158191575872, Desviación Estándar: 4.2540023843702155, Varianza: 18.096536286227476, Incertidumbre: 0.7405267635011167\n", + "Predicción obtenida: -13.387\n", + "\tR²: 0.6780488774864877, Desviación Estándar: 4.21598158894776, Varianza: 17.77450075834648, Incertidumbre: 0.7339081925564045\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Empty weight (r = 0.778) ---\n", @@ -32595,140 +36989,160 @@ "Predicción obtenida: 5.053\n", "\tR²: 0.6045881019855628, Desviación Estándar: 3.3770579595870056, Varianza: 11.404520462409948, Incertidumbre: 0.8719526157771782\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 1.05', 'Relación de aspecto del ala: 1.896', 'Peso máximo al despegue (MTOW): 3.714', 'Velocidad máxima (KIAS): 6.284', 'envergadura: 4.659', 'Cuerda: -13.11', 'Empty weight: 5.053']\n", - "**Mediana calculada:** 3.714\n", + "Valores imputados: ['Área del ala: 1.05', 'Relación de aspecto del ala: 1.804', 'Peso máximo al despegue (MTOW): 3.714', 'Alcance de la aeronave: 111.989', 'Velocidad máxima (KIAS): 6.288', 'envergadura: 4.659', 'Cuerda: -13.387', 'Empty weight: 5.053']\n", + "**Mediana calculada:** 4.186\n", "\n", "--- Imputación para aeronave: **Fulmar X** ---\n", "\n", "--- Correlación: Área del ala (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 10.219x + -4.535\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 10.186x + -4.474\n", "Valor del parámetro correlacionado para la aeronave: 0.94\n", - "Predicción obtenida: 5.071\n", - "\tR²: 0.7043549197034407, Desviación Estándar: 4.019442149271862, Varianza: 16.155915191343205, Incertidumbre: 0.699695067594778\n", + "Predicción obtenida: 5.101\n", + "\tR²: 0.702030027451888, Desviación Estándar: 4.0288222779643945, Varianza: 16.231408947422214, Incertidumbre: 0.7013279384101367\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = -7.761x + 116.556\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = -7.725x + 115.975\n", "Valor del parámetro correlacionado para la aeronave: 13.218\n", - "Predicción obtenida: 13.965\n", - "\tR²: 0.7200402040647889, Desviación Estándar: 3.9193674746851532, Varianza: 15.361441401619876, Incertidumbre: 0.6721659765620716\n", + "Predicción obtenida: 13.864\n", + "\tR²: 0.7182610183328181, Desviación Estándar: 3.9255462621148403, Varianza: 15.409913456003792, Incertidumbre: 0.6732256298641592\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.875) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 55.0, 32.0, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.204x + 1.036\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.204x + 1.071\n", "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 5.125\n", - "\tR²: 0.7248470361708765, Desviación Estándar: 3.675385156868235, Varianza: 13.508456051327343, Incertidumbre: 0.649722441973478\n", + "Predicción obtenida: 5.151\n", + "\tR²: 0.7237880605522922, Desviación Estándar: 3.67627307760161, Varianza: 13.514983741098414, Incertidumbre: 0.6498794056664093\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652, 506.641, 504.283]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.001x + 9.864\n", + "Valor del parámetro correlacionado para la aeronave: 800.0\n", + "Predicción obtenida: 10.419\n", + "\tR²: 0.002146729462209951, Desviación Estándar: 7.387712262058516, Varianza: 54.578292466969756, Incertidumbre: 1.266982200382999\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 30.846, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.28, 30.0, 35.102, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.887x + -21.192\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 30.846, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.275, 30.0, 35.098, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.885x + -21.108\n", "Valor del parámetro correlacionado para la aeronave: 41.7\n", - "Predicción obtenida: 15.784\n", - "\tR²: 0.5574907931449841, Desviación Estándar: 5.037319182991233, Varianza: 25.374584551331463, Incertidumbre: 0.9047292332664074\n", + "Predicción obtenida: 15.791\n", + "\tR²: 0.5575443463725547, Desviación Estándar: 5.028702836489781, Varianza: 25.287852217720367, Incertidumbre: 0.9031816917506468\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.734) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 5.2, 4.4, 3.1, 4.8, 5.033, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 4.819x + -9.371\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 4.807x + -9.308\n", "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 5.085\n", - "\tR²: 0.5654022085753986, Desviación Estándar: 4.9488715578269735, Varianza: 24.491329695868778, Incertidumbre: 0.8748451594401715\n", + "Predicción obtenida: 5.112\n", + "\tR²: 0.5643721514307216, Desviación Estándar: 4.946883176427056, Varianza: 24.471653161217034, Incertidumbre: 0.8744936599473048\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29, 0.312]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 155.509x + -38.199\n", - "Valor del parámetro correlacionado para la aeronave: 0.313\n", - "Predicción obtenida: 10.475\n", - "\tR²: 0.5775590357480013, Desviación Estándar: 4.814499686360729, Varianza: 23.179407229967552, Incertidumbre: 0.8256798843799175\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291, 0.313]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 154.23x + -37.928\n", + "Valor del parámetro correlacionado para la aeronave: 0.319\n", + "Predicción obtenida: 11.271\n", + "\tR²: 0.573081677467914, Desviación Estándar: 4.832245905238442, Varianza: 23.350600488693686, Incertidumbre: 0.8287233358090745\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 5.071', 'Relación de aspecto del ala: 13.965', 'Peso máximo al despegue (MTOW): 5.125', 'Velocidad máxima (KIAS): 15.784', 'envergadura: 5.085', 'Cuerda: 10.475']\n", - "**Mediana calculada:** 7.8\n", + "Valores imputados: ['Área del ala: 5.101', 'Relación de aspecto del ala: 13.864', 'Peso máximo al despegue (MTOW): 5.151', 'Alcance de la aeronave: 10.419', 'Velocidad máxima (KIAS): 15.791', 'envergadura: 5.112', 'Cuerda: 11.271']\n", + "**Mediana calculada:** 10.419\n", "\n", "--- Imputación para aeronave: **Mantis** ---\n", "\n", "--- Correlación: Área del ala (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 10.116x + -4.31\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 9.986x + -4.035\n", "Valor del parámetro correlacionado para la aeronave: 0.754\n", - "Predicción obtenida: 3.318\n", - "\tR²: 0.7011908920545251, Desviación Estándar: 3.986164457115577, Varianza: 15.889507079171521, Incertidumbre: 0.6836215645406782\n", + "Predicción obtenida: 3.494\n", + "\tR²: 0.687066646727224, Desviación Estándar: 4.067760372560328, Varianza: 16.546674448572137, Incertidumbre: 0.6976151485928609\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Relación de aspecto del ala (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 14.012, 14.067, 12.923, 12.654, 12.859, 13.774, 12.973, 14.599, 14.717, 14.578, 14.435, 14.194, 13.909, 14.054, 13.657, 14.116, 14.013, 13.722, 13.684, 12.713, 13.046, 12.876, 13.114, 14.357, 14.233, 13.683]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = -7.633x + 114.627\n", - "Valor del parámetro correlacionado para la aeronave: 14.767\n", - "Predicción obtenida: 1.904\n", - "\tR²: 0.701365229774406, Desviación Estándar: 3.9958441696300517, Varianza: 15.96677062796648, Incertidumbre: 0.6754209402389949\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = -7.656x + 114.927\n", + "Valor del parámetro correlacionado para la aeronave: 14.755\n", + "Predicción obtenida: 1.967\n", + "\tR²: 0.712125594047297, Desviación Estándar: 3.9109945361870477, Varianza: 15.29587826208494, Incertidumbre: 0.6610787344956929\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.875) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.202x + 1.199\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.2x + 1.393\n", "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 2.515\n", - "\tR²: 0.7210133339229867, Desviación Estándar: 3.6477290781179126, Varianza: 13.305927427346957, Incertidumbre: 0.6349881274802048\n", + "Predicción obtenida: 2.693\n", + "\tR²: 0.7070179545639659, Desviación Estándar: 3.729320581026571, Varianza: 13.90783199606836, Incertidumbre: 0.6491913850524466\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Alcance de la aeronave (r = 0.804) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652, 506.641, 504.283]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.001x + 9.864\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 9.882\n", + "\tR²: 0.002163345486388857, Desviación Estándar: 7.281408701826373, Varianza: 53.01891268303283, Incertidumbre: 1.2307827089531607\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Velocidad máxima (KIAS) (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.941, 29.09, 33.0, 33.0, 33.0, 33.0, 33.0, 33.28, 30.0, 35.102, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.849x + -20.097\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.275, 30.0, 35.098, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 0.859x + -20.372\n", "Valor del parámetro correlacionado para la aeronave: 25.6\n", - "Predicción obtenida: 1.635\n", - "\tR²: 0.5252245995470497, Desviación Estándar: 5.143299088683377, Varianza: 26.453525515651258, Incertidumbre: 0.9092154158196514\n", + "Predicción obtenida: 1.629\n", + "\tR²: 0.5422563989605891, Desviación Estándar: 5.034375894779248, Varianza: 25.34494064993435, Incertidumbre: 0.8899603335601247\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.734) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 3.1, 4.8, 5.033, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 4.759x + -9.051\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 4.69x + -8.682\n", "Valor del parámetro correlacionado para la aeronave: 2.1\n", - "Predicción obtenida: 0.943\n", - "\tR²: 0.5626551847696982, Desviación Estándar: 4.894993387759511, Varianza: 23.960960266209337, Incertidumbre: 0.852108974859816\n", + "Predicción obtenida: 1.167\n", + "\tR²: 0.5495595055917364, Desviación Estándar: 4.953710222200966, Varianza: 24.539244965538344, Incertidumbre: 0.8623302637645233\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.334, 0.286, 0.29, 0.312]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 155.416x + -38.247\n", - "Valor del parámetro correlacionado para la aeronave: 0.27\n", - "Predicción obtenida: 3.715\n", - "\tR²: 0.5751366916639009, Desviación Estándar: 4.766098029962729, Varianza: 22.715690431214608, Incertidumbre: 0.8056176056952402\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291, 0.313]\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", + "Ecuación de regresión: y = 154.107x + -37.914\n", + "Valor del parámetro correlacionado para la aeronave: 0.271\n", + "Predicción obtenida: 3.849\n", + "\tR²: 0.5727096856576819, Desviación Estándar: 4.764827757793928, Varianza: 22.703583561443512, Incertidumbre: 0.8054028905096152\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 3.318', 'Relación de aspecto del ala: 1.904', 'Peso máximo al despegue (MTOW): 2.515', 'Velocidad máxima (KIAS): 1.635', 'envergadura: 0.943', 'Cuerda: 3.715']\n", - "**Mediana calculada:** 2.21\n", + "Valores imputados: ['Área del ala: 3.494', 'Relación de aspecto del ala: 1.967', 'Peso máximo al despegue (MTOW): 2.693', 'Alcance de la aeronave: 9.882', 'Velocidad máxima (KIAS): 1.629', 'envergadura: 1.167', 'Cuerda: 3.849']\n", + "**Mediana calculada:** 2.693\n", "\n", "=== Imputación para el parámetro: **Empty weight** ===\n", "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 Fixed Wing** ---\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.7 Fixed Wing** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", @@ -32772,22 +37186,22 @@ "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 168.652x + -36.821\n", + "Ecuación de regresión: y = 166.311x + -36.293\n", "Valor del parámetro correlacionado para la aeronave: 0.352\n", - "Predicción obtenida: 22.545\n", - "\tR²: 0.3232917045220116, Desviación Estándar: 8.579771360926884, Varianza: 73.61247660578115, Incertidumbre: 2.144942840231721\n", + "Predicción obtenida: 22.248\n", + "\tR²: 0.32488694567254894, Desviación Estándar: 8.569652613963415, Varianza: 73.43894592400999, Incertidumbre: 2.1424131534908537\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 3.714, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para payload: [2.495, 2.495, 4.186, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.551x + 3.249\n", + "Ecuación de regresión: y = 1.555x + 3.179\n", "Valor del parámetro correlacionado para la aeronave: 14.5\n", - "Predicción obtenida: 25.745\n", - "\tR²: 0.6053918907187015, Desviación Estándar: 6.551759806427765, Varianza: 42.92555656112239, Incertidumbre: 1.6379399516069413\n", + "Predicción obtenida: 25.733\n", + "\tR²: 0.6059393093606185, Desviación Estándar: 6.547213776970333, Varianza: 42.86600824135013, Incertidumbre: 1.6368034442425832\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", @@ -32799,13 +37213,13 @@ "Predicción obtenida: 13.161\n", "\tR²: 0.6929626692113744, Desviación Estándar: 2.790886287308021, Varianza: 7.78904626868395, Incertidumbre: 1.1393745556725372\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 17.253', 'Longitud del fuselaje: 19.796', 'Peso máximo al despegue (MTOW): 15.517', 'envergadura: 20.292', 'Cuerda: 22.545', 'payload: 25.745', 'Rango de comunicación: 13.161']\n", + "Valores imputados: ['Área del ala: 17.253', 'Longitud del fuselaje: 19.796', 'Peso máximo al despegue (MTOW): 15.517', 'envergadura: 20.292', 'Cuerda: 22.248', 'payload: 25.733', 'Rango de comunicación: 13.161']\n", "**Mediana calculada:** 19.796\n", "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 VTOL** ---\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.7 VTOL** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 15.104x + -5.974\n", @@ -32815,7 +37229,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 8.792x + -6.58\n", @@ -32825,7 +37239,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Ancho del fuselaje (r = 0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 32.0, 35.0]\n", "Ecuación de regresión: y = 113.452x + -9.6\n", @@ -32835,7 +37249,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 0.327x + 1.985\n", @@ -32845,7 +37259,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 8.53x + -17.284\n", @@ -32855,27 +37269,27 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 161.947x + -35.001\n", + "Ecuación de regresión: y = 160.56x + -34.733\n", "Valor del parámetro correlacionado para la aeronave: 0.352\n", - "Predicción obtenida: 22.004\n", - "\tR²: 0.3392826165393894, Desviación Estándar: 8.345019309965002, Varianza: 69.63934728368875, Incertidumbre: 2.02396447428263\n", + "Predicción obtenida: 21.784\n", + "\tR²: 0.34149882368086004, Desviación Estándar: 8.331011939712234, Varianza: 69.4057599396278, Incertidumbre: 2.0205671879832594\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 3.714, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 4.186, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.457x + 3.507\n", + "Ecuación de regresión: y = 1.461x + 3.44\n", "Valor del parámetro correlacionado para la aeronave: 11.3\n", - "Predicción obtenida: 19.967\n", - "\tR²: 0.600650247472835, Desviación Estándar: 6.487781114213371, Varianza: 42.091303785943694, Incertidumbre: 1.5735180476346566\n", + "Predicción obtenida: 19.946\n", + "\tR²: 0.6012377913575799, Desviación Estándar: 6.483006778963267, Varianza: 42.029376896083676, Incertidumbre: 1.5723601012506396\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: RTF (Including fuel & Batteries) (r = 0.996) ---\n", - "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", "Valores para RTF (Including fuel & Batteries): [27.7, 6.8, 8.9, 16.5, 84.0]\n", "Valores para Empty weight: [19.796, 4.8, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 0.356x + 2.621\n", @@ -32885,7 +37299,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 2.65, 3.45, 6.45]\n", "Ecuación de regresión: y = 0.09x + 2.413\n", @@ -32893,13 +37307,13 @@ "Predicción obtenida: 14.967\n", "\tR²: 0.6992353116426389, Desviación Estándar: 3.354572744999882, Varianza: 11.253158301496043, Incertidumbre: 1.267909319734997\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 17.436', 'Longitud del fuselaje: 19.796', 'Ancho del fuselaje: 21.827', 'Peso máximo al despegue (MTOW): 19.49', 'envergadura: 20.246', 'Cuerda: 22.004', 'payload: 19.967', 'RTF (Including fuel & Batteries): 17.662', 'Rango de comunicación: 14.967']\n", + "Valores imputados: ['Área del ala: 17.436', 'Longitud del fuselaje: 19.796', 'Ancho del fuselaje: 21.827', 'Peso máximo al despegue (MTOW): 19.49', 'envergadura: 20.246', 'Cuerda: 21.784', 'payload: 19.946', 'RTF (Including fuel & Batteries): 17.662', 'Rango de comunicación: 14.967']\n", "**Mediana calculada:** 19.796\n", "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 Fixed wing** ---\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 Fixed wing** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 15.104x + -5.974\n", @@ -32909,7 +37323,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 8.792x + -6.58\n", @@ -32919,7 +37333,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Ancho del fuselaje (r = 0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 32.0, 35.0]\n", "Ecuación de regresión: y = 113.452x + -9.6\n", @@ -32929,7 +37343,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 0.328x + 1.989\n", @@ -32939,7 +37353,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 8.53x + -17.284\n", @@ -32949,27 +37363,27 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 161.947x + -35.001\n", + "Ecuación de regresión: y = 160.56x + -34.733\n", "Valor del parámetro correlacionado para la aeronave: 0.352\n", - "Predicción obtenida: 22.004\n", - "\tR²: 0.3392826165393894, Desviación Estándar: 8.345019309965002, Varianza: 69.63934728368875, Incertidumbre: 2.02396447428263\n", + "Predicción obtenida: 21.784\n", + "\tR²: 0.34149882368086004, Desviación Estándar: 8.331011939712234, Varianza: 69.4057599396278, Incertidumbre: 2.0205671879832594\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 3.714, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 4.186, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.455x + 3.508\n", + "Ecuación de regresión: y = 1.459x + 3.441\n", "Valor del parámetro correlacionado para la aeronave: 17.7\n", - "Predicción obtenida: 29.263\n", - "\tR²: 0.6105487089694287, Desviación Estándar: 6.305105946732535, Varianza: 39.75436099952198, Incertidumbre: 1.4861277236780677\n", + "Predicción obtenida: 29.272\n", + "\tR²: 0.6111249176575178, Desviación Estándar: 6.300439892571702, Varianza: 39.69554283990892, Incertidumbre: 1.4850279241652313\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: RTF (Including fuel & Batteries) (r = 0.996) ---\n", - "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 6.8, 8.9, 16.5, 84.0]\n", "Valores para Empty weight: [19.796, 19.796, 4.8, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 0.362x + 2.802\n", @@ -32979,7 +37393,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 2.65, 3.45, 6.45]\n", "Ecuación de regresión: y = 0.09x + 2.413\n", @@ -32987,13 +37401,13 @@ "Predicción obtenida: 14.967\n", "\tR²: 0.6992353116426389, Desviación Estándar: 3.354572744999882, Varianza: 11.253158301496043, Incertidumbre: 1.267909319734997\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 17.436', 'Longitud del fuselaje: 19.796', 'Ancho del fuselaje: 21.827', 'Peso máximo al despegue (MTOW): 19.809', 'envergadura: 20.246', 'Cuerda: 22.004', 'payload: 29.263', 'RTF (Including fuel & Batteries): 16.09', 'Rango de comunicación: 14.967']\n", + "Valores imputados: ['Área del ala: 17.436', 'Longitud del fuselaje: 19.796', 'Ancho del fuselaje: 21.827', 'Peso máximo al despegue (MTOW): 19.809', 'envergadura: 20.246', 'Cuerda: 21.784', 'payload: 29.272', 'RTF (Including fuel & Batteries): 16.09', 'Rango de comunicación: 14.967']\n", "**Mediana calculada:** 19.809\n", "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 VTOL FTUAS** ---\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 15.199x + -5.963\n", @@ -33003,7 +37417,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 8.793x + -6.581\n", @@ -33013,7 +37427,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 0.328x + 1.989\n", @@ -33023,7 +37437,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 8.513x + -17.25\n", @@ -33033,27 +37447,27 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 157.473x + -33.787\n", + "Ecuación de regresión: y = 156.666x + -33.677\n", "Valor del parámetro correlacionado para la aeronave: 0.394\n", - "Predicción obtenida: 28.257\n", - "\tR²: 0.35355225050821804, Desviación Estándar: 8.123678231024497, Varianza: 65.9941480012213, Incertidumbre: 1.9147693217783197\n", + "Predicción obtenida: 28.049\n", + "\tR²: 0.35611948927363, Desviación Estándar: 8.10753139727324, Varianza: 65.73206535777136, Incertidumbre: 1.9109634765649177\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 3.714, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 4.186, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.302x + 4.121\n", + "Ecuación de regresión: y = 1.306x + 4.062\n", "Valor del parámetro correlacionado para la aeronave: 22.7\n", - "Predicción obtenida: 33.679\n", - "\tR²: 0.5820027961619212, Desviación Estándar: 6.42836622157291, Varianza: 41.32389227865957, Incertidumbre: 1.4747683543108792\n", + "Predicción obtenida: 33.704\n", + "\tR²: 0.5824993045960247, Desviación Estándar: 6.424547193133453, Varianza: 41.27480663679893, Incertidumbre: 1.4738922090987687\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: RTF (Including fuel & Batteries) (r = 0.996) ---\n", - "Aeronaves utilizadas: ['Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", "Valores para Empty weight: [19.796, 19.796, 19.809, 4.8, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 0.366x + 3.198\n", @@ -33061,13 +37475,13 @@ "Predicción obtenida: 28.95\n", "\tR²: 0.8686932854431193, Desviación Estándar: 3.5123478434354816, Varianza: 12.336587373285877, Incertidumbre: 1.3275427016691874\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 32.08', 'Longitud del fuselaje: 25.02', 'Peso máximo al despegue (MTOW): 32.453', 'envergadura: 30.798', 'Cuerda: 28.257', 'payload: 33.679', 'RTF (Including fuel & Batteries): 28.95']\n", + "Valores imputados: ['Área del ala: 32.08', 'Longitud del fuselaje: 25.02', 'Peso máximo al despegue (MTOW): 32.453', 'envergadura: 30.798', 'Cuerda: 28.049', 'payload: 33.704', 'RTF (Including fuel & Batteries): 28.95']\n", "**Mediana calculada:** 30.798\n", "\n", "--- Imputación para aeronave: **Fulmar X** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 15.024x + -5.798\n", @@ -33077,7 +37491,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 9.152x + -7.113\n", @@ -33087,7 +37501,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 0.323x + 2.09\n", @@ -33097,7 +37511,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 8.513x + -17.25\n", @@ -33107,31 +37521,31 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 164.068x + -35.656\n", - "Valor del parámetro correlacionado para la aeronave: 0.313\n", - "Predicción obtenida: 15.697\n", - "\tR²: 0.4401012275044295, Desviación Estándar: 7.922310441477786, Varianza: 62.76300273114796, Incertidumbre: 1.8175026638820404\n", + "Ecuación de regresión: y = 163.675x + -35.666\n", + "Valor del parámetro correlacionado para la aeronave: 0.319\n", + "Predicción obtenida: 16.546\n", + "\tR²: 0.44192713119646354, Desviación Estándar: 7.9093820433108375, Varianza: 62.558324307047926, Incertidumbre: 1.814536685928799\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.254x + 4.367\n", - "Valor del parámetro correlacionado para la aeronave: 7.8\n", - "Predicción obtenida: 14.145\n", - "\tR²: 0.6338082409254504, Desviación Estándar: 6.288836818337574, Varianza: 39.54946852767826, Incertidumbre: 1.406226662520631\n", + "Ecuación de regresión: y = 1.257x + 4.312\n", + "Valor del parámetro correlacionado para la aeronave: 10.419\n", + "Predicción obtenida: 17.406\n", + "\tR²: 0.6341935967623096, Desviación Estándar: 6.28552697067258, Varianza: 39.50784929905242, Incertidumbre: 1.4054865580832214\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 8.325', 'Longitud del fuselaje: 3.87', 'Peso máximo al despegue (MTOW): 8.545', 'envergadura: 8.289', 'Cuerda: 15.697', 'payload: 14.145']\n", + "Valores imputados: ['Área del ala: 8.325', 'Longitud del fuselaje: 3.87', 'Peso máximo al despegue (MTOW): 8.545', 'envergadura: 8.289', 'Cuerda: 16.546', 'payload: 17.406']\n", "**Mediana calculada:** 8.435\n", "\n", "--- Imputación para aeronave: **Orbiter 4** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 15.019x + -5.786\n", @@ -33141,7 +37555,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 8.927x + -6.373\n", @@ -33151,7 +37565,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 0.323x + 2.081\n", @@ -33161,7 +37575,7 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", "Ecuación de regresión: y = 8.51x + -17.23\n", @@ -33171,27 +37585,27 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 162.214x + -35.455\n", - "Valor del parámetro correlacionado para la aeronave: 0.334\n", - "Predicción obtenida: 18.724\n", - "\tR²: 0.4250328821813467, Desviación Estándar: 7.881879841341832, Varianza: 62.124029833350754, Incertidumbre: 1.7624419115725594\n", + "Ecuación de regresión: y = 160.493x + -35.101\n", + "Valor del parámetro correlacionado para la aeronave: 0.332\n", + "Predicción obtenida: 18.183\n", + "\tR²: 0.42120352079680623, Desviación Estándar: 7.908083494559376, Varianza: 62.53778455692244, Incertidumbre: 1.7683012265578852\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.255x + 4.083\n", + "Ecuación de regresión: y = 1.234x + 4.074\n", "Valor del parámetro correlacionado para la aeronave: 12.0\n", - "Predicción obtenida: 19.144\n", - "\tR²: 0.6253889798074073, Desviación Estándar: 6.256578342524824, Varianza: 39.14477255615067, Incertidumbre: 1.3652973260019692\n", + "Predicción obtenida: 18.877\n", + "\tR²: 0.6052009839355558, Desviación Estándar: 6.422951508721367, Varianza: 41.25430608338608, Incertidumbre: 1.401602927321261\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 30.0, 30.0, 30.0]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 2.65, 3.45, 6.45]\n", "Ecuación de regresión: y = 0.099x + 2.179\n", @@ -33199,955 +37613,955 @@ "Predicción obtenida: 16.991\n", "\tR²: 0.7177783310839329, Desviación Estándar: 3.4856268169459126, Varianza: 12.149594307012496, Incertidumbre: 1.2323551794740677\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 18.365', 'Longitud del fuselaje: 4.34', 'Peso máximo al despegue (MTOW): 19.838', 'envergadura: 27.02', 'Cuerda: 18.724', 'payload: 19.144', 'Rango de comunicación: 16.991']\n", - "**Mediana calculada:** 18.724\n", + "Valores imputados: ['Área del ala: 18.365', 'Longitud del fuselaje: 4.34', 'Peso máximo al despegue (MTOW): 19.838', 'envergadura: 27.02', 'Cuerda: 18.183', 'payload: 18.877', 'Rango de comunicación: 16.991']\n", + "**Mediana calculada:** 18.365\n", "\n", "--- Imputación para aeronave: **Orbiter 3** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.031x + -5.784\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.019x + -5.786\n", "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 12.253\n", - "\tR²: 0.9136497392693784, Desviación Estándar: 2.996311656277053, Varianza: 8.977883541541738, Incertidumbre: 0.6538488081222905\n", + "Predicción obtenida: 12.237\n", + "\tR²: 0.9135790496566742, Desviación Estándar: 2.995343734099099, Varianza: 8.972084085406735, Incertidumbre: 0.6536375901867048\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.282x + -4.258\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.298x + -4.311\n", "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 5.68\n", - "\tR²: 0.7127494541740498, Desviación Estándar: 5.464942394654294, Varianza: 29.865595376889807, Incertidumbre: 1.192548199622692\n", + "Predicción obtenida: 5.647\n", + "\tR²: 0.7165705648434354, Desviación Estándar: 5.424498885276192, Varianza: 29.42518815636265, Incertidumbre: 1.183722702332463\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.322x + 2.067\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.322x + 2.063\n", "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 12.368\n", - "\tR²: 0.9094761068700505, Desviación Estándar: 3.0184944982890896, Varianza: 9.111309036201503, Incertidumbre: 0.6435451893507028\n", + "Predicción obtenida: 12.354\n", + "\tR²: 0.9089579741392599, Desviación Estándar: 3.0250631475449667, Varianza: 9.15100704663466, Incertidumbre: 0.6449456300775446\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.083x + -16.034\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.064x + -15.982\n", "Valor del parámetro correlacionado para la aeronave: 4.4\n", - "Predicción obtenida: 19.531\n", - "\tR²: 0.8563105110981454, Desviación Estándar: 3.8651616084856775, Varianza: 14.93947425971159, Incertidumbre: 0.8434474116248993\n", + "Predicción obtenida: 19.501\n", + "\tR²: 0.8536528012452334, Desviación Estándar: 3.8978879953283716, Varianza: 15.193530824125032, Incertidumbre: 0.850588894716759\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 162.214x + -35.455\n", - "Valor del parámetro correlacionado para la aeronave: 0.301\n", - "Predicción obtenida: 13.371\n", - "\tR²: 0.4309374492611131, Desviación Estándar: 7.691927112179356, Varianza: 59.16574269907985, Incertidumbre: 1.6785161062688088\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 160.622x + -35.131\n", + "Valor del parámetro correlacionado para la aeronave: 0.304\n", + "Predicción obtenida: 13.698\n", + "\tR²: 0.4262936940175942, Desviación Estándar: 7.717594578111506, Varianza: 59.56126607209612, Incertidumbre: 1.6841172065322267\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.253x + 4.079\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.232x + 4.068\n", "Valor del parámetro correlacionado para la aeronave: 5.5\n", - "Predicción obtenida: 10.972\n", - "\tR²: 0.6286868594150173, Desviación Estándar: 6.113345437183864, Varianza: 37.37299243433677, Incertidumbre: 1.3033696265369021\n", + "Predicción obtenida: 10.841\n", + "\tR²: 0.6081114488646513, Desviación Estándar: 6.2761711228549695, Varianza: 39.39032396335861, Incertidumbre: 1.3380841793630753\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'V21', 'V25', 'V32']\n", "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.102x + 2.062\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.101x + 2.086\n", "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 7.156\n", - "\tR²: 0.7467331385836531, Desviación Estándar: 3.326478501613054, Varianza: 11.065459221693828, Incertidumbre: 1.1088261672043513\n", + "Predicción obtenida: 7.148\n", + "\tR²: 0.7460116085943318, Desviación Estándar: 3.3116063271343945, Varianza: 10.966736465916554, Incertidumbre: 1.103868775711465\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 12.253', 'Longitud del fuselaje: 5.68', 'Peso máximo al despegue (MTOW): 12.368', 'envergadura: 19.531', 'Cuerda: 13.371', 'payload: 10.972', 'Rango de comunicación: 7.156']\n", - "**Mediana calculada:** 12.253\n", + "Valores imputados: ['Área del ala: 12.237', 'Longitud del fuselaje: 5.647', 'Peso máximo al despegue (MTOW): 12.354', 'envergadura: 19.501', 'Cuerda: 13.698', 'payload: 10.841', 'Rango de comunicación: 7.148']\n", + "**Mediana calculada:** 12.237\n", "\n", "--- Imputación para aeronave: **Mantis** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.031x + -5.784\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.019x + -5.786\n", "Valor del parámetro correlacionado para la aeronave: 0.754\n", - "Predicción obtenida: 5.549\n", - "\tR²: 0.913778739631991, Desviación Estándar: 2.9274217216946905, Varianza: 8.569797936649906, Incertidumbre: 0.6241284081402825\n", + "Predicción obtenida: 5.538\n", + "\tR²: 0.913708191825763, Desviación Estándar: 2.9264760519606896, Varianza: 8.564262082699425, Incertidumbre: 0.6239267906755577\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.012x + -3.374\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.028x + -3.425\n", "Valor del parámetro correlacionado para la aeronave: 1.48\n", - "Predicción obtenida: 8.484\n", - "\tR²: 0.695106219357537, Desviación Estándar: 5.50493747043158, Varianza: 30.304336553361637, Incertidumbre: 1.1736566121888548\n", + "Predicción obtenida: 8.456\n", + "\tR²: 0.6987989734892859, Desviación Estándar: 5.467495240618404, Varianza: 29.893504206184897, Incertidumbre: 1.1656739019707343\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.322x + 2.061\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.322x + 2.056\n", "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 4.154\n", - "\tR²: 0.9096439599147235, Desviación Estándar: 2.952238871692774, Varianza: 8.715714355533823, Incertidumbre: 0.6155843584875383\n", + "Predicción obtenida: 4.147\n", + "\tR²: 0.9091268370272325, Desviación Estándar: 2.9586655428005026, Varianza: 8.753701794154992, Incertidumbre: 0.6169244120479039\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.922x + -15.761\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.904x + -15.71\n", "Valor del parámetro correlacionado para la aeronave: 2.1\n", - "Predicción obtenida: 0.876\n", - "\tR²: 0.8337456079246974, Desviación Estándar: 4.065038574534986, Varianza: 16.524538612457434, Incertidumbre: 0.8666691361040485\n", + "Predicción obtenida: 0.889\n", + "\tR²: 0.8311430591942646, Desviación Estándar: 4.0937342671353925, Varianza: 16.758660249918545, Incertidumbre: 0.8727870783227756\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 162.348x + -35.547\n", - "Valor del parámetro correlacionado para la aeronave: 0.27\n", - "Predicción obtenida: 8.287\n", - "\tR²: 0.43124218687960514, Desviación Estándar: 7.518683358339971, Varianza: 56.530599442978435, Incertidumbre: 1.6029886780490887\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 160.716x + -35.226\n", + "Valor del parámetro correlacionado para la aeronave: 0.271\n", + "Predicción obtenida: 8.327\n", + "\tR²: 0.42621839297901487, Desviación Estándar: 7.54629017734252, Varianza: 56.94649544065621, Incertidumbre: 1.6088744716367318\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.25x + 4.165\n", - "Valor del parámetro correlacionado para la aeronave: 2.21\n", - "Predicción obtenida: 6.926\n", - "\tR²: 0.6286967759718571, Desviación Estándar: 5.984629370732705, Varianza: 35.81578870503653, Incertidumbre: 1.2478814865870902\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.227x + 4.163\n", + "Valor del parámetro correlacionado para la aeronave: 2.693\n", + "Predicción obtenida: 7.469\n", + "\tR²: 0.6080299406703621, Desviación Estándar: 6.144753971108537, Varianza: 37.758001365454135, Incertidumbre: 1.28126977381058\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'V21', 'V25', 'V32']\n", "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.095x + 3.264\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.094x + 3.287\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 5.627\n", - "\tR²: 0.6914792906735672, Desviación Estándar: 3.4830980292244655, Varianza: 12.131971881187356, Incertidumbre: 1.1014523085993035\n", + "Predicción obtenida: 5.633\n", + "\tR²: 0.6902774142105856, Desviación Estándar: 3.4693600600060193, Varianza: 12.03645922596497, Incertidumbre: 1.0971079812837463\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 5.549', 'Longitud del fuselaje: 8.484', 'Peso máximo al despegue (MTOW): 4.154', 'envergadura: 0.876', 'Cuerda: 8.287', 'payload: 6.926', 'Rango de comunicación: 5.627']\n", - "**Mediana calculada:** 5.627\n", + "Valores imputados: ['Área del ala: 5.538', 'Longitud del fuselaje: 8.456', 'Peso máximo al despegue (MTOW): 4.147', 'envergadura: 0.889', 'Cuerda: 8.327', 'payload: 7.469', 'Rango de comunicación: 5.633']\n", + "**Mediana calculada:** 5.633\n", "\n", "--- Imputación para aeronave: **ScanEagle** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.026x + -5.775\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.014x + -5.775\n", "Valor del parámetro correlacionado para la aeronave: 1.063\n", - "Predicción obtenida: 10.198\n", - "\tR²: 0.9163529546189084, Desviación Estándar: 2.863117876012014, Varianza: 8.197443971939547, Incertidumbre: 0.5970013462929816\n", + "Predicción obtenida: 10.185\n", + "\tR²: 0.916273259509183, Desviación Estándar: 2.8622130745125545, Varianza: 8.19226368391061, Incertidumbre: 0.5968126821384838\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.091x + -3.666\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.105x + -3.713\n", "Valor del parámetro correlacionado para la aeronave: 1.71\n", - "Predicción obtenida: 10.169\n", - "\tR²: 0.7008193572895111, Desviación Estándar: 5.414778194269882, Varianza: 29.319822893140607, Incertidumbre: 1.12905930242721\n", + "Predicción obtenida: 10.147\n", + "\tR²: 0.7044409378663876, Desviación Estándar: 5.377643509962671, Varianza: 28.919049720243635, Incertidumbre: 1.1213161854876974\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.32x + 2.202\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.319x + 2.199\n", "Valor del parámetro correlacionado para la aeronave: 26.5\n", - "Predicción obtenida: 10.676\n", - "\tR²: 0.9116020723584356, Desviación Estándar: 2.90434700702091, Varianza: 8.435231537191319, Incertidumbre: 0.5928473502650619\n", + "Predicción obtenida: 10.664\n", + "\tR²: 0.9110745074819713, Desviación Estándar: 2.910850755492127, Varianza: 8.473052120749086, Incertidumbre: 0.5941749223625525\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.684x + -14.677\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.667x + -14.628\n", "Valor del parámetro correlacionado para la aeronave: 3.1\n", - "Predicción obtenida: 9.144\n", - "\tR²: 0.8299304395789938, Desviación Estándar: 4.0825096549566275, Varianza: 16.66688508281408, Incertidumbre: 0.8512621085856318\n", + "Predicción obtenida: 9.139\n", + "\tR²: 0.827398243173229, Desviación Estándar: 4.1095330263226915, Varianza: 16.888261694436938, Incertidumbre: 0.8568968710318733\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 164.778x + -36.401\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 163.16x + -36.089\n", "Valor del parámetro correlacionado para la aeronave: 0.298\n", - "Predicción obtenida: 12.703\n", - "\tR²: 0.44533403577557296, Desviación Estándar: 7.372752999831995, Varianza: 54.35748679653168, Incertidumbre: 1.5373252717475088\n", + "Predicción obtenida: 12.533\n", + "\tR²: 0.44031188669232135, Desviación Estándar: 7.400190688413107, Varianza: 54.76282222487606, Incertidumbre: 1.54304642530508\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.257x + 4.049\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.238x + 4.003\n", "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 10.336\n", - "\tR²: 0.6396223521327932, Desviación Estándar: 5.8641677511942225, Varianza: 34.38846341414631, Incertidumbre: 1.1970182297091787\n", + "Predicción obtenida: 10.192\n", + "\tR²: 0.618870296719052, Desviación Estándar: 6.026195235106751, Varianza: 36.31502901162332, Incertidumbre: 1.2300919513669042\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'V21', 'V25', 'V32']\n", "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.095x + 3.264\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.094x + 3.287\n", "Valor del parámetro correlacionado para la aeronave: 101.86\n", - "Predicción obtenida: 12.891\n", - "\tR²: 0.7190956882541144, Desviación Estándar: 3.321003665545642, Varianza: 11.029065346567588, Incertidumbre: 1.0013202805463481\n", + "Predicción obtenida: 12.848\n", + "\tR²: 0.7179611867018045, Desviación Estándar: 3.307905028059083, Varianza: 10.942235674658562, Incertidumbre: 0.9973708927456391\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 10.198', 'Longitud del fuselaje: 10.169', 'Peso máximo al despegue (MTOW): 10.676', 'envergadura: 9.144', 'Cuerda: 12.703', 'payload: 10.336', 'Rango de comunicación: 12.891']\n", - "**Mediana calculada:** 10.336\n", + "Valores imputados: ['Área del ala: 10.185', 'Longitud del fuselaje: 10.147', 'Peso máximo al despegue (MTOW): 10.664', 'envergadura: 9.139', 'Cuerda: 12.533', 'payload: 10.192', 'Rango de comunicación: 12.848']\n", + "**Mediana calculada:** 10.192\n", "\n", "--- Imputación para aeronave: **Integrator** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.023x + -5.765\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.013x + -5.774\n", "Valor del parámetro correlacionado para la aeronave: 1.872\n", - "Predicción obtenida: 22.358\n", - "\tR²: 0.9167346130104, Desviación Estándar: 2.8029703167934756, Varianza: 7.856642596825316, Incertidumbre: 0.5721539200259446\n", + "Predicción obtenida: 22.331\n", + "\tR²: 0.9166944834584114, Desviación Estándar: 2.801949594727719, Varianza: 7.850921531394829, Incertidumbre: 0.5719455660067526\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.088x + -3.653\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.104x + -3.71\n", "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 16.567\n", - "\tR²: 0.702201492451173, Desviación Estándar: 5.300874285112574, Varianza: 28.099268186567745, Incertidumbre: 1.0820364324305303\n", + "Predicción obtenida: 16.551\n", + "\tR²: 0.7059270986898859, Desviación Estándar: 5.264424973725865, Varianza: 27.714170303988578, Incertidumbre: 1.0745962478994258\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.32x + 2.183\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.32x + 2.172\n", "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 26.113\n", - "\tR²: 0.9120249989617861, Desviación Estándar: 2.846441429999798, Varianza: 8.102228814419295, Incertidumbre: 0.5692882859999596\n", + "Predicción obtenida: 26.082\n", + "\tR²: 0.9114909858720777, Desviación Estándar: 2.8535287759093366, Varianza: 8.142626474942638, Incertidumbre: 0.5707057551818673\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.663x + -14.551\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.648x + -14.516\n", "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 22.234\n", - "\tR²: 0.8301280134060601, Desviación Estándar: 4.003567260287771, Varianza: 16.02855080764813, Incertidumbre: 0.8172247448847872\n", + "Predicción obtenida: 22.195\n", + "\tR²: 0.8278014537455741, Desviación Estándar: 4.028451857224602, Varianza: 16.22842436597635, Incertidumbre: 0.8223042919639589\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 165.132x + -36.607\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 163.568x + -36.31\n", "Valor del parámetro correlacionado para la aeronave: 0.338\n", - "Predicción obtenida: 19.208\n", - "\tR²: 0.44554931519439944, Desviación Estándar: 7.232987396297254, Varianza: 52.316106674994934, Incertidumbre: 1.476427369742511\n", + "Predicción obtenida: 18.976\n", + "\tR²: 0.4408083829979076, Desviación Estándar: 7.259450756578375, Varianza: 52.69962528718634, Incertidumbre: 1.4818291805398596\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.257x + 4.049\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.238x + 4.003\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 26.683\n", - "\tR²: 0.6415415848013848, Desviación Estándar: 5.74568750261759, Varianza: 33.01292487773596, Incertidumbre: 1.149137500523518\n", + "Predicción obtenida: 26.284\n", + "\tR²: 0.6210511278262185, Desviación Estándar: 5.904441366561173, Varianza: 34.86242785115877, Incertidumbre: 1.1808882733122346\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'V21', 'V25', 'V32']\n", "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 10.336, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.094x + 3.136\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 10.192, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.093x + 3.153\n", "Valor del parámetro correlacionado para la aeronave: 92.6\n", - "Predicción obtenida: 11.799\n", - "\tR²: 0.7062398188700879, Desviación Estándar: 3.2566903822374726, Varianza: 10.606032245758056, Incertidumbre: 0.9401255344260351\n", + "Predicción obtenida: 11.754\n", + "\tR²: 0.7040073224712778, Desviación Estándar: 3.250633447126605, Varianza: 10.566617807578197, Incertidumbre: 0.9383770478676734\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 22.358', 'Longitud del fuselaje: 16.567', 'Peso máximo al despegue (MTOW): 26.113', 'envergadura: 22.234', 'Cuerda: 19.208', 'payload: 26.683', 'Rango de comunicación: 11.799']\n", - "**Mediana calculada:** 22.234\n", + "Valores imputados: ['Área del ala: 22.331', 'Longitud del fuselaje: 16.551', 'Peso máximo al despegue (MTOW): 26.082', 'envergadura: 22.195', 'Cuerda: 18.976', 'payload: 26.284', 'Rango de comunicación: 11.754']\n", + "**Mediana calculada:** 22.195\n", "\n", "--- Imputación para aeronave: **Integrator VTOL** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.016x + -5.76\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 15.005x + -5.769\n", "Valor del parámetro correlacionado para la aeronave: 2.09\n", - "Predicción obtenida: 25.622\n", - "\tR²: 0.9193277412989592, Desviación Estándar: 2.7464421762175815, Varianza: 7.542944627306766, Incertidumbre: 0.5492884352435163\n", + "Predicción obtenida: 25.592\n", + "\tR²: 0.9192804737551298, Desviación Estándar: 2.745463286837539, Varianza: 7.537568659372784, Incertidumbre: 0.5490926573675078\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.172x + -3.606\n", - "Valor del parámetro correlacionado para la aeronave: 3.004\n", - "Predicción obtenida: 20.942\n", - "\tR²: 0.6983801297433365, Desviación Estándar: 5.310535615782573, Varianza: 28.201788526495196, Incertidumbre: 1.0621071231565147\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.188x + -3.662\n", + "Valor del parámetro correlacionado para la aeronave: 2.998\n", + "Predicción obtenida: 20.885\n", + "\tR²: 0.7020575415560586, Desviación Estándar: 5.274636514397158, Varianza: 27.821790359011803, Incertidumbre: 1.0549273028794315\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.313x + 2.281\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.313x + 2.27\n", "Valor del parámetro correlacionado para la aeronave: 75.0\n", - "Predicción obtenida: 25.783\n", - "\tR²: 0.9088807177212644, Desviación Estándar: 2.882816344795206, Varianza: 8.310630077818393, Incertidumbre: 0.5653667998544224\n", + "Predicción obtenida: 25.751\n", + "\tR²: 0.9083231176697067, Desviación Estándar: 2.889927154659528, Varianza: 8.351678959238516, Incertidumbre: 0.5667613444027156\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.663x + -14.551\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.648x + -14.516\n", "Valor del parámetro correlacionado para la aeronave: 5.033\n", - "Predicción obtenida: 24.019\n", - "\tR²: 0.835430712623076, Desviación Estándar: 3.922678776357705, Varianza: 15.387408782487181, Incertidumbre: 0.7845357552715411\n", + "Predicción obtenida: 23.977\n", + "\tR²: 0.833162024179038, Desviación Estándar: 3.947060601866186, Varianza: 15.579287394804256, Incertidumbre: 0.7894121203732372\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 167.757x + -37.286\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 166.269x + -37.008\n", "Valor del parámetro correlacionado para la aeronave: 0.341\n", - "Predicción obtenida: 19.919\n", - "\tR²: 0.45920799755943364, Desviación Estándar: 7.110882159300929, Varianza: 50.56464508346425, Incertidumbre: 1.4221764318601857\n", + "Predicción obtenida: 19.69\n", + "\tR²: 0.45407381641801514, Desviación Estándar: 7.1399192582523305, Varianza: 50.97844701436251, Incertidumbre: 1.4279838516504662\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.215x + 4.222\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.199x + 4.165\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 26.093\n", - "\tR²: 0.6447227222524704, Desviación Estándar: 5.692401793705153, Varianza: 32.403438180977645, Incertidumbre: 1.1163718394351212\n", + "Predicción obtenida: 25.75\n", + "\tR²: 0.6258944719318421, Desviación Estándar: 5.837864906102878, Varianza: 34.08066666190756, Incertidumbre: 1.144899502843734\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 25.622', 'Longitud del fuselaje: 20.942', 'Peso máximo al despegue (MTOW): 25.783', 'envergadura: 24.019', 'Cuerda: 19.919', 'payload: 26.093']\n", - "**Mediana calculada:** 24.82\n", + "Valores imputados: ['Área del ala: 25.592', 'Longitud del fuselaje: 20.885', 'Peso máximo al despegue (MTOW): 25.751', 'envergadura: 23.977', 'Cuerda: 19.69', 'payload: 25.75']\n", + "**Mediana calculada:** 24.784\n", "\n", "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.956x + -5.711\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.945x + -5.72\n", "Valor del parámetro correlacionado para la aeronave: 1.872\n", - "Predicción obtenida: 22.286\n", - "\tR²: 0.9228857881228267, Desviación Estándar: 2.697264613869004, Varianza: 7.275236397229909, Incertidumbre: 0.5289771115169755\n", + "Predicción obtenida: 22.258\n", + "\tR²: 0.9228323594939006, Desviación Estándar: 2.6963684551592366, Varianza: 7.2704028459778085, Incertidumbre: 0.5288013603343454\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.3x + -3.735\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.316x + -3.791\n", "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 17.015\n", - "\tR²: 0.7068456492535431, Desviación Estándar: 5.25901627247607, Varianza: 27.657252154168102, Incertidumbre: 1.031377945986826\n", + "Predicción obtenida: 17.0\n", + "\tR²: 0.7102604918578905, Desviación Estándar: 5.224749658509504, Varianza: 27.298008994095174, Incertidumbre: 1.024657710091431\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.312x + 2.303\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.312x + 2.292\n", "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 25.631\n", - "\tR²: 0.912609810603614, Desviación Estándar: 2.834428027141342, Varianza: 8.033982241044361, Incertidumbre: 0.5454859281562245\n", + "Predicción obtenida: 25.599\n", + "\tR²: 0.9120674450382346, Desviación Estándar: 2.8414417914279144, Varianza: 8.073791454073076, Incertidumbre: 0.5468357277225196\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.693x + -14.63\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.677x + -14.595\n", "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 22.295\n", - "\tR²: 0.8429342446881969, Desviación Estándar: 3.8494359348535636, Varianza: 14.818157016541928, Incertidumbre: 0.7549364980055233\n", + "Predicción obtenida: 22.256\n", + "\tR²: 0.8407587886499855, Desviación Estándar: 3.873373378087582, Varianza: 15.003021326077608, Incertidumbre: 0.7596310168576687\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 172.075x + -38.42\n", - "Valor del parámetro correlacionado para la aeronave: 0.344\n", - "Predicción obtenida: 20.773\n", - "\tR²: 0.4755347143919866, Desviación Estándar: 7.034200066776385, Varianza: 49.479970579436895, Incertidumbre: 1.3795201308849407\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 170.626x + -38.15\n", + "Valor del parámetro correlacionado para la aeronave: 0.345\n", + "Predicción obtenida: 20.716\n", + "\tR²: 0.4698496594948569, Desviación Estándar: 7.067424038440677, Varianza: 49.94848253912913, Incertidumbre: 1.3860358878016823\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.204x + 4.266\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.191x + 4.199\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 25.944\n", - "\tR²: 0.6600077706694365, Desviación Estándar: 5.590730903029895, Varianza: 31.256272030093463, Incertidumbre: 1.0759366639436898\n", + "Predicción obtenida: 25.639\n", + "\tR²: 0.6422377954896081, Desviación Estándar: 5.73140563845551, Varianza: 32.84901059251961, Incertidumbre: 1.1030095293990758\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 22.286', 'Longitud del fuselaje: 17.015', 'Peso máximo al despegue (MTOW): 25.631', 'envergadura: 22.295', 'Cuerda: 20.773', 'payload: 25.944']\n", - "**Mediana calculada:** 22.29\n", + "Valores imputados: ['Área del ala: 22.258', 'Longitud del fuselaje: 17.0', 'Peso máximo al despegue (MTOW): 25.599', 'envergadura: 22.256', 'Cuerda: 20.716', 'payload: 25.639']\n", + "**Mediana calculada:** 22.257\n", "\n", "--- Imputación para aeronave: **ScanEagle 3** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.956x + -5.712\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.945x + -5.72\n", "Valor del parámetro correlacionado para la aeronave: 1.349\n", - "Predicción obtenida: 14.464\n", - "\tR²: 0.9247777030959305, Desviación Estándar: 2.6468441167859273, Varianza: 7.005783778564275, Incertidumbre: 0.5093853877764442\n", + "Predicción obtenida: 14.441\n", + "\tR²: 0.9247233040619992, Desviación Estándar: 2.645964603028454, Varianza: 7.001128680479524, Incertidumbre: 0.5092161252748997\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.366x + -3.684\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.382x + -3.74\n", "Valor del parámetro correlacionado para la aeronave: 2.4\n", - "Predicción obtenida: 16.395\n", - "\tR²: 0.7034262913324543, Desviación Estándar: 5.255588982695431, Varianza: 27.6212155550296, Incertidumbre: 1.0114385713030796\n", + "Predicción obtenida: 16.377\n", + "\tR²: 0.7068047947675931, Desviación Estándar: 5.221943728574231, Varianza: 27.268696304395736, Incertidumbre: 1.00496353912847\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.307x + 2.373\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.307x + 2.362\n", "Valor del parámetro correlacionado para la aeronave: 36.3\n", - "Predicción obtenida: 13.524\n", - "\tR²: 0.9106032203613208, Desviación Estándar: 2.8482011972816106, Varianza: 8.1122500601964, Incertidumbre: 0.5382594322773969\n", + "Predicción obtenida: 13.503\n", + "\tR²: 0.9100626005198136, Desviación Estándar: 2.854969820151899, Varianza: 8.150852673978166, Incertidumbre: 0.5395385817654803\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.692x + -14.629\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.677x + -14.596\n", "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 16.14\n", - "\tR²: 0.8467876896895489, Desviación Estándar: 3.7774776218534605, Varianza: 14.269337183603675, Incertidumbre: 0.7269759072782942\n", + "Predicción obtenida: 16.114\n", + "\tR²: 0.8446608947110245, Desviación Estándar: 3.800967492809924, Varianza: 14.447353881397758, Incertidumbre: 0.7314965350516088\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 173.426x + -38.78\n", - "Valor del parámetro correlacionado para la aeronave: 0.31\n", - "Predicción obtenida: 14.982\n", - "\tR²: 0.4875507277051119, Desviación Estándar: 6.908448268587538, Varianza: 47.726657479750145, Incertidumbre: 1.329531489183873\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 171.998x + -38.517\n", + "Valor del parámetro correlacionado para la aeronave: 0.312\n", + "Predicción obtenida: 15.147\n", + "\tR²: 0.4819605292975089, Desviación Estándar: 6.941209958097174, Varianza: 48.18039568238737, Incertidumbre: 1.3358364792697046\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.177x + 4.379\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.166x + 4.305\n", "Valor del parámetro correlacionado para la aeronave: 8.6\n", - "Predicción obtenida: 14.499\n", - "\tR²: 0.6631553407358013, Desviación Estándar: 5.528719549623086, Varianza: 30.566739858384498, Incertidumbre: 1.044829785494551\n", + "Predicción obtenida: 14.33\n", + "\tR²: 0.6464449368019647, Desviación Estándar: 5.6605664016485475, Varianza: 32.042011987472385, Incertidumbre: 1.0697464984664153\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 14.464', 'Longitud del fuselaje: 16.395', 'Peso máximo al despegue (MTOW): 13.524', 'envergadura: 16.14', 'Cuerda: 14.982', 'payload: 14.499']\n", - "**Mediana calculada:** 14.74\n", + "Valores imputados: ['Área del ala: 14.441', 'Longitud del fuselaje: 16.377', 'Peso máximo al despegue (MTOW): 13.503', 'envergadura: 16.114', 'Cuerda: 15.147', 'payload: 14.33']\n", + "**Mediana calculada:** 14.794\n", "\n", - "--- Imputación para aeronave: **RQNan21A Blackjack** ---\n", + "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.956x + -5.701\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.945x + -5.707\n", "Valor del parámetro correlacionado para la aeronave: 1.802\n", - "Predicción obtenida: 21.249\n", - "\tR²: 0.9247492382330068, Desviación Estándar: 2.59965345007093, Varianza: 6.758198060465689, Incertidumbre: 0.4912883231313392\n", + "Predicción obtenida: 21.224\n", + "\tR²: 0.9246771071689462, Desviación Estándar: 2.599110307951027, Varianza: 6.7553743928972825, Incertidumbre: 0.49118567891878007\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.353x + -3.713\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.369x + -3.768\n", "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 17.168\n", - "\tR²: 0.7023810376902668, Desviación Estándar: 5.16999718239943, Varianza: 26.728870866018045, Incertidumbre: 0.9770376302523951\n", + "Predicción obtenida: 17.155\n", + "\tR²: 0.7058504559348706, Desviación Estándar: 5.136239966459277, Varianza: 26.380960993053595, Incertidumbre: 0.9706581160858557\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.307x + 2.423\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.307x + 2.415\n", "Valor del parámetro correlacionado para la aeronave: 61.0\n", - "Predicción obtenida: 21.149\n", - "\tR²: 0.9100414966060446, Desviación Estándar: 2.8074428350263565, Varianza: 7.881735271940825, Incertidumbre: 0.5213290466629605\n", + "Predicción obtenida: 21.123\n", + "\tR²: 0.9094286866168957, Desviación Estándar: 2.8151847172798137, Varianza: 7.925264992405825, Incertidumbre: 0.5227666781061432\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.685x + -14.651\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.67x + -14.615\n", "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 22.237\n", - "\tR²: 0.8460378541791183, Desviación Estándar: 3.7184931434823216, Varianza: 13.827191258125037, Incertidumbre: 0.7027291506823602\n", + "Predicción obtenida: 22.202\n", + "\tR²: 0.8439953830988041, Desviación Estándar: 3.740502434777401, Varianza: 13.991358464575665, Incertidumbre: 0.7068885157751859\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 173.412x + -38.784\n", - "Valor del parámetro correlacionado para la aeronave: 0.338\n", - "Predicción obtenida: 19.829\n", - "\tR²: 0.48753313542114285, Desviación Estándar: 6.784110329613901, Varianza: 46.024152964374025, Incertidumbre: 1.2820763427844863\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 171.97x + -38.52\n", + "Valor del parámetro correlacionado para la aeronave: 0.341\n", + "Predicción obtenida: 20.121\n", + "\tR²: 0.481923663962755, Desviación Estándar: 6.816447496640321, Varianza: 46.46395647445409, Incertidumbre: 1.2881874929313626\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.177x + 4.387\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.166x + 4.322\n", "Valor del parámetro correlacionado para la aeronave: 17.7\n", - "Predicción obtenida: 25.215\n", - "\tR²: 0.6631333634856997, Desviación Estándar: 5.432738626672287, Varianza: 29.514648985737086, Incertidumbre: 1.0088342365074427\n", + "Predicción obtenida: 24.954\n", + "\tR²: 0.6463635003419814, Desviación Estándar: 5.562757206524997, Varianza: 30.944267738745786, Incertidumbre: 1.0329780806624915\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'V32']\n", "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 10.336, 22.234, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.095x + 3.847\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 10.192, 22.195, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.094x + 3.865\n", "Valor del parámetro correlacionado para la aeronave: 92.6\n", - "Predicción obtenida: 12.606\n", - "\tR²: 0.5786689229639067, Desviación Estándar: 4.18551671444084, Varianza: 17.51855016686364, Incertidumbre: 1.160853471402155\n", + "Predicción obtenida: 12.561\n", + "\tR²: 0.5755594637290282, Desviación Estándar: 4.182273841525787, Varianza: 17.491414485510862, Incertidumbre: 1.1599540602809986\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 21.249', 'Longitud del fuselaje: 17.168', 'Peso máximo al despegue (MTOW): 21.149', 'envergadura: 22.237', 'Cuerda: 19.829', 'payload: 25.215', 'Rango de comunicación: 12.606']\n", - "**Mediana calculada:** 21.149\n", + "Valores imputados: ['Área del ala: 21.224', 'Longitud del fuselaje: 17.155', 'Peso máximo al despegue (MTOW): 21.123', 'envergadura: 22.202', 'Cuerda: 20.121', 'payload: 24.954', 'Rango de comunicación: 12.561']\n", + "**Mediana calculada:** 21.123\n", "\n", "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.952x + -5.699\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.941x + -5.705\n", "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 4.767\n", - "\tR²: 0.9259690891919676, Desviación Estándar: 2.554502626662271, Varianza: 6.525483669624441, Incertidumbre: 0.4743592291322177\n", + "Predicción obtenida: 4.754\n", + "\tR²: 0.9258976032052461, Desviación Estándar: 2.5539701136688935, Varianza: 6.522763341513901, Incertidumbre: 0.4742603439518288\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.399x + -3.679\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.416x + -3.734\n", "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 3.88\n", - "\tR²: 0.7012558792426988, Desviación Estándar: 5.131560941639433, Varianza: 26.332917697759388, Incertidumbre: 0.9529069444338854\n", + "Predicción obtenida: 3.84\n", + "\tR²: 0.704696721359465, Desviación Estándar: 5.098398969474347, Varianza: 25.993672051937075, Incertidumbre: 0.9467489207980716\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.307x + 2.423\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.307x + 2.415\n", "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 4.326\n", - "\tR²: 0.9114139885923979, Desviación Estándar: 2.760255561189478, Varianza: 7.61901076307744, Incertidumbre: 0.5039514117808528\n", + "Predicción obtenida: 4.317\n", + "\tR²: 0.9108095208047919, Desviación Estándar: 2.7678673189624066, Varianza: 7.661089495380141, Incertidumbre: 0.5053411222590188\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.657x + -14.58\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.642x + -14.545\n", "Valor del parámetro correlacionado para la aeronave: 2.35\n", - "Predicción obtenida: 3.413\n", - "\tR²: 0.8481057631774891, Desviación Estándar: 3.659067788491134, Varianza: 13.388777080773398, Incertidumbre: 0.6794718303966184\n", + "Predicción obtenida: 3.414\n", + "\tR²: 0.846101938815913, Desviación Estándar: 3.680579963754588, Varianza: 13.546668869591723, Incertidumbre: 0.6834665410570895\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 174.337x + -39.024\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 172.715x + -38.717\n", "Valor del parámetro correlacionado para la aeronave: 0.272\n", - "Predicción obtenida: 8.395\n", - "\tR²: 0.49522154420699915, Desviación Estándar: 6.670374425052742, Varianza: 44.493894970397704, Incertidumbre: 1.2386574346277268\n", + "Predicción obtenida: 8.262\n", + "\tR²: 0.4899739920102165, Desviación Estándar: 6.700324629668004, Varianza: 44.89435014293567, Incertidumbre: 1.24421904800223\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.15x + 4.498\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.14x + 4.428\n", "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 5.878\n", - "\tR²: 0.6624428309267374, Desviación Estándar: 5.388158809639675, Varianza: 29.032255357897643, Incertidumbre: 0.9837387078199448\n", + "Predicción obtenida: 5.796\n", + "\tR²: 0.6465679119191974, Desviación Estándar: 5.509840252613488, Varianza: 30.358339609319863, Incertidumbre: 1.0059545982021234\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQNan21A Blackjack', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32']\n", "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 10.336, 22.234, 21.149, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.095x + 4.388\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 10.192, 22.195, 21.123, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.095x + 4.407\n", "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 9.156\n", - "\tR²: 0.518357595351121, Desviación Estándar: 4.594228397572152, Varianza: 21.106934569058385, Incertidumbre: 1.2278591871644609\n", + "Predicción obtenida: 9.141\n", + "\tR²: 0.5148731942546723, Desviación Estándar: 4.5938015964161, Varianza: 21.103013107235103, Incertidumbre: 1.2277451197574445\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 4.767', 'Longitud del fuselaje: 3.88', 'Peso máximo al despegue (MTOW): 4.326', 'envergadura: 3.413', 'Cuerda: 8.395', 'payload: 5.878', 'Rango de comunicación: 9.156']\n", - "**Mediana calculada:** 4.767\n", + "Valores imputados: ['Área del ala: 4.754', 'Longitud del fuselaje: 3.84', 'Peso máximo al despegue (MTOW): 4.317', 'envergadura: 3.414', 'Cuerda: 8.262', 'payload: 5.796', 'Rango de comunicación: 9.141']\n", + "**Mediana calculada:** 4.754\n", "\n", "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.952x + -5.699\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.941x + -5.705\n", "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 4.767\n", - "\tR²: 0.9286912161672043, Desviación Estándar: 2.5115667515700397, Varianza: 6.3079675475920824, Incertidumbre: 0.4585472548382948\n", + "Predicción obtenida: 4.754\n", + "\tR²: 0.9286220934085586, Desviación Estándar: 2.511043189243422, Varianza: 6.305337898245776, Incertidumbre: 0.4584516658727787\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.358x + -3.56\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.373x + -3.612\n", "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 3.962\n", - "\tR²: 0.7119717257129345, Desviación Estándar: 5.047667874355293, Varianza: 25.47895096979848, Incertidumbre: 0.9215738525261822\n", + "Predicción obtenida: 3.924\n", + "\tR²: 0.7152679648938791, Desviación Estándar: 5.015225272771336, Varianza: 25.15248453664432, Incertidumbre: 0.9156506709556202\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.306x + 2.46\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.306x + 2.452\n", "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 4.359\n", - "\tR²: 0.9146821995211808, Desviación Estándar: 2.7164377856628272, Varianza: 7.379034243376764, Incertidumbre: 0.4878866289309157\n", + "Predicción obtenida: 4.35\n", + "\tR²: 0.9141014032234078, Desviación Estándar: 2.7239052914347277, Varianza: 7.419660036706109, Incertidumbre: 0.4892278325604646\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.607x + -14.346\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.593x + -14.314\n", "Valor del parámetro correlacionado para la aeronave: 2.35\n", "Predicción obtenida: 3.53\n", - "\tR²: 0.8530601734425255, Desviación Estándar: 3.605312977255013, Varianza: 12.998281663963407, Incertidumbre: 0.6582370815028935\n", + "\tR²: 0.851141505775779, Desviación Estándar: 3.626261257188199, Varianza: 13.149770705384139, Incertidumbre: 0.6620616966563397\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 177.343x + -40.07\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 175.676x + -39.747\n", "Valor del parámetro correlacionado para la aeronave: 0.272\n", - "Predicción obtenida: 8.167\n", - "\tR²: 0.5091333952756019, Desviación Estándar: 6.589537801021175, Varianza: 43.42200843108698, Incertidumbre: 1.203079499050762\n", + "Predicción obtenida: 8.037\n", + "\tR²: 0.5043808003522512, Desviación Estándar: 6.616774593333738, Varianza: 43.78170601898685, Incertidumbre: 1.2080522342319868\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.156x + 4.406\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.146x + 4.34\n", "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 5.794\n", - "\tR²: 0.6747265207014675, Desviación Estándar: 5.304009156303675, Varianza: 28.132513130153214, Incertidumbre: 0.952628166470701\n", + "Predicción obtenida: 5.716\n", + "\tR²: 0.6594997555877751, Desviación Estándar: 5.423225746126612, Varianza: 29.411377493450544, Incertidumbre: 0.9740400980924518\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQNan21A Blackjack', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 50.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 10.336, 22.234, 21.149, 4.767, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.1x + 3.711\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 10.192, 22.195, 21.123, 4.754, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.099x + 3.73\n", "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 6.705\n", - "\tR²: 0.5369948479446686, Desviación Estándar: 4.566261608746338, Varianza: 20.850745079510695, Incertidumbre: 1.17900367767339\n", + "Predicción obtenida: 6.703\n", + "\tR²: 0.5336804190590896, Desviación Estándar: 4.565757595526708, Varianza: 20.846142421109825, Incertidumbre: 1.1788735420196639\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 4.767', 'Longitud del fuselaje: 3.962', 'Peso máximo al despegue (MTOW): 4.359', 'envergadura: 3.53', 'Cuerda: 8.167', 'payload: 5.794', 'Rango de comunicación: 6.705']\n", - "**Mediana calculada:** 4.767\n", + "Valores imputados: ['Área del ala: 4.754', 'Longitud del fuselaje: 3.924', 'Peso máximo al despegue (MTOW): 4.35', 'envergadura: 3.53', 'Cuerda: 8.037', 'payload: 5.716', 'Rango de comunicación: 6.703']\n", + "**Mediana calculada:** 4.754\n", "\n", "--- Imputación para aeronave: **V35** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.952x + -5.699\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.941x + -5.705\n", "Valor del parámetro correlacionado para la aeronave: 1.202\n", - "Predicción obtenida: 12.273\n", - "\tR²: 0.9286912161672043, Desviación Estándar: 2.5115667515700397, Varianza: 6.3079675475920824, Incertidumbre: 0.4585472548382948\n", + "Predicción obtenida: 12.254\n", + "\tR²: 0.9286220934085586, Desviación Estándar: 2.511043189243422, Varianza: 6.305337898245776, Incertidumbre: 0.4584516658727787\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.358x + -3.56\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.373x + -3.612\n", "Valor del parámetro correlacionado para la aeronave: 1.88\n", - "Predicción obtenida: 12.153\n", - "\tR²: 0.7119717257129345, Desviación Estándar: 5.047667874355293, Varianza: 25.47895096979848, Incertidumbre: 0.9215738525261822\n", + "Predicción obtenida: 12.13\n", + "\tR²: 0.7152679648938791, Desviación Estándar: 5.015225272771336, Varianza: 25.15248453664432, Incertidumbre: 0.9156506709556202\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.306x + 2.46\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.306x + 2.452\n", "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 12.265\n", - "\tR²: 0.9146821995211808, Desviación Estándar: 2.7164377856628272, Varianza: 7.379034243376764, Incertidumbre: 0.4878866289309157\n", + "Predicción obtenida: 12.248\n", + "\tR²: 0.9141014032234078, Desviación Estándar: 2.7239052914347277, Varianza: 7.419660036706109, Incertidumbre: 0.4892278325604646\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.607x + -14.346\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.593x + -14.314\n", "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 12.278\n", - "\tR²: 0.8530601734425255, Desviación Estándar: 3.605312977255013, Varianza: 12.998281663963407, Incertidumbre: 0.6582370815028935\n", + "Predicción obtenida: 12.262\n", + "\tR²: 0.851141505775779, Desviación Estándar: 3.626261257188199, Varianza: 13.149770705384139, Incertidumbre: 0.6620616966563397\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 177.343x + -40.07\n", - "Valor del parámetro correlacionado para la aeronave: 0.304\n", - "Predicción obtenida: 13.842\n", - "\tR²: 0.5091333952756019, Desviación Estándar: 6.589537801021175, Varianza: 43.42200843108698, Incertidumbre: 1.203079499050762\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 175.676x + -39.747\n", + "Valor del parámetro correlacionado para la aeronave: 0.306\n", + "Predicción obtenida: 14.01\n", + "\tR²: 0.5043808003522512, Desviación Estándar: 6.616774593333738, Varianza: 43.78170601898685, Incertidumbre: 1.2080522342319868\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.156x + 4.406\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.146x + 4.34\n", "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 15.967\n", - "\tR²: 0.6747265207014675, Desviación Estándar: 5.304009156303675, Varianza: 28.132513130153214, Incertidumbre: 0.952628166470701\n", + "Predicción obtenida: 15.804\n", + "\tR²: 0.6594997555877751, Desviación Estándar: 5.423225746126612, Varianza: 29.411377493450544, Incertidumbre: 0.9740400980924518\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQNan21A Blackjack', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'V21', 'V25', 'V32']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'V21', 'V25', 'V32']\n", "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 50.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 10.336, 22.234, 21.149, 4.767, 4.767, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.102x + 3.372\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 10.192, 22.195, 21.123, 4.754, 4.754, 2.65, 3.45, 6.45]\n", + "Ecuación de regresión: y = 0.102x + 3.389\n", "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 6.444\n", - "\tR²: 0.5669527647105395, Desviación Estándar: 4.444173630434083, Varianza: 19.750679257445654, Incertidumbre: 1.1110434076085207\n", + "Predicción obtenida: 6.441\n", + "\tR²: 0.5638096764017799, Desviación Estándar: 4.443968751728997, Varianza: 19.74885826634378, Incertidumbre: 1.1109921879322493\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 12.273', 'Longitud del fuselaje: 12.153', 'Peso máximo al despegue (MTOW): 12.265', 'envergadura: 12.278', 'Cuerda: 13.842', 'payload: 15.967', 'Rango de comunicación: 6.444']\n", - "**Mediana calculada:** 12.273\n", + "Valores imputados: ['Área del ala: 12.254', 'Longitud del fuselaje: 12.13', 'Peso máximo al despegue (MTOW): 12.248', 'envergadura: 12.262', 'Cuerda: 14.01', 'payload: 15.804', 'Rango de comunicación: 6.441']\n", + "**Mediana calculada:** 12.254\n", "\n", "--- Imputación para aeronave: **V39** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.952x + -5.699\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.941x + -5.705\n", "Valor del parámetro correlacionado para la aeronave: 1.203\n", - "Predicción obtenida: 12.288\n", - "\tR²: 0.9288175658243848, Desviación Estándar: 2.470725546458319, Varianza: 6.104484725921759, Incertidumbre: 0.4437554079969149\n", + "Predicción obtenida: 12.269\n", + "\tR²: 0.9287486031571285, Desviación Estándar: 2.4702104975198655, Varianza: 6.101939902057342, Incertidumbre: 0.4436629024767654\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.357x + -3.554\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.372x + -3.605\n", "Valor del parámetro correlacionado para la aeronave: 1.954\n", - "Predicción obtenida: 12.775\n", - "\tR²: 0.7124768203038293, Desviación Estándar: 4.965631866447997, Varianza: 24.657499833083815, Incertidumbre: 0.8918538111271705\n", + "Predicción obtenida: 12.754\n", + "\tR²: 0.7157670307935512, Desviación Estándar: 4.933719987077154, Varianza: 24.341592910884593, Incertidumbre: 0.8861222683945283\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.306x + 2.46\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.306x + 2.452\n", "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 9.814\n", - "\tR²: 0.9148561084451423, Desviación Estándar: 2.6736569224974254, Varianza: 7.148441339218403, Incertidumbre: 0.47264023511607123\n", + "Predicción obtenida: 9.799\n", + "\tR²: 0.9142766506576697, Desviación Estándar: 2.6810066815314575, Varianza: 7.187796826416317, Incertidumbre: 0.47393950122933404\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.607x + -14.346\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.593x + -14.314\n", "Valor del parámetro correlacionado para la aeronave: 3.9\n", - "Predicción obtenida: 15.321\n", - "\tR²: 0.8533205216960276, Desviación Estándar: 3.5466862107514427, Varianza: 12.578983077534426, Incertidumbre: 0.6370036480762108\n", + "Predicción obtenida: 15.299\n", + "\tR²: 0.8514053187249602, Desviación Estándar: 3.567294004696238, Varianza: 12.725586515941725, Incertidumbre: 0.6407049171317712\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 177.47x + -40.16\n", - "Valor del parámetro correlacionado para la aeronave: 0.304\n", - "Predicción obtenida: 13.791\n", - "\tR²: 0.5091069267607173, Desviación Estándar: 6.488309232157318, Varianza: 42.098156692097895, Incertidumbre: 1.1653347392847273\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 175.776x + -39.835\n", + "Valor del parámetro correlacionado para la aeronave: 0.307\n", + "Predicción obtenida: 14.129\n", + "\tR²: 0.5041358522783776, Desviación Estándar: 6.516563415932599, Varianza: 42.46559875387115, Incertidumbre: 1.1704093404952065\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.153x + 4.318\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.144x + 4.253\n", "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 10.083\n", - "\tR²: 0.6704748222680529, Desviación Estándar: 5.259848463722186, Varianza: 27.666005861320635, Incertidumbre: 0.9298186291779004\n", + "Predicción obtenida: 9.972\n", + "\tR²: 0.6556479703357065, Desviación Estándar: 5.373405979420902, Varianza: 28.8734918196763, Incertidumbre: 0.9498929515292154\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQNan21A Blackjack', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'V21', 'V25', 'V32', 'V35']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'V21', 'V25', 'V32', 'V35']\n", "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 50.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.724, 12.253, 5.627, 10.336, 22.234, 21.149, 4.767, 4.767, 2.65, 3.45, 6.45, 12.273]\n", - "Ecuación de regresión: y = 0.095x + 4.271\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 10.192, 22.195, 21.123, 4.754, 4.754, 2.65, 3.45, 6.45, 12.254]\n", + "Ecuación de regresión: y = 0.095x + 4.285\n", "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 7.135\n", - "\tR²: 0.5259965377098035, Desviación Estándar: 4.511169408629526, Varianza: 20.35064943335487, Incertidumbre: 1.0941192921667469\n", + "Predicción obtenida: 7.13\n", + "\tR²: 0.5227923448909729, Desviación Estándar: 4.50989094884209, Varianza: 20.339116370447805, Incertidumbre: 1.093809220123117\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 12.288', 'Longitud del fuselaje: 12.775', 'Peso máximo al despegue (MTOW): 9.814', 'envergadura: 15.321', 'Cuerda: 13.791', 'payload: 10.083', 'Rango de comunicación: 7.135']\n", - "**Mediana calculada:** 12.288\n", + "Valores imputados: ['Área del ala: 12.269', 'Longitud del fuselaje: 12.754', 'Peso máximo al despegue (MTOW): 9.799', 'envergadura: 15.299', 'Cuerda: 14.129', 'payload: 9.972', 'Rango de comunicación: 7.13']\n", + "**Mediana calculada:** 12.269\n", "\n", "--- Imputación para aeronave: **Volitation VT370** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.952x + -5.699\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.941x + -5.705\n", "Valor del parámetro correlacionado para la aeronave: 1.424\n", - "Predicción obtenida: 15.592\n", - "\tR²: 0.9289339624984088, Desviación Estándar: 2.4318140515483493, Varianza: 5.913719581307998, Incertidumbre: 0.4298880516086425\n", + "Predicción obtenida: 15.571\n", + "\tR²: 0.9288651463121561, Desviación Estándar: 2.4313071135199693, Varianza: 5.911254280252805, Incertidumbre: 0.4297984367792653\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.36x + -3.576\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.375x + -3.627\n", "Valor del parámetro correlacionado para la aeronave: 2.02\n", - "Predicción obtenida: 13.311\n", - "\tR²: 0.7128606994643418, Desviación Estándar: 4.888162469550296, Varianza: 23.894132328720048, Incertidumbre: 0.8641132074401486\n", + "Predicción obtenida: 13.291\n", + "\tR²: 0.7161464221538233, Desviación Estándar: 4.856750413201213, Varianza: 23.58802457613015, Incertidumbre: 0.858560287926286\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.305x + 2.59\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.305x + 2.582\n", "Valor del parámetro correlacionado para la aeronave: 40.0\n", - "Predicción obtenida: 14.79\n", - "\tR²: 0.9128312102468352, Desviación Estándar: 2.6664750643886856, Varianza: 7.110089269006646, Incertidumbre: 0.4641737288731009\n", + "Predicción obtenida: 14.771\n", + "\tR²: 0.9122573197462216, Desviación Estándar: 2.6735124201413973, Varianza: 7.147668660650313, Incertidumbre: 0.46539877526665924\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.598x + -14.407\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 7.584x + -14.375\n", "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 34.979\n", - "\tR²: 0.8502155711682761, Desviación Estándar: 3.5304709885139887, Varianza: 12.46422540073894, Incertidumbre: 0.6241049941901538\n", + "Predicción obtenida: 34.922\n", + "\tR²: 0.8483051549539458, Desviación Estándar: 3.5504549853464953, Varianza: 12.605730602971782, Incertidumbre: 0.6276376991090227\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 177.588x + -40.243\n", - "Valor del parámetro correlacionado para la aeronave: 0.313\n", - "Predicción obtenida: 15.342\n", - "\tR²: 0.5090877540596378, Desviación Estándar: 6.391477230982371, Varianza: 40.85098119416608, Incertidumbre: 1.129864222956763\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 175.837x + -39.912\n", + "Valor del parámetro correlacionado para la aeronave: 0.314\n", + "Predicción obtenida: 15.301\n", + "\tR²: 0.503687273356439, Desviación Estándar: 6.42208878399288, Varianza: 41.24322434948715, Incertidumbre: 1.1352756321358586\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.147x + 4.438\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.137x + 4.381\n", "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 25.083\n", - "\tR²: 0.6693635088936598, Desviación Estándar: 5.193168453194595, Varianza: 26.968998583255537, Incertidumbre: 0.9040145913151921\n", + "Predicción obtenida: 24.85\n", + "\tR²: 0.6544160881835673, Desviación Estándar: 5.30583225901069, Varianza: 28.151855960758482, Incertidumbre: 0.923626842542706\n", "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 15.592', 'Longitud del fuselaje: 13.311', 'Peso máximo al despegue (MTOW): 14.79', 'envergadura: 34.979', 'Cuerda: 15.342', 'payload: 25.083']\n", - "**Mediana calculada:** 15.467\n", + "Valores imputados: ['Área del ala: 15.571', 'Longitud del fuselaje: 13.291', 'Peso máximo al despegue (MTOW): 14.771', 'envergadura: 34.922', 'Cuerda: 15.301', 'payload: 24.85']\n", + "**Mediana calculada:** 15.436\n", "\n", "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.951x + -5.702\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.94x + -5.708\n", "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 33.395\n", - "\tR²: 0.928961023163967, Desviación Estándar: 2.394780806275851, Varianza: 5.734975110107215, Incertidumbre: 0.41687782928419626\n", + "Predicción obtenida: 33.36\n", + "\tR²: 0.9288907011950363, Desviación Estándar: 2.394297517401611, Varianza: 5.7326606018355175, Incertidumbre: 0.41679369948981904\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.351x + -3.491\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.366x + -3.542\n", "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 25.737\n", - "\tR²: 0.7113023376551475, Desviación Estándar: 4.827686425855041, Varianza: 23.306556226385016, Incertidumbre: 0.8403923367019864\n", + "Predicción obtenida: 25.738\n", + "\tR²: 0.714598445858678, Desviación Estándar: 4.796705817916089, Varianza: 23.008386703630055, Incertidumbre: 0.8349993050918911\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Ancho del fuselaje (r = 0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.277, 0.375, 0.375]\n", "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 32.0, 35.0]\n", "Ecuación de regresión: y = 114.066x + -10.111\n", @@ -34157,132 +38571,15355 @@ "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.305x + 2.609\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.305x + 2.601\n", "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 33.112\n", - "\tR²: 0.9126951112649697, Desviación Estándar: 2.6294562235715246, Varianza: 6.914040031679023, Incertidumbre: 0.45094802203668083\n", + "Predicción obtenida: 33.074\n", + "\tR²: 0.9121259725181001, Desviación Estándar: 2.6363012992791948, Varianza: 6.950084540581171, Incertidumbre: 0.45212194283573964\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 6.49x + -10.713\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 6.477x + -10.686\n", "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 21.735\n", - "\tR²: 0.7330823891778183, Desviación Estándar: 4.642009599703794, Varianza: 21.548253123742178, Incertidumbre: 0.8080701500402837\n", + "Predicción obtenida: 21.7\n", + "\tR²: 0.7313279100692804, Desviación Estándar: 4.653998017305745, Varianza: 21.659697545085802, Incertidumbre: 0.8101570656750493\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 177.604x + -40.244\n", - "Valor del parámetro correlacionado para la aeronave: 0.336\n", - "Predicción obtenida: 19.431\n", - "\tR²: 0.5093083046494281, Desviación Estándar: 6.293928048096407, Varianza: 39.61353027461465, Incertidumbre: 1.0956322413664432\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 175.854x + -39.913\n", + "Valor del parámetro correlacionado para la aeronave: 0.338\n", + "Predicción obtenida: 19.526\n", + "\tR²: 0.5039052742482211, Desviación Estándar: 6.324078061960279, Varianza: 39.993963333767276, Incertidumbre: 1.1008806851068977\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.088x + 4.692\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.079x + 4.633\n", "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 31.883\n", - "\tR²: 0.6380380391473043, Desviación Estándar: 5.353997072660648, Varianza: 28.665284654058784, Incertidumbre: 0.9182029228184352\n", + "Predicción obtenida: 31.618\n", + "\tR²: 0.624328313685615, Desviación Estándar: 5.450909982107748, Varianza: 29.712419633041897, Incertidumbre: 0.934823349670657\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Capacidad combustible (r = 0.995) ---\n", "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", "Valores para Capacidad combustible: [13.0, 11.5, 11.5, 28.0, 28.0]\n", - "Valores para Empty weight: [15.467, 11.5, 11.0, 32.0, 35.0]\n", - "Ecuación de regresión: y = 1.31x + -3.112\n", + "Valores para Empty weight: [15.436, 11.5, 11.0, 32.0, 35.0]\n", + "Ecuación de regresión: y = 1.311x + -3.129\n", "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 33.57\n", - "\tR²: 0.98508534505441, Desviación Estándar: 1.2666533895780039, Varianza: 1.6044108093294462, Incertidumbre: 0.566464616605388\n", + "Predicción obtenida: 33.569\n", + "\tR²: 0.9852719437774983, Desviación Estándar: 1.259106590568882, Varianza: 1.5853494064139946, Incertidumbre: 0.5630895854860032\n", "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Área del ala: 33.395', 'Longitud del fuselaje: 25.737', 'Ancho del fuselaje: 32.663', 'Peso máximo al despegue (MTOW): 33.112', 'envergadura: 21.735', 'Cuerda: 19.431', 'payload: 31.883', 'Capacidad combustible: 33.57']\n", - "**Mediana calculada:** 32.273\n", + "Valores imputados: ['Área del ala: 33.36', 'Longitud del fuselaje: 25.738', 'Ancho del fuselaje: 32.663', 'Peso máximo al despegue (MTOW): 33.074', 'envergadura: 21.7', 'Cuerda: 19.526', 'payload: 31.618', 'Capacidad combustible: 33.569']\n", + "**Mediana calculada:** 32.14\n", "\n", "--- Imputación para aeronave: **Volitation VT510** ---\n", "\n", "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 32.273, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.841x + -5.584\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 32.14, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 14.821x + -5.579\n", "Valor del parámetro correlacionado para la aeronave: 1.993\n", - "Predicción obtenida: 23.995\n", - "\tR²: 0.9360251057500628, Desviación Estándar: 2.3659573340086495, Varianza: 5.597754106349317, Incertidumbre: 0.4057583352900256\n", + "Predicción obtenida: 23.959\n", + "\tR²: 0.9358207202237538, Desviación Estándar: 2.3667022774990354, Varianza: 5.601279670319122, Incertidumbre: 0.4058860920446379\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 3.004, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 32.273, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.649x + -3.948\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 1.562, 2.3, 4.712]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 32.14, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 8.658x + -3.99\n", "Valor del parámetro correlacionado para la aeronave: 2.905\n", - "Predicción obtenida: 21.176\n", - "\tR²: 0.7283975003859964, Desviación Estándar: 4.874936932144367, Varianza: 23.765010092385136, Incertidumbre: 0.8360447865217215\n", + "Predicción obtenida: 21.161\n", + "\tR²: 0.7315519871473125, Desviación Estándar: 4.840344870982094, Varianza: 23.42893847004267, Incertidumbre: 0.8301122969752286\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 32.273, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.303x + 2.653\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 32.14, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 0.303x + 2.649\n", "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 32.99\n", - "\tR²: 0.9213940860587302, Desviación Estándar: 2.5949358780767224, Varianza: 6.733692211329811, Incertidumbre: 0.4386242196208442\n", + "Predicción obtenida: 32.939\n", + "\tR²: 0.920742065863033, Desviación Estándar: 2.6024670945613564, Varianza: 6.772834978274627, Incertidumbre: 0.4398972275518982\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 32.273, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 6.733x + -11.353\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 32.14, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 6.719x + -11.32\n", "Valor del parámetro correlacionado para la aeronave: 5.1\n", - "Predicción obtenida: 22.986\n", - "\tR²: 0.7256919483141002, Desviación Estándar: 4.899157460266217, Varianza: 24.001743820482126, Incertidumbre: 0.8401985728260232\n", + "Predicción obtenida: 22.945\n", + "\tR²: 0.7244083786781033, Desviación Estándar: 4.904324680873634, Varianza: 24.052400575426272, Incertidumbre: 0.8410847438493143\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.313, 0.334, 0.301, 0.27, 0.298, 0.338, 0.341, 0.344, 0.31, 0.338, 0.276, 0.272, 0.278, 0.281, 0.291, 0.304, 0.304, 0.313, 0.295, 0.299, 0.309, 0.312, 0.346, 0.336, 0.342, 0.286, 0.29, 0.312]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 32.273, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 185.703x + -42.364\n", - "Valor del parámetro correlacionado para la aeronave: 0.334\n", - "Predicción obtenida: 19.661\n", - "\tR²: 0.5077463639535145, Desviación Estándar: 6.562912085594877, Varianza: 43.07181504324729, Incertidumbre: 1.1255301370941933\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.287, 0.291, 0.313]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 32.14, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 184.0x + -42.063\n", + "Valor del parámetro correlacionado para la aeronave: 0.335\n", + "Predicción obtenida: 19.577\n", + "\tR²: 0.5041716405668544, Desviación Estándar: 6.578272967275993, Varianza: 43.27367523199409, Incertidumbre: 1.128164506569046\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.778) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 32.14, 35.0, 3.0, 5.8, 31.0]\n", + "Ecuación de regresión: y = 1.084x + 4.602\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 31.707\n", + "\tR²: 0.6621543183227474, Desviación Estándar: 5.373076933587957, Varianza: 28.869955734254965, Incertidumbre: 0.9082157662811027\n", "\tNivel de confianza: Confianza Muy Baja\n", "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde® Mk. 4.7 Fixed Wing', 'Aerosonde® Mk. 4.7 VTOL', 'Aerosonde® Mk. 4.8 Fixed wing', 'Aerosonde® Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQNan21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.714, 7.8, 12.0, 5.5, 2.21, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.724, 12.253, 5.627, 10.336, 22.234, 24.82, 22.29, 14.74, 21.149, 4.8, 4.767, 2.65, 3.45, 6.45, 12.273, 12.288, 15.467, 6.5, 7.1, 11.5, 11.0, 32.0, 32.273, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.091x + 4.669\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 31.949\n", - "\tR²: 0.674893773503893, Desviación Estándar: 5.277298544902269, Varianza: 27.849879932027605, Incertidumbre: 0.8920262637393525\n", - "\tNivel de confianza: Confianza Muy Baja\n", + "--- Correlación: Capacidad combustible (r = 0.995) ---\n", + "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", + "Valores para Capacidad combustible: [13.0, 11.5, 11.5, 28.0, 28.0, 28.0]\n", + "Valores para Empty weight: [15.436, 11.5, 11.0, 32.0, 32.14, 35.0]\n", + "Ecuación de regresión: y = 1.281x + -2.774\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 29.251\n", + "\tR²: 0.9855262376673756, Desviación Estándar: 1.244330432674912, Varianza: 1.5483582256809336, Incertidumbre: 0.5079957719116919\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Área del ala: 23.959', 'Longitud del fuselaje: 21.161', 'Peso máximo al despegue (MTOW): 32.939', 'envergadura: 22.945', 'Cuerda: 19.577', 'payload: 31.707', 'Capacidad combustible: 29.251']\n", + "**Mediana calculada:** 23.959\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Reporte Final de Imputaciones

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AeronaveParámetroValor ImputadoNivel de Confianza
8Aerosonde Mk. 4.8 VTOL FTUASÁrea del ala2.5030.804
9Fulmar XÁrea del ala0.9400.804
10Orbiter 4Área del ala1.6080.804
11Orbiter 3Área del ala1.2000.804
12MantisÁrea del ala0.7540.804
13ScanEagleÁrea del ala1.0630.804
14IntegratorÁrea del ala1.8720.804
15Integrator VTOLÁrea del ala2.0900.804
16Integrator Extended Range (ER)Área del ala1.8720.804
17ScanEagle 3Área del ala1.3490.804
18RQ Nan 21A BlackjackÁrea del ala1.8020.804
19DeltaQuad Pro #MAPÁrea del ala0.7000.804
20DeltaQuad Pro #CARGOÁrea del ala0.7000.804
21V32Área del ala1.0300.804
29Fulmar XRelación de aspecto del ala13.2180.963
30Orbiter 4Relación de aspecto del ala13.4430.752
31Orbiter 3Relación de aspecto del ala13.9340.752
32MantisRelación de aspecto del ala14.7550.752
33ScanEagleRelación de aspecto del ala14.0570.776
34IntegratorRelación de aspecto del ala12.9080.632
35Integrator VTOLRelación de aspecto del ala12.6480.677
36Integrator Extended Range (ER)Relación de aspecto del ala12.8400.677
37ScanEagle 3Relación de aspecto del ala13.7650.677
38RQ Nan 21A BlackjackRelación de aspecto del ala12.9140.697
39DeltaQuad EvoRelación de aspecto del ala14.5890.720
40DeltaQuad Pro #MAPRelación de aspecto del ala14.7140.730
41DeltaQuad Pro #CARGORelación de aspecto del ala14.7140.743
42V21Relación de aspecto del ala14.5680.743
43V25Relación de aspecto del ala14.4210.755
44V32Relación de aspecto del ala14.1820.764
45V35Relación de aspecto del ala13.8980.770
46V39Relación de aspecto del ala14.0410.771
47Volitation VT370Relación de aspecto del ala13.6450.766
48Skyeye 2600Relación de aspecto del ala14.1030.773
49Skyeye 2930 VTOLRelación de aspecto del ala14.0010.671
50Skyeye 3600Relación de aspecto del ala13.7100.677
51Skyeye 3600 VTOLRelación de aspecto del ala13.6710.677
52Skyeye 5000Relación de aspecto del ala12.6950.676
53Skyeye 5000 VTOLRelación de aspecto del ala13.0320.700
54Skyeye 5000 VTOL octoRelación de aspecto del ala12.8550.705
55Volitation VT510Relación de aspecto del ala13.0990.719
56AscendRelación de aspecto del ala14.3490.727
57TransitionRelación de aspecto del ala14.2230.733
58ReachRelación de aspecto del ala13.6690.737
59Aerosonde Mk. 4.8 VTOL FTUASLongitud del fuselaje3.5950.831
60Integrator VTOLLongitud del fuselaje2.9980.831
61V39Longitud del fuselaje1.9540.831
95Aerosonde Mk. 4.8 VTOL FTUASenvergadura5.6440.805
96Integrator VTOLenvergadura5.0330.805
97Aerosonde Mk. 4.8 VTOL FTUASCuerda0.3940.827
98Fulmar XCuerda0.3190.827
99Orbiter 4Cuerda0.3320.758
100Orbiter 3Cuerda0.3040.758
101MantisCuerda0.2710.758
102ScanEagleCuerda0.2980.782
103IntegratorCuerda0.3390.725
104Integrator VTOLCuerda0.3410.757
105Integrator Extended Range (ER)Cuerda0.3450.757
106ScanEagle 3Cuerda0.3110.757
107RQ Nan 21A BlackjackCuerda0.3410.759
108DeltaQuad EvoCuerda0.2760.784
109DeltaQuad Pro #MAPCuerda0.2720.803
110DeltaQuad Pro #CARGOCuerda0.2720.819
111V21Cuerda0.2780.819
112V25Cuerda0.2810.602
113V32Cuerda0.2920.591
114V35Cuerda0.3060.590
115V39Cuerda0.3070.590
116Volitation VT370Cuerda0.3140.590
117Skyeye 2600Cuerda0.2960.590
118Skyeye 2930 VTOLCuerda0.3000.589
119Skyeye 3600Cuerda0.3110.590
120Skyeye 3600 VTOLCuerda0.3150.594
121Skyeye 5000Cuerda0.3480.599
122Skyeye 5000 VTOLCuerda0.3380.684
123Skyeye 5000 VTOL octoCuerda0.3440.684
124Volitation VT510Cuerda0.3350.729
125AscendCuerda0.2870.729
126TransitionCuerda0.2910.725
127ReachCuerda0.3130.723
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Resumen de Imputaciones

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AeronaveCantidad de Valores Imputados
0Aerosonde Mk. 4.8 VTOL FTUAS4.000
1Ascend2.000
2DeltaQuad Evo2.000
3DeltaQuad Pro #CARGO3.000
4DeltaQuad Pro #MAP3.000
5Fulmar X3.000
6Integrator3.000
7Integrator Extended Range (ER)3.000
8Integrator VTOL5.000
9Mantis3.000
10Orbiter 33.000
11Orbiter 43.000
12RQ Nan 21A Blackjack3.000
13Reach2.000
14ScanEagle3.000
15ScanEagle 33.000
16Skyeye 26002.000
17Skyeye 2930 VTOL2.000
18Skyeye 36002.000
19Skyeye 3600 VTOL2.000
20Skyeye 50002.000
21Skyeye 5000 VTOL2.000
22Skyeye 5000 VTOL octo2.000
23Transition2.000
24V212.000
25V252.000
26V323.000
27V352.000
28V393.000
29Volitation VT3702.000
30Volitation VT5102.000
TotalTotal80.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m>>> RESULTADOS DE IMPUTACIÓN POR CORRELACIÓN\u001b[0m\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 30.228668858925072 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - AAI Aerosonde = 36.09414654431562 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Orbiter 4 = 30.466419244333917 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Orbiter 3 = 27.426371766294327 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Integrator Extended Range (ER) = 31.89437620999355 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Skyeye 5000 VTOL octo = 30.290908946357952 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Orbiter 4 = 9403.635180379342 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Orbiter 3 = 6839.1446057940275 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 2600 = 14972.955913461341 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 2930 VTOL = 15999.999999999998 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 3600 VTOL = 16959.091874090493 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 VTOL = 16009.435943246384 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 VTOL octo = 16009.476366047784 (Similitud)\n", + "Imputación final aplicada: Área del ala - Aerosonde Mk. 4.8 VTOL FTUAS = 2.503 (Correlación)\n", + "Imputación final aplicada: Área del ala - Fulmar X = 0.94 (Correlación)\n", + "Imputación final aplicada: Área del ala - Orbiter 4 = 1.608 (Correlación)\n", + "Imputación final aplicada: Área del ala - ScanEagle 3 = 1.349 (Correlación)\n", + "Imputación final aplicada: Área del ala - RQ Nan 21A Blackjack = 1.802 (Correlación)\n", + "Imputación final aplicada: Área del ala - V32 = 1.03 (Correlación)\n", + "Imputación final aplicada: Área del ala - V35 = 1.202 (Correlación)\n", + "Imputación final aplicada: Área del ala - V39 = 1.203 (Correlación)\n", + "Imputación final aplicada: Área del ala - Volitation VT370 = 1.424 (Correlación)\n", + "Imputación final aplicada: Área del ala - Volitation VT510 = 1.993 (Correlación)\n", + "Imputación final aplicada: Área del ala - Ascend = 0.771 (Correlación)\n", + "Imputación final aplicada: Área del ala - Transition = 0.986 (Correlación)\n", + "Imputación final aplicada: Área del ala - Reach = 2.329 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Fulmar X = 13.217500000000001 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Orbiter 4 = 13.443 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - ScanEagle 3 = 13.765 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - RQ Nan 21A Blackjack = 12.914 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - V25 = 14.421 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 2600 = 14.103 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 VTOL = 13.032 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 VTOL octo = 12.8555 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Volitation VT510 = 13.099 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Reach = 13.669 (Correlación)\n", + "Imputación final aplicada: Longitud del fuselaje - Aerosonde Mk. 4.8 VTOL FTUAS = 3.5945 (Correlación)\n", + "Imputación final aplicada: Longitud del fuselaje - V39 = 1.954 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - Aerosonde Mk. 4.8 VTOL FTUAS = 800.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Integrator = 499.99999999999994 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - ScanEagle 3 = 50.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - V21 = 270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - V25 = 270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Volitation VT370 = 300.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 2600 = 3270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 5000 VTOL octo = 800.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Volitation VT510 = 800.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Ascend = 270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Reach = 800.0 (Similitud)\n", + "Imputación final aplicada: Autonomía de la aeronave - Skyeye 5000 VTOL octo = 11.672906868436865 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 42.25267526977939 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Evo = 33.0 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - Skyeye 2600 = 30.8342888378733 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 18.90746548752 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - AAI Aerosonde = 10.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - DeltaQuad Evo = 14.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - V35 = 18.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - V39 = 17.397389995852386 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Volitation VT370 = 24.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Skyeye 5000 VTOL = 19.109224697504906 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 25.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - AAI Aerosonde = 10.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - DeltaQuad Evo = 14.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - V35 = 18.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - V39 = 17.397389995852386 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Volitation VT370 = 24.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Skyeye 5000 VTOL = 25.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Skyeye 5000 VTOL octo = 25.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Ascend = 14.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Transition = 10.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Reach = 25.0 (Similitud)\n", + "Imputación final aplicada: envergadura - Aerosonde Mk. 4.8 VTOL FTUAS = 5.644 (Correlación)\n", + "Imputación final aplicada: Cuerda - Fulmar X = 0.319 (Correlación)\n", + "Imputación final aplicada: Cuerda - Orbiter 4 = 0.332 (Correlación)\n", + "Imputación final aplicada: Cuerda - ScanEagle 3 = 0.3115 (Correlación)\n", + "Imputación final aplicada: Cuerda - RQ Nan 21A Blackjack = 0.341 (Correlación)\n", + "Imputación final aplicada: Cuerda - V25 = 0.281 (Correlación)\n", + "Imputación final aplicada: Cuerda - Skyeye 2600 = 0.296 (Correlación)\n", + "Imputación final aplicada: payload - AAI Aerosonde = 4.0 (Similitud)\n", + "Imputación final aplicada: payload - Fulmar X = 2.4947559999999998 (Similitud)\n", + "Imputación final aplicada: Empty weight - Aerosonde Mk. 4.8 VTOL FTUAS = 31.0 (Similitud)\n", + "Imputación final aplicada: Empty weight - Fulmar X = 17.463292 (Similitud)\n", + "Imputación final aplicada: Empty weight - V35 = 7.1 (Similitud)\n", + "Imputación final aplicada: Empty weight - V39 = 6.708303497304052 (Similitud)\n", + "Imputación final aplicada: Empty weight - Volitation VT370 = 10.999999999999998 (Similitud)\n", + "Imputación final aplicada: Empty weight - Skyeye 5000 VTOL = 32.140499999999996 (Correlación)\n", + "Imputación final aplicada: Empty weight - Volitation VT510 = 23.959 (Correlación)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Integrator VTOL = 21.463 (Correlación)\n", + "Imputación final aplicada: Techo de servicio máximo - Integrator VTOL = 7013.834 (Correlación)\n", + "Imputación final aplicada: Área del ala - Orbiter 3 = 1.2 (Correlación)\n", + "Imputación final aplicada: Área del ala - Mantis = 0.754 (Correlación)\n", + "Imputación final aplicada: Área del ala - ScanEagle = 1.063 (Correlación)\n", + "Imputación final aplicada: Área del ala - Integrator = 1.872 (Correlación)\n", + "Imputación final aplicada: Área del ala - Integrator VTOL = 2.0895 (Correlación)\n", + "Imputación final aplicada: Área del ala - Integrator Extended Range (ER) = 1.872 (Correlación)\n", + "Imputación final aplicada: Área del ala - DeltaQuad Pro #MAP = 0.7 (Correlación)\n", + "Imputación final aplicada: Área del ala - DeltaQuad Pro #CARGO = 0.7 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Orbiter 3 = 13.9345 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Mantis = 14.755 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - ScanEagle = 14.057 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Integrator = 12.908 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Integrator VTOL = 12.648 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Integrator Extended Range (ER) = 12.84 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - DeltaQuad Evo = 14.589 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - DeltaQuad Pro #MAP = 14.714 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - DeltaQuad Pro #CARGO = 14.714 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - V21 = 14.568 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - V32 = 14.182 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - V35 = 13.898 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - V39 = 14.0415 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Volitation VT370 = 13.645 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 2930 VTOL = 14.001 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 3600 = 13.7095 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 3600 VTOL = 13.6715 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 = 12.695 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Ascend = 14.349 (Correlación)\n", + "Imputación final aplicada: Relación de aspecto del ala - Transition = 14.223 (Correlación)\n", + "Imputación final aplicada: Longitud del fuselaje - Integrator VTOL = 2.998 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - Aerosonde Mk. 4.7 Fixed Wing = 518.9225 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - Aerosonde Mk. 4.7 VTOL = 481.428 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - Aerosonde Mk. 4.8 Fixed wing = 535.2755 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - Orbiter 4 = 509.5565 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - ScanEagle = 503.5155 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - Integrator VTOL = 646.0835 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - RQ Nan 21A Blackjack = 565.912 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - V32 = 412.68600000000004 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - V35 = 456.221 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - V39 = 413.556 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 2930 VTOL = 425.273 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 3600 = 458.1245 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 5000 = 530.401 (Correlación)\n", + "Imputación final aplicada: Alcance de la aeronave - Transition = 506.641 (Correlación)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - Integrator VTOL = 40.216 (Correlación)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Pro #MAP = 29.009 (Correlación)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Pro #CARGO = 29.009 (Correlación)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - Skyeye 3600 = 35.0985 (Correlación)\n", + "Imputación final aplicada: envergadura - Integrator VTOL = 5.033 (Correlación)\n", + "Imputación final aplicada: Cuerda - Aerosonde Mk. 4.8 VTOL FTUAS = 0.394 (Correlación)\n", + "Imputación final aplicada: Cuerda - Orbiter 3 = 0.304 (Correlación)\n", + "Imputación final aplicada: Cuerda - Mantis = 0.271 (Correlación)\n", + "Imputación final aplicada: Cuerda - ScanEagle = 0.2985 (Correlación)\n", + "Imputación final aplicada: Cuerda - Integrator = 0.3385 (Correlación)\n", + "Imputación final aplicada: Cuerda - Integrator VTOL = 0.341 (Correlación)\n", + "Imputación final aplicada: Cuerda - Integrator Extended Range (ER) = 0.345 (Correlación)\n", + "Imputación final aplicada: Cuerda - DeltaQuad Evo = 0.2755 (Correlación)\n", + "Imputación final aplicada: Cuerda - DeltaQuad Pro #MAP = 0.272 (Correlación)\n", + "Imputación final aplicada: Cuerda - DeltaQuad Pro #CARGO = 0.272 (Correlación)\n", + "Imputación final aplicada: Cuerda - V21 = 0.278 (Correlación)\n", + "Imputación final aplicada: Cuerda - V32 = 0.292 (Correlación)\n", + "Imputación final aplicada: Cuerda - V35 = 0.306 (Correlación)\n", + "Imputación final aplicada: Cuerda - V39 = 0.307 (Correlación)\n", + "Imputación final aplicada: Cuerda - Volitation VT370 = 0.314 (Correlación)\n", + "Imputación final aplicada: Cuerda - Skyeye 2930 VTOL = 0.3 (Correlación)\n", + "Imputación final aplicada: Cuerda - Skyeye 3600 = 0.311 (Correlación)\n", + "Imputación final aplicada: Cuerda - Skyeye 3600 VTOL = 0.315 (Correlación)\n", + "Imputación final aplicada: Cuerda - Skyeye 5000 = 0.3485 (Correlación)\n", + "Imputación final aplicada: Cuerda - Skyeye 5000 VTOL = 0.338 (Correlación)\n", + "Imputación final aplicada: Cuerda - Skyeye 5000 VTOL octo = 0.3445 (Correlación)\n", + "Imputación final aplicada: Cuerda - Volitation VT510 = 0.335 (Correlación)\n", + "Imputación final aplicada: Cuerda - Ascend = 0.287 (Correlación)\n", + "Imputación final aplicada: Cuerda - Transition = 0.291 (Correlación)\n", + "Imputación final aplicada: Cuerda - Reach = 0.313 (Correlación)\n", + "Imputación final aplicada: payload - Mantis = 2.693 (Correlación)\n", + "Imputación final aplicada: Empty weight - Aerosonde Mk. 4.7 Fixed Wing = 19.796 (Correlación)\n", + "Imputación final aplicada: Empty weight - Aerosonde Mk. 4.7 VTOL = 19.796 (Correlación)\n", + "Imputación final aplicada: Empty weight - Aerosonde Mk. 4.8 Fixed wing = 19.809 (Correlación)\n", + "Imputación final aplicada: Empty weight - Orbiter 4 = 18.365 (Correlación)\n", + "Imputación final aplicada: Empty weight - Orbiter 3 = 12.237 (Correlación)\n", + "Imputación final aplicada: Empty weight - Mantis = 5.633 (Correlación)\n", + "Imputación final aplicada: Empty weight - ScanEagle = 10.192 (Correlación)\n", + "Imputación final aplicada: Empty weight - Integrator = 22.195 (Correlación)\n", + "Imputación final aplicada: Empty weight - Integrator VTOL = 24.7845 (Correlación)\n", + "Imputación final aplicada: Empty weight - Integrator Extended Range (ER) = 22.256999999999998 (Correlación)\n", + "Imputación final aplicada: Empty weight - ScanEagle 3 = 14.794 (Correlación)\n", + "Imputación final aplicada: Empty weight - RQ Nan 21A Blackjack = 21.123 (Correlación)\n", + "Imputación final aplicada: Empty weight - DeltaQuad Pro #MAP = 4.754 (Correlación)\n", + "Imputación final aplicada: Empty weight - DeltaQuad Pro #CARGO = 4.754 (Correlación)\n", + "\n", + "=== Iteración 1: Resumen después de imputaciones ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de Valores Faltantes Después de Iteración 1

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ColumnaValores Faltantes
0Stalker XE2.000
1Stalker VXE302.000
2Aerosonde Mk. 4.7 Fixed Wing2.000
3Aerosonde Mk. 4.7 VTOL2.000
4Aerosonde Mk. 4.8 Fixed wing2.000
5Aerosonde Mk. 4.8 VTOL FTUAS0.000
6AAI Aerosonde0.000
7Fulmar X2.000
8Orbiter 42.000
9Orbiter 32.000
10Mantis3.000
11ScanEagle2.000
12Integrator2.000
13Integrator VTOL2.000
14Integrator Extended Range (ER)2.000
15ScanEagle 32.000
16RQ Nan 21A Blackjack2.000
17DeltaQuad Evo0.000
18DeltaQuad Pro #MAP2.000
19DeltaQuad Pro #CARGO2.000
20V210.000
21V250.000
22V320.000
23V350.000
24V390.000
25Volitation VT3700.000
26Skyeye 26000.000
27Skyeye 2930 VTOL0.000
28Skyeye 36002.000
29Skyeye 3600 VTOL0.000
30Skyeye 50001.000
31Skyeye 5000 VTOL0.000
32Skyeye 5000 VTOL octo0.000
33Volitation VT5100.000
34Ascend0.000
35Transition0.000
36Reach0.000
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Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes38.000
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Resumen de Valores Faltantes Antes de Iteración 2

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ColumnaValores Faltantes
0Stalker XE32.000
1Stalker VXE3033.000
2Aerosonde Mk. 4.7 Fixed Wing30.000
3Aerosonde Mk. 4.7 VTOL29.000
4Aerosonde Mk. 4.8 Fixed wing33.000
5Aerosonde Mk. 4.8 VTOL FTUAS33.000
6AAI Aerosonde30.000
7Fulmar X36.000
8Orbiter 436.000
9Orbiter 336.000
10Mantis36.000
11ScanEagle35.000
12Integrator35.000
13Integrator VTOL34.000
14Integrator Extended Range (ER)37.000
15ScanEagle 335.000
16RQ Nan 21A Blackjack34.000
17DeltaQuad Evo28.000
18DeltaQuad Pro #MAP32.000
19DeltaQuad Pro #CARGO32.000
20V2128.000
21V2528.000
22V3228.000
23V3531.000
24V3931.000
25Volitation VT37030.000
26Skyeye 260033.000
27Skyeye 2930 VTOL32.000
28Skyeye 360034.000
29Skyeye 3600 VTOL31.000
30Skyeye 500030.000
31Skyeye 5000 VTOL30.000
32Skyeye 5000 VTOL octo30.000
33Volitation VT51030.000
34Ascend29.000
35Transition29.000
36Reach29.000
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Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes1179.000
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Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker XE - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker VXE30 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker XE - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker VXE30 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1mNo se realizaron imputaciones por similitud en esta iteración.\u001b[0m\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN 2 ***\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "\n", + "=== DataFrame inicial ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())

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Stalker XEStalker VXE30Aerosonde Mk. 4.7 Fixed WingAerosonde Mk. 4.7 VTOLAerosonde Mk. 4.8 Fixed wingAerosonde Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQ Nan 21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Modelo
Distancia de carrera requerida para despegue0.00.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.0
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.3440530.22866936.09414730.40658430.46641927.42637218.26582630.62533630.95346521.46331.89437625.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.62533630.29090932.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.8429403.635186839.144606NaN19500.019500.07013.83419500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.014972.95591316000.0NaN16959.091874NaN16009.43594316009.47636617000.010000.013000.016000.0
Velocidad de pérdida limpia (KCAS)NaNNaNNaNNaNNaN25.010.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.0NaNNaN14.015.517.018.017.3973924.010.018.012.524.015.025.025.025.014.010.025.0
Área del ala0.871.1582831.551.551.552.5030.570.941.6081.20.7541.0631.8722.08951.8721.3491.8020.840.70.70.80.521.031.2021.2031.4240.881.01.331.322.6152.6152.6151.9930.7710.9862.329
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.75438613.217513.44313.934514.75514.05712.90812.64812.8413.76512.91414.58914.71414.71414.56814.42114.18213.89814.041513.64514.10314.00113.709513.671512.69513.03212.855513.09914.34914.22313.669
Longitud del fuselaje2.12.59083.03.03.03.59451.71.21.21.21.481.712.52.9982.52.42.50.750.90.90.930.931.01.881.9542.022.052.032.4882.423.53.53.52.9051.5622.34.712
Profundidad del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Ancho del fuselaje0.2110.20.2770.2770.277NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.3750.3750.375NaNNaNNaNNaN
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0518.9225481.428535.2755800.03270.0800.0509.556550.025.0503.5155500.0646.0835500.050.0565.912270.0100.0100.0270.0270.0412.686456.221413.556300.03270.0425.273458.1245300.0530.401800.0800.0800.0270.0506.641800.0
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.011.6729075.06.012.020.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.4388642.25267530.84572541.736.036.025.641.246.340.21646.341.246.333.029.00929.00933.033.033.033.033.033.030.83428930.035.098533.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaN18.90746510.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.0NaNNaN14.015.517.018.017.3973924.010.018.012.524.015.019.10922524.025.013.013.013.0
Tasa de ascensoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Radio de giroNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100.0120.0150.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
envergadura3.6574.87684.44.44.45.6442.93.05.24.42.13.14.85.0334.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.3520.3940.1965520.3190.3320.3040.2710.29850.33850.3410.3450.31150.3410.27550.2720.2720.2780.2810.2920.3060.3070.3140.2960.30.3110.3150.34850.3380.34450.3350.2870.2910.313
payload2.4947562.49475614.511.317.722.74.02.49475612.05.52.6935.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
duracion en VTOL2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050.050.05
Crucero KIAS15.4333216.09342225.025.025.0NaNNaN27.8NaNNaN16.728.028.3NaNNaN23.530.916.5416.016.018.020.020.025.025.025.033.024.0NaN30.033.028.035.030.020.020.025.0
RTF (dry weight)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.011.854.0
RTF (Including fuel & Batteries)NaNNaN27.742.236.770.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8.916.584.0
Empty weight10.88620817.46329219.79619.79619.80931.010.017.46329218.36512.2375.63310.19222.19524.784522.25714.79421.1234.84.7544.7542.653.456.457.16.70830311.06.57.111.511.032.032.140535.023.9593.05.831.0
Maximum CrosswindNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.0NaNNaNNaN45.050.050.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15.015.015.0
Rango de comunicación59.0161.0140.0140.0140.0NaN150.0NaN150.050.025.0101.8692.6NaNNaNNaN92.6NaN50.030.030.030.030.030.030.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Wing LoadingNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.524.025.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Potencia específica (P/W)NaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Capacidad combustibleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.0NaNNaN11.511.528.028.028.025.0NaNNaNNaN
ConsumoNaNNaN0.60.6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.96NaNNaNNaNNaN1.2NaNNaN5.0NaNNaNNaN
Potencia WattsNaNNaN2980.02980.0NaNNaN1280.0NaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Potencia HPNaNNaN4.04.0NaNNaN1.74NaNNaNNaNNaNNaNNaNNaNNaNNaN8.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
PrecioNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.0NaNNaNNaN
Tiempo de emergencia en vueloNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.1080.1080.108NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Distancia de aterrizajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Despegue1.01.01.02.01.02.02.01.01.01.01.01.01.02.01.01.01.02.02.02.02.02.02.02.02.02.02.02.03.02.03.02.02.02.02.02.02.0
Propulsión horizontal2.02.02.02.02.02.02.02.02.01.01.02.02.02.02.02.02.01.01.01.01.01.02.02.02.02.02.02.02.02.02.02.02.02.02.02.02.0
Propulsión vertical5.05.05.01.05.01.01.05.05.05.05.05.05.01.05.05.05.01.01.01.01.01.01.01.01.01.01.01.05.01.05.01.01.01.01.01.01.0
Cantidad de motores propulsión vertical0.00.00.04.00.04.04.00.00.00.00.00.00.04.00.00.00.04.04.04.04.04.04.04.04.04.04.04.00.04.00.04.08.04.04.04.04.0
Cantidad de motores propulsión horizontal1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
Misión1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Battery Power SupplyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Modelo Motor Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Modelo Motor VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
PortabilidadNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
CámaraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Despegue todos los tiposNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Datalink banksNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Motor recomendadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Hélice recomendada VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Hélice recomendada Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Sistema de controlNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Características adicionalesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
indice_desconocidoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Convertir todo a numérico ===\n", + "\n", + "\n", + "=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===\n", + "\n", + "Umbral seleccionado para correlaciones significativas: 0.7\n", + "\n", + "=== Cálculo de tabla completa ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Tabla de Correlaciones con todos los parametros(tabla_completa)

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ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Modelo
Distancia de carrera requerida para despegue1.0000.0630.427nan-0.3420.269-0.2500.2220.4250.1680.054-0.0780.241-0.3110.1050.2000.229nan0.389nan0.225nannan-0.018-0.240-0.1560.7350.1540.671-0.598nannan
Altitud a la que se realiza el crucero0.0631.0000.0110.0900.331-0.0750.105-0.092nan-0.095-0.309-0.278-0.0640.343-0.0800.109-0.114nannannan-0.1210.038-0.325nannannan-0.119-0.1090.187-0.159nannan
Velocidad a la que se realiza el crucero (KTAS)0.4270.0111.0000.1220.2080.497-0.6070.4280.9170.5530.5450.4230.6340.1610.4210.3960.567-0.6940.9730.7900.503-0.8540.4800.3650.461-0.2900.0630.5630.120-0.076nannan
Techo de servicio máximonan0.0900.1221.0000.0070.083-0.0170.1210.5460.1170.1010.0160.0050.0260.0170.0180.107-0.8750.0990.579-0.018-0.961-0.118-0.7570.515-0.1710.1740.058-0.1740.176nannan
Velocidad de pérdida limpia (KCAS)-0.3420.3310.2080.0071.0000.711-0.6890.595nan0.768-0.3430.1600.5870.8170.7740.6700.705-0.1810.4280.9360.662-0.181-0.8740.2240.5320.118-0.2530.261-0.2530.365nannan
Área del ala0.269-0.0750.4970.0830.7111.000-0.7780.8350.9840.970-0.0230.3830.6480.4570.8250.7870.854-0.3830.6920.9650.944-0.4660.4910.9740.2880.0360.1250.4760.0720.037nannan
Relación de aspecto del ala-0.2500.105-0.607-0.017-0.689-0.7781.000-0.624-0.681-0.790-0.003-0.456-0.730-0.567-0.630-0.862-0.8260.519-0.769-0.497-0.7460.432-0.409-0.9700.2960.024-0.001-0.471-0.1410.075nannan
Longitud del fuselaje0.222-0.0920.4280.1210.5950.835-0.6241.0000.9380.8060.1400.4030.3630.2450.7190.6230.660-0.6170.5740.9260.834-0.6960.6460.9290.036-0.2030.1380.6120.0340.040nannan
Ancho del fuselaje0.425nan0.9170.546nan0.984-0.6810.9381.0000.9860.833-0.0890.940nan0.6710.5570.868nan0.944nan0.954nan0.323nan1.000nan0.794nan-0.5350.574nannan
Peso máximo al despegue (MTOW)0.168-0.0950.5530.1170.7680.970-0.7900.8060.9861.0000.0300.4200.7170.5230.8110.7510.882-0.4010.7080.9790.933-0.4640.5140.9760.7580.0520.0900.4670.0230.075nannan
Alcance de la aeronave0.054-0.3090.5450.101-0.343-0.023-0.0030.1400.8330.0301.0000.2240.013-0.429-0.043-0.2240.019-0.5250.5240.9360.081-0.7110.4670.8480.837-0.1480.1570.317-0.2130.210nannan
Autonomía de la aeronave-0.078-0.2780.4230.0160.1600.383-0.4560.403-0.0890.4200.2241.0000.378-0.0980.5410.2690.400-0.5940.3370.6340.486-0.7150.8020.056-0.7320.033-0.4200.4780.353-0.314nannan
Velocidad máxima (KIAS)0.241-0.0640.6340.0050.5870.648-0.7300.3630.9400.7170.0130.3781.0000.5000.4910.6270.718-0.0770.7000.7260.613-0.2230.1510.7270.9100.067-0.0570.3000.178-0.141nannan
Velocidad de pérdida (KCAS)-0.3110.3430.1610.0260.8170.457-0.5670.245nan0.523-0.429-0.0980.5001.0000.5570.5570.6401.0000.4140.4340.3701.000-0.8740.0360.5320.121-0.2220.211-0.2220.381nannan
envergadura0.105-0.0800.4210.0170.7740.825-0.6300.7190.6710.811-0.0430.5410.4910.5571.0000.6860.775-0.2580.5010.9500.806-0.4140.6480.2970.0850.032-0.0810.5160.167-0.106nannan
Cuerda0.2000.1090.3960.0180.6700.787-0.8620.6230.5570.751-0.2240.2690.6270.5570.6861.0000.758-0.4990.7300.5950.724-0.4980.3550.975-0.228-0.041-0.0650.4180.193-0.129nannan
payload0.229-0.1140.5670.1070.7050.854-0.8260.6600.8680.8820.0190.4000.7180.6400.7750.7581.000-0.0240.6700.5590.784-0.1420.4890.7110.846-0.0080.0530.4620.100-0.055nannan
duracion en VTOLnannan-0.694-0.875-0.181-0.3830.519-0.617nan-0.401-0.525-0.594-0.0771.000-0.258-0.499-0.0241.000-0.694-0.402-0.3151.000nannannannan-0.188-0.9040.188-0.188nannan
Crucero KIAS0.389nan0.9730.0990.4280.692-0.7690.5740.9440.7080.5240.3370.7000.4140.5010.7300.670-0.6941.0000.7230.636-0.8550.3590.5810.461-0.2430.1430.6080.0650.063nannan
RTF (Including fuel & Batteries)nannan0.7900.5790.9360.965-0.4970.926nan0.9790.9360.6340.7260.4340.9500.5950.559-0.4020.7231.0000.948-0.402nannannannan0.0970.428-0.0970.097nannan
Empty weight0.225-0.1210.503-0.0180.6620.944-0.7460.8340.9540.9330.0810.4860.6130.3700.8060.7240.784-0.3150.6360.9481.000-0.3860.7850.9800.2510.023-0.0290.4800.195-0.070nannan
Maximum Crosswindnan0.038-0.854-0.961-0.181-0.4660.432-0.696nan-0.464-0.711-0.715-0.2231.000-0.414-0.498-0.1421.000-0.855-0.402-0.3861.000nannannannannan-0.943nannannannan
Rango de comunicaciónnan-0.3250.480-0.118-0.8740.491-0.4090.6460.3230.5140.4670.8020.151-0.8740.6480.3550.489nan0.359nan0.785nan1.000nannannan-0.4300.6040.430-0.430nannan
Capacidad combustible-0.018nan0.365-0.7570.2240.974-0.9700.929nan0.9760.8480.0560.7270.0360.2970.9750.711nan0.581nan0.980nannan1.0000.3770.817-0.080nan-0.0800.270nannan
Consumo-0.240nan0.4610.5150.5320.2880.2960.0361.0000.7580.837-0.7320.9100.5320.085-0.2280.846nan0.461nan0.251nannan0.3771.0000.9980.113nan-0.3750.375nannan
Precio-0.156nan-0.290-0.1710.1180.0360.024-0.203nan0.052-0.1480.0330.0670.1210.032-0.041-0.008nan-0.243nan0.023nannan0.8170.9981.000-0.1380.217-0.1380.134nannan
Despegue0.735-0.1190.0630.174-0.2530.125-0.0010.1380.7940.0900.157-0.420-0.057-0.222-0.081-0.0650.053-0.1880.1430.097-0.029nan-0.430-0.0800.113-0.1381.000-0.010-0.6390.610nannan
Propulsión horizontal0.154-0.1090.5630.0580.2610.476-0.4710.612nan0.4670.3170.4780.3000.2110.5160.4180.462-0.9040.6080.4280.480-0.9430.604nannan0.217-0.0101.0000.118-0.083nannan
Propulsión vertical0.6710.1870.120-0.174-0.2530.072-0.1410.034-0.5350.023-0.2130.3530.178-0.2220.1670.1930.1000.1880.065-0.0970.195nan0.430-0.080-0.375-0.138-0.6390.1181.000-0.954nannan
Cantidad de motores propulsión vertical-0.598-0.159-0.0760.1760.3650.0370.0750.0400.5740.0750.210-0.314-0.1410.381-0.106-0.129-0.055-0.1880.0630.097-0.070nan-0.4300.2700.3750.1340.610-0.083-0.9541.000nannan
Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores1024.000
1Valores numéricos820.000
2Valores NaN204.000
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Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

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ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Modelo
Velocidad a la que se realiza el crucero (KTAS)1.0000.1220.497-0.6070.4280.5530.5450.4230.6340.1610.2080.4210.3960.5670.503
Techo de servicio máximo0.1221.0000.083-0.0170.1210.1170.1010.0160.0050.0260.0070.0170.0180.107-0.018
Área del ala0.4970.0831.000-0.7780.8350.970-0.0230.3830.6480.4570.7110.8250.7870.8540.944
Relación de aspecto del ala-0.607-0.017-0.7781.000-0.624-0.790-0.003-0.456-0.730-0.567-0.689-0.630-0.862-0.826-0.746
Longitud del fuselaje0.4280.1210.835-0.6241.0000.8060.1400.4030.3630.2450.5950.7190.6230.6600.834
Peso máximo al despegue (MTOW)0.5530.1170.970-0.7900.8061.0000.0300.4200.7170.5230.7680.8110.7510.8820.933
Alcance de la aeronave0.5450.101-0.023-0.0030.1400.0301.0000.2240.013-0.429-0.343-0.043-0.2240.0190.081
Autonomía de la aeronave0.4230.0160.383-0.4560.4030.4200.2241.0000.378-0.0980.1600.5410.2690.4000.486
Velocidad máxima (KIAS)0.6340.0050.648-0.7300.3630.7170.0130.3781.0000.5000.5870.4910.6270.7180.613
Velocidad de pérdida (KCAS)0.1610.0260.457-0.5670.2450.523-0.429-0.0980.5001.0000.8170.5570.5570.6400.370
Velocidad de pérdida limpia (KCAS)0.2080.0070.711-0.6890.5950.768-0.3430.1600.5870.8171.0000.7740.6700.7050.662
envergadura0.4210.0170.825-0.6300.7190.811-0.0430.5410.4910.5570.7741.0000.6860.7750.806
Cuerda0.3960.0180.787-0.8620.6230.751-0.2240.2690.6270.5570.6700.6861.0000.7580.724
payload0.5670.1070.854-0.8260.6600.8820.0190.4000.7180.6400.7050.7750.7581.0000.784
Empty weight0.503-0.0180.944-0.7460.8340.9330.0810.4860.6130.3700.6620.8060.7240.7841.000
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Resumen de la Tabla

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ResumenCantidad
0Total de valores225.000
1Valores numéricos225.000
2Valores NaN0.000
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Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

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ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Modelo
Velocidad a la que se realiza el crucero (KTAS)nannannannannannannannannannannannannannannan
Techo de servicio máximonannannannannannannannannannannannannannannan
Área del alanannannan-0.7780.8350.970nannannannan0.7110.8250.7870.8540.944
Relación de aspecto del alanannan-0.778nannan-0.790nannan-0.730nannannan-0.862-0.826-0.746
Longitud del fuselajenannan0.835nannan0.806nannannannannan0.719nannan0.834
Peso máximo al despegue (MTOW)nannan0.970-0.7900.806nannannan0.717nan0.7680.8110.7510.8820.933
Alcance de la aeronavenannannannannannannannannannannannannannannan
Autonomía de la aeronavenannannannannannannannannannannannannannannan
Velocidad máxima (KIAS)nannannan-0.730nan0.717nannannannannannannan0.718nan
Velocidad de pérdida (KCAS)nannannannannannannannannannan0.817nannannannan
Velocidad de pérdida limpia (KCAS)nannan0.711nannan0.768nannannan0.817nan0.774nan0.705nan
envergaduranannan0.825nan0.7190.811nannannannan0.774nannan0.7750.806
Cuerdanannan0.787-0.862nan0.751nannannannannannannan0.7580.724
payloadnannan0.854-0.826nan0.882nannan0.718nan0.7050.7750.758nan0.784
Empty weightnannan0.944-0.7460.8340.933nannannannannan0.8060.7240.784nan
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de la Tabla

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ResumenCantidad
0Total de valores225.000
1Valores numéricos62.000
2Valores NaN163.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Preparando datos para el heatmap ===\n", + "\n", + "=== Generando heatmap ===\n" + ] + }, + { + "data": { + "image/png": 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Tabla de correlaciones con filtro de umbral de correlación

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ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Modelo
Distancia de carrera requerida para despeguenannannannannannannannannannannannannannannannannannannannannannannannannannan0.735nannannannannan
Altitud a la que se realiza el cruceronannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Velocidad a la que se realiza el crucero (KTAS)nannannannannannannannan0.917nannannannannannannannannan0.9730.790nan-0.854nannannannannannannannannannan
Techo de servicio máximonannannannannannannannannannannannannannannannannan-0.875nannannan-0.961nan-0.757nannannannannannannannan
Velocidad de pérdida limpia (KCAS)nannannannannan0.711nannannan0.768nannannan0.8170.774nan0.705nannan0.936nannan-0.874nannannannannannannannannan
Área del alanannannannan0.711nan-0.7780.8350.9840.970nannannannan0.8250.7870.854nannan0.9650.944nannan0.974nannannannannannannannan
Relación de aspecto del alanannannannannan-0.778nannannan-0.790nannan-0.730nannan-0.862-0.826nan-0.769nan-0.746nannan-0.970nannannannannannannannan
Longitud del fuselajenannannannannan0.835nannan0.9380.806nannannannan0.719nannannannan0.9260.834nannan0.929nannannannannannannannan
Ancho del fuselajenannan0.917nannan0.984nan0.938nan0.9860.833nan0.940nannannan0.868nan0.944nan0.954nannannannannan0.794nannannannannan
Peso máximo al despegue (MTOW)nannannannan0.7680.970-0.7900.8060.986nannannan0.717nan0.8110.7510.882nan0.7080.9790.933nannan0.9760.758nannannannannannannan
Alcance de la aeronavenannannannannannannannan0.833nannannannannannannannannannan0.936nan-0.711nan0.8480.837nannannannannannannan
Autonomía de la aeronavenannannannannannannannannannannannannannannannannannannannannan-0.7150.802nan-0.732nannannannannannannan
Velocidad máxima (KIAS)nannannannannannan-0.730nan0.9400.717nannannannannannan0.718nannan0.726nannannan0.7270.910nannannannannannannan
Velocidad de pérdida (KCAS)nannannannan0.817nannannannannannannannannannannannan1.000nannannan1.000-0.874nannannannannannannannannan
envergaduranannannannan0.7740.825nan0.719nan0.811nannannannannannan0.775nannan0.9500.806nannannannannannannannannannannan
Cuerdanannannannannan0.787-0.862nannan0.751nannannannannannan0.758nan0.730nan0.724nannan0.975nannannannannannannannan
payloadnannannannan0.7050.854-0.826nan0.8680.882nannan0.718nan0.7750.758nannannannan0.784nannan0.7110.846nannannannannannannan
duracion en VTOLnannannan-0.875nannannannannannannannannan1.000nannannannannannannannannannannannannan-0.904nannannannan
Crucero KIASnannan0.973nannannan-0.769nan0.9440.708nannannannannan0.730nannannan0.723nan-0.855nannannannannannannannannannan
RTF (Including fuel & Batteries)nannan0.790nan0.9360.965nan0.926nan0.9790.936nan0.726nan0.950nannannan0.723nan0.948nannannannannannannannannannannan
Empty weightnannannannannan0.944-0.7460.8340.9540.933nannannannan0.8060.7240.784nannan0.948nannan0.7850.980nannannannannannannannan
Maximum Crosswindnannan-0.854-0.961nannannannannannan-0.711-0.715nan1.000nannannannan-0.855nannannannannannannannan-0.943nannannannan
Rango de comunicaciónnannannannan-0.874nannannannannannan0.802nan-0.874nannannannannannan0.785nannannannannannannannannannannan
Capacidad combustiblenannannan-0.757nan0.974-0.9700.929nan0.9760.848nan0.727nannan0.9750.711nannannan0.980nannannannan0.817nannannannannannan
Consumonannannannannannannannannan0.7580.837-0.7320.910nannannan0.846nannannannannannannannan0.998nannannannannannan
Precionannannannannannannannannannannannannannannannannannannannannannannan0.8170.998nannannannannannannan
Despegue0.735nannannannannannannan0.794nannannannannannannannannannannannannannannannannannannannannannannan
Propulsión horizontalnannannannannannannannannannannannannannannannannan-0.904nannannan-0.943nannannannannannannannannannan
Propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannan
Cantidad de motores propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannannan
Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", + "\n", + "=== Imputación para el parámetro: **Velocidad a la que se realiza el crucero (KTAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Skyeye 3600** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad a la que se realiza el crucero (KTAS)' para la aeronave 'Skyeye 3600'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Imputación para el parámetro: **Techo de servicio máximo** ===\n", + "\n", + "--- Imputación para aeronave: **Mantis** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Mantis'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Skyeye 3600** ---\n", + "\n", + "--- Correlación: Capacidad combustible (r = -0.757) ---\n", + "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Capacidad combustible: [13.0, 11.5, 28.0, 28.0, 25.0]\n", + "Valores para Techo de servicio máximo: [17000.0, 16959.092, 16009.436, 16009.476, 17000.0]\n", + "Ecuación de regresión: y = -49.503x + 17640.121\n", + "Valor del parámetro correlacionado para la aeronave: 11.5\n", + "Predicción obtenida: 17070.833\n", + "\tR²: 0.5733448646440649, Desviación Estándar: 312.7585032074662, Varianza: 97817.88132857464, Incertidumbre: 139.8698547425961\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Capacidad combustible: 17070.833']\n", + "**Mediana calculada:** 17070.833\n", + "\n", + "--- Imputación para aeronave: **Skyeye 5000** ---\n", + "\n", + "--- Correlación: Capacidad combustible (r = -0.757) ---\n", + "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", + "Valores para Capacidad combustible: [13.0, 11.5, 11.5, 28.0, 28.0, 25.0]\n", + "Valores para Techo de servicio máximo: [17000.0, 17070.833, 16959.092, 16009.436, 16009.476, 17000.0]\n", + "Ecuación de regresión: y = -49.503x + 17640.121\n", + "Valor del parámetro correlacionado para la aeronave: 28.0\n", + "Predicción obtenida: 16254.028\n", + "\tR²: 0.6335143266174253, Desviación Estándar: 285.5081454305007, Varianza: 81514.90110716393, Incertidumbre: 116.5582122855099\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Capacidad combustible: 16254.028']\n", + "**Mediana calculada:** 16254.028\n", + "\n", + "=== Área del ala: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Relación de aspecto del ala: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Longitud del fuselaje: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Peso máximo al despegue (MTOW): No hay valores faltantes para imputar. ===\n", + "\n", + "=== Alcance de la aeronave: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Autonomía de la aeronave: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Velocidad máxima (KIAS): No hay valores faltantes para imputar. ===\n", + "\n", + "=== Imputación para el parámetro: **Velocidad de pérdida (KCAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Stalker XE** ---\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 59.0\n", + "Predicción obtenida: 14.838\n", + "\tR²: 0.7638900941970123, Desviación Estándar: 1.321784213429975, Varianza: 1.747113506872698, Incertidumbre: 0.5396161454949092\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Rango de comunicación: 14.838']\n", + "**Mediana calculada:** 14.838\n", + "\n", + "--- Imputación para aeronave: **Stalker VXE30** ---\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 161.0\n", + "Predicción obtenida: 9.415\n", + "\tR²: 0.7649280338966065, Desviación Estándar: 1.2237343949682917, Varianza: 1.4975258694284113, Incertidumbre: 0.4625281256974559\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Rango de comunicación: 9.415']\n", + "**Mediana calculada:** 9.415\n", + "\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.7 Fixed Wing** ---\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 140.0\n", + "Predicción obtenida: 10.532\n", + "\tR²: 0.8590346121694943, Desviación Estándar: 1.1446987113646354, Varianza: 1.3103351397998568, Incertidumbre: 0.40471211061071805\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Rango de comunicación: 10.532']\n", + "**Mediana calculada:** 10.532\n", + "\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.7 VTOL** ---\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 140.0\n", + "Predicción obtenida: 10.532\n", + "\tR²: 0.8815409323186313, Desviación Estándar: 1.0792323024785884, Varianza: 1.1647423627132352, Incertidumbre: 0.3597441008261961\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Rango de comunicación: 10.532']\n", + "**Mediana calculada:** 10.532\n", + "\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 Fixed wing** ---\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 140.0\n", + "Predicción obtenida: 10.532\n", + "\tR²: 0.8815409323186313, Desviación Estándar: 1.0792323024785884, Varianza: 1.1647423627132352, Incertidumbre: 0.3597441008261961\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Rango de comunicación: 10.532']\n", + "**Mediana calculada:** 10.532\n", + "\n", + "--- Imputación para aeronave: **Fulmar X** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Fulmar X'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 150.0\n", + "Predicción obtenida: 10.0\n", + "\tR²: 0.8815409323186313, Desviación Estándar: 1.0792323024785884, Varianza: 1.1647423627132352, Incertidumbre: 0.3597441008261961\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Rango de comunicación: 10.0']\n", + "**Mediana calculada:** 10.0\n", + "\n", + "--- Imputación para aeronave: **Orbiter 3** ---\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 50.0\n", + "Predicción obtenida: 15.316\n", + "\tR²: 0.8815409323186313, Desviación Estándar: 1.0792323024785884, Varianza: 1.1647423627132352, Incertidumbre: 0.3597441008261961\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Rango de comunicación: 15.316']\n", + "**Mediana calculada:** 15.316\n", + "\n", + "--- Imputación para aeronave: **Mantis** ---\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Orbiter 3', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 50.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 15.316, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 16.645\n", + "\tR²: 0.8833653923911279, Desviación Estándar: 1.02384966328724, Varianza: 1.048268133013395, Incertidumbre: 0.32376969175841563\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Rango de comunicación: 16.645']\n", + "**Mediana calculada:** 16.645\n", + "\n", + "--- Imputación para aeronave: **ScanEagle** ---\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Orbiter 3', 'Mantis', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 50.0, 25.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 15.316, 16.645, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 101.86\n", + "Predicción obtenida: 12.559\n", + "\tR²: 0.8900166558794294, Desviación Estándar: 0.9762023543141057, Varianza: 0.9529710365684029, Incertidumbre: 0.29433608442922443\n", + "\tNivel de confianza: Confianza Baja\n", + "Valores imputados: ['Rango de comunicación: 12.559']\n", + "**Mediana calculada:** 12.559\n", + "\n", + "--- Imputación para aeronave: **Integrator** ---\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Orbiter 3', 'Mantis', 'ScanEagle', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 50.0, 25.0, 101.86, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 15.316, 16.645, 12.559, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 92.6\n", + "Predicción obtenida: 13.051\n", + "\tR²: 0.8935698204333948, Desviación Estándar: 0.934642599085376, Varianza: 0.8735567880250669, Incertidumbre: 0.26980807808901663\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Rango de comunicación: 13.051']\n", + "**Mediana calculada:** 13.051\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator VTOL'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator Extended Range (ER)'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **ScanEagle 3** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle 3'.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 50.0, 25.0, 101.86, 92.6, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 15.316, 16.645, 12.559, 13.051, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 92.6\n", + "Predicción obtenida: 13.051\n", + "\tR²: 0.895024240813695, Desviación Estándar: 0.8979755727825594, Varianza: 0.8063601293141656, Incertidumbre: 0.2490536132139741\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Rango de comunicación: 13.051']\n", + "**Mediana calculada:** 13.051\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 50.0, 25.0, 101.86, 92.6, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 15.316, 16.645, 12.559, 13.051, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 50.0\n", + "Predicción obtenida: 15.316\n", + "\tR²: 0.895024240813695, Desviación Estándar: 0.8979755727825594, Varianza: 0.8063601293141656, Incertidumbre: 0.2490536132139741\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Rango de comunicación: 15.316']\n", + "**Mediana calculada:** 15.316\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", + "\n", + "--- Correlación: Maximum Crosswind (r = 1.0) ---\n", + "Aeronaves utilizadas: ['DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Ascend']\n", + "Valores para Maximum Crosswind: [45.0, 50.0, 15.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.0, 15.316, 13.0]\n", + "Ecuación de regresión: y = 0.055x + 12.099\n", + "Valor del parámetro correlacionado para la aeronave: 50.0\n", + "Predicción obtenida: 14.835\n", + "\tR²: 0.7950904219131606, Desviación Estándar: 0.42932620726909115, Varianza: 0.18432099224806262, Incertidumbre: 0.24787160133697086\n", + "\tNivel de confianza: Confianza Media\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 50.0, 25.0, 101.86, 92.6, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 15.316, 16.645, 12.559, 13.051, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 16.379\n", + "\tR²: 0.895024240813695, Desviación Estándar: 0.8979755727825594, Varianza: 0.8063601293141656, Incertidumbre: 0.2490536132139741\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Maximum Crosswind: 14.835', 'Rango de comunicación: 16.379']\n", + "**Mediana calculada:** 15.607\n", + "\n", + "=== Imputación para el parámetro: **Velocidad de pérdida limpia (KCAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Stalker XE** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.382x + 10.202\n", + "Valor del parámetro correlacionado para la aeronave: 0.87\n", + "Predicción obtenida: 14.884\n", + "\tR²: 0.4584975382227796, Desviación Estándar: 3.9626586397738124, Varianza: 15.70266349537404, Incertidumbre: 0.9090962399222311\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [93.0, 13.1, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.118x + 12.28\n", + "Valor del parámetro correlacionado para la aeronave: 13.6\n", + "Predicción obtenida: 13.882\n", + "\tR²: 0.5362410361538301, Desviación Estándar: 3.548785552305845, Varianza: 12.5938788962547, Incertidumbre: 0.8871963880764613\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [18.907, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.983x + 1.727\n", + "Valor del parámetro correlacionado para la aeronave: 14.838\n", + "Predicción obtenida: 16.309\n", + "\tR²: 0.6019452091484776, Desviación Estándar: 3.479540061453548, Varianza: 12.107199039260163, Incertidumbre: 0.869885015363387\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 3.108x + 5.81\n", + "Valor del parámetro correlacionado para la aeronave: 3.657\n", + "Predicción obtenida: 17.174\n", + "\tR²: 0.5835699593207654, Desviación Estándar: 3.4750212924510513, Varianza: 12.075772982988175, Incertidumbre: 0.7972245600234859\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [22.7, 4.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.46x + 13.713\n", + "Valor del parámetro correlacionado para la aeronave: 2.495\n", + "Predicción obtenida: 14.86\n", + "\tR²: 0.43900880754018157, Desviación Estándar: 3.9112272862931547, Varianza: 15.297698885044115, Incertidumbre: 0.921885112299916\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.974\n", + "Valor del parámetro correlacionado para la aeronave: 59.0\n", + "Predicción obtenida: 14.838\n", + "\tR²: 0.7638900941970123, Desviación Estándar: 1.321784213429975, Varianza: 1.747113506872698, Incertidumbre: 0.5396161454949092\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 14.884', 'Peso máximo al despegue (MTOW): 13.882', 'Velocidad de pérdida (KCAS): 16.309', 'envergadura: 17.174', 'payload: 14.86', 'Rango de comunicación: 14.838']\n", + "**Mediana calculada:** 14.872\n", + "\n", + "--- Imputación para aeronave: **Stalker VXE30** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.383x + 10.201\n", + "Valor del parámetro correlacionado para la aeronave: 1.158\n", + "Predicción obtenida: 16.435\n", + "\tR²: 0.4650980437290636, Desviación Estándar: 3.86232278712243, Varianza: 14.917537311925178, Incertidumbre: 0.8636416303052202\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 93.0, 13.1, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.116x + 12.39\n", + "Valor del parámetro correlacionado para la aeronave: 19.958\n", + "Predicción obtenida: 14.713\n", + "\tR²: 0.5383061046948842, Desviación Estándar: 3.4504190564798676, Varianza: 11.905391665319419, Incertidumbre: 0.8368495425006189\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 18.907, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.992x + 1.495\n", + "Valor del parámetro correlacionado para la aeronave: 9.415\n", + "Predicción obtenida: 10.831\n", + "\tR²: 0.606805757387085, Desviación Estándar: 3.392334540315432, Varianza: 11.507933633417112, Incertidumbre: 0.8227619780677445\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 3.115x + 5.665\n", + "Valor del parámetro correlacionado para la aeronave: 4.877\n", + "Predicción obtenida: 20.858\n", + "\tR²: 0.5796228985588339, Desviación Estándar: 3.4239782633031473, Varianza: 11.723627147572437, Incertidumbre: 0.765624815022751\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 22.7, 4.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.46x + 13.714\n", + "Valor del parámetro correlacionado para la aeronave: 2.495\n", + "Predicción obtenida: 14.861\n", + "\tR²: 0.44918896341251313, Desviación Estándar: 3.806910014724386, Varianza: 14.492563860208826, Incertidumbre: 0.8733650548071861\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.053x + 17.978\n", + "Valor del parámetro correlacionado para la aeronave: 161.0\n", + "Predicción obtenida: 9.423\n", + "\tR²: 0.7647638318484331, Desviación Estándar: 1.2237926717855518, Varianza: 1.4976685035160195, Incertidumbre: 0.4625501522639803\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 16.435', 'Peso máximo al despegue (MTOW): 14.713', 'Velocidad de pérdida (KCAS): 10.831', 'envergadura: 20.858', 'payload: 14.861', 'Rango de comunicación: 9.423']\n", + "**Mediana calculada:** 14.787\n", + "\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.7 Fixed Wing** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.414x + 10.08\n", + "Valor del parámetro correlacionado para la aeronave: 1.55\n", + "Predicción obtenida: 18.472\n", + "\tR²: 0.466757382312631, Desviación Estándar: 3.7854894974433573, Varianza: 14.32993073525396, Incertidumbre: 0.8260615316425965\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 93.0, 13.1, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.116x + 12.397\n", + "Valor del parámetro correlacionado para la aeronave: 42.2\n", + "Predicción obtenida: 17.306\n", + "\tR²: 0.5422143559645116, Desviación Estándar: 3.3532462483094934, Varianza: 11.244260401801691, Incertidumbre: 0.7903677203893309\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 18.907, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.915x + 2.961\n", + "Valor del parámetro correlacionado para la aeronave: 10.532\n", + "Predicción obtenida: 12.6\n", + "\tR²: 0.5897032702850062, Desviación Estándar: 3.402063815159601, Varianza: 11.5740382024183, Incertidumbre: 0.8018741312429105\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.93x + 6.085\n", + "Valor del parámetro correlacionado para la aeronave: 4.4\n", + "Predicción obtenida: 18.977\n", + "\tR²: 0.5244094532346639, Desviación Estándar: 3.5750014852544703, Varianza: 12.780635619571669, Incertidumbre: 0.7801292817027697\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 22.7, 4.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.46x + 13.707\n", + "Valor del parámetro correlacionado para la aeronave: 14.5\n", + "Predicción obtenida: 20.38\n", + "\tR²: 0.4585425146715313, Desviación Estándar: 3.710550701599703, Varianza: 13.768186509142046, Incertidumbre: 0.8297043602736472\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.936) ---\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [70.3, 6.8, 8.9, 16.5, 84.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [25.0, 14.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.176x + 11.018\n", + "Valor del parámetro correlacionado para la aeronave: 27.7\n", + "Predicción obtenida: 15.906\n", + "\tR²: 0.8767961918805747, Desviación Estándar: 2.18187881096421, Varianza: 4.760595145734596, Incertidumbre: 0.9757658679964775\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.03x + 17.168\n", + "Valor del parámetro correlacionado para la aeronave: 140.0\n", + "Predicción obtenida: 12.918\n", + "\tR²: 0.46532681815645593, Desviación Estándar: 1.7295254821806558, Varianza: 2.99125839351223, Incertidumbre: 0.6114795983424375\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 18.472', 'Peso máximo al despegue (MTOW): 17.306', 'Velocidad de pérdida (KCAS): 12.6', 'envergadura: 18.977', 'payload: 20.38', 'RTF (Including fuel & Batteries): 15.906', 'Rango de comunicación: 12.918']\n", + "**Mediana calculada:** 17.306\n", + "\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.7 VTOL** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.387x + 10.063\n", + "Valor del parámetro correlacionado para la aeronave: 1.55\n", + "Predicción obtenida: 18.413\n", + "\tR²: 0.46446962919077694, Desviación Estándar: 3.7063812757992287, Varianza: 13.737262161595117, Incertidumbre: 0.7902031430874169\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 93.0, 13.1, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.116x + 12.397\n", + "Valor del parámetro correlacionado para la aeronave: 53.5\n", + "Predicción obtenida: 18.62\n", + "\tR²: 0.5426110461297494, Desviación Estándar: 3.2638102308142223, Varianza: 10.652457222767586, Incertidumbre: 0.7487694193164923\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 18.907, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.849x + 4.269\n", + "Valor del parámetro correlacionado para la aeronave: 10.532\n", + "Predicción obtenida: 13.21\n", + "\tR²: 0.5520283845821775, Desviación Estándar: 3.4611244919244952, Varianza: 11.979382748599594, Incertidumbre: 0.7940364153322332\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.904x + 6.111\n", + "Valor del parámetro correlacionado para la aeronave: 4.4\n", + "Predicción obtenida: 18.886\n", + "\tR²: 0.5197301153510105, Desviación Estándar: 3.5099482875201593, Varianza: 12.319736981065699, Incertidumbre: 0.748323489270446\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 22.7, 4.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.445x + 13.696\n", + "Valor del parámetro correlacionado para la aeronave: 11.3\n", + "Predicción obtenida: 18.723\n", + "\tR²: 0.4414953610991542, Desviación Estándar: 3.6781026044838465, Varianza: 13.528438769110855, Incertidumbre: 0.8026277904219737\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.936) ---\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 70.3, 6.8, 8.9, 16.5, 84.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [17.306, 25.0, 14.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.174x + 11.323\n", + "Valor del parámetro correlacionado para la aeronave: 42.2\n", + "Predicción obtenida: 18.685\n", + "\tR²: 0.8685070668946828, Desviación Estándar: 2.0580697312416656, Varianza: 4.235651018653141, Incertidumbre: 0.840203449434832\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.02x + 16.878\n", + "Valor del parámetro correlacionado para la aeronave: 140.0\n", + "Predicción obtenida: 14.112\n", + "\tR²: 0.22114113946870317, Desviación Estándar: 2.053329630048995, Varianza: 4.2161625696371425, Incertidumbre: 0.6844432100163317\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 18.413', 'Peso máximo al despegue (MTOW): 18.62', 'Velocidad de pérdida (KCAS): 13.21', 'envergadura: 18.886', 'payload: 18.723', 'RTF (Including fuel & Batteries): 18.685', 'Rango de comunicación: 14.112']\n", + "**Mediana calculada:** 18.62\n", + "\n", + "--- Imputación para aeronave: **Aerosonde Mk. 4.8 Fixed wing** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.392x + 10.066\n", + "Valor del parámetro correlacionado para la aeronave: 1.55\n", + "Predicción obtenida: 18.423\n", + "\tR²: 0.4660005657286891, Desviación Estándar: 3.6251561480011025, Varianza: 13.141757097390192, Incertidumbre: 0.7558973100658521\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 93.0, 13.1, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.116x + 12.397\n", + "Valor del parámetro correlacionado para la aeronave: 54.4\n", + "Predicción obtenida: 18.725\n", + "\tR²: 0.5461903226551696, Desviación Estándar: 3.181168711678456, Varianza: 10.119834372161968, Incertidumbre: 0.7113309487208457\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 18.907, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.782x + 5.601\n", + "Valor del parámetro correlacionado para la aeronave: 10.532\n", + "Predicción obtenida: 13.832\n", + "\tR²: 0.5015824133754161, Desviación Estándar: 3.5602796721277223, Varianza: 12.675591343765882, Incertidumbre: 0.796102736578825\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.9x + 6.115\n", + "Valor del parámetro correlacionado para la aeronave: 4.4\n", + "Predicción obtenida: 18.873\n", + "\tR²: 0.5210484825351553, Desviación Estándar: 3.4332236552626645, Varianza: 11.78702466705513, Incertidumbre: 0.715876618803983\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 22.7, 4.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.445x + 13.693\n", + "Valor del parámetro correlacionado para la aeronave: 17.7\n", + "Predicción obtenida: 21.564\n", + "\tR²: 0.44248774210834096, Desviación Estándar: 3.5936014722335075, Varianza: 12.913971541238833, Incertidumbre: 0.7661584081767601\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: RTF (Including fuel & Batteries) (r = 0.936) ---\n", + "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 70.3, 6.8, 8.9, 16.5, 84.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [17.306, 18.62, 25.0, 14.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.174x + 11.317\n", + "Valor del parámetro correlacionado para la aeronave: 36.7\n", + "Predicción obtenida: 17.716\n", + "\tR²: 0.8691516645551287, Desviación Estándar: 1.9055366919971601, Varianza: 3.63107008454748, Incertidumbre: 0.7202251715904527\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.011x + 16.644\n", + "Valor del parámetro correlacionado para la aeronave: 140.0\n", + "Predicción obtenida: 15.076\n", + "\tR²: 0.0684597878447748, Desviación Estándar: 2.3220684254768322, Varianza: 5.392001772596454, Incertidumbre: 0.7343025107267749\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 18.423', 'Peso máximo al despegue (MTOW): 18.725', 'Velocidad de pérdida (KCAS): 13.832', 'envergadura: 18.873', 'payload: 21.564', 'RTF (Including fuel & Batteries): 17.716', 'Rango de comunicación: 15.076']\n", + "**Mediana calculada:** 18.423\n", + "\n", + "--- Imputación para aeronave: **Fulmar X** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.392x + 10.066\n", + "Valor del parámetro correlacionado para la aeronave: 0.94\n", + "Predicción obtenida: 15.134\n", + "\tR²: 0.4670451470194883, Desviación Estándar: 3.5488285233364496, Varianza: 12.594183888046365, Incertidumbre: 0.7244015889007506\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.116x + 12.393\n", + "Valor del parámetro correlacionado para la aeronave: 20.0\n", + "Predicción obtenida: 14.714\n", + "\tR²: 0.5485403649598684, Desviación Estándar: 3.1051599496166142, Varianza: 9.642018312703055, Incertidumbre: 0.677601453050638\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.893x + 6.121\n", + "Valor del parámetro correlacionado para la aeronave: 3.0\n", + "Predicción obtenida: 14.8\n", + "\tR²: 0.5216461178243376, Desviación Estándar: 3.362129644762508, Varianza: 11.30391574819087, Incertidumbre: 0.6862918398960846\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.423x + 13.759\n", + "Valor del parámetro correlacionado para la aeronave: 2.495\n", + "Predicción obtenida: 14.814\n", + "\tR²: 0.42567155526653766, Desviación Estándar: 3.5690658775926396, Varianza: 12.738231238596118, Incertidumbre: 0.7442016801973287\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 15.134', 'Peso máximo al despegue (MTOW): 14.714', 'envergadura: 14.8', 'payload: 14.814']\n", + "**Mediana calculada:** 14.807\n", + "\n", + "--- Imputación para aeronave: **Orbiter 4** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.406x + 10.034\n", + "Valor del parámetro correlacionado para la aeronave: 1.608\n", + "Predicción obtenida: 18.727\n", + "\tR²: 0.4728295759055522, Desviación Estándar: 3.477707911789329, Varianza: 12.094452319722095, Incertidumbre: 0.6955415823578658\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.116x + 12.4\n", + "Valor del parámetro correlacionado para la aeronave: 55.0\n", + "Predicción obtenida: 18.779\n", + "\tR²: 0.552600150975832, Desviación Estándar: 3.033828161728033, Varianza: 9.204113314894094, Incertidumbre: 0.6468143373802413\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.73x + 6.614\n", + "Valor del parámetro correlacionado para la aeronave: 10.0\n", + "Predicción obtenida: 13.916\n", + "\tR²: 0.46468134584634513, Desviación Estándar: 3.601724880792143, Varianza: 12.972422116917178, Incertidumbre: 0.7859608046969426\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.893x + 6.122\n", + "Valor del parámetro correlacionado para la aeronave: 5.2\n", + "Predicción obtenida: 21.165\n", + "\tR²: 0.5269957515236472, Desviación Estándar: 3.2942010830487516, Varianza: 10.851760775559567, Incertidumbre: 0.6588402166097503\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.423x + 13.758\n", + "Valor del parámetro correlacionado para la aeronave: 12.0\n", + "Predicción obtenida: 18.835\n", + "\tR²: 0.43464239567025376, Desviación Estándar: 3.4939195040094204, Varianza: 12.207473500497436, Incertidumbre: 0.7131933322650971\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.006x + 16.501\n", + "Valor del parámetro correlacionado para la aeronave: 150.0\n", + "Predicción obtenida: 15.606\n", + "\tR²: 0.019263283730102443, Desviación Estándar: 2.3959493405082912, Varianza: 5.7405732422821165, Incertidumbre: 0.7224059071968609\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 18.727', 'Peso máximo al despegue (MTOW): 18.779', 'Velocidad de pérdida (KCAS): 13.916', 'envergadura: 21.165', 'payload: 18.835', 'Rango de comunicación: 15.606']\n", + "**Mediana calculada:** 18.753\n", + "\n", + "--- Imputación para aeronave: **Orbiter 3** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.407x + 10.034\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 16.522\n", + "\tR²: 0.47471713659572623, Desviación Estándar: 3.4101769025502042, Varianza: 11.629306506686904, Incertidumbre: 0.6687907142656497\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.116x + 12.4\n", + "Valor del parámetro correlacionado para la aeronave: 32.0\n", + "Predicción obtenida: 16.111\n", + "\tR²: 0.5562407359989936, Desviación Estándar: 2.9671471323451786, Varianza: 8.803962104984215, Incertidumbre: 0.6186929457220847\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.677x + 7.652\n", + "Valor del parámetro correlacionado para la aeronave: 15.316\n", + "Predicción obtenida: 18.014\n", + "\tR²: 0.42421279321722494, Desviación Estándar: 3.651853558055984, Varianza: 13.33603440948615, Incertidumbre: 0.7785777946033205\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.811x + 6.35\n", + "Valor del parámetro correlacionado para la aeronave: 4.4\n", + "Predicción obtenida: 18.718\n", + "\tR²: 0.5194126656316639, Desviación Estándar: 3.261868529196971, Varianza: 10.639786301765609, Incertidumbre: 0.6397050492749827\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.423x + 13.756\n", + "Valor del parámetro correlacionado para la aeronave: 5.5\n", + "Predicción obtenida: 16.082\n", + "\tR²: 0.4360376843534042, Desviación Estándar: 3.4233658161858984, Varianza: 11.719433511430143, Incertidumbre: 0.6846731632371796\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.001x + 16.326\n", + "Valor del parámetro correlacionado para la aeronave: 50.0\n", + "Predicción obtenida: 16.269\n", + "\tR²: 0.0007196998998141302, Desviación Estándar: 2.438000130498692, Varianza: 5.943844636311639, Incertidumbre: 0.7037900158138813\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 16.522', 'Peso máximo al despegue (MTOW): 16.111', 'Velocidad de pérdida (KCAS): 18.014', 'envergadura: 18.718', 'payload: 16.082', 'Rango de comunicación: 16.269']\n", + "**Mediana calculada:** 16.396\n", + "\n", + "--- Imputación para aeronave: **Mantis** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.409x + 10.026\n", + "Valor del parámetro correlacionado para la aeronave: 0.754\n", + "Predicción obtenida: 14.105\n", + "\tR²: 0.47548065381933924, Desviación Estándar: 3.346513898608693, Varianza: 11.199155273581153, Incertidumbre: 0.6440369000695176\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.116x + 12.416\n", + "Valor del parámetro correlacionado para la aeronave: 6.5\n", + "Predicción obtenida: 13.169\n", + "\tR²: 0.5562776352004176, Desviación Estándar: 2.9052327113675602, Varianza: 8.440377107200106, Incertidumbre: 0.59302814390775\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 15.316, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 16.396, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.676x + 7.585\n", + "Valor del parámetro correlacionado para la aeronave: 16.645\n", + "Predicción obtenida: 18.842\n", + "\tR²: 0.4219605998717829, Desviación Estándar: 3.586786423534771, Varianza: 12.865036848053354, Incertidumbre: 0.7478966694512236\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.782x + 6.377\n", + "Valor del parámetro correlacionado para la aeronave: 2.1\n", + "Predicción obtenida: 12.22\n", + "\tR²: 0.5111829799239707, Desviación Estándar: 3.230613748948095, Varianza: 10.436865194892464, Incertidumbre: 0.6217319058676296\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.422x + 13.776\n", + "Valor del parámetro correlacionado para la aeronave: 2.693\n", + "Predicción obtenida: 14.913\n", + "\tR²: 0.4374652379037306, Desviación Estándar: 3.357422336000781, Varianza: 11.272284742276943, Incertidumbre: 0.6584446925630868\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 50.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 16.396, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = -0.001x + 16.346\n", + "Valor del parámetro correlacionado para la aeronave: 25.0\n", + "Predicción obtenida: 16.315\n", + "\tR²: 0.0009110570695874953, Desviación Estándar: 2.342591413436464, Varianza: 5.48773453030625, Incertidumbre: 0.6497179583543717\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 14.105', 'Peso máximo al despegue (MTOW): 13.169', 'Velocidad de pérdida (KCAS): 18.842', 'envergadura: 12.22', 'payload: 14.913', 'Rango de comunicación: 16.315']\n", + "**Mediana calculada:** 14.509\n", + "\n", + "--- Imputación para aeronave: **ScanEagle** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.385x + 10.072\n", + "Valor del parámetro correlacionado para la aeronave: 1.063\n", + "Predicción obtenida: 15.797\n", + "\tR²: 0.4820128146718825, Desviación Estándar: 3.2870383565993335, Varianza: 10.804621157755248, Incertidumbre: 0.6211918601065917\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.114x + 12.545\n", + "Valor del parámetro correlacionado para la aeronave: 26.5\n", + "Predicción obtenida: 15.561\n", + "\tR²: 0.5577729360322713, Desviación Estándar: 2.858039912868348, Varianza: 8.168392143548514, Incertidumbre: 0.5716079825736695\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 15.316, 16.645, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 16.396, 14.509, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.664x + 7.593\n", + "Valor del parámetro correlacionado para la aeronave: 12.559\n", + "Predicción obtenida: 15.932\n", + "\tR²: 0.3998213157109771, Desviación Estándar: 3.6160406734700388, Varianza: 13.07575015218965, Incertidumbre: 0.7381212115959697\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.684x + 6.837\n", + "Valor del parámetro correlacionado para la aeronave: 3.1\n", + "Predicción obtenida: 15.158\n", + "\tR²: 0.5095357739591448, Desviación Estándar: 3.198518978768509, Varianza: 10.230523657542346, Incertidumbre: 0.6044632701101256\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.424x + 13.745\n", + "Valor del parámetro correlacionado para la aeronave: 5.0\n", + "Predicción obtenida: 15.864\n", + "\tR²: 0.44688240705153315, Desviación Estándar: 3.295517822105962, Varianza: 10.860437715818021, Incertidumbre: 0.6342227005706954\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 16.396, 14.509, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = 0.001x + 16.018\n", + "Valor del parámetro correlacionado para la aeronave: 101.86\n", + "Predicción obtenida: 16.133\n", + "\tR²: 0.0007471972665133997, Desviación Estándar: 2.300874321451344, Varianza: 5.294022643114182, Incertidumbre: 0.614934528635493\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 15.797', 'Peso máximo al despegue (MTOW): 15.561', 'Velocidad de pérdida (KCAS): 15.932', 'envergadura: 15.158', 'payload: 15.864', 'Rango de comunicación: 16.133']\n", + "**Mediana calculada:** 15.83\n", + "\n", + "--- Imputación para aeronave: **Integrator** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.384x + 10.075\n", + "Valor del parámetro correlacionado para la aeronave: 1.872\n", + "Predicción obtenida: 20.154\n", + "\tR²: 0.4836358210043519, Desviación Estándar: 3.2298738173488455, Varianza: 10.432084875995603, Incertidumbre: 0.5997725107817994\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.114x + 12.56\n", + "Valor del parámetro correlacionado para la aeronave: 74.8\n", + "Predicción obtenida: 21.063\n", + "\tR²: 0.5584094722839654, Desviación Estándar: 2.803013662480037, Varianza: 7.856885592049752, Incertidumbre: 0.5497162062251112\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 15.316, 16.645, 12.559, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 16.396, 14.509, 15.83, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.665x + 7.58\n", + "Valor del parámetro correlacionado para la aeronave: 13.051\n", + "Predicción obtenida: 16.253\n", + "\tR²: 0.40384854496261013, Desviación Estándar: 3.543037251696186, Varianza: 12.553112966906864, Incertidumbre: 0.7086074503392372\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.673x + 6.905\n", + "Valor del parámetro correlacionado para la aeronave: 4.8\n", + "Predicción obtenida: 19.733\n", + "\tR²: 0.51034062753414, Desviación Estándar: 3.145245417564829, Varianza: 9.892568736712557, Incertidumbre: 0.5840574114645236\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.424x + 13.743\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 21.375\n", + "\tR²: 0.44959491624800174, Desviación Estándar: 3.2361405160283896, Varianza: 10.47260543948049, Incertidumbre: 0.6115730723622395\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 16.396, 14.509, 15.83, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = 0.001x + 16.008\n", + "Valor del parámetro correlacionado para la aeronave: 92.6\n", + "Predicción obtenida: 16.101\n", + "\tR²: 0.0006009919023085564, Desviación Estándar: 2.2241277836738833, Varianza: 4.946744398110101, Incertidumbre: 0.5742673244004108\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 20.154', 'Peso máximo al despegue (MTOW): 21.063', 'Velocidad de pérdida (KCAS): 16.253', 'envergadura: 19.733', 'payload: 21.375', 'Rango de comunicación: 16.101']\n", + "**Mediana calculada:** 19.944\n", + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.373x + 10.083\n", + "Valor del parámetro correlacionado para la aeronave: 2.09\n", + "Predicción obtenida: 21.309\n", + "\tR²: 0.49007651885627856, Desviación Estándar: 3.1758029669681096, Varianza: 10.085724485003448, Incertidumbre: 0.5798196410667559\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.112x + 12.592\n", + "Valor del parámetro correlacionado para la aeronave: 75.0\n", + "Predicción obtenida: 20.973\n", + "\tR²: 0.5653829737578795, Desviación Estándar: 2.7581885003227513, Varianza: 7.607603803312669, Incertidumbre: 0.5308136243790236\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.677x + 6.895\n", + "Valor del parámetro correlacionado para la aeronave: 5.033\n", + "Predicción obtenida: 20.368\n", + "\tR²: 0.5164434789260648, Desviación Estándar: 3.092606517663101, Varianza: 9.56421507309229, Incertidumbre: 0.5646301170705265\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 18.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.415x + 13.775\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 21.244\n", + "\tR²: 0.45243459789702367, Desviación Estándar: 3.1899291559574228, Varianza: 10.175648020027236, Incertidumbre: 0.5923549733763512\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 21.309', 'Peso máximo al despegue (MTOW): 20.973', 'envergadura: 20.368', 'payload: 21.244']\n", + "**Mediana calculada:** 21.108\n", + "\n", + "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.359x + 10.095\n", + "Valor del parámetro correlacionado para la aeronave: 1.872\n", + "Predicción obtenida: 20.127\n", + "\tR²: 0.5020709354180335, Desviación Estándar: 3.1243525541900556, Varianza: 9.761578882873925, Incertidumbre: 0.5611502841334306\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.112x + 12.589\n", + "Valor del parámetro correlacionado para la aeronave: 74.8\n", + "Predicción obtenida: 20.963\n", + "\tR²: 0.5809327536697524, Desviación Estándar: 2.7085965129650105, Varianza: 7.336495070046214, Incertidumbre: 0.5118766268087254\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.696x + 6.845\n", + "Valor del parámetro correlacionado para la aeronave: 4.8\n", + "Predicción obtenida: 19.785\n", + "\tR²: 0.5270307946868045, Desviación Estándar: 3.045038079257076, Varianza: 9.272256904125623, Incertidumbre: 0.5469049839079977\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.414x + 13.778\n", + "Valor del parámetro correlacionado para la aeronave: 18.0\n", + "Predicción obtenida: 21.233\n", + "\tR²: 0.46486409456400923, Desviación Estándar: 3.1364030940850203, Varianza: 9.837024368586087, Incertidumbre: 0.5726262413531211\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 20.127', 'Peso máximo al despegue (MTOW): 20.963', 'envergadura: 19.785', 'payload: 21.233']\n", + "**Mediana calculada:** 20.545\n", + "\n", + "--- Imputación para aeronave: **ScanEagle 3** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.378x + 10.082\n", + "Valor del parámetro correlacionado para la aeronave: 1.349\n", + "Predicción obtenida: 17.337\n", + "\tR²: 0.509633634350891, Desviación Estándar: 3.0759861908690347, Varianza: 9.461691046416995, Incertidumbre: 0.543762673599918\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.111x + 12.598\n", + "Valor del parámetro correlacionado para la aeronave: 36.3\n", + "Predicción obtenida: 16.641\n", + "\tR²: 0.5907860344296275, Desviación Estándar: 2.6625239079484526, Varianza: 7.089033560397101, Incertidumbre: 0.49441827749097367\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.711x + 6.811\n", + "Valor del parámetro correlacionado para la aeronave: 4.0\n", + "Predicción obtenida: 17.653\n", + "\tR²: 0.5335788768451615, Desviación Estándar: 2.9999440120772443, Varianza: 8.999664075598112, Incertidumbre: 0.5303201885299493\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.41x + 13.791\n", + "Valor del parámetro correlacionado para la aeronave: 8.6\n", + "Predicción obtenida: 17.321\n", + "\tR²: 0.4719735374132813, Desviación Estándar: 3.0876799487134994, Varianza: 9.533767465687397, Incertidumbre: 0.5545636897507049\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 17.337', 'Peso máximo al despegue (MTOW): 16.641', 'envergadura: 17.653', 'payload: 17.321']\n", + "**Mediana calculada:** 17.329\n", + "\n", + "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.378x + 10.081\n", + "Valor del parámetro correlacionado para la aeronave: 1.802\n", + "Predicción obtenida: 19.773\n", + "\tR²: 0.5096506431121606, Desviación Estándar: 3.0290221145859624, Varianza: 9.174974970650814, Incertidumbre: 0.5272850695450955\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.111x + 12.626\n", + "Valor del parámetro correlacionado para la aeronave: 61.0\n", + "Predicción obtenida: 19.412\n", + "\tR²: 0.5899119116525353, Desviación Estándar: 2.6206837047329836, Varianza: 6.867983080252995, Incertidumbre: 0.47846919372282115\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 15.316, 16.645, 12.559, 13.051, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 16.396, 14.509, 15.83, 19.944, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.649x + 7.958\n", + "Valor del parámetro correlacionado para la aeronave: 13.051\n", + "Predicción obtenida: 16.427\n", + "\tR²: 0.38497447747770597, Desviación Estándar: 3.545344634636701, Varianza: 12.569468578347243, Incertidumbre: 0.695299287477847\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.71x + 6.803\n", + "Valor del parámetro correlacionado para la aeronave: 4.8\n", + "Predicción obtenida: 19.812\n", + "\tR²: 0.53342997768055, Desviación Estándar: 2.954663687517189, Varianza: 8.730037506332673, Incertidumbre: 0.5143409288604107\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.41x + 13.792\n", + "Valor del parámetro correlacionado para la aeronave: 17.7\n", + "Predicción obtenida: 21.056\n", + "\tR²: 0.4721125878507184, Desviación Estándar: 3.039052343695801, Varianza: 9.235839147722942, Incertidumbre: 0.5372336301520427\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 16.396, 14.509, 15.83, 19.944, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = 0.002x + 16.192\n", + "Valor del parámetro correlacionado para la aeronave: 92.6\n", + "Predicción obtenida: 16.346\n", + "\tR²: 0.0013961471345631526, Desviación Estándar: 2.3455650352893986, Varianza: 5.501675334772157, Incertidumbre: 0.5863912588223497\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 19.773', 'Peso máximo al despegue (MTOW): 19.412', 'Velocidad de pérdida (KCAS): 16.427', 'envergadura: 19.812', 'payload: 21.056', 'Rango de comunicación: 16.346']\n", + "**Mediana calculada:** 19.592\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.371x + 10.086\n", + "Valor del parámetro correlacionado para la aeronave: 0.7\n", + "Predicción obtenida: 13.846\n", + "\tR²: 0.5130352786837467, Desviación Estándar: 2.984299405987304, Varianza: 8.906042944576173, Incertidumbre: 0.5118031257684521\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.111x + 12.626\n", + "Valor del parámetro correlacionado para la aeronave: 6.2\n", + "Predicción obtenida: 13.317\n", + "\tR²: 0.5946184657373111, Desviación Estándar: 2.5782613181332947, Varianza: 6.647431424582433, Incertidumbre: 0.4630694027472812\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 15.316, 16.645, 12.559, 13.051, 13.051, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 16.396, 14.509, 15.83, 19.944, 19.592, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.636x + 8.268\n", + "Valor del parámetro correlacionado para la aeronave: 15.316\n", + "Predicción obtenida: 18.011\n", + "\tR²: 0.3707919802946765, Desviación Estándar: 3.529606447333878, Varianza: 12.458121673060882, Incertidumbre: 0.6792730775005511\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.706x + 6.812\n", + "Valor del parámetro correlacionado para la aeronave: 2.35\n", + "Predicción obtenida: 13.172\n", + "\tR²: 0.5366242582049442, Desviación Estándar: 2.9111212195272542, Varianza: 8.474626754781848, Incertidumbre: 0.49925317032725897\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.403x + 13.817\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 14.301\n", + "\tR²: 0.4717960925463479, Desviación Estándar: 3.002745499394878, Varianza: 9.016480534136194, Incertidumbre: 0.5227108979661816\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 16.396, 14.509, 15.83, 19.944, 19.592, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = 0.002x + 16.338\n", + "Valor del parámetro correlacionado para la aeronave: 50.0\n", + "Predicción obtenida: 16.448\n", + "\tR²: 0.0021729000438440726, Desviación Estándar: 2.4001143739811432, Varianza: 5.760549008190895, Incertidumbre: 0.5821132398522036\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 13.846', 'Peso máximo al despegue (MTOW): 13.317', 'Velocidad de pérdida (KCAS): 18.011', 'envergadura: 13.172', 'payload: 14.301', 'Rango de comunicación: 16.448']\n", + "**Mediana calculada:** 14.074\n", + "\n", + "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", + "\n", + "--- Correlación: Área del ala (r = 0.711) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.074, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 5.358x + 10.11\n", + "Valor del parámetro correlacionado para la aeronave: 0.7\n", + "Predicción obtenida: 13.861\n", + "\tR²: 0.5219079320200679, Desviación Estándar: 2.941594049068034, Varianza: 8.65297554951247, Incertidumbre: 0.4972201452507867\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.074, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.11x + 12.69\n", + "Valor del parámetro correlacionado para la aeronave: 6.2\n", + "Predicción obtenida: 13.374\n", + "\tR²: 0.6010085744446478, Desviación Estándar: 2.5409184754535725, Varianza: 6.456266698901308, Incertidumbre: 0.4491751711088513\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 15.316, 16.645, 12.559, 13.051, 13.051, 14.0, 15.316, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 16.396, 14.509, 15.83, 19.944, 19.592, 14.0, 14.074, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.634x + 8.16\n", + "Valor del parámetro correlacionado para la aeronave: 15.607\n", + "Predicción obtenida: 18.054\n", + "\tR²: 0.3591533059239339, Desviación Estándar: 3.5421565229641567, Varianza: 12.546872833177526, Incertidumbre: 0.669404661759172\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: envergadura (r = 0.774) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.074, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 2.677x + 6.953\n", + "Valor del parámetro correlacionado para la aeronave: 2.35\n", + "Predicción obtenida: 13.244\n", + "\tR²: 0.5439573083064899, Desviación Estándar: 2.87296092523383, Varianza: 8.253904477920424, Incertidumbre: 0.4856190299260294\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: payload (r = 0.705) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", + "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.074, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", + "Ecuación de regresión: y = 0.404x + 13.799\n", + "Valor del parámetro correlacionado para la aeronave: 1.2\n", + "Predicción obtenida: 14.285\n", + "\tR²: 0.48382243545428616, Desviación Estándar: 2.95849521726017, Varianza: 8.7526939505513, Incertidumbre: 0.5073777439109984\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "\n", + "--- Correlación: Rango de comunicación (r = -0.874) ---\n", + "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", + "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 50.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", + "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 16.396, 14.509, 15.83, 19.944, 19.592, 14.074, 14.0, 15.5, 17.0, 18.0, 17.397]\n", + "Ecuación de regresión: y = 0.004x + 16.06\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 16.179\n", + "\tR²: 0.006797842032161827, Desviación Estándar: 2.3934134703855308, Varianza: 5.72842804022291, Incertidumbre: 0.5641329650309457\n", + "\tNivel de confianza: Confianza Muy Baja\n", + "Valores imputados: ['Área del ala: 13.861', 'Peso máximo al despegue (MTOW): 13.374', 'Velocidad de pérdida (KCAS): 18.054', 'envergadura: 13.244', 'payload: 14.285', 'Rango de comunicación: 16.179']\n", + "**Mediana calculada:** 14.073\n", + "\n", + "=== envergadura: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Cuerda: No hay valores faltantes para imputar. ===\n", + "\n", + "=== payload: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Empty weight: No hay valores faltantes para imputar. ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Reporte Final de Imputaciones

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AeronaveParámetroValor ImputadoNivel de Confianza
5Aerosonde Mk. 4.7 VTOLVelocidad de pérdida (KCAS)10.5320.512
6Aerosonde Mk. 4.8 Fixed wingVelocidad de pérdida (KCAS)10.5320.512
7Orbiter 4Velocidad de pérdida (KCAS)10.0000.512
8Orbiter 3Velocidad de pérdida (KCAS)15.3160.512
9MantisVelocidad de pérdida (KCAS)16.6450.547
10ScanEagleVelocidad de pérdida (KCAS)12.5590.577
11IntegratorVelocidad de pérdida (KCAS)13.0510.602
12RQ Nan 21A BlackjackVelocidad de pérdida (KCAS)13.0510.623
13DeltaQuad Pro #MAPVelocidad de pérdida (KCAS)15.3160.623
14DeltaQuad Pro #CARGOVelocidad de pérdida (KCAS)15.6070.623
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de Imputaciones

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AeronaveCantidad de Valores Imputados
0Aerosonde Mk. 4.7 VTOL1.000
1Aerosonde Mk. 4.8 Fixed wing1.000
2DeltaQuad Pro #CARGO1.000
3DeltaQuad Pro #MAP1.000
4Integrator1.000
5Mantis1.000
6Orbiter 31.000
7Orbiter 41.000
8RQ Nan 21A Blackjack1.000
9ScanEagle1.000
TotalTotal10.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m>>> RESULTADOS DE IMPUTACIÓN POR CORRELACIÓN\u001b[0m\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 3600 = 17070.833 (Correlación)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 = 16254.028 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Stalker XE = 14.838 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Stalker VXE30 = 9.415 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde Mk. 4.7 Fixed Wing = 10.532 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde Mk. 4.7 VTOL = 10.532 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde Mk. 4.8 Fixed wing = 10.532 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Orbiter 4 = 10.0 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Orbiter 3 = 15.316 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Mantis = 16.645 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - ScanEagle = 12.559 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Integrator = 13.051 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - RQ Nan 21A Blackjack = 13.051 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - DeltaQuad Pro #MAP = 15.316 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - DeltaQuad Pro #CARGO = 15.607000000000001 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Stalker XE = 14.872 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Stalker VXE30 = 14.786999999999999 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Aerosonde Mk. 4.7 Fixed Wing = 17.306 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Aerosonde Mk. 4.7 VTOL = 18.62 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Aerosonde Mk. 4.8 Fixed wing = 18.423 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Fulmar X = 14.807 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Orbiter 4 = 18.753 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Orbiter 3 = 16.3955 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Mantis = 14.509 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - ScanEagle = 15.8305 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Integrator = 19.9435 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Integrator VTOL = 21.1085 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Integrator Extended Range (ER) = 20.545 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - ScanEagle 3 = 17.329 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - RQ Nan 21A Blackjack = 19.5925 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - DeltaQuad Pro #MAP = 14.0735 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - DeltaQuad Pro #CARGO = 14.073 (Correlación)\n", + "\n", + "=== Iteración 2: Resumen después de imputaciones ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de Valores Faltantes Después de Iteración 2

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ColumnaValores Faltantes
0Stalker XE0.000
1Stalker VXE300.000
2Aerosonde Mk. 4.7 Fixed Wing0.000
3Aerosonde Mk. 4.7 VTOL0.000
4Aerosonde Mk. 4.8 Fixed wing0.000
5Aerosonde Mk. 4.8 VTOL FTUAS0.000
6AAI Aerosonde0.000
7Fulmar X1.000
8Orbiter 40.000
9Orbiter 30.000
10Mantis1.000
11ScanEagle0.000
12Integrator0.000
13Integrator VTOL1.000
14Integrator Extended Range (ER)1.000
15ScanEagle 31.000
16RQ Nan 21A Blackjack0.000
17DeltaQuad Evo0.000
18DeltaQuad Pro #MAP0.000
19DeltaQuad Pro #CARGO0.000
20V210.000
21V250.000
22V320.000
23V350.000
24V390.000
25Volitation VT3700.000
26Skyeye 26000.000
27Skyeye 2930 VTOL0.000
28Skyeye 36001.000
29Skyeye 3600 VTOL0.000
30Skyeye 50000.000
31Skyeye 5000 VTOL0.000
32Skyeye 5000 VTOL octo0.000
33Volitation VT5100.000
34Ascend0.000
35Transition0.000
36Reach0.000
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Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes6.000
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Resumen de Valores Faltantes Antes de Iteración 3

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ColumnaValores Faltantes
0Stalker XE30.000
1Stalker VXE3031.000
2Aerosonde Mk. 4.7 Fixed Wing28.000
3Aerosonde Mk. 4.7 VTOL27.000
4Aerosonde Mk. 4.8 Fixed wing31.000
5Aerosonde Mk. 4.8 VTOL FTUAS33.000
6AAI Aerosonde30.000
7Fulmar X35.000
8Orbiter 434.000
9Orbiter 334.000
10Mantis34.000
11ScanEagle33.000
12Integrator33.000
13Integrator VTOL33.000
14Integrator Extended Range (ER)36.000
15ScanEagle 334.000
16RQ Nan 21A Blackjack32.000
17DeltaQuad Evo28.000
18DeltaQuad Pro #MAP30.000
19DeltaQuad Pro #CARGO30.000
20V2128.000
21V2528.000
22V3228.000
23V3531.000
24V3931.000
25Volitation VT37030.000
26Skyeye 260033.000
27Skyeye 2930 VTOL32.000
28Skyeye 360033.000
29Skyeye 3600 VTOL31.000
30Skyeye 500029.000
31Skyeye 5000 VTOL30.000
32Skyeye 5000 VTOL octo30.000
33Volitation VT51030.000
34Ascend29.000
35Transition29.000
36Reach29.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes1147.000
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Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1mNo se realizaron imputaciones por similitud en esta iteración.\u001b[0m\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN 3 ***\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "\n", + "=== DataFrame inicial ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

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Stalker XEStalker VXE30Aerosonde Mk. 4.7 Fixed WingAerosonde Mk. 4.7 VTOLAerosonde Mk. 4.8 Fixed wingAerosonde Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQ Nan 21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Modelo
Distancia de carrera requerida para despegue0.00.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.0
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.3440530.22866936.09414730.40658430.46641927.42637218.26582630.62533630.95346521.46331.89437625.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.62533630.29090932.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.8429403.635186839.144606NaN19500.019500.07013.83419500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.014972.95591316000.017070.83316959.09187416254.02816009.43594316009.47636617000.010000.013000.016000.0
Velocidad de pérdida limpia (KCAS)14.87214.78717.30618.6218.42325.010.014.80718.75316.395514.50915.830519.943521.108520.54517.32919.592514.014.073514.07314.015.517.018.017.3973924.010.018.012.524.015.025.025.025.014.010.025.0
Área del ala0.871.1582831.551.551.552.5030.570.941.6081.20.7541.0631.8722.08951.8721.3491.8020.840.70.70.80.521.031.2021.2031.4240.881.01.331.322.6152.6152.6151.9930.7710.9862.329
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.75438613.217513.44313.934514.75514.05712.90812.64812.8413.76512.91414.58914.71414.71414.56814.42114.18213.89814.041513.64514.10314.00113.709513.671512.69513.03212.855513.09914.34914.22313.669
Longitud del fuselaje2.12.59083.03.03.03.59451.71.21.21.21.481.712.52.9982.52.42.50.750.90.90.930.931.01.881.9542.022.052.032.4882.423.53.53.52.9051.5622.34.712
Profundidad del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Ancho del fuselaje0.2110.20.2770.2770.277NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.3750.3750.375NaNNaNNaNNaN
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0518.9225481.428535.2755800.03270.0800.0509.556550.025.0503.5155500.0646.0835500.050.0565.912270.0100.0100.0270.0270.0412.686456.221413.556300.03270.0425.273458.1245300.0530.401800.0800.0800.0270.0506.641800.0
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.011.6729075.06.012.020.0
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.4388642.25267530.84572541.736.036.025.641.246.340.21646.341.246.333.029.00929.00933.033.033.033.033.033.030.83428930.035.098533.042.042.038.050.030.030.035.0
Velocidad de pérdida (KCAS)14.8389.41510.53210.53210.53218.90746510.0NaN10.015.31616.64512.55913.051NaNNaNNaN13.05114.015.31615.60714.015.517.018.017.3973924.010.018.012.524.015.019.10922524.025.013.013.013.0
Tasa de ascensoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Radio de giroNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100.0120.0150.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
envergadura3.6574.87684.44.44.45.6442.93.05.24.42.13.14.85.0334.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
Cuerda0.2390.3181950.3520.3520.3520.3940.1965520.3190.3320.3040.2710.29850.33850.3410.3450.31150.3410.27550.2720.2720.2780.2810.2920.3060.3070.3140.2960.30.3110.3150.34850.3380.34450.3350.2870.2910.313
payload2.4947562.49475614.511.317.722.74.02.49475612.05.52.6935.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
duracion en VTOL2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050.050.05
Crucero KIAS15.4333216.09342225.025.025.0NaNNaN27.8NaNNaN16.728.028.3NaNNaN23.530.916.5416.016.018.020.020.025.025.025.033.024.0NaN30.033.028.035.030.020.020.025.0
RTF (dry weight)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.011.854.0
RTF (Including fuel & Batteries)NaNNaN27.742.236.770.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8.916.584.0
Empty weight10.88620817.46329219.79619.79619.80931.010.017.46329218.36512.2375.63310.19222.19524.784522.25714.79421.1234.84.7544.7542.653.456.457.16.70830311.06.57.111.511.032.032.140535.023.9593.05.831.0
Maximum CrosswindNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.0NaNNaNNaN45.050.050.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15.015.015.0
Rango de comunicación59.0161.0140.0140.0140.0NaN150.0NaN150.050.025.0101.8692.6NaNNaNNaN92.6NaN50.030.030.030.030.030.030.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Wing LoadingNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.524.025.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Potencia específica (P/W)NaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Capacidad combustibleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.0NaNNaN11.511.528.028.028.025.0NaNNaNNaN
ConsumoNaNNaN0.60.6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.96NaNNaNNaNNaN1.2NaNNaN5.0NaNNaNNaN
Potencia WattsNaNNaN2980.02980.0NaNNaN1280.0NaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Potencia HPNaNNaN4.04.0NaNNaN1.74NaNNaNNaNNaNNaNNaNNaNNaNNaN8.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
PrecioNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.0NaNNaNNaN
Tiempo de emergencia en vueloNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.1080.1080.108NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Distancia de aterrizajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Despegue1.01.01.02.01.02.02.01.01.01.01.01.01.02.01.01.01.02.02.02.02.02.02.02.02.02.02.02.03.02.03.02.02.02.02.02.02.0
Propulsión horizontal2.02.02.02.02.02.02.02.02.01.01.02.02.02.02.02.02.01.01.01.01.01.02.02.02.02.02.02.02.02.02.02.02.02.02.02.02.0
Propulsión vertical5.05.05.01.05.01.01.05.05.05.05.05.05.01.05.05.05.01.01.01.01.01.01.01.01.01.01.01.05.01.05.01.01.01.01.01.01.0
Cantidad de motores propulsión vertical0.00.00.04.00.04.04.00.00.00.00.00.00.04.00.00.00.04.04.04.04.04.04.04.04.04.04.04.00.04.00.04.08.04.04.04.04.0
Cantidad de motores propulsión horizontal1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
Misión1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Battery Power SupplyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Modelo Motor Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Modelo Motor VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
PortabilidadNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
CámaraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Despegue todos los tiposNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Datalink banksNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Motor recomendadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Hélice recomendada VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Hélice recomendada Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Sistema de controlNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Características adicionalesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
indice_desconocidoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Convertir todo a numérico ===\n", + "\n", + "\n", + "=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===\n", + "\n", + "Umbral seleccionado para correlaciones significativas: 0.7\n", + "\n", + "=== Cálculo de tabla completa ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Tabla de Correlaciones con todos los parametros(tabla_completa)

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ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Modelo
Distancia de carrera requerida para despegue1.0000.0630.4270.082-0.2730.269-0.2500.2220.4250.1680.054-0.0780.241-0.1950.1050.2000.229nan0.389nan0.225nannan-0.018-0.240-0.1560.7350.1540.671-0.598nannan
Altitud a la que se realiza el crucero0.0631.0000.0110.0950.008-0.0750.105-0.092nan-0.095-0.309-0.278-0.0640.216-0.0800.109-0.114nannannan-0.1210.038-0.325nannannan-0.119-0.1090.187-0.159nannan
Velocidad a la que se realiza el crucero (KTAS)0.4270.0111.0000.1340.3160.497-0.6070.4280.9170.5530.5450.4230.6340.1060.4210.3960.567-0.6940.9730.7900.503-0.8540.4800.3650.461-0.2900.0630.5630.120-0.076nannan
Techo de servicio máximo0.0820.0950.1341.0000.1030.096-0.0290.1350.5900.1220.0970.0010.0140.1050.0220.0280.118-0.8750.1120.579-0.004-0.961-0.118-0.8190.461-0.1560.1960.068-0.1360.140nannan
Velocidad de pérdida limpia (KCAS)-0.2730.0080.3160.1031.0000.753-0.5980.5940.6750.798-0.2300.2600.5160.6440.7470.6430.731-0.1900.4930.9310.678-0.2370.1290.2240.6300.118-0.0280.322-0.1600.232nannan
Área del ala0.269-0.0750.4970.0960.7531.000-0.7780.8350.9840.970-0.0230.3830.6480.3050.8250.7870.854-0.3830.6920.9650.944-0.4660.4910.9740.2880.0360.1250.4760.0720.037nannan
Relación de aspecto del ala-0.2500.105-0.607-0.029-0.598-0.7781.000-0.624-0.681-0.790-0.003-0.456-0.730-0.149-0.630-0.862-0.8260.519-0.769-0.497-0.7460.432-0.409-0.9700.2960.024-0.001-0.471-0.1410.075nannan
Longitud del fuselaje0.222-0.0920.4280.1350.5940.835-0.6241.0000.9380.8060.1400.4030.3630.1170.7190.6230.660-0.6170.5740.9260.834-0.6960.6460.9290.036-0.2030.1380.6120.0340.040nannan
Ancho del fuselaje0.425nan0.9170.5900.6750.984-0.6810.9381.0000.9860.833-0.0890.9400.7110.6710.5570.868nan0.944nan0.954nan0.323nan1.000nan0.794nan-0.5350.574nannan
Peso máximo al despegue (MTOW)0.168-0.0950.5530.1220.7980.970-0.7900.8060.9861.0000.0300.4200.7170.3510.8110.7510.882-0.4010.7080.9790.933-0.4640.5140.9760.7580.0520.0900.4670.0230.075nannan
Alcance de la aeronave0.054-0.3090.5450.097-0.230-0.023-0.0030.1400.8330.0301.0000.2240.013-0.258-0.043-0.2240.019-0.5250.5240.9360.081-0.7110.4670.8480.837-0.1480.1570.317-0.2130.210nannan
Autonomía de la aeronave-0.078-0.2780.4230.0010.2600.383-0.4560.403-0.0890.4200.2241.0000.378-0.3430.5410.2690.400-0.5940.3370.6340.486-0.7150.8020.056-0.7320.033-0.4200.4780.353-0.314nannan
Velocidad máxima (KIAS)0.241-0.0640.6340.0140.5160.648-0.7300.3630.9400.7170.0130.3781.0000.2610.4910.6270.718-0.0770.7000.7260.613-0.2230.1510.7270.9100.067-0.0570.3000.178-0.141nannan
Velocidad de pérdida (KCAS)-0.1950.2160.1060.1050.6440.305-0.1490.1170.7110.351-0.258-0.3430.2611.0000.2340.1680.3740.6720.2840.3230.1620.934-0.9610.0360.6850.1210.3010.005-0.4330.508nannan
envergadura0.105-0.0800.4210.0220.7470.825-0.6300.7190.6710.811-0.0430.5410.4910.2341.0000.6860.775-0.2580.5010.9500.806-0.4140.6480.2970.0850.032-0.0810.5160.167-0.106nannan
Cuerda0.2000.1090.3960.0280.6430.787-0.8620.6230.5570.751-0.2240.2690.6270.1680.6861.0000.758-0.4990.7300.5950.724-0.4980.3550.975-0.228-0.041-0.0650.4180.193-0.129nannan
payload0.229-0.1140.5670.1180.7310.854-0.8260.6600.8680.8820.0190.4000.7180.3740.7750.7581.000-0.0240.6700.5590.784-0.1420.4890.7110.846-0.0080.0530.4620.100-0.055nannan
duracion en VTOLnannan-0.694-0.875-0.190-0.3830.519-0.617nan-0.401-0.525-0.594-0.0770.672-0.258-0.499-0.0241.000-0.694-0.402-0.3151.000nannannannan-0.188-0.9040.188-0.188nannan
Crucero KIAS0.389nan0.9730.1120.4930.692-0.7690.5740.9440.7080.5240.3370.7000.2840.5010.7300.670-0.6941.0000.7230.636-0.8550.3590.5810.461-0.2430.1430.6080.0650.063nannan
RTF (Including fuel & Batteries)nannan0.7900.5790.9310.965-0.4970.926nan0.9790.9360.6340.7260.3230.9500.5950.559-0.4020.7231.0000.948-0.402nannannannan0.0970.428-0.0970.097nannan
Empty weight0.225-0.1210.503-0.0040.6780.944-0.7460.8340.9540.9330.0810.4860.6130.1620.8060.7240.784-0.3150.6360.9481.000-0.3860.7850.9800.2510.023-0.0290.4800.195-0.070nannan
Maximum Crosswindnan0.038-0.854-0.961-0.237-0.4660.432-0.696nan-0.464-0.711-0.715-0.2230.934-0.414-0.498-0.1421.000-0.855-0.402-0.3861.000nannannannannan-0.943nannannannan
Rango de comunicaciónnan-0.3250.480-0.1180.1290.491-0.4090.6460.3230.5140.4670.8020.151-0.9610.6480.3550.489nan0.359nan0.785nan1.000nannannan-0.4300.6040.430-0.430nannan
Capacidad combustible-0.018nan0.365-0.8190.2240.974-0.9700.929nan0.9760.8480.0560.7270.0360.2970.9750.711nan0.581nan0.980nannan1.0000.3770.817-0.080nan-0.0800.270nannan
Consumo-0.240nan0.4610.4610.6300.2880.2960.0361.0000.7580.837-0.7320.9100.6850.085-0.2280.846nan0.461nan0.251nannan0.3771.0000.9980.113nan-0.3750.375nannan
Precio-0.156nan-0.290-0.1560.1180.0360.024-0.203nan0.052-0.1480.0330.0670.1210.032-0.041-0.008nan-0.243nan0.023nannan0.8170.9981.000-0.1380.217-0.1380.134nannan
Despegue0.735-0.1190.0630.196-0.0280.125-0.0010.1380.7940.0900.157-0.420-0.0570.301-0.081-0.0650.053-0.1880.1430.097-0.029nan-0.430-0.0800.113-0.1381.000-0.010-0.6390.610nannan
Propulsión horizontal0.154-0.1090.5630.0680.3220.476-0.4710.612nan0.4670.3170.4780.3000.0050.5160.4180.462-0.9040.6080.4280.480-0.9430.604nannan0.217-0.0101.0000.118-0.083nannan
Propulsión vertical0.6710.1870.120-0.136-0.1600.072-0.1410.034-0.5350.023-0.2130.3530.178-0.4330.1670.1930.1000.1880.065-0.0970.195nan0.430-0.080-0.375-0.138-0.6390.1181.000-0.954nannan
Cantidad de motores propulsión vertical-0.598-0.159-0.0760.1400.2320.0370.0750.0400.5740.0750.210-0.314-0.1410.508-0.106-0.129-0.055-0.1880.0630.097-0.070nan-0.4300.2700.3750.1340.610-0.083-0.9541.000nannan
Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores1024.000
1Valores numéricos826.000
2Valores NaN198.000
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Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

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ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Modelo
Velocidad a la que se realiza el crucero (KTAS)1.0000.1340.497-0.6070.4280.5530.5450.4230.6340.1060.3160.4210.3960.5670.503
Techo de servicio máximo0.1341.0000.096-0.0290.1350.1220.0970.0010.0140.1050.1030.0220.0280.118-0.004
Área del ala0.4970.0961.000-0.7780.8350.970-0.0230.3830.6480.3050.7530.8250.7870.8540.944
Relación de aspecto del ala-0.607-0.029-0.7781.000-0.624-0.790-0.003-0.456-0.730-0.149-0.598-0.630-0.862-0.826-0.746
Longitud del fuselaje0.4280.1350.835-0.6241.0000.8060.1400.4030.3630.1170.5940.7190.6230.6600.834
Peso máximo al despegue (MTOW)0.5530.1220.970-0.7900.8061.0000.0300.4200.7170.3510.7980.8110.7510.8820.933
Alcance de la aeronave0.5450.097-0.023-0.0030.1400.0301.0000.2240.013-0.258-0.230-0.043-0.2240.0190.081
Autonomía de la aeronave0.4230.0010.383-0.4560.4030.4200.2241.0000.378-0.3430.2600.5410.2690.4000.486
Velocidad máxima (KIAS)0.6340.0140.648-0.7300.3630.7170.0130.3781.0000.2610.5160.4910.6270.7180.613
Velocidad de pérdida (KCAS)0.1060.1050.305-0.1490.1170.351-0.258-0.3430.2611.0000.6440.2340.1680.3740.162
Velocidad de pérdida limpia (KCAS)0.3160.1030.753-0.5980.5940.798-0.2300.2600.5160.6441.0000.7470.6430.7310.678
envergadura0.4210.0220.825-0.6300.7190.811-0.0430.5410.4910.2340.7471.0000.6860.7750.806
Cuerda0.3960.0280.787-0.8620.6230.751-0.2240.2690.6270.1680.6430.6861.0000.7580.724
payload0.5670.1180.854-0.8260.6600.8820.0190.4000.7180.3740.7310.7750.7581.0000.784
Empty weight0.503-0.0040.944-0.7460.8340.9330.0810.4860.6130.1620.6780.8060.7240.7841.000
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Resumen de la Tabla

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ResumenCantidad
0Total de valores225.000
1Valores numéricos225.000
2Valores NaN0.000
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Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

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ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Modelo
Velocidad a la que se realiza el crucero (KTAS)nannannannannannannannannannannannannannannan
Techo de servicio máximonannannannannannannannannannannannannannannan
Área del alanannannan-0.7780.8350.970nannannannan0.7530.8250.7870.8540.944
Relación de aspecto del alanannan-0.778nannan-0.790nannan-0.730nannannan-0.862-0.826-0.746
Longitud del fuselajenannan0.835nannan0.806nannannannannan0.719nannan0.834
Peso máximo al despegue (MTOW)nannan0.970-0.7900.806nannannan0.717nan0.7980.8110.7510.8820.933
Alcance de la aeronavenannannannannannannannannannannannannannannan
Autonomía de la aeronavenannannannannannannannannannannannannannannan
Velocidad máxima (KIAS)nannannan-0.730nan0.717nannannannannannannan0.718nan
Velocidad de pérdida (KCAS)nannannannannannannannannannannannannannannan
Velocidad de pérdida limpia (KCAS)nannan0.753nannan0.798nannannannannan0.747nan0.731nan
envergaduranannan0.825nan0.7190.811nannannannan0.747nannan0.7750.806
Cuerdanannan0.787-0.862nan0.751nannannannannannannan0.7580.724
payloadnannan0.854-0.826nan0.882nannan0.718nan0.7310.7750.758nan0.784
Empty weightnannan0.944-0.7460.8340.933nannannannannan0.8060.7240.784nan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores225.000
1Valores numéricos60.000
2Valores NaN165.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Preparando datos para el heatmap ===\n", "\n", - "--- Correlación: Capacidad combustible (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Capacidad combustible: [13.0, 11.5, 11.5, 28.0, 28.0, 28.0]\n", - "Valores para Empty weight: [15.467, 11.5, 11.0, 32.0, 32.273, 35.0]\n", - "Ecuación de regresión: y = 1.283x + -2.79\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 29.289\n", - "\tR²: 0.9857968254239403, Desviación Estándar: 1.2345889100883332, Varianza: 1.5242097769130982, Incertidumbre: 0.504018811969206\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Área del ala: 23.995', 'Longitud del fuselaje: 21.176', 'Peso máximo al despegue (MTOW): 32.99', 'envergadura: 22.986', 'Cuerda: 19.661', 'payload: 31.949', 'Capacidad combustible: 29.289']\n", - "**Mediana calculada:** 23.995\n" + "=== Generando heatmap ===\n" ] }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -34318,576 +53955,1268 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

Reporte Final de Imputaciones

\n", + "

Tabla de correlaciones con filtro de umbral de correlación

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ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
ModeloAeronaveParámetroValor ImputadoNivel de Confianza
8Aerosonde® Mk. 4.8 VTOL FTUASÁrea del ala2.5030.804
9Fulmar XÁrea del ala0.9400.804
10Orbiter 4Área del ala1.6080.804
11Orbiter 3Área del ala1.2000.804
12MantisÁrea del ala0.7540.804
13ScanEagleÁrea del ala1.0630.804
14IntegratorÁrea del ala1.8720.804
15Integrator VTOLÁrea del ala2.0900.804
16Integrator Extended Range (ER)Área del ala1.8720.804
17ScanEagle 3Área del ala1.3490.804
18RQNan21A BlackjackÁrea del ala1.8020.804
19DeltaQuad Pro #MAPÁrea del ala0.7000.804
20DeltaQuad Pro #CARGOÁrea del ala0.7000.804
21V32Área del ala1.0300.804
29Fulmar XRelación de aspecto del ala13.2180.963
30Orbiter 4Relación de aspecto del ala13.4430.752
31Orbiter 3Relación de aspecto del ala14.0120.752
32MantisRelación de aspecto del ala14.7670.752
33ScanEagleRelación de aspecto del ala14.0670.777
34IntegratorRelación de aspecto del ala12.9230.631
35Integrator VTOLRelación de aspecto del ala12.6540.676
36Integrator Extended Range (ER)Relación de aspecto del ala12.8590.676
37ScanEagle 3Relación de aspecto del ala13.7740.676
38RQNan21A BlackjackRelación de aspecto del ala12.9730.695
39DeltaQuad EvoRelación de aspecto del ala14.5990.716
40DeltaQuad Pro #MAPRelación de aspecto del ala14.7170.726
41DeltaQuad Pro #CARGORelación de aspecto del ala14.7170.740
42V21Relación de aspecto del ala14.5780.740
43V25Relación de aspecto del ala14.4350.752
44V32Relación de aspecto del ala14.1940.761
45V35Relación de aspecto del ala13.9090.767
46V39Relación de aspecto del ala14.0530.768
47Volitation VT370Relación de aspecto del ala13.6570.764
48Skyeye 2600Relación de aspecto del ala14.1160.771
49Skyeye 2930 VTOLRelación de aspecto del ala14.0130.669
50Skyeye 3600Relación de aspecto del ala13.7230.675
51Skyeye 3600 VTOLRelación de aspecto del ala13.6840.675
52Skyeye 5000Relación de aspecto del ala12.7130.675
53Skyeye 5000 VTOLRelación de aspecto del ala13.0460.699
54Skyeye 5000 VTOL octoRelación de aspecto del ala12.8770.703
55Volitation VT510Relación de aspecto del ala13.1140.718
56AscendRelación de aspecto del ala14.3570.726
57TransitionRelación de aspecto del ala14.2330.732
58ReachRelación de aspecto del ala13.6830.736
59Aerosonde® Mk. 4.8 VTOL FTUASLongitud del fuselaje3.5950.831
60Integrator VTOLLongitud del fuselaje3.0030.831
61V39Longitud del fuselaje1.9540.831Distancia de carrera requerida para despeguenannannannannannannannannannannannannannannannannannannannannannannannannannan0.735nannannannannan
92Aerosonde® Mk. 4.8 VTOL FTUASenvergadura5.6440.805Altitud a la que se realiza el cruceronannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
93Integrator VTOLenvergadura5.0330.805Velocidad a la que se realiza el crucero (KTAS)nannannannannannannannan0.917nannannannannannannannannan0.9730.790nan-0.854nannannannannannannannannannan
94Aerosonde® Mk. 4.8 VTOL FTUASCuerda0.3940.827Techo de servicio máximonannannannannannannannannannannannannannannannannan-0.875nannannan-0.961nan-0.819nannannannannannannannan
95Fulmar XCuerda0.3130.827Velocidad de pérdida limpia (KCAS)nannannannannan0.753nannannan0.798nannannannan0.747nan0.731nannan0.931nannannannannannannannannannannannan
96Orbiter 4Cuerda0.3340.737Área del alanannannannan0.753nan-0.7780.8350.9840.970nannannannan0.8250.7870.854nannan0.9650.944nannan0.974nannannannannannannannan
97Orbiter 3Cuerda0.3010.737Relación de aspecto del alanannannannannan-0.778nannannan-0.790nannan-0.730nannan-0.862-0.826nan-0.769nan-0.746nannan-0.970nannannannannannannannan
98MantisCuerda0.2700.737Longitud del fuselajenannannannannan0.835nannan0.9380.806nannannannan0.719nannannannan0.9260.834nannan0.929nannannannannannannannan
99ScanEagleCuerda0.2970.763Ancho del fuselajenannan0.917nannan0.984nan0.938nan0.9860.833nan0.9400.711nannan0.868nan0.944nan0.954nannannannannan0.794nannannannannan
100IntegratorCuerda0.3380.714Peso máximo al despegue (MTOW)nannannannan0.7980.970-0.7900.8060.986nannannan0.717nan0.8110.7510.882nan0.7080.9790.933nannan0.9760.758nannannannannannannan
101Integrator VTOLCuerda0.3410.749Alcance de la aeronavenannannannannannannannan0.833nannannannannannannannannannan0.936nan-0.711nan0.8480.837nannannannannannannan
102Integrator Extended Range (ER)Cuerda0.3440.749Autonomía de la aeronavenannannannannannannannannannannannannannannannannannannannannan-0.7150.802nan-0.732nannannannannannannan
103ScanEagle 3Cuerda0.3100.749Velocidad máxima (KIAS)nannannannannannan-0.730nan0.9400.717nannannannannannan0.718nannan0.726nannannan0.7270.910nannannannannannannan
104RQNan21A BlackjackCuerda0.3390.750Velocidad de pérdida (KCAS)nannannannannannannannan0.711nannannannannannannannannannannannan0.934-0.961nannannannannannannannannan
105DeltaQuad EvoCuerda0.2760.774envergaduranannannannan0.7470.825nan0.719nan0.811nannannannannannan0.775nannan0.9500.806nannannannannannannannannannannan
106DeltaQuad Pro #MAPCuerda0.2720.793Cuerdanannannannannan0.787-0.862nannan0.751nannannannannannan0.758nan0.730nan0.724nannan0.975nannannannannannannannan
107DeltaQuad Pro #CARGOCuerda0.2720.809payloadnannannannan0.7310.854-0.826nan0.8680.882nannan0.718nan0.7750.758nannannannan0.784nannan0.7110.846nannannannannannannan
108V21Cuerda0.2780.809duracion en VTOLnannannan-0.875nannannannannannannannannannannannannannannannannannannannannannannan-0.904nannannannan
109V25Cuerda0.2810.602Crucero KIASnannan0.973nannannan-0.769nan0.9440.708nannannannannan0.730nannannan0.723nan-0.855nannannannannannannannannannan
110V32Cuerda0.2910.591RTF (Including fuel & Batteries)nannan0.790nan0.9310.965nan0.926nan0.9790.936nan0.726nan0.950nannannan0.723nan0.948nannannannannannannannannannannan
111V35Cuerda0.3030.590Empty weightnannannannannan0.944-0.7460.8340.9540.933nannannannan0.8060.7240.784nannan0.948nannan0.7850.980nannannannannannannannan
112V39Cuerda0.3040.590Maximum Crosswindnannan-0.854-0.961nannannannannannan-0.711-0.715nan0.934nannannannan-0.855nannannannannannannannan-0.943nannannannan
113Volitation VT370Cuerda0.3130.590Rango de comunicaciónnannannannannannannannannannannan0.802nan-0.961nannannannannannan0.785nannannannannannannannannannannan
114Skyeye 2600Cuerda0.2950.590Capacidad combustiblenannannan-0.819nan0.974-0.9700.929nan0.9760.848nan0.727nannan0.9750.711nannannan0.980nannannannan0.817nannannannannannan
115Skyeye 2930 VTOLCuerda0.2990.590Consumonannannannannannannannannan0.7580.837-0.7320.910nannannan0.846nannannannannannannannan0.998nannannannannannan
116Skyeye 3600Cuerda0.3090.590Precionannannannannannannannannannannannannannannannannannannannannannannan0.8170.998nannannannannannannan
117Skyeye 3600 VTOLCuerda0.3120.594Despegue0.735nannannannannannannan0.794nannannannannannannannannannannannannannannannannannannannannannannan
118Skyeye 5000Cuerda0.3460.599Propulsión horizontalnannannannannannannannannannannannannannannannannan-0.904nannannan-0.943nannannannannannannannannannan
119Skyeye 5000 VTOLCuerda0.3360.683Propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannan
120Skyeye 5000 VTOL octoCuerda0.3430.683Cantidad de motores propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannannan
121Volitation VT510Cuerda0.3340.728Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
122AscendCuerda0.2860.728Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
123TransitionCuerda0.2900.724
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", + "\n", + "=== Imputación para el parámetro: **Velocidad a la que se realiza el crucero (KTAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Skyeye 3600** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
124ReachCuerda0.3120.7220No se pudo imputar el parámetro 'Velocidad a la que se realiza el crucero (KTAS)' para la aeronave 'Skyeye 3600'.
" @@ -34899,6 +55228,16 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Imputación para el parámetro: **Techo de servicio máximo** ===\n", + "\n", + "--- Imputación para aeronave: **Mantis** ---\n" + ] + }, { "data": { "text/html": [ @@ -34934,174 +55273,385 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

Resumen de Imputaciones

\n", + "

Imputación no Exitosa

\n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + "
AeronaveCantidad de Valores ImputadosMensaje
0Aerosonde® Mk. 4.8 VTOL FTUAS4.000No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Mantis'.
1Ascend2.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Área del ala: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Relación de aspecto del ala: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Longitud del fuselaje: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Peso máximo al despegue (MTOW): No hay valores faltantes para imputar. ===\n", + "\n", + "=== Alcance de la aeronave: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Autonomía de la aeronave: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Velocidad máxima (KIAS): No hay valores faltantes para imputar. ===\n", + "\n", + "=== Imputación para el parámetro: **Velocidad de pérdida (KCAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Fulmar X** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

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Mensaje
2DeltaQuad Evo2.0000No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Fulmar X'.
3DeltaQuad Pro #CARGO3.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **Integrator VTOL** ---\n", + "\n", + "--- Correlación: Maximum Crosswind (r = 0.934) ---\n", + "Aeronaves utilizadas: ['DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'Ascend']\n", + "Valores para Maximum Crosswind: [45.0, 50.0, 50.0, 15.0]\n", + "Valores para Velocidad de pérdida (KCAS): [14.0, 15.316, 15.607, 13.0]\n", + "Ecuación de regresión: y = 0.064x + 11.929\n", + "Valor del parámetro correlacionado para la aeronave: 30.0\n", + "Predicción obtenida: 13.843\n", + "\tR²: 0.7881580184530225, Desviación Estándar: 0.48216925192301585, Varianza: 0.23248718750000075, Incertidumbre: 0.24108462596150793\n", + "\tNivel de confianza: Confianza Media\n", + "Valores imputados: ['Maximum Crosswind: 13.843']\n", + "**Mediana calculada:** 13.843\n", + "\n", + "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + "
Mensaje
4DeltaQuad Pro #MAP3.0000No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator Extended Range (ER)'.
5Fulmar X3.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Imputación para aeronave: **ScanEagle 3** ---\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Imputación no Exitosa

\n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + "
Mensaje
6Integrator3.0000No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle 3'.
7Integrator Extended Range (ER)3.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Velocidad de pérdida limpia (KCAS): No hay valores faltantes para imputar. ===\n", + "\n", + "=== envergadura: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Cuerda: No hay valores faltantes para imputar. ===\n", + "\n", + "=== payload: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Empty weight: No hay valores faltantes para imputar. ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Reporte Final de Imputaciones

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AeronaveParámetroValor ImputadoNivel de Confianza
80Integrator VTOL5.000
9Mantis3.000
10Orbiter 33.000
11Orbiter 43.000
12RQNan21A Blackjack3.000
13Reach2.000
14ScanEagle3.000
15ScanEagle 33.000
16Skyeye 26002.000
17Skyeye 2930 VTOL2.000
18Skyeye 36002.000
19Skyeye 3600 VTOL2.000
20Skyeye 50002.000
21Skyeye 5000 VTOL2.000
22Skyeye 5000 VTOL octo2.000
23Transition2.000
24V212.000
25V252.000
26V323.000
27V352.000
28V393.000Velocidad de pérdida (KCAS)13.8430.630
29Volitation VT3702.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de Imputaciones

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AeronaveCantidad de Valores Imputados
30Volitation VT5102.0000Integrator VTOL1.000
TotalTotal80.0001.000
" @@ -35118,183 +55668,9 @@ "output_type": "stream", "text": [ "\u001b[1m>>> RESULTADOS DE IMPUTACIÓN POR CORRELACIÓN\u001b[0m\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Aerosonde® Mk. 4.8 VTOL FTUAS = 32.31596323465078 (Similitud)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - AAI Aerosonde = 21.624760478782665 (Similitud)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Orbiter 4 = 27.26947572941752 (Similitud)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Orbiter 3 = 26.566881229546517 (Similitud)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Integrator VTOL = 30.93282942341402 (Similitud)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Integrator Extended Range (ER) = 30.953465066791882 (Similitud)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Skyeye 3600 = 27.344050412360318 (Similitud)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Skyeye 5000 VTOL octo = 31.71909847833797 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Orbiter 4 = 9633.863636363636 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Orbiter 3 = 8010.3359375 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Mantis = 12.97158076923077 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Integrator VTOL = 19487.0 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 2600 = 13122.5 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 2930 VTOL = 16571.42857142857 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 3600 = 16571.42857142857 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 3600 VTOL = 14902.124999999998 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 = 16044.444444444443 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 VTOL = 15640.0 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 VTOL octo = 15640.0 (Similitud)\n", - "Imputación final aplicada: Área del ala - Aerosonde® Mk. 4.8 VTOL FTUAS = 2.503 (Correlación)\n", - "Imputación final aplicada: Área del ala - Fulmar X = 0.94 (Correlación)\n", - "Imputación final aplicada: Área del ala - Orbiter 4 = 1.608 (Correlación)\n", - "Imputación final aplicada: Área del ala - Orbiter 3 = 1.12859375 (Similitud)\n", - "Imputación final aplicada: Área del ala - ScanEagle = 1.1814858490566036 (Similitud)\n", - "Imputación final aplicada: Área del ala - ScanEagle 3 = 1.349 (Correlación)\n", - "Imputación final aplicada: Área del ala - V35 = 1.12859375 (Similitud)\n", - "Imputación final aplicada: Área del ala - Volitation VT370 = 1.4456562499999999 (Similitud)\n", - "Imputación final aplicada: Área del ala - Volitation VT510 = 2.615 (Similitud)\n", - "Imputación final aplicada: Área del ala - Ascend = 0.8307894736842105 (Similitud)\n", - "Imputación final aplicada: Área del ala - Transition = 1.1897828403221333 (Similitud)\n", - "Imputación final aplicada: Área del ala - Reach = 2.6796565934065932 (Similitud)\n", - "Imputación final aplicada: Relación de aspecto del ala - Fulmar X = 13.217500000000001 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Orbiter 4 = 13.443 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - V25 = 14.435 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Volitation VT370 = 13.657 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 3600 VTOL = 13.6845 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 = 12.713000000000001 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 VTOL = 13.046 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 VTOL octo = 12.8765 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Volitation VT510 = 13.114 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Transition = 14.233 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Reach = 13.683 (Correlación)\n", - "Imputación final aplicada: Longitud del fuselaje - Aerosonde® Mk. 4.8 VTOL FTUAS = 3.5658602150537635 (Similitud)\n", - "Imputación final aplicada: Longitud del fuselaje - Integrator VTOL = 3.0035 (Correlación)\n", - "Imputación final aplicada: Longitud del fuselaje - V39 = 1.3746614583333334 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Aerosonde® Mk. 4.7 Fixed Wing = 296.09004739336496 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Aerosonde® Mk. 4.7 VTOL = 123.4483644859813 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Aerosonde® Mk. 4.8 Fixed wing = 122.9111213235294 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Aerosonde® Mk. 4.8 VTOL FTUAS = 815.0537634408602 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Integrator = 500.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Integrator VTOL = 499.66666666666663 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - ScanEagle 3 = 178.08195592286503 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - V21 = 270.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - V25 = 1843.69 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - V32 = 770.2127659574468 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - V35 = 50.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Volitation VT370 = 300.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Skyeye 2600 = 1763.9083333333333 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Skyeye 2930 VTOL = 51.78571428571428 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Skyeye 3600 = 51.78571428571428 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Skyeye 5000 = 822.2222222222222 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Skyeye 5000 VTOL octo = 800.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Volitation VT510 = 800.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Ascend = 273.55263157894734 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Transition = 633.4988526666666 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Reach = 819.7802197802197 (Similitud)\n", - "Imputación final aplicada: Autonomía de la aeronave - Skyeye 5000 VTOL octo = 8.0 (Similitud)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - Aerosonde® Mk. 4.8 VTOL FTUAS = 41.66129032258065 (Similitud)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - Integrator VTOL = 46.26913333333333 (Similitud)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Evo = 31.3125 (Similitud)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Pro #MAP = 25.909677419354843 (Similitud)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Pro #CARGO = 25.909677419354843 (Similitud)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - Skyeye 2600 = 24.701138406926347 (Similitud)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - Skyeye 3600 = 34.17857142857142 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Stalker XE = 12.721966911764707 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Stalker VXE30 = 12.68114837683525 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde® Mk. 4.7 Fixed Wing = 23.687203791469194 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde® Mk. 4.8 VTOL FTUAS = 19.66532258064516 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - AAI Aerosonde = 12.842557251908397 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Fulmar X = 12.674999999999999 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Orbiter 3 = 14.7734375 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - ScanEagle = 16.5188679245283 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - ScanEagle 3 = 24.611570247933887 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - DeltaQuad Evo = 13.41875 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - V35 = 14.7734375 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - V39 = 16.911458333333332 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Volitation VT370 = 24.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Skyeye 5000 VTOL = 19.3125 (Similitud)\n", - "Imputación final aplicada: envergadura - Aerosonde® Mk. 4.8 VTOL FTUAS = 5.094086021505376 (Similitud)\n", - "Imputación final aplicada: envergadura - Integrator VTOL = 5.033 (Correlación)\n", - "Imputación final aplicada: Cuerda - Fulmar X = 0.313 (Correlación)\n", - "Imputación final aplicada: Cuerda - Orbiter 4 = 0.334 (Correlación)\n", - "Imputación final aplicada: Cuerda - V25 = 0.22158417241379308 (Similitud)\n", - "Imputación final aplicada: Cuerda - Volitation VT370 = 0.35683999999999994 (Similitud)\n", - "Imputación final aplicada: Cuerda - Skyeye 2600 = 0.2118754597701149 (Similitud)\n", - "Imputación final aplicada: Cuerda - Skyeye 3600 VTOL = 0.35683999999999994 (Similitud)\n", - "Imputación final aplicada: Cuerda - Transition = 0.29 (Correlación)\n", - "Imputación final aplicada: payload - AAI Aerosonde = 2.518560923664122 (Similitud)\n", - "Imputación final aplicada: payload - Fulmar X = 1.9779738749767999 (Similitud)\n", - "Imputación final aplicada: payload - Mantis = 1.1861538461538461 (Similitud)\n", - "Imputación final aplicada: Empty weight - Aerosonde® Mk. 4.7 Fixed Wing = 10.856635071090047 (Similitud)\n", - "Imputación final aplicada: Empty weight - Aerosonde® Mk. 4.8 VTOL FTUAS = 31.741935483870968 (Similitud)\n", - "Imputación final aplicada: Empty weight - Fulmar X = 11.5545671248376 (Similitud)\n", - "Imputación final aplicada: Empty weight - Orbiter 3 = 9.009375 (Similitud)\n", - "Imputación final aplicada: Empty weight - ScanEagle = 7.200471698113207 (Similitud)\n", - "Imputación final aplicada: Empty weight - ScanEagle 3 = 11.280303030303031 (Similitud)\n", - "Imputación final aplicada: Empty weight - V35 = 9.009375 (Similitud)\n", - "Imputación final aplicada: Empty weight - V39 = 6.41640625 (Similitud)\n", - "Imputación final aplicada: Empty weight - Volitation VT370 = 11.0 (Similitud)\n", - "Imputación final aplicada: Empty weight - Skyeye 5000 VTOL = 31.2 (Similitud)\n", - "Imputación final aplicada: Empty weight - Volitation VT510 = 31.2 (Similitud)\n", - "Imputación final aplicada: Área del ala - Mantis = 0.754 (Correlación)\n", - "Imputación final aplicada: Área del ala - Integrator = 1.872 (Correlación)\n", - "Imputación final aplicada: Área del ala - Integrator VTOL = 2.0895 (Correlación)\n", - "Imputación final aplicada: Área del ala - Integrator Extended Range (ER) = 1.872 (Correlación)\n", - "Imputación final aplicada: Área del ala - RQNan21A Blackjack = 1.802 (Correlación)\n", - "Imputación final aplicada: Área del ala - DeltaQuad Pro #MAP = 0.7 (Correlación)\n", - "Imputación final aplicada: Área del ala - DeltaQuad Pro #CARGO = 0.7 (Correlación)\n", - "Imputación final aplicada: Área del ala - V32 = 1.03 (Correlación)\n", - "Imputación final aplicada: Área del ala - V39 = 1.203 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Orbiter 3 = 14.012 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Mantis = 14.767 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - ScanEagle = 14.067 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Integrator = 12.923 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Integrator VTOL = 12.654499999999999 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Integrator Extended Range (ER) = 12.859 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - ScanEagle 3 = 13.774 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - RQNan21A Blackjack = 12.972999999999999 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - DeltaQuad Evo = 14.599 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - DeltaQuad Pro #MAP = 14.717 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - DeltaQuad Pro #CARGO = 14.717 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - V21 = 14.578 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - V32 = 14.194 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - V35 = 13.909 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - V39 = 14.0535 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 2600 = 14.116 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 2930 VTOL = 14.013 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 3600 = 13.7225 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Ascend = 14.357 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - ScanEagle = 418.78 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - V39 = 475.37699999999995 (Correlación)\n", - "Imputación final aplicada: Cuerda - Aerosonde® Mk. 4.8 VTOL FTUAS = 0.394 (Correlación)\n", - "Imputación final aplicada: Cuerda - Orbiter 3 = 0.301 (Correlación)\n", - "Imputación final aplicada: Cuerda - Mantis = 0.27 (Correlación)\n", - "Imputación final aplicada: Cuerda - ScanEagle = 0.2975 (Correlación)\n", - "Imputación final aplicada: Cuerda - Integrator = 0.33799999999999997 (Correlación)\n", - "Imputación final aplicada: Cuerda - Integrator VTOL = 0.341 (Correlación)\n", - "Imputación final aplicada: Cuerda - Integrator Extended Range (ER) = 0.344 (Correlación)\n", - "Imputación final aplicada: Cuerda - ScanEagle 3 = 0.3105 (Correlación)\n", - "Imputación final aplicada: Cuerda - RQNan21A Blackjack = 0.3385 (Correlación)\n", - "Imputación final aplicada: Cuerda - DeltaQuad Evo = 0.2755 (Correlación)\n", - "Imputación final aplicada: Cuerda - DeltaQuad Pro #MAP = 0.272 (Correlación)\n", - "Imputación final aplicada: Cuerda - DeltaQuad Pro #CARGO = 0.272 (Correlación)\n", - "Imputación final aplicada: Cuerda - V21 = 0.2775 (Correlación)\n", - "Imputación final aplicada: Cuerda - V32 = 0.291 (Correlación)\n", - "Imputación final aplicada: Cuerda - V35 = 0.3035 (Correlación)\n", - "Imputación final aplicada: Cuerda - V39 = 0.3045 (Correlación)\n", - "Imputación final aplicada: Cuerda - Skyeye 2930 VTOL = 0.299 (Correlación)\n", - "Imputación final aplicada: Cuerda - Skyeye 3600 = 0.309 (Correlación)\n", - "Imputación final aplicada: Cuerda - Skyeye 5000 = 0.3465 (Correlación)\n", - "Imputación final aplicada: Cuerda - Skyeye 5000 VTOL = 0.336 (Correlación)\n", - "Imputación final aplicada: Cuerda - Skyeye 5000 VTOL octo = 0.3425 (Correlación)\n", - "Imputación final aplicada: Cuerda - Volitation VT510 = 0.334 (Correlación)\n", - "Imputación final aplicada: Cuerda - Ascend = 0.286 (Correlación)\n", - "Imputación final aplicada: Cuerda - Reach = 0.312 (Correlación)\n", - "Imputación final aplicada: Empty weight - Aerosonde® Mk. 4.7 VTOL = 19.796 (Correlación)\n", - "Imputación final aplicada: Empty weight - Aerosonde® Mk. 4.8 Fixed wing = 19.809 (Correlación)\n", - "Imputación final aplicada: Empty weight - Orbiter 4 = 18.724 (Correlación)\n", - "Imputación final aplicada: Empty weight - Mantis = 5.627 (Correlación)\n", - "Imputación final aplicada: Empty weight - Integrator = 22.234 (Correlación)\n", - "Imputación final aplicada: Empty weight - Integrator VTOL = 24.8205 (Correlación)\n", - "Imputación final aplicada: Empty weight - Integrator Extended Range (ER) = 22.2905 (Correlación)\n", - "Imputación final aplicada: Empty weight - RQNan21A Blackjack = 21.149 (Correlación)\n", - "Imputación final aplicada: Empty weight - DeltaQuad Pro #MAP = 4.767 (Correlación)\n", - "Imputación final aplicada: Empty weight - DeltaQuad Pro #CARGO = 4.767 (Correlación)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Integrator VTOL = 13.843 (Correlación)\n", "\n", - "=== Iteración 1: Resumen después de imputaciones ===\n" + "=== Iteración 3: Resumen después de imputaciones ===\n" ] }, { @@ -35332,7 +55708,7 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - 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Resumen de Valores Faltantes Después de Iteración 1

\n", + "

Resumen de Valores Faltantes Después de Iteración 3

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2Aerosonde® Mk. 4.7 Fixed WingAerosonde Mk. 4.7 Fixed Wing0.000
3Aerosonde® Mk. 4.7 VTOL1.000Aerosonde Mk. 4.7 VTOL0.000
4Aerosonde® Mk. 4.8 Fixed wing1.000Aerosonde Mk. 4.8 Fixed wing0.000
5Aerosonde® Mk. 4.8 VTOL FTUASAerosonde Mk. 4.8 VTOL FTUAS0.000
7Fulmar X0.0001.000
8Orbiter 41.0000.000
9
12Integrator1.0000.000
13Integrator VTOL1.0000.000
14
15ScanEagle 30.0001.000
16RQNan21A Blackjack1.000RQ Nan 21A Blackjack0.000
17
18DeltaQuad Pro #MAP1.0000.000
19DeltaQuad Pro #CARGO1.0000.000
20
28Skyeye 36000.0001.000
29
0Total de Valores Faltantes10.0005.000
" @@ -35601,14 +55977,14 @@ "text": [ "\n", "================================================================================\n", - "\u001b[1m=== FIN DE ITERACIÓN 1 ===\u001b[0m\n", + "\u001b[1m=== FIN DE ITERACIÓN 3 ===\u001b[0m\n", "================================================================================\n", "\n", "================================================================================\n", - "\u001b[1m=== INICIO DE ITERACIÓN 2 ===\u001b[0m\n", + "\u001b[1m=== INICIO DE ITERACIÓN 4 ===\u001b[0m\n", "================================================================================\n", "\n", - "=== Iteración 2: Resumen antes de imputaciones ===\n" + "=== Iteración 4: Resumen antes de imputaciones ===\n" ] }, { @@ -35646,7 +56022,7 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

Resumen de Valores Faltantes Antes de Iteración 2

\n", + "

Resumen de Valores Faltantes Antes de Iteración 4

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0Stalker XE32.00030.000
1Stalker VXE3033.00031.000
2Aerosonde® Mk. 4.7 Fixed Wing30.000Aerosonde Mk. 4.7 Fixed Wing28.000
3Aerosonde® Mk. 4.7 VTOL30.000Aerosonde Mk. 4.7 VTOL27.000
4Aerosonde® Mk. 4.8 Fixed wing34.000Aerosonde Mk. 4.8 Fixed wing31.000
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7Fulmar X36.00035.000
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11ScanEagle35.00033.000
12Integrator36.00033.000
13Integrator VTOL35.00032.000
14Integrator Extended Range (ER)38.00036.000
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16RQNan21A Blackjack35.000RQ Nan 21A Blackjack32.000
17DeltaQuad Evo30.00028.000
18DeltaQuad Pro #MAP33.00030.000
19DeltaQuad Pro #CARGO33.00030.000
20V2129.00028.000
21V2529.00028.000
22V3229.00028.000
23V3532.00031.000
24V3933.00031.000
25Volitation VT37031.00030.000
26Skyeye 260034.000
27Skyeye 2930 VTOL33.000
28Skyeye 360033.000
29Skyeye 3600 VTOL32.000
30Skyeye 500030.000
31Skyeye 5000 VTOL32.000
32Skyeye 5000 VTOL octo32.000
33Volitation VT51030.000
34Ascend31.000
35Transition31.000
36Reach31.000
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Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes1212.000
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Datos Filtrados por aeronaves seleccionadas antes de imputar(df_resultado_por_similitud)

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Stalker XEStalker VXE30Aerosonde® Mk. 4.7 Fixed WingAerosonde® Mk. 4.7 VTOLAerosonde® Mk. 4.8 Fixed wingAerosonde® Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQNan21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.3440532.31596321.6247630.40658427.26947626.56688118.26582630.62533630.95346530.93282930.95346525.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.25028827.3440532.8128636.09414730.62533631.71909832.8128621.8752421.8752427.34405
Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.8429633.8636368010.33593812.97158119500.019500.019487.019500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.013122.516571.42857116571.42857114902.12516044.44444415640.015640.017000.010000.013000.016000.0
Área del ala0.871.1582831.551.551.552.5030.570.941.6081.1285940.7541.1814861.8722.08951.8721.3491.8020.840.70.70.80.521.031.1285941.2031.4456560.881.01.331.322.6152.6152.6152.6150.8307891.1897832.679657
Relación de aspecto del ala15.30125515.32644912.512.512.512.514.75438613.217513.44314.01214.76714.06712.92312.654512.85913.77412.97314.59914.71714.71714.57814.43514.19413.90914.053513.65714.11614.01313.722513.684512.71313.04612.876513.11414.35714.23313.683
Longitud del fuselaje2.12.59083.03.03.03.565861.71.21.21.21.481.712.53.00352.52.42.50.750.90.90.930.931.01.881.3746612.022.052.032.4882.423.53.53.52.9051.5622.34.712
Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
Alcance de la aeronave370.0433.0296.090047123.448364122.911121815.0537633270.0800.0150.050.025.0418.78500.0499.666667500.0178.08195692.6270.0100.0100.0270.01843.69770.21276650.0475.377300.01763.90833351.78571451.785714300.0822.222222800.0800.0800.0273.552632633.498853819.7802227Skyeye 2930 VTOL32.000
Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.08.05.06.012.020.028Skyeye 360033.000
Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.4388641.6612930.84572541.736.036.025.641.246.346.26913346.341.246.331.312525.90967725.90967733.033.033.033.033.033.024.70113830.034.17857133.042.042.038.050.030.030.035.029Skyeye 3600 VTOL31.000
Velocidad de pérdida (KCAS)12.72196712.68114823.687204NaNNaN19.66532312.84255712.675NaN14.773438NaN16.518868NaNNaNNaN24.61157NaN13.41875NaNNaN14.015.517.014.77343816.91145824.010.018.012.524.015.019.312524.025.013.013.013.030Skyeye 500029.000
envergadura3.6574.87684.44.44.45.0940862.93.05.24.42.13.14.85.0334.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.031Skyeye 5000 VTOL30.000
Cuerda0.2390.3181950.3520.3520.3520.3940.1965520.3130.3340.3010.270.29750.3380.3410.3440.31050.33850.27550.2720.2720.27750.2215840.2910.30350.30450.356840.2118750.2990.3090.356840.34650.3360.34250.3340.2860.290.31232Skyeye 5000 VTOL octo30.000
payload2.4947562.49475614.511.317.722.72.5185611.97797412.05.51.1861545.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.033Volitation VT51030.000
Empty weight10.88620817.46329210.85663519.79619.80931.74193510.011.55456718.7249.0093755.6277.20047222.23424.820522.290511.28030321.1494.84.7674.7672.653.456.459.0093756.41640611.06.57.111.511.032.031.235.031.23.05.831.034Ascend29.000
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Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes1146.000
" @@ -36575,34 +56289,74 @@ "name": "stdout", "output_type": "stream", "text": [ - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.7 VTOL'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Aerosonde® Mk. 4.7 VTOL.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Aerosonde® Mk. 4.8 Fixed wing'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Aerosonde® Mk. 4.8 Fixed wing.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Orbiter 4'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Orbiter 4.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Mantis'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Mantis.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Integrator.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator VTOL'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Integrator VTOL.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'Integrator Extended Range (ER)'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para Integrator Extended Range (ER).\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'RQNan21A Blackjack'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para RQNan21A Blackjack.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #MAP'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para DeltaQuad Pro #MAP.\n", - "Razón: Ninguna aeronave se encuentra dentro del rango MTOW de 'DeltaQuad Pro #CARGO'para el parametro 'Velocidad de pérdida (KCAS)'.\n", - "No se pudo imputar: Velocidad de pérdida (KCAS) para DeltaQuad Pro #CARGO.\n", - "\n", - "=== Generando reporte final ===\n", - "No se realizaron imputaciones con el nivel de confianza aceptable.\n", - "No se realizaron imputaciones con éxito.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m*** IMPUTACIÓN POR SIMILITUD - ITERACIÓN 4 ***\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", "\u001b[1mNo se realizaron imputaciones por similitud en esta iteración.\u001b[0m\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN 2 ***\u001b[0m\n", + "\u001b[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN 4 ***\u001b[0m\n", "--------------------------------------------------------------------------------\n", "\n", "=== DataFrame inicial ===\n" @@ -36649,10 +56403,10 @@ " \n", " Stalker XE\n", " Stalker VXE30\n", - " Aerosonde® Mk. 4.7 Fixed Wing\n", - " Aerosonde® Mk. 4.7 VTOL\n", - " Aerosonde® Mk. 4.8 Fixed wing\n", - " Aerosonde® Mk. 4.8 VTOL FTUAS\n", + " Aerosonde Mk. 4.7 Fixed Wing\n", + " Aerosonde Mk. 4.7 VTOL\n", + " Aerosonde Mk. 4.8 Fixed wing\n", + " Aerosonde Mk. 4.8 VTOL FTUAS\n", " AAI Aerosonde\n", " Fulmar X\n", " Orbiter 4\n", @@ -36663,7 +56417,7 @@ " Integrator VTOL\n", " Integrator Extended Range (ER)\n", " ScanEagle 3\n", - " RQNan21A Blackjack\n", + " RQ Nan 21A Blackjack\n", " DeltaQuad Evo\n", " DeltaQuad Pro #MAP\n", " DeltaQuad Pro #CARGO\n", @@ -36685,6 +56439,46 @@ " Transition\n", " Reach\n", " \n", + " \n", + " Modelo\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -36728,46 +56522,6 @@ " 0.0\n", " \n", " \n", - " Tasa de ascenso\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 2.49936\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 5.0\n", - " NaN\n", - " 5.0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 5.0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", " Altitud a la que se realiza el crucero\n", " 6000.0\n", " 6000.0\n", @@ -36814,16 +56568,16 @@ " 27.34405\n", " 27.34405\n", " 27.34405\n", - " 32.315963\n", - " 21.62476\n", + " 30.228669\n", + " 36.094147\n", " 30.406584\n", - " 27.269476\n", - " 26.566881\n", + " 30.466419\n", + " 27.426372\n", " 18.265826\n", " 30.625336\n", " 30.953465\n", - " 30.932829\n", - " 30.953465\n", + " 21.463\n", + " 31.894376\n", " 25.703407\n", " 33.797246\n", " 18.090824\n", @@ -36837,11 +56591,11 @@ " 27.34405\n", " 36.094147\n", " 26.250288\n", - " 27.34405\n", + " NaN\n", " 32.81286\n", " 36.094147\n", " 30.625336\n", - " 31.719098\n", + " 30.290909\n", " 32.81286\n", " 21.87524\n", " 21.87524\n", @@ -36857,12 +56611,12 @@ " 15000.0\n", " 15000.0\n", " 9.842\n", - " 9633.863636\n", - " 8010.335938\n", - " 12.971581\n", + " 9403.63518\n", + " 6839.144606\n", + " NaN\n", " 19500.0\n", " 19500.0\n", - " 19487.0\n", + " 7013.834\n", " 19500.0\n", " 20.0\n", " 20.0\n", @@ -36875,13 +56629,13 @@ " 16000.0\n", " 16000.0\n", " 17000.0\n", - " 13122.5\n", - " 16571.428571\n", - " 16571.428571\n", - " 14902.125\n", - " 16044.444444\n", - " 15640.0\n", - " 15640.0\n", + " 14972.955913\n", + " 16000.0\n", + " 17070.833\n", + " 16959.091874\n", + " 16254.028\n", + " 16009.435943\n", + " 16009.476366\n", " 17000.0\n", " 10000.0\n", " 13000.0\n", @@ -36889,43 +56643,43 @@ " \n", " \n", " Velocidad de pérdida limpia (KCAS)\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 14.872\n", + " 14.787\n", + " 17.306\n", + " 18.62\n", + " 18.423\n", + " 25.0\n", + " 10.0\n", + " 14.807\n", + " 18.753\n", + " 16.3955\n", + " 14.509\n", + " 15.8305\n", + " 19.9435\n", + " 21.1085\n", + " 20.545\n", + " 17.329\n", + " 19.5925\n", + " 14.0\n", + " 14.0735\n", + " 14.073\n", " 14.0\n", " 15.5\n", " 17.0\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 18.0\n", + " 17.39739\n", + " 24.0\n", " 10.0\n", " 18.0\n", " 12.5\n", " 24.0\n", " 15.0\n", - " NaN\n", - " NaN\n", " 25.0\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 25.0\n", + " 25.0\n", + " 14.0\n", + " 10.0\n", + " 25.0\n", " \n", " \n", " Área del ala\n", @@ -36938,9 +56692,9 @@ " 0.57\n", " 0.94\n", " 1.608\n", - " 1.128594\n", + " 1.2\n", " 0.754\n", - " 1.181486\n", + " 1.063\n", " 1.872\n", " 2.0895\n", " 1.872\n", @@ -36952,9 +56706,9 @@ " 0.8\n", " 0.52\n", " 1.03\n", - " 1.128594\n", + " 1.202\n", " 1.203\n", - " 1.445656\n", + " 1.424\n", " 0.88\n", " 1.0\n", " 1.33\n", @@ -36962,10 +56716,10 @@ " 2.615\n", " 2.615\n", " 2.615\n", - " 2.615\n", - " 0.830789\n", - " 1.189783\n", - " 2.679657\n", + " 1.993\n", + " 0.771\n", + " 0.986\n", + " 2.329\n", " \n", " \n", " Relación de aspecto del ala\n", @@ -36978,34 +56732,34 @@ " 14.754386\n", " 13.2175\n", " 13.443\n", - " 14.012\n", - " 14.767\n", - " 14.067\n", - " 12.923\n", - " 12.6545\n", - " 12.859\n", - " 13.774\n", - " 12.973\n", - " 14.599\n", - " 14.717\n", - " 14.717\n", - " 14.578\n", - " 14.435\n", - " 14.194\n", - " 13.909\n", - " 14.0535\n", - " 13.657\n", - " 14.116\n", - " 14.013\n", - " 13.7225\n", - " 13.6845\n", - " 12.713\n", - " 13.046\n", - " 12.8765\n", - " 13.114\n", - " 14.357\n", - " 14.233\n", - " 13.683\n", + " 13.9345\n", + " 14.755\n", + " 14.057\n", + " 12.908\n", + " 12.648\n", + " 12.84\n", + " 13.765\n", + " 12.914\n", + " 14.589\n", + " 14.714\n", + " 14.714\n", + " 14.568\n", + " 14.421\n", + " 14.182\n", + " 13.898\n", + " 14.0415\n", + " 13.645\n", + " 14.103\n", + " 14.001\n", + " 13.7095\n", + " 13.6715\n", + " 12.695\n", + " 13.032\n", + " 12.8555\n", + " 13.099\n", + " 14.349\n", + " 14.223\n", + " 13.669\n", " \n", " \n", " Longitud del fuselaje\n", @@ -37014,7 +56768,7 @@ " 3.0\n", " 3.0\n", " 3.0\n", - " 3.56586\n", + " 3.5945\n", " 1.7\n", " 1.2\n", " 1.2\n", @@ -37022,7 +56776,7 @@ " 1.48\n", " 1.71\n", " 2.5\n", - " 3.0035\n", + " 2.998\n", " 2.5\n", " 2.4\n", " 2.5\n", @@ -37033,7 +56787,7 @@ " 0.93\n", " 1.0\n", " 1.88\n", - " 1.374661\n", + " 1.954\n", " 2.02\n", " 2.05\n", " 2.03\n", @@ -37171,41 +56925,41 @@ " Alcance de la aeronave\n", " 370.0\n", " 433.0\n", - " 296.090047\n", - " 123.448364\n", - " 122.911121\n", - " 815.053763\n", + " 518.9225\n", + " 481.428\n", + " 535.2755\n", + " 800.0\n", " 3270.0\n", " 800.0\n", - " 150.0\n", + " 509.5565\n", " 50.0\n", " 25.0\n", - " 418.78\n", + " 503.5155\n", " 500.0\n", - " 499.666667\n", + " 646.0835\n", " 500.0\n", - " 178.081956\n", - " 92.6\n", + " 50.0\n", + " 565.912\n", " 270.0\n", " 100.0\n", " 100.0\n", " 270.0\n", - " 1843.69\n", - " 770.212766\n", - " 50.0\n", - " 475.377\n", + " 270.0\n", + " 412.686\n", + " 456.221\n", + " 413.556\n", " 300.0\n", - " 1763.908333\n", - " 51.785714\n", - " 51.785714\n", + " 3270.0\n", + " 425.273\n", + " 458.1245\n", " 300.0\n", - " 822.222222\n", + " 530.401\n", " 800.0\n", " 800.0\n", " 800.0\n", - " 273.552632\n", - " 633.498853\n", - " 819.78022\n", + " 270.0\n", + " 506.641\n", + " 800.0\n", " \n", " \n", " Autonomía de la aeronave\n", @@ -37241,7 +56995,7 @@ " 6.0\n", " 8.0\n", " 8.0\n", - " 8.0\n", + " 11.672907\n", " 5.0\n", " 6.0\n", " 12.0\n", @@ -37254,7 +57008,7 @@ " 33.43886\n", " 33.43886\n", " 33.43886\n", - " 41.66129\n", + " 42.252675\n", " 30.845725\n", " 41.7\n", " 36.0\n", @@ -37262,22 +57016,22 @@ " 25.6\n", " 41.2\n", " 46.3\n", - " 46.269133\n", + " 40.216\n", " 46.3\n", " 41.2\n", " 46.3\n", - " 31.3125\n", - " 25.909677\n", - " 25.909677\n", + " 33.0\n", + " 29.009\n", + " 29.009\n", " 33.0\n", " 33.0\n", " 33.0\n", " 33.0\n", " 33.0\n", " 33.0\n", - " 24.701138\n", + " 30.834289\n", " 30.0\n", - " 34.178571\n", + " 35.0985\n", " 33.0\n", " 42.0\n", " 42.0\n", @@ -37289,38 +57043,38 @@ " \n", " \n", " Velocidad de pérdida (KCAS)\n", - " 12.721967\n", - " 12.681148\n", - " 23.687204\n", - " NaN\n", - " NaN\n", - " 19.665323\n", - " 12.842557\n", - " 12.675\n", - " NaN\n", - " 14.773438\n", - " NaN\n", - " 16.518868\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 24.61157\n", + " 14.838\n", + " 9.415\n", + " 10.532\n", + " 10.532\n", + " 10.532\n", + " 18.907465\n", + " 10.0\n", " NaN\n", - " 13.41875\n", + " 10.0\n", + " 15.316\n", + " 16.645\n", + " 12.559\n", + " 13.051\n", + " 13.843\n", " NaN\n", " NaN\n", + " 13.051\n", + " 14.0\n", + " 15.316\n", + " 15.607\n", " 14.0\n", " 15.5\n", " 17.0\n", - " 14.773438\n", - " 16.911458\n", + " 18.0\n", + " 17.39739\n", " 24.0\n", " 10.0\n", " 18.0\n", " 12.5\n", " 24.0\n", " 15.0\n", - " 19.3125\n", + " 19.109225\n", " 24.0\n", " 25.0\n", " 13.0\n", @@ -37328,6 +57082,46 @@ " 13.0\n", " \n", " \n", + " Tasa de ascenso\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", " Radio de giro\n", " NaN\n", " NaN\n", @@ -37374,7 +57168,7 @@ " 4.4\n", " 4.4\n", " 4.4\n", - " 5.094086\n", + " 5.644\n", " 2.9\n", " 3.0\n", " 5.2\n", @@ -37416,36 +57210,36 @@ " 0.352\n", " 0.394\n", " 0.196552\n", - " 0.313\n", - " 0.334\n", - " 0.301\n", - " 0.27\n", - " 0.2975\n", - " 0.338\n", - " 0.341\n", - " 0.344\n", - " 0.3105\n", + " 0.319\n", + " 0.332\n", + " 0.304\n", + " 0.271\n", + " 0.2985\n", " 0.3385\n", + " 0.341\n", + " 0.345\n", + " 0.3115\n", + " 0.341\n", " 0.2755\n", " 0.272\n", " 0.272\n", - " 0.2775\n", - " 0.221584\n", + " 0.278\n", + " 0.281\n", + " 0.292\n", + " 0.306\n", + " 0.307\n", + " 0.314\n", + " 0.296\n", + " 0.3\n", + " 0.311\n", + " 0.315\n", + " 0.3485\n", + " 0.338\n", + " 0.3445\n", + " 0.335\n", + " 0.287\n", " 0.291\n", - " 0.3035\n", - " 0.3045\n", - " 0.35684\n", - " 0.211875\n", - " 0.299\n", - " 0.309\n", - " 0.35684\n", - " 0.3465\n", - " 0.336\n", - " 0.3425\n", - " 0.334\n", - " 0.286\n", - " 0.29\n", - " 0.312\n", + " 0.313\n", " \n", " \n", " payload\n", @@ -37455,11 +57249,11 @@ " 11.3\n", " 17.7\n", " 22.7\n", - " 2.518561\n", - " 1.977974\n", + " 4.0\n", + " 2.494756\n", " 12.0\n", " 5.5\n", - " 1.186154\n", + " 2.693\n", " 5.0\n", " 18.0\n", " 18.0\n", @@ -37651,38 +57445,38 @@ " Empty weight\n", " 10.886208\n", " 17.463292\n", - " 10.856635\n", + " 19.796\n", " 19.796\n", " 19.809\n", - " 31.741935\n", + " 31.0\n", " 10.0\n", - " 11.554567\n", - " 18.724\n", - " 9.009375\n", - " 5.627\n", - " 7.200472\n", - " 22.234\n", - " 24.8205\n", - " 22.2905\n", - " 11.280303\n", - " 21.149\n", + " 17.463292\n", + " 18.365\n", + " 12.237\n", + " 5.633\n", + " 10.192\n", + " 22.195\n", + " 24.7845\n", + " 22.257\n", + " 14.794\n", + " 21.123\n", " 4.8\n", - " 4.767\n", - " 4.767\n", + " 4.754\n", + " 4.754\n", " 2.65\n", " 3.45\n", " 6.45\n", - " 9.009375\n", - " 6.416406\n", + " 7.1\n", + " 6.708303\n", " 11.0\n", " 6.5\n", " 7.1\n", " 11.5\n", " 11.0\n", " 32.0\n", - " 31.2\n", + " 32.1405\n", " 35.0\n", - " 31.2\n", + " 23.959\n", " 3.0\n", " 5.8\n", " 31.0\n", @@ -37808,7 +57602,7 @@ " NaN\n", " \n", " \n", - " Potencia/Peso\n", + " Potencia específica (P/W)\n", " NaN\n", " NaN\n", " NaN\n", @@ -37928,7 +57722,7 @@ " NaN\n", " \n", " \n", - " Potencia(W)\n", + " Potencia Watts\n", " NaN\n", " NaN\n", " 2980.0\n", @@ -37968,7 +57762,7 @@ " NaN\n", " \n", " \n", - " Potencia(HP)\n", + " Potencia HP\n", " NaN\n", " NaN\n", " 4.0\n", @@ -38128,6 +57922,246 @@ " NaN\n", " \n", " \n", + " Despegue\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 2.0\n", + " 1.0\n", + " 2.0\n", + " 2.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 2.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 3.0\n", + " 2.0\n", + " 3.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " \n", + " \n", + " Propulsión horizontal\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 1.0\n", + " 1.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " 2.0\n", + " \n", + " \n", + " Propulsión vertical\n", + " 5.0\n", + " 5.0\n", + " 5.0\n", + " 1.0\n", + " 5.0\n", + " 1.0\n", + " 1.0\n", + " 5.0\n", + " 5.0\n", + " 5.0\n", + " 5.0\n", + " 5.0\n", + " 5.0\n", + " 1.0\n", + " 5.0\n", + " 5.0\n", + " 5.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 5.0\n", + " 1.0\n", + " 5.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " \n", + " \n", + " Cantidad de motores propulsión vertical\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 4.0\n", + " 0.0\n", + " 4.0\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 0.0\n", + " 4.0\n", + " 0.0\n", + " 4.0\n", + " 8.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " \n", + " \n", + " Cantidad de motores propulsión horizontal\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " \n", + " \n", + " Misión\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " \n", + " \n", " Dimensiones de la bahía de carga útil\n", " NaN\n", " NaN\n", @@ -38368,7 +58402,7 @@ " NaN\n", " \n", " \n", - " Despegue\n", + " Despegue todos los tipos\n", " NaN\n", " NaN\n", " NaN\n", @@ -38688,47 +58722,7 @@ " NaN\n", " \n", " \n", - " Empresa\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " kjbk\n", + " indice_desconocido\n", " NaN\n", " NaN\n", " NaN\n", @@ -38830,7 +58824,7 @@ "

Tabla de Correlaciones con todos los parametros(tabla_completa)

\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -38857,6 +58851,47 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -38864,494 +58899,596 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - 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" \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -39359,18 +59496,18 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -39381,112 +59518,136 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -39497,122 +59658,362 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - 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ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Capacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Modelo
Distancia de carrera requerida para despegue1.0000.0630.3460.058-0.5050.230-0.2490.2270.4270.082-0.2730.269-0.2500.2220.4250.168-0.014-0.0690.207-0.2350.1130.1510.054-0.0780.241-0.1850.1050.2000.229nan0.389nan0.2090.225nannan-0.018-0.240-0.1560.7350.1540.671-0.598nannan
Altitud a la que se realiza el crucero0.0631.000-0.056-0.125nan-0.0590.108-0.0960.0110.0950.008-0.0750.105-0.092nan-0.095-0.324-0.281-0.1970.170-0.0840.079-0.101-0.309-0.278-0.0640.147-0.0800.109-0.114nannannan-0.127-0.1210.038-0.325nannannan-0.119-0.1090.187-0.159nannan
Velocidad a la que se realiza el crucero (KTAS)0.346-0.0560.4270.0111.0000.1660.1390.672-0.7850.5550.9320.7020.1020.2720.7030.3690.5310.5420.708-0.6940.9820.7970.608-0.6020.3050.5950.1340.3160.497-0.6070.4280.9170.5530.5450.4230.6340.1140.4210.3960.567-0.6940.9730.7900.503-0.8540.4800.3650.461-0.272-0.2900.0630.5630.120-0.076nannan
Techo de servicio máximo0.058-0.1250.1661.000-0.1660.187-0.1290.1780.5530.2040.1470.0840.158-0.0020.1250.0820.0950.2000.1341.0000.1030.096-0.0290.1350.5900.1220.0970.0010.0140.1110.0220.0280.118-0.8750.1440.1120.5790.118-0.798-0.036-0.0900.469-0.138-0.004-0.961-0.118-0.8190.461-0.1560.1960.068-0.1360.140nannan
Velocidad de pérdida limpia (KCAS)-0.505nan0.139-0.166-0.2730.0080.3160.1031.0000.439-0.3700.753-0.5980.5940.6750.798-0.2300.260nan0.546-0.2890.4010.5931.0000.5050.6530.536nan0.128nan0.4110.5160.6290.7470.6430.731-0.1900.4930.9310.678-0.2370.1290.2240.6300.118-0.0280.322-0.1600.232nannan0.0681.0000.163
Área del ala0.230-0.0590.6720.1870.4390.269-0.0750.4970.0960.7531.000-0.7460.846-0.7780.8350.9840.979-0.0490.3460.6840.4620.8090.7230.845-0.4430.6720.9860.949-0.5430.5130.9920.6870.0480.970-0.0230.3830.6480.2870.8250.7870.854-0.3830.6920.9650.944-0.4660.4910.9740.2880.0360.1250.4760.0720.037nannan
Relación de aspecto del ala-0.2490.108-0.785-0.129-0.370-0.746-0.2500.105-0.607-0.029-0.598-0.7781.000-0.626-0.676-0.7890.108-0.444-0.740-0.572-0.618-0.779-0.8280.521-0.765-0.495-0.6960.429-0.416-0.624-0.681-0.790-0.003-0.456-0.730-0.132-0.630-0.862-0.8260.519-0.769-0.497-0.7460.432-0.409-0.9700.3020.0250.2960.024-0.001-0.471-0.1410.075nannan
Longitud del fuselaje0.227-0.0960.5550.1780.2600.846-0.6260.222-0.0920.4280.1350.5940.835-0.6241.0000.9380.8080.0430.3930.3970.3120.7040.5770.6650.8060.1400.4030.3630.1070.7190.6230.660-0.6170.5640.9250.8320.5740.9260.834-0.6960.6820.6460.9290.036-0.186-0.2030.1380.6120.0340.040nannan
Ancho del fuselaje0.425nan0.9320.553nan0.9170.5900.6750.984-0.676-0.6810.9381.0000.9860.721-0.1940.833-0.0890.9400.5470.7110.6710.5360.5570.868nan0.944nan0.8820.954nan0.323nan1.000nan0.794nan-0.5350.574nannan
Peso máximo al despegue (MTOW)0.168-0.0950.7020.2040.5460.979-0.7890.8080.5530.1220.7980.970-0.7900.8060.9861.0000.0130.3930.7360.5120.8020.7070.8840.0300.4200.7170.3330.8110.7510.882-0.4010.7080.9790.9580.933-0.4640.5140.9760.7580.0520.0900.4670.0230.075nannan
Alcance de la aeronave-0.014-0.3240.1020.147-0.289-0.0490.1080.0430.7210.0130.054-0.3090.5450.097-0.230-0.023-0.0030.1400.8330.0301.0000.2000.001-0.227-0.095-0.492-0.074-0.5780.3320.6240.073-0.7860.1750.9660.6680.0370.2240.013-0.258-0.043-0.2240.019-0.5250.5240.9360.081-0.7110.4670.8480.837-0.1480.1570.317-0.2130.210nannan
Autonomía de la aeronave-0.069-0.2810.2720.0840.4010.346-0.4440.393-0.1940.3930.200-0.078-0.2780.4230.0010.2600.383-0.4560.403-0.0890.4200.2241.0000.4220.2030.5300.3180.3800.378-0.3470.5410.2690.400-0.5940.3010.3370.6340.3840.486-0.7150.802-0.0930.056-0.7320.0210.033-0.4200.4780.353-0.314nannan
Velocidad máxima (KIAS)0.207-0.1970.7030.1580.5930.684-0.7400.3970.241-0.0640.6340.0140.5160.648-0.7300.3630.9400.7360.0010.4220.7170.0130.3781.0000.5150.5310.5930.732-0.2020.6640.7840.632-0.3390.1840.7420.2490.4910.6270.718-0.0770.7000.7260.613-0.2230.1510.7270.9100.1140.067-0.0570.3000.178-0.141nannan
Velocidad de pérdida (KCAS)-0.2350.1700.369-0.0021.0000.462-0.5720.3120.5470.512-0.2270.2030.5151.0000.4930.6340.6540.5850.3940.1740.357-0.1850.1470.1140.1110.6290.287-0.1320.1070.7110.333-0.258-0.3470.2491.0000.0890.0420.3540.1360.2210.1570.3560.6720.2840.3230.1480.934-0.9610.0360.6850.1210.295-0.000-0.4210.498nannan
envergadura0.113-0.0840.5310.1250.5050.809-0.6180.7040.105-0.0800.4210.0220.7470.825-0.6300.7190.6710.802-0.0950.5300.5310.4930.811-0.0430.5410.4910.2211.0000.7190.7700.6860.775-0.2580.5010.9340.7930.9500.806-0.4140.6480.2970.0850.032-0.0810.5160.167-0.106nannan
Cuerda0.1510.0790.5420.0950.6530.723-0.7790.5770.5360.707-0.4920.3180.5930.6340.7190.2000.1090.3960.0280.6430.787-0.8620.6230.5570.751-0.2240.2690.6270.1570.6861.0000.754-0.4890.5000.5910.642-0.4810.4120.014-0.9220.0750.758-0.4990.7300.5950.724-0.4980.3550.975-0.228-0.041-0.0650.4180.193-0.129nannan
payload0.229-0.1010.7080.2000.5360.845-0.8280.665-0.1140.5670.1180.7310.854-0.8260.6600.8680.884-0.0740.3800.7320.6540.7700.7540.8820.0190.4000.7180.3560.7750.7581.000-0.0240.6710.6700.5590.8080.784-0.1420.4750.4890.7110.846-0.0080.0530.4620.100-0.055nannan
duracion en VTOLnan-0.694-0.875nan-0.4430.521-0.190-0.3830.519-0.617nan-0.401-0.578-0.525-0.594-0.2020.585-0.0770.672-0.258-0.489-0.499-0.0241.000-0.694nannannan-0.188-0.9040.188-0.188nannan
Crucero KIAS0.389nan0.9820.1440.1280.672-0.7650.5640.9730.1120.4930.692-0.7690.5740.9440.7080.3320.3010.6640.3940.5240.3370.7000.2840.5010.5000.6710.7300.670-0.6941.0000.7230.6200.636-0.8550.3590.5810.461-0.2430.1430.6080.0650.063nannan
RTF (Including fuel & Batteries)nannan0.7970.7900.579nan0.986-0.4950.9250.9310.965-0.4970.926nan0.9790.6240.9360.6340.7840.1740.9340.5910.7260.3230.9500.5950.559-0.4020.7231.0000.9730.948-0.402nannannannan0.0970.428-0.0970.097nannan
Empty weight0.209-0.1270.6080.1180.4110.949-0.6960.8320.8820.9580.0730.3840.6320.3570.7930.6420.8080.225-0.1210.503-0.0040.6780.944-0.7460.8340.9540.9330.0810.4860.6130.1480.8060.7240.784-0.3150.6200.9730.6360.9481.000-0.3860.7210.9890.6170.0280.7850.9800.2510.023-0.0290.4800.195-0.070nannan
Maximum Crosswindnan0.038-0.602-0.798nan-0.5430.429-0.854-0.961-0.237-0.4660.432-0.696nan-0.464-0.786-0.711-0.715-0.3391.000-0.2230.934-0.414-0.481-0.498-0.1421.000-0.855nannannannan-0.943nannannannan
Rango de comunicaciónnan-0.3250.305-0.036nan0.513-0.4160.6820.480-0.1180.1290.491-0.4090.6460.3230.5140.1750.4670.8020.1840.0890.151-0.9610.6480.4120.4750.3550.489nan0.359nan0.7210.785nan1.000nannannan-0.4300.6040.430-0.430nannan
Capacidad combustible-0.018nan0.595-0.0900.0680.9920.365-0.8190.2240.974-0.9700.929nan0.9760.966-0.0930.7420.0420.8480.0560.7270.0360.2970.0140.9750.711nan0.581nan0.9890.980nannan1.0000.3770.817-0.080nan-0.0800.270nannan
Consumo-0.240nan0.4610.4691.0000.6870.3020.4610.6300.2880.2960.0361.0000.7580.6680.837-0.7320.9100.3540.6850.085-0.922-0.2280.846nan0.461nan0.6170.251nannan0.3771.0000.9980.113nan-0.3750.375nannan
Precio-0.156nan-0.272-0.1380.1630.0480.025-0.186-0.290-0.1560.1180.0360.024-0.203nan0.0520.0370.0210.1140.136-0.1480.0330.0670.1210.0320.075-0.041-0.008nan-0.243nan0.0280.023nannan0.8170.9981.000-0.1380.217-0.1380.134nannan
Despegue0.735-0.1190.0630.196-0.0280.125-0.0010.1380.7940.0900.157-0.420-0.0570.295-0.081-0.0650.053-0.1880.1430.097-0.029nan-0.430-0.0800.113-0.1381.000-0.010-0.6390.610nannan
Propulsión horizontal0.154-0.1090.5630.0680.3220.476-0.4710.612nan0.4670.3170.4780.300-0.0000.5160.4180.462-0.9040.6080.4280.480-0.9430.604nannan0.217-0.0101.0000.118-0.083nannan
Propulsión vertical0.6710.1870.120-0.136-0.1600.072-0.1410.034-0.5350.023-0.2130.3530.178-0.4210.1670.1930.1000.1880.065-0.0970.195nan0.430-0.080-0.375-0.138-0.6390.1181.000-0.954nannan
Cantidad de motores propulsión vertical-0.598-0.159-0.0760.1400.2320.0370.0750.0400.5740.0750.210-0.314-0.1410.498-0.106-0.129-0.055-0.1880.0630.097-0.070nan-0.4300.2700.3750.1340.610-0.083-0.9541.000nannan
Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
" @@ -39671,17 +60072,17 @@ " \n", " 0\n", " Total de valores\n", - " 676.000\n", + " 1024.000\n", " \n", " \n", " 1\n", " Valores numéricos\n", - " 602.000\n", + " 826.000\n", " \n", " \n", " 2\n", " Valores NaN\n", - " 74.000\n", + " 198.000\n", " \n", " \n", "" @@ -39741,7 +60142,7 @@ "

Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -39752,249 +60153,300 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40054,12 +60506,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -40114,7 +60566,7 @@ "

Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Modelo
Velocidad a la que se realiza el crucero (KTAS)1.0000.1660.672-0.7850.5550.7020.1020.2720.7030.3690.5310.5420.7080.6080.1340.497-0.6070.4280.5530.5450.4230.6340.1140.3160.4210.3960.5670.503
Techo de servicio máximo0.1660.1341.0000.187-0.1290.1780.2040.1470.0840.158-0.0020.1250.0950.2000.096-0.0290.1350.1220.0970.0010.0140.1110.1030.0220.0280.118-0.004
Área del ala0.6720.1870.4970.0961.000-0.7460.8460.979-0.0490.3460.6840.4620.8090.7230.8450.949-0.7780.8350.970-0.0230.3830.6480.2870.7530.8250.7870.8540.944
Relación de aspecto del ala-0.785-0.129-0.746-0.607-0.029-0.7781.000-0.626-0.7890.108-0.444-0.740-0.572-0.618-0.779-0.828-0.696-0.624-0.790-0.003-0.456-0.730-0.132-0.598-0.630-0.862-0.826-0.746
Longitud del fuselaje0.5550.1780.846-0.6261.0000.8080.0430.3930.3970.3120.7040.5770.6650.8320.4280.1350.835-0.6241.0000.8060.1400.4030.3630.1070.5940.7190.6230.6600.834
Peso máximo al despegue (MTOW)0.7020.2040.979-0.7890.8080.5530.1220.970-0.7900.8061.0000.0130.3930.7360.5120.8020.7070.8840.9580.0300.4200.7170.3330.7980.8110.7510.8820.933
Alcance de la aeronave0.1020.147-0.0490.1080.0430.0130.5450.097-0.023-0.0030.1400.0301.0000.2000.001-0.227-0.095-0.492-0.0740.0730.2240.013-0.258-0.230-0.043-0.2240.0190.081
Autonomía de la aeronave0.2720.0840.346-0.4440.3930.3930.2000.4230.0010.383-0.4560.4030.4200.2241.0000.4220.2030.5300.3180.3800.3840.378-0.3470.2600.5410.2690.4000.486
Velocidad máxima (KIAS)0.7030.1580.684-0.7400.3970.7360.0010.4220.6340.0140.648-0.7300.3630.7170.0130.3781.0000.5150.5310.5930.7320.6320.2490.5160.4910.6270.7180.613
Velocidad de pérdida (KCAS)0.369-0.0020.462-0.5720.3120.512-0.2270.2030.5150.1140.1110.287-0.1320.1070.333-0.258-0.3470.2491.0000.4930.6340.6540.3570.6290.2210.1570.3560.148
envergadura0.5310.1250.809-0.6180.7040.802-0.0950.5300.5310.493Velocidad de pérdida limpia (KCAS)0.3160.1030.753-0.5980.5940.798-0.2300.2600.5160.6291.0000.7470.6430.7310.678
envergadura0.4210.0220.825-0.6300.7190.7700.7930.811-0.0430.5410.4910.2210.7471.0000.6860.7750.806
Cuerda0.5420.0950.723-0.7790.5770.707-0.4920.3180.5930.6340.7190.3960.0280.787-0.8620.6230.751-0.2240.2690.6270.1570.6430.6861.0000.7540.6420.7580.724
payload0.7080.2000.845-0.8280.6650.884-0.0740.3800.7320.6540.7700.7540.5670.1180.854-0.8260.6600.8820.0190.4000.7180.3560.7310.7750.7581.0000.8080.784
Empty weight0.6080.1180.949-0.6960.8320.9580.0730.3840.6320.3570.7930.6420.8080.503-0.0040.944-0.7460.8340.9330.0810.4860.6130.1480.6780.8060.7240.7841.000
0Total de valores196.000225.000
1Valores numéricos196.000225.000
2
\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -40125,11 +60577,30 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40137,16 +60608,17 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40165,74 +60637,79 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40250,6 +60727,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -40267,22 +60745,24 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -40301,73 +60781,96 @@ " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40427,17 +60930,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "
ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Modelo
nannannan-0.785nan0.702nannan0.703nannannan0.708nannannannannannan
nannannannan
Área del alanannannan-0.7460.8460.979-0.7780.8350.970nannannannan0.8090.7230.8450.9490.7530.8250.7870.8540.944
Relación de aspecto del ala-0.785nan-0.746nan-0.778nannan-0.789-0.790nannan-0.740-0.730nannan-0.779-0.828nan-0.862-0.826-0.746
Longitud del fuselajenannan0.8460.835nannan0.8080.806nannannannannan0.7040.719nannan0.8320.834
Peso máximo al despegue (MTOW)0.702nan0.979-0.7890.808nan0.970-0.7900.806nannan0.736nan0.8020.7070.8840.9580.717nan0.7980.8110.7510.8820.933
Alcance de la aeronavenannannannan
Autonomía de la aeronavenannannannan
Velocidad máxima (KIAS)0.703nannan-0.740nan0.736-0.730nan0.717nannannannannannannan0.7320.718nan
nannannannan
envergaduraVelocidad de pérdida limpia (KCAS)nannan0.8090.753nannan0.798nannan0.7040.802nannannan0.747nan0.731nan
envergaduranannan0.825nan0.7190.7700.7930.811nannannannan0.747nannan0.7750.806
Cuerdanannan0.723-0.7790.787-0.862nan0.7070.751nannannannan0.719nan0.754nannan0.7580.724
payload0.708nan0.845-0.828nan0.8840.854-0.826nan0.882nan0.732nan0.7700.7540.718nan0.8080.7310.7750.758nan0.784
Empty weightnannan0.949nan0.8320.9580.944-0.7460.8340.933nannannannan0.793nan0.8080.8060.7240.784nan
0Total de valores196.000225.000
1Valores numéricos58.00060.000
2Valores NaN138.000165.000
" @@ -40461,7 +60964,7 @@ }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -40507,7 +61010,7 @@ "

Tabla de correlaciones con filtro de umbral de correlación

\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -40534,6 +61037,47 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40565,9 +61109,30 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40585,6 +61150,9 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40593,30 +61161,24 @@ " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -40647,7 +61209,13 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40660,16 +61228,22 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40688,26 +61262,32 @@ " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -40715,30 +61295,36 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40747,33 +61333,39 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -40781,17 +61373,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40802,30 +61400,36 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40837,7 +61441,10 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40845,14 +61452,17 @@ " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", @@ -40884,35 +61494,47 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40924,6 +61546,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40933,6 +61559,8 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40949,23 +61577,29 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -40979,18 +61613,25 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -40998,37 +61639,42 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -41058,16 +61704,22 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -41076,7 +61728,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -41087,30 +61739,42 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -41124,25 +61788,31 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -41150,18 +61820,18 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -41174,6 +61844,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -41190,14 +61866,20 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -41209,29 +61891,35 @@ " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -41245,12 +61933,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -41261,6 +61949,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -41290,93 +61984,222 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - "
ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Capacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Modelo
nannannan
Altitud a la que se realiza el crucero0.735nannannannannan
Altitud a la que se realiza el cruceronannannannannannannannannannannannannannannannannannannannannan
Velocidad a la que se realiza el crucero (KTAS)nannannannannannan0.917nan
Velocidad a la que se realiza el crucero (KTAS)nannannannannannan-0.785nan0.9320.702nan0.9730.790nan0.703-0.854nannannan0.708nan0.9820.797nannannannannannan-0.798-0.961nan-0.819nannannannannannannannannannan0.753nannannan0.798nannannannan0.747nan0.731nannan0.931nannannannannannannan0.753nannan-0.7460.846-0.7780.8350.9840.9790.970nannannannan0.8090.7230.8450.8250.7870.854nannan0.9650.944nannan0.974nannannannan0.9860.949nannan0.992nannan
Relación de aspecto del alanannan-0.785nannan-0.746nan-0.778nannan-0.789nan-0.790nan-0.740nan-0.730nan-0.779-0.828nan-0.765-0.862-0.826nan-0.769nan-0.746nannan-0.970nannannannannannannannan
Longitud del fuselajenannannan0.8460.835nannan0.9380.8080.806nannannannan0.7040.719nannannannan0.9250.8320.9260.834nannan0.929nannannannannannannannan
Ancho del fuselajenannan0.9320.917nannan0.9840.938nan0.9860.7210.833nan0.940nan0.711nannan0.868nan0.944nan0.8820.954nannannannannan0.794nannannanPeso máximo al despegue (MTOW)nannan0.702nannan0.979-0.7890.8080.7980.970-0.7900.8060.986nannannan0.7360.717nan0.8020.7070.8840.8110.7510.882nan0.7080.9790.9580.933nannan0.9760.758nannannannannannannan
Alcance de la aeronavenannannan0.7210.833nannannannannannannannannan0.936nan-0.711nan0.8480.837nannannannan-0.786nan0.966nannan
nan-0.732nannannannannannannan
Velocidad máxima (KIAS)nannan0.703nannannan-0.740nan-0.730nan0.9400.7360.717nannannannannannan0.7320.718nannan0.7840.726nannannan0.7420.7270.910nannannannannannannan
Velocidad de pérdida (KCAS)nannannan0.711nannannannannannannannannan0.934-0.961nannannannannannan0.7470.825nan0.719nan0.8090.811nan0.704nan0.802nannannannan0.775nannan0.9500.806nannannannan0.7190.770nannan0.9340.793nannannannannannan0.723-0.7790.787-0.862nannan0.7070.751nannannannan0.719nan0.754nan0.758nan0.730nan0.724nannan0.975nannannannannannan-0.922nan
payloadnannan0.708nannan0.845-0.8280.7310.854-0.826nan0.8680.8840.882nannan0.7320.718nan0.7700.7540.7750.758nannannannan0.8080.784nannan0.7110.846nannannannannannannan
duracion en VTOLnannannannan-0.904nannannannan
Crucero KIASnannan0.9820.973nannannan-0.765-0.769nan0.9440.708nannannannan0.730nannannannannannannannannannannannan
RTF (Including fuel & Batteries)nannan0.7970.790nan0.9310.965nan0.986nan0.9250.926nan0.9790.936nan0.726nan0.784nan0.9340.950nannannan0.723nan0.9730.948nannannannannannannannannannannannan0.9490.944-0.7460.8340.9540.933nannan0.8320.8820.958nannan0.8060.7240.784nannan0.7930.948nannan0.7850.980nannan0.808nannan0.973nannan0.7210.989nannan
Maximum Crosswindnannannan-0.798-0.854-0.961nannannannannannan-0.786-0.711-0.715nannan0.934nannannannannannannan-0.943nannannannan
Rango de comunicaciónnan0.802nan-0.961nannannannannannan0.785nannannannannannan0.721nannannannannannan-0.819nannan0.9920.974-0.9700.929nan0.9760.966nan0.7420.848nan0.727nannan0.9750.711nannannan0.9890.980nannannannan0.817nannannannannannan
Consumonannan0.758nan0.837-0.7320.910nannan-0.922nan0.846nannannannan0.998nannannannannannan
Precio0.8170.998nannannannannannannan
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", - "\n", - "=== Velocidad a la que se realiza el crucero (KTAS): No hay valores faltantes para imputar. ===\n", - "\n", - "=== Techo de servicio máximo: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Área del ala: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Relación de aspecto del ala: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Longitud del fuselaje: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Peso máximo al despegue (MTOW): No hay valores faltantes para imputar. ===\n", - "\n", - "=== Alcance de la aeronave: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Autonomía de la aeronave: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Velocidad máxima (KIAS): No hay valores faltantes para imputar. ===\n", - "\n", - "=== Imputación para el parámetro: **Velocidad de pérdida (KCAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.7 VTOL** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

Imputación no Exitosa

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Mensaje
Despegue0.735nannannannannannannan0.794nannannannannannannannannannannannannannannannannannannannannannannan
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde® Mk. 4.7 VTOL'.Propulsión horizontalnannannannannannannannannannannannannannannannannan-0.904nannannan-0.943nannannannannannannannannannan
Propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannan
Cantidad de motores propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannannan
Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
" @@ -41393,7 +62216,11 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Aerosonde® Mk. 4.8 Fixed wing** ---\n" + "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", + "\n", + "=== Imputación para el parámetro: **Velocidad a la que se realiza el crucero (KTAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Skyeye 3600** ---\n" ] }, { @@ -41441,7 +62268,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde® Mk. 4.8 Fixed wing'.\n", + " No se pudo imputar el parámetro 'Velocidad a la que se realiza el crucero (KTAS)' para la aeronave 'Skyeye 3600'.\n", " \n", " \n", "" @@ -41458,70 +62285,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

Imputación no Exitosa

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Orbiter 4'.
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "=== Imputación para el parámetro: **Techo de servicio máximo** ===\n", "\n", "--- Imputación para aeronave: **Mantis** ---\n" ] @@ -41571,7 +62335,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Mantis'.\n", + " No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Mantis'.\n", " \n", " \n", "" @@ -41588,72 +62352,23 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Integrator** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

Imputación no Exitosa

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator'.
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "=== Área del ala: No hay valores faltantes para imputar. ===\n", "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n" + "=== Relación de aspecto del ala: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Longitud del fuselaje: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Peso máximo al despegue (MTOW): No hay valores faltantes para imputar. ===\n", + "\n", + "=== Alcance de la aeronave: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Autonomía de la aeronave: No hay valores faltantes para imputar. ===\n", + "\n", + "=== Velocidad máxima (KIAS): No hay valores faltantes para imputar. ===\n", + "\n", + "=== Imputación para el parámetro: **Velocidad de pérdida (KCAS)** ===\n", + "\n", + "--- Imputación para aeronave: **Fulmar X** ---\n" ] }, { @@ -41701,7 +62416,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator VTOL'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Fulmar X'.\n", " \n", " \n", "" @@ -41783,72 +62498,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **RQNan21A Blackjack** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

Imputación no Exitosa

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'RQNan21A Blackjack'.
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n" + "--- Imputación para aeronave: **ScanEagle 3** ---\n" ] }, { @@ -41896,7 +62546,7 @@ " \n", " \n", " 0\n", - " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Pro #MAP'.\n", + " No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle 3'.\n", " \n", " \n", "" @@ -41913,70 +62563,7 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

Imputación no Exitosa

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Mensaje
0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Pro #CARGO'.
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "=== Velocidad de pérdida limpia (KCAS): No hay valores faltantes para imputar. ===\n", "\n", "=== envergadura: No hay valores faltantes para imputar. ===\n", "\n", @@ -41988,7 +62575,7 @@ "La columna 'Nivel de Confianza' no está presente en df_reporte.\n", "\u001b[1mNo se realizaron imputaciones por correlación en esta iteración.\u001b[0m\n", "\n", - "=== Iteración 2: Resumen después de imputaciones ===\n" + "=== Iteración 4: Resumen después de imputaciones ===\n" ] }, { @@ -42026,7 +62613,7 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

Resumen de Valores Faltantes Después de Iteración 2

\n", + "

Resumen de Valores Faltantes Después de Iteración 4

\n", " \n", " \n", " \n", @@ -42047,22 +62634,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -42073,12 +62660,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -42098,12 +62685,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -42113,12 +62700,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -42128,12 +62715,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -42178,7 +62765,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -42277,7 +62864,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "
2Aerosonde® Mk. 4.7 Fixed WingAerosonde Mk. 4.7 Fixed Wing0.000
3Aerosonde® Mk. 4.7 VTOL1.000Aerosonde Mk. 4.7 VTOL0.000
4Aerosonde® Mk. 4.8 Fixed wing1.000Aerosonde Mk. 4.8 Fixed wing0.000
5Aerosonde® Mk. 4.8 VTOL FTUASAerosonde Mk. 4.8 VTOL FTUAS0.000
7Fulmar X0.0001.000
8Orbiter 41.0000.000
9
12Integrator1.0000.000
13Integrator VTOL1.0000.000
14
15ScanEagle 30.0001.000
16RQNan21A Blackjack1.000RQ Nan 21A Blackjack0.000
17
18DeltaQuad Pro #MAP1.0000.000
19DeltaQuad Pro #CARGO1.0000.000
20
28Skyeye 36000.0001.000
29
0Total de Valores Faltantes10.0005.000
" @@ -42294,6 +62881,7 @@ "output_type": "stream", "text": [ "\u001b[1mNo se realizaron nuevas imputaciones. Finalizando...\u001b[0m\n", + "Hola\n", "=== Exportando datos al archivo: C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Results\\Datos_imputados.xlsx ===\n", "Exportación completada. El archivo se guardó como 'C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Results\\Datos_imputados.xlsx'.\n", "\n", diff --git a/ADRpy/analisis/main.py b/ADRpy/analisis/main.py index 07d33cae..def5be8f 100644 --- a/ADRpy/analisis/main.py +++ b/ADRpy/analisis/main.py @@ -65,7 +65,7 @@ # IMPORTAR MÓDULOS # # ===================== # -from Modulos.config_and_loading import configurar_entorno, cargar_datos +from Modulos.config_and_loading import configurar_entorno, cargar_datos, normalizar_encabezados from Modulos.data_processing import procesar_datos_y_manejar_duplicados from Modulos.user_interaction import seleccionar_parametros_por_indices, solicitar_umbral from Modulos.correlation_analysis import calcular_correlaciones_y_generar_heatmap_con_resumen @@ -88,6 +88,10 @@ print(f"Error al cargar datos: {e}") exit(1) # Detiene el programa si hay un error +# Normalizar encabezados del DataFrame +#print("\n=== Normalizando encabezados del DataFrame ===") +#df_inicial = normalizar_encabezados(df_inicial) + # Validar que los datos se hayan cargado correctamente print("\n=== Validando datos cargados ===") if df_inicial.empty: @@ -142,6 +146,7 @@ "Autonomía de la aeronave", "Velocidad máxima (KIAS)", "Velocidad de pérdida (KCAS)", + "Velocidad de pérdida limpia (KCAS)", "envergadura", "Cuerda", "payload", @@ -216,7 +221,7 @@ df_procesado_actualizado, resumen_imputaciones = bucle_imputacion_similitud_correlacion( df_parametros=df_parametros, df_atributos=df_atributos, - parametros_preseleccionados=parametros_preseleccionados, + parametros_preseleccionados=parametros_preseleccionados, bloques_rasgos=bloques_rasgos, capas_familia=capas_familia, df_procesado=df_procesado, @@ -237,7 +242,9 @@ archivo_destino = args.archivo_destino if not archivo_destino: archivo_destino = input("Ingrese la ruta donde desea guardar el archivo con las imputaciones (incluya .xlsx): ") - +if not archivo_destino: + archivo_destino = r"C:\Users\delpi\OneDrive\Tesis\ADRpy-VTOL\ADRpy\analisis\Results\Datos_imputados.xlsx" + exportar_excel_con_imputaciones( archivo_origen=ruta_archivo, df_procesado=df_procesado_actualizado, From bee1e42485871f07264f87bb223a0704da088c6a Mon Sep 17 00:00:00 2001 From: Delpoo Date: Fri, 6 Jun 2025 14:32:29 -0300 Subject: [PATCH 7/9] Cambio de formato de tabla, rotacion de columnas por filas, imputacion similitud funcionando, imputacion por correlacion no, proximo paso crear la nueva imputacion por correlacion de la tesis --- ADRpy/analisis/Agents.md | 121 + .../analisis/Data/Datos_aeronaves - Copy.xlsx | Bin 0 -> 104311 bytes .../analisis/Data/Datos_aeronaves(vieja).xlsx | Bin 0 -> 71484 bytes ADRpy/analisis/Data/Datos_aeronaves.xlsx | Bin 75912 -> 378884 bytes .../Data/Datos_aeronaves_completo.xlsx | Bin 0 -> 486691 bytes .../Diagrama sin t\303\255tulo.drawio" | 408 +- .../analisis/Modulos/correlation_analysis.py | 14 +- .../Modulos/correlation_imputation.py | 52 +- ADRpy/analisis/Modulos/data_processing.py | 70 +- ADRpy/analisis/Modulos/excel_export.py | 45 +- .../Modulos/imputacion_similitud_flexible.py | 59 +- ADRpy/analisis/Modulos/imputation_loop.py | 14 +- ADRpy/analisis/Modulos/user_interaction.py | 2 +- ADRpy/analisis/Results/Datos_imputados.xlsx | Bin 76963 -> 491997 bytes .../archivo_salida antes de transpo.xlsx | Bin 0 -> 76971 bytes ADRpy/analisis/Results/archivo_salida.xlsx | Bin 62070 -> 274031 bytes ADRpy/analisis/aaa.ipynb | 62081 ++++------------ ADRpy/analisis/main.py | 18 +- ADRpy/analisis/tempCodeRunnerFile.py | 1 + 19 files changed, 13863 insertions(+), 49022 deletions(-) create mode 100644 ADRpy/analisis/Agents.md create mode 100644 ADRpy/analisis/Data/Datos_aeronaves - Copy.xlsx create mode 100644 ADRpy/analisis/Data/Datos_aeronaves(vieja).xlsx create mode 100644 ADRpy/analisis/Data/Datos_aeronaves_completo.xlsx create mode 100644 ADRpy/analisis/Results/archivo_salida antes de transpo.xlsx create mode 100644 ADRpy/analisis/tempCodeRunnerFile.py diff --git a/ADRpy/analisis/Agents.md b/ADRpy/analisis/Agents.md new file mode 100644 index 00000000..29ad70eb --- /dev/null +++ b/ADRpy/analisis/Agents.md @@ -0,0 +1,121 @@ +# Archivo agents.md para Codex - Script ADRpy de Imputación por Correlación + +## Contexto del proyecto + +El script pertenece al proyecto ADRpy, enfocado en la imputación de datos faltantes en un DataFrame con información técnica aeronáutica. El objetivo específico es imputar valores faltantes utilizando modelos predictivos basados en correlaciones estadísticas. + +## Objetivo del script + +* Automatizar la imputación de valores faltantes mediante regresión lineal y polinómica (grado 2). +* Evaluar todas las combinaciones posibles de hasta 2 predictores, considerando métricas como MAPE, R², coeficiente Corr, y Confianza. +* Seleccionar automáticamente el mejor modelo para imputar, priorizando la precisión, robustez estadística y capacidad de generalización (evaluada con LOOCV). + +## Entradas necesarias + +* importar excel con nombre datos aeronaves dentro del mismo directorio y que use la pestaña que se llama (data\_frame\_prueba) +* manejar errores comunes y como encabezados con caracteres o espacios raros, y detectar las celdas vacias manejando diferentes opciones en excel las celdas vacias estan escritas con "nan" + +## Salidas esperadas + +* DataFrame actualizado con valores imputados. +* Reporte detallado con: + + * Modelo usado (ecuación final). + * Métricas: MAPE, R², Corr, Confianza y Correlación final. + * Advertencias explícitas en caso de extrapolaciones (valores fuera del rango de entrenamiento). + +## Flujo lógico principal detallado + +### Paso 1: Detección de celda objetivo + +* Identificar la celda faltante a imputar. + +### Paso 2: Filtrado por familia + +* Filtrar por tipo de misión de la aeronave objetivo. +* Si este filtro deja menos de 5 muestras completas para entrenar, relajar el filtro y usar dataset completo, registrando advertencia explícita. + +### Paso 3: Selección inicial de predictores + +* En esta etapa realizamos dos verificaciones, por un lugar garantizamos la existencia de valores en la aeronave objetivo para utilizarlos como valor de entrada en la ecuación de regresión que va a resultar de entrenar el modelo. +* En segundo lugar, validamos que los valores de estos parámetros se encuentren dentro del dominio de entrenamiento del modelo (rango ±15%) de valores del parámetro predictor. o sea cada parametro disponible en la aeronave se debe chequear que se encuentre dentro del rango aceptable dentro de ese mismo parametro comparandolo con los valores minimos y maximos de ese parametro sin ser la aeronave que se está evaluando. + +### Paso 4: Generación de combinaciones + +* Crear todas las combinaciones posibles de hasta 2 predictores disponibles. + +### Paso 5: Evaluación de cantidad de datos + +* Para cada combinación, filtrar y asegurar la existencia mínima de datos válidos necesarios según la cantidad de coeficientes del modelo. + +### Paso 6: Entrenamiento del modelo + +* Entrenar modelos (lineales y polinómicos de grado 2). +* Aplicar normalización en caso de regresión polinómica. +* Calcular métricas iniciales para filtrar modelos: + + * Coeficiente Corr: + $Corr = 0.6 \times \frac{R^2}{0.7} + 0.4 \times \left(1 - \frac{MAPE}{15}\right)$ + * Confianza final (tras aplicar Corr) +* Manejar errores posibles por matrices indefinidas brindando las advertencias correspondientes. + +### Paso 7: Filtrado y selección de los mejores modelos + +* Filtrar modelos según MAPE y R² mínimos establecidos. +* Seleccionar los mejores 2 modelos de cada tipo basándose en Confianza final. + +### Paso 8: Validación cruzada (LOOCV) + +* Aplicar Leave-One-Out Cross-Validation para evaluar capacidad de generalización de los modelos seleccionados. +* Seleccionar el modelo con mejor desempeño generalizado (menor error en LOOCV). + +### Paso 9: Imputación final + +* Imputar el valor faltante utilizando el mejor modelo seleccionado. +* Registrar advertencias en caso de extrapolación fuera del dominio entrenado. + +### Paso 10: Generación del reporte + +* Crear un DataFrame con un resumen detallado de imputaciones realizadas. +* Registrar métricas detalladas: + + * El valor imputado + * El valor de confianza (calculado tras validación) + * El número de iteración (en verdad esto se agrega en la función del Loop) + * El coeficiente Corr del modelo original + * El número de observaciones k usado para la función + * El tipo de modelo utilizado (lineal / polinómico) + * La ecuación del modelo entrenado + * La cantidad y nombres de predictores involucrados + * El factor de penalización aplicado (k) + * Cualquier advertencia relevante (ej. extrapolación, uso de datos sin filtrar, validación inestable) + * El estado de validez del modelo final (válido / parcialmente válido) + * Comparación entre coeficientes MAE y R2 de modelo entrenado con todos los datos vs. validación LOOCV    + +## Consideraciones técnicas importantes + +* Aplicar normalización a predictores al entrenar modelos polinómicos. +* Calcular número de condición para evaluar estabilidad numérica. +* Penalización en la confianza final por baja cantidad de datos: corr x f(k) (con función empírica definida según cantidad de datos disponibles). +* f (k) **=** 0.00002281 **\*** (k/**2)**\*\***5 **************************************************************************************************************************************************************************************************************-************************************************************************************************************************************************************************************************************** 0.00024 **************************************************************************************************************************************************************************************************************\*************************************************************************************************************************************************************************************************************** (k**/**2)**\*\***4 **************************************************************************************************************************************************************************************************************-************************************************************************************************************************************************************************************************************** 0.0036 **************************************************************************************************************************************************************************************************************\*************************************************************************************************************************************************************************************************************** (k**/**2)**\*\***3 **************************************************************************************************************************************************************************************************************+************************************************************************************************************************************************************************************************************** 0.046 **************************************************************************************************************************************************************************************************************\*************************************************************************************************************************************************************************************************************** (k**/**2)**\*\***2 **************************************************************************************************************************************************************************************************************+************************************************************************************************************************************************************************************************************** 0.0095 **************************************************************************************************************************************************************************************************************\*************************************************************************************************************************************************************************************************************** (k**/\*\*2) **+** 0.024 +* Validar dominio estricto: valores fuera del rango entrenado (±15%) deben generar advertencias explícitas. + +## Estructura del código sugerida + +dentro de una capeta que sea especificamente (imputación\_correlacion) hacer la modularización de la función adrpy/analisis/modulos/(aqui carpeta)/aqui modulos + +* Claridad modular con funciones específicas: + + * `cargar_y_validar_datos()` + * `filtrar_por_familia()` + * `seleccionar_predictores_validos()` + * `generar_combinaciones()` + * `entrenar_modelos_y_evaluar()` + * `filtrar_mejores_modelos()` + * `validar_con_loocv()` + * `imputar_valores()` + * `generar_reporte_final()` + +## Paquetes necesarios + +* `pandas`, `numpy`, `scikit-learn`, `matplotlib` (opcional). diff --git a/ADRpy/analisis/Data/Datos_aeronaves - Copy.xlsx b/ADRpy/analisis/Data/Datos_aeronaves - Copy.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..2797f899b0a9e0a439e0038ba42fbdfbe0a873c3 GIT binary patch literal 104311 zcmeFYWmH^k((er+Ktiw}fuJD-cMmj9a0~9zc;oIGf?IHR8VT;f35`SJ?(U7dzT}>H zo|!rGyz9(|^X;7e(A}$hueGnL`qjVc+Pn5wX@r+Ja7b{k;NalM;EvgDf`wkd!SQ0i 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/ADRpy/analisis/Modulos/correlation_analysis.py b/ADRpy/analisis/Modulos/correlation_analysis.py index 66cc2881..03da95ab 100644 --- a/ADRpy/analisis/Modulos/correlation_analysis.py +++ b/ADRpy/analisis/Modulos/correlation_analysis.py @@ -48,7 +48,7 @@ def agregar_resumen_a_tabla(tabla, titulo): # === Validación de parámetros seleccionados === parametros_no_encontrados = [ - v for v in parametros_seleccionados if v not in df_procesado.index + v for v in parametros_seleccionados if v not in df_procesado.columns ] if parametros_no_encontrados: raise ValueError( @@ -57,7 +57,7 @@ def agregar_resumen_a_tabla(tabla, titulo): # === Tabla completa (sin filtrar) === print("\n=== Cálculo de tabla completa ===") - tabla_completa = df_procesado.transpose().corr() + tabla_completa = df_procesado.corr() agregar_resumen_a_tabla( tabla_completa.round(3), "Tabla de Correlaciones con todos los parametros(tabla_completa)", @@ -72,14 +72,14 @@ def agregar_resumen_a_tabla(tabla, titulo): # === Filtrar datos seleccionados === print("\n=== Filtrando datos seleccionados ===") - df_filtrado_transpuesto = df_procesado.loc[parametros_seleccionados].transpose() + df_filtrado = df_procesado[parametros_seleccionados] # Tabla filtrada print("\n=== Cálculo de correlaciones filtradas ===") - tabla_filtrada = df_filtrado_transpuesto.corr() + tabla_filtrada = df_filtrado.corr() agregar_resumen_a_tabla( tabla_filtrada.round(3), - "Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)", + "Tabla de Correlaciones Filtradas por parametros seleccionados (Para Heatmap)", ) # Filtrar correlaciones por el umbral_heat_map para la tabla filtrada @@ -93,7 +93,7 @@ def agregar_resumen_a_tabla(tabla, titulo): # Preparar datos para el heatmap print("\n=== Preparando datos para el heatmap ===") - heatmap_data = df_filtrado_transpuesto.dropna( + heatmap_data = df_filtrado.dropna( thresh=2 ) # Excluir variables con menos de 2 valores válidos heatmap_correlaciones = heatmap_data.corr() @@ -118,10 +118,12 @@ def agregar_resumen_a_tabla(tabla, titulo): except ValueError as e: print(f"Error: {e}. Por favor verifica los parámetros seleccionados.") + tabla_completa = None # Asegurar que la variable esté definida except KeyError as e: print( f"Error: {e}. Asegúrate de que las variables seleccionadas existen en los datos." ) + tabla_completa = None # Asegurar que la variable esté definida if devolver_tabla: return tabla_completa diff --git a/ADRpy/analisis/Modulos/correlation_imputation.py b/ADRpy/analisis/Modulos/correlation_imputation.py index 9dbe7dba..b0832a74 100644 --- a/ADRpy/analisis/Modulos/correlation_imputation.py +++ b/ADRpy/analisis/Modulos/correlation_imputation.py @@ -113,38 +113,38 @@ def evaluar_confianza(puntaje): for parametro in parametros_preseleccionados: - if parametro not in correlaciones_aceptables.index: + if parametro not in correlaciones_aceptables.columns: # Cambiar lógica para trabajar con columnas print(f"\n=== {parametro}: Sin correlaciones significativas (|r| < 0.7) ===") continue - - valores_faltantes = df.loc[parametro][df.loc[parametro].isna()].index.tolist() + + valores_faltantes = df[parametro][df[parametro].isna()].index.tolist() # Ajustar para trabajar con columnas if not valores_faltantes: print(f"\n=== {parametro}: No hay valores faltantes para imputar. ===") continue - + print(f"\n=== Imputación para el parámetro: **{parametro}** ===") for aeronave in valores_faltantes: if lineas_impresas >= MAX_LINEAS_CONSOLA: print("\n--- Límite de impresión alcanzado. ---") break - + print(f"\n--- Imputación para aeronave: **{aeronave}** ---") valores_predichos = [] - - correlaciones_parametro = correlaciones_aceptables.loc[parametro].dropna() - + + correlaciones_parametro = correlaciones_aceptables[parametro].dropna() # Ajustar para trabajar con columnas + for parametro_correlacionado, correlacion in correlaciones_parametro.items(): - datos_validos = df.loc[[parametro, parametro_correlacionado]].dropna(axis=1) - - if datos_validos.shape[1] < 5: + datos_validos = df[[parametro, parametro_correlacionado]].dropna(axis=0) # Ajustar para trabajar con filas + + if datos_validos.shape[0] < 5: # Cambiar a filas continue - + # Evitar duplicados - datos_validos = datos_validos.T.drop_duplicates().T - - X = datos_validos.loc[parametro_correlacionado].values.reshape(-1, 1) - y = datos_validos.loc[parametro].values - + datos_validos = datos_validos.drop_duplicates() + + X = datos_validos[parametro_correlacionado].values.reshape(-1, 1) + y = datos_validos[parametro].values + # Entrenar modelo de regresión modelo = LinearRegression().fit(X, y) r2 = modelo.score(X, y) @@ -153,32 +153,32 @@ def evaluar_confianza(puntaje): incertidumbre = desviacion_std / np.sqrt(len(y)) puntaje_confianza = 0.4 * r2 + 0.3 * (1 - incertidumbre) + 0.2 * (1 - desviacion_std) + 0.1 * (1 - varianza) nivel_confianza = evaluar_confianza(puntaje_confianza) - - if pd.notna(df.loc[parametro_correlacionado, aeronave]): - valor_imputado = modelo.predict([[df.loc[parametro_correlacionado, aeronave]]])[0] + + if pd.notna(df.at[aeronave, parametro_correlacionado]): # Ajustar para trabajar con filas + valor_imputado = modelo.predict(np.array([[df.at[aeronave, parametro_correlacionado]]]))[0] # Convertir a numpy.ndarray valores_predichos.append( (parametro_correlacionado, round(valor_imputado, 3), round(r2, 3), round(desviacion_std, 3)) ) - + # Detalle de datos usados print(f"\n--- Correlación: {parametro_correlacionado} (r = {round(correlacion, 3)}) ---") - print(f"Aeronaves utilizadas: {datos_validos.columns.tolist()}") + print(f"Aeronaves utilizadas: {datos_validos.index.tolist()}") print(f"Valores para {parametro_correlacionado}: {X.flatten().round(3).tolist()}") print(f"Valores para {parametro}: {y.round(3).tolist()}") print(f"Ecuación de regresión: y = {round(modelo.coef_[0], 3)}x + {round(modelo.intercept_, 3)}") - print(f"Valor del parámetro correlacionado para la aeronave: {round(df.loc[parametro_correlacionado, aeronave], 3)}") + print(f"Valor del parámetro correlacionado para la aeronave: {round(df.at[aeronave, parametro_correlacionado], 3)}") print(f"Predicción obtenida: {round(valor_imputado, 3)}") print(f"\tR²: {r2}, Desviación Estándar: {desviacion_std}, Varianza: {varianza}, Incertidumbre: {incertidumbre}") print(f"\tNivel de confianza: {nivel_confianza}") lineas_impresas += 1 - + if valores_predichos: valor_final = np.median([pred[1] for pred in valores_predichos]) - df.loc[parametro, aeronave] = round(valor_final, 3) + df.at[aeronave, parametro] = round(valor_final, 3) # Ajustar para trabajar con filas valores_imputados += 1 print(f"Valores imputados: {[f'{pred[0]}: {pred[1]}' for pred in valores_predichos]}") print(f"**Mediana calculada:** {round(valor_final, 3)}") - + # Registro correcto reporte_imputaciones.append({ "Aeronave": aeronave, diff --git a/ADRpy/analisis/Modulos/data_processing.py b/ADRpy/analisis/Modulos/data_processing.py index 5d5fcd99..e94c653c 100644 --- a/ADRpy/analisis/Modulos/data_processing.py +++ b/ADRpy/analisis/Modulos/data_processing.py @@ -114,76 +114,76 @@ def procesar_datos_y_manejar_duplicados(df, respuesta_global=None): raise ValueError(f"Error durante el procesamiento y manejo de duplicados: {e}") -def mostrar_celdas_faltantes_con_seleccion(df, columna_seleccionada=None, debug_mode=False): +def mostrar_celdas_faltantes_con_seleccion(df, fila_seleccionada=None, debug_mode=False): """ - Muestra las celdas faltantes de una columna específica elegida por el usuario o automáticamente. + Muestra las celdas faltantes de una fila específica elegida por el usuario o automáticamente. :param df: DataFrame a analizar. - :param columna_seleccionada: Nombre de la columna a analizar. Si None, se pedirá al usuario o se usará el modo automático. - :param debug_mode: Si True, selecciona automáticamente la primera columna con datos faltantes si no se pasa ninguna. - :return: DataFrame con las celdas faltantes de la columna seleccionada. + :param fila_seleccionada: Nombre de la fila a analizar. Si None, se pedirá al usuario o se usará el modo automático. + :param debug_mode: Si True, selecciona automáticamente la primera fila con datos faltantes si no se pasa ninguna. + :return: DataFrame con las celdas faltantes de la fila seleccionada. """ - columnas_con_nulos = df.columns[df.isnull().any()].tolist() + aeronaves_con_nulos = df.index[df.isnull().any(axis=1)].tolist() - if not columnas_con_nulos: - print("✅ No hay columnas con valores faltantes.") + if not aeronaves_con_nulos: + print("✅ No hay filas con valores faltantes.") return pd.DataFrame() - if debug_mode and not columna_seleccionada: - columna_seleccionada = columnas_con_nulos[0] - print(f"[DEBUG] Seleccionando automáticamente la primera columna con nulos: '{columna_seleccionada}'") + if debug_mode and not fila_seleccionada: + fila_seleccionada = aeronaves_con_nulos[0] + print(f"[DEBUG] Seleccionando automáticamente la primera fila con nulos: '{fila_seleccionada}'") - elif not columna_seleccionada: - print("\n=== Columnas con datos faltantes ===") - for i, col in enumerate(columnas_con_nulos, start=1): - print(f"{i}. {col}") + elif not fila_seleccionada: + print("\n=== Filas con datos faltantes ===") + for i, fila in enumerate(aeronaves_con_nulos, start=1): + print(f"{i}. {fila}") - seleccion = input("Selecciona el número de la columna a analizar (presiona Enter para seleccionar la primera): ").strip() + seleccion = input("Selecciona el número de la fila a analizar (presiona Enter para seleccionar la primera): ").strip() if not seleccion.isdigit(): - print("🔁 Entrada inválida o vacía. Seleccionando la primera columna por defecto.") - columna_seleccionada = columnas_con_nulos[0] + print("🔁 Entrada inválida o vacía. Seleccionando la primera fila por defecto.") + fila_seleccionada = aeronaves_con_nulos[0] else: seleccion = int(seleccion) - 1 - if 0 <= seleccion < len(columnas_con_nulos): - columna_seleccionada = columnas_con_nulos[seleccion] + if 0 <= seleccion < len(aeronaves_con_nulos): + fila_seleccionada = aeronaves_con_nulos[seleccion] else: - print("🔁 Número fuera de rango. Seleccionando la primera columna por defecto.") - columna_seleccionada = columnas_con_nulos[0] + print("🔁 Número fuera de rango. Seleccionando la primera fila por defecto.") + fila_seleccionada = aeronaves_con_nulos[0] - print(f"\n=== Analizando celdas faltantes en la columna: '{columna_seleccionada}' ===") - celdas_faltantes = df[df[columna_seleccionada].isnull()][[columna_seleccionada]] + print(f"\n=== Analizando celdas faltantes en la fila: '{fila_seleccionada}' ===") + celdas_faltantes = df.loc[fila_seleccionada][df.loc[fila_seleccionada].isnull()] return celdas_faltantes def generar_resumen_faltantes( - df, titulo="Resumen de Valores Faltantes por Columna", ancho="50%", alto="300px" + df, titulo="Resumen de Valores Faltantes por Fila", ancho="50%", alto="300px" ): """ - Genera un resumen de los valores faltantes por columna en un DataFrame. - También genera una tabla HTML con la sumatoria total de los valores faltantes de todas las columnas. + Genera un resumen de los valores faltantes por fila en un DataFrame. + También genera una tabla HTML con la sumatoria total de los valores faltantes de todas las filas. :param df: DataFrame a analizar. :param titulo: Título opcional para mostrar en la tabla HTML. :param ancho: Ancho del contenedor HTML. :param alto: Alto del contenedor HTML. - :return: Tuple con dos DataFrames: resumen de valores faltantes por columna y sumatoria total. + :return: Tuple con dos DataFrames: resumen de valores faltantes por fila y sumatoria total. """ - # Calcular la cantidad de valores faltantes por columna - faltantes_por_columna = df.isnull().sum() + # Calcular la cantidad de valores faltantes por fila + faltantes_por_fila = df.isnull().sum(axis=1) - # Crear un DataFrame con el resumen por columna - resumen_faltantes = faltantes_por_columna.reset_index() - resumen_faltantes.columns = ["Columna", "Valores Faltantes"] + # Crear un DataFrame con el resumen por fila + resumen_faltantes = faltantes_por_fila.reset_index() + resumen_faltantes.columns = ["Fila", "Valores Faltantes"] # Calcular la sumatoria total de los valores faltantes - total_faltantes = faltantes_por_columna.sum() + total_faltantes = faltantes_por_fila.sum() resumen_total = pd.DataFrame( {"Resumen": ["Total de Valores Faltantes"], "Cantidad": [total_faltantes]} ) - # Mostrar el resumen por columna como una tabla HTML + # Mostrar el resumen por fila como una tabla HTML convertir_a_html( resumen_faltantes, titulo=titulo, ancho=ancho, alto=alto, mostrar=True ) diff --git a/ADRpy/analisis/Modulos/excel_export.py b/ADRpy/analisis/Modulos/excel_export.py index ef20ffd1..2a1239ba 100644 --- a/ADRpy/analisis/Modulos/excel_export.py +++ b/ADRpy/analisis/Modulos/excel_export.py @@ -41,28 +41,29 @@ def exportar_excel_con_imputaciones(archivo_origen, df_procesado, resumen_imputa # Recorrer las celdas del archivo original y actualizar según las imputaciones for fila in ws.iter_rows(min_row=2, min_col=2): # Ajustar filas/columnas según tu estructura for celda in fila: - aeronave = ws.cell(row=1, column=celda.column).value # Obtener nombre del parámetro - parametro = ws.cell(row=celda.row, column=1).value # Obtener nombre de la aeronave - - if (parametro, aeronave) in imputaciones_por_celda: - registro = imputaciones_por_celda[(parametro, aeronave)] - valor_imputado = df_procesado.loc[parametro, aeronave] - - # Actualizar el valor en la celda - celda.value = valor_imputado - - # Asignar color según el tipo de imputación - if registro["Método"] == "Similitud": - celda.fill = color_similitud - elif registro["Método"] == "Correlación": - celda.fill = color_correlacion - - # Agregar comentario con el nivel de confianza y la iteración - comentario = ( - f"Nivel de confianza: {registro['Nivel de Confianza']*100:.2f}%\n" - f"Iteración: {registro['Iteración']}" - ) - celda.comment = Comment(comentario, "Sistema") + if celda and celda.column and celda.row: # Validar que la celda y sus propiedades no sean None + parametro = ws.cell(row=1, column=celda.column).value # Obtener nombre del parámetro + aeronave = ws.cell(row=celda.row, column=1).value # Obtener nombre de la aeronave + + if (parametro, aeronave) in imputaciones_por_celda: + registro = imputaciones_por_celda[(parametro, aeronave)] + valor_imputado = df_procesado.at[aeronave, parametro] # Acceder usando filas como aeronaves y columnas como parámetros + + # Actualizar el valor en la celda + celda.value = valor_imputado + + # Asignar color según el tipo de imputación + if registro["Método"] == "Similitud": + celda.fill = color_similitud + elif registro["Método"] == "Correlación": + celda.fill = color_correlacion + + # Agregar comentario con el nivel de confianza y la iteración + comentario = ( + f"Nivel de confianza: {registro['Nivel de Confianza']*100:.2f}%\n" + f"Iteración: {registro['Iteración']}" + ) + celda.comment = Comment(comentario, "Sistema") # Guardar el archivo con las imputaciones wb.save(archivo_destino) diff --git a/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py b/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py index c3badfc2..79b4f84d 100644 --- a/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py +++ b/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py @@ -1,14 +1,28 @@ +""" +imputacion_similitud_flexible.py +-------------------------------- +Implementa la lógica de K‑NN con 3 ejes obligatorios (físico, geométrico, prestacional) +y filtrado progresivo de familia (F0‑F3). Diseñado para integrarse sin romper +los nombres ni los flujos que ya existen en tu proyecto ADRpy. + +Uso rápido: + python imputacion_similitud_flexible.py --ruta_excel Datos_aeronaves.xlsx \ + --aeronave "Stalker XE" \ + --parametro "Velocidad a la que se realiza el crucero (KTAS)" +""" + +import argparse import pandas as pd import numpy as np - # ------------------------ HELPERS ------------------------ def imprimir(msg, bold=False): prefix = "\033[1m" if bold else "" suffix = "\033[0m" if bold else "" print(f"{prefix}{msg}{suffix}") - -# ------------------------ CONFIGURACIÓN ------------------------ +# ------------------------------------------------------------------ # +# CONFIGURACIÓN DE BLOQUES Y CAPAS DE FAMILIA +# ------------------------------------------------------------------ # def configurar_similitud(): """ @@ -59,10 +73,10 @@ def imputar_por_similitud( imprimir(f"\n=== Imputación por similitud: {aeronave_obj} - {parametro_objetivo} ===", True) # Validaciones - if parametro_objetivo not in df_parametros.index: + if parametro_objetivo not in df_parametros.columns: imprimir(f"⚠️ Parámetro '{parametro_objetivo}' no encontrado.", True) return None - if aeronave_obj not in df_parametros.columns: + if aeronave_obj not in df_parametros.index: imprimir(f"⚠️ Aeronave '{aeronave_obj}' no encontrada.", True) return None @@ -71,28 +85,27 @@ def imputar_por_similitud( familia = f"F{capa_idx}" imprimir(f"\n--- Capa {familia}: criterios {list(criterios.keys())} ---", True) # Filtrar familia - mask = np.ones(df_parametros.shape[1], dtype=bool) + mask = np.ones(df_parametros.shape[0], dtype=bool) for fila, modo in criterios.items(): - val = df_atributos.at[fila, aeronave_obj] - mask &= (df_atributos.loc[fila] == val).values - df_familia = df_parametros.loc[:, mask] - if df_familia.shape[1] == 0: - imprimir(f"❌ Sin drones en {familia}. Continuando...", True) + val = df_atributos.loc[aeronave_obj, fila] + mask &= (df_atributos[fila] == val).values + df_familia = df_parametros.loc[mask, :] + if df_familia.shape[0] == 0: + imprimir(f"❌ Sin drones en {familia}. Relajando...", True) continue # —> Validar que haya vecinos con el parámetro objetivo - cols_validas = df_familia.columns[df_familia.loc[parametro_objetivo].notna()] - if len(cols_validas) == 0: + rows_validas = df_familia.index[df_familia[parametro_objetivo].notna()] + if len(rows_validas) == 0: imprimir(f"❌ Ningún dron en {familia} tiene '{parametro_objetivo}'.", True) continue - # Parámetros MTOW y filtro ±20% - mtow_obj = df_familia.at["Peso máximo al despegue (MTOW)", aeronave_obj] - mtow_vec = df_familia.loc["Peso máximo al despegue (MTOW)", cols_validas].values + mtow_obj = df_familia.loc[aeronave_obj, "Peso máximo al despegue (MTOW)"] + mtow_vec = df_familia.loc[rows_validas, "Peso máximo al despegue (MTOW)"].values delta_mtow = np.abs(mtow_vec - mtow_obj) / mtow_obj * 100 mask_mtow = delta_mtow <= 20 - cols_filtrados = cols_validas[mask_mtow] - if len(cols_filtrados) == 0: + rows_filtrados = rows_validas[mask_mtow] + if len(rows_filtrados) == 0: imprimir(f"❌ Sin vecinos ±20% MTOW en {familia}.", True) continue @@ -110,8 +123,8 @@ def calcular_bono(tipo): for parametro in parametros: try: # Valores de la aeronave objetivo y los vecinos - valor_objetivo = df_parametros.at[parametro, aeronave_obj] - valores_vecinos = df_familia.loc[parametro, cols_filtrados].values + valor_objetivo = df_parametros.loc[aeronave_obj, parametro] + valores_vecinos = df_familia.loc[rows_filtrados, parametro].values # Si el valor de la aeronave objetivo es NaN, el bono es 0 if pd.isna(valor_objetivo): @@ -164,20 +177,20 @@ def calcular_bono(tipo): sim_i = fam_score * mtow_scores + bonus_geom + bonus_prest # Mostrar similitudes - for nbr, s in zip(cols_filtrados, sim_i): + for nbr, s in zip(rows_filtrados, sim_i): imprimir(f" vecino '{nbr}' → sim_i: {s:.3f}") # Filtrar por umbral umbral = 0.0 mask_sim = sim_i >= umbral - vecinos_val = cols_filtrados[mask_sim] + vecinos_val = rows_filtrados[mask_sim] sim_vals = sim_i[mask_sim] if len(vecinos_val) == 0 or sim_vals.sum() < 1e-6: imprimir(f"❌ Sin vecinos ≥{umbral} en {familia}.", True) continue # Imputación y confianza - y = df_familia.loc[parametro_objetivo, vecinos_val].values + y = df_familia.loc[vecinos_val, parametro_objetivo].values valor_imp = np.dot(sim_vals, y) / sim_vals.sum() # Cálculo de métricas estadísticas diff --git a/ADRpy/analisis/Modulos/imputation_loop.py b/ADRpy/analisis/Modulos/imputation_loop.py index 02b777e1..682c36ec 100644 --- a/ADRpy/analisis/Modulos/imputation_loop.py +++ b/ADRpy/analisis/Modulos/imputation_loop.py @@ -114,8 +114,8 @@ def bucle_imputacion_similitud_correlacion( reporte_similitud = [] for parametro in parametros_preseleccionados: - for aeronave in df_similitud_resultado.columns: - if pd.isna(df_similitud_resultado.loc[parametro, aeronave]): + for aeronave in df_similitud_resultado.index: # Acceder usando filas como aeronaves y columnas como parámetros + if pd.isna(df_similitud_resultado.at[aeronave, parametro]): resultado = imputar_por_similitud( df_parametros=df_parametros, df_atributos=df_atributos, @@ -126,14 +126,14 @@ def bucle_imputacion_similitud_correlacion( ) if resultado is not None: - df_similitud_resultado.loc[parametro, aeronave] = resultado["valor"] + df_similitud_resultado.at[aeronave, parametro] = resultado["valor"] # Corregir lógica para asignar valores reporte_similitud.append({ "Aeronave": aeronave, "Parámetro": parametro, "Valor Imputado": resultado["valor"], "Nivel de Confianza": resultado["confianza"] }) - + if reporte_similitud and len(reporte_similitud) > 0: print("\033[1m>>> RESULTADOS DE IMPUTACIÓN POR SIMILITUD\033[0m") # Se guardan las imputaciones de similitud, pero NO se actualiza el DataFrame aún. @@ -201,7 +201,7 @@ def registrar_imputacion(regs): for key, candidatos in imputaciones_candidatas.items(): parametro, aeronave = key # Si ya hay un valor en df_procesado_base, no imputar. - if not pd.isna(df_procesado_base.loc[parametro, aeronave]): + if not pd.isna(df_procesado_base.at[aeronave, parametro]): # Cambiar lógica para trabajar con filas como aeronaves y columnas como parámetros continue # Escoger la de mayor confianza mejor = max(candidatos, key=lambda x: x["Nivel de Confianza"]) @@ -213,8 +213,8 @@ def registrar_imputacion(regs): aeronave = imp["Aeronave"] valor = imp["Valor Imputado"] metodo = imp["Método"] - df_procesado_base.loc[parametro, aeronave] = valor - df_filtrado_base.loc[parametro, aeronave] = valor + df_procesado_base.at[aeronave, parametro] = valor # Corregir lógica para asignar valores + df_filtrado_base.at[aeronave, parametro] = valor # Corregir lógica para asignar valores resumen_imputaciones.append(imp) print( f"Imputación final aplicada: {parametro} - {aeronave} = {valor} ({metodo})" diff --git a/ADRpy/analisis/Modulos/user_interaction.py b/ADRpy/analisis/Modulos/user_interaction.py index 35fb8b0a..52705dce 100644 --- a/ADRpy/analisis/Modulos/user_interaction.py +++ b/ADRpy/analisis/Modulos/user_interaction.py @@ -4,7 +4,7 @@ def seleccionar_parametros_por_indices(parametros_disponibles, parametros_presel for i, parametro in enumerate(parametros_disponibles, 1): print(f"{i}. {parametro}") - print(f"\nPreseleccionados: {', '.join(str(parametros_disponibles.index(p) + 1) for p in parametros_preseleccionados)}") + print(f"\nPreseleccionados: {', '.join(str(parametros_disponibles.index(p) + 1) for p in parametros_preseleccionados if p in parametros_disponibles)}") if entrada_indices is None: indices = input("Ingresa los números separados por coma (o presiona Enter para usar los preseleccionados): ") diff --git a/ADRpy/analisis/Results/Datos_imputados.xlsx b/ADRpy/analisis/Results/Datos_imputados.xlsx index 3bb5164c10e6747648746d4d43531de7d6071663..ed01aed44a435525e1dab67fe6df9649d8f5e532 100644 GIT binary patch literal 491997 zcmeEtWmp_-wk;mqg1c*Q3+`^g-5PgyXbA4^POuQ%n&9s44#7RR+a=$eIWu$4+~=JC z_x6vjdWw2@Ys*?|@3(h-k%ff90D}R81p@;k1q)5BDMf?;1ItDM1N#UD3!x)wZ|7oa 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- " 6 AAI Aerosonde 57 non-null object\n", - " 7 Fulmar X 57 non-null object\n", - " 8 Orbiter 4 57 non-null object\n", - " 9 Orbiter 3 57 non-null object\n", - " 10 Mantis 57 non-null object\n", - " 11 ScanEagle 57 non-null object\n", - " 12 Integrator 57 non-null object\n", - " 13 Integrator VTOL 57 non-null object\n", - " 14 Integrator Extended Range (ER) 57 non-null object\n", - " 15 ScanEagle 3 57 non-null object\n", - " 16 RQ Nan 21A Blackjack 57 non-null object\n", - " 17 DeltaQuad Evo 57 non-null object\n", - " 18 DeltaQuad Pro #MAP 57 non-null object\n", - " 19 DeltaQuad Pro #CARGO 57 non-null object\n", - " 20 V21 57 non-null object\n", - " 21 V25 57 non-null object\n", - " 22 V32 57 non-null object\n", - " 23 V35 57 non-null object\n", - " 24 V39 57 non-null object\n", - " 25 Volitation VT370 57 non-null object\n", - " 26 Skyeye 2600 57 non-null object\n", - " 27 Skyeye 2930 VTOL 57 non-null object\n", - " 28 Skyeye 3600 57 non-null object\n", - " 29 Skyeye 3600 VTOL 57 non-null object\n", - " 30 Skyeye 5000 57 non-null object\n", - " 31 Skyeye 5000 VTOL 57 non-null object\n", - " 32 Skyeye 5000 VTOL octo 57 non-null object\n", - " 33 Volitation VT510 57 non-null object\n", - " 34 Ascend 57 non-null object\n", - " 35 Transition 57 non-null object\n", - " 36 Reach 57 non-null object\n", - "dtypes: object(37)\n", - "memory usage: 16.9+ KB\n", + "Index: 37 entries, Stalker XE to Reach\n", + "Data columns (total 42 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Distancia de carrera requerida para despegue 37 non-null object \n", + " 1 Altitud a la que se realiza el crucero 37 non-null int64 \n", + " 2 Velocidad a la que se realiza el crucero (KTAS) 37 non-null object \n", + " 3 Techo de servicio máximo 37 non-null object \n", + " 4 Velocidad de pérdida limpia (KCAS) 37 non-null object \n", + " 5 Área del ala 37 non-null object \n", + " 6 Relación de aspecto del ala 37 non-null object \n", + " 7 Longitud del fuselaje 37 non-null object \n", + " 8 Profundidad del fuselaje 37 non-null object \n", + " 9 Ancho del fuselaje 37 non-null object \n", + " 10 Peso máximo al despegue (MTOW) 37 non-null float64\n", + " 11 Alcance de la aeronave 37 non-null object \n", + " 12 Autonomía de la aeronave 37 non-null object \n", + " 13 Velocidad máxima (KIAS) 37 non-null object \n", + " 14 Velocidad de pérdida (KCAS) 37 non-null object \n", + " 15 Tasa de ascenso 37 non-null object \n", + " 16 Radio de giro 37 non-null object \n", + " 17 envergadura 37 non-null object \n", + " 18 Cuerda 37 non-null object \n", + " 19 payload 37 non-null object \n", + " 20 duracion en VTOL 37 non-null object \n", + " 21 Crucero KIAS 37 non-null object \n", + " 22 RTF (dry weight) 37 non-null object \n", + " 23 RTF (Including fuel & Batteries) 37 non-null object \n", + " 24 Empty weight 37 non-null object \n", + " 25 Maximum Crosswind 37 non-null object \n", + " 26 Rango de comunicación 37 non-null object \n", + " 27 Wing Loading 37 non-null object \n", + " 28 Potencia específica (P/W) 37 non-null object \n", + " 29 Capacidad combustible 37 non-null object \n", + " 30 Consumo 37 non-null object \n", + " 31 Potencia Watts 37 non-null object \n", + " 32 Potencia HP 37 non-null object \n", + " 33 Precio 37 non-null object \n", + " 34 Tiempo de emergencia en vuelo 37 non-null object \n", + " 35 Distancia de aterrizaje 37 non-null object \n", + " 36 Despegue 37 non-null int64 \n", + " 37 Propulsión horizontal 37 non-null int64 \n", + " 38 Propulsión vertical 37 non-null int64 \n", + " 39 Cantidad de motores propulsión vertical 37 non-null int64 \n", + " 40 Cantidad de motores propulsión horizontal 37 non-null int64 \n", + " 41 Misión 37 non-null int64 \n", + "dtypes: float64(1), int64(7), object(34)\n", + "memory usage: 12.4+ KB\n", "None\n", "Datos cargados correctamente desde: C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Data\\Datos_aeronaves.xlsx\n", "\n", @@ -67,7 +71,7 @@ "=== Continuando con el procesamiento de datos ===\n", "\n", "Encabezados iniciales cargados:\n", - "['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", + "['Distancia de carrera requerida para despegue', 'Altitud a la que se realiza el crucero', 'Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Velocidad de pérdida limpia (KCAS)', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Profundidad del fuselaje', 'Ancho del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Tasa de ascenso', 'Radio de giro', 'envergadura', 'Cuerda', 'payload', 'duracion en VTOL', 'Crucero KIAS', 'RTF (dry weight)', 'RTF (Including fuel & Batteries)', 'Empty weight', 'Maximum Crosswind', 'Rango de comunicación', 'Wing Loading', 'Potencia específica (P/W)', 'Capacidad combustible', 'Consumo', 'Potencia Watts', 'Potencia HP', 'Precio', 'Tiempo de emergencia en vuelo', 'Distancia de aterrizaje', 'Despegue', 'Propulsión horizontal', 'Propulsión vertical', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal', 'Misión']\n", "\n", "=== Mostrando datos iniciales en formato HTML ===\n" ] @@ -111,43 +115,48 @@ " \n", " \n", " \n", - " Stalker XE\n", - " Stalker VXE30\n", - " Aerosonde Mk. 4.7 Fixed Wing\n", - " Aerosonde Mk. 4.7 VTOL\n", - " Aerosonde Mk. 4.8 Fixed wing\n", - " Aerosonde Mk. 4.8 VTOL FTUAS\n", - " AAI Aerosonde\n", - " Fulmar X\n", - " Orbiter 4\n", - " Orbiter 3\n", - " Mantis\n", - " ScanEagle\n", - " Integrator\n", - " Integrator VTOL\n", - " Integrator Extended Range (ER)\n", - " ScanEagle 3\n", - " RQ Nan 21A Blackjack\n", - " DeltaQuad Evo\n", - " DeltaQuad Pro #MAP\n", - " DeltaQuad Pro #CARGO\n", - " V21\n", - " V25\n", - " V32\n", - " V35\n", - " V39\n", - " Volitation VT370\n", - " Skyeye 2600\n", - " Skyeye 2930 VTOL\n", - " Skyeye 3600\n", - " Skyeye 3600 VTOL\n", - " Skyeye 5000\n", - " Skyeye 5000 VTOL\n", - " Skyeye 5000 VTOL octo\n", - " Volitation VT510\n", - " Ascend\n", - " Transition\n", - " Reach\n", + " Distancia de carrera requerida para despegue\n", + " Altitud a la que se realiza el crucero\n", + " Velocidad a la que se realiza el crucero (KTAS)\n", + " Techo de servicio máximo\n", + " Velocidad de pérdida limpia (KCAS)\n", + " Área del ala\n", + " Relación de aspecto del ala\n", + " Longitud del fuselaje\n", + " Profundidad del fuselaje\n", + " Ancho del fuselaje\n", + " Peso máximo al despegue (MTOW)\n", + " Alcance de la aeronave\n", + " Autonomía de la aeronave\n", + " Velocidad máxima (KIAS)\n", + " Velocidad de pérdida (KCAS)\n", + " Tasa de ascenso\n", + " Radio de giro\n", + " envergadura\n", + " Cuerda\n", + " payload\n", + " duracion en VTOL\n", + " Crucero KIAS\n", + " RTF (dry weight)\n", + " RTF (Including fuel & Batteries)\n", + " Empty weight\n", + " Maximum Crosswind\n", + " Rango de comunicación\n", + " Wing Loading\n", + " Potencia específica (P/W)\n", + " Capacidad combustible\n", + " Consumo\n", + " Potencia Watts\n", + " Potencia HP\n", + " Precio\n", + " Tiempo de emergencia en vuelo\n", + " Distancia de aterrizaje\n", + " Despegue\n", + " Propulsión horizontal\n", + " Propulsión vertical\n", + " Cantidad de motores propulsión vertical\n", + " Cantidad de motores propulsión horizontal\n", + " Misión\n", " \n", " \n", " Modelo\n", @@ -188,157 +197,90 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " Distancia de carrera requerida para despegue\n", - " 0\n", + " Stalker XE\n", " 0\n", + " 6000.000\n", + " 16.880379\n", + " 12000\n", " Nan\n", - " 0\n", + " 0.87\n", + " 15.301255\n", + " 2.1\n", " Nan\n", - " 0\n", + " 0.211\n", + " 13.600\n", + " 370\n", + " 8\n", + " 20\n", " Nan\n", " Nan\n", " Nan\n", + " 3.657\n", + " 0.239\n", + " 2.494756\n", + " 2\n", + " 15.43332\n", " Nan\n", " Nan\n", + " 10.886208\n", " Nan\n", + " 59\n", " Nan\n", - " 0\n", " Nan\n", " Nan\n", " Nan\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " Nan\n", - " 0\n", - " 50\n", - " 0\n", - " 60\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " \n", - " \n", - " Altitud a la que se realiza el crucero\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 5500\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 5000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", - " 6000\n", + " Nan\n", + " Nan\n", + " Nan\n", + " Nan\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Velocidad a la que se realiza el crucero (KTAS)\n", - " 16.880379\n", + " Stalker VXE30\n", + " 0\n", + " 6000.000\n", " 17.602373\n", - " 27.34405\n", - " 27.34405\n", - " 27.34405\n", - " Nan\n", + " 12000\n", " Nan\n", - " 30.406584\n", + " 1.158283\n", + " 15.326449\n", + " 2.5908\n", " Nan\n", + " 0.2\n", + " 19.958\n", + " 433\n", + " 8\n", + " 25.034211\n", " Nan\n", - " 18.265826\n", - " 30.625336\n", - " 30.953465\n", " Nan\n", " Nan\n", - " 25.703407\n", - " 33.797246\n", - " 18.090824\n", - " 17.500192\n", - " 17.500192\n", - " 19.687716\n", - " 21.87524\n", - " 21.87524\n", - " 27.34405\n", - " 27.34405\n", - " 27.34405\n", - " 36.094147\n", - " 26.250288\n", + " 4.8768\n", + " 0.318195\n", + " 2.494756\n", " Nan\n", - " 32.81286\n", - " 36.094147\n", - " 30.625336\n", + " 16.093422\n", " Nan\n", - " 32.81286\n", - " 21.87524\n", - " 21.87524\n", - " 27.34405\n", - " \n", - " \n", - " Techo de servicio máximo\n", - " 12000\n", - " 12000\n", - " 14700\n", - " 9700\n", - " 18200\n", - " 15000\n", - " 15000\n", - " 9.842\n", " Nan\n", + " 17.463292\n", " Nan\n", + " 161\n", " Nan\n", - " 19500\n", - " 19500\n", " Nan\n", - " 19500\n", - " 20\n", - " 20\n", - " 13\n", - " 13.123\n", - " 13.123\n", - " 48800\n", - " 16000\n", - " 16000\n", - " 16000\n", - " 16000\n", - " 17000\n", " Nan\n", " Nan\n", " Nan\n", @@ -346,118 +288,174 @@ " Nan\n", " Nan\n", " Nan\n", - " 17000\n", - " 10000\n", - " 13000\n", - " 16000\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Velocidad de pérdida limpia (KCAS)\n", - " Nan\n", + " Aerosonde Mk. 4.7 Fixed Wing\n", " Nan\n", + " 6000.000\n", + " 27.34405\n", + " 14700\n", " Nan\n", + " 1.55\n", + " 12.5\n", + " 3\n", " Nan\n", + " 0.277\n", + " 42.200\n", " Nan\n", + " 19.8\n", + " 33.43886\n", " Nan\n", " Nan\n", " Nan\n", + " 4.4\n", + " 0.352\n", + " 14.5\n", " Nan\n", + " 25\n", " Nan\n", + " 27.7\n", " Nan\n", " Nan\n", + " 140\n", " Nan\n", " Nan\n", " Nan\n", + " 0.6\n", + " 2980\n", + " 4\n", " Nan\n", " Nan\n", " Nan\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Aerosonde Mk. 4.7 VTOL\n", + " 0\n", + " 6000.000\n", + " 27.34405\n", + " 9700\n", " Nan\n", + " 1.55\n", + " 12.5\n", + " 3\n", " Nan\n", - " 14\n", - " 15.5\n", - " 17\n", + " 0.277\n", + " 53.500\n", " Nan\n", + " 12\n", + " 33.43886\n", " Nan\n", " Nan\n", - " 10\n", - " 18\n", - " 12.5\n", - " 24\n", - " 15\n", " Nan\n", + " 4.4\n", + " 0.352\n", + " 11.3\n", " Nan\n", " 25\n", " Nan\n", + " 42.2\n", + " Nan\n", + " Nan\n", + " 140\n", + " Nan\n", " Nan\n", " Nan\n", + " 0.6\n", + " 2980\n", + " 4\n", + " Nan\n", + " Nan\n", + " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Área del ala\n", - " 0.87\n", - " 1.158283\n", - " 1.55\n", - " 1.55\n", - " 1.55\n", - " Nan\n", - " 0.57\n", + " Aerosonde Mk. 4.8 Fixed wing\n", " Nan\n", + " 6000.000\n", + " 27.34405\n", + " 18200\n", " Nan\n", + " 1.55\n", + " 12.5\n", + " 3\n", " Nan\n", + " 0.277\n", + " 54.400\n", " Nan\n", + " 19.8\n", + " 33.43886\n", " Nan\n", " Nan\n", " Nan\n", + " 4.4\n", + " 0.352\n", + " 17.7\n", " Nan\n", + " 25\n", " Nan\n", + " 36.7\n", " Nan\n", - " 0.84\n", " Nan\n", + " 140\n", " Nan\n", - " 0.8\n", - " 0.52\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", - " 0.88\n", - " 1\n", - " 1.33\n", - " 1.32\n", - " 2.615\n", - " 2.615\n", - " 2.615\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Relación de aspecto del ala\n", - " 15.301255\n", - " 15.326449\n", - " 12.5\n", - " 12.5\n", - " 12.5\n", - " 12.5\n", - " 14.754386\n", - " Nan\n", - " Nan\n", + " Aerosonde Mk. 4.8 VTOL FTUAS\n", + " 0\n", + " 6000.000\n", " Nan\n", + " 15000\n", " Nan\n", " Nan\n", + " 12.5\n", " Nan\n", " Nan\n", " Nan\n", + " 93.000\n", " Nan\n", + " 14\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 22.7\n", " Nan\n", " Nan\n", " Nan\n", + " 70.3\n", " Nan\n", " Nan\n", " Nan\n", @@ -470,72 +468,82 @@ " Nan\n", " Nan\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Longitud del fuselaje\n", - " 2.1\n", - " 2.5908\n", - " 3\n", - " 3\n", - " 3\n", + " AAI Aerosonde\n", + " Nan\n", + " 5500.000\n", + " Nan\n", + " 15000\n", " Nan\n", + " 0.57\n", + " 14.754386\n", " 1.7\n", - " 1.2\n", - " 1.2\n", - " 1.2\n", - " 1.48\n", - " 1.71\n", - " 2.5\n", " Nan\n", - " 2.5\n", - " 2.4\n", - " 2.5\n", - " 0.75\n", - " 0.9\n", - " 0.9\n", - " 0.93\n", - " 0.93\n", - " 1\n", - " 1.88\n", " Nan\n", - " 2.02\n", - " 2.05\n", - " 2.03\n", - " 2.488\n", - " 2.42\n", - " 3.5\n", - " 3.5\n", - " 3.5\n", - " 2.905\n", - " 1.562\n", - " 2.3\n", - " 4.712\n", - " \n", - " \n", - " Profundidad del fuselaje\n", + " 13.100\n", + " 3270\n", + " 26\n", + " 30.845725\n", " Nan\n", " Nan\n", " Nan\n", + " 2.9\n", + " 0.196552\n", + " Nan\n", + " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 10\n", + " Nan\n", + " 150\n", + " 23\n", + " 98\n", " Nan\n", " Nan\n", + " 1280\n", + " 1.74\n", " Nan\n", " Nan\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Fulmar X\n", " Nan\n", + " 6000.000\n", + " 30.406584\n", + " 9.842\n", " Nan\n", " Nan\n", " Nan\n", + " 1.2\n", " Nan\n", " Nan\n", + " 20.000\n", + " 800\n", + " 8\n", + " 41.7\n", " Nan\n", " Nan\n", " Nan\n", + " 3\n", " Nan\n", " Nan\n", " Nan\n", + " 27.8\n", " Nan\n", " Nan\n", " Nan\n", @@ -550,218 +558,132 @@ " Nan\n", " Nan\n", " Nan\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Ancho del fuselaje\n", - " 0.211\n", - " 0.2\n", - " 0.277\n", - " 0.277\n", - " 0.277\n", - " Nan\n", + " Orbiter 4\n", " Nan\n", + " 6000.000\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 1.2\n", " Nan\n", " Nan\n", + " 55.000\n", " Nan\n", + " 24\n", + " 36\n", " Nan\n", " Nan\n", " Nan\n", + " 5.2\n", " Nan\n", + " 12\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 150\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", - " 0.375\n", - " 0.375\n", - " 0.375\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Peso máximo al despegue (MTOW)\n", - " 13.6\n", - " 19.958048\n", - " 42.2\n", - " 53.5\n", - " 54.4\n", - " 93\n", - " 13.1\n", - " 20\n", - " 55\n", - " 32\n", - " 6.5\n", - " 26.5\n", - " 74.8\n", - " 75\n", - " 74.8\n", - " 36.3\n", - " 61\n", - " 10\n", - " 6.2\n", - " 6.2\n", - " 10\n", - " 12.5\n", - " 23.5\n", - " 32\n", - " 24\n", - " 40\n", - " 15\n", - " 28\n", - " 28\n", - " 40\n", - " 90\n", - " 100\n", - " 100\n", - " 100\n", - " 9.5\n", - " 18\n", - " 91\n", - " \n", - " \n", - " Alcance de la aeronave\n", - " 370\n", - " 433\n", + " Orbiter 3\n", " Nan\n", + " 6000.000\n", " Nan\n", " Nan\n", " Nan\n", - " 3270\n", - " 800\n", " Nan\n", - " 50\n", - " 25\n", " Nan\n", + " 1.2\n", " Nan\n", " Nan\n", - " 500\n", + " 32.000\n", + " 50\n", + " 6\n", + " 36\n", " Nan\n", " Nan\n", - " 270\n", - " 100\n", - " 100\n", " Nan\n", + " 4.4\n", + " Nan\n", + " 5.5\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 50\n", " Nan\n", " Nan\n", - " 300\n", " Nan\n", - " 800\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " Nan\n", + " 1.000\n", + " 1.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Autonomía de la aeronave\n", - " 8\n", - " 8\n", - " 19.8\n", - " 12\n", - " 19.8\n", - " 14\n", - " 26\n", - " 8\n", - " 24\n", - " 6\n", - " 2\n", - " 18\n", - " 24\n", - " 16\n", - " 19\n", - " 18\n", - " 16\n", - " 4.53\n", - " 1.83\n", - " 1.83\n", - " 3\n", - " 4\n", - " 4.5\n", - " 2.8\n", - " 4.5\n", - " 15\n", - " 2\n", - " 3\n", - " 4.5\n", - " 6\n", - " 8\n", - " 8\n", + " Mantis\n", " Nan\n", - " 5\n", - " 6\n", - " 12\n", - " 20\n", - " \n", - " \n", - " Velocidad máxima (KIAS)\n", - " 20\n", - " 25.034211\n", - " 33.43886\n", - " 33.43886\n", - " 33.43886\n", + " 6000.000\n", + " 18.265826\n", " Nan\n", - " 30.845725\n", - " 41.7\n", - " 36\n", - " 36\n", - " 25.6\n", - " 41.2\n", - " 46.3\n", " Nan\n", - " 46.3\n", - " 41.2\n", - " 46.3\n", " Nan\n", " Nan\n", + " 1.48\n", " Nan\n", - " 33\n", - " 33\n", - " 33\n", - " 33\n", - " 33\n", - " 33\n", " Nan\n", - " 30\n", + " 6.500\n", + " 25\n", + " 2\n", + " 25.6\n", " Nan\n", - " 33\n", - " 42\n", - " 42\n", - " 38\n", - " 50\n", - " 30\n", - " 30\n", - " 35\n", - " \n", - " \n", - " Velocidad de pérdida (KCAS)\n", " Nan\n", " Nan\n", + " 2.1\n", " Nan\n", " Nan\n", " Nan\n", + " 16.7\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 25\n", " Nan\n", " Nan\n", " Nan\n", @@ -771,38 +693,42 @@ " Nan\n", " Nan\n", " Nan\n", + " 1.000\n", + " 1.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " ScanEagle\n", " Nan\n", + " 6000.000\n", + " 30.625336\n", + " 19500\n", " Nan\n", - " 14\n", - " 15.5\n", - " 17\n", " Nan\n", " Nan\n", + " 1.71\n", " Nan\n", - " 10\n", - " 18\n", - " 12.5\n", - " 24\n", - " 15\n", " Nan\n", - " 24\n", - " 25\n", - " 13\n", - " 13\n", - " 13\n", - " \n", - " \n", - " Tasa de ascenso\n", + " 26.500\n", " Nan\n", + " 18\n", + " 41.2\n", " Nan\n", " Nan\n", " Nan\n", + " 3.1\n", " Nan\n", + " 5\n", " Nan\n", + " 28\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 101.86\n", " Nan\n", " Nan\n", " Nan\n", @@ -812,27 +738,44 @@ " Nan\n", " Nan\n", " Nan\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Integrator\n", " Nan\n", + " 6000.000\n", + " 30.953465\n", + " 19500\n", " Nan\n", " Nan\n", " Nan\n", + " 2.5\n", " Nan\n", " Nan\n", + " 74.800\n", " Nan\n", + " 24\n", + " 46.3\n", " Nan\n", " Nan\n", " Nan\n", + " 4.8\n", " Nan\n", + " 18\n", " Nan\n", + " 28.3\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 92.6\n", " Nan\n", " Nan\n", - " \n", - " \n", - " Radio de giro\n", " Nan\n", " Nan\n", " Nan\n", @@ -840,6 +783,17 @@ " Nan\n", " Nan\n", " Nan\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Integrator VTOL\n", + " 0\n", + " 5000.000\n", " Nan\n", " Nan\n", " Nan\n", @@ -848,21 +802,22 @@ " Nan\n", " Nan\n", " Nan\n", + " 75.000\n", " Nan\n", + " 16\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", - " 100\n", - " 120\n", - " 150\n", " Nan\n", " Nan\n", + " 18\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 30\n", " Nan\n", " Nan\n", " Nan\n", @@ -870,66 +825,38 @@ " Nan\n", " Nan\n", " Nan\n", - " \n", - " \n", - " envergadura\n", - " 3.657\n", - " 4.8768\n", - " 4.4\n", - " 4.4\n", - " 4.4\n", " Nan\n", - " 2.9\n", - " 3\n", - " 5.2\n", - " 4.4\n", - " 2.1\n", - " 3.1\n", - " 4.8\n", " Nan\n", - " 4.8\n", - " 4\n", - " 4.8\n", - " 2.69\n", - " 2.35\n", - " 2.35\n", - " 2.15\n", - " 2.45\n", - " 3.2\n", - " 3.5\n", - " 3.9\n", - " 6.5\n", - " 2.6\n", - " 2.93\n", - " 3.6\n", - " 3.6\n", - " 5\n", - " 5\n", - " 5\n", - " 5.1\n", - " 2\n", - " 3\n", - " 6\n", + " 0\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Cuerda\n", - " 0.239\n", - " 0.318195\n", - " 0.352\n", - " 0.352\n", - " 0.352\n", + " Integrator Extended Range\n", " Nan\n", - " 0.196552\n", + " 6000.000\n", " Nan\n", + " 19500\n", " Nan\n", " Nan\n", " Nan\n", + " 2.5\n", " Nan\n", " Nan\n", + " 74.800\n", + " 500\n", + " 19\n", + " 46.3\n", " Nan\n", " Nan\n", " Nan\n", + " 4.8\n", " Nan\n", + " 18\n", " Nan\n", " Nan\n", " Nan\n", @@ -946,56 +873,37 @@ " Nan\n", " Nan\n", " Nan\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " ScanEagle 3\n", " Nan\n", + " 6000.000\n", + " 25.703407\n", + " 20\n", " Nan\n", " Nan\n", " Nan\n", - " \n", - " \n", - " payload\n", - " 2.494756\n", - " 2.494756\n", - " 14.5\n", - " 11.3\n", - " 17.7\n", - " 22.7\n", + " 2.4\n", " Nan\n", " Nan\n", - " 12\n", - " 5.5\n", + " 36.300\n", " Nan\n", - " 5\n", - " 18\n", - " 18\n", - " 18\n", - " 8.6\n", - " 17.7\n", - " 3\n", - " 1.2\n", - " 1.2\n", - " 1.5\n", - " 2.2\n", - " 5\n", - " 10\n", - " 5\n", " 18\n", + " 41.2\n", + " Nan\n", + " Nan\n", + " Nan\n", " 4\n", - " 6\n", - " 10\n", - " 10\n", - " 20\n", - " 25\n", - " 15\n", - " 25\n", - " 0.6\n", - " 1.5\n", - " 7\n", - " \n", - " \n", - " duracion en VTOL\n", - " 2\n", " Nan\n", + " 8.6\n", " Nan\n", + " 23.5\n", " Nan\n", " Nan\n", " Nan\n", @@ -1005,83 +913,91 @@ " Nan\n", " Nan\n", " Nan\n", + " 170\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " RQ Nan 21A Blackjack\n", " Nan\n", - " 4.53\n", + " 6000.000\n", + " 33.797246\n", + " 20\n", " Nan\n", " Nan\n", " Nan\n", + " 2.5\n", " Nan\n", " Nan\n", + " 61.000\n", " Nan\n", + " 16\n", + " 46.3\n", " Nan\n", " Nan\n", " Nan\n", + " 4.8\n", " Nan\n", + " 17.7\n", " Nan\n", + " 30.9\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 92.6\n", " Nan\n", - " 0.05\n", - " 0.05\n", - " 0.05\n", - " \n", - " \n", - " Crucero KIAS\n", - " 15.43332\n", - " 16.093422\n", - " 25\n", - " 25\n", - " 25\n", " Nan\n", " Nan\n", - " 27.8\n", " Nan\n", " Nan\n", - " 16.7\n", - " 28\n", - " 28.3\n", + " 8\n", " Nan\n", " Nan\n", - " 23.5\n", - " 30.9\n", - " 16.54\n", - " 16\n", - " 16\n", - " 18\n", - " 20\n", - " 20\n", - " 25\n", - " 25\n", - " 25\n", - " 33\n", - " 24\n", " Nan\n", - " 30\n", - " 33\n", - " 28\n", - " 35\n", - " 30\n", - " 20\n", - " 20\n", - " 25\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " RTF (dry weight)\n", + " DeltaQuad Evo\n", + " 0\n", + " 6000.000\n", + " 18.090824\n", + " 13\n", " Nan\n", + " 0.84\n", " Nan\n", + " 0.75\n", " Nan\n", " Nan\n", + " 10.000\n", + " 270\n", + " 4.53\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 2.69\n", " Nan\n", + " 3\n", + " 4.53\n", + " 16.54\n", + " 4.8\n", + " 6.8\n", + " 4.8\n", + " 45\n", " Nan\n", " Nan\n", " Nan\n", @@ -1090,55 +1006,89 @@ " Nan\n", " Nan\n", " Nan\n", - " 4.8\n", " Nan\n", + " 0\n", + " 2.000\n", + " 1.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " DeltaQuad Pro #MAP\n", + " 0\n", + " 6000.000\n", + " 17.500192\n", + " 13.123\n", " Nan\n", " Nan\n", " Nan\n", + " 0.9\n", " Nan\n", " Nan\n", + " 6.200\n", + " 100\n", + " 1.83\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 2.35\n", " Nan\n", + " 1.2\n", " Nan\n", + " 16\n", " Nan\n", " Nan\n", " Nan\n", + " 50\n", + " 50\n", " Nan\n", - " 6\n", - " 11.8\n", - " 54\n", - " \n", - " \n", - " RTF (Including fuel & Batteries)\n", " Nan\n", " Nan\n", - " 27.7\n", - " 42.2\n", - " 36.7\n", - " 70.3\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 0\n", + " 2.000\n", + " 1.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " DeltaQuad Pro #CARGO\n", + " 0\n", + " 6000.000\n", + " 17.500192\n", + " 13.123\n", " Nan\n", " Nan\n", " Nan\n", + " 0.9\n", " Nan\n", " Nan\n", + " 6.200\n", + " 100\n", + " 1.83\n", " Nan\n", - " 6.8\n", " Nan\n", " Nan\n", " Nan\n", + " 2.35\n", " Nan\n", + " 1.2\n", " Nan\n", + " 16\n", " Nan\n", " Nan\n", " Nan\n", + " 50\n", + " 30\n", " Nan\n", " Nan\n", " Nan\n", @@ -1147,184 +1097,263 @@ " Nan\n", " Nan\n", " Nan\n", - " 8.9\n", - " 16.5\n", - " 84\n", + " 0\n", + " 2.000\n", + " 1.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Empty weight\n", - " 10.886208\n", - " 17.463292\n", - " Nan\n", - " Nan\n", + " V21\n", + " 0\n", + " 6000.000\n", + " 19.687716\n", + " 48800\n", + " 14\n", + " 0.8\n", " Nan\n", + " 0.93\n", " Nan\n", - " 10\n", " Nan\n", + " 10.000\n", " Nan\n", + " 3\n", + " 33\n", + " 14\n", " Nan\n", + " 100\n", + " 2.15\n", " Nan\n", + " 1.5\n", " Nan\n", + " 18\n", " Nan\n", " Nan\n", + " 2.65\n", " Nan\n", + " 30\n", + " 12.5\n", " Nan\n", " Nan\n", - " 4.8\n", " Nan\n", " Nan\n", - " 2.65\n", - " 3.45\n", - " 6.45\n", " Nan\n", + " 3999\n", + " 0.108\n", " Nan\n", + " 2.000\n", + " 1.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " V25\n", + " 0\n", + " 6000.000\n", + " 21.87524\n", + " 16000\n", + " 15.5\n", + " 0.52\n", " Nan\n", - " 6.5\n", - " 7.1\n", - " 11.5\n", - " 11\n", - " 32\n", + " 0.93\n", " Nan\n", - " 35\n", " Nan\n", - " 3\n", - " 5.8\n", - " 31\n", - " \n", - " \n", - " Maximum Crosswind\n", + " 12.500\n", " Nan\n", + " 4\n", + " 33\n", + " 15.5\n", " Nan\n", + " 120\n", + " 2.45\n", " Nan\n", + " 2.2\n", " Nan\n", + " 20\n", " Nan\n", " Nan\n", + " 3.45\n", " Nan\n", + " 30\n", + " 24\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 4679\n", + " 0.108\n", " Nan\n", - " 30\n", + " 2.000\n", + " 1.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " V32\n", + " 0\n", + " 6000.000\n", + " 21.87524\n", + " 16000\n", + " 17\n", " Nan\n", " Nan\n", + " 1\n", " Nan\n", - " 45\n", - " 50\n", - " 50\n", " Nan\n", + " 23.500\n", " Nan\n", + " 4.5\n", + " 33\n", + " 17\n", " Nan\n", + " 150\n", + " 3.2\n", " Nan\n", + " 5\n", " Nan\n", + " 20\n", " Nan\n", " Nan\n", + " 6.45\n", " Nan\n", + " 30\n", + " 25\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 69999\n", + " 0.108\n", " Nan\n", - " 15\n", - " 15\n", - " 15\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Rango de comunicación\n", - " 59\n", - " 161\n", - " 140\n", - " 140\n", - " 140\n", - " Nan\n", - " 150\n", - " Nan\n", - " 150\n", - " 50\n", - " 25\n", - " 101.86\n", - " 92.6\n", + " V35\n", + " 0\n", + " 6000.000\n", + " 27.34405\n", + " 16000\n", " Nan\n", " Nan\n", " Nan\n", - " 92.6\n", + " 1.88\n", " Nan\n", - " 50\n", - " 30\n", - " 30\n", - " 30\n", - " 30\n", - " 30\n", - " 30\n", " Nan\n", + " 32.000\n", " Nan\n", + " 2.8\n", + " 33\n", " Nan\n", " Nan\n", " Nan\n", + " 3.5\n", " Nan\n", + " 10\n", " Nan\n", + " 25\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 30\n", " Nan\n", - " \n", - " \n", - " Wing Loading\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 7999\n", " Nan\n", - " 23\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " V39\n", + " 0\n", + " 6000.000\n", + " 27.34405\n", + " 16000\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 24.000\n", " Nan\n", + " 4.5\n", + " 33\n", " Nan\n", " Nan\n", " Nan\n", + " 3.9\n", " Nan\n", + " 5\n", " Nan\n", - " 12.5\n", - " 24\n", " 25\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 30\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 8999\n", " Nan\n", " Nan\n", - " Nan\n", - " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Potencia específica (P/W)\n", + " Volitation VT370\n", + " 0\n", + " 6000.000\n", + " 27.34405\n", + " 17000\n", " Nan\n", " Nan\n", " Nan\n", + " 2.02\n", " Nan\n", " Nan\n", + " 40.000\n", " Nan\n", - " 98\n", + " 15\n", + " 33\n", " Nan\n", " Nan\n", " Nan\n", + " 6.5\n", " Nan\n", + " 18\n", " Nan\n", + " 25\n", " Nan\n", " Nan\n", " Nan\n", @@ -1332,27 +1361,48 @@ " Nan\n", " Nan\n", " Nan\n", + " 13\n", + " 0.96\n", " Nan\n", " Nan\n", + " 8999\n", " Nan\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Skyeye 2600\n", " Nan\n", + " 6000.000\n", + " 36.094147\n", " Nan\n", + " 10\n", + " 0.88\n", " Nan\n", + " 2.05\n", " Nan\n", " Nan\n", + " 15.000\n", " Nan\n", + " 2\n", " Nan\n", + " 10\n", " Nan\n", " Nan\n", + " 2.6\n", " Nan\n", + " 4\n", " Nan\n", + " 33\n", " Nan\n", " Nan\n", + " 6.5\n", " Nan\n", - " \n", - " \n", - " Capacidad combustible\n", " Nan\n", " Nan\n", " Nan\n", @@ -1360,222 +1410,361 @@ " Nan\n", " Nan\n", " Nan\n", + " 2299\n", " Nan\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Skyeye 2930 VTOL\n", + " 0\n", + " 6000.000\n", + " 26.250288\n", " Nan\n", + " 18\n", + " 1\n", " Nan\n", + " 2.03\n", " Nan\n", " Nan\n", + " 28.000\n", " Nan\n", + " 3\n", + " 30\n", + " 18\n", " Nan\n", " Nan\n", + " 2.93\n", " Nan\n", + " 6\n", " Nan\n", + " 24\n", " Nan\n", " Nan\n", + " 7.1\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", - " 13\n", " Nan\n", " Nan\n", - " 11.5\n", - " 11.5\n", - " 28\n", - " 28\n", - " 28\n", - " 25\n", " Nan\n", + " 6799\n", " Nan\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Consumo\n", - " Nan\n", - " Nan\n", - " 0.6\n", - " 0.6\n", - " Nan\n", - " Nan\n", - " Nan\n", + " Skyeye 3600\n", + " 50\n", + " 6000.000\n", " Nan\n", " Nan\n", + " 12.5\n", + " 1.33\n", " Nan\n", + " 2.488\n", " Nan\n", " Nan\n", + " 28.000\n", " Nan\n", + " 4.5\n", " Nan\n", + " 12.5\n", " Nan\n", " Nan\n", + " 3.6\n", " Nan\n", + " 10\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 11.5\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", - " 0.96\n", + " 11.5\n", " Nan\n", " Nan\n", " Nan\n", + " 4999\n", " Nan\n", - " 1.2\n", " Nan\n", + " 3.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Skyeye 3600 VTOL\n", + " 0\n", + " 6000.000\n", + " 32.81286\n", " Nan\n", - " 5\n", + " 24\n", + " 1.32\n", " Nan\n", + " 2.42\n", " Nan\n", " Nan\n", - " \n", - " \n", - " Potencia Watts\n", + " 40.000\n", + " 300\n", + " 6\n", + " 33\n", + " 24\n", " Nan\n", " Nan\n", - " 2980\n", - " 2980\n", + " 3.6\n", " Nan\n", + " 10\n", " Nan\n", - " 1280\n", + " 30\n", " Nan\n", " Nan\n", + " 11\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 11.5\n", " Nan\n", " Nan\n", - " 170\n", " Nan\n", + " 6999\n", " Nan\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Skyeye 5000\n", + " 60\n", + " 6000.000\n", + " 36.094147\n", " Nan\n", + " 15\n", + " 2.615\n", " Nan\n", + " 3.5\n", " Nan\n", + " 0.375\n", + " 90.000\n", " Nan\n", + " 8\n", + " 42\n", + " 15\n", " Nan\n", " Nan\n", + " 5\n", " Nan\n", + " 20\n", " Nan\n", + " 33\n", " Nan\n", " Nan\n", + " 32\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 28\n", + " 1.2\n", " Nan\n", " Nan\n", + " 9999\n", " Nan\n", " Nan\n", + " 3.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Potencia HP\n", - " Nan\n", - " Nan\n", - " 4\n", - " 4\n", + " Skyeye 5000 VTOL\n", + " 0\n", + " 6000.000\n", + " 30.625336\n", " Nan\n", " Nan\n", - " 1.74\n", + " 2.615\n", " Nan\n", + " 3.5\n", " Nan\n", + " 0.375\n", + " 100.000\n", + " 800\n", + " 8\n", + " 42\n", " Nan\n", " Nan\n", " Nan\n", + " 5\n", " Nan\n", + " 25\n", " Nan\n", + " 28\n", " Nan\n", " Nan\n", - " 8\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 28\n", " Nan\n", " Nan\n", " Nan\n", + " 13900\n", " Nan\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Skyeye 5000 VTOL octo\n", + " 0\n", + " 6000.000\n", " Nan\n", " Nan\n", " Nan\n", + " 2.615\n", " Nan\n", + " 3.5\n", " Nan\n", + " 0.375\n", + " 100.000\n", " Nan\n", " Nan\n", + " 38\n", + " 24\n", " Nan\n", " Nan\n", + " 5\n", " Nan\n", - " \n", - " \n", - " Precio\n", + " 15\n", " Nan\n", + " 35\n", " Nan\n", " Nan\n", + " 35\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 28\n", " Nan\n", " Nan\n", " Nan\n", + " 15999\n", " Nan\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 8.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Volitation VT510\n", + " 0\n", + " 6000.000\n", + " 32.81286\n", + " 17000\n", + " 25\n", " Nan\n", " Nan\n", + " 2.905\n", " Nan\n", " Nan\n", + " 100.000\n", " Nan\n", + " 5\n", + " 50\n", + " 25\n", " Nan\n", " Nan\n", + " 5.1\n", " Nan\n", - " 3999\n", - " 4679\n", - " 69999\n", - " 7999\n", - " 8999\n", - " 8999\n", - " 2299\n", - " 6799\n", - " 4999\n", - " 6999\n", - " 9999\n", - " 13900\n", - " 15999\n", - " 16599\n", + " 25\n", " Nan\n", + " 30\n", " Nan\n", " Nan\n", - " \n", - " \n", - " Tiempo de emergencia en vuelo\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", " Nan\n", + " 25\n", + " 5\n", " Nan\n", " Nan\n", + " 16599\n", " Nan\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Ascend\n", + " 0\n", + " 6000.000\n", + " 21.87524\n", + " 10000\n", " Nan\n", " Nan\n", " Nan\n", + " 1.562\n", " Nan\n", " Nan\n", + " 9.500\n", " Nan\n", + " 6\n", + " 30\n", + " 13\n", " Nan\n", " Nan\n", + " 2\n", " Nan\n", + " 0.6\n", + " 0.05\n", + " 20\n", + " 6\n", + " 8.9\n", + " 3\n", + " 15\n", " Nan\n", " Nan\n", - " 0.108\n", - " 0.108\n", - " 0.108\n", " Nan\n", " Nan\n", " Nan\n", @@ -1584,18 +1773,41 @@ " Nan\n", " Nan\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Transition\n", + " 0\n", + " 6000.000\n", + " 21.87524\n", + " 13000\n", " Nan\n", " Nan\n", " Nan\n", + " 2.3\n", " Nan\n", " Nan\n", + " 18.000\n", " Nan\n", - " \n", - " \n", - " Distancia de aterrizaje\n", + " 12\n", + " 30\n", + " 13\n", " Nan\n", " Nan\n", + " 3\n", " Nan\n", + " 1.5\n", + " 0.05\n", + " 20\n", + " 11.8\n", + " 16.5\n", + " 5.8\n", + " 15\n", " Nan\n", " Nan\n", " Nan\n", @@ -1606,20 +1818,41 @@ " Nan\n", " Nan\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Reach\n", " 0\n", + " 6000.000\n", + " 27.34405\n", + " 16000\n", " Nan\n", " Nan\n", " Nan\n", - " 0\n", - " 0\n", - " 0\n", - " Nan\n", + " 4.712\n", " Nan\n", " Nan\n", + " 91.000\n", " Nan\n", + " 20\n", + " 35\n", + " 13\n", " Nan\n", " Nan\n", + " 6\n", " Nan\n", + " 7\n", + " 0.05\n", + " 25\n", + " 54\n", + " 84\n", + " 31\n", + " 15\n", " Nan\n", " Nan\n", " Nan\n", @@ -1630,1143 +1863,382 @@ " Nan\n", " Nan\n", " Nan\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", - " \n", + " \n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Procesando los datos ===\n", + "=== Inicio del procesamiento de datos ===\n", + "\n", + "=== Comprobación de duplicados ===\n", + "No se encontraron duplicados en índices o columnas.\n", + "\n", + "=== Convirtiendo valores a numéricos donde sea posible ===\n", + "=== Procesamiento completado ===\n", + "\n", + "✅ Los encabezados se preservaron correctamente.\n", + "\n", + "=== Mostrando datos procesados en formato HTML ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Datos Procesados

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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespegue1112122111111211122222222222323222222
    Propulsión horizontal2222222221122222211111222222222222222
    Propulsión vertical5551511555555155511111111111515111111
    Cantidad de motores propulsión vertical0004044000000400044444444444040484444
    Cantidad de motores propulsión horizontal1111111111111111111111111111111111111
    Misión1111111111111111111111111111111111111
    Dimensiones de la bahía de carga útilNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan0.2 x 0.2 x 0.11Nan15 x 10 x 9NanNanNanNanNanNanNan560 x 210 x 185 mm560 x 260 x 270 mm560 x 260 x 270 mm920 x 340 x 350 mmNanNanNanNanNanNanModelo
    Battery Power SupplyNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanSemi Solid State LiNanion, 22AhNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan2 x 3800mAh 4S Lipo2 x 9000mAh 4S Lipo12 x 16000mAh 4S LipoStalker XE0.06000.00016.88037912000.0NaN0.8715.3012552.1NaN0.21113.600370.08.020.0NaNNaNNaN3.6570.2392.4947562.015.43332NaNNaN10.886208NaN59.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    Modelo Motor Fixed WingNanNanELNan005ELNan005NanNanEnya R120NanNanNanBatería recargableMotor de combustible pesado (JPNan5 o JPNan8)EFI con JPNan5/JPNan8NanEFI con JPNan5/JPNan8Heavy Fuel (JPNan5/JPNan8)EFI, JPNan5/JPNan8NanNanNanNanNanNanNanNanEFI de gasolina20–35 cc gasolinaCompatible con motores eléctricos o de gasolina50–100 cc gasolina50–100 cc gasolinaDLA 180cc EFINanNanMotor pistón EFITNanMotor MN501Saito FG21HFE International DA100EFIStalker VXE300.06000.00017.60237312000.0NaN1.15828315.3264492.5908NaN0.219.958433.08.025.034211NaNNaNNaN4.87680.3181952.494756NaN16.093422NaNNaN17.463292NaN161.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    Modelo Motor VTOLNanNanNanNanNanNanNanNanNanNanNanNanNanFLARES eléctricoNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanTNanMotor MN501TNanMotor ALTI U7TNanMotor ALTI U15 II KV100Aerosonde Mk. 4.7 Fixed WingNaN6000.00027.3440514700.0NaN1.5512.53.0NaN0.27742.200NaN19.833.43886NaNNaNNaN4.40.35214.5NaN25.0NaN27.7NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN1.0002.0005.0000.0001.0001.000
    PortabilidadsisisisisisiNanAltaAltamente transportableAltamente transportableSistema transportable en cajas ligerasNanTransportable por barco y aviónOperaciones en espacios reducidosTransportable por barco y aviónAltaOperable desde tierra y marIncluye maleta de transporteMontaje en 2 minutosMontaje en 2 minutosAltaAltaAltaAltaAltaMediaNanNanNanNanNanNanNanNanNanNanNan
    CámaraNanNanNanNanNanNanNanNanEO/IR, WAMI, SAR, COMINT, ELINTEO/Cooled IR, WAMI, COMINT, ELINTEO, IR giroestabilizadasEO, MWIR, ViDAREO, MWIR, ViDAR, LRFModular payloadsEO, MWIR, ViDAR, LRFEO, MWIR/EO, ViDAREO, MWIR, LRF, IR MarkerNanRGB, Multiespectral, TérmicaNo aplicaNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan
    Despegue todos los tiposBungee, rail, VTOLBungee, rail, VTOLRailVTOLRailVTOLRailRailRailRailRailRailRailVTOLRailRailRailVTOLVTOLVTOLVTOLVTOLVTOLVTOLVTOLVTOLNanVTOLNanVTOLNanVTOLNanVTOLNanNanNan
    Datalink banksNanNanL,S,CL,S,CL,S,CL,S,C,KUNanNanLOS, BLOSLOS, AESNan256, RelayDigital, varias frecuenciasEnlace de datos digital encriptadoEnlace de datos encriptadoNanSATCOM, BLOS, robustoEncrypted/UnencryptedEncriptadoNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan
    Material del fuselajeNanNanNanNanNanNanNanFibra de carbonoNanNanNanNanNanNanNanNanNanNanNanNanFibra de carbono, Kevlar, PVCFibra de carbono, Kevlar, PVCFibra de carbono y compuestosFibra de carbono, Kevlar, fibra de vidrioFibra de carbono completaNanFibra de carbonoFibra de carbonoFibra de carbonoFibra de carbonoFibra de carbonoFibra de carbonoCarbon FiberFibra de carbono compuestaNanNanNan
    Motor recomendadoNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanEFI de gasolinaGasolina 20–35 ccGasolina o eléctricaGasolina 50–100 ccGasolina 50–100 ccGasolina EFI DLA 180 ccNanNanGasolina o diéselNanNanNan
    Hélice recomendada VTOLNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanMaster Airscrew 3x Power 13x12TNanMotor 18x6.1 Carbon FiberTNanMotor 40x13.1 Carbon Fiber
    Hélice recomendada Fixed WingNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan16 pulgadas16Nan17 pulgadas17Nan18 pulgadasVTOL: 26 pulgadas; Ala fija: 24 pulgadasVTOL: 22 pulgadas; Ala fija: 21 pulgadasNan19 x 8 pulgadasNanNanNan32 x 10 pulgadasNanNanNanNanNanNan
    Sistema de controlNanNanNanNanNanNanNanNanNavegación avanzada, redundanteNavegación avanzada, precisiónGPS/INS, navegación manual o automáticaArquitectura abiertaArquitectura abiertaModular y portátilNavegación avanzadaModularModular y portátilNanControlador DeltaQuadControlador DeltaQuadNanNanNanNanLightning X7NanNanNanNanNanNanNanNanNanNanNanNan
    Características adicionalesNanNanNanNanNanNanNanAlta fiabilidad y facilidad de usoMultiNanpayload, operable en clima extremoMultiNanpayload, operable en clima extremoModular, navegación spline, ATOLModular, flexibleModular, multiNanmisiónResistente a alta mar y vientosSeguridad aumentadaCapacidad MultiNanINTModularidad y flexibilidadDespegue y aterrizaje vertical, resistente a lluvias ligerasDespegue y aterrizaje vertical, transmisión de video en vivo, integración de múltiples sensoresDespegue y aterrizaje vertical, transmisión de video en vivo, mecanismo de liberación de carga útil personalizableAlta portabilidadAlta portabilidadAlta portabilidadMayor capacidad de cargaLarga autonomíaAutonomía extendidaUso optimizado para misiones prolongadasCompatible VTOL; configuración flexibleDiseñado para vigilancia y cartografíaAlta capacidad VTOL; alcance extendidoTanque de combustible de Kevlar; sistema de freno en las ruedasConfiguración VTOL avanzadaNanDiseño modular de ala tándemNanNanNan
    indice_desconocidopdfpdfLinkLinkLinkLinkLinkLinkLinkLinkLinkLinkLinkNanNanLinkLinkLinkLinkLinkLinkLinkLinkLinkLinkLinkLinkLinkLinkLinkLinkLinkNanLinkpdfpdfpdf
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Procesando los datos ===\n", - "=== Inicio del procesamiento de datos ===\n", - "\n", - "=== Comprobación de duplicados ===\n", - "No se encontraron duplicados en índices o columnas.\n", - "\n", - "=== Convirtiendo valores a numéricos donde sea posible ===\n", - "=== Procesamiento completado ===\n", - "\n", - "✅ Los encabezados se preservaron correctamente.\n", - "\n", - "=== Mostrando datos procesados en formato HTML ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Datos Procesados

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" \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2774,111 +2246,127 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -2893,65 +2381,42 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -2961,32 +2426,42 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -2996,194 +2471,132 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -3193,6 +2606,17 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3201,30 +2625,24 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3232,16 +2650,36 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", @@ -3258,21 +2696,37 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", @@ -3281,94 +2735,92 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3378,75 +2830,43 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3455,61 +2875,43 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3518,216 +2920,311 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -3736,26 +3233,43 @@ " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -3764,196 +3278,359 @@ " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - 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    Stalker XEStalker VXE30Aerosonde Mk. 4.7 Fixed WingAerosonde Mk. 4.7 VTOLAerosonde Mk. 4.8 Fixed wingAerosonde Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQ Nan 21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
    Modelo
    Distancia de carrera requerida para despegue0.00.06000.00027.344059700.0NaN0.01.5512.53.0NaN0.00.27753.500NaN12.033.43886NaNNaNNaN4.40.35211.3NaN25.0NaN42.2NaN0.0NaN140.0NaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
    Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.00.62980.04.0NaNNaNNaN2.0002.0001.0004.0001.0001.000
    Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.34405Aerosonde Mk. 4.8 Fixed wingNaN6000.00027.3440518200.0NaN1.5512.53.0NaN30.4065840.27754.400NaN19.833.43886NaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.2502884.40.35217.7NaN32.8128636.09414730.62533625.0NaN32.8128621.8752421.8752427.34405
    Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.84236.7NaNNaN140.0NaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaN17000.010000.013000.016000.01.0002.0005.0000.0001.0001.000
    Velocidad de pérdida limpia (KCAS)Aerosonde Mk. 4.8 VTOL FTUAS0.06000.000NaN15000.0NaNNaN12.5NaNNaNNaN93.000NaN14.0NaNNaNNaNNaNNaNNaN22.7NaNNaNNaN70.3NaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaNNaN25.0NaNNaNNaN2.0002.0001.0004.0001.0001.000
    Área del ala0.871.1582831.551.551.55NaN0.57NaNAAI AerosondeNaN5500.000NaN15000.0NaN0.5714.7543861.7NaNNaN13.1003270.026.030.845725NaNNaNNaN2.90.196552NaN0.84NaNNaN0.80.52NaNNaN10.0NaN150.023.098.0NaN0.881.01.331.322.6152.6152.615NaN1280.01.74NaNNaNNaN2.0002.0001.0004.0001.0001.000
    Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386Fulmar XNaN6000.00030.4065849.842NaNNaNNaN1.2NaNNaN20.000800.08.041.7NaNNaNNaN3.0NaNNaNNaN27.8NaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    Orbiter 4NaN6000.000NaNNaNNaNNaN
    Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.712
    Profundidad del fuselaje55.000NaN24.036.0NaNNaNNaN5.2NaN12.0NaNNaNNaNNaNNaNNaN150.0NaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    Orbiter 3NaN6000.000NaNNaNNaNNaNNaN1.2NaNNaN32.00050.06.036.0NaNNaNNaN4.4NaN5.5NaNNaNNaNNaNNaN
    Ancho del fuselaje0.2110.20.2770.2770.277NaN50.0NaNNaNNaNNaNNaNNaN1.0001.0005.0000.0001.0001.000
    MantisNaN6000.00018.265826NaNNaNNaNNaN1.48NaNNaN6.50025.02.025.6NaNNaNNaN2.1NaNNaNNaN16.7NaNNaN0.3750.3750.375NaNNaN25.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0005.0000.0001.0001.000
    Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
    Alcance de la aeronave370.0433.0ScanEagleNaN6000.00030.62533619500.0NaNNaNNaN3270.0800.01.71NaN50.025.0NaN26.500NaN18.041.2NaN500.0NaNNaN270.0100.0100.03.1NaN5.0NaN28.0NaNNaNNaNNaN101.86NaNNaNNaN300.0NaN800.0NaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0IntegratorNaN5.06.012.020.0
    Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.438866000.00030.95346519500.0NaN30.84572541.736.036.025.641.246.3NaN46.341.2NaN2.5NaNNaN74.800NaN24.046.3NaNNaNNaN33.033.033.033.033.033.04.8NaN30.018.0NaN28.3NaN33.042.042.038.050.030.030.035.0
    Velocidad de pérdida (KCAS)NaNNaNNaN92.6NaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    Integrator VTOL0.05000.000NaNNaNNaNNaNNaNNaN14.015.517.075.000NaN16.0NaNNaNNaNNaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.0
    Tasa de ascensoNaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaNNaNNaN0.02.0002.0001.0004.0001.0001.000
    Integrator Extended RangeNaN6000.000NaN19500.0NaNNaNNaN2.5NaNNaN74.800500.019.046.3NaNNaNNaN4.8NaN18.0NaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    Radio de giroScanEagle 3NaN6000.00025.70340720.0NaNNaNNaN2.4NaNNaN36.300NaN18.041.2NaNNaNNaN4.0NaN8.6NaN23.5NaNNaNNaNNaNNaNNaN100.0120.0150.0NaN170.0NaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    RQ Nan 21A BlackjackNaN6000.00033.79724620.0NaNNaNNaN2.5NaNNaN61.000NaN16.046.3NaNNaN
    envergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
    Cuerda0.2390.3181950.3520.3520.35217.7NaN0.19655230.9NaNNaNNaNNaN92.6NaNNaNNaNNaNNaN8.0NaNNaNNaN1.0002.0005.0000.0001.0001.000
    DeltaQuad Evo0.06000.00018.09082413.0NaN0.84NaN0.75NaNNaN10.000270.04.53NaNNaNNaNNaN2.69NaN3.04.5316.544.86.84.845.0NaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
    payload2.4947562.49475614.511.317.722.7NaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
    duracion en VTOL2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDeltaQuad Pro #MAP0.06000.00017.50019213.123NaNNaNNaN0.9NaNNaN6.200100.01.83NaN4.53NaNNaNNaN2.35NaN1.2NaN16.0NaNNaNNaN50.050.0NaNNaNNaNNaNNaNNaN0.050.050.050.02.0001.0001.0004.0001.0001.000
    Crucero KIAS15.4333216.09342225.025.025.0NaNNaN27.8DeltaQuad Pro #CARGO0.06000.00017.50019213.123NaNNaN16.728.028.3NaN0.9NaN23.530.916.5416.016.018.020.020.025.025.025.033.024.0NaN30.033.028.035.030.020.020.025.0
    RTF (dry weight)6.200100.01.83NaNNaNNaNNaN2.35NaN1.2NaN16.0NaNNaNNaN50.030.0NaNNaNNaNNaNNaNNaN4.80.02.0001.0001.0004.0001.0001.000
    V210.06000.00019.68771648800.014.00.8NaN0.93NaNNaN10.000NaN3.033.014.0NaN100.02.15NaN1.5NaN18.0NaNNaN2.65NaN30.012.5NaNNaNNaNNaNNaN3999.00.108NaN6.011.854.02.0001.0001.0004.0001.0001.000
    RTF (Including fuel & Batteries)V250.06000.00021.8752416000.015.50.52NaN0.93NaN27.742.236.770.3NaN12.500NaN4.033.015.5NaN120.02.45NaN2.2NaN20.0NaNNaN3.45NaN30.024.0NaNNaNNaN6.8NaNNaN4679.00.108NaN2.0001.0001.0004.0001.0001.000
    V320.06000.00021.8752416000.017.0NaNNaN1.0NaNNaN23.500NaN4.533.017.0NaN150.03.2NaN5.0NaN20.0NaNNaN6.45NaN30.025.0NaNNaN8.916.584.0
    Empty weight10.88620817.463292NaNNaNNaN69999.00.108NaN10.02.0002.0001.0004.0001.0001.000
    V350.06000.00027.3440516000.0NaNNaNNaN1.88NaNNaN32.000NaN2.833.0NaNNaNNaN3.5NaN4.810.0NaN25.0NaN2.653.456.45NaNNaNNaN6.57.111.511.032.030.0NaN35.0NaN3.05.831.0
    Maximum CrosswindNaNNaNNaNNaN7999.0NaNNaN2.0002.0001.0004.0001.0001.000
    V390.06000.00027.3440516000.0NaNNaNNaNNaNNaNNaN24.000NaN30.04.533.0NaNNaNNaN45.050.050.03.9NaN5.0NaN25.0NaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaN8999.0NaNNaN15.015.015.02.0002.0001.0004.0001.0001.000
    Rango de comunicación59.0161.0140.0140.0140.0NaN150.0NaN150.050.025.0101.8692.6NaNNaNNaN92.6NaN50.030.030.030.030.030.030.0Volitation VT3700.06000.00027.3440517000.0NaNNaNNaN2.02NaNNaN40.000NaN15.033.0NaNNaNNaN6.5NaN18.0NaN25.0NaN
    Wing LoadingNaNNaNNaNNaNNaNNaN23.013.00.96NaNNaN8999.0NaNNaN2.0002.0001.0004.0001.0001.000
    Skyeye 2600NaN6000.00036.094147NaN10.00.88NaN2.05NaNNaN15.000NaN2.0NaN10.0NaNNaN12.524.025.02.6NaN4.0NaN33.0NaNNaN6.5NaNNaNNaNNaNNaNNaN2299.0NaNNaN2.0002.0001.0004.0001.0001.000
    Potencia específica (P/W)NaNNaNNaNSkyeye 2930 VTOL0.06000.00026.250288NaN18.01.0NaN2.03NaN98.0NaN28.000NaN3.030.018.0NaNNaN2.93NaN6.0NaN24.0NaNNaN7.1NaNNaNNaNNaNNaNNaN6799.0NaNNaN2.0002.0001.0004.0001.0001.000
    Skyeye 360050.06000.000NaNNaN12.51.33NaN2.488NaNNaN28.000NaN4.5NaN12.5NaNNaN3.6NaN10.0NaNNaN
    Capacidad combustibleNaNNaN11.5NaNNaNNaNNaN11.5NaNNaNNaN4999.0NaNNaN3.0002.0005.0000.0001.0001.000
    Skyeye 3600 VTOL0.06000.00032.81286NaN24.01.32NaN2.42NaNNaN40.000300.06.033.024.0NaNNaN3.6NaN10.0NaN30.0NaNNaN11.0NaNNaNNaNNaN13.011.5NaNNaN11.511.528.028.028.025.0NaN6999.0NaNNaN2.0002.0001.0004.0001.0001.000
    ConsumoNaNNaN0.60.6NaNNaNSkyeye 500060.06000.00036.094147NaN15.02.615NaN3.5NaN0.37590.000NaN8.042.015.0NaNNaN5.0NaN20.0NaN33.0NaNNaN32.0NaNNaNNaNNaN28.01.2NaNNaN9999.0NaNNaN3.0002.0005.0000.0001.0001.000
    Skyeye 5000 VTOL0.06000.00030.625336NaN0.96NaN2.615NaN3.5NaN0.375100.000800.08.042.0NaN1.2NaNNaN5.0NaN25.0NaN28.0NaN
    Potencia WattsNaNNaN2980.02980.0NaNNaN1280.0NaNNaN28.0NaNNaNNaN13900.0NaNNaN2.0002.0001.0004.0001.0001.000
    Skyeye 5000 VTOL octo0.06000.000NaN170.0NaNNaN2.615NaN3.5NaN0.375100.000NaNNaN38.024.0NaNNaN5.0NaN15.0NaN35.0NaNNaN35.0NaNNaNNaNNaN28.0NaNNaNNaN15999.0NaNNaN2.0002.0001.0008.0001.0001.000
    Potencia HPVolitation VT5100.06000.00032.8128617000.025.0NaNNaN4.04.02.905NaNNaN1.74100.000NaN5.050.025.0NaNNaN5.1NaN25.0NaN30.0NaNNaNNaNNaNNaN8.0NaNNaN25.05.0NaNNaN16599.0NaNNaN2.0002.0001.0004.0001.0001.000
    Ascend0.06000.00021.8752410000.0NaNNaNNaN1.562NaNNaN9.500NaN6.030.013.0NaNNaN2.0NaN0.60.0520.06.08.93.015.0NaNNaNNaNNaNNaNNaN
    PrecioNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
    Transition0.06000.00021.8752413000.0NaNNaNNaN2.3NaNNaN18.000NaN12.030.013.0NaNNaN3.0NaN1.50.0520.011.816.55.815.0NaNNaNNaNNaNNaNNaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.0NaNNaNNaN2.0002.0001.0004.0001.0001.000
    Tiempo de emergencia en vueloReach0.06000.00027.3440516000.0NaNNaNNaN4.712NaNNaN91.000NaN20.035.013.0NaNNaN6.0NaN7.00.0525.054.084.031.015.0NaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parámetros disponibles en df_procesado antes de seleccionar:\n", + "['Distancia de carrera requerida para despegue', 'Altitud a la que se realiza el crucero', 'Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Velocidad de pérdida limpia (KCAS)', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Profundidad del fuselaje', 'Ancho del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Tasa de ascenso', 'Radio de giro', 'envergadura', 'Cuerda', 'payload', 'duracion en VTOL', 'Crucero KIAS', 'RTF (dry weight)', 'RTF (Including fuel & Batteries)', 'Empty weight', 'Maximum Crosswind', 'Rango de comunicación', 'Wing Loading', 'Potencia específica (P/W)', 'Capacidad combustible', 'Consumo', 'Potencia Watts', 'Potencia HP', 'Precio', 'Tiempo de emergencia en vuelo', 'Distancia de aterrizaje', 'Despegue', 'Propulsión horizontal', 'Propulsión vertical', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal', 'Misión']\n", + "\n", + "=== Selección de Parámetros ===\n", + "Parámetros disponibles:\n", + "1. Distancia de carrera requerida para despegue\n", + "2. Altitud a la que se realiza el crucero\n", + "3. Velocidad a la que se realiza el crucero (KTAS)\n", + "4. Techo de servicio máximo\n", + "5. Velocidad de pérdida limpia (KCAS)\n", + "6. Área del ala\n", + "7. Relación de aspecto del ala\n", + "8. Longitud del fuselaje\n", + "9. Profundidad del fuselaje\n", + "10. Ancho del fuselaje\n", + "11. Peso máximo al despegue (MTOW)\n", + "12. Alcance de la aeronave\n", + "13. Autonomía de la aeronave\n", + "14. Velocidad máxima (KIAS)\n", + "15. Velocidad de pérdida (KCAS)\n", + "16. Tasa de ascenso\n", + "17. Radio de giro\n", + "18. envergadura\n", + "19. Cuerda\n", + "20. payload\n", + "21. duracion en VTOL\n", + "22. Crucero KIAS\n", + "23. RTF (dry weight)\n", + "24. RTF (Including fuel & Batteries)\n", + "25. Empty weight\n", + "26. Maximum Crosswind\n", + "27. Rango de comunicación\n", + "28. Wing Loading\n", + "29. Potencia específica (P/W)\n", + "30. Capacidad combustible\n", + "31. Consumo\n", + "32. Potencia Watts\n", + "33. Potencia HP\n", + "34. Precio\n", + "35. Tiempo de emergencia en vuelo\n", + "36. Distancia de aterrizaje\n", + "37. Despegue\n", + "38. Propulsión horizontal\n", + "39. Propulsión vertical\n", + "40. Cantidad de motores propulsión vertical\n", + "41. Cantidad de motores propulsión horizontal\n", + "42. Misión\n", + "\n", + "Preseleccionados: 3, 4, 6, 7, 8, 11, 12, 13, 14, 15, 5, 18, 19, 20, 25\n", + "Parámetros seleccionados después de filtrar:\n", + "['Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Velocidad de pérdida limpia (KCAS)', 'envergadura', 'Cuerda', 'payload', 'Empty weight']\n", + "Parámetros seleccionados después de filtrar:\n", + "['Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Velocidad de pérdida limpia (KCAS)', 'envergadura', 'Cuerda', 'payload', 'Empty weight']\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Datos Filtrados por Parámetros (df_filtrado)

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    Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
    Modelo
    Stalker XE16.88037912000.00.8715.3012552.113.600370.08.020.0NaNNaN0.1080.1080.1083.6570.2392.49475610.886208
    Stalker VXE3017.60237312000.01.15828315.3264492.590819.958433.08.025.034211NaNNaN4.87680.3181952.49475617.463292
    Aerosonde Mk. 4.7 Fixed Wing27.3440514700.01.5512.53.042.200NaN19.833.43886NaNNaN4.40.35214.5NaN
    Aerosonde Mk. 4.7 VTOL27.344059700.01.5512.53.053.500NaN12.033.43886NaNNaN4.40.35211.3NaN
    Aerosonde Mk. 4.8 Fixed wing27.3440518200.01.5512.53.054.400NaN19.833.43886NaNNaN4.40.35217.7NaN
    Distancia de aterrizajeAerosonde Mk. 4.8 VTOL FTUASNaN15000.0NaN12.5NaN93.000NaN14.0NaNNaNNaNNaNNaN22.7NaN
    AAI AerosondeNaN15000.00.5714.7543861.713.1003270.026.030.845725NaNNaN2.90.196552NaN10.0
    Fulmar X30.4065849.842NaNNaN1.220.000800.08.041.7NaNNaN0.03.0NaNNaNNaN0.00.00.0
    Orbiter 4NaNNaNNaNNaN1.255.000NaN24.036.0NaNNaN5.2NaN12.0NaN
    Orbiter 3NaNNaNNaNNaN1.232.00050.06.036.0NaNNaN4.4NaN5.5NaN
    Despegue1.01.01.02.01.02.02.01.01.01.01.01.01.02.01.01.01.02.02.02.02.02.02.02.02.02.02.02.03.02.03.02.02.02.02.02.0Mantis18.265826NaNNaNNaN1.486.50025.02.025.6NaNNaN2.1NaNNaNNaN
    Propulsión horizontal2.02.02.02.02.02.02.02.02.01.01.02.02.02.02.02.02.01.01.01.01.01.02.02.02.02.02.02.02.02.02.02.02.02.02.02.02.0ScanEagle30.62533619500.0NaNNaN1.7126.500NaN18.041.2NaNNaN3.1NaN5.0NaN
    Propulsión vertical5.05.05.01.05.01.01.05.05.05.05.05.05.01.05.05.05.01.01.01.01.01.01.01.01.01.01.01.05.01.05.01.01.01.01.01.01.0
    Cantidad de motores propulsión vertical0.00.00.04.00.04.04.00.00.00.00.00.00.04.00.00.00.04.04.04.04.04.04.04.04.04.04.04.00.04.00.04.08.04.04.04.04.0
    Cantidad de motores propulsión horizontal1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
    Misión1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
    Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNIntegrator30.95346519500.0NaNNaN2.574.800NaN24.046.3NaNNaN4.8NaN18.0NaN
    Battery Power SupplyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNIntegrator VTOLNaNNaNNaNNaNNaN75.000NaN16.0NaNNaNNaNNaNNaN18.0NaN
    Modelo Motor Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNIntegrator Extended RangeNaN19500.0NaNNaN2.574.800500.019.046.3NaNNaN4.8NaN18.0NaN
    Modelo Motor VTOLNaNNaNNaNNaNScanEagle 325.70340720.0NaNNaN2.436.300NaN18.041.2NaNNaN4.0NaN8.6NaN
    RQ Nan 21A Blackjack33.79724620.0NaNNaN2.561.000NaN16.046.3NaNNaN4.8NaN17.7NaN
    DeltaQuad Evo18.09082413.00.84NaN0.7510.000270.04.53NaNNaNNaN2.69NaN3.04.8
    DeltaQuad Pro #MAP17.50019213.123NaNNaN0.96.200100.01.83NaNNaNNaN2.35NaN1.2NaN
    DeltaQuad Pro #CARGO17.50019213.123NaNNaN0.96.200100.01.83NaNNaNNaN2.35NaN1.2NaN
    PortabilidadNaNNaNNaNNaNNaNV2119.68771648800.00.8NaN0.9310.000NaN3.033.014.014.02.15NaN1.52.65
    V2521.8752416000.00.52NaN0.9312.500NaN4.033.015.515.52.45NaN2.23.45
    V3221.8752416000.0NaNNaN1.023.500NaN4.533.017.017.03.2NaN5.06.45
    V3527.3440516000.0NaNNaN1.8832.000NaN2.833.0NaNNaN3.5NaN10.0NaN
    V3927.3440516000.0NaNNaNNaN24.000NaN4.533.0NaNNaN3.9NaN5.0NaN
    Volitation VT37027.3440517000.0NaNNaN2.0240.000NaN15.033.0NaNNaN6.5NaN18.0NaN
    CámaraNaNNaNSkyeye 260036.094147NaN0.88NaN2.0515.000NaN2.0NaN10.010.02.6NaN4.06.5
    Skyeye 2930 VTOL26.250288NaN1.0NaN2.0328.000NaN3.030.018.018.02.93NaN6.07.1
    Skyeye 3600NaNNaN1.33NaN2.48828.000NaN4.5NaN12.512.53.6NaN10.011.5
    Skyeye 3600 VTOL32.81286NaN1.32NaN2.4240.000300.06.033.024.024.03.6NaN10.011.0
    Skyeye 500036.094147NaN2.615NaN3.590.000NaN8.042.015.015.05.0NaN20.032.0
    Skyeye 5000 VTOL30.625336NaN2.615NaN3.5100.000800.08.042.0NaNNaN5.0NaN25.0NaN
    Skyeye 5000 VTOL octoNaNNaN2.615NaN3.5100.000NaNNaN38.024.0NaN5.0NaN15.035.0
    Despegue todos los tiposNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Datalink banksNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Motor recomendadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Hélice recomendada VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Hélice recomendada Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Sistema de controlNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Características adicionalesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    indice_desconocidoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNVolitation VT51032.8128617000.0NaNNaN2.905100.000NaN5.050.025.025.05.1NaN25.0NaN
    Ascend21.8752410000.0NaNNaN1.5629.500NaN6.030.013.0NaN2.0NaN0.63.0
    Transition21.8752413000.0NaNNaN2.318.000NaN12.030.013.0NaN3.0NaN1.55.8
    Reach27.3440516000.0NaNNaN4.71291.000NaN20.035.013.0NaN6.0NaN7.031.0
    " @@ -4913,74 +4517,48 @@ "name": "stdout", "output_type": "stream", "text": [ - "Parámetros disponibles en df_procesado antes de seleccionar:\n", - "['Distancia de carrera requerida para despegue', 'Altitud a la que se realiza el crucero', 'Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Velocidad de pérdida limpia (KCAS)', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Profundidad del fuselaje', 'Ancho del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Tasa de ascenso', 'Radio de giro', 'envergadura', 'Cuerda', 'payload', 'duracion en VTOL', 'Crucero KIAS', 'RTF (dry weight)', 'RTF (Including fuel & Batteries)', 'Empty weight', 'Maximum Crosswind', 'Rango de comunicación', 'Wing Loading', 'Potencia específica (P/W)', 'Capacidad combustible', 'Consumo', 'Potencia Watts', 'Potencia HP', 'Precio', 'Tiempo de emergencia en vuelo', 'Distancia de aterrizaje', 'Despegue', 'Propulsión horizontal', 'Propulsión vertical', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal', 'Misión', 'Dimensiones de la bahía de carga útil', 'Battery Power Supply', 'Modelo Motor Fixed Wing', 'Modelo Motor VTOL', 'Portabilidad', 'Cámara', 'Despegue todos los tipos', 'Datalink banks', 'Material del fuselaje', 'Motor recomendado', 'Hélice recomendada VTOL', 'Hélice recomendada Fixed Wing', 'Sistema de control', 'Características adicionales', 'indice_desconocido']\n", "\n", - "=== Selección de Parámetros ===\n", - "Parámetros disponibles:\n", - "1. Distancia de carrera requerida para despegue\n", - "2. Altitud a la que se realiza el crucero\n", - "3. Velocidad a la que se realiza el crucero (KTAS)\n", - "4. Techo de servicio máximo\n", - "5. Velocidad de pérdida limpia (KCAS)\n", - "6. Área del ala\n", - "7. Relación de aspecto del ala\n", - "8. Longitud del fuselaje\n", - "9. Profundidad del fuselaje\n", - "10. Ancho del fuselaje\n", - "11. Peso máximo al despegue (MTOW)\n", - "12. Alcance de la aeronave\n", - "13. Autonomía de la aeronave\n", - "14. Velocidad máxima (KIAS)\n", - "15. Velocidad de pérdida (KCAS)\n", - "16. Tasa de ascenso\n", - "17. Radio de giro\n", - "18. envergadura\n", - "19. Cuerda\n", - "20. payload\n", - "21. duracion en VTOL\n", - "22. Crucero KIAS\n", - "23. RTF (dry weight)\n", - "24. RTF (Including fuel & Batteries)\n", - "25. Empty weight\n", - "26. Maximum Crosswind\n", - "27. Rango de comunicación\n", - "28. Wing Loading\n", - "29. Potencia específica (P/W)\n", - "30. Capacidad combustible\n", - "31. Consumo\n", - "32. Potencia Watts\n", - "33. Potencia HP\n", - "34. Precio\n", - "35. Tiempo de emergencia en vuelo\n", - "36. Distancia de aterrizaje\n", - "37. Despegue\n", - "38. Propulsión horizontal\n", - "39. Propulsión vertical\n", - "40. Cantidad de motores propulsión vertical\n", - "41. Cantidad de motores propulsión horizontal\n", - "42. Misión\n", - "43. Dimensiones de la bahía de carga útil\n", - "44. Battery Power Supply\n", - "45. Modelo Motor Fixed Wing\n", - "46. Modelo Motor VTOL\n", - "47. Portabilidad\n", - "48. Cámara\n", - "49. Despegue todos los tipos\n", - "50. Datalink banks\n", - "51. Material del fuselaje\n", - "52. Motor recomendado\n", - "53. Hélice recomendada VTOL\n", - "54. Hélice recomendada Fixed Wing\n", - "55. Sistema de control\n", - "56. Características adicionales\n", - "57. indice_desconocido\n", + "=== Filas con datos faltantes ===\n", + "1. Stalker XE\n", + "2. Stalker VXE30\n", + "3. Aerosonde Mk. 4.7 Fixed Wing\n", + "4. Aerosonde Mk. 4.7 VTOL\n", + "5. Aerosonde Mk. 4.8 Fixed wing\n", + "6. Aerosonde Mk. 4.8 VTOL FTUAS\n", + "7. AAI Aerosonde\n", + "8. Fulmar X\n", + "9. Orbiter 4\n", + "10. Orbiter 3\n", + "11. Mantis\n", + "12. ScanEagle\n", + "13. Integrator\n", + "14. Integrator VTOL\n", + "15. Integrator Extended Range\n", + "16. ScanEagle 3\n", + "17. RQ Nan 21A Blackjack\n", + "18. DeltaQuad Evo\n", + "19. DeltaQuad Pro #MAP\n", + "20. DeltaQuad Pro #CARGO\n", + "21. V21\n", + "22. V25\n", + "23. V32\n", + "24. V35\n", + "25. V39\n", + "26. Volitation VT370\n", + "27. Skyeye 2600\n", + "28. Skyeye 2930 VTOL\n", + "29. Skyeye 3600\n", + "30. Skyeye 3600 VTOL\n", + "31. Skyeye 5000\n", + "32. Skyeye 5000 VTOL\n", + "33. Skyeye 5000 VTOL octo\n", + "34. Volitation VT510\n", + "35. Ascend\n", + "36. Transition\n", + "37. Reach\n", + "🔁 Entrada inválida o vacía. Seleccionando la primera fila por defecto.\n", "\n", - "Preseleccionados: 3, 4, 6, 7, 8, 11, 12, 13, 14, 15, 5, 18, 19, 20, 25\n", - "Parámetros seleccionados después de filtrar:\n", - "['Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Velocidad de pérdida limpia (KCAS)', 'envergadura', 'Cuerda', 'payload', 'Empty weight']\n", - "Parámetros seleccionados después de filtrar:\n", - "['Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Velocidad de pérdida limpia (KCAS)', 'envergadura', 'Cuerda', 'payload', 'Empty weight']\n" + "=== Analizando celdas faltantes en la fila: 'Stalker XE' ===\n" ] }, { @@ -5018,919 +4596,142 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

    Datos Filtrados por Parámetros (df_filtrado)

    \n", + "

    Celdas Faltantes Identificadas en df_filtrado (df_celdas_faltantes)

    \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + "
    Stalker XEStalker VXE30Aerosonde Mk. 4.7 Fixed WingAerosonde Mk. 4.7 VTOLAerosonde Mk. 4.8 Fixed wingAerosonde Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQ Nan 21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReachValores
    ModeloÍndice
    Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NaNNaN30.406584NaNNaN18.26582630.62533630.953465Velocidad de pérdida (KCAS)NaN
    Velocidad de pérdida limpia (KCAS)NaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Generando resumen de valores faltantes por columna ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Resumen de Valores Faltantes de df_filtrado

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    FilaValores Faltantes
    Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaNNaN17000.010000.013000.016000.00Stalker XE2.000
    Área del ala0.871.1582831.551.551.55NaN0.57NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN1Stalker VXE302.000
    Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2Aerosonde Mk. 4.7 Fixed Wing4.000
    Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.7123Aerosonde Mk. 4.7 VTOL4.000
    Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.04Aerosonde Mk. 4.8 Fixed wing4.000
    Alcance de la aeronave370.0433.0NaNNaNNaNNaN3270.0800.0NaN50.025.0NaNNaNNaN500.0NaNNaN270.0100.0100.0NaNNaNNaNNaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaNNaN5Aerosonde Mk. 4.8 VTOL FTUAS10.000
    Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.06AAI Aerosonde4.000
    Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0NaN30.0NaN33.042.042.038.050.030.030.035.07Fulmar X7.000
    Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.08Orbiter 49.000
    Velocidad de pérdida limpia (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaNNaN25.0NaNNaNNaN9Orbiter 38.000
    envergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
    Cuerda0.2390.3181950.3520.3520.352NaN0.196552NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    payload2.4947562.49475614.511.317.722.7NaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
    Empty weight10.88620817.463292NaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Columnas con datos faltantes ===\n", - "1. Stalker XE\n", - "2. Stalker VXE30\n", - "3. Aerosonde Mk. 4.7 Fixed Wing\n", - "4. Aerosonde Mk. 4.7 VTOL\n", - "5. Aerosonde Mk. 4.8 Fixed wing\n", - "6. Aerosonde Mk. 4.8 VTOL FTUAS\n", - "7. AAI Aerosonde\n", - "8. Fulmar X\n", - "9. Orbiter 4\n", - "10. Orbiter 3\n", - "11. Mantis\n", - "12. ScanEagle\n", - "13. Integrator\n", - "14. Integrator VTOL\n", - "15. Integrator Extended Range (ER)\n", - "16. ScanEagle 3\n", - "17. RQ Nan 21A Blackjack\n", - "18. DeltaQuad Evo\n", - "19. DeltaQuad Pro #MAP\n", - "20. DeltaQuad Pro #CARGO\n", - "21. V21\n", - "22. V25\n", - "23. V32\n", - "24. V35\n", - "25. V39\n", - "26. Volitation VT370\n", - "27. Skyeye 2600\n", - "28. Skyeye 2930 VTOL\n", - "29. Skyeye 3600\n", - "30. Skyeye 3600 VTOL\n", - "31. Skyeye 5000\n", - "32. Skyeye 5000 VTOL\n", - "33. Skyeye 5000 VTOL octo\n", - "34. Volitation VT510\n", - "35. Ascend\n", - "36. Transition\n", - "37. Reach\n", - "🔁 Entrada inválida o vacía. Seleccionando la primera columna por defecto.\n", - "\n", - "=== Analizando celdas faltantes en la columna: 'Stalker XE' ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Celdas Faltantes Identificadas en df_filtrado (df_celdas_faltantes)

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Stalker XE
    Modelo
    Velocidad de pérdida (KCAS)NaN
    Velocidad de pérdida limpia (KCAS)NaN
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Generando resumen de valores faltantes por columna ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de Valores Faltantes de df_filtrado

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    Tabla de Correlaciones con todos los parametros(tabla_completa)

    ColumnaValores Faltantes
    0Stalker XE2.000
    1Stalker VXE302.000
    2Aerosonde Mk. 4.7 Fixed Wing4.000
    3Aerosonde Mk. 4.7 VTOL4.000
    4Aerosonde Mk. 4.8 Fixed wing4.000
    5Aerosonde Mk. 4.8 VTOL FTUAS10.000
    6AAI Aerosonde4.000
    7Fulmar X7.000
    8Orbiter 49.000
    9Orbiter 38.000
    10Mantis8.00010Mantis8.000
    11
    14Integrator Extended Range (ER)Integrator Extended Range7.000
    \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6224,81 +5025,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6346,21 +5072,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6406,21 +5117,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6466,21 +5162,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6526,21 +5207,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6586,21 +5252,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6646,21 +5297,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6706,21 +5342,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6766,21 +5387,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6826,21 +5432,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6886,21 +5477,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6946,21 +5522,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7006,21 +5567,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7066,21 +5612,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7126,21 +5657,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7186,21 +5702,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7246,21 +5747,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7306,21 +5792,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7366,21 +5837,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7426,21 +5882,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7486,21 +5927,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7546,21 +5972,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7606,21 +6017,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7666,21 +6062,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7726,21 +6107,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7786,21 +6152,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7846,21 +6197,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7906,21 +6242,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7966,21 +6287,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8026,21 +6332,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8086,21 +6377,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8146,21 +6422,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8206,21 +6467,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8266,21 +6512,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8326,6 +6557,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8341,9 +6576,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8370,6 +6602,20 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8403,33 +6649,187 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8461,53 +6861,20 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8521,49 +6888,18 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8582,350 +6918,578 @@ " \n", " \n", " \n", + " \n", + "
    ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Cantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisiónDimensiones de la bahía de carga útilBattery Power SupplyModelo Motor Fixed WingModelo Motor VTOLPortabilidadCámaraDespegue todos los tiposDatalink banksMaterial del fuselajeMotor recomendadoHélice recomendada VTOLHélice recomendada Fixed WingSistema de controlCaracterísticas adicionalesindice_desconocido
    Modelo
    -0.598nannannannannannannannannannannannannannannannannan
    Altitud a la que se realiza el crucero-0.159nannannannannannannannannannannannannannannannannan
    Velocidad a la que se realiza el crucero (KTAS)-0.126nannannannannannannannannannannannannannannannannan
    Techo de servicio máximo0.125nannannannannannannannannannannannannannannannannan
    Velocidad de pérdida limpia (KCAS)0.345nannannannannannannannannannannannannannannannannan
    Área del ala0.055nannannannannannannannannannannannannannannannannan
    Relación de aspecto del ala-0.247nannannannannannannannannannannannannannannannannan
    Longitud del fuselaje-0.004nannannannannannannannannannannannannannannannannan
    Profundidad del fuselajenannannannannannannannannannannannannannannannannannan
    Ancho del fuselaje0.574nannannannannannannannannannannannannannannannannan
    Peso máximo al despegue (MTOW)0.075nannannannannannannannannannannannannannannannannan
    Alcance de la aeronave0.262nannannannannannannannannannannannannannannannannan
    Autonomía de la aeronave-0.361nannannannannannannannannannannannannannannannannan
    Velocidad máxima (KIAS)-0.133nannannannannannannannannannannannannannannannannan
    Velocidad de pérdida (KCAS)0.444nannannannannannannannannannannannannannannannannan
    Tasa de ascensonannannannannannannannannannannannannannannannannannan
    Radio de gironannannannannannannannannannannannannannannannannannan
    envergadura-0.164nannannannannannannannannannannannannannannannannan
    Cuerda-0.313nannannannannannannannannannannannannannannannannan
    payload-0.111nannannannannannannannannannannannannannannannannan
    duracion en VTOL-0.188nannannannannannannannannannannannannannannannannan
    Crucero KIAS0.063nannannannannannannannannannannannannannannannannan
    RTF (dry weight)nannannannannannannannannannannannannannannannannannan
    RTF (Including fuel & Batteries)0.097nannannannannannannannannannannannannannannannannan
    Empty weight0.004nannannannannannannannannannannannannannannannannan
    Maximum Crosswindnannannannannannannannannannannannannannannannannannan
    Rango de comunicación-0.430nannannannannannannannannannannannannannannannannan
    Wing Loadingnannannannannannannannannannannannannannannannannannan
    Potencia específica (P/W)nannannannannannannannannannannannannannannannannannan
    Capacidad combustible0.270nannannannannannannannannannannannannannannannannan
    Consumo0.375nannannannannannannannannannannannannannannannannan
    Potencia Watts0.232nannannannannannannannannannannannannannannannannan
    Potencia HP-0.694nannannannannannannannannannannannannannannannannan
    Precio0.134nannan
    Tiempo de emergencia en vuelonannannannannannannan
    Tiempo de emergencia en vuelonannannannannannan
    Distancia de aterrizajenannannannannannannannannannannannannannannan
    Distancia de aterrizajeDespegue0.735-0.1190.1130.125-0.3450.236-0.2470.111nan0.7940.0900.262-0.424-0.041-0.244nannan-0.124-0.313-0.004-0.1880.143nan0.0970.182nan-0.430nannan-0.0800.1130.232-0.694-0.138nannan1.000-0.010-0.6390.610nannan
    Propulsión horizontal0.154-0.1090.6190.0070.2310.453nan0.615nannan0.4670.4740.4770.2080.154nan0.9180.508nan0.477-0.9040.6080.4080.4280.404-0.9430.6040.572nannannannannan0.217nannan-0.0101.0000.118-0.083nannan
    Propulsión vertical0.6710.1870.126-0.125-0.3450.1720.2470.093nan-0.5350.023-0.2620.3610.174-0.244nannan0.2390.3130.1620.1880.065nan-0.0970.307nan0.430nannan-0.080-0.375-0.2320.694-0.138nannan-0.6390.1181.000-0.954nannan
    Cantidad de motores propulsión vertical-0.598-0.159-0.1260.1250.3450.055-0.247-0.004nan0.5740.0750.262-0.361-0.1330.444nannan-0.164-0.313-0.111-0.1880.063nan0.0970.004nan-0.430nannan0.2700.3750.232-0.6940.134nannan0.610-0.083-0.9541.000nannan
    Cantidad de motores propulsión horizontalnannannannannannan
    Despegue0.735-0.1190.1130.125-0.3450.236-0.2470.111nan0.7940.0900.262-0.424-0.041-0.244nannan-0.124-0.313-0.004-0.1880.143nan0.0970.182nan-0.430nannan-0.0800.1130.232-0.694-0.138nannan1.000-0.010-0.6390.610nannannannan
    Misiónnannannannannannan
    Propulsión horizontal0.154-0.1090.6190.0070.2310.453nan0.615nannan0.4670.4740.4770.2080.154nan0.9180.508nan0.477-0.9040.6080.4080.4280.404-0.9430.6040.572nannannannannan0.217nannan-0.0101.0000.118-0.083nannannannannan
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Resumen de la Tabla

    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    ResumenCantidad
    Propulsión vertical0.6710.1870.126-0.125-0.3450.1720.2470.093nan-0.5350.023-0.2620.3610.174-0.244nannan0.2390.3130.1620.1880.065nan-0.0970.307nan0.430nannan-0.080-0.375-0.2320.6940Total de valores1764.000
    1Valores numéricos907.000
    2Valores NaN857.000
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Filtrando datos seleccionados ===\n", + "\n", + "=== Cálculo de correlaciones filtradas ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Tabla de Correlaciones Filtradas por parametros seleccionados (Para Heatmap)

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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
    Velocidad a la que se realiza el crucero (KTAS)1.0000.0410.587-0.9990.5350.6630.6650.3360.8150.2570.1280.4720.8460.6960.426
    Techo de servicio máximo0.0411.000-0.152-0.3140.0820.1370.4280.079-0.111-0.071-0.5020.0570.0170.087-0.138nannan-0.6390.118
    Área del ala0.587-0.1521.000-0.954nannannannannannannannannannannannannannannan-0.8310.8670.977-0.3010.0810.7370.4230.0970.8410.9840.8990.941
    Relación de aspecto del ala-0.999-0.314-0.8311.000-0.790-0.823-0.998-0.305-0.859nannan-0.349-0.744-0.8880.622
    Cantidad de motores propulsión vertical-0.598-0.159-0.1260.1250.3450.055-0.247-0.004nan0.5740.0750.262-0.361-0.1330.444Longitud del fuselaje0.5350.0820.867-0.7901.0000.7860.1500.3890.2560.1800.2600.6930.9950.5990.880
    Peso máximo al despegue (MTOW)0.6630.1370.977-0.8230.7861.0000.0250.4340.6780.5390.5460.7910.8580.8750.947
    Alcance de la aeronave0.6650.428-0.301-0.9980.1500.0251.0000.8430.042nannan-0.010-0.7550.804-0.059
    Autonomía de la aeronave0.3360.0790.081-0.3050.3890.4340.8431.0000.297-0.164-0.3130.4010.532-0.2010.4610.428
    Velocidad máxima (KIAS)0.815-0.111-0.1880.063nan0.0970.004nan-0.430nannan0.2700.3750.232-0.6940.134nannan0.610-0.083-0.9540.737-0.8590.2560.6780.0420.2971.000nannannannannannannannannannannannannannannannannan0.5390.4460.4000.5120.7150.517
    Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanVelocidad de pérdida (KCAS)0.257-0.0710.423nan0.1800.539nan-0.1640.5391.0001.0000.401nan0.6270.321
    Velocidad de pérdida limpia (KCAS)0.128-0.5020.097nan0.2600.546nan0.4010.4461.0001.0000.505nan0.5360.038
    Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanenvergadura0.4720.0570.841-0.3490.6930.791-0.0100.5320.4000.4010.5051.0000.8850.7340.924
    Dimensiones de la bahía de carga útilnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanCuerda0.8460.0170.984-0.7440.9950.858-0.755-0.2010.512nannan0.8851.0000.7760.971
    Battery Power Supplynannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanpayload0.6960.0870.899-0.8880.5990.8750.8040.4610.7150.6270.5360.7340.7761.0000.778
    Empty weight0.426-0.1380.9410.6220.8800.947-0.0590.4280.5170.3210.0380.9240.9710.7781.000
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    Resumen de la Tabla

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    ResumenCantidad
    0Total de valores225.000
    1Valores numéricos213.000
    2Valores NaN12.000
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8943,75 +7507,104 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -9020,7 +7613,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9030,7 +7629,11 @@ " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9046,6 +7649,9 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9063,808 +7669,257 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + "
    Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
    Velocidad a la que se realiza el crucero (KTAS)nannannan-0.999nannannannan0.815nannannan0.846nannan
    Techo de servicio máximonannannannan
    Modelo Motor Fixed WingnannannannannannannannannannannannannannanÁrea del alanannannan-0.8310.8670.977nannan0.737nannan0.8410.9840.8990.941
    Relación de aspecto del ala-0.999nan-0.831nan-0.790-0.823-0.998nan-0.859nannannan-0.744-0.888nan
    Longitud del fuselajenannan0.867-0.790nan0.786nannannannannannan0.995nan0.880
    Peso máximo al despegue (MTOW)nannan0.977-0.8230.786nannannannannannan0.7910.8580.8750.947
    Alcance de la aeronavenannannan-0.998nannannan0.843nannannannan-0.7550.804nan
    Modelo Motor VTOLnannanAutonomía de la aeronavenannannannannannan0.843nannannannannannan
    Velocidad máxima (KIAS)0.815nan0.737-0.859nannannannannannan0.715nan
    Velocidad de pérdida (KCAS)nannannannannannan
    Velocidad de pérdida limpia (KCAS)nannannannan
    Portabilidadnannannannannannannannannannannannannannannannannannannannannannannannannanenvergaduranannan0.841nannan0.791nannannannannannan0.8850.7340.924
    Cuerda0.846nan0.984-0.7440.9950.858-0.755nannannannan0.885nan0.7760.971
    payloadnannan0.899-0.888nan0.8750.804nan0.715nannan0.7340.776nan0.778
    Empty weightnannan0.941nan0.8800.947nannannannannan0.9240.9710.778nan
    Cámaranannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Resumen de la Tabla

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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    ResumenCantidad
    Despegue todos los tiposnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan0Total de valores225.000
    Datalink banksnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan1Valores numéricos68.000
    Material del fuselajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan2Valores NaN157.000
    Motor recomendadonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Hélice recomendada VTOLnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Hélice recomendada Fixed Wingnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Sistema de controlnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Características adicionalesnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    indice_desconocidonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de la Tabla

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    ResumenCantidad
    0Total de valores3249.000
    1Valores numéricos907.000
    2Valores NaN2342.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Filtrando datos seleccionados ===\n", - "\n", - "=== Cálculo de correlaciones filtradas ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

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    ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Preparando datos para el heatmap ===\n", + "\n", + "=== Generando heatmap ===\n" + ] + }, + { + "data": { + "image/png": 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    Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
    Modelo
    Velocidad a la que se realiza el crucero (KTAS)Stalker XE0.06000.00016.88037912000.0NaN0.8715.3012552.1NaN0.21113.600370.08.020.0NaNNaNNaN3.6570.2392.4947562.015.43332NaNNaN10.886208NaN59.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0000.0410.587-0.9990.5350.6630.6650.3360.8150.2570.1280.4720.8460.6960.426
    Techo de servicio máximo0.0411.000-0.152-0.3140.0820.1370.4280.079-0.111-0.071-0.5020.0570.0170.087-0.138
    Área del ala0.587-0.1521.000-0.8310.8670.977-0.3010.0810.7370.4230.0970.8410.9840.8990.941
    Relación de aspecto del ala-0.999-0.314-0.831Stalker VXE300.06000.00017.60237312000.0NaN1.15828315.3264492.5908NaN0.219.958433.08.025.034211NaNNaNNaN4.87680.3181952.494756NaN16.093422NaNNaN17.463292NaN161.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.000-0.790-0.823-0.998-0.305-0.859nannan-0.349-0.744-0.8880.622
    Longitud del fuselaje0.5350.0820.867-0.7901.0000.7860.1500.3890.2560.1800.2600.6930.9950.5990.880
    Peso máximo al despegue (MTOW)0.6630.1370.977-0.8230.786Aerosonde Mk. 4.7 Fixed WingNaN6000.00027.3440514700.0NaN1.5512.53.0NaN0.27742.200NaN19.833.43886NaNNaNNaN4.40.35214.5NaN25.0NaN27.7NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN1.0002.0005.0000.0001.0001.0000.0250.4340.6780.5390.5460.7910.8580.8750.947
    Alcance de la aeronave0.6650.428-0.301-0.9980.1500.025Aerosonde Mk. 4.7 VTOL0.06000.00027.344059700.0NaN1.5512.53.0NaN0.27753.500NaN12.033.43886NaNNaNNaN4.40.35211.3NaN25.0NaN42.2NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN2.0002.0001.0004.0001.0001.0000.8430.042nannan-0.010-0.7550.804-0.059
    Autonomía de la aeronave0.3360.0790.081-0.3050.3890.4340.843Aerosonde Mk. 4.8 Fixed wingNaN6000.00027.3440518200.0NaN1.5512.53.0NaN0.27754.400NaN19.833.43886NaNNaNNaN4.40.35217.7NaN25.0NaN36.7NaNNaN140.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.0000.297-0.1640.4010.532-0.2010.4610.428
    Velocidad máxima (KIAS)0.815-0.1110.737-0.8590.2560.6780.0420.297Aerosonde Mk. 4.8 VTOL FTUAS0.06000.000NaN15000.0NaNNaN12.5NaNNaNNaN93.000NaN14.0NaNNaNNaNNaNNaNNaN22.7NaNNaNNaN70.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.0000.5390.4460.4000.5120.7150.517
    Velocidad de pérdida (KCAS)0.257-0.0710.423nan0.1800.539nan-0.1640.539AAI AerosondeNaN5500.000NaN15000.0NaN0.5714.7543861.7NaNNaN13.1003270.026.030.845725NaNNaNNaN2.90.196552NaNNaNNaNNaNNaN10.0NaN150.023.098.0NaNNaN1280.01.74NaNNaNNaN2.0002.0001.0004.0001.0001.0000.401nan0.6270.321
    Velocidad de pérdida limpia (KCAS)0.128-0.5020.097nan0.2600.546nan0.4010.446Fulmar XNaN6000.00030.4065849.842NaNNaNNaN1.2NaNNaN20.000800.08.041.7NaNNaNNaN3.0NaNNaNNaN27.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.0000.505nan0.5360.038
    envergadura0.4720.0570.841-0.3490.6930.791-0.0100.5320.4000.4010.505Orbiter 4NaN6000.000NaNNaNNaNNaNNaN1.2NaNNaN55.000NaN24.036.0NaNNaNNaN5.2NaN12.0NaNNaNNaNNaNNaNNaN150.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.0000.8850.7340.924
    Cuerda0.8460.0170.984-0.7440.9950.858-0.755-0.2010.512nannan0.885Orbiter 3NaN6000.000NaNNaNNaNNaNNaN1.2NaNNaN32.00050.06.036.0NaNNaNNaN4.4NaN5.5NaNNaNNaNNaNNaNNaN50.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0005.0000.0001.0001.0000.7760.971
    payload0.6960.0870.899-0.8880.5990.8750.8040.4610.7150.6270.5360.7340.776MantisNaN6000.00018.265826NaNNaNNaNNaN1.48NaNNaN6.50025.02.025.6NaNNaNNaN2.1NaNNaNNaN16.7NaNNaNNaNNaN25.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0005.0000.0001.0000.778
    Empty weight0.426-0.1380.9410.6220.8800.947-0.0590.4280.5170.3210.0380.9240.9710.7781.000
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    Resumen de la Tabla

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    ResumenCantidad
    0Total de valores225.000
    1Valores numéricos213.000ScanEagleNaN6000.00030.62533619500.0NaNNaNNaN1.71NaNNaN26.500NaN18.041.2NaNNaNNaN3.1NaN5.0NaN28.0NaNNaNNaNNaN101.86NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    2Valores NaN12.000
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    Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

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    ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weightIntegratorNaN6000.00030.95346519500.0NaNNaNNaN2.5NaNNaN74.800NaN24.046.3NaNNaNNaN4.8NaN18.0NaN28.3NaNNaNNaNNaN92.6NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    ModeloIntegrator VTOL0.05000.000NaNNaNNaNNaNNaNNaNNaNNaN75.000NaN16.0NaNNaNNaNNaNNaNNaN18.0NaNNaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaNNaNNaNNaN0.02.0002.0001.0004.0001.0001.000
    Velocidad a la que se realiza el crucero (KTAS)nannannan-0.999nannannannan0.815nannannan0.846nannanIntegrator Extended RangeNaN6000.000NaN19500.0NaNNaNNaN2.5NaNNaN74.800500.019.046.3NaNNaNNaN4.8NaN18.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    Techo de servicio máximonannannannannannannannannannannannannannannanScanEagle 3NaN6000.00025.70340720.0NaNNaNNaN2.4NaNNaN36.300NaN18.041.2NaNNaNNaN4.0NaN8.6NaN23.5NaNNaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    Área del alanannannan-0.8310.8670.977nannan0.737nannan0.8410.9840.8990.941RQ Nan 21A BlackjackNaN6000.00033.79724620.0NaNNaNNaN2.5NaNNaN61.000NaN16.046.3NaNNaNNaN4.8NaN17.7NaN30.9NaNNaNNaNNaN92.6NaNNaNNaNNaNNaN8.0NaNNaNNaN1.0002.0005.0000.0001.0001.000
    Relación de aspecto del ala-0.999nan-0.831nan-0.790-0.823-0.998nan-0.859nannannan-0.744-0.888nan
    Longitud del fuselajenannan0.867-0.790nan0.786nannannannannannan0.995nan0.880
    Peso máximo al despegue (MTOW)nannan0.977-0.8230.786nannannannannannan0.7910.8580.8750.947
    Alcance de la aeronavenannannan-0.998nannannan0.843nannannannan-0.7550.804nan
    Autonomía de la aeronavenannannannannannan0.843nannannannannannannannan
    Velocidad máxima (KIAS)0.815nan0.737-0.859nannannannannannannannannan0.715nan
    Velocidad de pérdida (KCAS)nannannannannannannannannannannannannannannan
    Velocidad de pérdida limpia (KCAS)nannannannannannannannannannannannannannannan
    envergaduranannan0.841nannan0.791nannannannannannan0.8850.7340.924
    Cuerda0.846nan0.984-0.7440.9950.858-0.755nannannannan0.885nan0.7760.971
    payloadnannan0.899-0.888nan0.8750.804nan0.715nannan0.7340.776nan0.778
    Empty weightnannan0.941nan0.8800.947nannannannannan0.9240.9710.778nan
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    Resumen de la Tabla

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    ResumenCantidad
    0Total de valores225.000
    1Valores numéricos68.000
    2Valores NaN157.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Preparando datos para el heatmap ===\n", - "\n", - "=== Generando heatmap ===\n" - ] - }, - { - "data": { - "image/png": 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" \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -11374,235 +9218,265 @@ " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -11610,20 +9484,48 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11634,56 +9536,41 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11694,16 +9581,41 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11714,1369 +9626,1018 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Stalker XEStalker VXE30Aerosonde Mk. 4.7 Fixed WingAerosonde Mk. 4.7 VTOLAerosonde Mk. 4.8 Fixed wingAerosonde Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQ Nan 21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
    Modelo
    Distancia de carrera requerida para despegue0.00.06000.00018.09082413.0NaN0.00.84NaN0.00.75NaNNaN10.000270.04.53NaNNaNNaNNaN2.69NaN3.04.5316.544.86.84.845.0NaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
    Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.02.0001.0001.0004.0001.0001.000
    Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405DeltaQuad Pro #MAP0.06000.00017.50019213.123NaNNaN30.406584NaN0.9NaN18.26582630.62533630.953465NaN6.200100.01.83NaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
    Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.02.35NaN1.2NaN16.0NaNNaNNaN50.050.0NaNNaN17000.010000.013000.016000.0
    Velocidad de pérdida limpia (KCAS)NaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
    DeltaQuad Pro #CARGO0.06000.00017.50019213.123NaNNaNNaN0.9NaNNaN6.200100.01.83NaNNaNNaNNaN2.35NaN1.2NaN16.0NaNNaNNaN14.015.517.050.030.0NaNNaNNaN10.018.012.524.015.0NaNNaN25.0NaNNaNNaN0.02.0001.0001.0004.0001.0001.000
    Área del ala0.871.1582831.551.551.55NaN0.57NaNNaNNaNNaNV210.06000.00019.68771648800.014.00.8NaN0.93NaNNaN10.000NaN3.033.014.0NaN100.02.15NaN0.841.5NaN18.0NaN0.80.52NaN2.65NaN30.012.5NaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaN3999.00.108NaN2.0001.0001.0004.0001.0001.000
    Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386V250.06000.00021.8752416000.015.50.52NaN0.93NaNNaN12.500NaN4.033.015.5NaN120.02.45NaN2.2NaN20.0NaNNaN3.45NaN30.024.0NaNNaNNaNNaNNaN4679.00.108NaN2.0001.0001.0004.0001.0001.000
    V320.06000.00021.8752416000.017.0NaNNaN1.0NaNNaN23.500NaN4.533.017.0NaN150.03.2NaN5.0NaN20.0NaNNaN6.45NaN30.025.0NaNNaNNaN
    Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.8869999.00.108NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.7122.0002.0001.0004.0001.0001.000
    Profundidad del fuselajeV350.06000.00027.3440516000.0NaNNaNNaN1.88NaNNaN32.000NaN2.833.0NaNNaNNaN3.5NaN10.0NaN25.0NaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaN7999.0NaNNaN2.0002.0001.0004.0001.0001.000
    V390.06000.00027.3440516000.0NaNNaNNaNNaNNaNNaN24.000NaN4.533.0NaNNaNNaN3.9NaN5.0NaN25.0NaNNaN
    Ancho del fuselaje0.2110.20.2770.2770.277NaNNaN30.0NaNNaNNaNNaNNaNNaN8999.0NaNNaN2.0002.0001.0004.0001.0001.000
    Volitation VT3700.06000.00027.3440517000.0NaNNaNNaN2.02NaNNaN40.000NaN15.033.0NaNNaNNaN6.5NaN18.0NaN25.0NaNNaNNaNNaN0.3750.3750.375NaNNaNNaN13.00.96NaN
    Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
    Alcance de la aeronave370.0433.0NaN8999.0NaNNaN2.0002.0001.0004.0001.0001.000
    Skyeye 2600NaN3270.0800.06000.00036.094147NaN50.025.010.00.88NaN2.05NaNNaN500.015.000NaN2.0NaN270.0100.0100.010.0NaNNaN2.6NaN4.0NaN33.0NaNNaN6.5NaNNaNNaN300.0NaN800.0NaNNaNNaNNaN2299.0NaN
    Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.02.0002.0001.0004.0001.0001.000
    Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3Skyeye 2930 VTOL0.06000.00026.250288NaN18.01.0NaN2.03NaN33.033.033.033.033.033.0NaN30.028.000NaN33.042.042.038.050.030.03.030.035.0
    Velocidad de pérdida (KCAS)18.0NaNNaN2.93NaN6.0NaN24.0NaNNaN7.1NaNNaNNaNNaNNaNNaN6799.0NaNNaN2.0002.0001.0004.0001.0001.000
    Skyeye 360050.06000.000NaNNaN12.51.33NaN2.488NaN14.015.517.0NaN28.000NaN4.5NaN10.018.012.524.015.0NaN24.025.013.013.013.0
    Tasa de ascensoNaNNaNNaNNaN3.6NaN10.0NaNNaNNaNNaN11.5NaNNaNNaNNaN11.5NaNNaNNaN4999.0NaNNaN3.0002.0005.0000.0001.0001.000
    Skyeye 3600 VTOL0.06000.00032.81286NaN24.01.32NaN2.42NaNNaN40.000300.06.033.024.0NaNNaN3.6NaN10.0NaN30.0NaNNaN11.0NaNNaNNaNNaN11.5NaNNaNNaN6999.0NaNNaN2.0002.0001.0004.0001.0001.000
    Radio de giroNaNSkyeye 500060.06000.00036.094147NaN15.02.615NaN3.5NaN0.37590.000NaN8.042.015.0NaNNaN5.0NaN20.0NaN33.0NaNNaN32.0NaNNaNNaNNaN28.01.2NaNNaN9999.0NaNNaN3.0002.0005.0000.0001.0001.000
    Skyeye 5000 VTOL0.06000.00030.625336NaN100.0120.0150.0NaN2.615NaN3.5NaN0.375100.000800.08.042.0NaNNaNNaN5.0NaN25.0NaN28.0NaNNaNNaNNaNNaNNaN
    envergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.828.0NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
    Cuerda0.2390.3181950.3520.3520.352NaN0.196552NaN13900.0NaNNaN2.0002.0001.0004.0001.0001.000
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    ScanEagle 325.70340720.0NaNNaN2.436.300NaN18.041.2NaNNaN4.0NaN8.6NaN
    RQ Nan 21A Blackjack33.79724620.0NaNNaN2.561.000NaN16.046.3NaNNaN4.8NaN17.7NaN
    DeltaQuad Evo18.09082413.00.84NaN0.7510.000270.04.53NaN11.511.528.028.028.025.0NaNNaN2.69NaN3.04.8
    ConsumoNaNNaN0.60.6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDeltaQuad Pro #MAP17.50019213.123NaNNaN0.960.96.200100.01.83NaNNaNNaN2.35NaN1.2NaNNaN5.0NaNNaNNaN
    Potencia WattsNaNNaN2980.02980.0NaNNaN1280.0NaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDeltaQuad Pro #CARGO17.50019213.123NaNNaN0.96.200100.01.83NaNNaNNaN2.35NaN1.2NaN
    V2119.68771648800.00.8NaN0.9310.000NaN3.033.014.014.02.15NaN1.52.65
    Potencia HPV2521.8752416000.00.52NaN0.9312.500NaN4.04.0NaNNaN1.74NaNNaNNaNNaNNaNNaNNaNNaNNaN8.033.015.515.52.45NaN2.23.45
    V3221.8752416000.0NaNNaN1.023.500NaN4.533.017.017.03.2NaN5.06.45
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    Skyeye 260036.094147NaN0.88NaN2.0515.000NaN2.0NaN10.010.02.6NaN4.06.5
    Skyeye 2930 VTOL26.250288NaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.01.0NaN2.0328.000NaN3.030.018.018.02.93NaN6.07.1
    Tiempo de emergencia en vueloNaNNaNNaNSkyeye 3600NaNNaN1.33NaN2.48828.000NaN4.5NaN12.512.53.6NaN10.011.5
    Skyeye 3600 VTOL32.81286NaN1.32NaN2.4240.000300.06.033.024.024.03.6NaN10.011.0
    Skyeye 500036.094147NaN2.615NaN3.590.000NaN8.042.015.015.05.0NaN20.032.0
    Skyeye 5000 VTOL30.625336NaN2.615NaN3.5100.000800.08.042.0NaNNaN0.1080.1080.1085.0NaN25.0NaN
    Skyeye 5000 VTOL octoNaNNaN2.615NaN3.5100.000NaNNaN38.024.0NaN5.0NaN15.035.0
    Volitation VT51032.8128617000.0NaNNaN2.905100.000NaN5.050.025.025.05.1NaN25.0NaN
    Distancia de aterrizajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.0NaNNaNAscend21.8752410000.0NaNNaN1.5629.500NaN6.030.013.0NaN2.0NaN0.63.0
    Transition21.8752413000.0NaNNaN2.318.000NaN12.030.013.0NaN3.0NaN1.55.8
    Reach27.3440516000.0NaNNaN4.71291.000NaN20.035.013.0NaN6.0NaN7.031.0
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Configuración Inicial ===\n", + "\n", + "Valores configurados: Rango MTOW [85% - 115%], Confianza Mínima: 0.50\n", + "\n", + "================================================================================\n", + "\u001b[1m=== INICIO DE ITERACIÓN 1 ===\u001b[0m\n", + "================================================================================\n", + "\n", + "=== Iteración 1: Resumen antes de imputaciones ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Resumen de Valores Faltantes Antes de Iteración 1

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    FilaValores Faltantes
    Despegue1.01.01.02.01.02.02.01.01.01.01.01.01.02.01.01.01.02.02.02.02.02.02.02.02.02.02.02.03.02.03.02.02.02.02.02.02.00Stalker XE17.000
    Propulsión horizontal2.02.02.02.02.02.02.02.02.01.01.02.02.02.02.02.02.01.01.01.01.01.02.02.02.02.02.02.02.02.02.02.02.02.02.02.02.01Stalker VXE3018.000
    Propulsión vertical5.05.05.01.05.01.01.05.05.05.05.05.05.01.05.05.05.01.01.01.01.01.01.01.01.01.01.01.05.01.05.01.01.01.01.01.01.02Aerosonde Mk. 4.7 Fixed Wing17.000
    Cantidad de motores propulsión vertical0.00.00.04.00.04.04.00.00.00.00.00.00.04.00.00.00.04.04.04.04.04.04.04.04.04.04.04.00.04.00.04.08.04.04.04.04.03Aerosonde Mk. 4.7 VTOL16.000
    Cantidad de motores propulsión horizontal1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.04Aerosonde Mk. 4.8 Fixed wing20.000
    Misión1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.05Aerosonde Mk. 4.8 VTOL FTUAS28.000
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    Sistema de controlNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18DeltaQuad Pro #MAP22.000
    Características adicionalesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN19DeltaQuad Pro #CARGO22.000
    indice_desconocidoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20V2116.000
    21V2516.000
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    23V3523.000
    24V3924.000
    25Volitation VT37022.000
    26Skyeye 260023.000
    27Skyeye 2930 VTOL21.000
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    29Skyeye 3600 VTOL19.000
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    32Skyeye 5000 VTOL octo22.000
    33Volitation VT51020.000
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    " @@ -13096,8 +10657,8 @@ " .scroll-table {\n", " overflow-x: auto;\n", " overflow-y: auto;\n", - " max-height: 400px;\n", - " max-width: 100%;\n", + " max-height: 100px;\n", + " max-width: 50%;\n", " display: block;\n", " border: 1px solid #ccc;\n", " margin-bottom: 20px;\n", @@ -13123,1003 +10684,19 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

    df_filtrado_base

    \n", + "

    Sumatoria Total de Valores Faltantes

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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Stalker XEStalker VXE30Aerosonde Mk. 4.7 Fixed WingAerosonde Mk. 4.7 VTOLAerosonde Mk. 4.8 Fixed wingAerosonde Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQ Nan 21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReachResumenCantidad
    Modelo
    Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.34405NaNNaN30.406584NaNNaN18.26582630.62533630.953465NaNNaN25.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.625336NaN32.8128621.8752421.8752427.34405
    Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.842NaNNaNNaN19500.019500.0NaN19500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.0NaNNaNNaNNaNNaNNaNNaN17000.010000.013000.016000.0
    Área del ala0.871.1582831.551.551.55NaN0.57NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.84NaNNaN0.80.52NaNNaNNaNNaN0.881.01.331.322.6152.6152.615NaNNaNNaNNaN
    Relación de aspecto del ala15.30125515.32644912.512.512.512.514.754386NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Longitud del fuselaje2.12.59083.03.03.0NaN1.71.21.21.21.481.712.5NaN2.52.42.50.750.90.90.930.931.01.88NaN2.022.052.032.4882.423.53.53.52.9051.5622.34.712
    Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
    Alcance de la aeronave370.0433.0NaNNaNNaNNaN3270.0800.0NaN50.025.0NaNNaNNaN500.0NaNNaN270.0100.0100.0NaNNaNNaNNaNNaNNaNNaNNaNNaN300.0NaN800.0NaNNaNNaNNaNNaN
    Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.0NaN5.06.012.020.0
    Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.43886NaN30.84572541.736.036.025.641.246.3NaN46.341.246.3NaNNaNNaN33.033.033.033.033.033.0NaN30.0NaN33.042.042.038.050.030.030.035.0
    Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaN24.025.013.013.013.0
    Velocidad de pérdida limpia (KCAS)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.015.517.0NaNNaNNaN10.018.012.524.015.0NaNNaN25.0NaNNaNNaN
    envergadura3.6574.87684.44.44.4NaN2.93.05.24.42.13.14.8NaN4.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
    Cuerda0.2390.3181950.3520.3520.352NaN0.196552NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    payload2.4947562.49475614.511.317.722.7NaNNaN12.05.5NaN5.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
    Empty weight10.88620817.463292NaNNaNNaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaN2.653.456.45NaNNaNNaN6.57.111.511.032.0NaN35.0NaN3.05.831.0
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Configuración Inicial ===\n", - "\n", - "Valores configurados: Rango MTOW [85% - 115%], Confianza Mínima: 0.50\n", - "\n", - "================================================================================\n", - "\u001b[1m=== INICIO DE ITERACIÓN 1 ===\u001b[0m\n", - "================================================================================\n", - "\n", - "=== Iteración 1: Resumen antes de imputaciones ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de Valores Faltantes Antes de Iteración 1

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    ColumnaValores Faltantes
    0Stalker XE32.000
    1Stalker VXE3033.000
    2Aerosonde Mk. 4.7 Fixed Wing32.000
    3Aerosonde Mk. 4.7 VTOL31.000
    4Aerosonde Mk. 4.8 Fixed wing35.000
    5Aerosonde Mk. 4.8 VTOL FTUAS43.000
    6AAI Aerosonde34.000
    7Fulmar X41.000
    8Orbiter 443.000
    9Orbiter 342.000
    10Mantis41.000
    11ScanEagle40.000
    12Integrator40.000
    13Integrator VTOL44.000
    14Integrator Extended Range (ER)42.000
    15ScanEagle 340.000
    16RQ Nan 21A Blackjack39.000
    17DeltaQuad Evo33.000
    18DeltaQuad Pro #MAP37.000
    19DeltaQuad Pro #CARGO37.000
    20V2131.000
    21V2531.000
    22V3232.000
    23V3538.000
    24V3939.000
    25Volitation VT37037.000
    26Skyeye 260038.000
    27Skyeye 2930 VTOL36.000
    28Skyeye 360038.000
    29Skyeye 3600 VTOL34.000
    30Skyeye 500033.000
    31Skyeye 5000 VTOL36.000
    32Skyeye 5000 VTOL octo37.000
    33Volitation VT51035.000
    34Ascend34.000
    35Transition34.000
    36Reach34.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Sumatoria Total de Valores Faltantes

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", "
    ResumenCantidad
    0Total de Valores Faltantes1356.0000Total de Valores Faltantes801.000
    " @@ -14291,7 +10868,7 @@ "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", "\u001b[1m\n", - "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "=== Imputación por similitud: Integrator Extended Range - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", "\u001b[1m\n", "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", @@ -14852,7 +11429,7 @@ "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", "\u001b[1m\n", - "=== Imputación por similitud: Integrator Extended Range (ER) - Área del ala ===\u001b[0m\n", + "=== Imputación por similitud: Integrator Extended Range - Área del ala ===\u001b[0m\n", "\u001b[1m\n", "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", @@ -15308,7 +11885,7 @@ "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", "\u001b[1m\n", - "=== Imputación por similitud: Integrator Extended Range (ER) - Relación de aspecto del ala ===\u001b[0m\n", + "=== Imputación por similitud: Integrator Extended Range - Relación de aspecto del ala ===\u001b[0m\n", "\u001b[1m\n", "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", @@ -15915,7 +12492,7 @@ " Bono total para 'prest': -0.00001\n", " Bono geométrico: 0.100\n", " Bono prestacional: -0.000\n", - " vecino 'Integrator Extended Range (ER)' → sim_i: 1.049\n", + " vecino 'Integrator Extended Range' → sim_i: 1.049\n", "\n", "Detalles del cálculo de confianza:\n", " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.049']\n", @@ -16711,7 +13288,7 @@ "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", "\u001b[1m\n", - "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "=== Imputación por similitud: Integrator Extended Range - Velocidad de pérdida (KCAS) ===\u001b[0m\n", "\u001b[1m\n", "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", @@ -17131,7 +13708,7 @@ "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", "\u001b[1m\n", - "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "=== Imputación por similitud: Integrator Extended Range - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", "\u001b[1m\n", "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", @@ -17643,7 +14220,7 @@ "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", "\u001b[1m\n", - "=== Imputación por similitud: Integrator Extended Range (ER) - Cuerda ===\u001b[0m\n", + "=== Imputación por similitud: Integrator Extended Range - Cuerda ===\u001b[0m\n", "\u001b[1m\n", "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", @@ -18229,7 +14806,7 @@ "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", "\u001b[1m\n", - "=== Imputación por similitud: Integrator Extended Range (ER) - Empty weight ===\u001b[0m\n", + "=== Imputación por similitud: Integrator Extended Range - Empty weight ===\u001b[0m\n", "\u001b[1m\n", "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", @@ -18486,43 +15063,48 @@ " \n", " \n", " \n", - " Stalker XE\n", - " Stalker VXE30\n", - " Aerosonde Mk. 4.7 Fixed Wing\n", - " Aerosonde Mk. 4.7 VTOL\n", - " Aerosonde Mk. 4.8 Fixed wing\n", - " Aerosonde Mk. 4.8 VTOL FTUAS\n", - " AAI Aerosonde\n", - " Fulmar X\n", - " Orbiter 4\n", - " Orbiter 3\n", - " Mantis\n", - " ScanEagle\n", - " Integrator\n", - " Integrator VTOL\n", - " Integrator Extended Range (ER)\n", - " ScanEagle 3\n", - " RQ Nan 21A Blackjack\n", - " DeltaQuad Evo\n", - " DeltaQuad Pro #MAP\n", - " DeltaQuad Pro #CARGO\n", - " V21\n", - " V25\n", - " V32\n", - " V35\n", - " V39\n", - " Volitation VT370\n", - " Skyeye 2600\n", - " Skyeye 2930 VTOL\n", - " Skyeye 3600\n", - " Skyeye 3600 VTOL\n", - " Skyeye 5000\n", - " Skyeye 5000 VTOL\n", - " Skyeye 5000 VTOL octo\n", - " Volitation VT510\n", - " Ascend\n", - " Transition\n", - " Reach\n", + " Distancia de carrera requerida para despegue\n", + " Altitud a la que se realiza el crucero\n", + " Velocidad a la que se realiza el crucero (KTAS)\n", + " Techo de servicio máximo\n", + " Velocidad de pérdida limpia (KCAS)\n", + " Área del ala\n", + " Relación de aspecto del ala\n", + " Longitud del fuselaje\n", + " Profundidad del fuselaje\n", + " Ancho del fuselaje\n", + " Peso máximo al despegue (MTOW)\n", + " Alcance de la aeronave\n", + " Autonomía de la aeronave\n", + " Velocidad máxima (KIAS)\n", + " Velocidad de pérdida (KCAS)\n", + " Tasa de ascenso\n", + " Radio de giro\n", + " envergadura\n", + " Cuerda\n", + " payload\n", + " duracion en VTOL\n", + " Crucero KIAS\n", + " RTF (dry weight)\n", + " RTF (Including fuel & Batteries)\n", + " Empty weight\n", + " Maximum Crosswind\n", + " Rango de comunicación\n", + " Wing Loading\n", + " Potencia específica (P/W)\n", + " Capacidad combustible\n", + " Consumo\n", + " Potencia Watts\n", + " Potencia HP\n", + " Precio\n", + " Tiempo de emergencia en vuelo\n", + " Distancia de aterrizaje\n", + " Despegue\n", + " Propulsión horizontal\n", + " Propulsión vertical\n", + " Cantidad de motores propulsión vertical\n", + " Cantidad de motores propulsión horizontal\n", + " Misión\n", " \n", " \n", " Modelo\n", @@ -18563,157 +15145,90 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " Distancia de carrera requerida para despegue\n", - " 0.0\n", + " Stalker XE\n", " 0.0\n", + " 6000.000\n", + " 16.880379\n", + " 12000.0\n", " NaN\n", - " 0.0\n", + " 0.87\n", + " 15.301255\n", + " 2.1\n", " NaN\n", - " 0.0\n", + " 0.211\n", + " 13.600\n", + " 370.0\n", + " 8.0\n", + " 20.0\n", " NaN\n", " NaN\n", " NaN\n", + " 3.657\n", + " 0.239\n", + " 2.494756\n", + " 2.0\n", + " 15.43332\n", " NaN\n", " NaN\n", + " 10.886208\n", " NaN\n", + " 59.0\n", " NaN\n", - " 0.0\n", " NaN\n", " NaN\n", " NaN\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", " NaN\n", - " 0.0\n", - " 50.0\n", - " 0.0\n", - " 60.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " Altitud a la que se realiza el crucero\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 5500.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 5000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", - " 6000.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Velocidad a la que se realiza el crucero (KTAS)\n", - " 16.880379\n", + " Stalker VXE30\n", + " 0.0\n", + " 6000.000\n", " 17.602373\n", - " 27.34405\n", - " 27.34405\n", - " 27.34405\n", - " NaN\n", + " 12000.0\n", " NaN\n", - " 30.406584\n", + " 1.158283\n", + " 15.326449\n", + " 2.5908\n", " NaN\n", + " 0.2\n", + " 19.958\n", + " 433.0\n", + " 8.0\n", + " 25.034211\n", " NaN\n", - " 18.265826\n", - " 30.625336\n", - " 30.953465\n", " NaN\n", " NaN\n", - " 25.703407\n", - " 33.797246\n", - " 18.090824\n", - " 17.500192\n", - " 17.500192\n", - " 19.687716\n", - " 21.87524\n", - " 21.87524\n", - " 27.34405\n", - " 27.34405\n", - " 27.34405\n", - " 36.094147\n", - " 26.250288\n", + " 4.8768\n", + " 0.318195\n", + " 2.494756\n", " NaN\n", - " 32.81286\n", - " 36.094147\n", - " 30.625336\n", + " 16.093422\n", " NaN\n", - " 32.81286\n", - " 21.87524\n", - " 21.87524\n", - " 27.34405\n", - " \n", - " \n", - " Techo de servicio máximo\n", - " 12000.0\n", - " 12000.0\n", - " 14700.0\n", - " 9700.0\n", - " 18200.0\n", - " 15000.0\n", - " 15000.0\n", - " 9.842\n", " NaN\n", + " 17.463292\n", " NaN\n", + " 161.0\n", " NaN\n", - " 19500.0\n", - " 19500.0\n", " NaN\n", - " 19500.0\n", - " 20.0\n", - " 20.0\n", - " 13.0\n", - " 13.123\n", - " 13.123\n", - " 48800.0\n", - " 16000.0\n", - " 16000.0\n", - " 16000.0\n", - " 16000.0\n", - " 17000.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -18721,118 +15236,174 @@ " NaN\n", " NaN\n", " NaN\n", - " 17000.0\n", - " 10000.0\n", - " 13000.0\n", - " 16000.0\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Velocidad de pérdida limpia (KCAS)\n", - " NaN\n", + " Aerosonde Mk. 4.7 Fixed Wing\n", " NaN\n", + " 6000.000\n", + " 27.34405\n", + " 14700.0\n", " NaN\n", + " 1.55\n", + " 12.5\n", + " 3.0\n", " NaN\n", + " 0.277\n", + " 42.200\n", " NaN\n", + " 19.8\n", + " 33.43886\n", " NaN\n", " NaN\n", " NaN\n", + " 4.4\n", + " 0.352\n", + " 14.5\n", " NaN\n", + " 25.0\n", " NaN\n", + " 27.7\n", " NaN\n", " NaN\n", + " 140.0\n", " NaN\n", " NaN\n", " NaN\n", + " 0.6\n", + " 2980.0\n", + " 4.0\n", " NaN\n", " NaN\n", " NaN\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Aerosonde Mk. 4.7 VTOL\n", + " 0.0\n", + " 6000.000\n", + " 27.34405\n", + " 9700.0\n", " NaN\n", + " 1.55\n", + " 12.5\n", + " 3.0\n", " NaN\n", - " 14.0\n", - " 15.5\n", - " 17.0\n", + " 0.277\n", + " 53.500\n", " NaN\n", + " 12.0\n", + " 33.43886\n", " NaN\n", " NaN\n", - " 10.0\n", - " 18.0\n", - " 12.5\n", - " 24.0\n", - " 15.0\n", " NaN\n", + " 4.4\n", + " 0.352\n", + " 11.3\n", " NaN\n", " 25.0\n", " NaN\n", + " 42.2\n", + " NaN\n", + " NaN\n", + " 140.0\n", + " NaN\n", " NaN\n", " NaN\n", + " 0.6\n", + " 2980.0\n", + " 4.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Área del ala\n", - " 0.87\n", - " 1.158283\n", - " 1.55\n", - " 1.55\n", - " 1.55\n", - " NaN\n", - " 0.57\n", + " Aerosonde Mk. 4.8 Fixed wing\n", " NaN\n", + " 6000.000\n", + " 27.34405\n", + " 18200.0\n", " NaN\n", + " 1.55\n", + " 12.5\n", + " 3.0\n", " NaN\n", + " 0.277\n", + " 54.400\n", " NaN\n", + " 19.8\n", + " 33.43886\n", " NaN\n", " NaN\n", " NaN\n", + " 4.4\n", + " 0.352\n", + " 17.7\n", " NaN\n", + " 25.0\n", " NaN\n", + " 36.7\n", " NaN\n", - " 0.84\n", " NaN\n", + " 140.0\n", " NaN\n", - " 0.8\n", - " 0.52\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 0.88\n", - " 1.0\n", - " 1.33\n", - " 1.32\n", - " 2.615\n", - " 2.615\n", - " 2.615\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Relación de aspecto del ala\n", - " 15.301255\n", - " 15.326449\n", - " 12.5\n", - " 12.5\n", - " 12.5\n", - " 12.5\n", - " 14.754386\n", - " NaN\n", - " NaN\n", + " Aerosonde Mk. 4.8 VTOL FTUAS\n", + " 0.0\n", + " 6000.000\n", " NaN\n", + " 15000.0\n", " NaN\n", " NaN\n", + " 12.5\n", " NaN\n", " NaN\n", " NaN\n", + " 93.000\n", " NaN\n", + " 14.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 22.7\n", " NaN\n", " NaN\n", " NaN\n", + " 70.3\n", " NaN\n", " NaN\n", " NaN\n", @@ -18845,72 +15416,82 @@ " NaN\n", " NaN\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Longitud del fuselaje\n", - " 2.1\n", - " 2.5908\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", + " AAI Aerosonde\n", + " NaN\n", + " 5500.000\n", + " NaN\n", + " 15000.0\n", " NaN\n", + " 0.57\n", + " 14.754386\n", " 1.7\n", - " 1.2\n", - " 1.2\n", - " 1.2\n", - " 1.48\n", - " 1.71\n", - " 2.5\n", " NaN\n", - " 2.5\n", - " 2.4\n", - " 2.5\n", - " 0.75\n", - " 0.9\n", - " 0.9\n", - " 0.93\n", - " 0.93\n", - " 1.0\n", - " 1.88\n", " NaN\n", - " 2.02\n", - " 2.05\n", - " 2.03\n", - " 2.488\n", - " 2.42\n", - " 3.5\n", - " 3.5\n", - " 3.5\n", - " 2.905\n", - " 1.562\n", - " 2.3\n", - " 4.712\n", - " \n", - " \n", - " Profundidad del fuselaje\n", + " 13.100\n", + " 3270.0\n", + " 26.0\n", + " 30.845725\n", " NaN\n", " NaN\n", " NaN\n", + " 2.9\n", + " 0.196552\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 10.0\n", + " NaN\n", + " 150.0\n", + " 23.0\n", + " 98.0\n", " NaN\n", " NaN\n", + " 1280.0\n", + " 1.74\n", " NaN\n", " NaN\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Fulmar X\n", " NaN\n", + " 6000.000\n", + " 30.406584\n", + " 9.842\n", " NaN\n", " NaN\n", " NaN\n", + " 1.2\n", " NaN\n", " NaN\n", + " 20.000\n", + " 800.0\n", + " 8.0\n", + " 41.7\n", " NaN\n", " NaN\n", " NaN\n", + " 3.0\n", " NaN\n", " NaN\n", " NaN\n", + " 27.8\n", " NaN\n", " NaN\n", " NaN\n", @@ -18925,218 +15506,132 @@ " NaN\n", " NaN\n", " NaN\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Ancho del fuselaje\n", - " 0.211\n", - " 0.2\n", - " 0.277\n", - " 0.277\n", - " 0.277\n", - " NaN\n", + " Orbiter 4\n", " NaN\n", + " 6000.000\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 1.2\n", " NaN\n", " NaN\n", + " 55.000\n", " NaN\n", + " 24.0\n", + " 36.0\n", " NaN\n", " NaN\n", " NaN\n", + " 5.2\n", " NaN\n", + " 12.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 150.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 0.375\n", - " 0.375\n", - " 0.375\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Peso máximo al despegue (MTOW)\n", - " 13.6\n", - " 19.958048\n", - " 42.2\n", - " 53.5\n", - " 54.4\n", - " 93.0\n", - " 13.1\n", - " 20.0\n", - " 55.0\n", - " 32.0\n", - " 6.5\n", - " 26.5\n", - " 74.8\n", - " 75.0\n", - " 74.8\n", - " 36.3\n", - " 61.0\n", - " 10.0\n", - " 6.2\n", - " 6.2\n", - " 10.0\n", - " 12.5\n", - " 23.5\n", - " 32.0\n", - " 24.0\n", - " 40.0\n", - " 15.0\n", - " 28.0\n", - " 28.0\n", - " 40.0\n", - " 90.0\n", - " 100.0\n", - " 100.0\n", - " 100.0\n", - " 9.5\n", - " 18.0\n", - " 91.0\n", - " \n", - " \n", - " Alcance de la aeronave\n", - " 370.0\n", - " 433.0\n", + " Orbiter 3\n", " NaN\n", + " 6000.000\n", " NaN\n", " NaN\n", " NaN\n", - " 3270.0\n", - " 800.0\n", " NaN\n", - " 50.0\n", - " 25.0\n", " NaN\n", + " 1.2\n", " NaN\n", " NaN\n", - " 500.0\n", + " 32.000\n", + " 50.0\n", + " 6.0\n", + " 36.0\n", " NaN\n", " NaN\n", - " 270.0\n", - " 100.0\n", - " 100.0\n", " NaN\n", + " 4.4\n", + " NaN\n", + " 5.5\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 50.0\n", " NaN\n", " NaN\n", - " 300.0\n", " NaN\n", - " 800.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " 1.000\n", + " 1.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Autonomía de la aeronave\n", - " 8.0\n", - " 8.0\n", - " 19.8\n", - " 12.0\n", - " 19.8\n", - " 14.0\n", - " 26.0\n", - " 8.0\n", - " 24.0\n", - " 6.0\n", - " 2.0\n", - " 18.0\n", - " 24.0\n", - " 16.0\n", - " 19.0\n", - " 18.0\n", - " 16.0\n", - " 4.53\n", - " 1.83\n", - " 1.83\n", - " 3.0\n", - " 4.0\n", - " 4.5\n", - " 2.8\n", - " 4.5\n", - " 15.0\n", - " 2.0\n", - " 3.0\n", - " 4.5\n", - " 6.0\n", - " 8.0\n", - " 8.0\n", + " Mantis\n", " NaN\n", - " 5.0\n", - " 6.0\n", - " 12.0\n", - " 20.0\n", - " \n", - " \n", - " Velocidad máxima (KIAS)\n", - " 20.0\n", - " 25.034211\n", - " 33.43886\n", - " 33.43886\n", - " 33.43886\n", + " 6000.000\n", + " 18.265826\n", " NaN\n", - " 30.845725\n", - " 41.7\n", - " 36.0\n", - " 36.0\n", - " 25.6\n", - " 41.2\n", - " 46.3\n", " NaN\n", - " 46.3\n", - " 41.2\n", - " 46.3\n", " NaN\n", " NaN\n", + " 1.48\n", " NaN\n", - " 33.0\n", - " 33.0\n", - " 33.0\n", - " 33.0\n", - " 33.0\n", - " 33.0\n", " NaN\n", - " 30.0\n", + " 6.500\n", + " 25.0\n", + " 2.0\n", + " 25.6\n", " NaN\n", - " 33.0\n", - " 42.0\n", - " 42.0\n", - " 38.0\n", - " 50.0\n", - " 30.0\n", - " 30.0\n", - " 35.0\n", - " \n", - " \n", - " Velocidad de pérdida (KCAS)\n", " NaN\n", " NaN\n", + " 2.1\n", " NaN\n", " NaN\n", " NaN\n", + " 16.7\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 25.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -19146,38 +15641,42 @@ " NaN\n", " NaN\n", " NaN\n", + " 1.000\n", + " 1.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " ScanEagle\n", " NaN\n", + " 6000.000\n", + " 30.625336\n", + " 19500.0\n", " NaN\n", - " 14.0\n", - " 15.5\n", - " 17.0\n", " NaN\n", " NaN\n", + " 1.71\n", " NaN\n", - " 10.0\n", - " 18.0\n", - " 12.5\n", - " 24.0\n", - " 15.0\n", " NaN\n", - " 24.0\n", - " 25.0\n", - " 13.0\n", - " 13.0\n", - " 13.0\n", - " \n", - " \n", - " Tasa de ascenso\n", + " 26.500\n", " NaN\n", + " 18.0\n", + " 41.2\n", " NaN\n", " NaN\n", " NaN\n", + " 3.1\n", " NaN\n", + " 5.0\n", " NaN\n", + " 28.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 101.86\n", " NaN\n", " NaN\n", " NaN\n", @@ -19187,27 +15686,44 @@ " NaN\n", " NaN\n", " NaN\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Integrator\n", " NaN\n", + " 6000.000\n", + " 30.953465\n", + " 19500.0\n", " NaN\n", " NaN\n", " NaN\n", + " 2.5\n", " NaN\n", " NaN\n", + " 74.800\n", " NaN\n", + " 24.0\n", + " 46.3\n", " NaN\n", " NaN\n", " NaN\n", + " 4.8\n", " NaN\n", + " 18.0\n", " NaN\n", + " 28.3\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 92.6\n", " NaN\n", " NaN\n", - " \n", - " \n", - " Radio de giro\n", " NaN\n", " NaN\n", " NaN\n", @@ -19215,6 +15731,17 @@ " NaN\n", " NaN\n", " NaN\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Integrator VTOL\n", + " 0.0\n", + " 5000.000\n", " NaN\n", " NaN\n", " NaN\n", @@ -19223,21 +15750,22 @@ " NaN\n", " NaN\n", " NaN\n", + " 75.000\n", " NaN\n", + " 16.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 100.0\n", - " 120.0\n", - " 150.0\n", " NaN\n", " NaN\n", + " 18.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 30.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -19245,66 +15773,38 @@ " NaN\n", " NaN\n", " NaN\n", - " \n", - " \n", - " envergadura\n", - " 3.657\n", - " 4.8768\n", - " 4.4\n", - " 4.4\n", - " 4.4\n", " NaN\n", - " 2.9\n", - " 3.0\n", - " 5.2\n", - " 4.4\n", - " 2.1\n", - " 3.1\n", - " 4.8\n", " NaN\n", - " 4.8\n", - " 4.0\n", - " 4.8\n", - " 2.69\n", - " 2.35\n", - " 2.35\n", - " 2.15\n", - " 2.45\n", - " 3.2\n", - " 3.5\n", - " 3.9\n", - " 6.5\n", - " 2.6\n", - " 2.93\n", - " 3.6\n", - " 3.6\n", - " 5.0\n", - " 5.0\n", - " 5.0\n", - " 5.1\n", - " 2.0\n", - " 3.0\n", - " 6.0\n", + " 0.0\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Cuerda\n", - " 0.239\n", - " 0.318195\n", - " 0.352\n", - " 0.352\n", - " 0.352\n", + " Integrator Extended Range\n", " NaN\n", - " 0.196552\n", + " 6000.000\n", " NaN\n", + " 19500.0\n", " NaN\n", " NaN\n", " NaN\n", + " 2.5\n", " NaN\n", " NaN\n", + " 74.800\n", + " 500.0\n", + " 19.0\n", + " 46.3\n", " NaN\n", " NaN\n", " NaN\n", + " 4.8\n", " NaN\n", + " 18.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -19321,56 +15821,37 @@ " NaN\n", " NaN\n", " NaN\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " ScanEagle 3\n", " NaN\n", + " 6000.000\n", + " 25.703407\n", + " 20.0\n", " NaN\n", " NaN\n", " NaN\n", - " \n", - " \n", - " payload\n", - " 2.494756\n", - " 2.494756\n", - " 14.5\n", - " 11.3\n", - " 17.7\n", - " 22.7\n", + " 2.4\n", " NaN\n", " NaN\n", - " 12.0\n", - " 5.5\n", + " 36.300\n", " NaN\n", - " 5.0\n", - " 18.0\n", - " 18.0\n", - " 18.0\n", - " 8.6\n", - " 17.7\n", - " 3.0\n", - " 1.2\n", - " 1.2\n", - " 1.5\n", - " 2.2\n", - " 5.0\n", - " 10.0\n", - " 5.0\n", " 18.0\n", + " 41.2\n", + " NaN\n", + " NaN\n", + " NaN\n", " 4.0\n", - " 6.0\n", - " 10.0\n", - " 10.0\n", - " 20.0\n", - " 25.0\n", - " 15.0\n", - " 25.0\n", - " 0.6\n", - " 1.5\n", - " 7.0\n", - " \n", - " \n", - " duracion en VTOL\n", - " 2.0\n", " NaN\n", + " 8.6\n", " NaN\n", + " 23.5\n", " NaN\n", " NaN\n", " NaN\n", @@ -19380,83 +15861,91 @@ " NaN\n", " NaN\n", " NaN\n", + " 170.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " RQ Nan 21A Blackjack\n", " NaN\n", - " 4.53\n", + " 6000.000\n", + " 33.797246\n", + " 20.0\n", " NaN\n", " NaN\n", " NaN\n", + " 2.5\n", " NaN\n", " NaN\n", + " 61.000\n", " NaN\n", + " 16.0\n", + " 46.3\n", " NaN\n", " NaN\n", " NaN\n", + " 4.8\n", " NaN\n", + " 17.7\n", " NaN\n", + " 30.9\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 92.6\n", " NaN\n", - " 0.05\n", - " 0.05\n", - " 0.05\n", - " \n", - " \n", - " Crucero KIAS\n", - " 15.43332\n", - " 16.093422\n", - " 25.0\n", - " 25.0\n", - " 25.0\n", " NaN\n", " NaN\n", - " 27.8\n", " NaN\n", " NaN\n", - " 16.7\n", - " 28.0\n", - " 28.3\n", + " 8.0\n", " NaN\n", " NaN\n", - " 23.5\n", - " 30.9\n", - " 16.54\n", - " 16.0\n", - " 16.0\n", - " 18.0\n", - " 20.0\n", - " 20.0\n", - " 25.0\n", - " 25.0\n", - " 25.0\n", - " 33.0\n", - " 24.0\n", " NaN\n", - " 30.0\n", - " 33.0\n", - " 28.0\n", - " 35.0\n", - " 30.0\n", - " 20.0\n", - " 20.0\n", - " 25.0\n", + " 1.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " RTF (dry weight)\n", + " DeltaQuad Evo\n", + " 0.0\n", + " 6000.000\n", + " 18.090824\n", + " 13.0\n", " NaN\n", + " 0.84\n", " NaN\n", + " 0.75\n", " NaN\n", " NaN\n", + " 10.000\n", + " 270.0\n", + " 4.53\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 2.69\n", " NaN\n", + " 3.0\n", + " 4.53\n", + " 16.54\n", + " 4.8\n", + " 6.8\n", + " 4.8\n", + " 45.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -19465,55 +15954,89 @@ " NaN\n", " NaN\n", " NaN\n", - " 4.8\n", " NaN\n", + " 0.0\n", + " 2.000\n", + " 1.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " DeltaQuad Pro #MAP\n", + " 0.0\n", + " 6000.000\n", + " 17.500192\n", + " 13.123\n", " NaN\n", " NaN\n", " NaN\n", + " 0.9\n", " NaN\n", " NaN\n", + " 6.200\n", + " 100.0\n", + " 1.83\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 2.35\n", " NaN\n", + " 1.2\n", " NaN\n", + " 16.0\n", " NaN\n", " NaN\n", " NaN\n", + " 50.0\n", + " 50.0\n", " NaN\n", - " 6.0\n", - " 11.8\n", - " 54.0\n", - " \n", - " \n", - " RTF (Including fuel & Batteries)\n", " NaN\n", " NaN\n", - " 27.7\n", - " 42.2\n", - " 36.7\n", - " 70.3\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 0.0\n", + " 2.000\n", + " 1.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " DeltaQuad Pro #CARGO\n", + " 0.0\n", + " 6000.000\n", + " 17.500192\n", + " 13.123\n", " NaN\n", " NaN\n", " NaN\n", + " 0.9\n", " NaN\n", " NaN\n", + " 6.200\n", + " 100.0\n", + " 1.83\n", " NaN\n", - " 6.8\n", " NaN\n", " NaN\n", " NaN\n", + " 2.35\n", " NaN\n", + " 1.2\n", " NaN\n", + " 16.0\n", " NaN\n", " NaN\n", " NaN\n", + " 50.0\n", + " 30.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -19522,184 +16045,263 @@ " NaN\n", " NaN\n", " NaN\n", - " 8.9\n", - " 16.5\n", - " 84.0\n", + " 0.0\n", + " 2.000\n", + " 1.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Empty weight\n", - " 10.886208\n", - " 17.463292\n", - " NaN\n", - " NaN\n", + " V21\n", + " 0.0\n", + " 6000.000\n", + " 19.687716\n", + " 48800.0\n", + " 14.0\n", + " 0.8\n", " NaN\n", + " 0.93\n", " NaN\n", - " 10.0\n", " NaN\n", + " 10.000\n", " NaN\n", + " 3.0\n", + " 33.0\n", + " 14.0\n", " NaN\n", + " 100.0\n", + " 2.15\n", " NaN\n", + " 1.5\n", " NaN\n", + " 18.0\n", " NaN\n", " NaN\n", + " 2.65\n", " NaN\n", + " 30.0\n", + " 12.5\n", " NaN\n", " NaN\n", - " 4.8\n", " NaN\n", " NaN\n", - " 2.65\n", - " 3.45\n", - " 6.45\n", " NaN\n", + " 3999.0\n", + " 0.108\n", " NaN\n", + " 2.000\n", + " 1.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " V25\n", + " 0.0\n", + " 6000.000\n", + " 21.87524\n", + " 16000.0\n", + " 15.5\n", + " 0.52\n", " NaN\n", - " 6.5\n", - " 7.1\n", - " 11.5\n", - " 11.0\n", - " 32.0\n", + " 0.93\n", " NaN\n", - " 35.0\n", " NaN\n", - " 3.0\n", - " 5.8\n", - " 31.0\n", - " \n", - " \n", - " Maximum Crosswind\n", + " 12.500\n", " NaN\n", + " 4.0\n", + " 33.0\n", + " 15.5\n", " NaN\n", + " 120.0\n", + " 2.45\n", " NaN\n", + " 2.2\n", " NaN\n", + " 20.0\n", " NaN\n", " NaN\n", + " 3.45\n", " NaN\n", + " 30.0\n", + " 24.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 4679.0\n", + " 0.108\n", " NaN\n", - " 30.0\n", + " 2.000\n", + " 1.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " V32\n", + " 0.0\n", + " 6000.000\n", + " 21.87524\n", + " 16000.0\n", + " 17.0\n", " NaN\n", " NaN\n", + " 1.0\n", " NaN\n", - " 45.0\n", - " 50.0\n", - " 50.0\n", " NaN\n", + " 23.500\n", " NaN\n", + " 4.5\n", + " 33.0\n", + " 17.0\n", " NaN\n", + " 150.0\n", + " 3.2\n", " NaN\n", + " 5.0\n", " NaN\n", + " 20.0\n", " NaN\n", " NaN\n", + " 6.45\n", " NaN\n", + " 30.0\n", + " 25.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 69999.0\n", + " 0.108\n", " NaN\n", - " 15.0\n", - " 15.0\n", - " 15.0\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Rango de comunicación\n", - " 59.0\n", - " 161.0\n", - " 140.0\n", - " 140.0\n", - " 140.0\n", - " NaN\n", - " 150.0\n", - " NaN\n", - " 150.0\n", - " 50.0\n", - " 25.0\n", - " 101.86\n", - " 92.6\n", + " V35\n", + " 0.0\n", + " 6000.000\n", + " 27.34405\n", + " 16000.0\n", " NaN\n", " NaN\n", " NaN\n", - " 92.6\n", + " 1.88\n", " NaN\n", - " 50.0\n", - " 30.0\n", - " 30.0\n", - " 30.0\n", - " 30.0\n", - " 30.0\n", - " 30.0\n", " NaN\n", + " 32.000\n", " NaN\n", + " 2.8\n", + " 33.0\n", " NaN\n", " NaN\n", " NaN\n", + " 3.5\n", " NaN\n", + " 10.0\n", " NaN\n", + " 25.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 30.0\n", " NaN\n", - " \n", - " \n", - " Wing Loading\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 7999.0\n", " NaN\n", - " 23.0\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " V39\n", + " 0.0\n", + " 6000.000\n", + " 27.34405\n", + " 16000.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 24.000\n", " NaN\n", + " 4.5\n", + " 33.0\n", " NaN\n", " NaN\n", " NaN\n", + " 3.9\n", " NaN\n", + " 5.0\n", " NaN\n", - " 12.5\n", - " 24.0\n", " 25.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 30.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 8999.0\n", " NaN\n", " NaN\n", - " NaN\n", - " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Potencia específica (P/W)\n", + " Volitation VT370\n", + " 0.0\n", + " 6000.000\n", + " 27.34405\n", + " 17000.0\n", " NaN\n", " NaN\n", " NaN\n", + " 2.02\n", " NaN\n", " NaN\n", + " 40.000\n", " NaN\n", - " 98.0\n", + " 15.0\n", + " 33.0\n", " NaN\n", " NaN\n", " NaN\n", + " 6.5\n", " NaN\n", + " 18.0\n", " NaN\n", + " 25.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -19707,27 +16309,48 @@ " NaN\n", " NaN\n", " NaN\n", + " 13.0\n", + " 0.96\n", " NaN\n", " NaN\n", + " 8999.0\n", " NaN\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Skyeye 2600\n", " NaN\n", + " 6000.000\n", + " 36.094147\n", " NaN\n", + " 10.0\n", + " 0.88\n", " NaN\n", + " 2.05\n", " NaN\n", " NaN\n", + " 15.000\n", " NaN\n", + " 2.0\n", " NaN\n", + " 10.0\n", " NaN\n", " NaN\n", + " 2.6\n", " NaN\n", + " 4.0\n", " NaN\n", + " 33.0\n", " NaN\n", " NaN\n", + " 6.5\n", " NaN\n", - " \n", - " \n", - " Capacidad combustible\n", " NaN\n", " NaN\n", " NaN\n", @@ -19735,222 +16358,361 @@ " NaN\n", " NaN\n", " NaN\n", + " 2299.0\n", " NaN\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Skyeye 2930 VTOL\n", + " 0.0\n", + " 6000.000\n", + " 26.250288\n", " NaN\n", + " 18.0\n", + " 1.0\n", " NaN\n", + " 2.03\n", " NaN\n", " NaN\n", + " 28.000\n", " NaN\n", + " 3.0\n", + " 30.0\n", + " 18.0\n", " NaN\n", " NaN\n", + " 2.93\n", " NaN\n", + " 6.0\n", " NaN\n", + " 24.0\n", " NaN\n", " NaN\n", + " 7.1\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 13.0\n", " NaN\n", " NaN\n", - " 11.5\n", - " 11.5\n", - " 28.0\n", - " 28.0\n", - " 28.0\n", - " 25.0\n", " NaN\n", + " 6799.0\n", " NaN\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Consumo\n", - " NaN\n", - " NaN\n", - " 0.6\n", - " 0.6\n", - " NaN\n", - " NaN\n", - " NaN\n", + " Skyeye 3600\n", + " 50.0\n", + " 6000.000\n", " NaN\n", " NaN\n", + " 12.5\n", + " 1.33\n", " NaN\n", + " 2.488\n", " NaN\n", " NaN\n", + " 28.000\n", " NaN\n", + " 4.5\n", " NaN\n", + " 12.5\n", " NaN\n", " NaN\n", + " 3.6\n", " NaN\n", + " 10.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 11.5\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 0.96\n", + " 11.5\n", " NaN\n", " NaN\n", " NaN\n", + " 4999.0\n", " NaN\n", - " 1.2\n", " NaN\n", + " 3.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Skyeye 3600 VTOL\n", + " 0.0\n", + " 6000.000\n", + " 32.81286\n", " NaN\n", - " 5.0\n", + " 24.0\n", + " 1.32\n", " NaN\n", + " 2.42\n", " NaN\n", " NaN\n", - " \n", - " \n", - " Potencia Watts\n", + " 40.000\n", + " 300.0\n", + " 6.0\n", + " 33.0\n", + " 24.0\n", " NaN\n", " NaN\n", - " 2980.0\n", - " 2980.0\n", + " 3.6\n", " NaN\n", + " 10.0\n", " NaN\n", - " 1280.0\n", + " 30.0\n", " NaN\n", " NaN\n", + " 11.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 11.5\n", " NaN\n", " NaN\n", - " 170.0\n", " NaN\n", + " 6999.0\n", " NaN\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Skyeye 5000\n", + " 60.0\n", + " 6000.000\n", + " 36.094147\n", " NaN\n", + " 15.0\n", + " 2.615\n", " NaN\n", + " 3.5\n", " NaN\n", + " 0.375\n", + " 90.000\n", " NaN\n", + " 8.0\n", + " 42.0\n", + " 15.0\n", " NaN\n", " NaN\n", + " 5.0\n", " NaN\n", + " 20.0\n", " NaN\n", + " 33.0\n", " NaN\n", " NaN\n", + " 32.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 28.0\n", + " 1.2\n", " NaN\n", " NaN\n", + " 9999.0\n", " NaN\n", " NaN\n", + " 3.000\n", + " 2.000\n", + " 5.000\n", + " 0.000\n", + " 1.000\n", + " 1.000\n", " \n", " \n", - " Potencia HP\n", - " NaN\n", - " NaN\n", - " 4.0\n", - " 4.0\n", + " Skyeye 5000 VTOL\n", + " 0.0\n", + " 6000.000\n", + " 30.625336\n", " NaN\n", " NaN\n", - " 1.74\n", + " 2.615\n", " NaN\n", + " 3.5\n", " NaN\n", + " 0.375\n", + " 100.000\n", + " 800.0\n", + " 8.0\n", + " 42.0\n", " NaN\n", " NaN\n", " NaN\n", + " 5.0\n", " NaN\n", + " 25.0\n", " NaN\n", + " 28.0\n", " NaN\n", " NaN\n", - " 8.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 28.0\n", " NaN\n", " NaN\n", " NaN\n", + " 13900.0\n", " NaN\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Skyeye 5000 VTOL octo\n", + " 0.0\n", + " 6000.000\n", " NaN\n", " NaN\n", " NaN\n", + " 2.615\n", " NaN\n", + " 3.5\n", " NaN\n", + " 0.375\n", + " 100.000\n", " NaN\n", " NaN\n", + " 38.0\n", + " 24.0\n", " NaN\n", " NaN\n", + " 5.0\n", " NaN\n", - " \n", - " \n", - " Precio\n", + " 15.0\n", " NaN\n", + " 35.0\n", " NaN\n", " NaN\n", + " 35.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 28.0\n", " NaN\n", " NaN\n", " NaN\n", + " 15999.0\n", " NaN\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 8.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Volitation VT510\n", + " 0.0\n", + " 6000.000\n", + " 32.81286\n", + " 17000.0\n", + " 25.0\n", " NaN\n", " NaN\n", + " 2.905\n", " NaN\n", " NaN\n", + " 100.000\n", " NaN\n", + " 5.0\n", + " 50.0\n", + " 25.0\n", " NaN\n", " NaN\n", + " 5.1\n", " NaN\n", - " 3999.0\n", - " 4679.0\n", - " 69999.0\n", - " 7999.0\n", - " 8999.0\n", - " 8999.0\n", - " 2299.0\n", - " 6799.0\n", - " 4999.0\n", - " 6999.0\n", - " 9999.0\n", - " 13900.0\n", - " 15999.0\n", - " 16599.0\n", + " 25.0\n", " NaN\n", + " 30.0\n", " NaN\n", " NaN\n", - " \n", - " \n", - " Tiempo de emergencia en vuelo\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 25.0\n", + " 5.0\n", " NaN\n", " NaN\n", + " 16599.0\n", " NaN\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Ascend\n", + " 0.0\n", + " 6000.000\n", + " 21.87524\n", + " 10000.0\n", " NaN\n", " NaN\n", " NaN\n", + " 1.562\n", " NaN\n", " NaN\n", + " 9.500\n", " NaN\n", + " 6.0\n", + " 30.0\n", + " 13.0\n", " NaN\n", " NaN\n", + " 2.0\n", " NaN\n", + " 0.6\n", + " 0.05\n", + " 20.0\n", + " 6.0\n", + " 8.9\n", + " 3.0\n", + " 15.0\n", " NaN\n", " NaN\n", - " 0.108\n", - " 0.108\n", - " 0.108\n", " NaN\n", " NaN\n", " NaN\n", @@ -19959,18 +16721,41 @@ " NaN\n", " NaN\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Transition\n", + " 0.0\n", + " 6000.000\n", + " 21.87524\n", + " 13000.0\n", " NaN\n", " NaN\n", " NaN\n", + " 2.3\n", " NaN\n", " NaN\n", + " 18.000\n", " NaN\n", - " \n", - " \n", - " Distancia de aterrizaje\n", + " 12.0\n", + " 30.0\n", + " 13.0\n", " NaN\n", " NaN\n", + " 3.0\n", " NaN\n", + " 1.5\n", + " 0.05\n", + " 20.0\n", + " 11.8\n", + " 16.5\n", + " 5.8\n", + " 15.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -19981,20 +16766,41 @@ " NaN\n", " NaN\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", + " \n", + " \n", + " Reach\n", " 0.0\n", + " 6000.000\n", + " 27.34405\n", + " 16000.0\n", " NaN\n", " NaN\n", " NaN\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " NaN\n", + " 4.712\n", " NaN\n", " NaN\n", + " 91.000\n", " NaN\n", + " 20.0\n", + " 35.0\n", + " 13.0\n", " NaN\n", " NaN\n", + " 6.0\n", " NaN\n", + " 7.0\n", + " 0.05\n", + " 25.0\n", + " 54.0\n", + " 84.0\n", + " 31.0\n", + " 15.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -20005,1394 +16811,700 @@ " NaN\n", " NaN\n", " NaN\n", + " 2.000\n", + " 2.000\n", + " 1.000\n", + " 4.000\n", + " 1.000\n", + " 1.000\n", " \n", - " \n", + " \n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Convertir todo a numérico ===\n", + "\n", + "\n", + "=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===\n", + "\n", + "Umbral seleccionado para correlaciones significativas: 0.7\n", + "\n", + "=== Cálculo de tabla completa ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Tabla de Correlaciones con todos los parametros(tabla_completa)

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    Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespegue1.01.01.02.01.02.02.01.01.01.01.01.01.02.01.01.01.02.02.02.02.02.02.02.02.02.02.02.03.02.03.02.02.02.02.02.02.0
    Propulsión horizontal2.02.02.02.02.02.02.02.02.01.01.02.02.02.02.02.02.01.01.01.01.01.02.02.02.02.02.02.02.02.02.02.02.02.02.02.02.0
    Propulsión vertical5.05.05.01.05.01.01.05.05.05.05.05.05.01.05.05.05.01.01.01.01.01.01.01.01.01.01.01.05.01.05.01.01.01.01.01.01.0
    Cantidad de motores propulsión vertical0.00.00.04.00.04.04.00.00.00.00.00.00.04.00.00.00.04.04.04.04.04.04.04.04.04.04.04.00.04.00.04.08.04.04.04.04.0
    Cantidad de motores propulsión horizontal1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0Misión
    Misión1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0Distancia de carrera requerida para despegue1.0000.0630.467nan-0.5050.363nan0.260nan0.4250.168nan-0.0680.316-0.308nannan0.145nan0.229nan0.389nannan0.357nannannannan-0.018-0.240nannan-0.156nannan0.7350.1540.671-0.598nannan
    Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAltitud a la que se realiza el crucero0.0631.000nan-0.038nan0.301-0.3510.081nannan-0.095-0.955-0.2800.128nannannan0.1360.761-0.183nannannannan0.0600.038-0.325-0.215nannannan0.2770.690nannannan-0.119-0.1090.187-0.159nannan
    Battery Power SupplyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNVelocidad a la que se realiza el crucero (KTAS)0.467nan1.0000.0410.1280.587-0.9990.535nan0.9360.6630.6650.3360.8150.257nan0.8030.4720.8460.696-0.6941.0000.9150.7230.426-0.8550.3590.997nan0.4910.4611.0001.000-0.296nannan0.1130.6190.126-0.126nannan
    Modelo Motor Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTecho de servicio máximonan-0.0380.0411.000-0.502-0.152-0.3140.082nan0.3690.1370.4280.079-0.111-0.071nan-0.8030.0570.0170.087-0.8750.0410.6770.579-0.138-0.961-0.120-0.986nannan0.5150.653-0.933-0.257nannan0.1250.007-0.1250.125nannan
    Modelo Motor VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    PortabilidadNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNVelocidad de pérdida limpia (KCAS)-0.505nan0.128-0.5021.0000.097nan0.260nannan0.546nan0.4010.4461.000nan0.9930.505nan0.536nan0.128nannan0.038nannan0.900nan0.0681.000nannan0.163nannan-0.3450.231-0.3450.345nannan
    CámaraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNÁrea del ala0.3630.3010.587-0.1520.0971.000-0.8310.867nan0.9840.977-0.3010.0810.7370.423nan-1.0000.8410.9840.899-1.0000.675nan0.9230.941nan0.595-0.996nan1.0001.0001.0001.0000.899nannan0.2360.4530.1720.055nannan
    Despegue todos los tiposNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNRelación de aspecto del alanan-0.351-0.999-0.314nan-0.8311.000-0.790nan-0.996-0.823-0.998-0.305-0.859nannannan-0.349-0.744-0.888nan-0.999nannan0.622nan-0.298nannannannan-1.000-1.000nannannan-0.247nan0.247-0.247nannan
    Datalink banksNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLongitud del fuselaje0.2600.0810.5350.0820.2600.867-0.7901.000nan0.9380.7860.1500.3890.2560.180nan0.9180.6930.9950.599-0.6170.5760.9660.9400.880-0.7180.6760.263nan0.9290.0360.6860.359-0.210nannan0.1110.6150.093-0.004nannan
    Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNProfundidad del fuselajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Motor recomendadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAncho del fuselaje0.425nan0.9360.369nan0.984-0.9960.938nan1.0000.9860.982-0.0900.940nannannan0.6710.7600.868nan0.944nannan0.955nan0.323nannannan1.000nannannannannan0.794nan-0.5350.574nannan
    Hélice recomendada VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Hélice recomendada Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Sistema de controlNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Características adicionalesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    indice_desconocidoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Convertir todo a numérico ===\n", - "\n", - "\n", - "=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===\n", - "\n", - "Umbral seleccionado para correlaciones significativas: 0.7\n", - "\n", - "=== Cálculo de tabla completa ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Tabla de Correlaciones con todos los parametros(tabla_completa)

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21410,24 +17522,34 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21446,23 +17568,33 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21471,6 +17603,96 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21480,24 +17702,34 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21515,24 +17747,34 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21550,24 +17792,34 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21586,16 +17838,20 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21603,6 +17859,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21620,24 +17882,34 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21646,6 +17918,51 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21656,23 +17973,33 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21690,24 +18017,34 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21726,16 +18063,20 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -21744,6 +18085,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21760,21 +18107,31 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21786,33 +18143,133 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21830,12 +18287,15 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21845,9 +18305,16 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21856,6 +18323,96 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21866,23 +18423,33 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21891,6 +18458,96 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21900,24 +18557,34 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21936,23 +18603,33 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -21970,24 +18647,34 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -22005,24 +18692,34 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -22064,6 +18761,16 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -22099,6 +18806,16 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
    ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
    Modelo
    Distancia de carrera requerida para despegue1.0000.0630.467nan-0.5050.363nan0.2600.4250.168-0.0950.6630.1370.5460.977-0.8230.786nan-0.0680.316-0.3080.145nan0.2290.9861.0000.0250.4340.6780.539nan0.3890.9730.7910.8580.875-0.4010.7080.9990.9790.947-0.4640.5140.628nan0.3570.9760.7580.5590.8550.052nannan-0.018-0.240-0.1560.7350.1540.671-0.5980.0900.4670.0230.075nannan
    Altitud a la que se realiza el crucero0.0631.000nan-0.038nan0.301-0.3510.081Alcance de la aeronavenan-0.095-0.955-0.2800.128nan0.1360.761-0.1830.6650.428nan-0.301-0.9980.150nan0.9820.0251.0000.8430.042nan0.0600.038-0.325nannan-0.010-0.7550.804-1.0000.665nan-0.119-0.1090.187-0.159nan-0.059-1.0000.670nan
    Velocidad a la que se realiza el crucero (KTAS)0.467nan1.0000.0410.1280.587-0.9990.5350.9360.6630.6650.3360.8150.2570.4720.8460.696-0.6941.0000.7230.426-0.8550.3590.4910.461-0.2960.1130.6190.126-0.126nannan
    Techo de servicio máximonan-0.0380.0411.000-0.502-0.152-0.3140.0820.3690.1370.4280.079-0.111-0.0710.0570.0170.087-0.8750.0410.579-0.138-0.961-0.120nan0.515-0.2570.1250.007-0.1250.125nan0.2620.474-0.2620.262nannan
    Velocidad de pérdida limpia (KCAS)-0.505Autonomía de la aeronave-0.068-0.2800.3360.0790.4010.081-0.3050.389nan0.128-0.5021.0000.097nan0.260nan0.546nan0.4010.4461.0000.505nan0.536nan0.128nan0.038nannan0.0681.0000.163-0.3450.231-0.3450.345nannan
    Área del ala0.3630.3010.587-0.1520.0971.000-0.8310.8670.9840.977-0.3010.0810.7370.4230.8410.9840.899-1.0000.6750.9230.941nan0.5951.0001.0000.8990.2360.4530.1720.055nannan
    Relación de aspecto del alanan-0.351-0.999-0.314nan-0.8311.000-0.790-0.996-0.823-0.998-0.305-0.859nan-0.349-0.744-0.888nan-0.999nan0.622nan-0.298nannannan-0.247nan0.247-0.247nannan
    Longitud del fuselaje0.2600.0810.5350.0820.2600.867-0.7901.0000.9380.7860.1500.3890.2560.1800.6930.9950.599-0.6170.5760.9400.880-0.7180.6760.9290.036-0.2100.1110.6150.093-0.004nannan
    Ancho del fuselaje0.425nan0.9360.369nan0.984-0.9960.9381.0000.9860.982-0.0900.940nan0.6710.7600.868nan0.944nan0.955nan0.323nan1.000nan0.794nan-0.5350.574nannan
    Peso máximo al despegue (MTOW)0.168-0.0950.6630.1370.5460.977-0.8230.7860.9861.0000.0250.4340.6780.5390.7910.8580.875-0.4010.7080.9790.947-0.4640.5140.9760.7580.0520.0900.4670.0230.075nannan
    Alcance de la aeronavenan-0.9550.6650.428nan-0.301-0.9980.1500.9820.0251.0000.8430.042nan-0.010-0.7550.804-1.0000.665nan-0.059-1.0000.6701.000nan1.0000.2620.474-0.2620.262nannan
    Autonomía de la aeronave-0.068-0.2800.3360.0790.4010.081-0.3050.389-0.0900.4340.843-0.0900.4340.8431.0000.297-0.164nan0.9540.532-0.2010.461-0.5940.3360.9400.6340.428-0.7150.8020.268nan-0.113-0.732-0.391-0.5770.011nannan-0.4240.4770.3610.737-0.8590.256nan0.9400.6780.0420.2971.0000.539nannan0.4000.5120.715-0.9270.7750.9940.8570.517nan0.094-0.215nan0.7050.910-0.6240.9700.015nannan-0.0410.2080.174nan0.180nannan0.539nan-0.1640.5391.000nan0.9930.401nan0.627nan0.417nannan0.321nannan0.900nan0.2301.000nannan0.160nannan-0.2440.154-0.244nan
    Tasa de ascensonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Radio de gironannan0.803-0.8030.993-1.000nan0.918nannan0.973nan0.954nan0.993nan1.0000.992nan0.976nan0.803nannan0.979nannan0.844nannannannannan0.921nannannan0.918nannannannan
    envergadura0.1450.1360.841-0.3490.693nan0.6710.791-0.0100.5320.4000.401nan0.9921.0000.8850.734-0.2580.5010.9830.9360.924-0.4520.6480.780nan0.2970.0850.5220.8350.032nannan-0.1240.5080.2390.984-0.7440.995nan0.7600.858-0.755-0.2010.512nannannan0.8851.0000.776nan0.846nannan0.971nan0.354nannannannan1.0001.000nannannan-0.313nan0.3130.899-0.8880.599nan0.8680.8750.8040.4610.7150.627nan0.9760.7340.7761.000-0.0240.6940.9150.5590.778-0.1420.5460.707nan0.7110.8460.8410.866-0.008nannan-0.0040.4770.162nan-0.617nannan-0.401-1.000-0.594-0.927nannannan-0.258nan-0.0241.000-0.694-0.408-0.402-0.3151.000nannannannannannannannannan-0.188-0.9040.1880.675-0.9990.576nan0.9440.7080.6650.3360.7750.417nan0.8030.5010.8460.694-0.6941.0000.9150.7230.583-0.8550.3590.997nan0.5810.4611.0001.000-0.243nannan0.1430.6080.065nan
    RTF (dry weight)nannan0.9150.677nannannan0.966nannan0.999nan0.9400.994nannannan0.983nan0.915-0.4080.9151.0001.0000.995-0.408nannannannannannannannannannannan0.408nannannannan
    RTF (Including fuel & Batteries)nannannan0.940nannan0.979nan0.6340.857nannannan0.936nan0.559-0.4020.7231.0001.0000.996-0.402nannannannannannannannannannan0.0970.428-0.0970.9410.6220.880nan0.9550.947-0.0590.4280.5170.321nan0.9790.9240.9710.778-0.3150.5830.9950.9961.000-0.3190.8320.530nan0.995nannannan-0.029nannan0.1820.4040.307nan-0.718nannan-0.464-1.000-0.715nannannannan-0.452nan-0.1421.000-0.855-0.408-0.402-0.3191.000nannannannannannannannannan-0.943nannan0.595-0.2980.676nan0.3230.5140.6700.8020.094nannannan0.6480.3540.546nan0.359nannan0.832nan1.0000.215nannannan-1.000-0.972nannannannan
    Capacidad combustible-0.018nan0.491nan0.0681.000Wing Loadingnan-0.2150.997-0.9860.900-0.996nan0.263nannan0.628nan0.268-0.2150.900nan0.8440.780nan0.707nan0.997nannan0.530nan0.2151.000nannannannannan0.568nannannan0.572nannannannan
    Potencia específica (P/W)nannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Capacidad combustible-0.018nan0.491nan0.0681.000nan0.929nannan0.9761.000-0.1130.7050.230nannan0.297nan0.711nan0.581nannan0.995nannannannan1.0000.377nannan0.817nannan-0.080nan-0.0801.000nan0.036nan1.0000.758nan-0.7320.9101.000nannan0.085nan0.846nannannannannannan0.3771.000nannan0.998nannan0.113nan-0.375nan
    Potencia Wattsnan0.2771.0000.653nan1.000-1.0000.686nannan0.559nan-0.391-0.624nannannan0.5221.0000.841nan1.000nannannannan-1.000nannannannan1.0001.000nannannan0.232nan-0.2320.232nannan
    Potencia HPnan0.6901.000-0.933nan1.000-1.0000.359nannan0.855nan-0.5770.970nannannan0.8351.0000.866nan1.000nannannannan-0.972nannannannan1.0001.000nannannan-0.694nan0.694-0.694nannan
    Precio-0.156nannan-0.210nannan0.0521.0000.0110.0150.160nan0.9210.032nan-0.008nan-0.243nannan-0.029nannan0.568nan0.8170.998nannan1.000nannan-0.1380.217-0.138nan
    Tiempo de emergencia en vuelonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Distancia de aterrizajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Despegue0.735-0.1190.236-0.2470.111nan0.7940.0900.262-0.424-0.041-0.244nannan-0.124-0.313-0.004-0.1880.143nan0.0970.182nan-0.430nannan-0.0800.1130.232-0.694-0.138nannan1.000-0.010-0.639nan0.615nannan0.4670.4740.4770.2080.154nan0.9180.508nan0.477-0.9040.6080.4080.4280.404-0.9430.6040.572nannannannannan0.217nannan-0.0101.0000.1180.1720.2470.093nan-0.5350.023-0.2620.3610.174-0.244nannan0.2390.3130.1620.1880.065nan-0.0970.307nan0.430nannan-0.080-0.375-0.2320.694-0.138nannan-0.6390.1181.0000.055-0.247-0.004nan0.5740.0750.262-0.361-0.1330.444nannan-0.164-0.313-0.111-0.1880.063nan0.0970.004nan-0.430nannan0.2700.3750.232-0.6940.134nannan0.610-0.083-0.954nannannannannannannannannannannannannan
    Misiónnannannannannannannannannannannannannan
    " @@ -22157,17 +18874,17 @@ " \n", " 0\n", " Total de valores\n", - " 1024.000\n", + " 1764.000\n", " \n", " \n", " 1\n", " Valores numéricos\n", - " 740.000\n", + " 907.000\n", " \n", " \n", " 2\n", " Valores NaN\n", - " 284.000\n", + " 857.000\n", " \n", " \n", "" @@ -22224,10 +18941,10 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

    Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

    \n", + "

    Tabla de Correlaciones Filtradas por parametros seleccionados (Para Heatmap)

    \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -22244,24 +18961,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -22651,7 +19350,7 @@ "

    Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

    ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alapayloadEmpty weight
    Modelo
    \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -22668,24 +19367,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -23049,7 +19730,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
    " ] @@ -23095,1196 +19776,377 @@ "

    Tabla de correlaciones con filtro de umbral de correlación

    ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alapayloadEmpty weight
    Modelo
    \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + "
    ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
    Modelo
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", + "\n", + "=== Velocidad a la que se realiza el crucero (KTAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Techo de servicio máximo: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Área del ala: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Relación de aspecto del ala: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Longitud del fuselaje: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Peso máximo al despegue (MTOW): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Alcance de la aeronave: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Autonomía de la aeronave: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Velocidad máxima (KIAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Velocidad de pérdida (KCAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Velocidad de pérdida limpia (KCAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== envergadura: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Cuerda: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== payload: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Empty weight: Sin correlaciones significativas (|r| < 0.7) ===\n", + "La columna 'Nivel de Confianza' no está presente en df_reporte.\n", + "\u001b[1mNo se realizaron imputaciones por correlación en esta iteración.\u001b[0m\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 30.228668858925072 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - AAI Aerosonde = 36.09414654431562 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Orbiter 4 = 30.466419244333917 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Orbiter 3 = 27.426371766294327 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Integrator Extended Range = 31.89437620999355 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Skyeye 5000 VTOL octo = 30.290908946357952 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Orbiter 4 = 9403.635180379342 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Orbiter 3 = 6839.1446057940275 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 2600 = 14972.955913461341 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 2930 VTOL = 15999.999999999998 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 3600 VTOL = 16959.091874090493 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 VTOL = 16009.435943246384 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 VTOL octo = 16009.476366047784 (Similitud)\n", + "Imputación final aplicada: Área del ala - Aerosonde Mk. 4.8 VTOL FTUAS = 2.615 (Similitud)\n", + "Imputación final aplicada: Área del ala - Fulmar X = 1.1582832 (Similitud)\n", + "Imputación final aplicada: Área del ala - Orbiter 4 = 1.55 (Similitud)\n", + "Imputación final aplicada: Área del ala - ScanEagle 3 = 1.5499999999999998 (Similitud)\n", + "Imputación final aplicada: Área del ala - RQ Nan 21A Blackjack = 1.55 (Similitud)\n", + "Imputación final aplicada: Área del ala - V32 = 1.0 (Similitud)\n", + "Imputación final aplicada: Área del ala - V35 = 1.0 (Similitud)\n", + "Imputación final aplicada: Área del ala - V39 = 1.0 (Similitud)\n", + "Imputación final aplicada: Área del ala - Volitation VT370 = 1.32 (Similitud)\n", + "Imputación final aplicada: Área del ala - Volitation VT510 = 2.615 (Similitud)\n", + "Imputación final aplicada: Área del ala - Ascend = 0.8200000000000001 (Similitud)\n", + "Imputación final aplicada: Área del ala - Transition = 0.8800000000000001 (Similitud)\n", + "Imputación final aplicada: Área del ala - Reach = 2.615 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Fulmar X = 15.326448834651856 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Orbiter 4 = 12.500000000000002 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - ScanEagle 3 = 12.500000000000002 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - RQ Nan 21A Blackjack = 12.500000000000002 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - V25 = 14.754385964912283 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 2600 = 14.75438596491228 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 VTOL = 12.5 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 VTOL octo = 12.500000000000002 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Volitation VT510 = 12.5 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Reach = 12.500000000000002 (Similitud)\n", + "Imputación final aplicada: Longitud del fuselaje - Aerosonde Mk. 4.8 VTOL FTUAS = 3.7167351527516352 (Similitud)\n", + "Imputación final aplicada: Longitud del fuselaje - V39 = 1.4093116957279586 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Aerosonde Mk. 4.8 VTOL FTUAS = 800.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Integrator = 499.99999999999994 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - ScanEagle 3 = 50.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - V21 = 270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - V25 = 270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Volitation VT370 = 300.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 2600 = 3270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 5000 VTOL octo = 800.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Volitation VT510 = 800.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Ascend = 270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Reach = 800.0 (Similitud)\n", + "Imputación final aplicada: Autonomía de la aeronave - Skyeye 5000 VTOL octo = 11.672906868436865 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 42.25267526977939 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Evo = 33.0 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - Skyeye 2600 = 30.8342888378733 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 18.90746548752 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - AAI Aerosonde = 10.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - DeltaQuad Evo = 14.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - V35 = 18.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - V39 = 17.397389995852386 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Volitation VT370 = 24.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Skyeye 5000 VTOL = 19.109224697504906 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 25.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - AAI Aerosonde = 10.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - DeltaQuad Evo = 14.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - V35 = 18.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - V39 = 17.397389995852386 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Volitation VT370 = 24.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Skyeye 5000 VTOL = 25.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Skyeye 5000 VTOL octo = 25.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Ascend = 14.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Transition = 10.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Reach = 25.0 (Similitud)\n", + "Imputación final aplicada: envergadura - Aerosonde Mk. 4.8 VTOL FTUAS = 5.373632590337233 (Similitud)\n", + "Imputación final aplicada: Cuerda - Fulmar X = 0.31819504 (Similitud)\n", + "Imputación final aplicada: Cuerda - Orbiter 4 = 0.352 (Similitud)\n", + "Imputación final aplicada: Cuerda - ScanEagle 3 = 0.3519999999999999 (Similitud)\n", + "Imputación final aplicada: Cuerda - RQ Nan 21A Blackjack = 0.352 (Similitud)\n", + "Imputación final aplicada: Cuerda - V25 = 0.196551724137931 (Similitud)\n", + "Imputación final aplicada: Cuerda - Skyeye 2600 = 0.196551724137931 (Similitud)\n", + "Imputación final aplicada: payload - AAI Aerosonde = 4.0 (Similitud)\n", + "Imputación final aplicada: payload - Fulmar X = 2.4947559999999998 (Similitud)\n", + "Imputación final aplicada: Empty weight - Aerosonde Mk. 4.8 VTOL FTUAS = 31.0 (Similitud)\n", + "Imputación final aplicada: Empty weight - Fulmar X = 17.463292 (Similitud)\n", + "Imputación final aplicada: Empty weight - V35 = 7.1 (Similitud)\n", + "Imputación final aplicada: Empty weight - V39 = 6.708303497304052 (Similitud)\n", + "Imputación final aplicada: Empty weight - Volitation VT370 = 10.999999999999998 (Similitud)\n", + "Imputación final aplicada: Empty weight - Skyeye 5000 VTOL = 31.0 (Similitud)\n", + "Imputación final aplicada: Empty weight - Volitation VT510 = 30.999999999999996 (Similitud)\n", + "\n", + "=== Iteración 1: Resumen después de imputaciones ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Resumen de Valores Faltantes Después de Iteración 1

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    FilaValores Faltantes
    Distancia de carrera requerida para despeguenannannannannannannannannannannannannannannannannannannannannannannannannannan0.735nannannannannan0Stalker XE2.000
    Altitud a la que se realiza el cruceronannannannannannannannannannan-0.955nannannannan0.761nannannannannannannannannannannannannannannannan1Stalker VXE302.000
    Velocidad a la que se realiza el crucero (KTAS)nannannannannannan-0.999nan0.936nannannan0.815nannan0.846nannan1.0000.723nan-0.855nannannannannannannannannannan2Aerosonde Mk. 4.7 Fixed Wing4.000
    Techo de servicio máximonannannannannannannannannannannannannannannannannan-0.875nannannan-0.961nannannannannannannannannannan3Aerosonde Mk. 4.7 VTOL4.000
    Velocidad de pérdida limpia (KCAS)nannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan4Aerosonde Mk. 4.8 Fixed wing4.000
    Área del alanannannannannannan-0.8310.8670.9840.977nannan0.737nan0.8410.9840.899nannan0.9230.941nannan5Aerosonde Mk. 4.8 VTOL FTUAS1.000nan0.899nannannannannannan
    Relación de aspecto del alanannan-0.999nannan-0.831nan-0.790-0.996-0.823-0.998nan-0.859nannan-0.744-0.888nan-0.999nannannannannannannannannannannannannan6AAI Aerosonde0.000
    Longitud del fuselajenannannannannan0.867-0.790nan0.9380.786nannannannannan0.995nannannan0.9400.880-0.718nan0.929nannannannannannannannan7Fulmar X2.000
    Ancho del fuselajenannan0.936nannan0.984-0.9960.938nan0.9860.982nan0.940nannan0.7600.868nan0.944nan0.955nannannannannan0.794nannannannannan8Orbiter 44.000
    Peso máximo al despegue (MTOW)nannannannannan0.977-0.8230.7860.986nannannannannan0.7910.8580.875nan0.7080.9790.947nannan0.9760.758nannannannannannannan9Orbiter 36.000
    Alcance de la aeronavenan-0.955nannannannan-0.998nan0.982nannan0.843nannannan-0.7550.804nannannannannannannannannannannannannannannan10Mantis8.000
    Autonomía de la aeronavenannannannannannannannannannan0.843nannannannannannannannannannan-0.7150.802nan-0.732nannannannannannannan11ScanEagle7.000
    Velocidad máxima (KIAS)nannan0.815nannan0.737-0.859nan0.940nannannannannannannan0.715-0.9270.7750.857nannannan0.7050.910nannannannannannannan12Integrator6.000
    Velocidad de pérdida (KCAS)nannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan13Integrator VTOL12.000
    envergaduranannannannannan0.841nannannan0.791nannannannannan0.8850.734nannan0.9360.924nannannannannannannannannannannan14Integrator Extended Range6.000
    Cuerdanan0.7610.846nannan0.984-0.7440.9950.7600.858-0.755nannannan0.885nan0.776nan0.846nan0.971nannannannannannannannannannannan15ScanEagle 33.000
    payloadnannannannannan0.899-0.888nan0.8680.8750.804nan0.715nan0.7340.776nannannannan0.778nannan0.7110.846nannannannannannannan16RQ Nan 21A Blackjack4.000
    duracion en VTOLnannannan-0.875nannannannannannannannan-0.927nannannannannannannannannannannannannannan-0.904nannannannan17DeltaQuad Evo2.000
    Crucero KIASnannan1.000nannannan-0.999nan0.9440.708nannan0.775nannan0.846nannannan0.723nan-0.855nannannannannannannannannannan18DeltaQuad Pro #MAP7.000
    RTF (Including fuel & Batteries)nannan0.723nannan0.923nan0.940nan0.979nannan0.857nan0.936nannannan0.723nan0.996nannannannannannannannannannannan19DeltaQuad Pro #CARGO7.000
    Empty weightnannannannannan0.941nan0.8800.9550.947nannannannan0.9240.9710.778nannan0.996nannan0.8320.995nannannannannannannannan20V212.000
    Maximum Crosswindnannan-0.855-0.961nannannan-0.718nannannan-0.715nannannannannannan-0.855nannannannannannannannan-0.943nannannannan21V250.000
    Rango de comunicaciónnannannannannannannannannannannan0.802nannannannannannannannan0.832nannannannannannannannannannannan22V323.000
    Capacidad combustiblenannannannannan1.000nan0.929nan0.976nannan0.705nannannan0.711nannannan0.995nannannannan0.817nannannannannannan23V353.000
    Consumonannannannannannannannannan0.758nan-0.7320.910nannannan0.846nannannannannannannannan0.998nannannannannannan24V393.000
    Precionannannannannan0.899nannannannannannannannannannannannannannannannannan0.8170.998nannannannannannannan25Volitation VT3702.000
    Despegue0.735nannannannannannannan0.794nannannannannannannannannannannannannannannannannannannannannannannan26Skyeye 26000.000
    Propulsión horizontalnannannannannannannannannannannannannannannannannan-0.904nannannan-0.943nannannannannannannannannannan27Skyeye 2930 VTOL3.000
    Propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannan28Skyeye 36006.000
    Cantidad de motores propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannannan29Skyeye 3600 VTOL2.000
    Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan30Skyeye 50004.000
    Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan31Skyeye 5000 VTOL1.000
    32Skyeye 5000 VTOL octo1.000
    33Volitation VT5101.000
    34Ascend2.000
    35Transition3.000
    36Reach1.000
    " @@ -24296,132 +20158,6 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", - "\n", - "=== Imputación para el parámetro: **Velocidad a la que se realiza el crucero (KTAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5]\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344]\n", - "Ecuación de regresión: y = -3.588x + 72.195\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 27.34\n", - "\tR²: 0.9951683800002252, Desviación Estándar: 0.3316703651380593, Varianza: 0.1100052311108136, Incertidumbre: 0.19148997459468003\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.723) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [27.344, 27.344, 27.344, 18.091, 21.875, 21.875, 27.344]\n", - "Ecuación de regresión: y = 0.104x + 21.164\n", - "Valor del parámetro correlacionado para la aeronave: 70.3\n", - "Predicción obtenida: 28.443\n", - "\tR²: 0.5234387684031696, Desviación Estándar: 2.436503148855875, Varianza: 5.936547594384592, Incertidumbre: 0.9209116286426334\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Relación de aspecto del ala: 27.34', 'RTF (Including fuel & Batteries): 28.443']\n", - "**Mediana calculada:** 27.892\n", - "\n", - "--- Imputación para aeronave: **AAI Aerosonde** ---\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5]\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892]\n", - "Ecuación de regresión: y = -3.686x + 73.696\n", - "Valor del parámetro correlacionado para la aeronave: 14.754\n", - "Predicción obtenida: 19.306\n", - "\tR²: 0.9955392528843181, Desviación Estándar: 0.3471824284258566, Varianza: 0.12053563860767504, Incertidumbre: 0.1735912142129283\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 30.407, 18.266, 30.625, 30.953, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", - "Ecuación de regresión: y = 0.607x + 4.971\n", - "Valor del parámetro correlacionado para la aeronave: 30.846\n", - "Predicción obtenida: 23.695\n", - "\tR²: 0.6883167394784258, Desviación Estándar: 3.20056844074234, Varianza: 10.243638343875855, Incertidumbre: 0.7342607576358634\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352]\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344]\n", - "Ecuación de regresión: y = 77.731x + -2.949\n", - "Valor del parámetro correlacionado para la aeronave: 0.197\n", - "Predicción obtenida: 12.33\n", - "\tR²: 0.5951449532870101, Desviación Estándar: 3.036057989288316, Varianza: 9.217648114321413, Incertidumbre: 1.7528688973909234\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Relación de aspecto del ala: 19.306', 'Velocidad máxima (KIAS): 23.695', 'Cuerda: 12.33']\n", - "**Mediana calculada:** 19.306\n", - "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 18.266, 30.625, 30.953, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", - "Ecuación de regresión: y = 0.625x + 4.123\n", - "Valor del parámetro correlacionado para la aeronave: 36.0\n", - "Predicción obtenida: 26.611\n", - "\tR²: 0.6860861539695317, Desviación Estándar: 3.2600537047404123, Varianza: 10.627950157791688, Incertidumbre: 0.728970169409959\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad máxima (KIAS): 26.611']\n", - "**Mediana calculada:** 26.611\n", - "\n", - "--- Imputación para aeronave: **Orbiter 3** ---\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", - "Ecuación de regresión: y = 0.625x + 4.123\n", - "Valor del parámetro correlacionado para la aeronave: 36.0\n", - "Predicción obtenida: 26.611\n", - "\tR²: 0.6861096464084044, Desviación Estándar: 3.181486648407253, Varianza: 10.121857293993614, Incertidumbre: 0.6942573042294092\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad máxima (KIAS): 26.611']\n", - "**Mediana calculada:** 26.611\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Maximum Crosswind (r = -0.855) ---\n", - "Aeronaves utilizadas: ['DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Ascend', 'Reach']\n", - "Valores para Maximum Crosswind: [45.0, 50.0, 15.0, 15.0]\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [18.091, 17.5, 21.875, 27.344]\n", - "Ecuación de regresión: y = -0.209x + 27.721\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 21.463\n", - "\tR²: 0.755405189129301, Desviación Estándar: 1.9401971688154946, Varianza: 3.764365053879661, Incertidumbre: 0.9700985844077473\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Maximum Crosswind: 21.463']\n", - "**Mediana calculada:** 21.463\n", - "\n", - "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", - "Ecuación de regresión: y = 0.625x + 4.123\n", - "Valor del parámetro correlacionado para la aeronave: 46.3\n", - "Predicción obtenida: 33.045\n", - "\tR²: 0.6861096464084044, Desviación Estándar: 3.181486648407253, Varianza: 10.121857293993614, Incertidumbre: 0.6942573042294092\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad máxima (KIAS): 33.045']\n", - "**Mediana calculada:** 33.045\n", - "\n", - "--- Imputación para aeronave: **Skyeye 3600** ---\n" - ] - }, { "data": { "text/html": [ @@ -24430,8 +20166,8 @@ " .scroll-table {\n", " overflow-x: auto;\n", " overflow-y: auto;\n", - " max-height: 400px;\n", - " max-width: 100%;\n", + " max-height: 100px;\n", + " max-width: 50%;\n", " display: block;\n", " border: 1px solid #ccc;\n", " margin-bottom: 20px;\n", @@ -24457,17 +20193,19 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

    Imputación no Exitosa

    \n", + "

    Sumatoria Total de Valores Faltantes

    \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", "
    MensajeResumenCantidad
    0No se pudo imputar el parámetro 'Velocidad a la que se realiza el crucero (KTAS)' para la aeronave 'Skyeye 3600'.Total de Valores Faltantes128.000
    " @@ -24484,43 +20222,15 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = 0.936) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 36.094, 30.625]\n", - "Ecuación de regresión: y = 93.547x + -1.195\n", - "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 33.885\n", - "\tR²: 0.9095474448825779, Desviación Estándar: 2.2440186570446126, Varianza: 5.035619733164306, Incertidumbre: 1.0035556519859081\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 50.0, 30.0, 35.0]\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 32.813, 21.875, 27.344]\n", - "Ecuación de regresión: y = 0.625x + 4.123\n", - "Valor del parámetro correlacionado para la aeronave: 38.0\n", - "Predicción obtenida: 27.86\n", - "\tR²: 0.7045474134573646, Desviación Estándar: 3.108339246562038, Varianza: 9.661772871717856, Incertidumbre: 0.6627001540432849\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Crucero KIAS (r = 1.0) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 33.0, 24.0, 30.0]\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 30.407, 18.266, 30.625, 30.953, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 36.094, 26.25, 32.813]\n", - "Ecuación de regresión: y = 1.094x + 0.0\n", - "Valor del parámetro correlacionado para la aeronave: 35.0\n", - "Predicción obtenida: 38.282\n", - "\tR²: 1.0, Desviación Estándar: 3.3158330576972436e-15, Varianza: 1.0994748866517852e-29, Incertidumbre: 8.289582644243109e-16\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "Valores imputados: ['Ancho del fuselaje: 33.885', 'Velocidad máxima (KIAS): 27.86', 'Crucero KIAS: 38.282']\n", - "**Mediana calculada:** 33.885\n", + "================================================================================\n", + "\u001b[1m=== FIN DE ITERACIÓN 1 ===\u001b[0m\n", + "================================================================================\n", "\n", - "=== Imputación para el parámetro: **Techo de servicio máximo** ===\n", + "================================================================================\n", + "\u001b[1m=== INICIO DE ITERACIÓN 2 ===\u001b[0m\n", + "================================================================================\n", "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n" + "=== Iteración 2: Resumen antes de imputaciones ===\n" ] }, { @@ -24531,8 +20241,8 @@ " .scroll-table {\n", " overflow-x: auto;\n", " overflow-y: auto;\n", - " max-height: 400px;\n", - " max-width: 100%;\n", + " max-height: 300px;\n", + " max-width: 50%;\n", " display: block;\n", " border: 1px solid #ccc;\n", " margin-bottom: 20px;\n", @@ -24558,551 +20268,199 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

    Imputación no Exitosa

    \n", + "

    Resumen de Valores Faltantes Antes de Iteración 2

    \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - "
    MensajeFilaValores Faltantes
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Orbiter 4'.Stalker XE17.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Orbiter 3** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

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    Mensaje
    1Stalker VXE3018.000
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Orbiter 3'.2Aerosonde Mk. 4.7 Fixed Wing17.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Mantis** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - "
    Mensaje
    3Aerosonde Mk. 4.7 VTOL16.000
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Mantis'.4Aerosonde Mk. 4.8 Fixed wing20.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Maximum Crosswind (r = -0.961) ---\n", - "Aeronaves utilizadas: ['DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Maximum Crosswind: [45.0, 50.0, 15.0, 15.0, 15.0]\n", - "Valores para Techo de servicio máximo: [13.0, 13.123, 10000.0, 13000.0, 16000.0]\n", - "Ecuación de regresión: y = -395.696x + 18884.7\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 7013.834\n", - "\tR²: 0.9093584510325812, Desviación Estándar: 1998.8363831923273, Varianza: 3995346.886773384, Incertidumbre: 893.9068057435724\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Maximum Crosswind: 7013.834']\n", - "**Mediana calculada:** 7013.834\n", - "\n", - "--- Imputación para aeronave: **Skyeye 2600** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - "
    Mensaje
    5Aerosonde Mk. 4.8 VTOL FTUAS19.000
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 2600'.6AAI Aerosonde15.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Skyeye 2930 VTOL** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - "
    Mensaje
    7Fulmar X21.000
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 2930 VTOL'.8Orbiter 423.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Skyeye 3600** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - "
    Mensaje
    9Orbiter 325.000
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 3600'.10Mantis26.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Skyeye 3600 VTOL** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - "
    Mensaje
    11ScanEagle25.000
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 3600 VTOL'.12Integrator24.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Skyeye 5000** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - "
    Mensaje
    13Integrator VTOL29.000
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 5000'.14Integrator Extended Range26.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
    Mensaje
    15ScanEagle 321.000
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 5000 VTOL'.16RQ Nan 21A Blackjack21.000
    17DeltaQuad Evo15.000
    18DeltaQuad Pro #MAP22.000
    19DeltaQuad Pro #CARGO22.000
    20V2115.000
    21V2513.000
    22V3216.000
    23V3519.000
    24V3919.000
    25Volitation VT37017.000
    26Skyeye 260018.000
    27Skyeye 2930 VTOL20.000
    28Skyeye 360023.000
    29Skyeye 3600 VTOL18.000
    30Skyeye 500018.000
    31Skyeye 5000 VTOL16.000
    32Skyeye 5000 VTOL octo16.000
    33Volitation VT51016.000
    34Ascend16.000
    35Transition17.000
    36Reach15.000
    " @@ -25114,14 +20472,6 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n" - ] - }, { "data": { "text/html": [ @@ -25130,8 +20480,8 @@ " .scroll-table {\n", " overflow-x: auto;\n", " overflow-y: auto;\n", - " max-height: 400px;\n", - " max-width: 100%;\n", + " max-height: 100px;\n", + " max-width: 50%;\n", " display: block;\n", " border: 1px solid #ccc;\n", " margin-bottom: 20px;\n", @@ -25157,17 +20507,19 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

    Imputación no Exitosa

    \n", + "

    Sumatoria Total de Valores Faltantes

    \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", "
    MensajeResumenCantidad
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Skyeye 5000 VTOL octo'.Total de Valores Faltantes714.000
    " @@ -25184,33213 +20536,1712 @@ "output_type": "stream", "text": [ "\n", - "=== Imputación para el parámetro: **Área del ala** ===\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57]\n", - "Ecuación de regresión: y = -0.219x + 4.203\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 1.468\n", - "\tR²: 0.4921072065029338, Desviación Estándar: 0.2579064700616838, Varianza: 0.06651574729967821, Incertidumbre: 0.1289532350308419\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.022x + 0.512\n", - "Valor del parámetro correlacionado para la aeronave: 93.0\n", - "Predicción obtenida: 2.528\n", - "\tR²: 0.9424993971942859, Desviación Estándar: 0.1480022127547218, Varianza: 0.02190465498029394, Incertidumbre: 0.038214007013392295\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.084x + 0.605\n", - "Valor del parámetro correlacionado para la aeronave: 22.7\n", - "Predicción obtenida: 2.503\n", - "\tR²: 0.8079809975569326, Desviación Estándar: 0.2933466953816814, Varianza: 0.08605228369135297, Incertidumbre: 0.07574179105854881\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Relación de aspecto del ala: 1.468', 'Peso máximo al despegue (MTOW): 2.528', 'payload: 2.503']\n", - "**Mediana calculada:** 2.503\n", - "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.546x + 0.007\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.662\n", - "\tR²: 0.6801892039390519, Desviación Estándar: 0.30468415865316995, Varianza: 0.09283243653419003, Incertidumbre: 0.08795474050811117\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.022x + 0.514\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 0.945\n", - "\tR²: 0.9537986325795035, Desviación Estándar: 0.14339927817107342, Varianza: 0.020563352979984892, Incertidumbre: 0.035849819542768356\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.082x + -1.305\n", - "Valor del parámetro correlacionado para la aeronave: 41.7\n", - "Predicción obtenida: 2.11\n", - "\tR²: 0.4361157359439931, Desviación Estándar: 0.5418933308069339, Varianza: 0.29364838197303306, Incertidumbre: 0.17136171742050005\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.46x + -0.446\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 0.935\n", - "\tR²: 0.606437991532139, Desviación Estándar: 0.33799470646338436, Varianza: 0.11424042159726937, Incertidumbre: 0.09757066738065176\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Longitud del fuselaje: 0.662', 'Peso máximo al despegue (MTOW): 0.945', 'Velocidad máxima (KIAS): 2.11', 'envergadura: 0.935']\n", - "**Mediana calculada:** 0.94\n", - "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.521x + 0.078\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.703\n", - "\tR²: 0.6641973297028555, Desviación Estándar: 0.30126149307901384, Varianza: 0.09075848721219672, Incertidumbre: 0.08355490466301725\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.022x + 0.513\n", - "Valor del parámetro correlacionado para la aeronave: 55.0\n", - "Predicción obtenida: 1.701\n", - "\tR²: 0.9548206367081808, Desviación Estándar: 0.1391232519560287, Varianza: 0.01935527923482064, Incertidumbre: 0.033742344870242656\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.057x + -0.607\n", - "Valor del parámetro correlacionado para la aeronave: 36.0\n", - "Predicción obtenida: 1.456\n", - "\tR²: 0.2635152280829376, Desviación Estándar: 0.5971883487670894, Varianza: 0.3566339239031628, Incertidumbre: 0.18005906200293956\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.46x + -0.445\n", - "Valor del parámetro correlacionado para la aeronave: 5.2\n", - "Predicción obtenida: 1.948\n", - "\tR²: 0.6098226097328528, Desviación Estándar: 0.32473761841891496, Varianza: 0.10545452081638883, Incertidumbre: 0.09006601032934286\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.084x + 0.605\n", - "Valor del parámetro correlacionado para la aeronave: 12.0\n", - "Predicción obtenida: 1.608\n", - "\tR²: 0.8352230239464027, Desviación Estándar: 0.2840317214888726, Varianza: 0.08067401881193251, Incertidumbre: 0.07100793037221816\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 0.703', 'Peso máximo al despegue (MTOW): 1.701', 'Velocidad máxima (KIAS): 1.456', 'envergadura: 1.948', 'payload: 1.608']\n", - "**Mediana calculada:** 1.608\n", - "\n", - "--- Imputación para aeronave: **Orbiter 3** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.45x + 0.279\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.819\n", - "\tR²: 0.49444751735768744, Desviación Estándar: 0.36781526151231503, Varianza: 0.1352880666013727, Incertidumbre: 0.09830276358612447\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.512\n", - "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 1.2\n", - "\tR²: 0.9541873450036837, Desviación Estándar: 0.1368279656080401, Varianza: 0.018721892172435, Incertidumbre: 0.03225066077913495\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.058x + -0.629\n", - "Valor del parámetro correlacionado para la aeronave: 36.0\n", - "Predicción obtenida: 1.472\n", - "\tR²: 0.2740142010052371, Desviación Estándar: 0.5732623728312028, Varianza: 0.328629748104061, Incertidumbre: 0.1654865926351893\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.417x + -0.317\n", - "Valor del parámetro correlacionado para la aeronave: 4.4\n", - "Predicción obtenida: 1.516\n", - "\tR²: 0.6121745707536088, Desviación Estándar: 0.3221549155644233, Varianza: 0.10378378962232071, Incertidumbre: 0.08609952282194\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.084x + 0.605\n", - "Valor del parámetro correlacionado para la aeronave: 5.5\n", - "Predicción obtenida: 1.065\n", - "\tR²: 0.8355353911029019, Desviación Estándar: 0.27555125326306473, Varianza: 0.07592849317484565, Incertidumbre: 0.06683099543970229\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 0.819', 'Peso máximo al despegue (MTOW): 1.2', 'Velocidad máxima (KIAS): 1.472', 'envergadura: 1.516', 'payload: 1.065']\n", - "**Mediana calculada:** 1.2\n", - "\n", - "--- Imputación para aeronave: **Mantis** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.423x + 0.354\n", - "Valor del parámetro correlacionado para la aeronave: 1.48\n", - "Predicción obtenida: 0.981\n", - "\tR²: 0.46052122430771714, Desviación Estándar: 0.36722158071804406, Varianza: 0.13485168934505895, Incertidumbre: 0.09481620443259649\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.512\n", - "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 0.651\n", - "\tR²: 0.9543119129686386, Desviación Estándar: 0.13317860331268058, Varianza: 0.017736540380316333, Incertidumbre: 0.03055326701483504\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.057x + -0.594\n", - "Valor del parámetro correlacionado para la aeronave: 25.6\n", - "Predicción obtenida: 0.857\n", - "\tR²: 0.26284243485241887, Desviación Estándar: 0.5554395050127472, Varianza: 0.30851304372880556, Incertidumbre: 0.1540512012109075\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.398x + -0.27\n", - "Valor del parámetro correlacionado para la aeronave: 2.1\n", - "Predicción obtenida: 0.565\n", - "\tR²: 0.5889263637114722, Desviación Estándar: 0.32055372387545367, Varianza: 0.10275468989042061, Incertidumbre: 0.08276661560896019\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Longitud del fuselaje: 0.981', 'Peso máximo al despegue (MTOW): 0.651', 'Velocidad máxima (KIAS): 0.857', 'envergadura: 0.565']\n", - "**Mediana calculada:** 0.754\n", - "\n", - "--- Imputación para aeronave: **ScanEagle** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.432x + 0.325\n", - "Valor del parámetro correlacionado para la aeronave: 1.71\n", - "Predicción obtenida: 1.063\n", - "\tR²: 0.46837090280435856, Desviación Estándar: 0.35970679644924114, Varianza: 0.1293889794117758, Incertidumbre: 0.08992669911231028\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.524\n", - "Valor del parámetro correlacionado para la aeronave: 26.5\n", - "Predicción obtenida: 1.088\n", - "\tR²: 0.9550142996680715, Desviación Estándar: 0.1316020238299309, Varianza: 0.0173190926761337, Incertidumbre: 0.029427107126027266\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.058x + -0.649\n", - "Valor del parámetro correlacionado para la aeronave: 41.2\n", - "Predicción obtenida: 1.746\n", - "\tR²: 0.29571820868524923, Desviación Estándar: 0.5358228587115724, Varianza: 0.2871061359178417, Incertidumbre: 0.14320468266432035\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.381x + -0.202\n", - "Valor del parámetro correlacionado para la aeronave: 3.1\n", - "Predicción obtenida: 0.98\n", - "\tR²: 0.5966556307390867, Desviación Estándar: 0.31331578218830264, Varianza: 0.0981667793682679, Incertidumbre: 0.07832894554707566\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.083x + 0.619\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 1.034\n", - "\tR²: 0.8350524298607005, Desviación Estándar: 0.2695310649903611, Varianza: 0.07264699499483826, Incertidumbre: 0.0635290812650388\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 1.063', 'Peso máximo al despegue (MTOW): 1.088', 'Velocidad máxima (KIAS): 1.746', 'envergadura: 0.98', 'payload: 1.034']\n", - "**Mediana calculada:** 1.063\n", - "\n", - "--- Imputación para aeronave: **Integrator** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.432x + 0.325\n", - "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 1.404\n", - "\tR²: 0.4688208790784759, Desviación Estándar: 0.34896686225957346, Varianza: 0.1217778709552921, Incertidumbre: 0.08463689605509357\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.522\n", - "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 2.117\n", - "\tR²: 0.9552685671013039, Desviación Estándar: 0.1285440575853638, Varianza: 0.016523574740509323, Incertidumbre: 0.028050613048652005\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.049x + -0.376\n", - "Valor del parámetro correlacionado para la aeronave: 46.3\n", - "Predicción obtenida: 1.872\n", - "\tR²: 0.23291649773094203, Desviación Estándar: 0.5418077102312508, Varianza: 0.29355559486603106, Incertidumbre: 0.13989414923816057\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.38x + -0.191\n", - "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 1.631\n", - "\tR²: 0.5953583197437493, Desviación Estándar: 0.30457832800349305, Varianza: 0.09276795788940341, Incertidumbre: 0.07387109515484842\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.083x + 0.622\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 2.113\n", - "\tR²: 0.8382132873731523, Desviación Estándar: 0.26242037008429453, Varianza: 0.06886445063517811, Incertidumbre: 0.060203361785472836\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 1.404', 'Peso máximo al despegue (MTOW): 2.117', 'Velocidad máxima (KIAS): 1.872', 'envergadura: 1.631', 'payload: 2.113']\n", - "**Mediana calculada:** 1.872\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.53\n", - "Valor del parámetro correlacionado para la aeronave: 75.0\n", - "Predicción obtenida: 2.091\n", - "\tR²: 0.9505024269111185, Desviación Estándar: 0.13477094387403904, Varianza: 0.018163207312699377, Incertidumbre: 0.02873326177786689\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.081x + 0.628\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 2.088\n", - "\tR²: 0.8352123461571666, Desviación Estándar: 0.2607982244843819, Varianza: 0.06801571389420606, Incertidumbre: 0.05831625583583279\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Peso máximo al despegue (MTOW): 2.091', 'payload: 2.088']\n", - "**Mediana calculada:** 2.09\n", - "\n", - "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.46x + 0.297\n", - "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 1.447\n", - "\tR²: 0.48817664948956896, Desviación Estándar: 0.3550488365774619, Varianza: 0.12605967635500923, Incertidumbre: 0.08368581333210588\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.53\n", - "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 2.087\n", - "\tR²: 0.9537268168880683, Desviación Estándar: 0.13180876658609073, Varianza: 0.017373550948946544, Incertidumbre: 0.027484027732284446\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.049x + -0.376\n", - "Valor del parámetro correlacionado para la aeronave: 46.3\n", - "Predicción obtenida: 1.872\n", - "\tR²: 0.2800468648798645, Desviación Estándar: 0.5246030655173786, Varianza: 0.27520837635023104, Incertidumbre: 0.13115076637934464\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.397x + -0.238\n", - "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 1.666\n", - "\tR²: 0.6331001883328717, Desviación Estándar: 0.30060886965213346, Varianza: 0.09036569251353338, Incertidumbre: 0.07085419007194885\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.081x + 0.628\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 2.088\n", - "\tR²: 0.8421455166049525, Desviación Estándar: 0.2545135233705542, Varianza: 0.06477713357849366, Incertidumbre: 0.055539404106451286\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 1.447', 'Peso máximo al despegue (MTOW): 2.087', 'Velocidad máxima (KIAS): 1.872', 'envergadura: 1.666', 'payload: 2.088']\n", - "**Mediana calculada:** 1.872\n", - "\n", - "--- Imputación para aeronave: **ScanEagle 3** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.46x + 0.297\n", - "Valor del parámetro correlacionado para la aeronave: 2.4\n", - "Predicción obtenida: 1.401\n", - "\tR²: 0.48817664948956896, Desviación Estándar: 0.3550488365774619, Varianza: 0.12605967635500923, Incertidumbre: 0.08368581333210588\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.53\n", - "Valor del parámetro correlacionado para la aeronave: 36.3\n", - "Predicción obtenida: 1.286\n", - "\tR²: 0.9537268168880683, Desviación Estándar: 0.13180876658609073, Varianza: 0.017373550948946544, Incertidumbre: 0.027484027732284446\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.049x + -0.376\n", - "Valor del parámetro correlacionado para la aeronave: 41.2\n", - "Predicción obtenida: 1.625\n", - "\tR²: 0.2800468648798645, Desviación Estándar: 0.5246030655173786, Varianza: 0.27520837635023104, Incertidumbre: 0.13115076637934464\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.397x + -0.238\n", - "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 1.349\n", - "\tR²: 0.6331001883328717, Desviación Estándar: 0.30060886965213346, Varianza: 0.09036569251353338, Incertidumbre: 0.07085419007194885\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.081x + 0.628\n", - "Valor del parámetro correlacionado para la aeronave: 8.6\n", - "Predicción obtenida: 1.326\n", - "\tR²: 0.8421455166049525, Desviación Estándar: 0.2545135233705542, Varianza: 0.06477713357849366, Incertidumbre: 0.055539404106451286\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 1.401', 'Peso máximo al despegue (MTOW): 1.286', 'Velocidad máxima (KIAS): 1.625', 'envergadura: 1.349', 'payload: 1.326']\n", - "**Mediana calculada:** 1.349\n", - "\n", - "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.458x + 0.299\n", - "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 1.443\n", - "\tR²: 0.49146211696239317, Desviación Estándar: 0.3457725283158643, Varianza: 0.11955864133794519, Incertidumbre: 0.0793256583358636\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.533\n", - "Valor del parámetro correlacionado para la aeronave: 61.0\n", - "Predicción obtenida: 1.802\n", - "\tR²: 0.9532832420705458, Desviación Estándar: 0.12965091612376284, Varianza: 0.016809360051730986, Incertidumbre: 0.026464882432299938\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.046x + -0.31\n", - "Valor del parámetro correlacionado para la aeronave: 46.3\n", - "Predicción obtenida: 1.828\n", - "\tR²: 0.2696364235152151, Desviación Estándar: 0.5128023719074978, Varianza: 0.2629662726339556, Incertidumbre: 0.12437284379069904\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.397x + -0.238\n", - "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 1.666\n", - "\tR²: 0.6358629871775192, Desviación Estándar: 0.2925911882905274, Varianza: 0.08560960346526285, Incertidumbre: 0.06712502218573105\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.081x + 0.63\n", - "Valor del parámetro correlacionado para la aeronave: 17.7\n", - "Predicción obtenida: 2.064\n", - "\tR²: 0.8424950545541062, Desviación Estándar: 0.24870967530649968, Varianza: 0.0618565025910645, Incertidumbre: 0.05302508093991701\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 1.443', 'Peso máximo al despegue (MTOW): 1.802', 'Velocidad máxima (KIAS): 1.828', 'envergadura: 1.666', 'payload: 2.064']\n", - "**Mediana calculada:** 1.802\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.476x + 0.281\n", - "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 0.709\n", - "\tR²: 0.5067722199589602, Desviación Estándar: 0.34569438035947625, Varianza: 0.11950460461212224, Incertidumbre: 0.0772996133923457\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.533\n", - "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 0.662\n", - "\tR²: 0.9542933408598602, Desviación Estándar: 0.12703143691612878, Varianza: 0.016136985964976404, Incertidumbre: 0.025406287383225756\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.405x + -0.261\n", - "Valor del parámetro correlacionado para la aeronave: 2.35\n", - "Predicción obtenida: 0.691\n", - "\tR²: 0.6610034143057635, Desviación Estándar: 0.2865934213089743, Varianza: 0.08213578913758324, Incertidumbre: 0.06408423719511033\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.08x + 0.635\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.73\n", - "\tR²: 0.8368924216256921, Desviación Estándar: 0.24881616391859884, Varianza: 0.061909483427167046, Incertidumbre: 0.05188175662741571\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 0.709', 'Peso máximo al despegue (MTOW): 0.662', 'envergadura: 0.691', 'payload: 0.73']\n", - "**Mediana calculada:** 0.7\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.477x + 0.279\n", - "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 0.708\n", - "\tR²: 0.530038527273323, Desviación Estándar: 0.3373686112553851, Varianza: 0.11381757986038715, Incertidumbre: 0.0736198665799968\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.537\n", - "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 0.666\n", - "\tR²: 0.9563099090104202, Desviación Estándar: 0.12476254599075952, Varianza: 0.015565692882096385, Incertidumbre: 0.024467948329707695\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.405x + -0.259\n", - "Valor del parámetro correlacionado para la aeronave: 2.35\n", - "Predicción obtenida: 0.692\n", - "\tR²: 0.676989457910925, Desviación Estándar: 0.2796931909209141, Varianza: 0.0782282810475229, Incertidumbre: 0.0610340580361345\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.08x + 0.631\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.727\n", - "\tR²: 0.8477300129544219, Desviación Estándar: 0.24364789703392117, Varianza: 0.05936429772905225, Incertidumbre: 0.04973441871960682\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 0.708', 'Peso máximo al despegue (MTOW): 0.666', 'envergadura: 0.692', 'payload: 0.727']\n", - "**Mediana calculada:** 0.7\n", - "\n", - "--- Imputación para aeronave: **V32** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.477x + 0.279\n", - "Valor del parámetro correlacionado para la aeronave: 1.0\n", - "Predicción obtenida: 0.756\n", - "\tR²: 0.530038527273323, Desviación Estándar: 0.3373686112553851, Varianza: 0.11381757986038715, Incertidumbre: 0.0736198665799968\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.537\n", - "Valor del parámetro correlacionado para la aeronave: 23.5\n", - "Predicción obtenida: 1.025\n", - "\tR²: 0.9563099090104202, Desviación Estándar: 0.12476254599075952, Varianza: 0.015565692882096385, Incertidumbre: 0.024467948329707695\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.046x + -0.3\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 1.213\n", - "\tR²: 0.2987388890678494, Desviación Estándar: 0.49838437168601263, Varianza: 0.24838698194086156, Incertidumbre: 0.1174703229521921\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.405x + -0.259\n", - "Valor del parámetro correlacionado para la aeronave: 3.2\n", - "Predicción obtenida: 1.036\n", - "\tR²: 0.676989457910925, Desviación Estándar: 0.2796931909209141, Varianza: 0.0782282810475229, Incertidumbre: 0.0610340580361345\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.08x + 0.631\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 1.03\n", - "\tR²: 0.8477300129544219, Desviación Estándar: 0.24364789703392117, Varianza: 0.05936429772905225, Incertidumbre: 0.04973441871960682\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0]\n", - "Valores para Área del ala: [0.87, 1.158, 0.57, 0.84, 0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.063x + 0.414\n", - "Valor del parámetro correlacionado para la aeronave: 6.45\n", - "Predicción obtenida: 0.818\n", - "\tR²: 0.8862827411226012, Desviación Estándar: 0.22697772477286166, Varianza: 0.05151888754306494, Incertidumbre: 0.06552282524883024\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Precio (r = 0.899) ---\n", - "Aeronaves utilizadas: ['V21', 'V25', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Precio: [3999.0, 4679.0, 2299.0, 6799.0, 4999.0, 6999.0, 9999.0, 13900.0, 15999.0]\n", - "Valores para Área del ala: [0.8, 0.52, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.0x + 0.241\n", - "Valor del parámetro correlacionado para la aeronave: 69999.0\n", - "Predicción obtenida: 11.824\n", - "\tR²: 0.8076686704270842, Desviación Estándar: 0.35422576262319455, Varianza: 0.12547589090598377, Incertidumbre: 0.11807525420773152\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 0.756', 'Peso máximo al despegue (MTOW): 1.025', 'Velocidad máxima (KIAS): 1.213', 'envergadura: 1.036', 'payload: 1.03', 'Empty weight: 0.818', 'Precio: 11.824']\n", - "**Mediana calculada:** 1.03\n", - "\n", - "--- Imputación para aeronave: **V35** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.459x + 0.325\n", - "Valor del parámetro correlacionado para la aeronave: 1.88\n", - "Predicción obtenida: 1.188\n", - "\tR²: 0.5187592344985992, Desviación Estándar: 0.33423029414950794, Varianza: 0.11170988952726661, Incertidumbre: 0.07125813814042148\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.538\n", - "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 1.201\n", - "\tR²: 0.9567615844762626, Desviación Estándar: 0.12243454518963205, Varianza: 0.014990217855792052, Incertidumbre: 0.023562539207781154\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.046x + -0.324\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 1.203\n", - "\tR²: 0.3019978039605754, Desviación Estándar: 0.4868033988346396, Varianza: 0.23697754911695715, Incertidumbre: 0.1116803589943326\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.405x + -0.26\n", - "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 1.157\n", - "\tR²: 0.678309022659344, Desviación Estándar: 0.27326502283958154, Varianza: 0.07467377270751702, Incertidumbre: 0.058260298624331026\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.08x + 0.631\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 1.429\n", - "\tR²: 0.850762300542554, Desviación Estándar: 0.23872521083313977, Varianza: 0.05698972628732704, Incertidumbre: 0.047745042166627956\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Precio (r = 0.899) ---\n", - "Aeronaves utilizadas: ['V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Precio: [3999.0, 4679.0, 69999.0, 2299.0, 6799.0, 4999.0, 6999.0, 9999.0, 13900.0, 15999.0]\n", - "Valores para Área del ala: [0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.0x + 1.468\n", - "Valor del parámetro correlacionado para la aeronave: 7999.0\n", - "Predicción obtenida: 1.471\n", - "\tR²: 5.510644397854758e-05, Desviación Estándar: 0.7803061547023157, Varianza: 0.6088776950663142, Incertidumbre: 0.24675447211070242\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Longitud del fuselaje: 1.188', 'Peso máximo al despegue (MTOW): 1.201', 'Velocidad máxima (KIAS): 1.203', 'envergadura: 1.157', 'payload: 1.429', 'Precio: 1.471']\n", - "**Mediana calculada:** 1.202\n", - "\n", - "--- Imputación para aeronave: **V39** ---\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.538\n", - "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 1.035\n", - "\tR²: 0.9568416510162402, Desviación Estándar: 0.12022845817986676, Varianza: 0.014454882156307969, Incertidumbre: 0.022721042918332626\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.046x + -0.324\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 1.203\n", - "\tR²: 0.30291737528467566, Desviación Estándar: 0.474477281198844, Varianza: 0.2251286903738469, Incertidumbre: 0.10609634545398981\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.405x + -0.257\n", - "Valor del parámetro correlacionado para la aeronave: 3.9\n", - "Predicción obtenida: 1.32\n", - "\tR²: 0.6779800432691134, Desviación Estándar: 0.26741850007254947, Varianza: 0.07151265418105215, Incertidumbre: 0.05576061185064946\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.08x + 0.622\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 1.021\n", - "\tR²: 0.8465859657242829, Desviación Estándar: 0.23811937696573565, Varianza: 0.05670083768655011, Incertidumbre: 0.0466990519120324\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Precio (r = 0.899) ---\n", - "Aeronaves utilizadas: ['V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Precio: [3999.0, 4679.0, 69999.0, 7999.0, 2299.0, 6799.0, 4999.0, 6999.0, 9999.0, 13900.0, 15999.0]\n", - "Valores para Área del ala: [0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.0x + 1.439\n", - "Valor del parámetro correlacionado para la aeronave: 8999.0\n", - "Predicción obtenida: 1.445\n", - "\tR²: 0.00029183893894446644, Desviación Estándar: 0.7479568811282364, Varianza: 0.5594394960270789, Incertidumbre: 0.22551748491516507\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Peso máximo al despegue (MTOW): 1.035', 'Velocidad máxima (KIAS): 1.203', 'envergadura: 1.32', 'payload: 1.021', 'Precio: 1.445']\n", - "**Mediana calculada:** 1.203\n", - "\n", - "--- Imputación para aeronave: **Volitation VT370** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.459x + 0.326\n", - "Valor del parámetro correlacionado para la aeronave: 2.02\n", - "Predicción obtenida: 1.253\n", - "\tR²: 0.5188052941313532, Desviación Estándar: 0.3268966538966882, Varianza: 0.10686142232885117, Incertidumbre: 0.06816266424448632\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.548\n", - "Valor del parámetro correlacionado para la aeronave: 40.0\n", - "Predicción obtenida: 1.373\n", - "\tR²: 0.9540497205736983, Desviación Estándar: 0.12200219933246954, Varianza: 0.014884536641959632, Incertidumbre: 0.022655239663387023\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.046x + -0.324\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 1.203\n", - "\tR²: 0.3037289741090303, Desviación Estándar: 0.46304242133609924, Varianza: 0.21440828395679767, Incertidumbre: 0.10104414027372904\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.403x + -0.255\n", - "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 2.362\n", - "\tR²: 0.6754640443421005, Desviación Estándar: 0.262831271883581, Varianza: 0.06908027747994087, Incertidumbre: 0.053650208713457374\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.079x + 0.636\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 2.06\n", - "\tR²: 0.8442705988266823, Desviación Estándar: 0.23612835689484077, Varianza: 0.05575660092985729, Incertidumbre: 0.04544292347218011\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Capacidad combustible (r = 1.0) ---\n", - "Aeronaves utilizadas: ['Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Capacidad combustible: [11.5, 11.5, 28.0]\n", - "Valores para Área del ala: [1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.078x + 0.426\n", - "Valor del parámetro correlacionado para la aeronave: 13.0\n", - "Predicción obtenida: 1.442\n", - "\tR²: 0.9999549326242733, Desviación Estándar: 0.004082482904638634, Varianza: 1.6666666666666698e-05, Incertidumbre: 0.002357022603955161\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Precio (r = 0.899) ---\n", - "Aeronaves utilizadas: ['V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Precio: [3999.0, 4679.0, 69999.0, 7999.0, 8999.0, 2299.0, 6799.0, 4999.0, 6999.0, 9999.0, 13900.0, 15999.0]\n", - "Valores para Área del ala: [0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.0x + 1.415\n", - "Valor del parámetro correlacionado para la aeronave: 8999.0\n", - "Predicción obtenida: 1.424\n", - "\tR²: 0.0005527163843784821, Desviación Estándar: 0.7192113906007205, Varianza: 0.5172650243698221, Incertidumbre: 0.20761844498378554\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Longitud del fuselaje: 1.253', 'Peso máximo al despegue (MTOW): 1.373', 'Velocidad máxima (KIAS): 1.203', 'envergadura: 2.362', 'payload: 2.06', 'Capacidad combustible: 1.442', 'Precio: 1.424']\n", - "**Mediana calculada:** 1.424\n", - "\n", - "--- Imputación para aeronave: **Volitation VT510** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.461x + 0.329\n", - "Valor del parámetro correlacionado para la aeronave: 2.905\n", - "Predicción obtenida: 1.668\n", - "\tR²: 0.5190019194394964, Desviación Estándar: 0.32183760586109134, Varianza: 0.10357944454639917, Incertidumbre: 0.06569482619988655\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.021x + 0.549\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 2.612\n", - "\tR²: 0.9538216857828705, Desviación Estándar: 0.12030701287802409, Varianza: 0.014473777347633054, Incertidumbre: 0.02196495492645277\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.046x + -0.299\n", - "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 1.993\n", - "\tR²: 0.2984442398036834, Desviación Estándar: 0.45473235019931246, Varianza: 0.20678151031779013, Incertidumbre: 0.09694926281256372\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.313x + 0.036\n", - "Valor del parámetro correlacionado para la aeronave: 5.1\n", - "Predicción obtenida: 1.632\n", - "\tR²: 0.5619559117531061, Desviación Estándar: 0.30093688814319053, Varianza: 0.09056301064530718, Incertidumbre: 0.06018737762863811\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.076x + 0.65\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 2.539\n", - "\tR²: 0.8057229726084794, Desviación Estándar: 0.25898874198312066, Varianza: 0.06707516847399946, Incertidumbre: 0.04894427168948646\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Capacidad combustible (r = 1.0) ---\n", - "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Capacidad combustible: [13.0, 11.5, 11.5, 28.0]\n", - "Valores para Área del ala: [1.424, 1.33, 1.32, 2.615]\n", - "Ecuación de regresión: y = 0.078x + 0.417\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 2.378\n", - "\tR²: 0.9997609226468374, Desviación Estándar: 0.008439335502332609, Varianza: 7.122238372093159e-05, Incertidumbre: 0.004219667751166304\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Precio (r = 0.899) ---\n", - "Aeronaves utilizadas: ['V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Precio: [3999.0, 4679.0, 69999.0, 7999.0, 8999.0, 8999.0, 2299.0, 6799.0, 4999.0, 6999.0, 9999.0, 13900.0, 15999.0]\n", - "Valores para Área del ala: [0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.0x + 1.415\n", - "Valor del parámetro correlacionado para la aeronave: 16599.0\n", - "Predicción obtenida: 1.431\n", - "\tR²: 0.0005545122547352399, Desviación Estándar: 0.69099596127085, Varianza: 0.4774754184926259, Incertidumbre: 0.19164779765389064\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Longitud del fuselaje: 1.668', 'Peso máximo al despegue (MTOW): 2.612', 'Velocidad máxima (KIAS): 1.993', 'envergadura: 1.632', 'payload: 2.539', 'Capacidad combustible: 2.378', 'Precio: 1.431']\n", - "**Mediana calculada:** 1.993\n", - "\n", - "--- Imputación para aeronave: **Ascend** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993]\n", - "Ecuación de regresión: y = 0.485x + 0.296\n", - "Valor del parámetro correlacionado para la aeronave: 1.562\n", - "Predicción obtenida: 1.054\n", - "\tR²: 0.5551210410771654, Desviación Estándar: 0.32120350506612844, Varianza: 0.10317169166676639, Incertidumbre: 0.06424070101322568\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993]\n", - "Ecuación de regresión: y = 0.019x + 0.589\n", - "Valor del parámetro correlacionado para la aeronave: 9.5\n", - "Predicción obtenida: 0.771\n", - "\tR²: 0.9237785089671043, Desviación Estándar: 0.15544994346429547, Varianza: 0.02416468492305266, Incertidumbre: 0.027919634045949875\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993]\n", - "Ecuación de regresión: y = 0.046x + -0.299\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 1.076\n", - "\tR²: 0.3456241929431657, Desviación Estándar: 0.44473701805432836, Varianza: 0.197791015227856, Incertidumbre: 0.09273407872908897\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993]\n", - "Ecuación de regresión: y = 0.329x + -0.009\n", - "Valor del parámetro correlacionado para la aeronave: 2.0\n", - "Predicción obtenida: 0.649\n", - "\tR²: 0.58926011492781, Desviación Estándar: 0.3026438405729111, Varianza: 0.09159329423672163, Incertidumbre: 0.05935334033653606\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993]\n", - "Ecuación de regresión: y = 0.071x + 0.685\n", - "Valor del parámetro correlacionado para la aeronave: 0.6\n", - "Predicción obtenida: 0.727\n", - "\tR²: 0.7863972465064352, Desviación Estándar: 0.27091760618201804, Varianza: 0.07339634933939503, Incertidumbre: 0.0503081364980872\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.923) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 6.8]\n", - "Valores para Área del ala: [1.55, 1.55, 1.55, 2.503, 0.84]\n", - "Ecuación de regresión: y = 0.025x + 0.682\n", - "Valor del parámetro correlacionado para la aeronave: 8.9\n", - "Predicción obtenida: 0.904\n", - "\tR²: 0.9483245120012642, Desviación Estándar: 0.12030916806448211, Varianza: 0.014474295920367803, Incertidumbre: 0.05380389562172576\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0]\n", - "Valores para Área del ala: [0.87, 1.158, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Ecuación de regresión: y = 0.062x + 0.442\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 0.627\n", - "\tR²: 0.8795539331981305, Desviación Estándar: 0.22504777591475647, Varianza: 0.05064650144417845, Incertidumbre: 0.0624170227299834\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 1.054', 'Peso máximo al despegue (MTOW): 0.771', 'Velocidad máxima (KIAS): 1.076', 'envergadura: 0.649', 'payload: 0.727', 'RTF (Including fuel & Batteries): 0.904', 'Empty weight: 0.627']\n", - "**Mediana calculada:** 0.771\n", - "\n", - "--- Imputación para aeronave: **Transition** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771]\n", - "Ecuación de regresión: y = 0.492x + 0.273\n", - "Valor del parámetro correlacionado para la aeronave: 2.3\n", - "Predicción obtenida: 1.404\n", - "\tR²: 0.5564924093650789, Desviación Estándar: 0.31959359329400444, Varianza: 0.10214006487457353, Incertidumbre: 0.0626774603317448\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771]\n", - "Ecuación de regresión: y = 0.019x + 0.589\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 0.933\n", - "\tR²: 0.9262301602367577, Desviación Estándar: 0.15300176981950134, Varianza: 0.023409541567899674, Incertidumbre: 0.02704714724322816\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771]\n", - "Ecuación de regresión: y = 0.047x + -0.36\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 1.056\n", - "\tR²: 0.36011776983130794, Desviación Estándar: 0.43951432810167035, Varianza: 0.19317284460666273, Incertidumbre: 0.08971548654094016\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771]\n", - "Ecuación de regresión: y = 0.323x + 0.017\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 0.986\n", - "\tR²: 0.600080995980541, Desviación Estándar: 0.29780984654529935, Varianza: 0.08869070469933475, Incertidumbre: 0.05731353169008324\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771]\n", - "Ecuación de regresión: y = 0.07x + 0.689\n", - "Valor del parámetro correlacionado para la aeronave: 1.5\n", - "Predicción obtenida: 0.795\n", - "\tR²: 0.7955552594057932, Desviación Estándar: 0.26647289638938826, Varianza: 0.07100780451014965, Incertidumbre: 0.04865107210540163\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.923) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 6.8, 8.9]\n", - "Valores para Área del ala: [1.55, 1.55, 1.55, 2.503, 0.84, 0.771]\n", - "Ecuación de regresión: y = 0.026x + 0.624\n", - "Valor del parámetro correlacionado para la aeronave: 16.5\n", - "Predicción obtenida: 1.054\n", - "\tR²: 0.9575373795814451, Desviación Estándar: 0.11811377037897985, Varianza: 0.01395086275313838, Incertidumbre: 0.04821974483745979\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0]\n", - "Valores para Área del ala: [0.87, 1.158, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771]\n", - "Ecuación de regresión: y = 0.061x + 0.463\n", - "Valor del parámetro correlacionado para la aeronave: 5.8\n", - "Predicción obtenida: 0.816\n", - "\tR²: 0.8799346208461828, Desviación Estándar: 0.21981540136860422, Varianza: 0.04831881067884058, Incertidumbre: 0.0587481371612512\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 1.404', 'Peso máximo al despegue (MTOW): 0.933', 'Velocidad máxima (KIAS): 1.056', 'envergadura: 0.986', 'payload: 0.795', 'RTF (Including fuel & Batteries): 1.054', 'Empty weight: 0.816']\n", - "**Mediana calculada:** 0.986\n", - "\n", - "--- Imputación para aeronave: **Reach** ---\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986]\n", - "Ecuación de regresión: y = 0.48x + 0.28\n", - "Valor del parámetro correlacionado para la aeronave: 4.712\n", - "Predicción obtenida: 2.54\n", - "\tR²: 0.5321565310192382, Desviación Estándar: 0.3232764059484733, Varianza: 0.1045076346429621, Incertidumbre: 0.06221457333233526\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.977) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986]\n", - "Ecuación de regresión: y = 0.019x + 0.592\n", - "Valor del parámetro correlacionado para la aeronave: 91.0\n", - "Predicción obtenida: 2.329\n", - "\tR²: 0.9268130919795654, Desviación Estándar: 0.15093208149961143, Varianza: 0.02278049322580535, Incertidumbre: 0.026273902955966707\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986]\n", - "Ecuación de regresión: y = 0.047x + -0.373\n", - "Valor del parámetro correlacionado para la aeronave: 35.0\n", - "Predicción obtenida: 1.289\n", - "\tR²: 0.36780246644034287, Desviación Estándar: 0.4308491629268742, Varianza: 0.18563100119478818, Incertidumbre: 0.08616983258537483\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0, 3.0]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986]\n", - "Ecuación de regresión: y = 0.323x + 0.017\n", - "Valor del parámetro correlacionado para la aeronave: 6.0\n", - "Predicción obtenida: 1.954\n", - "\tR²: 0.6029729846601317, Desviación Estándar: 0.2924434664063362, Varianza: 0.0855231810437539, Incertidumbre: 0.05526662033263126\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5]\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986]\n", - "Ecuación de regresión: y = 0.069x + 0.705\n", - "Valor del parámetro correlacionado para la aeronave: 7.0\n", - "Predicción obtenida: 1.191\n", - "\tR²: 0.7961115317688139, Desviación Estándar: 0.26421267929509595, Varianza: 0.06980833990029324, Incertidumbre: 0.047453998064099126\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.923) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend', 'Transition']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 6.8, 8.9, 16.5]\n", - "Valores para Área del ala: [1.55, 1.55, 1.55, 2.503, 0.84, 0.771, 0.986]\n", - "Ecuación de regresión: y = 0.026x + 0.605\n", - "Valor del parámetro correlacionado para la aeronave: 84.0\n", - "Predicción obtenida: 2.82\n", - "\tR²: 0.9596137014057883, Desviación Estándar: 0.11174694285275365, Varianza: 0.01248737923693659, Incertidumbre: 0.04223637436573326\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8]\n", - "Valores para Área del ala: [0.87, 1.158, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986]\n", - "Ecuación de regresión: y = 0.06x + 0.482\n", - "Valor del parámetro correlacionado para la aeronave: 31.0\n", - "Predicción obtenida: 2.346\n", - "\tR²: 0.8758975698949775, Desviación Estándar: 0.21647900912469786, Varianza: 0.046863161391611015, Incertidumbre: 0.05589463980956255\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Longitud del fuselaje: 2.54', 'Peso máximo al despegue (MTOW): 2.329', 'Velocidad máxima (KIAS): 1.289', 'envergadura: 1.954', 'payload: 1.191', 'RTF (Including fuel & Batteries): 2.82', 'Empty weight: 2.346']\n", - "**Mediana calculada:** 2.329\n", - "\n", - "=== Imputación para el parámetro: **Relación de aspecto del ala** ===\n", - "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754]\n", - "Ecuación de regresión: y = -0.27x + 19.967\n", - "Valor del parámetro correlacionado para la aeronave: 30.407\n", - "Predicción obtenida: 11.753\n", - "\tR²: 0.9958397104049683, Desviación Estándar: 0.08406165080066717, Varianza: 0.007066361135333309, Incertidumbre: 0.03759351309822828\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754]\n", - "Ecuación de regresión: y = -1.556x + 16.146\n", - "Valor del parámetro correlacionado para la aeronave: 0.94\n", - "Predicción obtenida: 14.684\n", - "\tR²: 0.6391425255554871, Desviación Estándar: 0.7828958406065879, Varianza: 0.6129258972390959, Incertidumbre: 0.3501216637796341\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", - "Ecuación de regresión: y = -1.519x + 18.038\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 16.214\n", - "\tR²: 0.4138028760447283, Desviación Estándar: 0.8884554790463566, Varianza: 0.789353138247491, Incertidumbre: 0.4442277395231783\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754]\n", - "Ecuación de regresión: y = -0.04x + 15.296\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 14.489\n", - "\tR²: 0.6775655448926923, Desviación Estándar: 0.7447916715878835, Varianza: 0.5547146340666738, Incertidumbre: 0.28150479165337583\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", - "Ecuación de regresión: y = -0.176x + 19.272\n", - "Valor del parámetro correlacionado para la aeronave: 41.7\n", - "Predicción obtenida: 11.946\n", - "\tR²: 0.6228198639466289, Desviación Estándar: 0.7126696016612697, Varianza: 0.5078979611320329, Incertidumbre: 0.35633480083063485\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5]\n", - "Ecuación de regresión: y = -0.303x + 20.091\n", - "Valor del parámetro correlacionado para la aeronave: 27.8\n", - "Predicción obtenida: 11.659\n", - "\tR²: 0.9951683800002252, Desviación Estándar: 0.0922049930648979, Varianza: 0.00850176074609787, Incertidumbre: 0.05323457756664639\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 11.753', 'Área del ala: 14.684', 'Longitud del fuselaje: 16.214', 'Peso máximo al despegue (MTOW): 14.489', 'Velocidad máxima (KIAS): 11.946', 'Crucero KIAS: 11.659']\n", - "**Mediana calculada:** 13.218\n", - "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218]\n", - "Ecuación de regresión: y = -0.211x + 18.836\n", - "Valor del parámetro correlacionado para la aeronave: 26.611\n", - "Predicción obtenida: 13.222\n", - "\tR²: 0.86788574295378, Desviación Estándar: 0.44779548895963533, Varianza: 0.2005207999325989, Incertidumbre: 0.1828117428452008\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218]\n", - "Ecuación de regresión: y = -1.355x + 15.647\n", - "Valor del parámetro correlacionado para la aeronave: 1.608\n", - "Predicción obtenida: 13.469\n", - "\tR²: 0.4774673753859955, Desviación Estándar: 0.8905567020216546, Varianza: 0.793091239515686, Incertidumbre: 0.3635682511614764\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218]\n", - "Ecuación de regresión: y = -0.157x + 14.552\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 14.364\n", - "\tR²: 0.0074891649212437406, Desviación Estándar: 1.1481791111587527, Varianza: 1.3183152713013033, Incertidumbre: 0.5134813085792517\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218]\n", - "Ecuación de regresión: y = -0.036x + 14.967\n", - "Valor del parámetro correlacionado para la aeronave: 55.0\n", - "Predicción obtenida: 12.99\n", - "\tR²: 0.5739892405979949, Desviación Estándar: 0.8056365874036895, Varianza: 0.6490503109634626, Incertidumbre: 0.28483554706256875\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218]\n", - "Ecuación de regresión: y = -0.122x + 17.914\n", - "Valor del parámetro correlacionado para la aeronave: 36.0\n", - "Predicción obtenida: 13.511\n", - "\tR²: 0.6167320222408146, Desviación Estándar: 0.7134988554846888, Varianza: 0.5090806167779609, Incertidumbre: 0.3190863885464126\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5]\n", - "Ecuación de regresión: y = -0.158x + 15.309\n", - "Valor del parámetro correlacionado para la aeronave: 12.0\n", - "Predicción obtenida: 13.417\n", - "\tR²: 0.7878676953385522, Desviación Estándar: 0.6109489630792406, Varianza: 0.3732586354875993, Incertidumbre: 0.24941886973776978\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.222', 'Área del ala: 13.469', 'Longitud del fuselaje: 14.364', 'Peso máximo al despegue (MTOW): 12.99', 'Velocidad máxima (KIAS): 13.511', 'payload: 13.417']\n", - "**Mediana calculada:** 13.443\n", - "\n", - "--- Imputación para aeronave: **Orbiter 3** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443]\n", - "Ecuación de regresión: y = -0.208x + 18.787\n", - "Valor del parámetro correlacionado para la aeronave: 26.611\n", - "Predicción obtenida: 13.263\n", - "\tR²: 0.866540858244522, Desviación Estándar: 0.42137143710599334, Varianza: 0.17755388800877012, Incertidumbre: 0.1592634331669075\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443]\n", - "Ecuación de regresión: y = -1.358x + 15.647\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 14.018\n", - "\tR²: 0.48897119697787084, Desviación Estándar: 0.8245435883924694, Varianza: 0.67987212915913, Incertidumbre: 0.31164818285989687\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443]\n", - "Ecuación de regresión: y = 0.102x + 13.89\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 14.012\n", - "\tR²: 0.003963308805261412, Desviación Estándar: 1.089046175187781, Varianza: 1.1860215716911349, Incertidumbre: 0.44460123925662026\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443]\n", - "Ecuación de regresión: y = -0.035x + 14.97\n", - "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 13.857\n", - "\tR²: 0.5601880164889009, Desviación Estándar: 0.7722572050409231, Varianza: 0.5963811907376184, Incertidumbre: 0.25741906834697437\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443]\n", - "Ecuación de regresión: y = -0.123x + 17.936\n", - "Valor del parámetro correlacionado para la aeronave: 36.0\n", - "Predicción obtenida: 13.495\n", - "\tR²: 0.6432306766680158, Desviación Estándar: 0.6517820231226291, Varianza: 0.4248198056658274, Incertidumbre: 0.2660888966948914\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443]\n", - "Ecuación de regresión: y = -0.158x + 15.313\n", - "Valor del parámetro correlacionado para la aeronave: 5.5\n", - "Predicción obtenida: 14.446\n", - "\tR²: 0.787811738102038, Desviación Estándar: 0.565704012619775, Varianza: 0.3200210298941146, Incertidumbre: 0.21381601900903846\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.263', 'Área del ala: 14.018', 'Longitud del fuselaje: 14.012', 'Peso máximo al despegue (MTOW): 13.857', 'Velocidad máxima (KIAS): 13.495', 'payload: 14.446']\n", - "**Mediana calculada:** 13.934\n", - "\n", - "--- Imputación para aeronave: **Mantis** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934]\n", - "Ecuación de regresión: y = -0.199x + 18.661\n", - "Valor del parámetro correlacionado para la aeronave: 18.266\n", - "Predicción obtenida: 15.029\n", - "\tR²: 0.8259424603654436, Desviación Estándar: 0.45023923501123037, Varianza: 0.2027153687434979, Incertidumbre: 0.1591836081163423\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934]\n", - "Ecuación de regresión: y = -1.354x + 15.633\n", - "Valor del parámetro correlacionado para la aeronave: 0.754\n", - "Predicción obtenida: 14.611\n", - "\tR²: 0.48854653028077, Desviación Estándar: 0.7717914841458512, Varianza: 0.5956620950000557, Incertidumbre: 0.27286949605078054\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934]\n", - "Ecuación de regresión: y = 0.118x + 13.849\n", - "Valor del parámetro correlacionado para la aeronave: 1.48\n", - "Predicción obtenida: 14.024\n", - "\tR²: 0.006260092700906439, Desviación Estándar: 1.0085765160538325, Varianza: 1.0172265887352867, Incertidumbre: 0.3812060913797692\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934]\n", - "Ecuación de regresión: y = -0.035x + 14.982\n", - "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 14.755\n", - "\tR²: 0.5642432263469788, Desviación Estándar: 0.732986855728399, Varianza: 0.5372697306706048, Incertidumbre: 0.23179079590669788\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934]\n", - "Ecuación de regresión: y = 0.0x + 14.449\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 14.451\n", - "\tR²: 0.00695796488843492, Desviación Estándar: 0.8160949284662801, Varianza: 0.6660109322683828, Incertidumbre: 0.36496874722868605\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934]\n", - "Ecuación de regresión: y = -0.118x + 17.819\n", - "Valor del parámetro correlacionado para la aeronave: 25.6\n", - "Predicción obtenida: 14.805\n", - "\tR²: 0.6226111062384435, Desviación Estándar: 0.6215370810067415, Varianza: 0.38630834306638073, Incertidumbre: 0.23491893527840643\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218]\n", - "Ecuación de regresión: y = -0.214x + 18.598\n", - "Valor del parámetro correlacionado para la aeronave: 16.7\n", - "Predicción obtenida: 15.024\n", - "\tR²: 0.8546770471425846, Desviación Estándar: 0.4778243263623709, Varianza: 0.2283160868636535, Incertidumbre: 0.23891216318118544\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 15.029', 'Área del ala: 14.611', 'Longitud del fuselaje: 14.024', 'Peso máximo al despegue (MTOW): 14.755', 'Alcance de la aeronave: 14.451', 'Velocidad máxima (KIAS): 14.805', 'Crucero KIAS: 15.024']\n", - "**Mediana calculada:** 14.755\n", - "\n", - "--- Imputación para aeronave: **ScanEagle** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755]\n", - "Ecuación de regresión: y = -0.193x + 18.483\n", - "Valor del parámetro correlacionado para la aeronave: 30.625\n", - "Predicción obtenida: 12.586\n", - "\tR²: 0.8322301664609455, Desviación Estándar: 0.43196932859791437, Varianza: 0.18659750084933294, Incertidumbre: 0.14398977619930478\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755]\n", - "Ecuación de regresión: y = -1.38x + 15.68\n", - "Valor del parámetro correlacionado para la aeronave: 1.063\n", - "Predicción obtenida: 14.213\n", - "\tR²: 0.5222926726573038, Desviación Estándar: 0.7289150646703415, Varianza: 0.5313171715033681, Incertidumbre: 0.24297168822344717\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755]\n", - "Ecuación de regresión: y = 0.046x + 14.071\n", - "Valor del parámetro correlacionado para la aeronave: 1.71\n", - "Predicción obtenida: 14.149\n", - "\tR²: 0.0009397134116212458, Desviación Estándar: 0.9728249313313656, Varianza: 0.9463883470198762, Incertidumbre: 0.34394555292587303\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755]\n", - "Ecuación de regresión: y = -0.035x + 14.982\n", - "Valor del parámetro correlacionado para la aeronave: 26.5\n", - "Predicción obtenida: 14.057\n", - "\tR²: 0.6034073830513715, Desviación Estándar: 0.698875551331174, Varianza: 0.48842703624845235, Incertidumbre: 0.21071890717438813\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755]\n", - "Ecuación de regresión: y = -0.117x + 17.789\n", - "Valor del parámetro correlacionado para la aeronave: 41.2\n", - "Predicción obtenida: 12.97\n", - "\tR²: 0.6429053256386628, Desviación Estándar: 0.5816082132170911, Varianza: 0.3382681136815773, Incertidumbre: 0.20562955577979825\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934]\n", - "Ecuación de regresión: y = -0.15x + 15.164\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 14.414\n", - "\tR²: 0.7728271432710303, Desviación Estándar: 0.5530726265073179, Varianza: 0.30588933019170317, Incertidumbre: 0.19554070234598958\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755]\n", - "Ecuación de regresión: y = -0.207x + 18.401\n", - "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 12.607\n", - "\tR²: 0.8547867905694756, Desviación Estándar: 0.43920425690338755, Varianza: 0.19290037928205683, Incertidumbre: 0.19641811488865116\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.586', 'Área del ala: 14.213', 'Longitud del fuselaje: 14.149', 'Peso máximo al despegue (MTOW): 14.057', 'Velocidad máxima (KIAS): 12.97', 'payload: 14.414', 'Crucero KIAS: 12.607']\n", - "**Mediana calculada:** 14.057\n", - "\n", - "--- Imputación para aeronave: **Integrator** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057]\n", - "Ecuación de regresión: y = -0.157x + 17.782\n", - "Valor del parámetro correlacionado para la aeronave: 30.953\n", - "Predicción obtenida: 12.908\n", - "\tR²: 0.6712253814763909, Desviación Estándar: 0.5738697173551367, Varianza: 0.32932645249726444, Incertidumbre: 0.18147353870392907\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057]\n", - "Ecuación de regresión: y = -1.371x + 15.653\n", - "Valor del parámetro correlacionado para la aeronave: 1.872\n", - "Predicción obtenida: 13.087\n", - "\tR²: 0.5204404709133397, Desviación Estándar: 0.6930832915381803, Varianza: 0.4803644490093982, Incertidumbre: 0.21917218094671553\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057]\n", - "Ecuación de regresión: y = 0.049x + 14.056\n", - "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 14.177\n", - "\tR²: 0.001042484555474843, Desviación Estándar: 0.917647035048045, Varianza: 0.8420760809324679, Incertidumbre: 0.305882345016015\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057]\n", - "Ecuación de regresión: y = -0.035x + 14.982\n", - "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 12.372\n", - "\tR²: 0.6067435478664236, Desviación Estándar: 0.669122407636399, Varianza: 0.4477247964011312, Incertidumbre: 0.19315900108484274\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057]\n", - "Ecuación de regresión: y = -0.095x + 17.205\n", - "Valor del parámetro correlacionado para la aeronave: 46.3\n", - "Predicción obtenida: 12.803\n", - "\tR²: 0.5330694834785845, Desviación Estándar: 0.6273765557788624, Varianza: 0.39360134274094805, Incertidumbre: 0.20912551859295414\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057]\n", - "Ecuación de regresión: y = -0.145x + 15.074\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 12.46\n", - "\tR²: 0.7690943364673855, Desviación Estándar: 0.53238130110139, Varianza: 0.28342984976240887, Incertidumbre: 0.17746043370046335\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057]\n", - "Ecuación de regresión: y = -0.156x + 17.537\n", - "Valor del parámetro correlacionado para la aeronave: 28.3\n", - "Predicción obtenida: 13.136\n", - "\tR²: 0.6651166916698918, Desviación Estándar: 0.6098787538174729, Varianza: 0.3719520943579537, Incertidumbre: 0.2489819586362145\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.908', 'Área del ala: 13.087', 'Longitud del fuselaje: 14.177', 'Peso máximo al despegue (MTOW): 12.372', 'Velocidad máxima (KIAS): 12.803', 'payload: 12.46', 'Crucero KIAS: 13.136']\n", - "**Mediana calculada:** 12.908\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908]\n", - "Ecuación de regresión: y = -0.157x + 17.782\n", - "Valor del parámetro correlacionado para la aeronave: 21.463\n", - "Predicción obtenida: 14.403\n", - "\tR²: 0.7022195601444223, Desviación Estándar: 0.5471633158350149, Varianza: 0.2993876941955682, Incertidumbre: 0.16497594706104288\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908]\n", - "Ecuación de regresión: y = -1.404x + 15.68\n", - "Valor del parámetro correlacionado para la aeronave: 2.09\n", - "Predicción obtenida: 12.745\n", - "\tR²: 0.5633237239923707, Desviación Estándar: 0.6625958144708088, Varianza: 0.43903321335423456, Incertidumbre: 0.199780154932692\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908]\n", - "Ecuación de regresión: y = -0.033x + 14.932\n", - "Valor del parámetro correlacionado para la aeronave: 75.0\n", - "Predicción obtenida: 12.491\n", - "\tR²: 0.6086276801287058, Desviación Estándar: 0.6558904966751431, Varianza: 0.4301923436287659, Incertidumbre: 0.18191129360398245\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908]\n", - "Ecuación de regresión: y = -0.138x + 15.044\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 12.551\n", - "\tR²: 0.7628945280960475, Desviación Estándar: 0.5206483180033946, Varianza: 0.2710746710397639, Incertidumbre: 0.1646434544826377\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.403', 'Área del ala: 12.745', 'Peso máximo al despegue (MTOW): 12.491', 'payload: 12.551']\n", - "**Mediana calculada:** 12.648\n", - "\n", - "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648]\n", - "Ecuación de regresión: y = -0.141x + 17.233\n", - "Valor del parámetro correlacionado para la aeronave: 33.045\n", - "Predicción obtenida: 12.574\n", - "\tR²: 0.5159927919290995, Desviación Estándar: 0.7087508493711017, Varianza: 0.502327766484258, Incertidumbre: 0.20459874683639073\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648]\n", - "Ecuación de regresión: y = -1.423x + 15.698\n", - "Valor del parámetro correlacionado para la aeronave: 1.872\n", - "Predicción obtenida: 13.033\n", - "\tR²: 0.6116464984342147, Desviación Estándar: 0.6348648613448064, Varianza: 0.4030533921703601, Incertidumbre: 0.1832696992982292\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908]\n", - "Ecuación de regresión: y = -0.163x + 14.323\n", - "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 13.917\n", - "\tR²: 0.011206334911924909, Desviación Estándar: 0.9412441639775184, Varianza: 0.8859405762217376, Incertidumbre: 0.2976475392509969\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648]\n", - "Ecuación de regresión: y = -0.032x + 14.92\n", - "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 12.528\n", - "\tR²: 0.6321568018371059, Desviación Estándar: 0.6331526544068619, Varianza: 0.4008822837824551, Incertidumbre: 0.16921716473692594\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755]\n", - "Ecuación de regresión: y = 0.0x + 14.526\n", - "Valor del parámetro correlacionado para la aeronave: 500.0\n", - "Predicción obtenida: 14.54\n", - "\tR²: 0.0015535116931560955, Desviación Estándar: 0.752709188227462, Varianza: 0.5665711220420449, Incertidumbre: 0.30729223931030347\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908]\n", - "Ecuación de regresión: y = -0.093x + 17.144\n", - "Valor del parámetro correlacionado para la aeronave: 46.3\n", - "Predicción obtenida: 12.84\n", - "\tR²: 0.6038367583191466, Desviación Estándar: 0.5957808601774445, Varianza: 0.35495483335377565, Incertidumbre: 0.18840245044950332\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648]\n", - "Ecuación de regresión: y = -0.137x + 15.038\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 12.567\n", - "\tR²: 0.7750989860654982, Desviación Estándar: 0.4971353116989971, Varianza: 0.24714351813805902, Incertidumbre: 0.14989193626745023\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.574', 'Área del ala: 13.033', 'Longitud del fuselaje: 13.917', 'Peso máximo al despegue (MTOW): 12.528', 'Alcance de la aeronave: 14.54', 'Velocidad máxima (KIAS): 12.84', 'payload: 12.567']\n", - "**Mediana calculada:** 12.84\n", - "\n", - "--- Imputación para aeronave: **ScanEagle 3** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84]\n", - "Ecuación de regresión: y = -0.136x + 17.118\n", - "Valor del parámetro correlacionado para la aeronave: 25.703\n", - "Predicción obtenida: 13.632\n", - "\tR²: 0.5415803492274942, Desviación Estándar: 0.6840025560016795, Varianza: 0.4678594966168307, Incertidumbre: 0.18970817601634496\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84]\n", - "Ecuación de regresión: y = -1.447x + 15.715\n", - "Valor del parámetro correlacionado para la aeronave: 1.349\n", - "Predicción obtenida: 13.764\n", - "\tR²: 0.6330253270433377, Desviación Estándar: 0.6119897631287073, Varianza: 0.37453147017433136, Incertidumbre: 0.16973542084766274\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84]\n", - "Ecuación de regresión: y = -0.311x + 14.512\n", - "Valor del parámetro correlacionado para la aeronave: 2.4\n", - "Predicción obtenida: 13.765\n", - "\tR²: 0.039424169255703134, Desviación Estándar: 0.9449335704173759, Varianza: 0.8928994525017299, Incertidumbre: 0.2849081913532099\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84]\n", - "Ecuación de regresión: y = -0.031x + 14.9\n", - "Valor del parámetro correlacionado para la aeronave: 36.3\n", - "Predicción obtenida: 13.773\n", - "\tR²: 0.6393936966590913, Desviación Estándar: 0.6161349689692996, Varianza: 0.3796222999867998, Incertidumbre: 0.15908536492227474\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84]\n", - "Ecuación de regresión: y = -0.093x + 17.144\n", - "Valor del parámetro correlacionado para la aeronave: 41.2\n", - "Predicción obtenida: 13.314\n", - "\tR²: 0.6528561194484994, Desviación Estándar: 0.5680547657716968, Varianza: 0.32268621691593735, Incertidumbre: 0.1712749562216309\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84]\n", - "Ecuación de regresión: y = -0.134x + 15.025\n", - "Valor del parámetro correlacionado para la aeronave: 8.6\n", - "Predicción obtenida: 13.87\n", - "\tR²: 0.7755555066361247, Desviación Estándar: 0.4815099276447935, Varianza: 0.2318518104204943, Incertidumbre: 0.1389999431715994\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908]\n", - "Ecuación de regresión: y = -0.161x + 17.639\n", - "Valor del parámetro correlacionado para la aeronave: 23.5\n", - "Predicción obtenida: 13.844\n", - "\tR²: 0.7192364194160978, Desviación Estándar: 0.5692595198003065, Varianza: 0.32405640088327564, Incertidumbre: 0.2151598744068086\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.632', 'Área del ala: 13.764', 'Longitud del fuselaje: 13.765', 'Peso máximo al despegue (MTOW): 13.773', 'Velocidad máxima (KIAS): 13.314', 'payload: 13.87', 'Crucero KIAS: 13.844']\n", - "**Mediana calculada:** 13.765\n", - "\n", - "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765]\n", - "Ecuación de regresión: y = -0.135x + 17.123\n", - "Valor del parámetro correlacionado para la aeronave: 33.797\n", - "Predicción obtenida: 12.545\n", - "\tR²: 0.5404546291978183, Desviación Estándar: 0.6600090502158572, Varianza: 0.43561194636683787, Incertidumbre: 0.17639483843412967\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765]\n", - "Ecuación de regresión: y = -1.447x + 15.715\n", - "Valor del parámetro correlacionado para la aeronave: 1.802\n", - "Predicción obtenida: 13.108\n", - "\tR²: 0.6331129407716816, Desviación Estándar: 0.5897281920140293, Varianza: 0.3477793404561358, Incertidumbre: 0.15761148898843813\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765]\n", - "Ecuación de regresión: y = -0.311x + 14.512\n", - "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 13.734\n", - "\tR²: 0.04129311851250839, Desviación Estándar: 0.9047050168185119, Varianza: 0.8184911674565839, Incertidumbre: 0.2611658424986864\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765]\n", - "Ecuación de regresión: y = -0.031x + 14.9\n", - "Valor del parámetro correlacionado para la aeronave: 61.0\n", - "Predicción obtenida: 13.006\n", - "\tR²: 0.6404167494362927, Desviación Estándar: 0.5965733368182722, Varianza: 0.35589974620248765, Incertidumbre: 0.14914333420456805\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765]\n", - "Ecuación de regresión: y = -0.09x + 17.066\n", - "Valor del parámetro correlacionado para la aeronave: 46.3\n", - "Predicción obtenida: 12.914\n", - "\tR²: 0.6362072062495043, Desviación Estándar: 0.5573026681001448, Varianza: 0.31058626387154015, Incertidumbre: 0.16087942272385766\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765]\n", - "Ecuación de regresión: y = -0.134x + 15.009\n", - "Valor del parámetro correlacionado para la aeronave: 17.7\n", - "Predicción obtenida: 12.643\n", - "\tR²: 0.7773273437346053, Desviación Estándar: 0.46343938731299195, Varianza: 0.21477606571304134, Incertidumbre: 0.1285349595405083\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765]\n", - "Ecuación de regresión: y = -0.162x + 17.636\n", - "Valor del parámetro correlacionado para la aeronave: 30.9\n", - "Predicción obtenida: 12.636\n", - "\tR²: 0.7203735267257434, Desviación Estándar: 0.533129992286115, Varianza: 0.2842275886749931, Incertidumbre: 0.18848991639972185\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.545', 'Área del ala: 13.108', 'Longitud del fuselaje: 13.734', 'Peso máximo al despegue (MTOW): 13.006', 'Velocidad máxima (KIAS): 12.914', 'payload: 12.643', 'Crucero KIAS: 12.636']\n", - "**Mediana calculada:** 12.914\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Evo** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914]\n", - "Ecuación de regresión: y = -0.129x + 16.982\n", - "Valor del parámetro correlacionado para la aeronave: 18.091\n", - "Predicción obtenida: 14.648\n", - "\tR²: 0.5523215209559584, Desviación Estándar: 0.6432370596243068, Varianza: 0.41375391487412394, Incertidumbre: 0.1660830946392245\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914]\n", - "Ecuación de regresión: y = -1.465x + 15.728\n", - "Valor del parámetro correlacionado para la aeronave: 0.84\n", - "Predicción obtenida: 14.498\n", - "\tR²: 0.6463458428923345, Desviación Estándar: 0.5717120330519926, Varianza: 0.3268546487364427, Incertidumbre: 0.14761541218911678\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914]\n", - "Ecuación de regresión: y = -0.399x + 14.624\n", - "Valor del parámetro correlacionado para la aeronave: 0.75\n", - "Predicción obtenida: 14.325\n", - "\tR²: 0.06604254768080076, Desviación Estándar: 0.8947186094782155, Varianza: 0.8005213901466314, Incertidumbre: 0.24815029412196515\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914]\n", - "Ecuación de regresión: y = -0.031x + 14.901\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 14.589\n", - "\tR²: 0.64862940600494, Desviación Estándar: 0.5791516164367155, Varianza: 0.33541659482126046, Incertidumbre: 0.14046489928328137\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755, 12.84]\n", - "Ecuación de regresión: y = 0.0x + 14.236\n", - "Valor del parámetro correlacionado para la aeronave: 270.0\n", - "Predicción obtenida: 14.259\n", - "\tR²: 0.010075858581318875, Desviación Estándar: 0.9138813895344626, Varianza: 0.8351791941374402, Incertidumbre: 0.34541469778833345\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914]\n", - "Ecuación de regresión: y = -0.131x + 14.998\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 14.604\n", - "\tR²: 0.7761013348288335, Desviación Estándar: 0.45173648062543886, Varianza: 0.2040658479278575, Incertidumbre: 0.12073165282910263\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914]\n", - "Ecuación de regresión: y = -0.155x + 17.498\n", - "Valor del parámetro correlacionado para la aeronave: 16.54\n", - "Predicción obtenida: 14.941\n", - "\tR²: 0.7453275443355423, Desviación Estándar: 0.5085476473721651, Varianza: 0.25862070964776396, Incertidumbre: 0.16951588245738836\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.648', 'Área del ala: 14.498', 'Longitud del fuselaje: 14.325', 'Peso máximo al despegue (MTOW): 14.589', 'Alcance de la aeronave: 14.259', 'payload: 14.604', 'Crucero KIAS: 14.941']\n", - "**Mediana calculada:** 14.589\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589]\n", - "Ecuación de regresión: y = -0.128x + 16.957\n", - "Valor del parámetro correlacionado para la aeronave: 17.5\n", - "Predicción obtenida: 14.714\n", - "\tR²: 0.5769309788482372, Desviación Estándar: 0.6229572635807227, Varianza: 0.38807575224798196, Incertidumbre: 0.15573931589518067\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589]\n", - "Ecuación de regresión: y = -1.476x + 15.749\n", - "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 14.716\n", - "\tR²: 0.6654453208012552, Desviación Estándar: 0.5539699350076821, Varianza: 0.3068826888924155, Incertidumbre: 0.13849248375192053\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589]\n", - "Ecuación de regresión: y = -0.449x + 14.74\n", - "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 14.335\n", - "\tR²: 0.10523063156350132, Desviación Estándar: 0.8642071856334788, Varianza: 0.746854059700538, Incertidumbre: 0.2309690857020446\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589]\n", - "Ecuación de regresión: y = -0.031x + 14.901\n", - "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 14.707\n", - "\tR²: 0.6704861354330247, Desviación Estándar: 0.5628342077229468, Varianza: 0.3167823453831172, Incertidumbre: 0.13266129498821788\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589]\n", - "Ecuación de regresión: y = 0.0x + 14.29\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 14.297\n", - "\tR²: 0.006384217652188662, Desviación Estándar: 0.861582649338378, Varianza: 0.7423246616409385, Incertidumbre: 0.30461546694991914\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589]\n", - "Ecuación de regresión: y = -0.131x + 14.994\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 14.837\n", - "\tR²: 0.7981390800156483, Desviación Estándar: 0.43643291254269034, Varianza: 0.19047368715049562, Incertidumbre: 0.11268649346764252\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589]\n", - "Ecuación de regresión: y = -0.148x + 17.309\n", - "Valor del parámetro correlacionado para la aeronave: 16.0\n", - "Predicción obtenida: 14.943\n", - "\tR²: 0.7479549673253836, Desviación Estándar: 0.49233652426651103, Varianza: 0.24239525312682883, Incertidumbre: 0.15569047919729348\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.714', 'Área del ala: 14.716', 'Longitud del fuselaje: 14.335', 'Peso máximo al despegue (MTOW): 14.707', 'Alcance de la aeronave: 14.297', 'payload: 14.837', 'Crucero KIAS: 14.943']\n", - "**Mediana calculada:** 14.714\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", - "Ecuación de regresión: y = -0.128x + 16.957\n", - "Valor del parámetro correlacionado para la aeronave: 17.5\n", - "Predicción obtenida: 14.714\n", - "\tR²: 0.6023394020952446, Desviación Estándar: 0.60435731717502, Varianza: 0.3652477668229877, Incertidumbre: 0.1465781796663248\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", - "Ecuación de regresión: y = -1.476x + 15.748\n", - "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 14.716\n", - "\tR²: 0.6855376342976433, Desviación Estándar: 0.5374299221408195, Varianza: 0.2888309212122873, Incertidumbre: 0.13034590207965144\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714]\n", - "Ecuación de regresión: y = -0.5x + 14.859\n", - "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 14.409\n", - "\tR²: 0.14307201753173704, Desviación Estándar: 0.8395031403094158, Varianza: 0.7047655225893706, Incertidumbre: 0.21675877876714636\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", - "Ecuación de regresión: y = -0.031x + 14.902\n", - "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 14.708\n", - "\tR²: 0.692130699174681, Desviación Estándar: 0.54782447874912, Varianza: 0.300111659516745, Incertidumbre: 0.1256795548244873\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714]\n", - "Ecuación de regresión: y = 0.0x + 14.354\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 14.358\n", - "\tR²: 0.002361606473490707, Desviación Estándar: 0.8223839641550523, Varianza: 0.6763153844993783, Incertidumbre: 0.2741279880516841\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", - "Ecuación de regresión: y = -0.129x + 14.969\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 14.813\n", - "\tR²: 0.81671987260067, Desviación Estándar: 0.4234784703530429, Varianza: 0.17933401485255304, Incertidumbre: 0.10586961758826073\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714]\n", - "Ecuación de regresión: y = -0.144x + 17.203\n", - "Valor del parámetro correlacionado para la aeronave: 16.0\n", - "Predicción obtenida: 14.898\n", - "\tR²: 0.7575308845016266, Desviación Estándar: 0.4734960530805051, Varianza: 0.22419851228281648, Incertidumbre: 0.1427644316165672\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.714', 'Área del ala: 14.716', 'Longitud del fuselaje: 14.409', 'Peso máximo al despegue (MTOW): 14.708', 'Alcance de la aeronave: 14.358', 'payload: 14.813', 'Crucero KIAS: 14.898']\n", - "**Mediana calculada:** 14.714\n", - "\n", - "--- Imputación para aeronave: **V21** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", - "Ecuación de regresión: y = -0.128x + 16.957\n", - "Valor del parámetro correlacionado para la aeronave: 19.688\n", - "Predicción obtenida: 14.434\n", - "\tR²: 0.6023394020952446, Desviación Estándar: 0.60435731717502, Varianza: 0.3652477668229877, Incertidumbre: 0.1465781796663248\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", - "Ecuación de regresión: y = -1.476x + 15.748\n", - "Valor del parámetro correlacionado para la aeronave: 0.8\n", - "Predicción obtenida: 14.568\n", - "\tR²: 0.6855376342976433, Desviación Estándar: 0.5374299221408195, Varianza: 0.2888309212122873, Incertidumbre: 0.13034590207965144\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714]\n", - "Ecuación de regresión: y = -0.5x + 14.859\n", - "Valor del parámetro correlacionado para la aeronave: 0.93\n", - "Predicción obtenida: 14.394\n", - "\tR²: 0.14307201753173704, Desviación Estándar: 0.8395031403094158, Varianza: 0.7047655225893706, Incertidumbre: 0.21675877876714636\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", - "Ecuación de regresión: y = -0.031x + 14.902\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 14.59\n", - "\tR²: 0.692130699174681, Desviación Estándar: 0.54782447874912, Varianza: 0.300111659516745, Incertidumbre: 0.1256795548244873\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914]\n", - "Ecuación de regresión: y = -0.09x + 17.066\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.107\n", - "\tR²: 0.6655168135921319, Desviación Estándar: 0.535439085733295, Varianza: 0.28669501453090684, Incertidumbre: 0.148504082961458\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", - "Ecuación de regresión: y = -0.129x + 14.969\n", - "Valor del parámetro correlacionado para la aeronave: 1.5\n", - "Predicción obtenida: 14.775\n", - "\tR²: 0.81671987260067, Desviación Estándar: 0.4234784703530429, Varianza: 0.17933401485255304, Incertidumbre: 0.10586961758826073\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714]\n", - "Ecuación de regresión: y = -0.144x + 17.203\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 14.61\n", - "\tR²: 0.7575308845016266, Desviación Estándar: 0.4734960530805051, Varianza: 0.22419851228281648, Incertidumbre: 0.1427644316165672\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.434', 'Área del ala: 14.568', 'Longitud del fuselaje: 14.394', 'Peso máximo al despegue (MTOW): 14.59', 'Velocidad máxima (KIAS): 14.107', 'payload: 14.775', 'Crucero KIAS: 14.61']\n", - "**Mediana calculada:** 14.568\n", - "\n", - "--- Imputación para aeronave: **V25** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568]\n", - "Ecuación de regresión: y = -0.129x + 16.992\n", - "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 14.164\n", - "\tR²: 0.6159186819254363, Desviación Estándar: 0.5881023640765761, Varianza: 0.34586439063245766, Incertidumbre: 0.13861705655679563\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568]\n", - "Ecuación de regresión: y = -1.476x + 15.748\n", - "Valor del parámetro correlacionado para la aeronave: 0.52\n", - "Predicción obtenida: 14.981\n", - "\tR²: 0.6970733829876015, Desviación Estándar: 0.5222880039127576, Varianza: 0.2727847590311727, Incertidumbre: 0.12310446309969901\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568]\n", - "Ecuación de regresión: y = -0.519x + 14.904\n", - "Valor del parámetro correlacionado para la aeronave: 0.93\n", - "Predicción obtenida: 14.421\n", - "\tR²: 0.16641438018273524, Desviación Estándar: 0.8138274929561254, Varianza: 0.6623151882912524, Incertidumbre: 0.20345687323903136\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568]\n", - "Ecuación de regresión: y = -0.031x + 14.899\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 14.509\n", - "\tR²: 0.7051231818210476, Desviación Estándar: 0.5339726907463312, Varianza: 0.2851268344628771, Incertidumbre: 0.11939992346372695\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568]\n", - "Ecuación de regresión: y = -0.091x + 17.151\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.144\n", - "\tR²: 0.6634608751199247, Desviación Estándar: 0.5293377198828594, Varianza: 0.2801984216907845, Incertidumbre: 0.14147145640698403\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568]\n", - "Ecuación de regresión: y = -0.127x + 14.933\n", - "Valor del parámetro correlacionado para la aeronave: 2.2\n", - "Predicción obtenida: 14.653\n", - "\tR²: 0.8257579107606323, Desviación Estándar: 0.41340767338372525, Varianza: 0.17090590441254486, Incertidumbre: 0.100266088458938\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568]\n", - "Ecuación de regresión: y = -0.144x + 17.191\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 14.318\n", - "\tR²: 0.7641303564183091, Desviación Estándar: 0.45348228917663347, Varianza: 0.20564618659687983, Incertidumbre: 0.13090906086442852\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.164', 'Área del ala: 14.981', 'Longitud del fuselaje: 14.421', 'Peso máximo al despegue (MTOW): 14.509', 'Velocidad máxima (KIAS): 14.144', 'payload: 14.653', 'Crucero KIAS: 14.318']\n", - "**Mediana calculada:** 14.421\n", - "\n", - "--- Imputación para aeronave: **V32** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", - "Ecuación de regresión: y = -0.13x + 17.031\n", - "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 14.18\n", - "\tR²: 0.6201449706987955, Desviación Estándar: 0.5752636617985344, Varianza: 0.3309282805858586, Incertidumbre: 0.13197453514057741\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", - "Ecuación de regresión: y = -1.403x + 15.627\n", - "Valor del parámetro correlacionado para la aeronave: 1.03\n", - "Predicción obtenida: 14.182\n", - "\tR²: 0.6872369206933386, Desviación Estándar: 0.5219942765247394, Varianza: 0.27247802472458604, Incertidumbre: 0.11975370002515603\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", - "Ecuación de regresión: y = -0.519x + 14.904\n", - "Valor del parámetro correlacionado para la aeronave: 1.0\n", - "Predicción obtenida: 14.385\n", - "\tR²: 0.17855538005879423, Desviación Estándar: 0.7895286400719489, Varianza: 0.623355473493861, Incertidumbre: 0.1914888222039361\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", - "Ecuación de regresión: y = -0.031x + 14.889\n", - "Valor del parámetro correlacionado para la aeronave: 23.5\n", - "Predicción obtenida: 14.16\n", - "\tR²: 0.7123924260887753, Desviación Estándar: 0.5214279546204159, Varianza: 0.2718871118596305, Incertidumbre: 0.11378490816733594\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421]\n", - "Ecuación de regresión: y = -0.092x + 17.198\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.164\n", - "\tR²: 0.6652782895416913, Desviación Estándar: 0.5160037700308047, Varianza: 0.2662598906860036, Incertidumbre: 0.1332316005273032\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", - "Ecuación de regresión: y = -0.125x + 14.9\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 14.274\n", - "\tR²: 0.8296041524851132, Desviación Estándar: 0.4050204961761552, Varianza: 0.16404160232277895, Incertidumbre: 0.09546424645523317\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", - "Ecuación de regresión: y = -0.144x + 17.209\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 14.327\n", - "\tR²: 0.7660206652821594, Desviación Estándar: 0.43655078246489853, Varianza: 0.19057658567071517, Incertidumbre: 0.12107740234777827\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.18', 'Área del ala: 14.182', 'Longitud del fuselaje: 14.385', 'Peso máximo al despegue (MTOW): 14.16', 'Velocidad máxima (KIAS): 14.164', 'payload: 14.274', 'Crucero KIAS: 14.327']\n", - "**Mediana calculada:** 14.182\n", - "\n", - "--- Imputación para aeronave: **V35** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", - "Ecuación de regresión: y = -0.13x + 17.032\n", - "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 13.467\n", - "\tR²: 0.6225279923162458, Desviación Estándar: 0.5606978547880688, Varianza: 0.31438208436394227, Incertidumbre: 0.12537585181444277\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", - "Ecuación de regresión: y = -1.403x + 15.627\n", - "Valor del parámetro correlacionado para la aeronave: 1.202\n", - "Predicción obtenida: 13.941\n", - "\tR²: 0.6891992560944142, Desviación Estándar: 0.5087770862467862, Varianza: 0.25885412348976977, Incertidumbre: 0.11376601502420872\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", - "Ecuación de regresión: y = -0.503x + 14.866\n", - "Valor del parámetro correlacionado para la aeronave: 1.88\n", - "Predicción obtenida: 13.92\n", - "\tR²: 0.17769866949605284, Desviación Estándar: 0.768609327920775, Varianza: 0.5907602989668254, Incertidumbre: 0.18116295595200493\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", - "Ecuación de regresión: y = -0.031x + 14.891\n", - "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 13.898\n", - "\tR²: 0.7152702339309572, Desviación Estándar: 0.5094595753554049, Varianza: 0.2595490589213094, Incertidumbre: 0.10861714641999741\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182]\n", - "Ecuación de regresión: y = -0.092x + 17.201\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.166\n", - "\tR²: 0.6671372923996113, Desviación Estándar: 0.4996364202272749, Varianza: 0.24963655241752605, Incertidumbre: 0.12490910505681872\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", - "Ecuación de regresión: y = -0.125x + 14.891\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 13.643\n", - "\tR²: 0.8319028879241045, Desviación Estándar: 0.3947401644934259, Varianza: 0.15581979746429697, Incertidumbre: 0.09055960452544556\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", - "Ecuación de regresión: y = -0.144x + 17.186\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 13.598\n", - "\tR²: 0.7644167301146686, Desviación Estándar: 0.42230373151011547, Varianza: 0.1783404416473677, Incertidumbre: 0.11286541974764448\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.467', 'Área del ala: 13.941', 'Longitud del fuselaje: 13.92', 'Peso máximo al despegue (MTOW): 13.898', 'Velocidad máxima (KIAS): 14.166', 'payload: 13.643', 'Crucero KIAS: 13.598']\n", - "**Mediana calculada:** 13.898\n", - "\n", - "--- Imputación para aeronave: **V39** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", - "Ecuación de regresión: y = -0.128x + 17.003\n", - "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 13.494\n", - "\tR²: 0.6120846873528347, Desviación Estándar: 0.5547180985616944, Varianza: 0.30771216887190167, Incertidumbre: 0.12104941314385424\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", - "Ecuación de regresión: y = -1.403x + 15.625\n", - "Valor del parámetro correlacionado para la aeronave: 1.203\n", - "Predicción obtenida: 13.937\n", - "\tR²: 0.6891124350538647, Desviación Estándar: 0.49659866715385637, Varianza: 0.24661023621898662, Incertidumbre: 0.10836671344032035\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", - "Ecuación de regresión: y = -0.031x + 14.891\n", - "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 14.146\n", - "\tR²: 0.7155976388717264, Desviación Estándar: 0.4982613003458266, Varianza: 0.24826432342231405, Incertidumbre: 0.10389466308892618\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898]\n", - "Ecuación de regresión: y = -0.091x + 17.161\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.148\n", - "\tR²: 0.6615818300119434, Desviación Estándar: 0.488766926306414, Varianza: 0.2388931082510195, Incertidumbre: 0.11854339196881342\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", - "Ecuación de regresión: y = -0.125x + 14.903\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 14.28\n", - "\tR²: 0.8289103542494105, Desviación Estándar: 0.38872886000472096, Varianza: 0.15111012660056994, Incertidumbre: 0.08692241557865553\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", - "Ecuación de regresión: y = -0.141x + 17.154\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 13.625\n", - "\tR²: 0.7574536879812513, Desviación Estándar: 0.4146205350250817, Varianza: 0.17191018806448502, Incertidumbre: 0.10705456180985003\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.494', 'Área del ala: 13.937', 'Peso máximo al despegue (MTOW): 14.146', 'Velocidad máxima (KIAS): 14.148', 'payload: 14.28', 'Crucero KIAS: 13.625']\n", - "**Mediana calculada:** 14.042\n", - "\n", - "--- Imputación para aeronave: **Volitation VT370** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042]\n", - "Ecuación de regresión: y = -0.126x + 16.968\n", - "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 13.525\n", - "\tR²: 0.5957680406681929, Desviación Estándar: 0.5537225849540539, Varianza: 0.3066087010881995, Incertidumbre: 0.11805405177448465\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042]\n", - "Ecuación de regresión: y = -1.404x + 15.631\n", - "Valor del parámetro correlacionado para la aeronave: 1.424\n", - "Predicción obtenida: 13.632\n", - "\tR²: 0.6890205544749124, Desviación Estándar: 0.48567165393621425, Varianza: 0.23587695543713788, Incertidumbre: 0.103545544532815\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", - "Ecuación de regresión: y = -0.504x + 14.866\n", - "Valor del parámetro correlacionado para la aeronave: 2.02\n", - "Predicción obtenida: 13.848\n", - "\tR²: 0.17842685862395946, Desviación Estándar: 0.7481250281419246, Varianza: 0.5596910577323555, Incertidumbre: 0.17163165235811428\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042]\n", - "Ecuación de regresión: y = -0.031x + 14.883\n", - "Valor del parámetro correlacionado para la aeronave: 40.0\n", - "Predicción obtenida: 13.645\n", - "\tR²: 0.7162472212244984, Desviación Estándar: 0.48820758180424606, Varianza: 0.2383466429311496, Incertidumbre: 0.09965495533154001\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042]\n", - "Ecuación de regresión: y = -0.091x + 17.146\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.142\n", - "\tR²: 0.6610410820046247, Desviación Estándar: 0.47561618075428896, Varianza: 0.22621075139529648, Incertidumbre: 0.11210380888446815\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042]\n", - "Ecuación de regresión: y = -0.124x + 14.881\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 12.657\n", - "\tR²: 0.8271914508346105, Desviación Estándar: 0.3826399844398456, Varianza: 0.1464133576921253, Incertidumbre: 0.08349889012439606\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042]\n", - "Ecuación de regresión: y = -0.138x + 17.114\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 13.66\n", - "\tR²: 0.7425159710448364, Desviación Estándar: 0.41365132002637955, Varianza: 0.17110741455956627, Incertidumbre: 0.10341283000659489\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.525', 'Área del ala: 13.632', 'Longitud del fuselaje: 13.848', 'Peso máximo al despegue (MTOW): 13.645', 'Velocidad máxima (KIAS): 14.142', 'payload: 12.657', 'Crucero KIAS: 13.66']\n", - "**Mediana calculada:** 13.645\n", - "\n", - "--- Imputación para aeronave: **Skyeye 2600** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", - "Ecuación de regresión: y = -0.125x + 16.96\n", - "Valor del parámetro correlacionado para la aeronave: 36.094\n", - "Predicción obtenida: 12.435\n", - "\tR²: 0.5961936628210269, Desviación Estándar: 0.5420953383497691, Varianza: 0.2938673558605506, Incertidumbre: 0.1130346918390738\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", - "Ecuación de regresión: y = -1.403x + 15.631\n", - "Valor del parámetro correlacionado para la aeronave: 0.88\n", - "Predicción obtenida: 14.396\n", - "\tR²: 0.6899612272653157, Desviación Estándar: 0.4750038307207795, Varianza: 0.22562863919941495, Incertidumbre: 0.09904514543760928\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645]\n", - "Ecuación de regresión: y = -0.51x + 14.867\n", - "Valor del parámetro correlacionado para la aeronave: 2.05\n", - "Predicción obtenida: 13.821\n", - "\tR²: 0.18318737073904479, Desviación Estándar: 0.7305092307354947, Varianza: 0.5336437361897641, Incertidumbre: 0.16334682981156448\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", - "Ecuación de regresión: y = -0.031x + 14.883\n", - "Valor del parámetro correlacionado para la aeronave: 15.0\n", - "Predicción obtenida: 14.419\n", - "\tR²: 0.7164307643262642, Desviación Estándar: 0.4783437902243881, Varianza: 0.2288127816462334, Incertidumbre: 0.09566875804487762\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", - "Ecuación de regresión: y = -0.116x + 14.849\n", - "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 14.385\n", - "\tR²: 0.7782311439723943, Desviación Estándar: 0.42352889042939196, Varianza: 0.17937672102835192, Incertidumbre: 0.0902966628368432\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", - "Ecuación de regresión: y = -0.138x + 17.115\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 12.553\n", - "\tR²: 0.7465935530165513, Desviación Estándar: 0.4013165330809285, Varianza: 0.16105495972409598, Incertidumbre: 0.0973335561881972\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.435', 'Área del ala: 14.396', 'Longitud del fuselaje: 13.821', 'Peso máximo al despegue (MTOW): 14.419', 'payload: 14.385', 'Crucero KIAS: 12.553']\n", - "**Mediana calculada:** 14.103\n", - "\n", - "--- Imputación para aeronave: **Skyeye 2930 VTOL** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103]\n", - "Ecuación de regresión: y = -0.101x + 16.425\n", - "Valor del parámetro correlacionado para la aeronave: 26.25\n", - "Predicción obtenida: 13.765\n", - "\tR²: 0.4647548108442666, Desviación Estándar: 0.6119553027249928, Varianza: 0.37448929253323754, Incertidumbre: 0.12491485308888704\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103]\n", - "Ecuación de regresión: y = -1.386x + 15.597\n", - "Valor del parámetro correlacionado para la aeronave: 1.0\n", - "Predicción obtenida: 14.211\n", - "\tR²: 0.6861710827108773, Desviación Estándar: 0.4685865248148631, Varianza: 0.21957333123807035, Incertidumbre: 0.0956498238450352\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103]\n", - "Ecuación de regresión: y = -0.501x + 14.864\n", - "Valor del parámetro correlacionado para la aeronave: 2.03\n", - "Predicción obtenida: 13.847\n", - "\tR²: 0.17824780030191256, Desviación Estándar: 0.7154090188937324, Varianza: 0.5118100643144928, Incertidumbre: 0.1561150467587915\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103]\n", - "Ecuación de regresión: y = -0.031x + 14.856\n", - "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 14.001\n", - "\tR²: 0.7134393100886465, Desviación Estándar: 0.4728662953123409, Varianza: 0.22360253324241802, Incertidumbre: 0.0927367102737675\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", - "Ecuación de regresión: y = -0.09x + 17.081\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 14.383\n", - "\tR²: 0.6440323429773995, Desviación Estándar: 0.4759723084801662, Varianza: 0.2265496384399385, Incertidumbre: 0.10919553645204273\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103]\n", - "Ecuación de regresión: y = -0.114x + 14.822\n", - "Valor del parámetro correlacionado para la aeronave: 6.0\n", - "Predicción obtenida: 14.136\n", - "\tR²: 0.7761269966964691, Desviación Estándar: 0.4180768449775009, Varianza: 0.17478824830634132, Incertidumbre: 0.0871750483613141\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103]\n", - "Ecuación de regresión: y = -0.109x + 16.524\n", - "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 13.919\n", - "\tR²: 0.5802472498582693, Desviación Estándar: 0.5020212903739392, Varianza: 0.25202537598871494, Incertidumbre: 0.11832755290781109\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.765', 'Área del ala: 14.211', 'Longitud del fuselaje: 13.847', 'Peso máximo al despegue (MTOW): 14.001', 'Velocidad máxima (KIAS): 14.383', 'payload: 14.136', 'Crucero KIAS: 13.919']\n", - "**Mediana calculada:** 14.001\n", - "\n", - "--- Imputación para aeronave: **Skyeye 3600** ---\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001]\n", - "Ecuación de regresión: y = -1.378x + 15.578\n", - "Valor del parámetro correlacionado para la aeronave: 1.33\n", - "Predicción obtenida: 13.746\n", - "\tR²: 0.6839560448092026, Desviación Estándar: 0.46094201511946703, Varianza: 0.21246754130239498, Incertidumbre: 0.0921884030238934\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001]\n", - "Ecuación de regresión: y = -0.496x + 14.863\n", - "Valor del parámetro correlacionado para la aeronave: 2.488\n", - "Predicción obtenida: 13.628\n", - "\tR²: 0.17653575217536233, Desviación Estándar: 0.6996903479356846, Varianza: 0.48956658299435946, Incertidumbre: 0.14917448340699427\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001]\n", - "Ecuación de regresión: y = -0.031x + 14.856\n", - "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 14.001\n", - "\tR²: 0.7141618045464115, Desviación Estándar: 0.46402689980399064, Varianza: 0.21532096374170276, Incertidumbre: 0.08930201850435383\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001]\n", - "Ecuación de regresión: y = -0.114x + 14.812\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 13.673\n", - "\tR²: 0.7761914041781102, Desviación Estándar: 0.41014763806155546, Varianza: 0.16822108500747268, Incertidumbre: 0.08372103603821063\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Área del ala: 13.746', 'Longitud del fuselaje: 13.628', 'Peso máximo al despegue (MTOW): 14.001', 'payload: 13.673']\n", - "**Mediana calculada:** 13.71\n", - "\n", - "--- Imputación para aeronave: **Skyeye 3600 VTOL** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001]\n", - "Ecuación de regresión: y = -0.101x + 16.427\n", - "Valor del parámetro correlacionado para la aeronave: 32.813\n", - "Predicción obtenida: 13.112\n", - "\tR²: 0.46205070143923843, Desviación Estándar: 0.6013720296168469, Varianza: 0.36164831800548575, Incertidumbre: 0.12027440592336938\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71]\n", - "Ecuación de regresión: y = -1.378x + 15.578\n", - "Valor del parámetro correlacionado para la aeronave: 1.32\n", - "Predicción obtenida: 13.758\n", - "\tR²: 0.6844100847233376, Desviación Estándar: 0.4520440536186202, Varianza: 0.20434382641195398, Incertidumbre: 0.08865313270788183\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71]\n", - "Ecuación de regresión: y = -0.49x + 14.856\n", - "Valor del parámetro correlacionado para la aeronave: 2.42\n", - "Predicción obtenida: 13.669\n", - "\tR²: 0.18085287153910534, Desviación Estándar: 0.6845046469228117, Varianza: 0.4685466116589231, Incertidumbre: 0.14272908537245493\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71]\n", - "Ecuación de regresión: y = -0.03x + 14.841\n", - "Valor del parámetro correlacionado para la aeronave: 40.0\n", - "Predicción obtenida: 13.625\n", - "\tR²: 0.7102145115795546, Desviación Estándar: 0.45885229575829384, Varianza: 0.21054542932265677, Incertidumbre: 0.0867149330776788\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714]\n", - "Ecuación de regresión: y = 0.0x + 14.354\n", - "Valor del parámetro correlacionado para la aeronave: 300.0\n", - "Predicción obtenida: 14.367\n", - "\tR²: 0.002361606473490707, Desviación Estándar: 0.8223839641550523, Varianza: 0.6763153844993783, Incertidumbre: 0.2741279880516841\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001]\n", - "Ecuación de regresión: y = -0.088x + 16.996\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 14.091\n", - "\tR²: 0.6330464973659589, Desviación Estándar: 0.4711473913359555, Varianza: 0.22197986436267603, Incertidumbre: 0.1053517594448892\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71]\n", - "Ecuación de regresión: y = -0.114x + 14.813\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 13.674\n", - "\tR²: 0.7761192880267639, Desviación Estándar: 0.4019269643743591, Varianza: 0.16154528469118734, Incertidumbre: 0.08038539287487181\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001]\n", - "Ecuación de regresión: y = -0.108x + 16.524\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 13.274\n", - "\tR²: 0.579753910323783, Desviación Estándar: 0.488970938859074, Varianza: 0.2390925790487243, Incertidumbre: 0.11217762677973205\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.112', 'Área del ala: 13.758', 'Longitud del fuselaje: 13.669', 'Peso máximo al despegue (MTOW): 13.625', 'Alcance de la aeronave: 14.367', 'Velocidad máxima (KIAS): 14.091', 'payload: 13.674', 'Crucero KIAS: 13.274']\n", - "**Mediana calculada:** 13.672\n", - "\n", - "--- Imputación para aeronave: **Skyeye 5000** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672]\n", - "Ecuación de regresión: y = -0.096x + 16.32\n", - "Valor del parámetro correlacionado para la aeronave: 36.094\n", - "Predicción obtenida: 12.855\n", - "\tR²: 0.44669483333289495, Desviación Estándar: 0.5987984426089217, Varianza: 0.35855957487087003, Incertidumbre: 0.11743403629122542\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672]\n", - "Ecuación de regresión: y = -1.379x + 15.576\n", - "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 11.969\n", - "\tR²: 0.6847227318034814, Desviación Estándar: 0.4438937988851134, Varianza: 0.19704170468865748, Incertidumbre: 0.08542740142597527\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672]\n", - "Ecuación de regresión: y = -0.49x + 14.856\n", - "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 13.14\n", - "\tR²: 0.18648513764579755, Desviación Estándar: 0.6700926766915704, Varianza: 0.4490241953556735, Incertidumbre: 0.13678209485584383\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = -0.996) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5]\n", - "Ecuación de regresión: y = -38.708x + 23.253\n", - "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 8.737\n", - "\tR²: 0.9845301318279952, Desviación Estándar: 0.16498762115286075, Varianza: 0.02722091513367991, Incertidumbre: 0.09525564748556016\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672]\n", - "Ecuación de regresión: y = -0.03x + 14.842\n", - "Valor del parámetro correlacionado para la aeronave: 90.0\n", - "Predicción obtenida: 12.108\n", - "\tR²: 0.710260620996489, Desviación Estándar: 0.45095219827945476, Varianza: 0.20335788513307268, Incertidumbre: 0.0837397209611883\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672]\n", - "Ecuación de regresión: y = -0.087x + 16.95\n", - "Valor del parámetro correlacionado para la aeronave: 42.0\n", - "Predicción obtenida: 13.284\n", - "\tR²: 0.6211901244543757, Desviación Estándar: 0.4683247546231655, Varianza: 0.21932807579284816, Incertidumbre: 0.102196839899156\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672]\n", - "Ecuación de regresión: y = -0.114x + 14.813\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 12.535\n", - "\tR²: 0.7761469380573378, Desviación Estándar: 0.39412206749545337, Varianza: 0.15533220408689072, Incertidumbre: 0.07729369664987883\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672]\n", - "Ecuación de regresión: y = -0.104x + 16.437\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 13.014\n", - "\tR²: 0.5722804312940908, Desviación Estándar: 0.4837831260054291, Varianza: 0.23404611300758488, Incertidumbre: 0.10817719561154858\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 12.855', 'Área del ala: 11.969', 'Longitud del fuselaje: 13.14', 'Ancho del fuselaje: 8.737', 'Peso máximo al despegue (MTOW): 12.108', 'Velocidad máxima (KIAS): 13.284', 'payload: 12.535', 'Crucero KIAS: 13.014']\n", - "**Mediana calculada:** 12.695\n", - "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695]\n", - "Ecuación de regresión: y = -0.098x + 16.36\n", - "Valor del parámetro correlacionado para la aeronave: 30.625\n", - "Predicción obtenida: 13.366\n", - "\tR²: 0.4861597087141194, Desviación Estándar: 0.5882900301706695, Varianza: 0.3460851595982073, Incertidumbre: 0.11321646909353636\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695]\n", - "Ecuación de regresión: y = -1.257x + 15.445\n", - "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 12.157\n", - "\tR²: 0.6861946820655023, Desviación Estándar: 0.4516214364570259, Varianza: 0.2039619218675075, Incertidumbre: 0.085348429115075\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695]\n", - "Ecuación de regresión: y = -0.546x + 14.942\n", - "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 13.032\n", - "\tR²: 0.2617903079866821, Desviación Estándar: 0.6611047129721297, Varianza: 0.437059441513962, Incertidumbre: 0.13222094259442593\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = -0.996) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.695]\n", - "Ecuación de regresión: y = -16.401x + 18.314\n", - "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 12.164\n", - "\tR²: 0.7048037347079352, Desviación Estándar: 0.7388896951434575, Varianza: 0.5459579815891915, Incertidumbre: 0.36944484757172874\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695]\n", - "Ecuación de regresión: y = -0.029x + 14.8\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 11.929\n", - "\tR²: 0.7123076858370674, Desviación Estándar: 0.45382550140258615, Varianza: 0.20595758572330874, Incertidumbre: 0.08285682142976294\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672]\n", - "Ecuación de regresión: y = 0.0x + 14.269\n", - "Valor del parámetro correlacionado para la aeronave: 800.0\n", - "Predicción obtenida: 14.323\n", - "\tR²: 0.005851871754970817, Desviación Estándar: 0.8072016250304093, Varianza: 0.6515744634517334, Incertidumbre: 0.2552595666085276\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695]\n", - "Ecuación de regresión: y = -0.091x + 17.059\n", - "Valor del parámetro correlacionado para la aeronave: 42.0\n", - "Predicción obtenida: 13.23\n", - "\tR²: 0.6374089155434113, Desviación Estándar: 0.4729515606700395, Varianza: 0.22368317874022606, Incertidumbre: 0.10083361153635637\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695]\n", - "Ecuación de regresión: y = -0.113x + 14.806\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 11.992\n", - "\tR²: 0.7868234537491392, Desviación Estándar: 0.3878273476620516, Varianza: 0.15041005159458187, Incertidumbre: 0.07463740785726136\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695]\n", - "Ecuación de regresión: y = -0.108x + 16.526\n", - "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 13.498\n", - "\tR²: 0.6230065242149951, Desviación Estándar: 0.47633025955044683, Varianza: 0.22689051616339603, Incertidumbre: 0.10394378429466117\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.366', 'Área del ala: 12.157', 'Longitud del fuselaje: 13.032', 'Ancho del fuselaje: 12.164', 'Peso máximo al despegue (MTOW): 11.929', 'Alcance de la aeronave: 14.323', 'Velocidad máxima (KIAS): 13.23', 'payload: 11.992', 'Crucero KIAS: 13.498']\n", - "**Mediana calculada:** 13.032\n", - "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032]\n", - "Ecuación de regresión: y = -0.099x + 16.39\n", - "Valor del parámetro correlacionado para la aeronave: 33.885\n", - "Predicción obtenida: 13.022\n", - "\tR²: 0.49733952651439894, Desviación Estándar: 0.5809229222603671, Varianza: 0.33747144160752457, Incertidumbre: 0.10978411308555996\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032]\n", - "Ecuación de regresión: y = -1.141x + 15.319\n", - "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 12.337\n", - "\tR²: 0.6639636558875286, Desviación Estándar: 0.4668168117669745, Varianza: 0.21791793574828286, Incertidumbre: 0.0866857057278015\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032]\n", - "Ecuación de regresión: y = -0.546x + 14.942\n", - "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 13.032\n", - "\tR²: 0.2974987662982014, Desviación Estándar: 0.6482665064635713, Varianza: 0.4202494634024835, Incertidumbre: 0.12713552178716822\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = -0.996) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.695, 13.032]\n", - "Ecuación de regresión: y = -13.779x + 17.734\n", - "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 12.567\n", - "\tR²: 0.6797443662647868, Desviación Estándar: 0.7194173301196193, Varianza: 0.5175612948764412, Incertidumbre: 0.3217332108677751\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032]\n", - "Ecuación de regresión: y = -0.026x + 14.718\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 12.147\n", - "\tR²: 0.6741658765761376, Desviación Estándar: 0.48040741723701913, Varianza: 0.2307912865363434, Incertidumbre: 0.08628371926875769\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032]\n", - "Ecuación de regresión: y = -0.092x + 17.093\n", - "Valor del parámetro correlacionado para la aeronave: 38.0\n", - "Predicción obtenida: 13.583\n", - "\tR²: 0.6514471014044674, Desviación Estándar: 0.4642468173732389, Varianza: 0.21552510744118147, Incertidumbre: 0.09680215310244272\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032]\n", - "Ecuación de regresión: y = -0.102x + 14.73\n", - "Valor del parámetro correlacionado para la aeronave: 15.0\n", - "Predicción obtenida: 13.203\n", - "\tR²: 0.74595960714623, Desviación Estándar: 0.42014445536437306, Varianza: 0.17652136337342567, Incertidumbre: 0.07939983882977203\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032]\n", - "Ecuación de regresión: y = -0.111x + 16.571\n", - "Valor del parámetro correlacionado para la aeronave: 35.0\n", - "Predicción obtenida: 12.689\n", - "\tR²: 0.6312746472246312, Desviación Estándar: 0.47515011887040703, Varianza: 0.22576763546256193, Incertidumbre: 0.10130234571962422\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.022', 'Área del ala: 12.337', 'Longitud del fuselaje: 13.032', 'Ancho del fuselaje: 12.567', 'Peso máximo al despegue (MTOW): 12.147', 'Velocidad máxima (KIAS): 13.583', 'payload: 13.203', 'Crucero KIAS: 12.689']\n", - "**Mediana calculada:** 12.856\n", - "\n", - "--- Imputación para aeronave: **Volitation VT510** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856]\n", - "Ecuación de regresión: y = -0.101x + 16.417\n", - "Valor del parámetro correlacionado para la aeronave: 32.813\n", - "Predicción obtenida: 13.115\n", - "\tR²: 0.5181009063264552, Desviación Estándar: 0.5715788333906897, Varianza: 0.3267023627802618, Incertidumbre: 0.10613952476132892\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856]\n", - "Ecuación de regresión: y = -1.083x + 15.257\n", - "Valor del parámetro correlacionado para la aeronave: 1.993\n", - "Predicción obtenida: 13.099\n", - "\tR²: 0.6672340951247093, Desviación Estándar: 0.4670272390002237, Varianza: 0.21811444196817206, Incertidumbre: 0.08526711792325965\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856]\n", - "Ecuación de regresión: y = -0.56x + 14.965\n", - "Valor del parámetro correlacionado para la aeronave: 2.905\n", - "Predicción obtenida: 13.337\n", - "\tR²: 0.3389580451352642, Desviación Estándar: 0.6369033781917752, Varianza: 0.40564591315209547, Incertidumbre: 0.12257211228226782\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856]\n", - "Ecuación de regresión: y = -0.024x + 14.674\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 12.265\n", - "\tR²: 0.6659304816788436, Desviación Estándar: 0.4864938763937024, Varianza: 0.236676291768571, Incertidumbre: 0.08600077975092925\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856]\n", - "Ecuación de regresión: y = -0.094x + 17.121\n", - "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 12.421\n", - "\tR²: 0.6386649948497098, Desviación Estándar: 0.4770137198562853, Varianza: 0.22754208893113068, Incertidumbre: 0.09737001782956828\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856]\n", - "Ecuación de regresión: y = -0.103x + 14.729\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 12.157\n", - "\tR²: 0.7481095898704668, Desviación Estándar: 0.41758688156853935, Varianza: 0.1743788036581373, Incertidumbre: 0.07754393719117042\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856]\n", - "Ecuación de regresión: y = -0.109x + 16.525\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 13.263\n", - "\tR²: 0.6572957320511896, Desviación Estándar: 0.4657670897559235, Varianza: 0.2169389818997025, Incertidumbre: 0.09711915180752535\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.115', 'Área del ala: 13.099', 'Longitud del fuselaje: 13.337', 'Peso máximo al despegue (MTOW): 12.265', 'Velocidad máxima (KIAS): 12.421', 'payload: 12.157', 'Crucero KIAS: 13.263']\n", - "**Mediana calculada:** 13.099\n", - "\n", - "--- Imputación para aeronave: **Ascend** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099]\n", - "Ecuación de regresión: y = -0.101x + 16.419\n", - "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 14.216\n", - "\tR²: 0.5284756398961317, Desviación Estándar: 0.5619789405509674, Varianza: 0.3158203296227878, Incertidumbre: 0.10260284752753986\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099]\n", - "Ecuación de regresión: y = -1.083x + 15.257\n", - "Valor del parámetro correlacionado para la aeronave: 0.771\n", - "Predicción obtenida: 14.422\n", - "\tR²: 0.6743723185855064, Desviación Estándar: 0.4594327962255891, Varianza: 0.21107849424766364, Incertidumbre: 0.08251656612710266\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099]\n", - "Ecuación de regresión: y = -0.572x + 14.979\n", - "Valor del parámetro correlacionado para la aeronave: 1.562\n", - "Predicción obtenida: 14.086\n", - "\tR²: 0.3566167414715793, Desviación Estándar: 0.6269166459626101, Varianza: 0.39302448098500864, Incertidumbre: 0.1184761098559851\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099]\n", - "Ecuación de regresión: y = -0.022x + 14.63\n", - "Valor del parámetro correlacionado para la aeronave: 9.5\n", - "Predicción obtenida: 14.417\n", - "\tR²: 0.6448304134719163, Desviación Estándar: 0.49760568948205036, Varianza: 0.24761142220490673, Incertidumbre: 0.08662203201525417\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099]\n", - "Ecuación de regresión: y = -0.087x + 16.885\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 14.284\n", - "\tR²: 0.6250452670025234, Desviación Estándar: 0.4830714233111504, Varianza: 0.23335800001986062, Incertidumbre: 0.09661428466223007\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099]\n", - "Ecuación de regresión: y = -0.095x + 14.67\n", - "Valor del parámetro correlacionado para la aeronave: 0.6\n", - "Predicción obtenida: 14.614\n", - "\tR²: 0.7145248980811096, Desviación Estándar: 0.44003758459985504, Varianza: 0.19363307586047457, Incertidumbre: 0.08033950374514283\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099]\n", - "Ecuación de regresión: y = -0.11x + 16.544\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 14.349\n", - "\tR²: 0.6687656157013713, Desviación Estándar: 0.45709955119724055, Varianza: 0.20893999970471874, Incertidumbre: 0.09330505517403626\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.216', 'Área del ala: 14.422', 'Longitud del fuselaje: 14.086', 'Peso máximo al despegue (MTOW): 14.417', 'Velocidad máxima (KIAS): 14.284', 'payload: 14.614', 'Crucero KIAS: 14.349']\n", - "**Mediana calculada:** 14.349\n", - "\n", - "--- Imputación para aeronave: **Transition** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", - "Ecuación de regresión: y = -0.101x + 16.438\n", - "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 14.223\n", - "\tR²: 0.5357657902510582, Desviación Estándar: 0.5533318320555499, Varianza: 0.30617611636595127, Incertidumbre: 0.09938133081736573\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", - "Ecuación de regresión: y = -1.079x + 15.25\n", - "Valor del parámetro correlacionado para la aeronave: 0.986\n", - "Predicción obtenida: 14.186\n", - "\tR²: 0.6797569865020562, Desviación Estándar: 0.4523706806342405, Varianza: 0.20463923269748602, Incertidumbre: 0.07996859397161137\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", - "Ecuación de regresión: y = -0.578x + 15.001\n", - "Valor del parámetro correlacionado para la aeronave: 2.3\n", - "Predicción obtenida: 13.672\n", - "\tR²: 0.36290901642874207, Desviación Estándar: 0.6178541362486212, Varianza: 0.3817437336795298, Incertidumbre: 0.11473263277477799\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0, 9.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", - "Ecuación de regresión: y = -0.022x + 14.625\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 14.222\n", - "\tR²: 0.6514348621062943, Desviación Estándar: 0.4903621688911602, Varianza: 0.2404550566796427, Incertidumbre: 0.08409641817224593\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", - "Ecuación de regresión: y = -0.087x + 16.898\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 14.288\n", - "\tR²: 0.6328453004599623, Desviación Estándar: 0.47385313335474916, Varianza: 0.22453679199011367, Incertidumbre: 0.09293024513665193\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", - "Ecuación de regresión: y = -0.093x + 14.645\n", - "Valor del parámetro correlacionado para la aeronave: 1.5\n", - "Predicción obtenida: 14.505\n", - "\tR²: 0.7186695306905169, Desviación Estándar: 0.43524135975547956, Varianza: 0.18943504124179877, Incertidumbre: 0.0781716558734271\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", - "Ecuación de regresión: y = -0.11x + 16.544\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 14.349\n", - "\tR²: 0.6740321107300629, Desviación Estándar: 0.4478642716421813, Varianza: 0.20058240581358153, Incertidumbre: 0.08957285432843626\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 14.223', 'Área del ala: 14.186', 'Longitud del fuselaje: 13.672', 'Peso máximo al despegue (MTOW): 14.222', 'Velocidad máxima (KIAS): 14.288', 'payload: 14.505', 'Crucero KIAS: 14.349']\n", - "**Mediana calculada:** 14.223\n", - "\n", - "--- Imputación para aeronave: **Reach** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 21.875]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", - "Ecuación de regresión: y = -0.101x + 16.438\n", - "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 13.669\n", - "\tR²: 0.5403248300026882, Desviación Estándar: 0.544617405388324, Varianza: 0.29660811825191014, Incertidumbre: 0.09627566512557671\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = -0.831) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", - "Ecuación de regresión: y = -1.08x + 15.252\n", - "Valor del parámetro correlacionado para la aeronave: 2.329\n", - "Predicción obtenida: 12.736\n", - "\tR²: 0.6828633114546836, Desviación Estándar: 0.44550944206432913, Varianza: 0.19847866296846983, Incertidumbre: 0.07755323938068918\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", - "Ecuación de regresión: y = -0.57x + 15.003\n", - "Valor del parámetro correlacionado para la aeronave: 4.712\n", - "Predicción obtenida: 12.318\n", - "\tR²: 0.35147007195166013, Desviación Estándar: 0.615443744042397, Varianza: 0.3787710020809235, Incertidumbre: 0.11236414049581885\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = -0.823) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0, 9.5, 18.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", - "Ecuación de regresión: y = -0.022x + 14.625\n", - "Valor del parámetro correlacionado para la aeronave: 91.0\n", - "Predicción obtenida: 12.587\n", - "\tR²: 0.6554972240005492, Desviación Estándar: 0.4833062580516597, Varianza: 0.23358493907189748, Incertidumbre: 0.08169366806585572\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", - "Ecuación de regresión: y = -0.087x + 16.886\n", - "Valor del parámetro correlacionado para la aeronave: 35.0\n", - "Predicción obtenida: 13.85\n", - "\tR²: 0.6369900213647028, Desviación Estándar: 0.46515224472263894, Varianza: 0.2163666107705098, Incertidumbre: 0.08951859123492475\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", - "Ecuación de regresión: y = -0.092x + 14.621\n", - "Valor del parámetro correlacionado para la aeronave: 7.0\n", - "Predicción obtenida: 13.978\n", - "\tR²: 0.7196334122958243, Desviación Estándar: 0.4310592638988115, Varianza: 0.1858120889929852, Incertidumbre: 0.07620123214903278\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = -0.999) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 20.0]\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 13.218, 14.755, 14.057, 12.908, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", - "Ecuación de regresión: y = -0.109x + 16.525\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 13.795\n", - "\tR²: 0.67567314504279, Desviación Estándar: 0.43981688532599306, Varianza: 0.1934388926178577, Incertidumbre: 0.08625518771864765\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 13.669', 'Área del ala: 12.736', 'Longitud del fuselaje: 12.318', 'Peso máximo al despegue (MTOW): 12.587', 'Velocidad máxima (KIAS): 13.85', 'payload: 13.978', 'Crucero KIAS: 13.795']\n", - "**Mediana calculada:** 13.669\n", - "\n", - "=== Imputación para el parámetro: **Longitud del fuselaje** ===\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = 1.343x + 0.345\n", - "Valor del parámetro correlacionado para la aeronave: 2.503\n", - "Predicción obtenida: 3.707\n", - "\tR²: 0.6086218910264541, Desviación Estándar: 0.5492734618495684, Varianza: 0.3017013358922092, Incertidumbre: 0.10380292727296299\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = -0.642x + 11.003\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 2.974\n", - "\tR²: 0.27906683530984955, Desviación Estándar: 0.7768274399509152, Varianza: 0.6034608714606927, Incertidumbre: 0.13952232697507047\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.786) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.0, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = 0.025x + 1.186\n", - "Valor del parámetro correlacionado para la aeronave: 93.0\n", - "Predicción obtenida: 3.482\n", - "\tR²: 0.5830919989131762, Desviación Estándar: 0.5850219632837886, Varianza: 0.3422506975244185, Incertidumbre: 0.10507304640788168\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.94) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", - "Valores para Longitud del fuselaje: [3.0, 3.0, 3.0, 0.75, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = 0.044x + 1.202\n", - "Valor del parámetro correlacionado para la aeronave: 70.3\n", - "Predicción obtenida: 4.329\n", - "\tR²: 0.8836944979649586, Desviación Estándar: 0.39788597040864104, Varianza: 0.15831324544802597, Incertidumbre: 0.150386761123267\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Área del ala: 3.707', 'Relación de aspecto del ala: 2.974', 'Peso máximo al despegue (MTOW): 3.482', 'RTF (Including fuel & Batteries): 4.329']\n", - "**Mediana calculada:** 3.594\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = 1.328x + 0.361\n", - "Valor del parámetro correlacionado para la aeronave: 2.09\n", - "Predicción obtenida: 3.135\n", - "\tR²: 0.6482130318947588, Desviación Estándar: 0.5400425508620562, Varianza: 0.29164595674159655, Incertidumbre: 0.10028338411232726\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = -0.684x + 11.599\n", - "Valor del parámetro correlacionado para la aeronave: 12.648\n", - "Predicción obtenida: 2.947\n", - "\tR²: 0.3216836388295986, Desviación Estándar: 0.7714753386968983, Varianza: 0.595174198217494, Incertidumbre: 0.13637886087769133\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.786) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = 0.025x + 1.181\n", - "Valor del parámetro correlacionado para la aeronave: 75.0\n", - "Predicción obtenida: 3.049\n", - "\tR²: 0.6148826876335616, Desviación Estándar: 0.5761037227120962, Varianza: 0.3318954993227359, Incertidumbre: 0.10184171224913441\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Maximum Crosswind (r = -0.718) ---\n", - "Aeronaves utilizadas: ['DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Maximum Crosswind: [45.0, 50.0, 15.0, 15.0, 15.0]\n", - "Valores para Longitud del fuselaje: [0.75, 0.9, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = -0.062x + 3.771\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 1.922\n", - "\tR²: 0.46781484146630226, Desviación Estándar: 1.0520768032800647, Varianza: 1.1068656, Incertidumbre: 0.47050304993697967\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 3.135', 'Relación de aspecto del ala: 2.947', 'Peso máximo al despegue (MTOW): 3.049', 'Maximum Crosswind: 1.922']\n", - "**Mediana calculada:** 2.998\n", - "\n", - "--- Imputación para aeronave: **V39** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.867) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = 1.316x + 0.371\n", - "Valor del parámetro correlacionado para la aeronave: 1.203\n", - "Predicción obtenida: 1.954\n", - "\tR²: 0.6597115958770696, Desviación Estándar: 0.5314985507982902, Varianza: 0.28249070950068267, Incertidumbre: 0.09703791518450966\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.79) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = -0.687x + 11.639\n", - "Valor del parámetro correlacionado para la aeronave: 14.042\n", - "Predicción obtenida: 1.994\n", - "\tR²: 0.3377422956329197, Desviación Estándar: 0.759743862528399, Varianza: 0.5772107366495708, Incertidumbre: 0.13225443071567164\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.786) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 2.905, 1.562, 2.3, 4.712]\n", - "Ecuación de regresión: y = 0.025x + 1.181\n", - "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 1.778\n", - "\tR²: 0.6241247541602877, Desviación Estándar: 0.5673714496579856, Varianza: 0.32191036188700406, Incertidumbre: 0.09876669201264836\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Área del ala: 1.954', 'Relación de aspecto del ala: 1.994', 'Peso máximo al despegue (MTOW): 1.778']\n", - "**Mediana calculada:** 1.954\n", - "\n", - "=== Peso máximo al despegue (MTOW): No hay valores faltantes para imputar. ===\n", - "\n", - "=== Imputación para el parámetro: **Alcance de la aeronave** ===\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.7 Fixed Wing** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.907x + 35759.111\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 316.444\n", - "\tR²: 0.941347558801303, Desviación Estándar: 221.17418977408332, Varianza: 48918.02222222222, Incertidumbre: 69.94141993284252\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 43.142x + 16.54\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 555.818\n", - "\tR²: 0.0017873010996798389, Desviación Estándar: 871.652385533205, Varianza: 759777.8812057271, Incertidumbre: 262.81308276653186\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 26.0, 8.0, 6.0, 2.0, 19.0, 4.53, 1.83, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = 106.024x + -335.63\n", - "Valor del parámetro correlacionado para la aeronave: 19.8\n", - "Predicción obtenida: 1763.644\n", - "\tR²: 0.7148030071984908, Desviación Estándar: 487.71248080374903, Varianza: 237863.46393174725, Incertidumbre: 154.2282282630995\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Orbiter 3', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 5.5, 18.0, 3.0, 1.2, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 50.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 21.387x + 171.916\n", - "Valor del parámetro correlacionado para la aeronave: 14.5\n", - "Predicción obtenida: 482.027\n", - "\tR²: 0.6151852469174406, Desviación Estándar: 137.60082187743836, Varianza: 18933.986181346518, Incertidumbre: 48.64923712318945\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 316.444', 'Relación de aspecto del ala: 555.818', 'Autonomía de la aeronave: 1763.644', 'payload: 482.027']\n", - "**Mediana calculada:** 518.922\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.7 VTOL** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.867x + 35536.386\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 336.692\n", - "\tR²: 0.9369828282191571, Desviación Estándar: 218.68983155385746, Varianza: 47825.242425054545, Incertidumbre: 65.9374651572882\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 48.512x + -61.995\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 544.404\n", - "\tR²: 0.002995498991272716, Desviación Estándar: 834.5905621601189, Varianza: 696541.4064467433, Incertidumbre: 240.92554286313288\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 26.0, 8.0, 6.0, 2.0, 19.0, 4.53, 1.83, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = 86.745x + -257.466\n", - "Valor del parámetro correlacionado para la aeronave: 12.0\n", - "Predicción obtenida: 783.47\n", - "\tR²: 0.5747433288658067, Desviación Estándar: 568.0998879939582, Varianza: 322737.4827387478, Incertidumbre: 171.28856108353526\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Orbiter 3', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 5.5, 18.0, 3.0, 1.2, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 50.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 21.739x + 172.796\n", - "Valor del parámetro correlacionado para la aeronave: 11.3\n", - "Predicción obtenida: 418.452\n", - "\tR²: 0.6350137109462142, Desviación Estándar: 130.21864982605558, Varianza: 16956.896762520882, Incertidumbre: 43.40621660868519\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 336.692', 'Relación de aspecto del ala: 544.404', 'Autonomía de la aeronave: 783.47', 'payload: 418.452']\n", - "**Mediana calculada:** 481.428\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 Fixed wing** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.84x + 35391.65\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 349.85\n", - "\tR²: 0.934808022871879, Desviación Estándar: 213.13565980536123, Varianza: 45426.80948066667, Incertidumbre: 61.526965281266904\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 55.512x + -164.373\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 529.525\n", - "\tR²: 0.004730674917466038, Desviación Estándar: 801.9939500096699, Varianza: 643194.2958521128, Incertidumbre: 222.43310072090526\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = 0.982) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.277, 0.375]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 800.0]\n", - "Ecuación de regresión: y = 2261.812x + -85.496\n", - "Valor del parámetro correlacionado para la aeronave: 0.277\n", - "Predicción obtenida: 541.026\n", - "\tR²: 0.9065116987148334, Desviación Estándar: 45.342194867988525, Varianza: 2055.914635446645, Incertidumbre: 20.27764599477289\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 26.0, 8.0, 6.0, 2.0, 19.0, 4.53, 1.83, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = 85.849x + -273.589\n", - "Valor del parámetro correlacionado para la aeronave: 19.8\n", - "Predicción obtenida: 1426.214\n", - "\tR²: 0.56549691959356, Desviación Estándar: 550.2441974157556, Varianza: 302768.67678970896, Incertidumbre: 158.84181774900804\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.197]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 3270.0]\n", - "Ecuación de regresión: y = -13031.161x + 4813.896\n", - "Valor del parámetro correlacionado para la aeronave: 0.352\n", - "Predicción obtenida: 226.928\n", - "\tR²: 0.5279015132670961, Desviación Estándar: 775.5687627719914, Varianza: 601506.9057876774, Incertidumbre: 346.8448949567162\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Orbiter 3', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 5.5, 18.0, 3.0, 1.2, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 50.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 21.957x + 177.064\n", - "Valor del parámetro correlacionado para la aeronave: 17.7\n", - "Predicción obtenida: 565.695\n", - "\tR²: 0.6360380648248155, Desviación Estándar: 124.96189515821898, Varianza: 15615.475241533713, Incertidumbre: 39.5164209431139\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 349.85', 'Relación de aspecto del ala: 529.525', 'Ancho del fuselaje: 541.026', 'Autonomía de la aeronave: 1426.214', 'Cuerda: 226.928', 'payload: 565.695']\n", - "**Mediana calculada:** 535.276\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.809x + 35221.676\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 365.302\n", - "\tR²: 0.9310643157348193, Desviación Estándar: 210.61070207807703, Varianza: 44356.86782982052, Incertidumbre: 58.412898884921304\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 54.995x + -156.81\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 530.624\n", - "\tR²: 0.005326717468706943, Desviación Estándar: 772.8219439192726, Varianza: 597253.7570031633, Incertidumbre: 206.5453525090388\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 26.0, 8.0, 6.0, 2.0, 19.0, 4.53, 1.83, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = 74.895x + -223.348\n", - "Valor del parámetro correlacionado para la aeronave: 14.0\n", - "Predicción obtenida: 825.177\n", - "\tR²: 0.48851416333887676, Desviación Estándar: 573.687248269721, Varianza: 329117.05882728443, Incertidumbre: 159.11221459356298\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Orbiter 3', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 5.5, 18.0, 3.0, 1.2, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 50.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 21.59x + 178.007\n", - "Valor del parámetro correlacionado para la aeronave: 22.7\n", - "Predicción obtenida: 668.093\n", - "\tR²: 0.6515452684663989, Desviación Estándar: 119.43474251813149, Varianza: 14264.657720372366, Incertidumbre: 36.01092980594082\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 365.302', 'Relación de aspecto del ala: 530.624', 'Autonomía de la aeronave: 825.177', 'payload: 668.093']\n", - "**Mediana calculada:** 599.358\n", - "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.773x + 35023.629\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 383.306\n", - "\tR²: 0.9250199701732349, Desviación Estándar: 211.66135430129737, Varianza: 44800.52890465933, Incertidumbre: 56.5688764154304\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 49.807x + -80.929\n", - "Valor del parámetro correlacionado para la aeronave: 13.443\n", - "Predicción obtenida: 588.62\n", - "\tR²: 0.004857101152760834, Desviación Estándar: 746.7939888417249, Varianza: 557701.2617701343, Incertidumbre: 192.82137878878717\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 6.0, 2.0, 19.0, 4.53, 1.83, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = 73.997x + -229.547\n", - "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 1546.391\n", - "\tR²: 0.48293085237829836, Desviación Estándar: 555.8314018926295, Varianza: 308948.5473299259, Incertidumbre: 148.55219076374803\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 3', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 5.5, 18.0, 3.0, 1.2, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 50.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 20.566x + 183.705\n", - "Valor del parámetro correlacionado para la aeronave: 12.0\n", - "Predicción obtenida: 430.493\n", - "\tR²: 0.6713351729638166, Desviación Estándar: 115.62496967809123, Varianza: 13369.133613059515, Incertidumbre: 33.378053684344145\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 383.306', 'Relación de aspecto del ala: 588.62', 'Autonomía de la aeronave: 1546.391', 'payload: 430.493']\n", - "**Mediana calculada:** 509.556\n", - "\n", - "--- Imputación para aeronave: **ScanEagle** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.755x + 34924.433\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 392.324\n", - "\tR²: 0.9233053265797887, Desviación Estándar: 206.88287418840918, Varianza: 42800.523632457145, Incertidumbre: 53.41692842314887\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 12.84, 14.589, 14.714, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 51.148x + -104.281\n", - "Valor del parámetro correlacionado para la aeronave: 14.057\n", - "Predicción obtenida: 614.707\n", - "\tR²: 0.0051433547185812944, Desviación Estándar: 723.3321143254109, Varianza: 523209.34761446924, Incertidumbre: 180.83302858135272\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 19.0, 4.53, 1.83, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = 59.997x + -131.637\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 948.312\n", - "\tR²: 0.38436660533416545, Desviación Estándar: 586.1422469494127, Varianza: 343562.73365890625, Incertidumbre: 151.3412773962447\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 18.0, 3.0, 1.2, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 20.645x + 188.896\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 292.121\n", - "\tR²: 0.6654030506878479, Desviación Estándar: 113.06733122345928, Varianza: 12784.22138999545, Incertidumbre: 31.359235408157918\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 392.324', 'Relación de aspecto del ala: 614.707', 'Autonomía de la aeronave: 948.312', 'payload: 292.121']\n", - "**Mediana calculada:** 503.516\n", - "\n", - "--- Imputación para aeronave: **Integrator** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.741x + 34842.892\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 399.737\n", - "\tR²: 0.9219836638407033, Desviación Estándar: 202.10566728677685, Varianza: 40846.70074943334, Incertidumbre: 50.52641682169421\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.84, 14.589, 14.714, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 49.053x + -82.026\n", - "Valor del parámetro correlacionado para la aeronave: 12.908\n", - "Predicción obtenida: 551.147\n", - "\tR²: 0.004755791997559489, Desviación Estándar: 702.2196991834805, Varianza: 493112.5059213379, Incertidumbre: 170.31329365429116\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 19.0, 4.53, 1.83, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = 57.271x + -125.878\n", - "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 1248.627\n", - "\tR²: 0.3634971486006614, Desviación Estándar: 577.2791780254325, Varianza: 333251.2493817191, Incertidumbre: 144.31979450635814\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 3.0, 1.2, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 19.14x + 220.22\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 564.734\n", - "\tR²: 0.5908236954277724, Desviación Estándar: 121.25887543058778, Varianza: 14703.714870690805, Incertidumbre: 32.407797640482855\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 399.737', 'Relación de aspecto del ala: 551.147', 'Autonomía de la aeronave: 1248.627', 'payload: 564.734']\n", - "**Mediana calculada:** 557.94\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -5.721x + 34734.128\n", - "Valor del parámetro correlacionado para la aeronave: 5000.0\n", - "Predicción obtenida: 6130.375\n", - "\tR²: 0.9191867670939571, Desviación Estándar: 199.55997470891685, Varianza: 39824.18350582354, Incertidumbre: 48.40040319826195\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 14.589, 14.714, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 48.743x + -77.408\n", - "Valor del parámetro correlacionado para la aeronave: 12.648\n", - "Predicción obtenida: 539.096\n", - "\tR²: 0.004881178077821846, Desviación Estándar: 682.436587225711, Varianza: 465719.69558427547, Incertidumbre: 160.8518461857017\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 19.0, 4.53, 1.83, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = 50.082x + -73.061\n", - "Valor del parámetro correlacionado para la aeronave: 16.0\n", - "Predicción obtenida: 728.245\n", - "\tR²: 0.31645626386358894, Desviación Estándar: 580.3838962714162, Varianza: 336845.4670511901, Incertidumbre: 140.76377104321017\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 3.0, 1.2, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 19.086x + 220.367\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 563.922\n", - "\tR²: 0.6034520565797792, Desviación Estándar: 117.1587675681931, Varianza: 13726.176818097896, Incertidumbre: 30.250263710253165\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 6130.375', 'Relación de aspecto del ala: 539.096', 'Autonomía de la aeronave: 728.245', 'payload: 563.922']\n", - "**Mediana calculada:** 646.084\n", - "\n", - "--- Imputación para aeronave: **ScanEagle 3** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -1.252x + 7989.744\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 477.332\n", - "\tR²: 0.21041072214297474, Desviación Estándar: 606.3660387299609, Varianza: 367679.7729250645, Incertidumbre: 142.9218459557267\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 14.589, 14.714, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 42.949x + 7.261\n", - "Valor del parámetro correlacionado para la aeronave: 13.765\n", - "Predicción obtenida: 598.458\n", - "\tR²: 0.004016554225731106, Desviación Estándar: 664.640093521826, Varianza: 441746.45391670155, Incertidumbre: 152.4788948151085\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 4.53, 1.83, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = 49.863x + -74.747\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 822.786\n", - "\tR²: 0.31605998443356176, Desviación Estándar: 564.3431464817085, Varianza: 318483.18698087503, Incertidumbre: 133.01695526445639\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 3.0, 1.2, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 19.662x + 218.783\n", - "Valor del parámetro correlacionado para la aeronave: 8.6\n", - "Predicción obtenida: 387.873\n", - "\tR²: 0.6221596490324965, Desviación Estándar: 115.08764274935331, Varianza: 13245.165513602775, Incertidumbre: 28.771910687338327\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 477.332', 'Relación de aspecto del ala: 598.458', 'Autonomía de la aeronave: 822.786', 'payload: 387.873']\n", - "**Mediana calculada:** 537.895\n", - "\n", - "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -1.248x + 7967.914\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 480.853\n", - "\tR²: 0.21016964286508355, Desviación Estándar: 590.3473792395638, Varianza: 348510.02817502135, Incertidumbre: 135.43497724680736\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 14.589, 14.714, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 42.563x + 9.506\n", - "Valor del parámetro correlacionado para la aeronave: 12.914\n", - "Predicción obtenida: 559.164\n", - "\tR²: 0.003946364727999141, Desviación Estándar: 647.945383208715, Varianza: 419833.21962148865, Incertidumbre: 144.88499225618375\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 4.53, 1.83, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = 48.663x + -73.639\n", - "Valor del parámetro correlacionado para la aeronave: 16.0\n", - "Predicción obtenida: 704.969\n", - "\tR²: 0.30722335636758213, Desviación Estándar: 552.8882334214711, Varianza: 305685.3986559151, Incertidumbre: 126.84125981878523\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 3.0, 1.2, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 19.184x + 233.095\n", - "Valor del parámetro correlacionado para la aeronave: 17.7\n", - "Predicción obtenida: 572.66\n", - "\tR²: 0.5902643693999969, Desviación Estándar: 117.04644073196869, Varianza: 13699.869288022259, Incertidumbre: 28.387931661206128\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 480.853', 'Relación de aspecto del ala: 559.164', 'Autonomía de la aeronave: 704.969', 'payload: 572.66']\n", - "**Mediana calculada:** 565.912\n", - "\n", - "--- Imputación para aeronave: **V21** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = -1.242x + 7938.938\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 485.527\n", - "\tR²: 0.20937029275663177, Desviación Estándar: 575.6964484514262, Varianza: 331426.4007595857, Incertidumbre: 128.72963931425926\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 42.313x + 13.229\n", - "Valor del parámetro correlacionado para la aeronave: 14.568\n", - "Predicción obtenida: 629.646\n", - "\tR²: 0.004008828742622317, Desviación Estándar: 632.3315354155332, Varianza: 399843.1706809657, Incertidumbre: 137.98605358806336\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 300.0]\n", - "Ecuación de regresión: y = 48.351x + -76.37\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 68.684\n", - "\tR²: 0.3050591302831972, Desviación Estándar: 539.7354115699455, Varianza: 291314.3145025785, Incertidumbre: 120.68850701342247\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 300.0, 800.0]\n", - "Ecuación de regresión: y = 19.143x + 233.214\n", - "Valor del parámetro correlacionado para la aeronave: 1.5\n", - "Predicción obtenida: 261.928\n", - "\tR²: 0.5985890782746399, Desviación Estándar: 113.75879351787289, Varianza: 12941.063102642038, Incertidumbre: 26.813204772029398\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 485.527', 'Relación de aspecto del ala: 629.646', 'Autonomía de la aeronave: 68.684', 'payload: 261.928']\n", - "**Mediana calculada:** 373.727\n", - "\n", - "--- Imputación para aeronave: **V25** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 300.0]\n", - "Ecuación de regresión: y = -1.249x + 7975.04\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 479.704\n", - "\tR²: 0.21171906549957753, Desviación Estándar: 562.3240970485209, Varianza: 316208.3901214344, Incertidumbre: 122.70917808678767\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 300.0, 800.0]\n", - "Ecuación de regresión: y = 30.498x + 162.947\n", - "Valor del parámetro correlacionado para la aeronave: 14.421\n", - "Predicción obtenida: 602.758\n", - "\tR²: 0.0021672955940218452, Desviación Estándar: 619.98780177568, Varianza: 384384.87435064, Incertidumbre: 132.1818434703152\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 300.0]\n", - "Ecuación de regresión: y = 45.798x + -28.528\n", - "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 154.662\n", - "\tR²: 0.2987687873424638, Desviación Estándar: 530.3673768842218, Varianza: 281289.5544630502, Incertidumbre: 115.73565003367233\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 300.0, 800.0]\n", - "Ecuación de regresión: y = 18.099x + 250.896\n", - "Valor del parámetro correlacionado para la aeronave: 2.2\n", - "Predicción obtenida: 290.713\n", - "\tR²: 0.5851882087506547, Desviación Estándar: 113.23855604202713, Varianza: 12822.970574483317, Incertidumbre: 25.978706436824385\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 479.704', 'Relación de aspecto del ala: 602.758', 'Autonomía de la aeronave: 154.662', 'payload: 290.713']\n", - "**Mediana calculada:** 385.208\n", - "\n", - "--- Imputación para aeronave: **V32** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 300.0]\n", - "Ecuación de regresión: y = -1.255x + 8004.043\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 475.026\n", - "\tR²: 0.21371901155960438, Desviación Estándar: 549.7463712197905, Varianza: 302221.07266932767, Incertidumbre: 117.20632015795667\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 300.0, 800.0]\n", - "Ecuación de regresión: y = 22.662x + 260.76\n", - "Valor del parámetro correlacionado para la aeronave: 14.182\n", - "Predicción obtenida: 582.152\n", - "\tR²: 0.0012253357519550478, Desviación Estándar: 607.9363047663803, Varianza: 369586.55065300124, Incertidumbre: 126.76348237238503\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 300.0]\n", - "Ecuación de regresión: y = 44.24x + 1.63\n", - "Valor del parámetro correlacionado para la aeronave: 4.5\n", - "Predicción obtenida: 200.71\n", - "\tR²: 0.2958075456148329, Desviación Estándar: 520.2584796289456, Varianza: 270668.88562582195, Incertidumbre: 110.91948054697195\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 300.0, 800.0]\n", - "Ecuación de regresión: y = 17.374x + 263.474\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 350.346\n", - "\tR²: 0.5751384226627532, Desviación Estándar: 112.14449166524236, Varianza: 12576.387010855611, Incertidumbre: 25.07627066656405\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 475.026', 'Relación de aspecto del ala: 582.152', 'Autonomía de la aeronave: 200.71', 'payload: 350.346']\n", - "**Mediana calculada:** 412.686\n", - "\n", - "--- Imputación para aeronave: **V35** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 300.0]\n", - "Ecuación de regresión: y = -1.258x + 8022.275\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 472.085\n", - "\tR²: 0.21522284145385784, Desviación Estándar: 537.8122565548367, Varianza: 289242.0233006055, Incertidumbre: 112.14160754824495\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 300.0, 800.0]\n", - "Ecuación de regresión: y = 18.768x + 307.082\n", - "Valor del parámetro correlacionado para la aeronave: 13.898\n", - "Predicción obtenida: 567.923\n", - "\tR²: 0.0008476663567767995, Desviación Estándar: 596.0879860214709, Varianza: 355320.88707913324, Incertidumbre: 121.6759506296563\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 300.0]\n", - "Ecuación de regresión: y = 43.01x + 25.948\n", - "Valor del parámetro correlacionado para la aeronave: 2.8\n", - "Predicción obtenida: 146.377\n", - "\tR²: 0.29271465280738995, Desviación Estándar: 510.56947779344574, Varianza: 260681.19165427188, Incertidumbre: 106.461095497527\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 300.0, 800.0]\n", - "Ecuación de regresión: y = 17.075x + 269.602\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 440.357\n", - "\tR²: 0.5701248711886335, Desviación Estándar: 110.22167028805497, Varianza: 12148.8166010887, Incertidumbre: 24.052340348546522\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 472.085', 'Relación de aspecto del ala: 567.923', 'Autonomía de la aeronave: 146.377', 'payload: 440.357']\n", - "**Mediana calculada:** 456.221\n", - "\n", - "--- Imputación para aeronave: **V39** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 300.0]\n", - "Ecuación de regresión: y = -1.259x + 8026.705\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 471.371\n", - "\tR²: 0.21604447260306026, Desviación Estándar: 526.4981352259283, Varianza: 277200.2863963799, Incertidumbre: 107.47098181919851\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 300.0, 800.0]\n", - "Ecuación de regresión: y = 17.789x + 316.05\n", - "Valor del parámetro correlacionado para la aeronave: 14.042\n", - "Predicción obtenida: 565.84\n", - "\tR²: 0.0007617532043836528, Desviación Estándar: 584.4539259103746, Varianza: 341586.3915120496, Incertidumbre: 116.89078518207491\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 300.0]\n", - "Ecuación de regresión: y = 41.038x + 62.298\n", - "Valor del parámetro correlacionado para la aeronave: 4.5\n", - "Predicción obtenida: 246.97\n", - "\tR²: 0.28329338577170626, Desviación Estándar: 503.409990644059, Varianza: 253421.61868025156, Incertidumbre: 102.7581340414332\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 300.0, 800.0]\n", - "Ecuación de regresión: y = 17.068x + 270.401\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 355.741\n", - "\tR²: 0.5697467595123673, Desviación Estándar: 107.73817669253191, Varianza: 11607.514717031225, Incertidumbre: 22.969856449695133\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 471.371', 'Relación de aspecto del ala: 565.84', 'Autonomía de la aeronave: 246.97', 'payload: 355.741']\n", - "**Mediana calculada:** 413.556\n", - "\n", - "--- Imputación para aeronave: **Volitation VT370** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 300.0]\n", - "Ecuación de regresión: y = -1.262x + 8042.156\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 468.879\n", - "\tR²: 0.21731767317513662, Desviación Estándar: 515.9847025403219, Varianza: 266240.21325562446, Incertidumbre: 103.19694050806439\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 300.0, 800.0]\n", - "Ecuación de regresión: y = 15.491x + 341.739\n", - "Valor del parámetro correlacionado para la aeronave: 13.645\n", - "Predicción obtenida: 553.116\n", - "\tR²: 0.0005791285523265577, Desviación Estándar: 573.8483135876736, Varianza: 329301.8870074169, Incertidumbre: 112.54091341637921\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 300.0]\n", - "Ecuación de regresión: y = 40.24x + 78.213\n", - "Valor del parámetro correlacionado para la aeronave: 15.0\n", - "Predicción obtenida: 681.814\n", - "\tR²: 0.2817793757671705, Desviación Estándar: 494.2799550877504, Varianza: 244312.67400154856, Incertidumbre: 98.85599101755008\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 300.0, 800.0]\n", - "Ecuación de regresión: y = 16.811x + 275.566\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 578.158\n", - "\tR²: 0.5654386769863811, Desviación Estándar: 106.0114127800309, Varianza: 11238.419639618098, Incertidumbre: 22.104907619190833\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 468.879', 'Relación de aspecto del ala: 553.116', 'Autonomía de la aeronave: 681.814', 'payload: 578.158']\n", - "**Mediana calculada:** 565.637\n", - "\n", - "--- Imputación para aeronave: **Skyeye 2600** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 300.0]\n", - "Ecuación de regresión: y = -1.257x + 8017.366\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 472.877\n", - "\tR²: 0.2163014350450687, Desviación Estándar: 506.30564828630486, Varianza: 256345.40948661542, Incertidumbre: 99.29470694054284\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 300.0, 800.0]\n", - "Ecuación de regresión: y = 15.442x + 342.884\n", - "Valor del parámetro correlacionado para la aeronave: 14.103\n", - "Predicción obtenida: 560.656\n", - "\tR²: 0.0005756116510963194, Desviación Estándar: 563.1261939754822, Varianza: 317111.1103413124, Incertidumbre: 108.37368655982469\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 300.0]\n", - "Ecuación de regresión: y = 39.985x + 76.738\n", - "Valor del parámetro correlacionado para la aeronave: 2.0\n", - "Predicción obtenida: 156.708\n", - "\tR²: 0.28030070432195453, Desviación Estándar: 485.1921720016292, Varianza: 235411.44377165852, Incertidumbre: 95.15401357226055\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 300.0, 800.0]\n", - "Ecuación de regresión: y = 16.737x + 275.831\n", - "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 342.778\n", - "\tR²: 0.5745690871363021, Desviación Estándar: 103.8081239768202, Varianza: 10776.126603586874, Incertidumbre: 21.1897445748988\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 472.877', 'Relación de aspecto del ala: 560.656', 'Autonomía de la aeronave: 156.708', 'payload: 342.778']\n", - "**Mediana calculada:** 407.828\n", - "\n", - "--- Imputación para aeronave: **Skyeye 2930 VTOL** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 300.0]\n", - "Ecuación de regresión: y = -1.261x + 8033.37\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 470.296\n", - "\tR²: 0.21750101210992656, Desviación Estándar: 496.99258866565987, Varianza: 247001.63318859378, Incertidumbre: 95.64626828378923\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 300.0, 800.0]\n", - "Ecuación de regresión: y = 12.779x + 374.006\n", - "Valor del parámetro correlacionado para la aeronave: 14.001\n", - "Predicción obtenida: 552.929\n", - "\tR²: 0.0003958766704269534, Desviación Estándar: 553.7010119743468, Varianza: 306584.8106614157, Incertidumbre: 104.63965559777989\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 300.0]\n", - "Ecuación de regresión: y = 38.499x + 102.922\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 218.418\n", - "\tR²: 0.2751249042805085, Desviación Estándar: 478.34320573310276, Varianza: 228812.2224710215, Incertidumbre: 92.0571928650118\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 300.0, 800.0]\n", - "Ecuación de regresión: y = 16.416x + 281.755\n", - "Valor del parámetro correlacionado para la aeronave: 6.0\n", - "Predicción obtenida: 380.25\n", - "\tR²: 0.5695278609951793, Desviación Estándar: 102.48058029141457, Varianza: 10502.269336865067, Incertidumbre: 20.496116058282915\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 470.296', 'Relación de aspecto del ala: 552.929', 'Autonomía de la aeronave: 218.418', 'payload: 380.25']\n", - "**Mediana calculada:** 425.273\n", - "\n", - "--- Imputación para aeronave: **Skyeye 3600** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 300.0]\n", - "Ecuación de regresión: y = -1.263x + 8044.025\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 468.577\n", - "\tR²: 0.218442742826649, Desviación Estándar: 488.10836630332125, Varianza: 238249.77725529726, Incertidumbre: 92.24381072061483\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 300.0, 800.0]\n", - "Ecuación de regresión: y = 11.241x + 390.755\n", - "Valor del parámetro correlacionado para la aeronave: 13.71\n", - "Predicción obtenida: 544.869\n", - "\tR²: 0.0003067365701517888, Desviación Estándar: 544.5675096094902, Varianza: 296553.7725222822, Incertidumbre: 101.12364785716923\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 300.0]\n", - "Ecuación de regresión: y = 37.488x + 121.496\n", - "Valor del parámetro correlacionado para la aeronave: 4.5\n", - "Predicción obtenida: 290.19\n", - "\tR²: 0.2715724467015357, Desviación Estándar: 471.2257714097305, Varianza: 222053.72764069558, Incertidumbre: 89.05330017962268\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 300.0, 800.0]\n", - "Ecuación de regresión: y = 16.272x + 284.956\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 447.672\n", - "\tR²: 0.5668554579176148, Desviación Estándar: 100.85760609280737, Varianza: 10172.256706771894, Incertidumbre: 19.779803906210745\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 468.577', 'Relación de aspecto del ala: 544.869', 'Autonomía de la aeronave: 290.19', 'payload: 447.672']\n", - "**Mediana calculada:** 458.124\n", - "\n", - "--- Imputación para aeronave: **Skyeye 5000** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0]\n", - "Ecuación de regresión: y = -1.263x + 8046.407\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 468.193\n", - "\tR²: 0.21896917958386974, Desviación Estándar: 479.6226611274876, Varianza: 230037.89706701285, Incertidumbre: 89.06369225544637\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 800.0]\n", - "Ecuación de regresión: y = 11.399x + 385.692\n", - "Valor del parámetro correlacionado para la aeronave: 12.695\n", - "Predicción obtenida: 530.401\n", - "\tR²: 0.00031517259985813784, Desviación Estándar: 535.6408225987375, Varianza: 286911.0908342521, Incertidumbre: 97.79418708598382\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = 0.982) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Skyeye 5000 VTOL']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.277, 0.277, 0.375]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 800.0]\n", - "Ecuación de regresión: y = 2259.606x + -85.86\n", - "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 761.493\n", - "\tR²: 0.9064132760257113, Desviación Estándar: 41.446819545787584, Varianza: 1717.8388504610796, Incertidumbre: 16.920593224732006\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 4.5, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0]\n", - "Ecuación de regresión: y = 36.858x + 134.106\n", - "Valor del parámetro correlacionado para la aeronave: 8.0\n", - "Predicción obtenida: 428.972\n", - "\tR²: 0.2689650439391573, Desviación Estándar: 464.0178425419368, Varianza: 215312.5581972736, Incertidumbre: 86.16595019101095\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 800.0]\n", - "Ecuación de regresión: y = 16.27x + 285.358\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 610.76\n", - "\tR²: 0.5667205055911326, Desviación Estándar: 98.99193323862133, Varianza: 9799.402846319661, Incertidumbre: 19.051006434306494\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 468.193', 'Relación de aspecto del ala: 530.401', 'Ancho del fuselaje: 761.493', 'Autonomía de la aeronave: 428.972', 'payload: 610.76']\n", - "**Mediana calculada:** 530.401\n", - "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401]\n", - "Ecuación de regresión: y = -1.26x + 8032.73\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 470.399\n", - "\tR²: 0.21853303784795208, Desviación Estándar: 471.6931077514649, Varianza: 222494.38790023504, Incertidumbre: 86.11898511173075\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0]\n", - "Ecuación de regresión: y = 11.399x + 385.691\n", - "Valor del parámetro correlacionado para la aeronave: 12.856\n", - "Predicción obtenida: 532.237\n", - "\tR²: 0.0003313628990518902, Desviación Estándar: 526.9306352412358, Varianza: 277655.89435573225, Incertidumbre: 94.63953588966253\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = 0.982) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.277, 0.277, 0.375, 0.375]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 530.401, 800.0]\n", - "Ecuación de regresión: y = 1541.282x + 85.542\n", - "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 663.523\n", - "\tR²: 0.6272402531593875, Desviación Estándar: 76.59759898413333, Varianza: 5867.192170134103, Incertidumbre: 28.95117113381007\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0]\n", - "Ecuación de regresión: y = 15.727x + 288.21\n", - "Valor del parámetro correlacionado para la aeronave: 15.0\n", - "Predicción obtenida: 524.112\n", - "\tR²: 0.5615645241082226, Desviación Estándar: 98.27029747742641, Varianza: 9657.05136630188, Incertidumbre: 18.57134059925773\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 470.399', 'Relación de aspecto del ala: 532.237', 'Ancho del fuselaje: 663.523', 'payload: 524.112']\n", - "**Mediana calculada:** 528.174\n", - "\n", - "--- Imputación para aeronave: **Volitation VT510** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 528.174]\n", - "Ecuación de regresión: y = -1.258x + 8020.463\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 472.377\n", - "\tR²: 0.21815805928069354, Desviación Estándar: 464.1348278963494, Varianza: 215421.1384663739, Incertidumbre: 83.36107594546903\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174]\n", - "Ecuación de regresión: y = 11.548x + 383.526\n", - "Valor del parámetro correlacionado para la aeronave: 13.099\n", - "Predicción obtenida: 534.792\n", - "\tR²: 0.0003511194348928548, Desviación Estándar: 518.6324629138196, Varianza: 268979.63158805453, Incertidumbre: 91.68213286746061\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 4.5, 6.0, 8.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401]\n", - "Ecuación de regresión: y = 36.695x + 139.245\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 322.718\n", - "\tR²: 0.2678068616286219, Desviación Estándar: 456.58017132018756, Varianza: 208465.45284277183, Incertidumbre: 83.35975304721335\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174]\n", - "Ecuación de regresión: y = 15.739x + 288.219\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 681.696\n", - "\tR²: 0.5652992923691292, Desviación Estándar: 96.5639245470936, Varianza: 9324.591523936788, Incertidumbre: 17.931470624475867\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 472.377', 'Relación de aspecto del ala: 534.792', 'Autonomía de la aeronave: 322.718', 'payload: 681.696']\n", - "**Mediana calculada:** 503.585\n", - "\n", - "--- Imputación para aeronave: **Ascend** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 528.174, 503.585]\n", - "Ecuación de regresión: y = -1.257x + 8014.056\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 473.411\n", - "\tR²: 0.21812584611652552, Desviación Estándar: 456.8573681831264, Varianza: 208718.65486321275, Incertidumbre: 80.76173576933198\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585]\n", - "Ecuación de regresión: y = 12.322x + 372.003\n", - "Valor del parámetro correlacionado para la aeronave: 14.349\n", - "Predicción obtenida: 548.807\n", - "\tR²: 0.00040559910055537607, Desviación Estándar: 510.74155237825306, Varianza: 260856.9333257478, Incertidumbre: 88.90869223718056\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 4.5, 6.0, 8.0, 5.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 503.585]\n", - "Ecuación de regresión: y = 36.114x + 151.21\n", - "Valor del parámetro correlacionado para la aeronave: 6.0\n", - "Predicción obtenida: 367.893\n", - "\tR²: 0.2642430210503296, Desviación Estándar: 450.27008149115517, Varianza: 202743.14628605152, Incertidumbre: 80.87089397983948\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585]\n", - "Ecuación de regresión: y = 14.216x + 299.289\n", - "Valor del parámetro correlacionado para la aeronave: 0.6\n", - "Predicción obtenida: 307.818\n", - "\tR²: 0.5236857913768209, Desviación Estándar: 99.5530852483226, Varianza: 9910.816782459788, Incertidumbre: 18.17582348658036\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 473.411', 'Relación de aspecto del ala: 548.807', 'Autonomía de la aeronave: 367.893', 'payload: 307.818']\n", - "**Mediana calculada:** 420.652\n", - "\n", - "--- Imputación para aeronave: **Transition** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 528.174, 503.585, 420.652]\n", - "Ecuación de regresión: y = -1.259x + 8024.54\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 471.72\n", - "\tR²: 0.21891613091083528, Desviación Estándar: 449.9727679396905, Varianza: 202475.4918873066, Incertidumbre: 78.33020468683851\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652]\n", - "Ecuación de regresión: y = 8.718x + 417.57\n", - "Valor del parámetro correlacionado para la aeronave: 14.223\n", - "Predicción obtenida: 541.562\n", - "\tR²: 0.00020665375027828503, Desviación Estándar: 503.6313668149641, Varianza: 253644.55363990893, Incertidumbre: 86.37206684215421\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 4.5, 6.0, 8.0, 5.0, 6.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 503.585, 420.652]\n", - "Ecuación de regresión: y = 35.981x + 154.244\n", - "Valor del parámetro correlacionado para la aeronave: 12.0\n", - "Predicción obtenida: 586.014\n", - "\tR²: 0.2649729523763782, Desviación Estándar: 443.27273842243636, Varianza: 196490.7206285257, Incertidumbre: 78.36028981340885\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652]\n", - "Ecuación de regresión: y = 13.547x + 310.162\n", - "Valor del parámetro correlacionado para la aeronave: 1.5\n", - "Predicción obtenida: 330.483\n", - "\tR²: 0.5062493497217644, Desviación Estándar: 99.81827758318798, Varianza: 9963.688539674367, Incertidumbre: 17.927891893120933\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 471.72', 'Relación de aspecto del ala: 541.562', 'Autonomía de la aeronave: 586.014', 'payload: 330.483']\n", - "**Mediana calculada:** 506.641\n", - "\n", - "--- Imputación para aeronave: **Reach** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = -0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 528.174, 503.585, 420.652, 506.641]\n", - "Ecuación de regresión: y = -1.258x + 8017.816\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 472.804\n", - "\tR²: 0.2188220660556668, Desviación Estándar: 443.34533244312075, Varianza: 196555.08379910127, Incertidumbre: 76.03309724353042\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.998) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652, 506.641]\n", - "Ecuación de regresión: y = 7.955x + 427.025\n", - "Valor del parámetro correlacionado para la aeronave: 13.669\n", - "Predicción obtenida: 535.762\n", - "\tR²: 0.0001740848142732787, Desviación Estándar: 496.4181896725954, Varianza: 246431.01903781694, Incertidumbre: 83.90998902528435\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Autonomía de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 4.5, 6.0, 8.0, 5.0, 6.0, 12.0]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 503.585, 420.652, 506.641]\n", - "Ecuación de regresión: y = 35.911x + 152.567\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 870.792\n", - "\tR²: 0.2643013375727117, Desviación Estándar: 436.7165278868632, Varianza: 190721.3257295574, Incertidumbre: 76.02258949165629\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5]\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652, 506.641]\n", - "Ecuación de regresión: y = 12.668x + 324.924\n", - "Valor del parámetro correlacionado para la aeronave: 7.0\n", - "Predicción obtenida: 413.601\n", - "\tR²: 0.46249934976527396, Desviación Estándar: 102.70373401095995, Varianza: 10548.056979794012, Incertidumbre: 18.155626693082308\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 472.804', 'Relación de aspecto del ala: 535.762', 'Autonomía de la aeronave: 870.792', 'payload: 413.601']\n", - "**Mediana calculada:** 504.283\n", - "\n", - "=== Imputación para el parámetro: **Autonomía de la aeronave** ===\n", - "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = 0.843) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 503.585, 420.652, 506.641, 504.283]\n", - "Valores para Autonomía de la aeronave: [8.0, 8.0, 19.8, 12.0, 19.8, 14.0, 26.0, 8.0, 24.0, 6.0, 2.0, 18.0, 24.0, 16.0, 19.0, 18.0, 16.0, 4.53, 1.83, 3.0, 4.0, 4.5, 2.8, 4.5, 15.0, 2.0, 3.0, 4.5, 6.0, 8.0, 5.0, 6.0, 12.0, 20.0]\n", - "Ecuación de regresión: y = 0.007x + 6.874\n", - "Valor del parámetro correlacionado para la aeronave: 528.174\n", - "Predicción obtenida: 10.747\n", - "\tR²: 0.2498408369463081, Desviación Estándar: 6.374288748814379, Varianza: 40.63155705326158, Incertidumbre: 1.093181501711479\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Alcance de la aeronave: 10.747']\n", - "**Mediana calculada:** 10.747\n", - "\n", - "=== Imputación para el parámetro: **Velocidad máxima (KIAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.072x + 7.262\n", - "Valor del parámetro correlacionado para la aeronave: 27.892\n", - "Predicción obtenida: 37.163\n", - "\tR²: 0.6785150773380106, Desviación Estándar: 4.266538520956066, Varianza: 18.20335095080197, Incertidumbre: 0.8896347797490924\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.786x + 25.969\n", - "Valor del parámetro correlacionado para la aeronave: 2.503\n", - "Predicción obtenida: 42.955\n", - "\tR²: 0.32230308247682127, Desviación Estándar: 5.586912076398391, Varianza: 31.213586549406177, Incertidumbre: 1.0956836037800992\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -7.332x + 136.73\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 45.084\n", - "\tR²: 0.6353583924337338, Desviación Estándar: 4.212708115387483, Varianza: 17.746909665451557, Incertidumbre: 0.7961270013870624\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 36.0, 36.0, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.629x + 28.996\n", - "Valor del parámetro correlacionado para la aeronave: 22.7\n", - "Predicción obtenida: 43.264\n", - "\tR²: 0.4900615381264627, Desviación Estándar: 4.799869922083653, Varianza: 23.03875126892334, Incertidumbre: 0.9797693450697835\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.857) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 8.9, 16.5, 84.0]\n", - "Valores para Velocidad máxima (KIAS): [33.439, 33.439, 33.439, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.067x + 30.152\n", - "Valor del parámetro correlacionado para la aeronave: 70.3\n", - "Predicción obtenida: 34.84\n", - "\tR²: 0.733701886636262, Desviación Estándar: 0.9740665033476542, Varianza: 0.9488055529439255, Incertidumbre: 0.39766098478979256\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 37.163', 'Área del ala: 42.955', 'Relación de aspecto del ala: 45.084', 'payload: 43.264', 'RTF (Including fuel & Batteries): 34.84']\n", - "**Mediana calculada:** 42.955\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.078x + 7.331\n", - "Valor del parámetro correlacionado para la aeronave: 21.463\n", - "Predicción obtenida: 30.477\n", - "\tR²: 0.6650554114482713, Desviación Estándar: 4.333938589551439, Varianza: 18.78302369800311, Incertidumbre: 0.8846615100798704\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.786x + 25.969\n", - "Valor del parámetro correlacionado para la aeronave: 2.09\n", - "Predicción obtenida: 40.152\n", - "\tR²: 0.3552344429430916, Desviación Estándar: 5.48247460485476, Varianza: 30.057527792877355, Incertidumbre: 1.0551027296460609\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -7.182x + 134.601\n", - "Valor del parámetro correlacionado para la aeronave: 12.648\n", - "Predicción obtenida: 43.762\n", - "\tR²: 0.6458631582625831, Desviación Estándar: 4.155986642108519, Varianza: 17.272224969384443, Incertidumbre: 0.7717473449656517\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.626x + 29.01\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 40.28\n", - "\tR²: 0.5133079427191782, Desviación Estándar: 4.703245083983008, Varianza: 22.12051432001033, Incertidumbre: 0.9406490167966016\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 30.477', 'Área del ala: 40.152', 'Relación de aspecto del ala: 43.762', 'payload: 40.28']\n", - "**Mediana calculada:** 40.216\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Evo** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.013x + 9.471\n", - "Valor del parámetro correlacionado para la aeronave: 18.091\n", - "Predicción obtenida: 27.799\n", - "\tR²: 0.6038420321629009, Desviación Estándar: 4.640881006314369, Varianza: 21.537776514769465, Incertidumbre: 0.9281762012628738\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.79x + 25.965\n", - "Valor del parámetro correlacionado para la aeronave: 0.84\n", - "Predicción obtenida: 31.669\n", - "\tR²: 0.3670434992715419, Desviación Estándar: 5.3836956297320535, Varianza: 28.98417863359601, Incertidumbre: 1.0174228407668775\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -6.983x + 131.755\n", - "Valor del parámetro correlacionado para la aeronave: 14.589\n", - "Predicción obtenida: 29.879\n", - "\tR²: 0.6423683336309061, Desviación Estándar: 4.132395523343531, Varianza: 17.076692761349655, Incertidumbre: 0.7544687482228727\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.626x + 29.011\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 30.888\n", - "\tR²: 0.5208842290733458, Desviación Estándar: 4.611927331533329, Varianza: 21.26987371134414, Incertidumbre: 0.904473363798475\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.775) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.065x + 9.368\n", - "Valor del parámetro correlacionado para la aeronave: 16.54\n", - "Predicción obtenida: 26.99\n", - "\tR²: 0.6084339451187571, Desviación Estándar: 4.787548453222305, Varianza: 22.920620191951283, Incertidumbre: 1.0705283786979045\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.857) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 70.3, 8.9, 16.5, 84.0]\n", - "Valores para Velocidad máxima (KIAS): [33.439, 33.439, 33.439, 42.955, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.119x + 29.159\n", - "Valor del parámetro correlacionado para la aeronave: 6.8\n", - "Predicción obtenida: 29.971\n", - "\tR²: 0.5655356424763149, Desviación Estándar: 2.661463952850547, Varianza: 7.0833903723228575, Incertidumbre: 1.0059388203722117\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 27.799', 'Área del ala: 31.669', 'Relación de aspecto del ala: 29.879', 'payload: 30.888', 'Crucero KIAS: 26.99', 'RTF (Including fuel & Batteries): 29.971']\n", - "**Mediana calculada:** 29.925\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.993x + 10.094\n", - "Valor del parámetro correlacionado para la aeronave: 17.5\n", - "Predicción obtenida: 27.466\n", - "\tR²: 0.6132641570429134, Desviación Estándar: 4.567543566658289, Varianza: 20.862454233321525, Incertidumbre: 0.8957689913683001\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.88x + 25.782\n", - "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 30.598\n", - "\tR²: 0.3791246211706657, Desviación Estándar: 5.299352481306049, Varianza: 28.083136721124575, Incertidumbre: 0.9840650511355258\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -6.981x + 131.729\n", - "Valor del parámetro correlacionado para la aeronave: 14.714\n", - "Predicción obtenida: 29.009\n", - "\tR²: 0.6511684506287918, Desviación Estándar: 4.065205545483665, Varianza: 16.525896127031146, Incertidumbre: 0.7301324697975139\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.631x + 28.923\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 29.68\n", - "\tR²: 0.5347441062393511, Desviación Estándar: 4.529221125565367, Varianza: 20.51384400426761, Incertidumbre: 0.8716490120215014\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.775) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 29.925, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.031x + 10.342\n", - "Valor del parámetro correlacionado para la aeronave: 16.0\n", - "Predicción obtenida: 26.846\n", - "\tR²: 0.6136657296272687, Desviación Estándar: 4.709745503173601, Varianza: 22.181702704663962, Incertidumbre: 1.0277507272509956\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 27.466', 'Área del ala: 30.598', 'Relación de aspecto del ala: 29.009', 'payload: 29.68', 'Crucero KIAS: 26.846']\n", - "**Mediana calculada:** 29.009\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.979x + 10.518\n", - "Valor del parámetro correlacionado para la aeronave: 17.5\n", - "Predicción obtenida: 27.645\n", - "\tR²: 0.6257444320590095, Desviación Estándar: 4.490845524887884, Varianza: 20.167693528405533, Incertidumbre: 0.8642636242276819\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.975x + 25.6\n", - "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 30.483\n", - "\tR²: 0.3948008000513574, Desviación Estándar: 5.217765466886563, Varianza: 27.225076467433954, Incertidumbre: 0.9526292819950818\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -6.981x + 131.729\n", - "Valor del parámetro correlacionado para la aeronave: 14.714\n", - "Predicción obtenida: 29.009\n", - "\tR²: 0.6615824796144654, Desviación Estándar: 4.001182559565268, Varianza: 16.00946187496927, Incertidumbre: 0.707315830158487\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.634x + 28.859\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 29.621\n", - "\tR²: 0.5513156760951279, Desviación Estándar: 4.4492527659965795, Varianza: 19.795850175728216, Incertidumbre: 0.8408297384923719\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.775) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.008x + 11.001\n", - "Valor del parámetro correlacionado para la aeronave: 16.0\n", - "Predicción obtenida: 27.134\n", - "\tR²: 0.6236400148434393, Desviación Estándar: 4.621454678981261, Varianza: 21.35784334987779, Incertidumbre: 0.9852974481637925\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 27.645', 'Área del ala: 30.483', 'Relación de aspecto del ala: 29.009', 'payload: 29.621', 'Crucero KIAS: 27.134']\n", - "**Mediana calculada:** 29.009\n", - "\n", - "--- Imputación para aeronave: **Skyeye 2600** ---\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.815) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 27.344, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 27.344]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.979x + 10.518\n", - "Valor del parámetro correlacionado para la aeronave: 36.094\n", - "Predicción obtenida: 45.843\n", - "\tR²: 0.6257444320590095, Desviación Estándar: 4.490845524887884, Varianza: 20.167693528405533, Incertidumbre: 0.8642636242276819\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.975x + 25.6\n", - "Valor del parámetro correlacionado para la aeronave: 0.88\n", - "Predicción obtenida: 31.738\n", - "\tR²: 0.3948008000513574, Desviación Estándar: 5.217765466886563, Varianza: 27.225076467433954, Incertidumbre: 0.9526292819950818\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -6.981x + 131.729\n", - "Valor del parámetro correlacionado para la aeronave: 14.103\n", - "Predicción obtenida: 33.275\n", - "\tR²: 0.6615824796144654, Desviación Estándar: 4.001182559565268, Varianza: 16.00946187496927, Incertidumbre: 0.707315830158487\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.634x + 28.859\n", - "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 31.397\n", - "\tR²: 0.5513156760951279, Desviación Estándar: 4.4492527659965795, Varianza: 19.795850175728216, Incertidumbre: 0.8408297384923719\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.775) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V35', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Reach']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 25.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 25.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 41.7, 25.6, 41.2, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 1.008x + 11.001\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 44.275\n", - "\tR²: 0.6236400148434393, Desviación Estándar: 4.621454678981261, Varianza: 21.35784334987779, Incertidumbre: 0.9852974481637925\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Velocidad a la que se realiza el crucero (KTAS): 45.843', 'Área del ala: 31.738', 'Relación de aspecto del ala: 33.275', 'payload: 31.397', 'Crucero KIAS: 44.275']\n", - "**Mediana calculada:** 33.275\n", - "\n", - "--- Imputación para aeronave: **Skyeye 3600** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.737) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.275, 30.0, 33.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 6.912x + 25.735\n", - "Valor del parámetro correlacionado para la aeronave: 1.33\n", - "Predicción obtenida: 34.927\n", - "\tR²: 0.394519537415028, Desviación Estándar: 5.139955848128364, Varianza: 26.419146120708966, Incertidumbre: 0.9231633225073828\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.859) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 25.6, 41.2, 46.3, 40.216, 46.3, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.275, 30.0, 33.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = -6.981x + 131.729\n", - "Valor del parámetro correlacionado para la aeronave: 13.71\n", - "Predicción obtenida: 36.018\n", - "\tR²: 0.6627761679084383, Desviación Estándar: 3.9400922159021547, Varianza: 15.524326669812753, Incertidumbre: 0.6858820171935385\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.275, 30.0, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Ecuación de regresión: y = 0.627x + 28.996\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 35.27\n", - "\tR²: 0.550504283331907, Desviación Estándar: 4.3849564059962995, Varianza: 19.227842682487985, Incertidumbre: 0.8142659627031128\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Capacidad combustible (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Capacidad combustible: [13.0, 11.5, 28.0, 28.0, 25.0]\n", - "Valores para Velocidad máxima (KIAS): [33.0, 33.0, 42.0, 38.0, 50.0]\n", - "Ecuación de regresión: y = 0.607x + 26.384\n", - "Valor del parámetro correlacionado para la aeronave: 11.5\n", - "Predicción obtenida: 33.369\n", - "\tR²: 0.487882729041738, Desviación Estándar: 4.5575735331497516, Varianza: 20.77147651006711, Incertidumbre: 2.038208846515347\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 34.927', 'Relación de aspecto del ala: 36.018', 'payload: 35.27', 'Capacidad combustible: 33.369']\n", - "**Mediana calculada:** 35.098\n", - "\n", - "=== Imputación para el parámetro: **Velocidad de pérdida (KCAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Stalker XE** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Stalker XE'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Stalker VXE30** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Stalker VXE30'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.7 Fixed Wing** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde Mk. 4.7 Fixed Wing'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.7 VTOL** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde Mk. 4.7 VTOL'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 Fixed wing** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde Mk. 4.8 Fixed wing'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Aerosonde Mk. 4.8 VTOL FTUAS'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **AAI Aerosonde** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'AAI Aerosonde'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Fulmar X'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Orbiter 4'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Orbiter 3** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Orbiter 3'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Mantis** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Mantis'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **ScanEagle** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Integrator** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator VTOL'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator Extended Range (ER)'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **ScanEagle 3** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle 3'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'RQ Nan 21A Blackjack'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **DeltaQuad Evo** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Evo'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Pro #MAP'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'DeltaQuad Pro #CARGO'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **V35** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'V35'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **V39** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'V39'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Volitation VT370** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Volitation VT370'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Skyeye 5000 VTOL'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Imputación para el parámetro: **Velocidad de pérdida limpia (KCAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Stalker XE** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Stalker XE'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Stalker VXE30** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Stalker VXE30'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.7 Fixed Wing** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Aerosonde Mk. 4.7 Fixed Wing'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.7 VTOL** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Aerosonde Mk. 4.7 VTOL'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 Fixed wing** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Aerosonde Mk. 4.8 Fixed wing'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Aerosonde Mk. 4.8 VTOL FTUAS'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **AAI Aerosonde** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'AAI Aerosonde'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Fulmar X'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Orbiter 4'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Orbiter 3** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Orbiter 3'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Mantis** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Mantis'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **ScanEagle** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'ScanEagle'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Integrator** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Integrator'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Integrator VTOL'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Integrator Extended Range (ER)'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **ScanEagle 3** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'ScanEagle 3'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'RQ Nan 21A Blackjack'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **DeltaQuad Evo** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'DeltaQuad Evo'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'DeltaQuad Pro #MAP'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'DeltaQuad Pro #CARGO'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **V35** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'V35'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **V39** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'V39'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Volitation VT370** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Volitation VT370'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Skyeye 5000 VTOL'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Skyeye 5000 VTOL octo'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Ascend** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Ascend'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Transition** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Transition'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Reach** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida limpia (KCAS)' para la aeronave 'Reach'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Imputación para el parámetro: **envergadura** ===\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 1.904x + 1.375\n", - "Valor del parámetro correlacionado para la aeronave: 2.503\n", - "Predicción obtenida: 6.14\n", - "\tR²: 0.6550202920419386, Desviación Estándar: 0.6922900390325586, Varianza: 0.47926549814370145, Incertidumbre: 0.1285550329147554\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.791) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 0.032x + 2.598\n", - "Valor del parámetro correlacionado para la aeronave: 93.0\n", - "Predicción obtenida: 5.614\n", - "\tR²: 0.6048248274212248, Desviación Estándar: 0.7257069176330723, Varianza: 0.5266505303004948, Incertidumbre: 0.12828807065308317\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = 0.734) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.2, 4.4, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 0.114x + 2.88\n", - "Valor del parámetro correlacionado para la aeronave: 22.7\n", - "Predicción obtenida: 5.476\n", - "\tR²: 0.5142554083484681, Desviación Estándar: 0.8073085344120734, Varianza: 0.6517470697345699, Incertidumbre: 0.1499134313108701\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.936) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", - "Valores para envergadura: [4.4, 4.4, 4.4, 2.69, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 0.048x + 2.326\n", - "Valor del parámetro correlacionado para la aeronave: 70.3\n", - "Predicción obtenida: 5.674\n", - "\tR²: 0.8762967968830506, Desviación Estándar: 0.44130001156979004, Varianza: 0.1947457002114968, Incertidumbre: 0.16679572631194156\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Área del ala: 6.14', 'Peso máximo al despegue (MTOW): 5.614', 'payload: 5.476', 'RTF (Including fuel & Batteries): 5.674']\n", - "**Mediana calculada:** 5.644\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.841) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 1.835x + 1.446\n", - "Valor del parámetro correlacionado para la aeronave: 2.09\n", - "Predicción obtenida: 5.281\n", - "\tR²: 0.6787677993689816, Desviación Estándar: 0.6854337532513997, Varianza: 0.46981943009630067, Incertidumbre: 0.12514250944374053\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.791) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 0.032x + 2.596\n", - "Valor del parámetro correlacionado para la aeronave: 75.0\n", - "Predicción obtenida: 5.033\n", - "\tR²: 0.6331651979245666, Desviación Estándar: 0.7146435397246005, Varianza: 0.5107153888701067, Incertidumbre: 0.12440347223914108\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: payload (r = 0.734) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 5.2, 4.4, 3.1, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Ecuación de regresión: y = 0.116x + 2.873\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 4.954\n", - "\tR²: 0.5454308932616603, Desviación Estándar: 0.794258196699784, Varianza: 0.6308460830247928, Incertidumbre: 0.14501104360528233\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Área del ala: 5.281', 'Peso máximo al despegue (MTOW): 5.033', 'payload: 4.954']\n", - "**Mediana calculada:** 5.033\n", - "\n", - "=== Imputación para el parámetro: **Cuerda** ===\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 5500.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", - "Ecuación de regresión: y = 0.0x + -0.975\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.303\n", - "\tR²: 0.5584422980021864, Desviación Estándar: 0.04101181707874288, Varianza: 0.0016819691401002662, Incertidumbre: 0.02050590853937144\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 19.306]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", - "Ecuación de regresión: y = 0.009x + 0.094\n", - "Valor del parámetro correlacionado para la aeronave: 27.892\n", - "Predicción obtenida: 0.345\n", - "\tR²: 0.37089288437785595, Desviación Estándar: 0.04895280456579205, Varianza: 0.0023963770748566308, Incertidumbre: 0.024476402282896024\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", - "Ecuación de regresión: y = 0.167x + 0.103\n", - "Valor del parámetro correlacionado para la aeronave: 2.503\n", - "Predicción obtenida: 0.521\n", - "\tR²: 0.9567278474032922, Desviación Estándar: 0.012838655021740699, Varianza: 0.00016483106276726767, Incertidumbre: 0.006419327510870349\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 14.754]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", - "Ecuación de regresión: y = -0.031x + 0.725\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 0.338\n", - "\tR²: 0.33955370790059436, Desviación Estándar: 0.050157286426532284, Varianza: 0.0025157533816731995, Incertidumbre: 0.025078643213266142\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", - "Ecuación de regresión: y = 0.125x + -0.016\n", - "Valor del parámetro correlacionado para la aeronave: 3.594\n", - "Predicción obtenida: 0.432\n", - "\tR²: 0.9863480800941506, Desviación Estándar: 0.007211276455846646, Varianza: 5.2002508122648166e-05, Incertidumbre: 0.003605638227923323\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.197]\n", - "Ecuación de regresión: y = 0.003x + 0.204\n", - "Valor del parámetro correlacionado para la aeronave: 93.0\n", - "Predicción obtenida: 0.481\n", - "\tR²: 0.736966419457739, Desviación Estándar: 0.03164979554992154, Varianza: 0.0010017095583518332, Incertidumbre: 0.012920974926786248\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 3270.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.197]\n", - "Ecuación de regresión: y = -0.0x + 0.342\n", - "Valor del parámetro correlacionado para la aeronave: 599.358\n", - "Predicción obtenida: 0.316\n", - "\tR²: 0.5312147113857546, Desviación Estándar: 0.04225248902560736, Varianza: 0.0017852728288590702, Incertidumbre: 0.01724950641254734\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197]\n", - "Ecuación de regresión: y = 0.074x + -0.018\n", - "Valor del parámetro correlacionado para la aeronave: 5.644\n", - "Predicción obtenida: 0.402\n", - "\tR²: 0.8190779024483189, Desviación Estándar: 0.026251920869893277, Varianza: 0.0006891633493591382, Incertidumbre: 0.013125960434946638\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352]\n", - "Ecuación de regresión: y = 0.005x + 0.27\n", - "Valor del parámetro correlacionado para la aeronave: 22.7\n", - "Predicción obtenida: 0.394\n", - "\tR²: 0.6020929401336428, Desviación Estándar: 0.027642283534153083, Varianza: 0.0007640958389825106, Incertidumbre: 0.012362005007137885\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.303', 'Velocidad a la que se realiza el crucero (KTAS): 0.345', 'Área del ala: 0.521', 'Relación de aspecto del ala: 0.338', 'Longitud del fuselaje: 0.432', 'Peso máximo al despegue (MTOW): 0.481', 'Alcance de la aeronave: 0.316', 'envergadura: 0.402', 'payload: 0.394']\n", - "**Mediana calculada:** 0.394\n", - "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = 0.0x + -1.225\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.326\n", - "\tR²: 0.5082545132230825, Desviación Estándar: 0.05085224756183586, Varianza: 0.002585951082090241, Incertidumbre: 0.022741816471382584\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = 0.012x + 0.047\n", - "Valor del parámetro correlacionado para la aeronave: 30.407\n", - "Predicción obtenida: 0.4\n", - "\tR²: 0.5916034080971878, Desviación Estándar: 0.0463426661702313, Varianza: 0.0021476427077655007, Incertidumbre: 0.020725070363043403\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = 0.1x + 0.166\n", - "Valor del parámetro correlacionado para la aeronave: 0.94\n", - "Predicción obtenida: 0.261\n", - "\tR²: 0.8603589273995997, Desviación Estándar: 0.027098580959037145, Varianza: 0.0007343330899934905, Incertidumbre: 0.0121188538236377\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = -0.041x + 0.884\n", - "Valor del parámetro correlacionado para la aeronave: 13.218\n", - "Predicción obtenida: 0.336\n", - "\tR²: 0.5556998392410524, Desviación Estándar: 0.048336833461136076, Varianza: 0.0023364494690496043, Incertidumbre: 0.02161688908723734\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = 0.108x + 0.02\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.15\n", - "\tR²: 0.9729483577129217, Desviación Estándar: 0.011927152694253538, Varianza: 0.00014225697139203943, Incertidumbre: 0.005333984840474135\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = 0.002x + 0.229\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 0.27\n", - "\tR²: 0.7196970118630204, Desviación Estándar: 0.034754348458380345, Varianza: 0.0012078647367665242, Incertidumbre: 0.013135908999850775\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = -0.0x + 0.356\n", - "Valor del parámetro correlacionado para la aeronave: 800.0\n", - "Predicción obtenida: 0.319\n", - "\tR²: 0.4747254035414423, Desviación Estándar: 0.04757606143631944, Varianza: 0.002263481621792442, Incertidumbre: 0.017982060988633097\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197]\n", - "Ecuación de regresión: y = 0.072x + -0.01\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 0.207\n", - "\tR²: 0.8941942208767555, Desviación Estándar: 0.023588192102840765, Varianza: 0.0005564028066805194, Incertidumbre: 0.010548960201655131\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352]\n", - "Ecuación de regresión: y = 0.008x + 0.145\n", - "Valor del parámetro correlacionado para la aeronave: 27.8\n", - "Predicción obtenida: 0.378\n", - "\tR²: 0.5951449532870101, Desviación Estándar: 0.03013201386753742, Varianza: 0.0009079382597134674, Incertidumbre: 0.017396726317648267\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.326', 'Velocidad a la que se realiza el crucero (KTAS): 0.4', 'Área del ala: 0.261', 'Relación de aspecto del ala: 0.336', 'Longitud del fuselaje: 0.15', 'Peso máximo al despegue (MTOW): 0.27', 'Alcance de la aeronave: 0.319', 'envergadura: 0.207', 'Crucero KIAS: 0.378']\n", - "**Mediana calculada:** 0.319\n", - "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319]\n", - "Ecuación de regresión: y = 0.0x + -1.21\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.324\n", - "\tR²: 0.5124560667085087, Desviación Estándar: 0.04648787282808511, Varianza: 0.0021611223200802133, Incertidumbre: 0.018978594609367214\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319]\n", - "Ecuación de regresión: y = 0.008x + 0.11\n", - "Valor del parámetro correlacionado para la aeronave: 26.611\n", - "Predicción obtenida: 0.331\n", - "\tR²: 0.46337237262389996, Desviación Estándar: 0.048771859040965634, Varianza: 0.0023786942343118215, Incertidumbre: 0.019911028076218723\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319]\n", - "Ecuación de regresión: y = 0.092x + 0.186\n", - "Valor del parámetro correlacionado para la aeronave: 1.608\n", - "Predicción obtenida: 0.335\n", - "\tR²: 0.7613504476982165, Desviación Estándar: 0.032524685665416886, Varianza: 0.0010578551776341746, Incertidumbre: 0.013278147320780956\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319]\n", - "Ecuación de regresión: y = -0.04x + 0.863\n", - "Valor del parámetro correlacionado para la aeronave: 13.443\n", - "Predicción obtenida: 0.323\n", - "\tR²: 0.5527436088338049, Desviación Estándar: 0.044525732581881805, Varianza: 0.001982540861953251, Incertidumbre: 0.018177554208204372\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319]\n", - "Ecuación de regresión: y = 0.056x + 0.17\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.238\n", - "\tR²: 0.45536965068723667, Desviación Estándar: 0.04913418031682804, Varianza: 0.0024141676754065723, Incertidumbre: 0.020058945117688236\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319]\n", - "Ecuación de regresión: y = 0.002x + 0.241\n", - "Valor del parámetro correlacionado para la aeronave: 55.0\n", - "Predicción obtenida: 0.346\n", - "\tR²: 0.6561535955657221, Desviación Estándar: 0.03601567329003582, Varianza: 0.0012971287225345997, Incertidumbre: 0.01273346340619177\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319]\n", - "Ecuación de regresión: y = -0.0x + 0.356\n", - "Valor del parámetro correlacionado para la aeronave: 509.556\n", - "Predicción obtenida: 0.332\n", - "\tR²: 0.4749907143714427, Desviación Estándar: 0.044503358493513535, Varianza: 0.0019805489172021835, Incertidumbre: 0.01573431328816968\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319]\n", - "Ecuación de regresión: y = 0.052x + 0.093\n", - "Valor del parámetro correlacionado para la aeronave: 5.2\n", - "Predicción obtenida: 0.361\n", - "\tR²: 0.5927043159163219, Desviación Estándar: 0.04249009480284112, Varianza: 0.0018054081563544256, Incertidumbre: 0.01734650856490736\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394]\n", - "Ecuación de regresión: y = 0.005x + 0.27\n", - "Valor del parámetro correlacionado para la aeronave: 12.0\n", - "Predicción obtenida: 0.335\n", - "\tR²: 0.7240526671985225, Desviación Estándar: 0.02523396237425335, Varianza: 0.0006367528571052337, Incertidumbre: 0.010301722000918372\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.324', 'Velocidad a la que se realiza el crucero (KTAS): 0.331', 'Área del ala: 0.335', 'Relación de aspecto del ala: 0.323', 'Longitud del fuselaje: 0.238', 'Peso máximo al despegue (MTOW): 0.346', 'Alcance de la aeronave: 0.332', 'envergadura: 0.361', 'payload: 0.335']\n", - "**Mediana calculada:** 0.332\n", - "\n", - "--- Imputación para aeronave: **Orbiter 3** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332]\n", - "Ecuación de regresión: y = 0.0x + -1.224\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.326\n", - "\tR²: 0.5234700162931528, Desviación Estándar: 0.043118399156113214, Varianza: 0.001859196345785905, Incertidumbre: 0.016297223014041837\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332]\n", - "Ecuación de regresión: y = 0.008x + 0.109\n", - "Valor del parámetro correlacionado para la aeronave: 26.611\n", - "Predicción obtenida: 0.331\n", - "\tR²: 0.4773971261284471, Desviación Estándar: 0.0451547465914678, Varianza: 0.0020389511397396727, Incertidumbre: 0.017066889999309325\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332]\n", - "Ecuación de regresión: y = 0.092x + 0.186\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.297\n", - "\tR²: 0.7673579176782551, Desviación Estándar: 0.030127399803337826, Varianza: 0.0009076602189101601, Incertidumbre: 0.011387086789806877\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332]\n", - "Ecuación de regresión: y = -0.041x + 0.87\n", - "Valor del parámetro correlacionado para la aeronave: 13.934\n", - "Predicción obtenida: 0.304\n", - "\tR²: 0.5618645680480575, Desviación Estándar: 0.0413448766429335, Varianza: 0.001709398824619388, Incertidumbre: 0.01562689451197787\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332]\n", - "Ecuación de regresión: y = 0.037x + 0.225\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.27\n", - "\tR²: 0.25541759559597876, Desviación Estándar: 0.053898154753589445, Varianza: 0.0029050110858418765, Incertidumbre: 0.02037158765761021\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332]\n", - "Ecuación de regresión: y = 0.002x + 0.241\n", - "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 0.301\n", - "\tR²: 0.6530681193804526, Desviación Estándar: 0.034246989275220595, Varianza: 0.0011728562744170745, Incertidumbre: 0.011415663091740198\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332]\n", - "Ecuación de regresión: y = -0.0x + 0.356\n", - "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 0.354\n", - "\tR²: 0.47924291008106135, Desviación Estándar: 0.04195830043487889, Varianza: 0.0017604989753835579, Incertidumbre: 0.013986100144959629\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332]\n", - "Ecuación de regresión: y = 0.048x + 0.105\n", - "Valor del parámetro correlacionado para la aeronave: 4.4\n", - "Predicción obtenida: 0.315\n", - "\tR²: 0.5811309424136593, Desviación Estándar: 0.04042561655887864, Varianza: 0.0016342304741654829, Incertidumbre: 0.015279446858749655\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332]\n", - "Ecuación de regresión: y = 0.005x + 0.269\n", - "Valor del parámetro correlacionado para la aeronave: 5.5\n", - "Predicción obtenida: 0.299\n", - "\tR²: 0.7234996015016496, Desviación Estándar: 0.02339015181341113, Varianza: 0.0005470992018544201, Incertidumbre: 0.008840646403761759\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.326', 'Velocidad a la que se realiza el crucero (KTAS): 0.331', 'Área del ala: 0.297', 'Relación de aspecto del ala: 0.304', 'Longitud del fuselaje: 0.27', 'Peso máximo al despegue (MTOW): 0.301', 'Alcance de la aeronave: 0.354', 'envergadura: 0.315', 'payload: 0.299']\n", - "**Mediana calculada:** 0.304\n", - "\n", - "--- Imputación para aeronave: **Mantis** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", - "Ecuación de regresión: y = 0.0x + -1.19\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.323\n", - "\tR²: 0.5088584866234673, Desviación Estándar: 0.04095418731504614, Varianza: 0.0016772454586358862, Incertidumbre: 0.014479491784226604\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", - "Ecuación de regresión: y = 0.008x + 0.114\n", - "Valor del parámetro correlacionado para la aeronave: 18.266\n", - "Predicción obtenida: 0.26\n", - "\tR²: 0.4544871001518501, Desviación Estándar: 0.043161596731449474, Varianza: 0.00186292343240827, Incertidumbre: 0.015259928867823522\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", - "Ecuación de regresión: y = 0.092x + 0.188\n", - "Valor del parámetro correlacionado para la aeronave: 0.754\n", - "Predicción obtenida: 0.257\n", - "\tR²: 0.7657531148702028, Desviación Estándar: 0.0282834186887527, Varianza: 0.0007999517727232854, Incertidumbre: 0.009999698574977681\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", - "Ecuación de regresión: y = -0.041x + 0.87\n", - "Valor del parámetro correlacionado para la aeronave: 14.755\n", - "Predicción obtenida: 0.271\n", - "\tR²: 0.5620081314167932, Desviación Estándar: 0.038674794449433975, Varianza: 0.0014957397257059691, Incertidumbre: 0.013673604708095305\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", - "Ecuación de regresión: y = 0.032x + 0.24\n", - "Valor del parámetro correlacionado para la aeronave: 1.48\n", - "Predicción obtenida: 0.288\n", - "\tR²: 0.22407617799783763, Desviación Estándar: 0.051475969281914856, Varianza: 0.002649775413512642, Incertidumbre: 0.018199503473696203\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", - "Ecuación de regresión: y = 0.002x + 0.242\n", - "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 0.254\n", - "\tR²: 0.654616976883781, Desviación Estándar: 0.0325002987324586, Varianza: 0.00105626941769905, Incertidumbre: 0.01027749686304525\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", - "Ecuación de regresión: y = -0.0x + 0.347\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.346\n", - "\tR²: 0.414987640780789, Desviación Estándar: 0.042297955918060494, Varianza: 0.0017891170748461885, Incertidumbre: 0.013375788107046958\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304]\n", - "Ecuación de regresión: y = 0.047x + 0.105\n", - "Valor del parámetro correlacionado para la aeronave: 2.1\n", - "Predicción obtenida: 0.204\n", - "\tR²: 0.5774901913231636, Desviación Estándar: 0.037985109973381545, Varianza: 0.0014428685796898902, Incertidumbre: 0.013429764423147424\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319]\n", - "Ecuación de regresión: y = 0.005x + 0.202\n", - "Valor del parámetro correlacionado para la aeronave: 16.7\n", - "Predicción obtenida: 0.285\n", - "\tR²: 0.42206898443002183, Desviación Estándar: 0.031616100972920246, Varianza: 0.0009995778407298887, Incertidumbre: 0.015808050486460123\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.323', 'Velocidad a la que se realiza el crucero (KTAS): 0.26', 'Área del ala: 0.257', 'Relación de aspecto del ala: 0.271', 'Longitud del fuselaje: 0.288', 'Peso máximo al despegue (MTOW): 0.254', 'Alcance de la aeronave: 0.346', 'envergadura: 0.204', 'Crucero KIAS: 0.285']\n", - "**Mediana calculada:** 0.271\n", - "\n", - "--- Imputación para aeronave: **ScanEagle** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", - "Ecuación de regresión: y = 0.0x + -1.119\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.316\n", - "\tR²: 0.4467125550050872, Desviación Estándar: 0.041829866636606275, Varianza: 0.0017497377428362668, Incertidumbre: 0.013943288878868759\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", - "Ecuación de regresión: y = 0.008x + 0.121\n", - "Valor del parámetro correlacionado para la aeronave: 30.625\n", - "Predicción obtenida: 0.359\n", - "\tR²: 0.4733288036155826, Desviación Estándar: 0.040811340123434084, Varianza: 0.0016655654826706208, Incertidumbre: 0.013603780041144695\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", - "Ecuación de regresión: y = 0.089x + 0.192\n", - "Valor del parámetro correlacionado para la aeronave: 1.063\n", - "Predicción obtenida: 0.287\n", - "\tR²: 0.7694013001888904, Desviación Estándar: 0.027004708852321632, Varianza: 0.0007292543001986583, Incertidumbre: 0.009001569617440544\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", - "Ecuación de regresión: y = -0.041x + 0.87\n", - "Valor del parámetro correlacionado para la aeronave: 14.057\n", - "Predicción obtenida: 0.299\n", - "\tR²: 0.5795819419058201, Desviación Estándar: 0.0364629459060155, Varianza: 0.0013295464241450125, Incertidumbre: 0.012154315302005168\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", - "Ecuación de regresión: y = 0.034x + 0.235\n", - "Valor del parámetro correlacionado para la aeronave: 1.71\n", - "Predicción obtenida: 0.293\n", - "\tR²: 0.24695440685743675, Desviación Estándar: 0.04880021191080425, Varianza: 0.0023814606825394006, Incertidumbre: 0.016266737303601415\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", - "Ecuación de regresión: y = 0.002x + 0.246\n", - "Valor del parámetro correlacionado para la aeronave: 26.5\n", - "Predicción obtenida: 0.294\n", - "\tR²: 0.6670417777815097, Desviación Estándar: 0.03132273145396624, Varianza: 0.000981113505737286, Incertidumbre: 0.009444158876533569\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", - "Ecuación de regresión: y = -0.0x + 0.336\n", - "Valor del parámetro correlacionado para la aeronave: 503.516\n", - "Predicción obtenida: 0.318\n", - "\tR²: 0.2988770873999306, Desviación Estándar: 0.04545292487076436, Varianza: 0.0020659683793073495, Incertidumbre: 0.013704572492776236\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271]\n", - "Ecuación de regresión: y = 0.036x + 0.158\n", - "Valor del parámetro correlacionado para la aeronave: 3.1\n", - "Predicción obtenida: 0.27\n", - "\tR²: 0.5074952168946643, Desviación Estándar: 0.03946538087998614, Varianza: 0.0015575162880023757, Incertidumbre: 0.01315512695999538\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304]\n", - "Ecuación de regresión: y = 0.005x + 0.271\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 0.298\n", - "\tR²: 0.7373011137977019, Desviación Estándar: 0.021931062094591367, Varianza: 0.0004809714845968222, Incertidumbre: 0.007753801362854401\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271]\n", - "Ecuación de regresión: y = 0.005x + 0.191\n", - "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 0.342\n", - "\tR²: 0.4796840662294731, Desviación Estándar: 0.028777205287537983, Varianza: 0.0008281275441611041, Incertidumbre: 0.012869557445080261\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.316', 'Velocidad a la que se realiza el crucero (KTAS): 0.359', 'Área del ala: 0.287', 'Relación de aspecto del ala: 0.299', 'Longitud del fuselaje: 0.293', 'Peso máximo al despegue (MTOW): 0.294', 'Alcance de la aeronave: 0.318', 'envergadura: 0.27', 'payload: 0.298', 'Crucero KIAS: 0.342']\n", - "**Mediana calculada:** 0.298\n", - "\n", - "--- Imputación para aeronave: **Integrator** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", - "Ecuación de regresión: y = 0.0x + -1.097\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.314\n", - "\tR²: 0.4368458746527305, Desviación Estándar: 0.04005051770019304, Varianza: 0.001604043968053476, Incertidumbre: 0.012665085740149871\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", - "Ecuación de regresión: y = 0.006x + 0.15\n", - "Valor del parámetro correlacionado para la aeronave: 30.953\n", - "Predicción obtenida: 0.345\n", - "\tR²: 0.37779542591176063, Desviación Estándar: 0.04209796464341744, Varianza: 0.0017722386271184249, Incertidumbre: 0.013312545313043725\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", - "Ecuación de regresión: y = 0.089x + 0.194\n", - "Valor del parámetro correlacionado para la aeronave: 1.872\n", - "Predicción obtenida: 0.36\n", - "\tR²: 0.765873381903867, Desviación Estándar: 0.025823784879448163, Varianza: 0.0006668678655000155, Incertidumbre: 0.008166197802527291\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", - "Ecuación de regresión: y = -0.041x + 0.87\n", - "Valor del parámetro correlacionado para la aeronave: 12.908\n", - "Predicción obtenida: 0.346\n", - "\tR²: 0.5798392420052842, Desviación Estándar: 0.034594116470138235, Varianza: 0.0011967528943494896, Incertidumbre: 0.010939620168678112\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", - "Ecuación de regresión: y = 0.034x + 0.236\n", - "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 0.32\n", - "\tR²: 0.24668522140873417, Desviación Estándar: 0.04632151372017501, Varianza: 0.002145682633328362, Incertidumbre: 0.014648148802249251\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", - "Ecuación de regresión: y = 0.002x + 0.247\n", - "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 0.38\n", - "\tR²: 0.6682782058720445, Desviación Estándar: 0.030013941218469384, Varianza: 0.0009008366674657355, Incertidumbre: 0.008664278520962451\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", - "Ecuación de regresión: y = -0.0x + 0.334\n", - "Valor del parámetro correlacionado para la aeronave: 557.94\n", - "Predicción obtenida: 0.315\n", - "\tR²: 0.29101241634623853, Desviación Estándar: 0.0438788603845218, Varianza: 0.0019253543886443567, Incertidumbre: 0.012666735927368835\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298]\n", - "Ecuación de regresión: y = 0.034x + 0.169\n", - "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 0.332\n", - "\tR²: 0.4843054651463291, Desviación Estándar: 0.03832575894901321, Varianza: 0.0014688637990178664, Incertidumbre: 0.012119669133346282\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298]\n", - "Ecuación de regresión: y = 0.005x + 0.271\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 0.368\n", - "\tR²: 0.7530217831084923, Desviación Estándar: 0.020677312991762044, Varianza: 0.00042755127255929146, Incertidumbre: 0.006892437663920681\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298]\n", - "Ecuación de regresión: y = 0.004x + 0.217\n", - "Valor del parámetro correlacionado para la aeronave: 28.3\n", - "Predicción obtenida: 0.325\n", - "\tR²: 0.33548537835814285, Desviación Estándar: 0.02969296652169765, Varianza: 0.0008816722608586575, Incertidumbre: 0.012122102821283785\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.314', 'Velocidad a la que se realiza el crucero (KTAS): 0.345', 'Área del ala: 0.36', 'Relación de aspecto del ala: 0.346', 'Longitud del fuselaje: 0.32', 'Peso máximo al despegue (MTOW): 0.38', 'Alcance de la aeronave: 0.315', 'envergadura: 0.332', 'payload: 0.368', 'Crucero KIAS: 0.325']\n", - "**Mediana calculada:** 0.338\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.0x + -1.123\n", - "Valor del parámetro correlacionado para la aeronave: 5000.0\n", - "Predicción obtenida: 0.077\n", - "\tR²: 0.44147114715305336, Desviación Estándar: 0.03879212590736499, Varianza: 0.0015048290324128585, Incertidumbre: 0.011696266041359519\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.006x + 0.153\n", - "Valor del parámetro correlacionado para la aeronave: 21.463\n", - "Predicción obtenida: 0.285\n", - "\tR²: 0.40064049329349527, Desviación Estándar: 0.040185046321322214, Varianza: 0.0016148379478468121, Incertidumbre: 0.012116247348261574\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.085x + 0.197\n", - "Valor del parámetro correlacionado para la aeronave: 2.09\n", - "Predicción obtenida: 0.374\n", - "\tR²: 0.7619020614155911, Desviación Estándar: 0.025327865532937314, Varianza: 0.0006415007724545539, Incertidumbre: 0.007636638792120725\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", - "Ecuación de regresión: y = -0.04x + 0.86\n", - "Valor del parámetro correlacionado para la aeronave: 12.648\n", - "Predicción obtenida: 0.355\n", - "\tR²: 0.5944802102759923, Desviación Estándar: 0.03305421142567004, Varianza: 0.001092580892972896, Incertidumbre: 0.009966219730911451\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.035x + 0.235\n", - "Valor del parámetro correlacionado para la aeronave: 2.998\n", - "Predicción obtenida: 0.34\n", - "\tR²: 0.2663264749195765, Desviación Estándar: 0.044460280463140905, Varianza: 0.001976716538861149, Incertidumbre: 0.01340527894274609\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.002x + 0.251\n", - "Valor del parámetro correlacionado para la aeronave: 75.0\n", - "Predicción obtenida: 0.371\n", - "\tR²: 0.6338748805446468, Desviación Estándar: 0.030614286662563286, Varianza: 0.0009372345478576003, Incertidumbre: 0.008490875409509635\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", - "Ecuación de regresión: y = -0.0x + 0.336\n", - "Valor del parámetro correlacionado para la aeronave: 646.084\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.2906940367251767, Desviación Estándar: 0.04261143763873455, Varianza: 0.0018157346176397638, Incertidumbre: 0.011818286409822586\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.034x + 0.168\n", - "Valor del parámetro correlacionado para la aeronave: 5.033\n", - "Predicción obtenida: 0.341\n", - "\tR²: 0.5033510515754096, Desviación Estándar: 0.036580154402799485, Varianza: 0.0013381076961326505, Incertidumbre: 0.011029331538850273\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.005x + 0.273\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 0.362\n", - "\tR²: 0.7098078178906966, Desviación Estándar: 0.021340119345216565, Varianza: 0.00045540069366808625, Incertidumbre: 0.006748338267070541\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.077', 'Velocidad a la que se realiza el crucero (KTAS): 0.285', 'Área del ala: 0.374', 'Relación de aspecto del ala: 0.355', 'Longitud del fuselaje: 0.34', 'Peso máximo al despegue (MTOW): 0.371', 'Alcance de la aeronave: 0.313', 'envergadura: 0.341', 'payload: 0.362']\n", - "**Mediana calculada:** 0.341\n", - "\n", - "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.0x + 0.178\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.01698088208648929, Desviación Estándar: 0.05021794969223678, Varianza: 0.002521842471292024, Incertidumbre: 0.014496673386481996\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.006x + 0.171\n", - "Valor del parámetro correlacionado para la aeronave: 33.045\n", - "Predicción obtenida: 0.357\n", - "\tR²: 0.333272374733811, Desviación Estándar: 0.04135730400423518, Varianza: 0.001710426594498727, Incertidumbre: 0.011938825299901186\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.078x + 0.203\n", - "Valor del parámetro correlacionado para la aeronave: 1.872\n", - "Predicción obtenida: 0.349\n", - "\tR²: 0.7434863405092712, Desviación Estándar: 0.025652709521725176, Varianza: 0.0006580615058060095, Incertidumbre: 0.007405299373905654\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = -0.039x + 0.841\n", - "Valor del parámetro correlacionado para la aeronave: 12.84\n", - "Predicción obtenida: 0.345\n", - "\tR²: 0.60452940995621, Desviación Estándar: 0.03185188148615243, Varianza: 0.0010145423542079, Incertidumbre: 0.009194846175113082\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.035x + 0.235\n", - "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 0.322\n", - "\tR²: 0.2936236395084689, Desviación Estándar: 0.04256925734185804, Varianza: 0.0018121416706373347, Incertidumbre: 0.012288686092762097\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.001x + 0.253\n", - "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 0.365\n", - "\tR²: 0.6208094760432097, Desviación Estándar: 0.030354867452664958, Varianza: 0.0009214179780688584, Incertidumbre: 0.008112679573486267\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = -0.0x + 0.338\n", - "Valor del parámetro correlacionado para la aeronave: 500.0\n", - "Predicción obtenida: 0.321\n", - "\tR²: 0.285409813514665, Desviación Estándar: 0.04167044389712285, Varianza: 0.0017364258945832627, Incertidumbre: 0.01113689458698706\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.034x + 0.168\n", - "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 0.333\n", - "\tR²: 0.5218690773685137, Desviación Estándar: 0.03502284412908208, Varianza: 0.001226599610889979, Incertidumbre: 0.010110224242855922\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341]\n", - "Ecuación de regresión: y = 0.005x + 0.274\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 0.358\n", - "\tR²: 0.6901146819400841, Desviación Estándar: 0.02113001704290527, Varianza: 0.0004464776202334672, Incertidumbre: 0.006370939849557429\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.311', 'Velocidad a la que se realiza el crucero (KTAS): 0.357', 'Área del ala: 0.349', 'Relación de aspecto del ala: 0.345', 'Longitud del fuselaje: 0.322', 'Peso máximo al despegue (MTOW): 0.365', 'Alcance de la aeronave: 0.321', 'envergadura: 0.333', 'payload: 0.358']\n", - "**Mediana calculada:** 0.345\n", - "\n", - "--- Imputación para aeronave: **ScanEagle 3** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", - "Ecuación de regresión: y = 0.0x + 0.16\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.314\n", - "\tR²: 0.022373451375729103, Desviación Estándar: 0.049063741225926084, Varianza: 0.002407250703084639, Incertidumbre: 0.013607833442782533\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", - "Ecuación de regresión: y = 0.005x + 0.176\n", - "Valor del parámetro correlacionado para la aeronave: 25.703\n", - "Predicción obtenida: 0.314\n", - "\tR²: 0.3555220685191681, Desviación Estándar: 0.0398362264992534, Varianza: 0.0015869249416998186, Incertidumbre: 0.011048581328004266\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", - "Ecuación de regresión: y = 0.077x + 0.204\n", - "Valor del parámetro correlacionado para la aeronave: 1.349\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.7527768409004458, Desviación Estándar: 0.024672817339078838, Varianza: 0.0006087479154475493, Incertidumbre: 0.0068430083097081335\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", - "Ecuación de regresión: y = -0.039x + 0.841\n", - "Valor del parámetro correlacionado para la aeronave: 13.765\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.6196701372371902, Desviación Estándar: 0.03060232203692826, Varianza: 0.000936502114051865, Incertidumbre: 0.00848755701941588\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", - "Ecuación de regresión: y = 0.036x + 0.234\n", - "Valor del parámetro correlacionado para la aeronave: 2.4\n", - "Predicción obtenida: 0.321\n", - "\tR²: 0.30616062286953594, Desviación Estándar: 0.04133363874625755, Varianza: 0.0017084696920061232, Incertidumbre: 0.011463888761625898\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", - "Ecuación de regresión: y = 0.001x + 0.254\n", - "Valor del parámetro correlacionado para la aeronave: 36.3\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.6206727625327728, Desviación Estándar: 0.029696344075245393, Varianza: 0.0008818728514353621, Incertidumbre: 0.007667563069778047\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", - "Ecuación de regresión: y = -0.0x + 0.34\n", - "Valor del parámetro correlacionado para la aeronave: 537.895\n", - "Predicción obtenida: 0.321\n", - "\tR²: 0.2869258951677305, Desviación Estándar: 0.04071583119142121, Varianza: 0.0016577789096083083, Incertidumbre: 0.01051278240875779\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345]\n", - "Ecuación de regresión: y = 0.035x + 0.166\n", - "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.5360863917912162, Desviación Estándar: 0.033798134019578256, Varianza: 0.001142313863205373, Incertidumbre: 0.009373915786353326\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345]\n", - "Ecuación de regresión: y = 0.005x + 0.274\n", - "Valor del parámetro correlacionado para la aeronave: 8.6\n", - "Predicción obtenida: 0.314\n", - "\tR²: 0.6852525799649505, Desviación Estándar: 0.020536902802633098, Varianza: 0.0004217643767247992, Incertidumbre: 0.0059284931807107\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338]\n", - "Ecuación de regresión: y = 0.004x + 0.212\n", - "Valor del parámetro correlacionado para la aeronave: 23.5\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.4148261927182897, Desviación Estándar: 0.027776222407102834, Varianza: 0.0007715185312088415, Incertidumbre: 0.01049842526428771\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.314', 'Velocidad a la que se realiza el crucero (KTAS): 0.314', 'Área del ala: 0.308', 'Relación de aspecto del ala: 0.309', 'Longitud del fuselaje: 0.321', 'Peso máximo al despegue (MTOW): 0.306', 'Alcance de la aeronave: 0.321', 'envergadura: 0.306', 'payload: 0.314', 'Crucero KIAS: 0.309']\n", - "**Mediana calculada:** 0.312\n", - "\n", - "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", - "Ecuación de regresión: y = 0.0x + 0.161\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.314\n", - "\tR²: 0.022227755119382575, Desviación Estándar: 0.04728280334178578, Varianza: 0.002235663491857988, Incertidumbre: 0.012636860742226598\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", - "Ecuación de regresión: y = 0.005x + 0.176\n", - "Valor del parámetro correlacionado para la aeronave: 33.797\n", - "Predicción obtenida: 0.358\n", - "\tR²: 0.3553724238219026, Desviación Estándar: 0.03839182920470344, Varianza: 0.0014739325496831198, Incertidumbre: 0.010260647952538724\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", - "Ecuación de regresión: y = 0.077x + 0.204\n", - "Valor del parámetro correlacionado para la aeronave: 1.802\n", - "Predicción obtenida: 0.344\n", - "\tR²: 0.7523839535914149, Desviación Estándar: 0.02379434517349416, Varianza: 0.0005661708622353848, Incertidumbre: 0.00635930624156095\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", - "Ecuación de regresión: y = -0.039x + 0.841\n", - "Valor del parámetro correlacionado para la aeronave: 12.914\n", - "Predicción obtenida: 0.342\n", - "\tR²: 0.6194315138686619, Desviación Estándar: 0.02949855729552859, Varianza: 0.000870164882517583, Incertidumbre: 0.007883821057427777\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", - "Ecuación de regresión: y = 0.036x + 0.234\n", - "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 0.324\n", - "\tR²: 0.303899170137669, Desviación Estándar: 0.03989518107787113, Varianza: 0.0015916254732361262, Incertidumbre: 0.010662435641192892\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", - "Ecuación de regresión: y = 0.001x + 0.255\n", - "Valor del parámetro correlacionado para la aeronave: 61.0\n", - "Predicción obtenida: 0.342\n", - "\tR²: 0.6200226373551951, Desviación Estándar: 0.0287868395998115, Varianza: 0.0008286821341452756, Incertidumbre: 0.007196709899952875\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", - "Ecuación de regresión: y = -0.0x + 0.339\n", - "Valor del parámetro correlacionado para la aeronave: 565.912\n", - "Predicción obtenida: 0.319\n", - "\tR²: 0.28524296729075405, Desviación Estándar: 0.0394815636598884, Varianza: 0.00155879386902982, Incertidumbre: 0.0098703909149721\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312]\n", - "Ecuación de regresión: y = 0.035x + 0.167\n", - "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 0.335\n", - "\tR²: 0.5351265760294277, Desviación Estándar: 0.032602561615663286, Varianza: 0.001062927023903121, Incertidumbre: 0.008713401106928507\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312]\n", - "Ecuación de regresión: y = 0.005x + 0.274\n", - "Valor del parámetro correlacionado para la aeronave: 17.7\n", - "Predicción obtenida: 0.355\n", - "\tR²: 0.6911388597155106, Desviación Estándar: 0.01973554103206761, Varianza: 0.00038949157982842426, Incertidumbre: 0.005473654241549482\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312]\n", - "Ecuación de regresión: y = 0.004x + 0.212\n", - "Valor del parámetro correlacionado para la aeronave: 30.9\n", - "Predicción obtenida: 0.34\n", - "\tR²: 0.41681296972136983, Desviación Estándar: 0.025997834126762907, Varianza: 0.000675887379282678, Incertidumbre: 0.009191622403598547\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.314', 'Velocidad a la que se realiza el crucero (KTAS): 0.358', 'Área del ala: 0.344', 'Relación de aspecto del ala: 0.342', 'Longitud del fuselaje: 0.324', 'Peso máximo al despegue (MTOW): 0.342', 'Alcance de la aeronave: 0.319', 'envergadura: 0.335', 'payload: 0.355', 'Crucero KIAS: 0.34']\n", - "**Mediana calculada:** 0.341\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Evo** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", - "Ecuación de regresión: y = 0.0x + 0.148\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.316\n", - "\tR²: 0.026268244411726593, Desviación Estándar: 0.04616322786282365, Varianza: 0.0021310436067149777, Incertidumbre: 0.011919294181326279\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", - "Ecuación de regresión: y = 0.005x + 0.182\n", - "Valor del parámetro correlacionado para la aeronave: 18.091\n", - "Predicción obtenida: 0.274\n", - "\tR²: 0.3645845882344463, Desviación Estándar: 0.03729111540765529, Varianza: 0.0013906272883470658, Incertidumbre: 0.00962852459568985\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", - "Ecuación de regresión: y = 0.077x + 0.204\n", - "Valor del parámetro correlacionado para la aeronave: 0.84\n", - "Predicción obtenida: 0.269\n", - "\tR²: 0.7583546241989619, Desviación Estándar: 0.02299671197634162, Varianza: 0.0005288487617228141, Incertidumbre: 0.00593772550012665\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", - "Ecuación de regresión: y = -0.039x + 0.84\n", - "Valor del parámetro correlacionado para la aeronave: 14.589\n", - "Predicción obtenida: 0.277\n", - "\tR²: 0.6288651605214148, Desviación Estándar: 0.028499836195337802, Varianza: 0.0008122406631610867, Incertidumbre: 0.007358626063612177\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", - "Ecuación de regresión: y = 0.037x + 0.234\n", - "Valor del parámetro correlacionado para la aeronave: 0.75\n", - "Predicción obtenida: 0.261\n", - "\tR²: 0.31289865533826733, Desviación Estándar: 0.038778133438712926, Varianza: 0.0015037436329906255, Incertidumbre: 0.010012471000342941\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", - "Ecuación de regresión: y = 0.001x + 0.255\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 0.269\n", - "\tR²: 0.626055259624176, Desviación Estándar: 0.027928418897225495, Varianza: 0.0007799965820989021, Incertidumbre: 0.006773636533515119\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", - "Ecuación de regresión: y = -0.0x + 0.341\n", - "Valor del parámetro correlacionado para la aeronave: 270.0\n", - "Predicción obtenida: 0.331\n", - "\tR²: 0.2841904292217742, Desviación Estándar: 0.03864037972786521, Varianza: 0.0014930789455136165, Incertidumbre: 0.009371668648939038\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", - "Ecuación de regresión: y = 0.035x + 0.166\n", - "Valor del parámetro correlacionado para la aeronave: 2.69\n", - "Predicción obtenida: 0.261\n", - "\tR²: 0.5455548123174172, Desviación Estándar: 0.03153677328028606, Varianza: 0.000994568068932165, Incertidumbre: 0.008142759847177798\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341]\n", - "Ecuación de regresión: y = 0.004x + 0.275\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 0.288\n", - "\tR²: 0.6833444264392312, Desviación Estándar: 0.019334288653334628, Varianza: 0.00037381471773046413, Incertidumbre: 0.0051673059969835075\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341]\n", - "Ecuación de regresión: y = 0.004x + 0.212\n", - "Valor del parámetro correlacionado para la aeronave: 16.54\n", - "Predicción obtenida: 0.281\n", - "\tR²: 0.47839540504657707, Desviación Estándar: 0.02451160448637781, Varianza: 0.0006008187544966167, Incertidumbre: 0.008170534828792602\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.316', 'Velocidad a la que se realiza el crucero (KTAS): 0.274', 'Área del ala: 0.269', 'Relación de aspecto del ala: 0.277', 'Longitud del fuselaje: 0.261', 'Peso máximo al despegue (MTOW): 0.269', 'Alcance de la aeronave: 0.331', 'envergadura: 0.261', 'payload: 0.288', 'Crucero KIAS: 0.281']\n", - "**Mediana calculada:** 0.276\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", - "Ecuación de regresión: y = 0.0x + 0.166\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.01967074482098219, Desviación Estándar: 0.04573470982394311, Varianza: 0.0020916636826802783, Incertidumbre: 0.011433677455985777\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", - "Ecuación de regresión: y = 0.005x + 0.183\n", - "Valor del parámetro correlacionado para la aeronave: 17.5\n", - "Predicción obtenida: 0.272\n", - "\tR²: 0.3888938502611704, Desviación Estándar: 0.03610923381670675, Varianza: 0.0013038767668295981, Incertidumbre: 0.009027308454176687\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", - "Ecuación de regresión: y = 0.076x + 0.206\n", - "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 0.259\n", - "\tR²: 0.7664145517248199, Desviación Estándar: 0.02232455641736923, Varianza: 0.0004983858192323016, Incertidumbre: 0.005581139104342307\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", - "Ecuación de regresión: y = -0.039x + 0.842\n", - "Valor del parámetro correlacionado para la aeronave: 14.714\n", - "Predicción obtenida: 0.272\n", - "\tR²: 0.6430546942477204, Desviación Estándar: 0.027596930750302347, Varianza: 0.0007615905868369832, Incertidumbre: 0.006899232687575587\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", - "Ecuación de regresión: y = 0.034x + 0.239\n", - "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 0.27\n", - "\tR²: 0.33445042360589083, Desviación Estándar: 0.037683406226531636, Varianza: 0.0014200391048338034, Incertidumbre: 0.009420851556632909\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", - "Ecuación de regresión: y = 0.001x + 0.256\n", - "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 0.265\n", - "\tR²: 0.6416207643515126, Desviación Estándar: 0.027185535937675302, Varianza: 0.0007390533642186353, Incertidumbre: 0.006407692270573599\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", - "Ecuación de regresión: y = -0.0x + 0.336\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 0.333\n", - "\tR²: 0.23999230022385276, Desviación Estándar: 0.03958908605237062, Varianza: 0.0015672957344620058, Incertidumbre: 0.009331237069536345\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", - "Ecuación de regresión: y = 0.034x + 0.172\n", - "Valor del parámetro correlacionado para la aeronave: 2.35\n", - "Predicción obtenida: 0.252\n", - "\tR²: 0.5575576859088149, Desviación Estándar: 0.030724744065250335, Varianza: 0.0009440098978751357, Incertidumbre: 0.007681186016312584\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276]\n", - "Ecuación de regresión: y = 0.005x + 0.272\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.278\n", - "\tR²: 0.721730846976111, Desviación Estándar: 0.018890410688664185, Varianza: 0.00035684761598639805, Incertidumbre: 0.004877483066680998\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276]\n", - "Ecuación de regresión: y = 0.004x + 0.209\n", - "Valor del parámetro correlacionado para la aeronave: 16.0\n", - "Predicción obtenida: 0.277\n", - "\tR²: 0.5239745729502132, Desviación Estándar: 0.023290105844576407, Varianza: 0.0005424290302515721, Incertidumbre: 0.007364978141526098\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.313', 'Velocidad a la que se realiza el crucero (KTAS): 0.272', 'Área del ala: 0.259', 'Relación de aspecto del ala: 0.272', 'Longitud del fuselaje: 0.27', 'Peso máximo al despegue (MTOW): 0.265', 'Alcance de la aeronave: 0.333', 'envergadura: 0.252', 'payload: 0.278', 'Crucero KIAS: 0.277']\n", - "**Mediana calculada:** 0.272\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.0x + 0.183\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.014305682652152818, Desviación Estándar: 0.04541585750246479, Varianza: 0.002062600112684187, Incertidumbre: 0.01101496338592133\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.005x + 0.183\n", - "Valor del parámetro correlacionado para la aeronave: 17.5\n", - "Predicción obtenida: 0.272\n", - "\tR²: 0.41353937190981327, Desviación Estándar: 0.03503126520298072, Varianza: 0.001227189541721568, Incertidumbre: 0.008496329801818471\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.075x + 0.209\n", - "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 0.261\n", - "\tR²: 0.771939412802745, Desviación Estándar: 0.021845475954437206, Varianza: 0.0004772248196758942, Incertidumbre: 0.005298306164825611\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = -0.039x + 0.842\n", - "Valor del parámetro correlacionado para la aeronave: 14.714\n", - "Predicción obtenida: 0.272\n", - "\tR²: 0.6574483990804236, Desviación Estándar: 0.0267731451760295, Varianza: 0.0007168013026167517, Incertidumbre: 0.006493441499456798\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.034x + 0.24\n", - "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 0.27\n", - "\tR²: 0.3612099344091373, Desviación Estándar: 0.03656078137464094, Varianza: 0.0013366907347242916, Incertidumbre: 0.008867291962515261\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.001x + 0.257\n", - "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 0.266\n", - "\tR²: 0.6569798349980132, Desviación Estándar: 0.026507694611626824, Varianza: 0.0007026578736232697, Incertidumbre: 0.006081282212543103\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = -0.0x + 0.331\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 0.328\n", - "\tR²: 0.18759571893960836, Desviación Estándar: 0.0407941824996777, Varianza: 0.0016641653258170103, Incertidumbre: 0.009358827315813186\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.032x + 0.18\n", - "Valor del parámetro correlacionado para la aeronave: 2.35\n", - "Predicción obtenida: 0.256\n", - "\tR²: 0.5664966665882962, Desviación Estándar: 0.030118449655234383, Varianza: 0.000907121009634888, Incertidumbre: 0.007304797012257599\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.005x + 0.271\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 0.277\n", - "\tR²: 0.7555202520970645, Desviación Estándar: 0.01833377542330803, Varianza: 0.0003361273212722936, Incertidumbre: 0.004583443855827008\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.004x + 0.207\n", - "Valor del parámetro correlacionado para la aeronave: 16.0\n", - "Predicción obtenida: 0.276\n", - "\tR²: 0.5632456052929734, Desviación Estándar: 0.022253056728350776, Varianza: 0.0004951985337551978, Incertidumbre: 0.006709549055130292\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.311', 'Velocidad a la que se realiza el crucero (KTAS): 0.272', 'Área del ala: 0.261', 'Relación de aspecto del ala: 0.272', 'Longitud del fuselaje: 0.27', 'Peso máximo al despegue (MTOW): 0.266', 'Alcance de la aeronave: 0.328', 'envergadura: 0.256', 'payload: 0.277', 'Crucero KIAS: 0.276']\n", - "**Mediana calculada:** 0.272\n", - "\n", - "--- Imputación para aeronave: **V21** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.0x + 0.183\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.014305682652152818, Desviación Estándar: 0.04541585750246479, Varianza: 0.002062600112684187, Incertidumbre: 0.01101496338592133\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.005x + 0.183\n", - "Valor del parámetro correlacionado para la aeronave: 19.688\n", - "Predicción obtenida: 0.283\n", - "\tR²: 0.41353937190981327, Desviación Estándar: 0.03503126520298072, Varianza: 0.001227189541721568, Incertidumbre: 0.008496329801818471\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.075x + 0.209\n", - "Valor del parámetro correlacionado para la aeronave: 0.8\n", - "Predicción obtenida: 0.269\n", - "\tR²: 0.771939412802745, Desviación Estándar: 0.021845475954437206, Varianza: 0.0004772248196758942, Incertidumbre: 0.005298306164825611\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = -0.039x + 0.842\n", - "Valor del parámetro correlacionado para la aeronave: 14.568\n", - "Predicción obtenida: 0.278\n", - "\tR²: 0.6574483990804236, Desviación Estándar: 0.0267731451760295, Varianza: 0.0007168013026167517, Incertidumbre: 0.006493441499456798\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.034x + 0.24\n", - "Valor del parámetro correlacionado para la aeronave: 0.93\n", - "Predicción obtenida: 0.271\n", - "\tR²: 0.3612099344091373, Desviación Estándar: 0.03656078137464094, Varianza: 0.0013366907347242916, Incertidumbre: 0.008867291962515261\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.001x + 0.257\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 0.271\n", - "\tR²: 0.6569798349980132, Desviación Estándar: 0.026507694611626824, Varianza: 0.0007026578736232697, Incertidumbre: 0.006081282212543103\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = -0.0x + 0.331\n", - "Valor del parámetro correlacionado para la aeronave: 373.727\n", - "Predicción obtenida: 0.32\n", - "\tR²: 0.18759571893960836, Desviación Estándar: 0.0407941824996777, Varianza: 0.0016641653258170103, Incertidumbre: 0.009358827315813186\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.032x + 0.18\n", - "Valor del parámetro correlacionado para la aeronave: 2.15\n", - "Predicción obtenida: 0.25\n", - "\tR²: 0.5664966665882962, Desviación Estándar: 0.030118449655234383, Varianza: 0.000907121009634888, Incertidumbre: 0.007304797012257599\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.005x + 0.271\n", - "Valor del parámetro correlacionado para la aeronave: 1.5\n", - "Predicción obtenida: 0.278\n", - "\tR²: 0.7555202520970645, Desviación Estándar: 0.01833377542330803, Varianza: 0.0003361273212722936, Incertidumbre: 0.004583443855827008\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272]\n", - "Ecuación de regresión: y = 0.004x + 0.207\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 0.285\n", - "\tR²: 0.5632456052929734, Desviación Estándar: 0.022253056728350776, Varianza: 0.0004951985337551978, Incertidumbre: 0.006709549055130292\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.311', 'Velocidad a la que se realiza el crucero (KTAS): 0.283', 'Área del ala: 0.269', 'Relación de aspecto del ala: 0.278', 'Longitud del fuselaje: 0.271', 'Peso máximo al despegue (MTOW): 0.271', 'Alcance de la aeronave: 0.32', 'envergadura: 0.25', 'payload: 0.278', 'Crucero KIAS: 0.285']\n", - "**Mediana calculada:** 0.278\n", - "\n", - "--- Imputación para aeronave: **V25** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.0x + 0.195\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.011044881710893129, Desviación Estándar: 0.044760694317023265, Varianza: 0.0020035197557419985, Incertidumbre: 0.010550196827395105\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.005x + 0.182\n", - "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 0.293\n", - "\tR²: 0.42735666066806766, Desviación Estándar: 0.034060469502456774, Varianza: 0.0011601155827277879, Incertidumbre: 0.008028129651861593\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.074x + 0.211\n", - "Valor del parámetro correlacionado para la aeronave: 0.52\n", - "Predicción obtenida: 0.249\n", - "\tR²: 0.7753395291556694, Desviación Estándar: 0.021333978629353077, Varianza: 0.0004551386441576937, Incertidumbre: 0.005028466986168149\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = -0.039x + 0.842\n", - "Valor del parámetro correlacionado para la aeronave: 14.421\n", - "Predicción obtenida: 0.284\n", - "\tR²: 0.6658371155959888, Desviación Estándar: 0.02601882192873693, Varianza: 0.0006769790945593217, Incertidumbre: 0.00613269514143171\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.034x + 0.241\n", - "Valor del parámetro correlacionado para la aeronave: 0.93\n", - "Predicción obtenida: 0.272\n", - "\tR²: 0.3758370724436967, Desviación Estándar: 0.035559652505826406, Varianza: 0.0012644888863351262, Incertidumbre: 0.008381490474502354\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.001x + 0.258\n", - "Valor del parámetro correlacionado para la aeronave: 12.5\n", - "Predicción obtenida: 0.275\n", - "\tR²: 0.6659897422136262, Desviación Estándar: 0.0258798006020757, Varianza: 0.0006697640792031978, Incertidumbre: 0.005786899339038125\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = -0.0x + 0.328\n", - "Valor del parámetro correlacionado para la aeronave: 385.208\n", - "Predicción obtenida: 0.317\n", - "\tR²: 0.17038629659156324, Desviación Estándar: 0.040786738838416804, Varianza: 0.0016635580650732177, Incertidumbre: 0.009120192062323078\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.03x + 0.19\n", - "Valor del parámetro correlacionado para la aeronave: 2.45\n", - "Predicción obtenida: 0.264\n", - "\tR²: 0.5591647654062968, Desviación Estándar: 0.029884546712702416, Varianza: 0.0008930861322236928, Incertidumbre: 0.007043855211079342\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.005x + 0.271\n", - "Valor del parámetro correlacionado para la aeronave: 2.2\n", - "Predicción obtenida: 0.281\n", - "\tR²: 0.7749229114992245, Desviación Estándar: 0.017786382883709647, Varianza: 0.00031635541608591945, Incertidumbre: 0.004313831489836053\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278]\n", - "Ecuación de regresión: y = 0.004x + 0.205\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 0.293\n", - "\tR²: 0.5795655402783035, Desviación Estándar: 0.02138953366069156, Varianza: 0.00045751215022185723, Incertidumbre: 0.006174626508420417\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278]\n", - "Ecuación de regresión: y = 0.002x + 0.247\n", - "Valor del parámetro correlacionado para la aeronave: 3.45\n", - "Predicción obtenida: 0.253\n", - "\tR²: 0.037864541216720005, Desviación Estándar: 0.04025536882356717, Varianza: 0.001620494719121424, Incertidumbre: 0.018002748229764387\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.309', 'Velocidad a la que se realiza el crucero (KTAS): 0.293', 'Área del ala: 0.249', 'Relación de aspecto del ala: 0.284', 'Longitud del fuselaje: 0.272', 'Peso máximo al despegue (MTOW): 0.275', 'Alcance de la aeronave: 0.317', 'envergadura: 0.264', 'payload: 0.281', 'Crucero KIAS: 0.293', 'Empty weight: 0.253']\n", - "**Mediana calculada:** 0.281\n", - "\n", - "--- Imputación para aeronave: **V32** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.0x + 0.205\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.307\n", - "\tR²: 0.008806840766555024, Desviación Estándar: 0.0439986702137573, Varianza: 0.0019358829805789736, Incertidumbre: 0.010093987216417957\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.005x + 0.18\n", - "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 0.293\n", - "\tR²: 0.4333816032073342, Desviación Estándar: 0.03326639487281157, Varianza: 0.001106653027833824, Incertidumbre: 0.007631834392973959\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.07x + 0.217\n", - "Valor del parámetro correlacionado para la aeronave: 1.03\n", - "Predicción obtenida: 0.289\n", - "\tR²: 0.7555854161374564, Desviación Estándar: 0.021848617461412912, Varianza: 0.00047736208497515723, Incertidumbre: 0.005012416608967213\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = -0.039x + 0.843\n", - "Valor del parámetro correlacionado para la aeronave: 14.182\n", - "Predicción obtenida: 0.293\n", - "\tR²: 0.6714366085489721, Desviación Estándar: 0.025332029736691172, Varianza: 0.0006417117305806058, Incertidumbre: 0.005811566192473899\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.033x + 0.243\n", - "Valor del parámetro correlacionado para la aeronave: 1.0\n", - "Predicción obtenida: 0.276\n", - "\tR²: 0.3848860095207083, Desviación Estándar: 0.0346607699064813, Varianza: 0.00120136897051004, Incertidumbre: 0.00795172596461409\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.001x + 0.259\n", - "Valor del parámetro correlacionado para la aeronave: 23.5\n", - "Predicción obtenida: 0.291\n", - "\tR²: 0.6724626600573362, Desviación Estándar: 0.025285590185798745, Varianza: 0.0006393610710441618, Incertidumbre: 0.005517768143716917\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = -0.0x + 0.326\n", - "Valor del parámetro correlacionado para la aeronave: 412.686\n", - "Predicción obtenida: 0.315\n", - "\tR²: 0.15808515960741942, Desviación Estándar: 0.04053934883017473, Varianza: 0.0016434388035745891, Incertidumbre: 0.008846411173261676\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.029x + 0.195\n", - "Valor del parámetro correlacionado para la aeronave: 3.2\n", - "Predicción obtenida: 0.288\n", - "\tR²: 0.5599804935133099, Desviación Estándar: 0.029315436669585263, Varianza: 0.0008593948271284642, Incertidumbre: 0.00672542241728888\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.005x + 0.271\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 0.294\n", - "\tR²: 0.7878383281156356, Desviación Estándar: 0.01728531402736029, Varianza: 0.0002987820810244584, Incertidumbre: 0.004074187587895138\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.004x + 0.203\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 0.292\n", - "\tR²: 0.5815487606672278, Desviación Estándar: 0.020794956088832327, Varianza: 0.0004324301987364647, Incertidumbre: 0.005767483111485157\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281]\n", - "Ecuación de regresión: y = 0.001x + 0.259\n", - "Valor del parámetro correlacionado para la aeronave: 6.45\n", - "Predicción obtenida: 0.264\n", - "\tR²: 0.009256571042910555, Desviación Estándar: 0.03798189451706237, Varianza: 0.0014426243111052524, Incertidumbre: 0.015506043505169485\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.307', 'Velocidad a la que se realiza el crucero (KTAS): 0.293', 'Área del ala: 0.289', 'Relación de aspecto del ala: 0.293', 'Longitud del fuselaje: 0.276', 'Peso máximo al despegue (MTOW): 0.291', 'Alcance de la aeronave: 0.315', 'envergadura: 0.288', 'payload: 0.294', 'Crucero KIAS: 0.292', 'Empty weight: 0.264']\n", - "**Mediana calculada:** 0.292\n", - "\n", - "--- Imputación para aeronave: **V35** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", - "Ecuación de regresión: y = 0.0x + 0.21\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.00781429009993051, Desviación Estándar: 0.0430085862012688, Varianza: 0.001849738487031969, Incertidumbre: 0.00961701223621965\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", - "Ecuación de regresión: y = 0.005x + 0.18\n", - "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 0.321\n", - "\tR²: 0.4360697755321421, Desviación Estándar: 0.03242435670008977, Varianza: 0.0010513389074146564, Incertidumbre: 0.007250306570810149\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", - "Ecuación de regresión: y = 0.07x + 0.218\n", - "Valor del parámetro correlacionado para la aeronave: 1.202\n", - "Predicción obtenida: 0.301\n", - "\tR²: 0.7565518672120739, Desviación Estándar: 0.021304036943021673, Varianza: 0.0004538619900696323, Incertidumbre: 0.0047637274799763275\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", - "Ecuación de regresión: y = -0.039x + 0.843\n", - "Valor del parámetro correlacionado para la aeronave: 13.898\n", - "Predicción obtenida: 0.304\n", - "\tR²: 0.6729848491652715, Desviación Estándar: 0.024691224016505753, Varianza: 0.0006096565434332705, Incertidumbre: 0.005521125534858225\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", - "Ecuación de regresión: y = 0.032x + 0.245\n", - "Valor del parámetro correlacionado para la aeronave: 1.88\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.38147480268763256, Desviación Estándar: 0.03395763069177028, Varianza: 0.001153120682198659, Incertidumbre: 0.007593157058163155\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", - "Ecuación de regresión: y = 0.001x + 0.259\n", - "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 0.302\n", - "\tR²: 0.6749008669048356, Desviación Estándar: 0.024705639698818668, Varianza: 0.0006103686329278449, Incertidumbre: 0.005267260081811748\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", - "Ecuación de regresión: y = -0.0x + 0.325\n", - "Valor del parámetro correlacionado para la aeronave: 456.221\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.15274102701798387, Desviación Estándar: 0.03988377763537778, Varianza: 0.0015907157184682607, Incertidumbre: 0.008503249962830417\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", - "Ecuación de regresión: y = 0.029x + 0.195\n", - "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 0.297\n", - "\tR²: 0.5616690703122301, Desviación Estándar: 0.028586418069702225, Varianza: 0.0008171832980557979, Incertidumbre: 0.006392117403708249\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", - "Ecuación de regresión: y = 0.005x + 0.271\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 0.317\n", - "\tR²: 0.7929138648348985, Desviación Estándar: 0.016831546785257487, Varianza: 0.0002833009671843116, Incertidumbre: 0.0038614216579165406\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292]\n", - "Ecuación de regresión: y = 0.004x + 0.203\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.314\n", - "\tR²: 0.5832259382889582, Desviación Estándar: 0.020038522534308566, Varianza: 0.00040154238535799216, Incertidumbre: 0.005355520418609409\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.321', 'Área del ala: 0.301', 'Relación de aspecto del ala: 0.304', 'Longitud del fuselaje: 0.306', 'Peso máximo al despegue (MTOW): 0.302', 'Alcance de la aeronave: 0.312', 'envergadura: 0.297', 'payload: 0.317', 'Crucero KIAS: 0.314']\n", - "**Mediana calculada:** 0.306\n", - "\n", - "--- Imputación para aeronave: **V39** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", - "Ecuación de regresión: y = 0.0x + 0.21\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.007839382716985033, Desviación Estándar: 0.04197210135142703, Varianza: 0.001761657291854463, Incertidumbre: 0.009159063405679652\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", - "Ecuación de regresión: y = 0.005x + 0.181\n", - "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 0.32\n", - "\tR²: 0.4305554307690891, Desviación Estándar: 0.031797681030392426, Varianza: 0.0010110925189105782, Incertidumbre: 0.006938822868849272\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", - "Ecuación de regresión: y = 0.07x + 0.218\n", - "Valor del parámetro correlacionado para la aeronave: 1.203\n", - "Predicción obtenida: 0.302\n", - "\tR²: 0.7560017518320579, Desviación Estándar: 0.02081436063167048, Varianza: 0.000433237608505234, Incertidumbre: 0.00454206586365423\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", - "Ecuación de regresión: y = -0.039x + 0.843\n", - "Valor del parámetro correlacionado para la aeronave: 14.042\n", - "Predicción obtenida: 0.298\n", - "\tR²: 0.6728675905615071, Desviación Estándar: 0.024100802676844793, Varianza: 0.0005808486896682092, Incertidumbre: 0.005259226313135029\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", - "Ecuación de regresión: y = 0.032x + 0.245\n", - "Valor del parámetro correlacionado para la aeronave: 1.954\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.381487984139919, Desviación Estándar: 0.033139335158115936, Varianza: 0.0010982155347219389, Incertidumbre: 0.007231595802027506\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", - "Ecuación de regresión: y = 0.001x + 0.259\n", - "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 0.292\n", - "\tR²: 0.6746862184494904, Desviación Estándar: 0.02417354407482776, Varianza: 0.0005843602331376403, Incertidumbre: 0.0050405323784455825\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", - "Ecuación de regresión: y = -0.0x + 0.324\n", - "Valor del parámetro correlacionado para la aeronave: 413.556\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.1520436185450239, Desviación Estándar: 0.03902796009378789, Varianza: 0.0015231816690822997, Incertidumbre: 0.008137892230799079\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", - "Ecuación de regresión: y = 0.029x + 0.196\n", - "Valor del parámetro correlacionado para la aeronave: 3.9\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.5595879139438245, Desviación Estándar: 0.027964002682235915, Varianza: 0.0007819854460120975, Incertidumbre: 0.006102245667871153\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", - "Ecuación de regresión: y = 0.005x + 0.27\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 0.294\n", - "\tR²: 0.7889957205554606, Desviación Estándar: 0.016594408157668158, Varianza: 0.00027537438210328347, Incertidumbre: 0.003710622468692305\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306]\n", - "Ecuación de regresión: y = 0.004x + 0.203\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.314\n", - "\tR²: 0.5799398889644451, Desviación Estándar: 0.01946734334447621, Varianza: 0.0003789774568917221, Incertidumbre: 0.005026446437870543\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.32', 'Área del ala: 0.302', 'Relación de aspecto del ala: 0.298', 'Longitud del fuselaje: 0.308', 'Peso máximo al despegue (MTOW): 0.292', 'Alcance de la aeronave: 0.313', 'envergadura: 0.309', 'payload: 0.294', 'Crucero KIAS: 0.314']\n", - "**Mediana calculada:** 0.307\n", - "\n", - "--- Imputación para aeronave: **Volitation VT370** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", - "Ecuación de regresión: y = 0.0x + 0.21\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.007920162700700728, Desviación Estándar: 0.04100745249090813, Varianza: 0.0016816111597940874, Incertidumbre: 0.008742818246980276\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", - "Ecuación de regresión: y = 0.005x + 0.181\n", - "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 0.319\n", - "\tR²: 0.4264004008303849, Desviación Estándar: 0.031181277073957195, Varianza: 0.0009722720399628886, Incertidumbre: 0.0066478706090504465\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", - "Ecuación de regresión: y = 0.069x + 0.218\n", - "Valor del parámetro correlacionado para la aeronave: 1.424\n", - "Predicción obtenida: 0.317\n", - "\tR²: 0.7552866449539557, Desviación Estándar: 0.020366589706083574, Varianza: 0.00041479797625594946, Incertidumbre: 0.004342171515057819\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", - "Ecuación de regresión: y = -0.039x + 0.842\n", - "Valor del parámetro correlacionado para la aeronave: 13.645\n", - "Predicción obtenida: 0.314\n", - "\tR²: 0.6709647919313728, Desviación Estándar: 0.023616235156659383, Varianza: 0.0005577265629746346, Incertidumbre: 0.005034998253022332\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", - "Ecuación de regresión: y = 0.032x + 0.245\n", - "Valor del parámetro correlacionado para la aeronave: 2.02\n", - "Predicción obtenida: 0.31\n", - "\tR²: 0.3815211319149636, Desviación Estándar: 0.03237814111230407, Varianza: 0.0010483440218882747, Incertidumbre: 0.006903042879406268\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", - "Ecuación de regresión: y = 0.001x + 0.26\n", - "Valor del parámetro correlacionado para la aeronave: 40.0\n", - "Predicción obtenida: 0.314\n", - "\tR²: 0.6693148854310667, Desviación Estándar: 0.023860379147788803, Varianza: 0.0005693176930762347, Incertidumbre: 0.004870479498452192\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", - "Ecuación de regresión: y = -0.0x + 0.324\n", - "Valor del parámetro correlacionado para la aeronave: 565.637\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.15125833221890828, Desviación Estándar: 0.038225905277819265, Varianza: 0.001461219834308811, Incertidumbre: 0.007802830240551638\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", - "Ecuación de regresión: y = 0.029x + 0.196\n", - "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 0.384\n", - "\tR²: 0.5595332664978436, Desviación Estándar: 0.027324110665788853, Varianza: 0.0007466070236762762, Incertidumbre: 0.005825519967726159\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", - "Ecuación de regresión: y = 0.005x + 0.271\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 0.354\n", - "\tR²: 0.7831688836414169, Desviación Estándar: 0.01644167434806251, Varianza: 0.00027032865536773677, Incertidumbre: 0.0035878674881814365\n", - "\tNivel de confianza: Confianza Muy Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307]\n", - "Ecuación de regresión: y = 0.004x + 0.204\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.578641185644784, Desviación Estándar: 0.018914583583003738, Varianza: 0.0003577614721184345, Incertidumbre: 0.0047286458957509344\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.319', 'Área del ala: 0.317', 'Relación de aspecto del ala: 0.314', 'Longitud del fuselaje: 0.31', 'Peso máximo al despegue (MTOW): 0.314', 'Alcance de la aeronave: 0.309', 'envergadura: 0.384', 'payload: 0.354', 'Crucero KIAS: 0.313']\n", - "**Mediana calculada:** 0.314\n", - "\n", - "--- Imputación para aeronave: **Skyeye 2600** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", - "Ecuación de regresión: y = 0.0x + 0.208\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.307\n", - "\tR²: 0.008376532013914084, Desviación Estándar: 0.04013734006562043, Varianza: 0.001611006067543259, Incertidumbre: 0.008369213945592396\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", - "Ecuación de regresión: y = 0.005x + 0.182\n", - "Valor del parámetro correlacionado para la aeronave: 36.094\n", - "Predicción obtenida: 0.363\n", - "\tR²: 0.4268824074716687, Desviación Estándar: 0.03051385371109099, Varianza: 0.0009310952683018614, Incertidumbre: 0.006362578327191434\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", - "Ecuación de regresión: y = 0.069x + 0.218\n", - "Valor del parámetro correlacionado para la aeronave: 0.88\n", - "Predicción obtenida: 0.279\n", - "\tR²: 0.7555152040915233, Desviación Estándar: 0.019929716367540293, Varianza: 0.0003971935944906035, Incertidumbre: 0.0041556331307013574\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", - "Ecuación de regresión: y = -0.039x + 0.842\n", - "Valor del parámetro correlacionado para la aeronave: 14.103\n", - "Predicción obtenida: 0.296\n", - "\tR²: 0.6716280796932008, Desviación Estándar: 0.02309713944013369, Varianza: 0.0005334778503169791, Incertidumbre: 0.004816086496754028\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", - "Ecuación de regresión: y = 0.032x + 0.245\n", - "Valor del parámetro correlacionado para la aeronave: 2.05\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.38238206414682874, Desviación Estándar: 0.03167635049099469, Varianza: 0.0010033911804283393, Incertidumbre: 0.006604975662096291\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", - "Ecuación de regresión: y = 0.001x + 0.26\n", - "Valor del parámetro correlacionado para la aeronave: 15.0\n", - "Predicción obtenida: 0.28\n", - "\tR²: 0.6695041439455878, Desviación Estándar: 0.02337831313115393, Varianza: 0.0005465455248582842, Incertidumbre: 0.004675662626230786\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", - "Ecuación de regresión: y = -0.0x + 0.324\n", - "Valor del parámetro correlacionado para la aeronave: 407.828\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.15110158401290597, Desviación Estándar: 0.03746778536542462, Varianza: 0.0014038349401895272, Incertidumbre: 0.007493557073084923\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", - "Ecuación de regresión: y = 0.023x + 0.215\n", - "Valor del parámetro correlacionado para la aeronave: 2.6\n", - "Predicción obtenida: 0.276\n", - "\tR²: 0.4627661042213257, Desviación Estándar: 0.029543156346368924, Varianza: 0.0008727980869059985, Incertidumbre: 0.006160173934959667\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", - "Ecuación de regresión: y = 0.004x + 0.273\n", - "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 0.29\n", - "\tR²: 0.7275683615430203, Desviación Estándar: 0.018006770737420567, Varianza: 0.0003242437923900256, Incertidumbre: 0.0038390564204693082\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314]\n", - "Ecuación de regresión: y = 0.004x + 0.204\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 0.348\n", - "\tR²: 0.5842116709677524, Desviación Estándar: 0.018351270832188404, Varianza: 0.0003367691411563289, Incertidumbre: 0.004450836941495841\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292]\n", - "Ecuación de regresión: y = 0.0x + 0.265\n", - "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 0.268\n", - "\tR²: 0.003486790166087661, Desviación Estándar: 0.03652495038432548, Varianza: 0.001334072000577438, Incertidumbre: 0.013805133623699752\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.307', 'Velocidad a la que se realiza el crucero (KTAS): 0.363', 'Área del ala: 0.279', 'Relación de aspecto del ala: 0.296', 'Longitud del fuselaje: 0.311', 'Peso máximo al despegue (MTOW): 0.28', 'Alcance de la aeronave: 0.313', 'envergadura: 0.276', 'payload: 0.29', 'Crucero KIAS: 0.348', 'Empty weight: 0.268']\n", - "**Mediana calculada:** 0.296\n", - "\n", - "--- Imputación para aeronave: **Skyeye 2930 VTOL** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", - "Ecuación de regresión: y = 0.0x + 0.211\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.007814506493702633, Desviación Estándar: 0.03934894530801304, Varianza: 0.0015483394968530018, Incertidumbre: 0.008032069826776185\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", - "Ecuación de regresión: y = 0.004x + 0.203\n", - "Valor del parámetro correlacionado para la aeronave: 26.25\n", - "Predicción obtenida: 0.31\n", - "\tR²: 0.3329612516575994, Desviación Estándar: 0.03226355293122838, Varianza: 0.0010409368477461755, Incertidumbre: 0.006585770164232172\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", - "Ecuación de regresión: y = 0.068x + 0.22\n", - "Valor del parámetro correlacionado para la aeronave: 1.0\n", - "Predicción obtenida: 0.289\n", - "\tR²: 0.7491292552639982, Desviación Estándar: 0.019786166950727165, Varianza: 0.00039149240260204785, Incertidumbre: 0.0040388344162334755\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", - "Ecuación de regresión: y = -0.039x + 0.842\n", - "Valor del parámetro correlacionado para la aeronave: 14.001\n", - "Predicción obtenida: 0.3\n", - "\tR²: 0.6723851116250787, Desviación Estándar: 0.022610932560392084, Varianza: 0.000511254271250599, Incertidumbre: 0.004615437281786883\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", - "Ecuación de regresión: y = 0.032x + 0.245\n", - "Valor del parámetro correlacionado para la aeronave: 2.03\n", - "Predicción obtenida: 0.31\n", - "\tR²: 0.3778731110352854, Desviación Estándar: 0.031158471570466775, Varianza: 0.0009708503506075861, Incertidumbre: 0.0063601963760549695\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", - "Ecuación de regresión: y = 0.001x + 0.261\n", - "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 0.299\n", - "\tR²: 0.66526195767342, Desviación Estándar: 0.023117889975755625, Varianza: 0.0005344368369311424, Incertidumbre: 0.00453379123459496\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", - "Ecuación de regresión: y = -0.0x + 0.324\n", - "Valor del parámetro correlacionado para la aeronave: 425.273\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.14767582813821534, Desviación Estándar: 0.036889099953161315, Varianza: 0.0013608056953543263, Incertidumbre: 0.007234547711540164\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", - "Ecuación de regresión: y = 0.022x + 0.219\n", - "Valor del parámetro correlacionado para la aeronave: 2.93\n", - "Predicción obtenida: 0.285\n", - "\tR²: 0.4538529022461858, Desviación Estándar: 0.029193856890482952, Varianza: 0.0008522812801419989, Incertidumbre: 0.005959171083793167\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", - "Ecuación de regresión: y = 0.004x + 0.273\n", - "Valor del parámetro correlacionado para la aeronave: 6.0\n", - "Predicción obtenida: 0.299\n", - "\tR²: 0.7300492378182392, Desviación Estándar: 0.0176537442574639, Varianza: 0.0003116546863079396, Incertidumbre: 0.0036810601397585336\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296]\n", - "Ecuación de regresión: y = 0.003x + 0.224\n", - "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 0.305\n", - "\tR²: 0.4379735968192333, Desviación Estándar: 0.020751260149570606, Varianza: 0.0004306147977951571, Incertidumbre: 0.0048911189233091824\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296]\n", - "Ecuación de regresión: y = 0.0x + 0.27\n", - "Valor del parámetro correlacionado para la aeronave: 7.1\n", - "Predicción obtenida: 0.272\n", - "\tR²: 0.0008846968281794876, Desviación Estándar: 0.03538270380705628, Varianza: 0.0012519357286978749, Incertidumbre: 0.012509674899342281\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.31', 'Área del ala: 0.289', 'Relación de aspecto del ala: 0.3', 'Longitud del fuselaje: 0.31', 'Peso máximo al despegue (MTOW): 0.299', 'Alcance de la aeronave: 0.312', 'envergadura: 0.285', 'payload: 0.299', 'Crucero KIAS: 0.305', 'Empty weight: 0.272']\n", - "**Mediana calculada:** 0.3\n", - "\n", - "--- Imputación para aeronave: **Skyeye 3600** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", - "Ecuación de regresión: y = 0.0x + 0.212\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.007526495051969895, Desviación Estándar: 0.03857245496275595, Varianza: 0.001487834281853836, Incertidumbre: 0.00771449099255119\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", - "Ecuación de regresión: y = 0.068x + 0.221\n", - "Valor del parámetro correlacionado para la aeronave: 1.33\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.745993216225064, Desviación Estándar: 0.019513738354102667, Varianza: 0.0003807859845523774, Incertidumbre: 0.0039027476708205335\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", - "Ecuación de regresión: y = -0.039x + 0.842\n", - "Valor del parámetro correlacionado para la aeronave: 13.71\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.6726026581884519, Desviación Estándar: 0.02215416542630856, Varianza: 0.0004908070457362455, Incertidumbre: 0.004430833085261712\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", - "Ecuación de regresión: y = 0.032x + 0.245\n", - "Valor del parámetro correlacionado para la aeronave: 2.488\n", - "Predicción obtenida: 0.324\n", - "\tR²: 0.37576047664868784, Desviación Estándar: 0.030590984286507895, Varianza: 0.000935808319617373, Incertidumbre: 0.006118196857301579\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", - "Ecuación de regresión: y = 0.001x + 0.261\n", - "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 0.299\n", - "\tR²: 0.6658077862482688, Desviación Estándar: 0.02268722584632889, Varianza: 0.0005147102166023337, Incertidumbre: 0.0043661586498479395\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", - "Ecuación de regresión: y = -0.0x + 0.323\n", - "Valor del parámetro correlacionado para la aeronave: 458.124\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.1458113421289795, Desviación Estándar: 0.03627105024977506, Varianza: 0.0013155890862217073, Incertidumbre: 0.006980366875166025\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", - "Ecuación de regresión: y = 0.022x + 0.221\n", - "Valor del parámetro correlacionado para la aeronave: 3.6\n", - "Predicción obtenida: 0.301\n", - "\tR²: 0.4484176985315972, Desviación Estándar: 0.028755636237230698, Varianza: 0.0008268866154079353, Incertidumbre: 0.00575112724744614\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", - "Ecuación de regresión: y = 0.004x + 0.273\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 0.316\n", - "\tR²: 0.7321183484558058, Desviación Estándar: 0.017283518765307298, Varianza: 0.00029872002091072946, Incertidumbre: 0.0035279834945684007\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3]\n", - "Ecuación de regresión: y = 0.0x + 0.274\n", - "Valor del parámetro correlacionado para la aeronave: 11.5\n", - "Predicción obtenida: 0.276\n", - "\tR²: 0.0002618966283592927, Desviación Estándar: 0.03450249234670619, Varianza: 0.0011904219781345188, Incertidumbre: 0.011500830782235396\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Área del ala: 0.312', 'Relación de aspecto del ala: 0.312', 'Longitud del fuselaje: 0.324', 'Peso máximo al despegue (MTOW): 0.299', 'Alcance de la aeronave: 0.311', 'envergadura: 0.301', 'payload: 0.316', 'Empty weight: 0.276']\n", - "**Mediana calculada:** 0.311\n", - "\n", - "--- Imputación para aeronave: **Skyeye 3600 VTOL** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", - "Ecuación de regresión: y = 0.0x + 0.211\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.0077910339066047385, Desviación Estándar: 0.037836352063430036, Varianza: 0.0014315895374678261, Incertidumbre: 0.0074203191344075535\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", - "Ecuación de regresión: y = 0.004x + 0.203\n", - "Valor del parámetro correlacionado para la aeronave: 32.813\n", - "Predicción obtenida: 0.336\n", - "\tR²: 0.33106974113438137, Desviación Estándar: 0.03166709614118016, Varianza: 0.001002804978014747, Incertidumbre: 0.0063334192282360315\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", - "Ecuación de regresión: y = 0.068x + 0.221\n", - "Valor del parámetro correlacionado para la aeronave: 1.32\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.7462264643720709, Desviación Estándar: 0.019135104991392145, Varianza: 0.0003661522430316006, Incertidumbre: 0.003752702836375202\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", - "Ecuación de regresión: y = -0.039x + 0.842\n", - "Valor del parámetro correlacionado para la aeronave: 13.672\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.6729065655813421, Desviación Estándar: 0.021724190260569178, Varianza: 0.0004719404424774087, Incertidumbre: 0.0042604642329094064\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", - "Ecuación de regresión: y = 0.031x + 0.245\n", - "Valor del parámetro correlacionado para la aeronave: 2.42\n", - "Predicción obtenida: 0.321\n", - "\tR²: 0.37206338538349726, Desviación Estándar: 0.030099937089659884, Varianza: 0.0009060062128014827, Incertidumbre: 0.005903083329926576\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", - "Ecuación de regresión: y = 0.001x + 0.262\n", - "Valor del parámetro correlacionado para la aeronave: 40.0\n", - "Predicción obtenida: 0.315\n", - "\tR²: 0.6623522424362198, Desviación Estándar: 0.02239505431218533, Varianza: 0.0005015384576457308, Incertidumbre: 0.004232267450559075\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", - "Ecuación de regresión: y = -0.0x + 0.323\n", - "Valor del parámetro correlacionado para la aeronave: 300.0\n", - "Predicción obtenida: 0.315\n", - "\tR²: 0.1459436125708079, Desviación Estándar: 0.03561750202712675, Varianza: 0.0012686064506523779, Incertidumbre: 0.006731075191794021\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", - "Ecuación de regresión: y = 0.022x + 0.222\n", - "Valor del parámetro correlacionado para la aeronave: 3.6\n", - "Predicción obtenida: 0.301\n", - "\tR²: 0.4461253083393548, Desviación Estándar: 0.028269195215371138, Varianza: 0.0007991473981247625, Incertidumbre: 0.005544045309105432\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311]\n", - "Ecuación de regresión: y = 0.004x + 0.273\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 0.316\n", - "\tR²: 0.7313295857648042, Desviación Estándar: 0.016962299070521318, Varianza: 0.00028771958975780837, Incertidumbre: 0.0033924598141042636\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3]\n", - "Ecuación de regresión: y = 0.003x + 0.224\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 0.325\n", - "\tR²: 0.4365151702095247, Desviación Estándar: 0.02022420538915309, Varianza: 0.00040901848362264885, Incertidumbre: 0.004639750921301526\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311]\n", - "Ecuación de regresión: y = 0.001x + 0.272\n", - "Valor del parámetro correlacionado para la aeronave: 11.0\n", - "Predicción obtenida: 0.281\n", - "\tR²: 0.009760346923073815, Desviación Estándar: 0.03428498479253269, Varianza: 0.0011754601822241977, Incertidumbre: 0.010841864148863874\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.336', 'Área del ala: 0.311', 'Relación de aspecto del ala: 0.313', 'Longitud del fuselaje: 0.321', 'Peso máximo al despegue (MTOW): 0.315', 'Alcance de la aeronave: 0.315', 'envergadura: 0.301', 'payload: 0.316', 'Crucero KIAS: 0.325', 'Empty weight: 0.281']\n", - "**Mediana calculada:** 0.315\n", - "\n", - "--- Imputación para aeronave: **Skyeye 5000** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", - "Ecuación de regresión: y = 0.0x + 0.209\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.306\n", - "\tR²: 0.008217398892604644, Desviación Estándar: 0.03716734216604148, Varianza: 0.001381411323687605, Incertidumbre: 0.007152858334875658\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315]\n", - "Ecuación de regresión: y = 0.004x + 0.207\n", - "Valor del parámetro correlacionado para la aeronave: 36.094\n", - "Predicción obtenida: 0.346\n", - "\tR²: 0.32262835377535615, Desviación Estándar: 0.031288239037149196, Varianza: 0.0009789539020457867, Incertidumbre: 0.006136128515245357\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", - "Ecuación de regresión: y = 0.068x + 0.221\n", - "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 0.399\n", - "\tR²: 0.7464203816084714, Desviación Estándar: 0.01879361973555014, Varianza: 0.00035320014276445967, Incertidumbre: 0.0036168338044557788\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", - "Ecuación de regresión: y = -0.039x + 0.843\n", - "Valor del parámetro correlacionado para la aeronave: 12.695\n", - "Predicción obtenida: 0.351\n", - "\tR²: 0.6736164896021335, Desviación Estándar: 0.02132148100602976, Varianza: 0.0004546055522904879, Incertidumbre: 0.004103320932784258\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", - "Ecuación de regresión: y = 0.031x + 0.246\n", - "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 0.354\n", - "\tR²: 0.3726704559110734, Desviación Estándar: 0.02955977566670113, Varianza: 0.0008737803374656961, Incertidumbre: 0.005688781479451615\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = 0.76) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352]\n", - "Ecuación de regresión: y = 0.883x + 0.101\n", - "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 0.432\n", - "\tR²: 0.40209852192250706, Desviación Estándar: 0.03661787969369935, Varianza: 0.001340869113262239, Incertidumbre: 0.02114134269831065\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", - "Ecuación de regresión: y = 0.001x + 0.262\n", - "Valor del parámetro correlacionado para la aeronave: 90.0\n", - "Predicción obtenida: 0.381\n", - "\tR²: 0.6626838320176878, Desviación Estándar: 0.022005551176023402, Varianza: 0.000484244282560585, Incertidumbre: 0.004086328267404087\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", - "Ecuación de regresión: y = -0.0x + 0.323\n", - "Valor del parámetro correlacionado para la aeronave: 530.401\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.14678275665337615, Desviación Estándar: 0.034998020711235714, Varianza: 0.0012248614537040841, Incertidumbre: 0.006498969291500381\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", - "Ecuación de regresión: y = 0.022x + 0.222\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 0.332\n", - "\tR²: 0.4424360357921081, Desviación Estándar: 0.02786766696740896, Varianza: 0.0007766068622064165, Incertidumbre: 0.005363135008440135\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315]\n", - "Ecuación de regresión: y = 0.004x + 0.273\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 0.358\n", - "\tR²: 0.731317018691535, Desviación Estándar: 0.016633562499201185, Varianza: 0.0002766754014148319, Incertidumbre: 0.00326210999092277\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315]\n", - "Ecuación de regresión: y = 0.003x + 0.226\n", - "Valor del parámetro correlacionado para la aeronave: 33.0\n", - "Predicción obtenida: 0.333\n", - "\tR²: 0.43886786937390043, Desviación Estándar: 0.019812140290383477, Varianza: 0.00039252090288583623, Incertidumbre: 0.0044301292469059875\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315]\n", - "Ecuación de regresión: y = 0.001x + 0.271\n", - "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.023639785146452374, Desviación Estándar: 0.0340515934789817, Varianza: 0.001159511018457829, Incertidumbre: 0.01026694173486318\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.306', 'Velocidad a la que se realiza el crucero (KTAS): 0.346', 'Área del ala: 0.399', 'Relación de aspecto del ala: 0.351', 'Longitud del fuselaje: 0.354', 'Ancho del fuselaje: 0.432', 'Peso máximo al despegue (MTOW): 0.381', 'Alcance de la aeronave: 0.309', 'envergadura: 0.332', 'payload: 0.358', 'Crucero KIAS: 0.333', 'Empty weight: 0.312']\n", - "**Mediana calculada:** 0.348\n", - "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", - "Ecuación de regresión: y = 0.0x + 0.199\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.009824679618514076, Desviación Estándar: 0.03730259846752296, Varianza: 0.0013914838524292463, Incertidumbre: 0.00704952848582606\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348]\n", - "Ecuación de regresión: y = 0.004x + 0.207\n", - "Valor del parámetro correlacionado para la aeronave: 30.625\n", - "Predicción obtenida: 0.325\n", - "\tR²: 0.35279566671506324, Desviación Estándar: 0.03070509946045511, Varianza: 0.0009428031328764408, Incertidumbre: 0.00590919914632933\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", - "Ecuación de regresión: y = 0.059x + 0.231\n", - "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 0.386\n", - "\tR²: 0.7085123361490802, Desviación Estándar: 0.020239189952971352, Varianza: 0.00040962480995245654, Incertidumbre: 0.0038248473823542318\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", - "Ecuación de regresión: y = -0.039x + 0.84\n", - "Valor del parámetro correlacionado para la aeronave: 13.032\n", - "Predicción obtenida: 0.338\n", - "\tR²: 0.6878639211364455, Desviación Estándar: 0.02094377780954981, Varianza: 0.0004386418289357911, Incertidumbre: 0.003958001971304421\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", - "Ecuación de regresión: y = 0.03x + 0.247\n", - "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 0.353\n", - "\tR²: 0.39957074018891947, Desviación Estándar: 0.029047847816911346, Varianza: 0.0008437774627944413, Incertidumbre: 0.0054895272460855635\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = 0.76) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.348]\n", - "Ecuación de regresión: y = 0.411x + 0.205\n", - "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 0.359\n", - "\tR²: 0.3978508461679193, Desviación Estándar: 0.03522448950237247, Varianza: 0.001240764660702748, Incertidumbre: 0.017612244751186234\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", - "Ecuación de regresión: y = 0.001x + 0.264\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 0.388\n", - "\tR²: 0.6531876768371664, Desviación Estándar: 0.02232772097728044, Varianza: 0.000498527124039289, Incertidumbre: 0.004076465478979263\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", - "Ecuación de regresión: y = -0.0x + 0.324\n", - "Valor del parámetro correlacionado para la aeronave: 800.0\n", - "Predicción obtenida: 0.303\n", - "\tR²: 0.14185181764220745, Desviación Estándar: 0.035121922429990356, Varianza: 0.0012335494351782598, Incertidumbre: 0.006412356392617459\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", - "Ecuación de regresión: y = 0.022x + 0.221\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 0.333\n", - "\tR²: 0.4612182728347505, Desviación Estándar: 0.027516263885293008, Varianza: 0.0007571447782050803, Incertidumbre: 0.005200085089293802\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", - "Ecuación de regresión: y = 0.004x + 0.274\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.378\n", - "\tR²: 0.7386270567836593, Desviación Estándar: 0.01642870017516188, Varianza: 0.00026990218944536394, Incertidumbre: 0.003161704822855121\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348]\n", - "Ecuación de regresión: y = 0.003x + 0.222\n", - "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 0.318\n", - "\tR²: 0.5000484040159869, Desviación Estándar: 0.019560275570138758, Varianza: 0.0003826043803797671, Incertidumbre: 0.0042684020673503026\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.308', 'Velocidad a la que se realiza el crucero (KTAS): 0.325', 'Área del ala: 0.386', 'Relación de aspecto del ala: 0.338', 'Longitud del fuselaje: 0.353', 'Ancho del fuselaje: 0.359', 'Peso máximo al despegue (MTOW): 0.388', 'Alcance de la aeronave: 0.303', 'envergadura: 0.333', 'payload: 0.378', 'Crucero KIAS: 0.318']\n", - "**Mediana calculada:** 0.338\n", - "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL octo** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348]\n", - "Ecuación de regresión: y = 0.0x + 0.199\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.308\n", - "\tR²: 0.009824679618514076, Desviación Estándar: 0.03730259846752296, Varianza: 0.0013914838524292463, Incertidumbre: 0.00704952848582606\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338]\n", - "Ecuación de regresión: y = 0.004x + 0.205\n", - "Valor del parámetro correlacionado para la aeronave: 33.885\n", - "Predicción obtenida: 0.339\n", - "\tR²: 0.364003579646475, Desviación Estándar: 0.030242361656466255, Varianza: 0.0009146004385605004, Incertidumbre: 0.005715269143020364\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", - "Ecuación de regresión: y = 0.053x + 0.237\n", - "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 0.376\n", - "\tR²: 0.6700337636483493, Desviación Estándar: 0.021406727235210983, Varianza: 0.0004582479709227237, Incertidumbre: 0.00397512945320641\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", - "Ecuación de regresión: y = -0.039x + 0.84\n", - "Valor del parámetro correlacionado para la aeronave: 12.856\n", - "Predicción obtenida: 0.344\n", - "\tR²: 0.6950392482679, Desviación Estándar: 0.020579626973971723, Varianza: 0.0004235210463878245, Incertidumbre: 0.003821540790489258\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", - "Ecuación de regresión: y = 0.029x + 0.248\n", - "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 0.351\n", - "\tR²: 0.40843076343231066, Desviación Estándar: 0.028662780269184288, Varianza: 0.0008215549727595402, Incertidumbre: 0.005322544675180707\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = 0.76) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.348, 0.338]\n", - "Ecuación de regresión: y = 0.347x + 0.219\n", - "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 0.349\n", - "\tR²: 0.401178655632655, Desviación Estándar: 0.03226372501937835, Varianza: 0.001040947952126061, Incertidumbre: 0.014428776470138143\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", - "Ecuación de regresión: y = 0.001x + 0.268\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 0.378\n", - "\tR²: 0.6142108778582203, Desviación Estándar: 0.02336858751606866, Varianza: 0.0005460908824961601, Incertidumbre: 0.004197122218762633\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", - "Ecuación de regresión: y = -0.0x + 0.325\n", - "Valor del parámetro correlacionado para la aeronave: 528.174\n", - "Predicción obtenida: 0.311\n", - "\tR²: 0.12969533614523188, Desviación Estándar: 0.03509886956318445, Varianza: 0.0012319306446134358, Incertidumbre: 0.00630394306869412\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", - "Ecuación de regresión: y = 0.023x + 0.221\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 0.334\n", - "\tR²: 0.47311026916495147, Desviación Estándar: 0.027050505844431656, Varianza: 0.0007317298664396311, Incertidumbre: 0.005023152830642055\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338]\n", - "Ecuación de regresión: y = 0.004x + 0.277\n", - "Valor del parámetro correlacionado para la aeronave: 15.0\n", - "Predicción obtenida: 0.333\n", - "\tR²: 0.6973906199473292, Desviación Estándar: 0.017512139839937605, Varianza: 0.00030667504177352985, Incertidumbre: 0.0033094833529329548\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338]\n", - "Ecuación de regresión: y = 0.004x + 0.22\n", - "Valor del parámetro correlacionado para la aeronave: 35.0\n", - "Predicción obtenida: 0.345\n", - "\tR²: 0.5123468231062337, Desviación Estándar: 0.019530346323899706, Varianza: 0.00038143442753146275, Incertidumbre: 0.0041638838269285805\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348]\n", - "Ecuación de regresión: y = 0.002x + 0.263\n", - "Valor del parámetro correlacionado para la aeronave: 35.0\n", - "Predicción obtenida: 0.346\n", - "\tR²: 0.23373450804691387, Desviación Estándar: 0.032994278206234205, Varianza: 0.0010886223943503814, Incertidumbre: 0.009524627702043362\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.308', 'Velocidad a la que se realiza el crucero (KTAS): 0.339', 'Área del ala: 0.376', 'Relación de aspecto del ala: 0.344', 'Longitud del fuselaje: 0.351', 'Ancho del fuselaje: 0.349', 'Peso máximo al despegue (MTOW): 0.378', 'Alcance de la aeronave: 0.311', 'envergadura: 0.334', 'payload: 0.333', 'Crucero KIAS: 0.345', 'Empty weight: 0.346']\n", - "**Mediana calculada:** 0.344\n", - "\n", - "--- Imputación para aeronave: **Volitation VT510** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344]\n", - "Ecuación de regresión: y = 0.0x + 0.191\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.011265848190217476, Desviación Estándar: 0.037236364860275865, Varianza: 0.0013865468680075873, Incertidumbre: 0.006914619365213018\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344]\n", - "Ecuación de regresión: y = 0.004x + 0.205\n", - "Valor del parámetro correlacionado para la aeronave: 32.813\n", - "Predicción obtenida: 0.335\n", - "\tR²: 0.38259337108241265, Desviación Estándar: 0.029730535611440134, Varianza: 0.00088390474774311, Incertidumbre: 0.005520821864551259\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", - "Ecuación de regresión: y = 0.049x + 0.241\n", - "Valor del parámetro correlacionado para la aeronave: 1.993\n", - "Predicción obtenida: 0.34\n", - "\tR²: 0.6594487293179985, Desviación Estándar: 0.021710134055956, Varianza: 0.00047132992072758054, Incertidumbre: 0.003963710049636075\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", - "Ecuación de regresión: y = -0.039x + 0.84\n", - "Valor del parámetro correlacionado para la aeronave: 13.099\n", - "Predicción obtenida: 0.335\n", - "\tR²: 0.704188353912186, Desviación Estándar: 0.020233866799229377, Varianza: 0.00040940936564895687, Incertidumbre: 0.0036941817571642614\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", - "Ecuación de regresión: y = 0.029x + 0.249\n", - "Valor del parámetro correlacionado para la aeronave: 2.905\n", - "Predicción obtenida: 0.333\n", - "\tR²: 0.42514465313889094, Desviación Estándar: 0.028206587035388978, Varianza: 0.0007956115521849735, Incertidumbre: 0.005149794663171771\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", - "Ecuación de regresión: y = 0.001x + 0.27\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 0.372\n", - "\tR²: 0.6021174048921634, Desviación Estándar: 0.023638617780023557, Varianza: 0.0005587842505500459, Incertidumbre: 0.004178756732532887\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", - "Ecuación de regresión: y = -0.0x + 0.326\n", - "Valor del parámetro correlacionado para la aeronave: 503.585\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.12714259346444579, Desviación Estándar: 0.035011946666754785, Varianza: 0.0012258364093956815, Incertidumbre: 0.006189296227651011\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", - "Ecuación de regresión: y = 0.023x + 0.22\n", - "Valor del parámetro correlacionado para la aeronave: 5.1\n", - "Predicción obtenida: 0.337\n", - "\tR²: 0.4865211118670584, Desviación Estándar: 0.026658305995461602, Varianza: 0.0007106652785476641, Incertidumbre: 0.004867118512863177\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344]\n", - "Ecuación de regresión: y = 0.004x + 0.277\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.371\n", - "\tR²: 0.7011767124714305, Desviación Estándar: 0.01732374391424772, Varianza: 0.00030011210320643483, Incertidumbre: 0.0032169384846488743\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344]\n", - "Ecuación de regresión: y = 0.004x + 0.22\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 0.327\n", - "\tR²: 0.5502375625525282, Desviación Estándar: 0.019101623043785508, Varianza: 0.00036487200290687756, Incertidumbre: 0.003982963736514053\n", - "\tNivel de confianza: Confianza Alta\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.309', 'Velocidad a la que se realiza el crucero (KTAS): 0.335', 'Área del ala: 0.34', 'Relación de aspecto del ala: 0.335', 'Longitud del fuselaje: 0.333', 'Peso máximo al despegue (MTOW): 0.372', 'Alcance de la aeronave: 0.313', 'envergadura: 0.337', 'payload: 0.371', 'Crucero KIAS: 0.327']\n", - "**Mediana calculada:** 0.335\n", - "\n", - "--- Imputación para aeronave: **Ascend** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.335]\n", - "Ecuación de regresión: y = 0.0x + 0.185\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.31\n", - "\tR²: 0.012363783169715314, Desviación Estándar: 0.0368988404995783, Varianza: 0.0013615244302133194, Incertidumbre: 0.006736775762468075\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344, 0.335]\n", - "Ecuación de regresión: y = 0.004x + 0.205\n", - "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 0.292\n", - "\tR²: 0.3919925997911332, Desviación Estándar: 0.029230827578736365, Varianza: 0.0008544412809378144, Incertidumbre: 0.005336794546472675\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", - "Ecuación de regresión: y = 0.049x + 0.241\n", - "Valor del parámetro correlacionado para la aeronave: 0.771\n", - "Predicción obtenida: 0.279\n", - "\tR²: 0.6641048421071282, Desviación Estándar: 0.021373342448267822, Varianza: 0.0004568197674109272, Incertidumbre: 0.003838765625742831\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", - "Ecuación de regresión: y = -0.039x + 0.84\n", - "Valor del parámetro correlacionado para la aeronave: 14.349\n", - "Predicción obtenida: 0.287\n", - "\tR²: 0.7086760340890808, Desviación Estándar: 0.019904839465063797, Varianza: 0.0003962026341299613, Incertidumbre: 0.0035750147039172522\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", - "Ecuación de regresión: y = 0.029x + 0.249\n", - "Valor del parámetro correlacionado para la aeronave: 1.562\n", - "Predicción obtenida: 0.294\n", - "\tR²: 0.43377431731307325, Desviación Estándar: 0.027750151250249728, Varianza: 0.0007700708944117365, Incertidumbre: 0.004984074296589788\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", - "Ecuación de regresión: y = 0.001x + 0.272\n", - "Valor del parámetro correlacionado para la aeronave: 9.5\n", - "Predicción obtenida: 0.281\n", - "\tR²: 0.5805077444550542, Desviación Estándar: 0.024037954911906977, Varianza: 0.0005778232763468728, Incertidumbre: 0.00418447084503352\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", - "Ecuación de regresión: y = -0.0x + 0.327\n", - "Valor del parámetro correlacionado para la aeronave: 420.652\n", - "Predicción obtenida: 0.316\n", - "\tR²: 0.1266463739361714, Desviación Estándar: 0.03468411410732085, Varianza: 0.001202987771409653, Incertidumbre: 0.0060377292827023645\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 5.1]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", - "Ecuación de regresión: y = 0.023x + 0.22\n", - "Valor del parámetro correlacionado para la aeronave: 2.0\n", - "Predicción obtenida: 0.266\n", - "\tR²: 0.494252346723298, Desviación Estándar: 0.026226329343117413, Varianza: 0.0006878203508136613, Incertidumbre: 0.004710387802724272\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335]\n", - "Ecuación de regresión: y = 0.003x + 0.279\n", - "Valor del parámetro correlacionado para la aeronave: 0.6\n", - "Predicción obtenida: 0.281\n", - "\tR²: 0.6661275841307379, Desviación Estándar: 0.01809912723389064, Varianza: 0.00032757840662856185, Incertidumbre: 0.0033044334190526613\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344, 0.335]\n", - "Ecuación de regresión: y = 0.004x + 0.219\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 0.291\n", - "\tR²: 0.5647788073941997, Desviación Estándar: 0.018767936696439058, Varianza: 0.00035223544784154386, Incertidumbre: 0.0038309890359276236\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344]\n", - "Ecuación de regresión: y = 0.002x + 0.263\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 0.27\n", - "\tR²: 0.3462930840767372, Desviación Estándar: 0.031703647782123416, Varianza: 0.0010051212826929388, Incertidumbre: 0.008793009822899704\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.31', 'Velocidad a la que se realiza el crucero (KTAS): 0.292', 'Área del ala: 0.279', 'Relación de aspecto del ala: 0.287', 'Longitud del fuselaje: 0.294', 'Peso máximo al despegue (MTOW): 0.281', 'Alcance de la aeronave: 0.316', 'envergadura: 0.266', 'payload: 0.281', 'Crucero KIAS: 0.291', 'Empty weight: 0.27']\n", - "**Mediana calculada:** 0.287\n", - "\n", - "--- Imputación para aeronave: **Transition** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.335, 0.287]\n", - "Ecuación de regresión: y = 0.0x + 0.19\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.011152514691267879, Desviación Estándar: 0.03652974557296492, Varianza: 0.0013344223116255503, Incertidumbre: 0.006560935986593608\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", - "Ecuación de regresión: y = 0.004x + 0.204\n", - "Valor del parámetro correlacionado para la aeronave: 21.875\n", - "Predicción obtenida: 0.291\n", - "\tR²: 0.39888174922260133, Desviación Estándar: 0.028766571435852473, Varianza: 0.0008275156321740035, Incertidumbre: 0.005166628751011079\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", - "Ecuación de regresión: y = 0.049x + 0.242\n", - "Valor del parámetro correlacionado para la aeronave: 0.986\n", - "Predicción obtenida: 0.29\n", - "\tR²: 0.6668892763992251, Desviación Estándar: 0.021077630404536665, Varianza: 0.00044426650347024837, Incertidumbre: 0.003726033847597907\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", - "Ecuación de regresión: y = -0.039x + 0.84\n", - "Valor del parámetro correlacionado para la aeronave: 14.223\n", - "Predicción obtenida: 0.292\n", - "\tR²: 0.7122103721028534, Desviación Estándar: 0.01959138083026252, Varianza: 0.0003838222028363777, Incertidumbre: 0.0034632995594716903\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", - "Ecuación de regresión: y = 0.029x + 0.248\n", - "Valor del parámetro correlacionado para la aeronave: 2.3\n", - "Predicción obtenida: 0.315\n", - "\tR²: 0.43946734247407293, Desviación Estándar: 0.027341852629340847, Varianza: 0.000747576905204593, Incertidumbre: 0.0048334023511025365\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0, 9.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", - "Ecuación de regresión: y = 0.001x + 0.273\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 0.29\n", - "\tR²: 0.585626194228399, Desviación Estándar: 0.023701762283004735, Varianza: 0.0005617735353200658, Incertidumbre: 0.004064818696919392\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", - "Ecuación de regresión: y = -0.0x + 0.326\n", - "Valor del parámetro correlacionado para la aeronave: 506.641\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.12137030661842563, Desviación Estándar: 0.03451337490962147, Varianza: 0.0011911730476520889, Incertidumbre: 0.005918994965493045\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", - "Ecuación de regresión: y = 0.022x + 0.224\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 0.29\n", - "\tR²: 0.49095069370026423, Desviación Estándar: 0.02605597944515681, Varianza: 0.0006789140648464343, Incertidumbre: 0.004606089939031919\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", - "Ecuación de regresión: y = 0.003x + 0.279\n", - "Valor del parámetro correlacionado para la aeronave: 1.5\n", - "Predicción obtenida: 0.285\n", - "\tR²: 0.6750655648466569, Desviación Estándar: 0.017835052168221725, Varianza: 0.0003180890858431905, Incertidumbre: 0.00320326993133675\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287]\n", - "Ecuación de regresión: y = 0.004x + 0.218\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 0.291\n", - "\tR²: 0.5731148377392137, Desviación Estándar: 0.018407788893284933, Varianza: 0.0003388466919397441, Incertidumbre: 0.0036815577786569868\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287]\n", - "Ecuación de regresión: y = 0.002x + 0.266\n", - "Valor del parámetro correlacionado para la aeronave: 5.8\n", - "Predicción obtenida: 0.279\n", - "\tR²: 0.3348753843733273, Desviación Estándar: 0.030833248903070595, Varianza: 0.000950689237918704, Incertidumbre: 0.008240532394029543\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.309', 'Velocidad a la que se realiza el crucero (KTAS): 0.291', 'Área del ala: 0.29', 'Relación de aspecto del ala: 0.292', 'Longitud del fuselaje: 0.315', 'Peso máximo al despegue (MTOW): 0.29', 'Alcance de la aeronave: 0.313', 'envergadura: 0.29', 'payload: 0.285', 'Crucero KIAS: 0.291', 'Empty weight: 0.279']\n", - "**Mediana calculada:** 0.291\n", - "\n", - "--- Imputación para aeronave: **Reach** ---\n", - "\n", - "--- Correlación: Altitud a la que se realiza el crucero (r = 0.761) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Altitud a la que se realiza el crucero: [6000.0, 6000.0, 6000.0, 6000.0, 5500.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 5000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0, 6000.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.335, 0.287, 0.291]\n", - "Ecuación de regresión: y = 0.0x + 0.194\n", - "Valor del parámetro correlacionado para la aeronave: 6000.0\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.010293782952762509, Desviación Estándar: 0.036097016178001054, Varianza: 0.00130299457695487, Incertidumbre: 0.006381111230016264\n", - "\tNivel de confianza: Confianza Baja\n", - "\n", - "--- Correlación: Velocidad a la que se realiza el crucero (KTAS) (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Velocidad a la que se realiza el crucero (KTAS): [16.88, 17.602, 27.344, 27.892, 19.306, 30.407, 26.611, 26.611, 18.266, 30.625, 30.953, 21.463, 33.045, 25.703, 33.797, 18.091, 17.5, 19.688, 21.875, 21.875, 27.344, 27.344, 27.344, 36.094, 26.25, 32.813, 36.094, 30.625, 33.885, 32.813, 21.875, 21.875]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", - "Ecuación de regresión: y = 0.004x + 0.204\n", - "Valor del parámetro correlacionado para la aeronave: 27.344\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.4034376616845361, Desviación Estándar: 0.02831358276093245, Varianza: 0.0008016589687601712, Incertidumbre: 0.005005181592485466\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Área del ala (r = 0.984) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", - "Ecuación de regresión: y = 0.049x + 0.242\n", - "Valor del parámetro correlacionado para la aeronave: 2.329\n", - "Predicción obtenida: 0.356\n", - "\tR²: 0.6694211053194665, Desviación Estándar: 0.02075614850551406, Varianza: 0.0004308177007829536, Incertidumbre: 0.0036131816785082483\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.744) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.755, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", - "Ecuación de regresión: y = -0.039x + 0.84\n", - "Valor del parámetro correlacionado para la aeronave: 13.669\n", - "Predicción obtenida: 0.313\n", - "\tR²: 0.7143961990441017, Desviación Estándar: 0.019292619508205277, Varianza: 0.00037220516748838283, Incertidumbre: 0.0033584139812335425\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 2.905, 1.562, 2.3]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", - "Ecuación de regresión: y = 0.029x + 0.248\n", - "Valor del parámetro correlacionado para la aeronave: 4.712\n", - "Predicción obtenida: 0.384\n", - "\tR²: 0.43033811917703635, Desviación Estándar: 0.027246943263226592, Varianza: 0.000742395917189489, Incertidumbre: 0.004743084015220378\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.858) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 100.0, 9.5, 18.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", - "Ecuación de regresión: y = 0.001x + 0.273\n", - "Valor del parámetro correlacionado para la aeronave: 91.0\n", - "Predicción obtenida: 0.358\n", - "\tR²: 0.5893624930927692, Desviación Estándar: 0.023361993268515235, Varianza: 0.0005457827294781511, Incertidumbre: 0.003948897601964923\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = -0.755) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 25.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652, 506.641]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", - "Ecuación de regresión: y = -0.0x + 0.325\n", - "Valor del parámetro correlacionado para la aeronave: 504.283\n", - "Predicción obtenida: 0.312\n", - "\tR²: 0.11957831865127477, Desviación Estándar: 0.03420786358834695, Varianza: 0.0011701779312789532, Incertidumbre: 0.005782184291372825\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: envergadura (r = 0.885) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", - "Ecuación de regresión: y = 0.022x + 0.224\n", - "Valor del parámetro correlacionado para la aeronave: 6.0\n", - "Predicción obtenida: 0.356\n", - "\tR²: 0.4948146918700289, Desviación Estándar: 0.025658694953824956, Varianza: 0.0006583686267334423, Incertidumbre: 0.004466605472444141\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: payload (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", - "Ecuación de regresión: y = 0.003x + 0.28\n", - "Valor del parámetro correlacionado para la aeronave: 7.0\n", - "Predicción obtenida: 0.304\n", - "\tR²: 0.6805607454161662, Desviación Estándar: 0.017587857145488283, Varianza: 0.00030933271897010323, Incertidumbre: 0.0031091232635287597\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Crucero KIAS (r = 0.846) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Fulmar X', 'Mantis', 'ScanEagle', 'Integrator', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition']\n", - "Valores para Crucero KIAS: [15.433, 16.093, 25.0, 27.8, 16.7, 28.0, 28.3, 23.5, 30.9, 16.54, 16.0, 18.0, 20.0, 20.0, 25.0, 25.0, 25.0, 33.0, 24.0, 30.0, 33.0, 28.0, 35.0, 30.0, 20.0, 20.0]\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.319, 0.271, 0.298, 0.338, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291]\n", - "Ecuación de regresión: y = 0.004x + 0.218\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 0.309\n", - "\tR²: 0.5784019179791874, Desviación Estándar: 0.018050324796678542, Varianza: 0.0003258142252655882, Incertidumbre: 0.0035399599371135236\n", - "\tNivel de confianza: Confianza Alta\n", - "\n", - "--- Correlación: Empty weight (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition']\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8]\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291]\n", - "Ecuación de regresión: y = 0.002x + 0.267\n", - "Valor del parámetro correlacionado para la aeronave: 31.0\n", - "Predicción obtenida: 0.335\n", - "\tR²: 0.3279695994564351, Desviación Estándar: 0.02994220141823091, Varianza: 0.0008965354257699091, Incertidumbre: 0.0077310431627730936\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Altitud a la que se realiza el crucero: 0.309', 'Velocidad a la que se realiza el crucero (KTAS): 0.313', 'Área del ala: 0.356', 'Relación de aspecto del ala: 0.313', 'Longitud del fuselaje: 0.384', 'Peso máximo al despegue (MTOW): 0.358', 'Alcance de la aeronave: 0.312', 'envergadura: 0.356', 'payload: 0.304', 'Crucero KIAS: 0.309', 'Empty weight: 0.335']\n", - "**Mediana calculada:** 0.313\n", - "\n", - "=== Imputación para el parámetro: **payload** ===\n", - "\n", - "--- Imputación para aeronave: **AAI Aerosonde** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 10.406x + -4.881\n", - "Valor del parámetro correlacionado para la aeronave: 0.57\n", - "Predicción obtenida: 1.05\n", - "\tR²: 0.7012035885121172, Desviación Estándar: 4.05696673307922, Varianza: 16.458979073311475, Incertidumbre: 0.7171771720021375\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = -7.839x + 117.46\n", - "Valor del parámetro correlacionado para la aeronave: 14.754\n", - "Predicción obtenida: 1.804\n", - "\tR²: 0.7152857656186882, Desviación Estándar: 3.964681680105561, Varianza: 15.718700824564651, Incertidumbre: 0.6901624934832739\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.875) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 55.0, 32.0, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.204x + 1.036\n", - "Valor del parámetro correlacionado para la aeronave: 13.1\n", - "Predicción obtenida: 3.714\n", - "\tR²: 0.7186101745846443, Desviación Estándar: 3.7341950529878645, Varianza: 13.944212693759042, Incertidumbre: 0.6706812303187741\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652, 506.641, 504.283]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.036x + -6.191\n", - "Valor del parámetro correlacionado para la aeronave: 3270.0\n", - "Predicción obtenida: 111.989\n", - "\tR²: 0.4516735209888093, Desviación Estándar: 5.50203492994826, Varianza: 30.272388370370752, Incertidumbre: 0.9577813435917653\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.275, 30.0, 35.098, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.877x + -20.776\n", - "Valor del parámetro correlacionado para la aeronave: 30.846\n", - "Predicción obtenida: 6.288\n", - "\tR²: 0.5505400445815896, Desviación Estándar: 5.098102304668767, Varianza: 25.99064710886899, Incertidumbre: 0.9307818775787194\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.734) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 5.2, 4.4, 3.1, 4.8, 5.033, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 4.794x + -9.245\n", - "Valor del parámetro correlacionado para la aeronave: 2.9\n", - "Predicción obtenida: 4.659\n", - "\tR²: 0.5558518681245512, Desviación Estándar: 5.025362155962813, Varianza: 25.25426479858321, Incertidumbre: 0.9025816878156258\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291, 0.313]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 199.636x + -52.626\n", - "Valor del parámetro correlacionado para la aeronave: 0.197\n", - "Predicción obtenida: -13.387\n", - "\tR²: 0.6780488774864877, Desviación Estándar: 4.21598158894776, Varianza: 17.77450075834648, Incertidumbre: 0.7339081925564045\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Empty weight (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Empty weight: [10.886, 17.463, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Valores para payload: [2.495, 2.495, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.388x + 1.17\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 5.053\n", - "\tR²: 0.6045881019855628, Desviación Estándar: 3.3770579595870056, Varianza: 11.404520462409948, Incertidumbre: 0.8719526157771782\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 1.05', 'Relación de aspecto del ala: 1.804', 'Peso máximo al despegue (MTOW): 3.714', 'Alcance de la aeronave: 111.989', 'Velocidad máxima (KIAS): 6.288', 'envergadura: 4.659', 'Cuerda: -13.387', 'Empty weight: 5.053']\n", - "**Mediana calculada:** 4.186\n", - "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 10.186x + -4.474\n", - "Valor del parámetro correlacionado para la aeronave: 0.94\n", - "Predicción obtenida: 5.101\n", - "\tR²: 0.702030027451888, Desviación Estándar: 4.0288222779643945, Varianza: 16.231408947422214, Incertidumbre: 0.7013279384101367\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = -7.725x + 115.975\n", - "Valor del parámetro correlacionado para la aeronave: 13.218\n", - "Predicción obtenida: 13.864\n", - "\tR²: 0.7182610183328181, Desviación Estándar: 3.9255462621148403, Varianza: 15.409913456003792, Incertidumbre: 0.6732256298641592\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.875) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 55.0, 32.0, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.204x + 1.071\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 5.151\n", - "\tR²: 0.7237880605522922, Desviación Estándar: 3.67627307760161, Varianza: 13.514983741098414, Incertidumbre: 0.6498794056664093\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652, 506.641, 504.283]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.001x + 9.864\n", - "Valor del parámetro correlacionado para la aeronave: 800.0\n", - "Predicción obtenida: 10.419\n", - "\tR²: 0.002146729462209951, Desviación Estándar: 7.387712262058516, Varianza: 54.578292466969756, Incertidumbre: 1.266982200382999\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 30.846, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.275, 30.0, 35.098, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.885x + -21.108\n", - "Valor del parámetro correlacionado para la aeronave: 41.7\n", - "Predicción obtenida: 15.791\n", - "\tR²: 0.5575443463725547, Desviación Estándar: 5.028702836489781, Varianza: 25.287852217720367, Incertidumbre: 0.9031816917506468\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.734) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 5.2, 4.4, 3.1, 4.8, 5.033, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 4.807x + -9.308\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 5.112\n", - "\tR²: 0.5643721514307216, Desviación Estándar: 4.946883176427056, Varianza: 24.471653161217034, Incertidumbre: 0.8744936599473048\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291, 0.313]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 154.23x + -37.928\n", - "Valor del parámetro correlacionado para la aeronave: 0.319\n", - "Predicción obtenida: 11.271\n", - "\tR²: 0.573081677467914, Desviación Estándar: 4.832245905238442, Varianza: 23.350600488693686, Incertidumbre: 0.8287233358090745\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 5.101', 'Relación de aspecto del ala: 13.864', 'Peso máximo al despegue (MTOW): 5.151', 'Alcance de la aeronave: 10.419', 'Velocidad máxima (KIAS): 15.791', 'envergadura: 5.112', 'Cuerda: 11.271']\n", - "**Mediana calculada:** 10.419\n", - "\n", - "--- Imputación para aeronave: **Mantis** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.899) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 1.063, 1.872, 2.09, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 9.986x + -4.035\n", - "Valor del parámetro correlacionado para la aeronave: 0.754\n", - "Predicción obtenida: 3.494\n", - "\tR²: 0.687066646727224, Desviación Estándar: 4.067760372560328, Varianza: 16.546674448572137, Incertidumbre: 0.6976151485928609\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Relación de aspecto del ala (r = -0.888) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Relación de aspecto del ala: [15.301, 15.326, 12.5, 12.5, 12.5, 12.5, 14.754, 13.218, 13.443, 13.934, 14.057, 12.908, 12.648, 12.84, 13.765, 12.914, 14.589, 14.714, 14.568, 14.421, 14.182, 13.898, 14.042, 13.645, 14.103, 14.001, 13.71, 13.672, 12.695, 13.032, 12.856, 13.099, 14.349, 14.223, 13.669]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = -7.656x + 114.927\n", - "Valor del parámetro correlacionado para la aeronave: 14.755\n", - "Predicción obtenida: 1.967\n", - "\tR²: 0.712125594047297, Desviación Estándar: 3.9109945361870477, Varianza: 15.29587826208494, Incertidumbre: 0.6610787344956929\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.875) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 26.5, 74.8, 75.0, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.2x + 1.393\n", - "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 2.693\n", - "\tR²: 0.7070179545639659, Desviación Estándar: 3.729320581026571, Varianza: 13.90783199606836, Incertidumbre: 0.6491913850524466\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Alcance de la aeronave (r = 0.804) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Alcance de la aeronave: [370.0, 433.0, 518.922, 481.428, 535.276, 599.358, 3270.0, 800.0, 509.556, 50.0, 503.516, 557.94, 646.084, 500.0, 537.895, 565.912, 270.0, 100.0, 373.727, 385.208, 412.686, 456.221, 413.556, 565.637, 407.828, 425.273, 458.124, 300.0, 530.401, 800.0, 528.174, 503.585, 420.652, 506.641, 504.283]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.001x + 9.864\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 9.882\n", - "\tR²: 0.002163345486388857, Desviación Estándar: 7.281408701826373, Varianza: 53.01891268303283, Incertidumbre: 1.2307827089531607\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad máxima (KIAS) (r = 0.715) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad máxima (KIAS): [20.0, 25.034, 33.439, 33.439, 33.439, 42.955, 30.846, 41.7, 36.0, 36.0, 41.2, 46.3, 40.216, 41.2, 46.3, 29.925, 29.009, 33.0, 33.0, 33.0, 33.0, 33.0, 33.275, 30.0, 35.098, 42.0, 42.0, 38.0, 50.0, 30.0, 30.0, 35.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 0.859x + -20.372\n", - "Valor del parámetro correlacionado para la aeronave: 25.6\n", - "Predicción obtenida: 1.629\n", - "\tR²: 0.5422563989605891, Desviación Estándar: 5.034375894779248, Varianza: 25.34494064993435, Incertidumbre: 0.8899603335601247\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.734) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 3.1, 4.8, 5.033, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 5.0, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 4.69x + -8.682\n", - "Valor del parámetro correlacionado para la aeronave: 2.1\n", - "Predicción obtenida: 1.167\n", - "\tR²: 0.5495595055917364, Desviación Estándar: 4.953710222200966, Varianza: 24.539244965538344, Incertidumbre: 0.8623302637645233\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.776) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.335, 0.287, 0.291, 0.313]\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 25.0, 0.6, 1.5, 7.0]\n", - "Ecuación de regresión: y = 154.107x + -37.914\n", - "Valor del parámetro correlacionado para la aeronave: 0.271\n", - "Predicción obtenida: 3.849\n", - "\tR²: 0.5727096856576819, Desviación Estándar: 4.764827757793928, Varianza: 22.703583561443512, Incertidumbre: 0.8054028905096152\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 3.494', 'Relación de aspecto del ala: 1.967', 'Peso máximo al despegue (MTOW): 2.693', 'Alcance de la aeronave: 9.882', 'Velocidad máxima (KIAS): 1.629', 'envergadura: 1.167', 'Cuerda: 3.849']\n", - "**Mediana calculada:** 2.693\n", - "\n", - "=== Imputación para el parámetro: **Empty weight** ===\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.7 Fixed Wing** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.994x + -5.988\n", - "Valor del parámetro correlacionado para la aeronave: 1.55\n", - "Predicción obtenida: 17.253\n", - "\tR²: 0.8989608005826268, Desviación Estándar: 3.3152777023765134, Varianza: 10.991066243874894, Incertidumbre: 0.8288194255941284\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.792x + -6.58\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 19.796\n", - "\tR²: 0.77484990052306, Desviación Estándar: 4.948926867225632, Varianza: 24.491877137147704, Incertidumbre: 1.237231716806408\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.325x + 1.819\n", - "Valor del parámetro correlacionado para la aeronave: 42.2\n", - "Predicción obtenida: 15.517\n", - "\tR²: 0.8966456395362715, Desviación Estándar: 3.3530448803440964, Varianza: 11.242909969601754, Incertidumbre: 0.8382612200860241\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.55x + -17.327\n", - "Valor del parámetro correlacionado para la aeronave: 4.4\n", - "Predicción obtenida: 20.292\n", - "\tR²: 0.854653857560669, Desviación Estándar: 3.9762778085330774, Varianza: 15.810785210632615, Incertidumbre: 0.9940694521332694\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 166.311x + -36.293\n", - "Valor del parámetro correlacionado para la aeronave: 0.352\n", - "Predicción obtenida: 22.248\n", - "\tR²: 0.32488694567254894, Desviación Estándar: 8.569652613963415, Varianza: 73.43894592400999, Incertidumbre: 2.1424131534908537\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 4.186, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.555x + 3.179\n", - "Valor del parámetro correlacionado para la aeronave: 14.5\n", - "Predicción obtenida: 25.733\n", - "\tR²: 0.6059393093606185, Desviación Estándar: 6.547213776970333, Varianza: 42.86600824135013, Incertidumbre: 1.6368034442425832\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 150.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 10.0, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.074x + 2.821\n", - "Valor del parámetro correlacionado para la aeronave: 140.0\n", - "Predicción obtenida: 13.161\n", - "\tR²: 0.6929626692113744, Desviación Estándar: 2.790886287308021, Varianza: 7.78904626868395, Incertidumbre: 1.1393745556725372\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 17.253', 'Longitud del fuselaje: 19.796', 'Peso máximo al despegue (MTOW): 15.517', 'envergadura: 20.292', 'Cuerda: 22.248', 'payload: 25.733', 'Rango de comunicación: 13.161']\n", - "**Mediana calculada:** 19.796\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.7 VTOL** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.104x + -5.974\n", - "Valor del parámetro correlacionado para la aeronave: 1.55\n", - "Predicción obtenida: 17.436\n", - "\tR²: 0.8985057224718374, Desviación Estándar: 3.270696585516228, Varianza: 10.697456154507513, Incertidumbre: 0.7932604406723784\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.792x + -6.58\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 19.796\n", - "\tR²: 0.7812972847516897, Desviación Estándar: 4.801164285068728, Varianza: 23.051178492219506, Incertidumbre: 1.1644533807812627\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = 0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 32.0, 35.0]\n", - "Ecuación de regresión: y = 113.452x + -9.6\n", - "Valor del parámetro correlacionado para la aeronave: 0.277\n", - "Predicción obtenida: 21.827\n", - "\tR²: 0.9023931014122297, Desviación Estándar: 2.838256825639311, Varianza: 8.055701808288138, Incertidumbre: 1.2693070399464534\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.327x + 1.985\n", - "Valor del parámetro correlacionado para la aeronave: 53.5\n", - "Predicción obtenida: 19.49\n", - "\tR²: 0.8900447434647047, Desviación Estándar: 3.404297276853743, Varianza: 11.58923994919381, Incertidumbre: 0.8256633678512089\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.53x + -17.284\n", - "Valor del parámetro correlacionado para la aeronave: 4.4\n", - "Predicción obtenida: 20.246\n", - "\tR²: 0.8586914485406882, Desviación Estándar: 3.859257009089604, Varianza: 14.893864662207235, Incertidumbre: 0.9360073108753961\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 160.56x + -34.733\n", - "Valor del parámetro correlacionado para la aeronave: 0.352\n", - "Predicción obtenida: 21.784\n", - "\tR²: 0.34149882368086004, Desviación Estándar: 8.331011939712234, Varianza: 69.4057599396278, Incertidumbre: 2.0205671879832594\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 4.186, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.461x + 3.44\n", - "Valor del parámetro correlacionado para la aeronave: 11.3\n", - "Predicción obtenida: 19.946\n", - "\tR²: 0.6012377913575799, Desviación Estándar: 6.483006778963267, Varianza: 42.029376896083676, Incertidumbre: 1.5723601012506396\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.996) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 6.8, 8.9, 16.5, 84.0]\n", - "Valores para Empty weight: [19.796, 4.8, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.356x + 2.621\n", - "Valor del parámetro correlacionado para la aeronave: 42.2\n", - "Predicción obtenida: 17.662\n", - "\tR²: 0.8796352899520189, Desviación Estándar: 3.766112128353929, Varianza: 14.18360056333456, Incertidumbre: 1.6842565459771597\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.09x + 2.413\n", - "Valor del parámetro correlacionado para la aeronave: 140.0\n", - "Predicción obtenida: 14.967\n", - "\tR²: 0.6992353116426389, Desviación Estándar: 3.354572744999882, Varianza: 11.253158301496043, Incertidumbre: 1.267909319734997\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 17.436', 'Longitud del fuselaje: 19.796', 'Ancho del fuselaje: 21.827', 'Peso máximo al despegue (MTOW): 19.49', 'envergadura: 20.246', 'Cuerda: 21.784', 'payload: 19.946', 'RTF (Including fuel & Batteries): 17.662', 'Rango de comunicación: 14.967']\n", - "**Mediana calculada:** 19.796\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 Fixed wing** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.104x + -5.974\n", - "Valor del parámetro correlacionado para la aeronave: 1.55\n", - "Predicción obtenida: 17.436\n", - "\tR²: 0.8985057224718374, Desviación Estándar: 3.270696585516228, Varianza: 10.697456154507513, Incertidumbre: 0.7932604406723784\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.792x + -6.58\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 19.796\n", - "\tR²: 0.7812972847516897, Desviación Estándar: 4.801164285068728, Varianza: 23.051178492219506, Incertidumbre: 1.1644533807812627\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = 0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.375, 0.375]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 32.0, 35.0]\n", - "Ecuación de regresión: y = 113.452x + -9.6\n", - "Valor del parámetro correlacionado para la aeronave: 0.277\n", - "Predicción obtenida: 21.827\n", - "\tR²: 0.9023931014122297, Desviación Estándar: 2.838256825639311, Varianza: 8.055701808288138, Incertidumbre: 1.2693070399464534\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.328x + 1.989\n", - "Valor del parámetro correlacionado para la aeronave: 54.4\n", - "Predicción obtenida: 19.809\n", - "\tR²: 0.8927270391262186, Desviación Estándar: 3.309108053534251, Varianza: 10.95019610996524, Incertidumbre: 0.7799642481110287\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.53x + -17.284\n", - "Valor del parámetro correlacionado para la aeronave: 4.4\n", - "Predicción obtenida: 20.246\n", - "\tR²: 0.8586914485406882, Desviación Estándar: 3.859257009089604, Varianza: 14.893864662207235, Incertidumbre: 0.9360073108753961\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 160.56x + -34.733\n", - "Valor del parámetro correlacionado para la aeronave: 0.352\n", - "Predicción obtenida: 21.784\n", - "\tR²: 0.34149882368086004, Desviación Estándar: 8.331011939712234, Varianza: 69.4057599396278, Incertidumbre: 2.0205671879832594\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 4.186, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.459x + 3.441\n", - "Valor del parámetro correlacionado para la aeronave: 17.7\n", - "Predicción obtenida: 29.272\n", - "\tR²: 0.6111249176575178, Desviación Estándar: 6.300439892571702, Varianza: 39.69554283990892, Incertidumbre: 1.4850279241652313\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.996) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 6.8, 8.9, 16.5, 84.0]\n", - "Valores para Empty weight: [19.796, 19.796, 4.8, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.362x + 2.802\n", - "Valor del parámetro correlacionado para la aeronave: 36.7\n", - "Predicción obtenida: 16.09\n", - "\tR²: 0.8814477024665971, Desviación Estándar: 3.525541028723161, Varianza: 12.429439545210364, Incertidumbre: 1.4392960979364398\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 10.0, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.09x + 2.413\n", - "Valor del parámetro correlacionado para la aeronave: 140.0\n", - "Predicción obtenida: 14.967\n", - "\tR²: 0.6992353116426389, Desviación Estándar: 3.354572744999882, Varianza: 11.253158301496043, Incertidumbre: 1.267909319734997\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 17.436', 'Longitud del fuselaje: 19.796', 'Ancho del fuselaje: 21.827', 'Peso máximo al despegue (MTOW): 19.809', 'envergadura: 20.246', 'Cuerda: 21.784', 'payload: 29.272', 'RTF (Including fuel & Batteries): 16.09', 'Rango de comunicación: 14.967']\n", - "**Mediana calculada:** 19.809\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 VTOL FTUAS** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.199x + -5.963\n", - "Valor del parámetro correlacionado para la aeronave: 2.503\n", - "Predicción obtenida: 32.08\n", - "\tR²: 0.8981760566509686, Desviación Estándar: 3.2241182006984137, Varianza: 10.394938172074777, Incertidumbre: 0.7599319476869396\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.793x + -6.581\n", - "Valor del parámetro correlacionado para la aeronave: 3.594\n", - "Predicción obtenida: 25.02\n", - "\tR²: 0.7867457415163526, Desviación Estándar: 4.665893862545674, Varianza: 21.770565536541387, Incertidumbre: 1.0997617301675797\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.328x + 1.989\n", - "Valor del parámetro correlacionado para la aeronave: 93.0\n", - "Predicción obtenida: 32.453\n", - "\tR²: 0.8950667903037404, Desviación Estándar: 3.220849267243446, Varianza: 10.373870002302642, Incertidumbre: 0.7389134983311162\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.513x + -17.25\n", - "Valor del parámetro correlacionado para la aeronave: 5.644\n", - "Predicción obtenida: 30.798\n", - "\tR²: 0.8621171121245332, Desviación Estándar: 3.751813006995635, Varianza: 14.076100839461628, Incertidumbre: 0.8843108063301686\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 156.666x + -33.677\n", - "Valor del parámetro correlacionado para la aeronave: 0.394\n", - "Predicción obtenida: 28.049\n", - "\tR²: 0.35611948927363, Desviación Estándar: 8.10753139727324, Varianza: 65.73206535777136, Incertidumbre: 1.9109634765649177\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 4.186, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.306x + 4.062\n", - "Valor del parámetro correlacionado para la aeronave: 22.7\n", - "Predicción obtenida: 33.704\n", - "\tR²: 0.5824993045960247, Desviación Estándar: 6.424547193133453, Varianza: 41.27480663679893, Incertidumbre: 1.4738922090987687\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.996) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 36.7, 6.8, 8.9, 16.5, 84.0]\n", - "Valores para Empty weight: [19.796, 19.796, 19.809, 4.8, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.366x + 3.198\n", - "Valor del parámetro correlacionado para la aeronave: 70.3\n", - "Predicción obtenida: 28.95\n", - "\tR²: 0.8686932854431193, Desviación Estándar: 3.5123478434354816, Varianza: 12.336587373285877, Incertidumbre: 1.3275427016691874\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 32.08', 'Longitud del fuselaje: 25.02', 'Peso máximo al despegue (MTOW): 32.453', 'envergadura: 30.798', 'Cuerda: 28.049', 'payload: 33.704', 'RTF (Including fuel & Batteries): 28.95']\n", - "**Mediana calculada:** 30.798\n", - "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.024x + -5.798\n", - "Valor del parámetro correlacionado para la aeronave: 0.94\n", - "Predicción obtenida: 8.325\n", - "\tR²: 0.9115417035933906, Desviación Estándar: 3.1489544926591027, Varianza: 9.915914396837946, Incertidumbre: 0.7224197058583904\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 9.152x + -7.113\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 3.87\n", - "\tR²: 0.8024007898945031, Desviación Estándar: 4.706409380156019, Varianza: 22.15028925362057, Incertidumbre: 1.0797243618437888\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.323x + 2.09\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 8.545\n", - "\tR²: 0.9077472362486133, Desviación Estándar: 3.1564995773900493, Varianza: 9.963489582063561, Incertidumbre: 0.7058147625993508\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.513x + -17.25\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 8.289\n", - "\tR²: 0.8810383235111052, Desviación Estándar: 3.6517466273004446, Varianza: 13.335253430000172, Incertidumbre: 0.8377681324114802\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 163.675x + -35.666\n", - "Valor del parámetro correlacionado para la aeronave: 0.319\n", - "Predicción obtenida: 16.546\n", - "\tR²: 0.44192713119646354, Desviación Estándar: 7.9093820433108375, Varianza: 62.558324307047926, Incertidumbre: 1.814536685928799\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.257x + 4.312\n", - "Valor del parámetro correlacionado para la aeronave: 10.419\n", - "Predicción obtenida: 17.406\n", - "\tR²: 0.6341935967623096, Desviación Estándar: 6.28552697067258, Varianza: 39.50784929905242, Incertidumbre: 1.4054865580832214\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 8.325', 'Longitud del fuselaje: 3.87', 'Peso máximo al despegue (MTOW): 8.545', 'envergadura: 8.289', 'Cuerda: 16.546', 'payload: 17.406']\n", - "**Mediana calculada:** 8.435\n", - "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.019x + -5.786\n", - "Valor del parámetro correlacionado para la aeronave: 1.608\n", - "Predicción obtenida: 18.365\n", - "\tR²: 0.9128101315191424, Desviación Estándar: 3.069313976161959, Varianza: 9.420688284263136, Incertidumbre: 0.6863194694988309\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.927x + -6.373\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 4.34\n", - "\tR²: 0.7965950223618128, Desviación Estándar: 4.688016094166544, Varianza: 21.977494899164537, Incertidumbre: 1.0482722666169448\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.323x + 2.081\n", - "Valor del parámetro correlacionado para la aeronave: 55.0\n", - "Predicción obtenida: 19.838\n", - "\tR²: 0.909185787337549, Desviación Estándar: 3.080516387452075, Varianza: 9.489581213360784, Incertidumbre: 0.6722237869071928\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.51x + -17.23\n", - "Valor del parámetro correlacionado para la aeronave: 5.2\n", - "Predicción obtenida: 27.02\n", - "\tR²: 0.882742078622475, Desviación Estándar: 3.5594213516060673, Varianza: 12.669480358269164, Incertidumbre: 0.7959108102755347\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 160.493x + -35.101\n", - "Valor del parámetro correlacionado para la aeronave: 0.332\n", - "Predicción obtenida: 18.183\n", - "\tR²: 0.42120352079680623, Desviación Estándar: 7.908083494559376, Varianza: 62.53778455692244, Incertidumbre: 1.7683012265578852\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.234x + 4.074\n", - "Valor del parámetro correlacionado para la aeronave: 12.0\n", - "Predicción obtenida: 18.877\n", - "\tR²: 0.6052009839355558, Desviación Estándar: 6.422951508721367, Varianza: 41.25430608338608, Incertidumbre: 1.401602927321261\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'V21', 'V25', 'V32']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.099x + 2.179\n", - "Valor del parámetro correlacionado para la aeronave: 150.0\n", - "Predicción obtenida: 16.991\n", - "\tR²: 0.7177783310839329, Desviación Estándar: 3.4856268169459126, Varianza: 12.149594307012496, Incertidumbre: 1.2323551794740677\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 18.365', 'Longitud del fuselaje: 4.34', 'Peso máximo al despegue (MTOW): 19.838', 'envergadura: 27.02', 'Cuerda: 18.183', 'payload: 18.877', 'Rango de comunicación: 16.991']\n", - "**Mediana calculada:** 18.365\n", - "\n", - "--- Imputación para aeronave: **Orbiter 3** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.019x + -5.786\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 12.237\n", - "\tR²: 0.9135790496566742, Desviación Estándar: 2.995343734099099, Varianza: 8.972084085406735, Incertidumbre: 0.6536375901867048\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.298x + -4.311\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 5.647\n", - "\tR²: 0.7165705648434354, Desviación Estándar: 5.424498885276192, Varianza: 29.42518815636265, Incertidumbre: 1.183722702332463\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.322x + 2.063\n", - "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 12.354\n", - "\tR²: 0.9089579741392599, Desviación Estándar: 3.0250631475449667, Varianza: 9.15100704663466, Incertidumbre: 0.6449456300775446\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.064x + -15.982\n", - "Valor del parámetro correlacionado para la aeronave: 4.4\n", - "Predicción obtenida: 19.501\n", - "\tR²: 0.8536528012452334, Desviación Estándar: 3.8978879953283716, Varianza: 15.193530824125032, Incertidumbre: 0.850588894716759\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 160.622x + -35.131\n", - "Valor del parámetro correlacionado para la aeronave: 0.304\n", - "Predicción obtenida: 13.698\n", - "\tR²: 0.4262936940175942, Desviación Estándar: 7.717594578111506, Varianza: 59.56126607209612, Incertidumbre: 1.6841172065322267\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.232x + 4.068\n", - "Valor del parámetro correlacionado para la aeronave: 5.5\n", - "Predicción obtenida: 10.841\n", - "\tR²: 0.6081114488646513, Desviación Estándar: 6.2761711228549695, Varianza: 39.39032396335861, Incertidumbre: 1.3380841793630753\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'V21', 'V25', 'V32']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.101x + 2.086\n", - "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 7.148\n", - "\tR²: 0.7460116085943318, Desviación Estándar: 3.3116063271343945, Varianza: 10.966736465916554, Incertidumbre: 1.103868775711465\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 12.237', 'Longitud del fuselaje: 5.647', 'Peso máximo al despegue (MTOW): 12.354', 'envergadura: 19.501', 'Cuerda: 13.698', 'payload: 10.841', 'Rango de comunicación: 7.148']\n", - "**Mediana calculada:** 12.237\n", - "\n", - "--- Imputación para aeronave: **Mantis** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.019x + -5.786\n", - "Valor del parámetro correlacionado para la aeronave: 0.754\n", - "Predicción obtenida: 5.538\n", - "\tR²: 0.913708191825763, Desviación Estándar: 2.9264760519606896, Varianza: 8.564262082699425, Incertidumbre: 0.6239267906755577\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.028x + -3.425\n", - "Valor del parámetro correlacionado para la aeronave: 1.48\n", - "Predicción obtenida: 8.456\n", - "\tR²: 0.6987989734892859, Desviación Estándar: 5.467495240618404, Varianza: 29.893504206184897, Incertidumbre: 1.1656739019707343\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.322x + 2.056\n", - "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 4.147\n", - "\tR²: 0.9091268370272325, Desviación Estándar: 2.9586655428005026, Varianza: 8.753701794154992, Incertidumbre: 0.6169244120479039\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.904x + -15.71\n", - "Valor del parámetro correlacionado para la aeronave: 2.1\n", - "Predicción obtenida: 0.889\n", - "\tR²: 0.8311430591942646, Desviación Estándar: 4.0937342671353925, Varianza: 16.758660249918545, Incertidumbre: 0.8727870783227756\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 160.716x + -35.226\n", - "Valor del parámetro correlacionado para la aeronave: 0.271\n", - "Predicción obtenida: 8.327\n", - "\tR²: 0.42621839297901487, Desviación Estándar: 7.54629017734252, Varianza: 56.94649544065621, Incertidumbre: 1.6088744716367318\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.227x + 4.163\n", - "Valor del parámetro correlacionado para la aeronave: 2.693\n", - "Predicción obtenida: 7.469\n", - "\tR²: 0.6080299406703621, Desviación Estándar: 6.144753971108537, Varianza: 37.758001365454135, Incertidumbre: 1.28126977381058\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'V21', 'V25', 'V32']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.094x + 3.287\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 5.633\n", - "\tR²: 0.6902774142105856, Desviación Estándar: 3.4693600600060193, Varianza: 12.03645922596497, Incertidumbre: 1.0971079812837463\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 5.538', 'Longitud del fuselaje: 8.456', 'Peso máximo al despegue (MTOW): 4.147', 'envergadura: 0.889', 'Cuerda: 8.327', 'payload: 7.469', 'Rango de comunicación: 5.633']\n", - "**Mediana calculada:** 5.633\n", - "\n", - "--- Imputación para aeronave: **ScanEagle** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.014x + -5.775\n", - "Valor del parámetro correlacionado para la aeronave: 1.063\n", - "Predicción obtenida: 10.185\n", - "\tR²: 0.916273259509183, Desviación Estándar: 2.8622130745125545, Varianza: 8.19226368391061, Incertidumbre: 0.5968126821384838\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.105x + -3.713\n", - "Valor del parámetro correlacionado para la aeronave: 1.71\n", - "Predicción obtenida: 10.147\n", - "\tR²: 0.7044409378663876, Desviación Estándar: 5.377643509962671, Varianza: 28.919049720243635, Incertidumbre: 1.1213161854876974\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.319x + 2.199\n", - "Valor del parámetro correlacionado para la aeronave: 26.5\n", - "Predicción obtenida: 10.664\n", - "\tR²: 0.9110745074819713, Desviación Estándar: 2.910850755492127, Varianza: 8.473052120749086, Incertidumbre: 0.5941749223625525\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.667x + -14.628\n", - "Valor del parámetro correlacionado para la aeronave: 3.1\n", - "Predicción obtenida: 9.139\n", - "\tR²: 0.827398243173229, Desviación Estándar: 4.1095330263226915, Varianza: 16.888261694436938, Incertidumbre: 0.8568968710318733\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 163.16x + -36.089\n", - "Valor del parámetro correlacionado para la aeronave: 0.298\n", - "Predicción obtenida: 12.533\n", - "\tR²: 0.44031188669232135, Desviación Estándar: 7.400190688413107, Varianza: 54.76282222487606, Incertidumbre: 1.54304642530508\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.238x + 4.003\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 10.192\n", - "\tR²: 0.618870296719052, Desviación Estándar: 6.026195235106751, Varianza: 36.31502901162332, Incertidumbre: 1.2300919513669042\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'V21', 'V25', 'V32']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.094x + 3.287\n", - "Valor del parámetro correlacionado para la aeronave: 101.86\n", - "Predicción obtenida: 12.848\n", - "\tR²: 0.7179611867018045, Desviación Estándar: 3.307905028059083, Varianza: 10.942235674658562, Incertidumbre: 0.9973708927456391\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 10.185', 'Longitud del fuselaje: 10.147', 'Peso máximo al despegue (MTOW): 10.664', 'envergadura: 9.139', 'Cuerda: 12.533', 'payload: 10.192', 'Rango de comunicación: 12.848']\n", - "**Mediana calculada:** 10.192\n", - "\n", - "--- Imputación para aeronave: **Integrator** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.013x + -5.774\n", - "Valor del parámetro correlacionado para la aeronave: 1.872\n", - "Predicción obtenida: 22.331\n", - "\tR²: 0.9166944834584114, Desviación Estándar: 2.801949594727719, Varianza: 7.850921531394829, Incertidumbre: 0.5719455660067526\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.104x + -3.71\n", - "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 16.551\n", - "\tR²: 0.7059270986898859, Desviación Estándar: 5.264424973725865, Varianza: 27.714170303988578, Incertidumbre: 1.0745962478994258\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.32x + 2.172\n", - "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 26.082\n", - "\tR²: 0.9114909858720777, Desviación Estándar: 2.8535287759093366, Varianza: 8.142626474942638, Incertidumbre: 0.5707057551818673\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.648x + -14.516\n", - "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 22.195\n", - "\tR²: 0.8278014537455741, Desviación Estándar: 4.028451857224602, Varianza: 16.22842436597635, Incertidumbre: 0.8223042919639589\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 163.568x + -36.31\n", - "Valor del parámetro correlacionado para la aeronave: 0.338\n", - "Predicción obtenida: 18.976\n", - "\tR²: 0.4408083829979076, Desviación Estándar: 7.259450756578375, Varianza: 52.69962528718634, Incertidumbre: 1.4818291805398596\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.238x + 4.003\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 26.284\n", - "\tR²: 0.6210511278262185, Desviación Estándar: 5.904441366561173, Varianza: 34.86242785115877, Incertidumbre: 1.1808882733122346\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'V21', 'V25', 'V32']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 10.192, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.093x + 3.153\n", - "Valor del parámetro correlacionado para la aeronave: 92.6\n", - "Predicción obtenida: 11.754\n", - "\tR²: 0.7040073224712778, Desviación Estándar: 3.250633447126605, Varianza: 10.566617807578197, Incertidumbre: 0.9383770478676734\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 22.331', 'Longitud del fuselaje: 16.551', 'Peso máximo al despegue (MTOW): 26.082', 'envergadura: 22.195', 'Cuerda: 18.976', 'payload: 26.284', 'Rango de comunicación: 11.754']\n", - "**Mediana calculada:** 22.195\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 15.005x + -5.769\n", - "Valor del parámetro correlacionado para la aeronave: 2.09\n", - "Predicción obtenida: 25.592\n", - "\tR²: 0.9192804737551298, Desviación Estándar: 2.745463286837539, Varianza: 7.537568659372784, Incertidumbre: 0.5490926573675078\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.188x + -3.662\n", - "Valor del parámetro correlacionado para la aeronave: 2.998\n", - "Predicción obtenida: 20.885\n", - "\tR²: 0.7020575415560586, Desviación Estándar: 5.274636514397158, Varianza: 27.821790359011803, Incertidumbre: 1.0549273028794315\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.313x + 2.27\n", - "Valor del parámetro correlacionado para la aeronave: 75.0\n", - "Predicción obtenida: 25.751\n", - "\tR²: 0.9083231176697067, Desviación Estándar: 2.889927154659528, Varianza: 8.351678959238516, Incertidumbre: 0.5667613444027156\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.648x + -14.516\n", - "Valor del parámetro correlacionado para la aeronave: 5.033\n", - "Predicción obtenida: 23.977\n", - "\tR²: 0.833162024179038, Desviación Estándar: 3.947060601866186, Varianza: 15.579287394804256, Incertidumbre: 0.7894121203732372\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 166.269x + -37.008\n", - "Valor del parámetro correlacionado para la aeronave: 0.341\n", - "Predicción obtenida: 19.69\n", - "\tR²: 0.45407381641801514, Desviación Estándar: 7.1399192582523305, Varianza: 50.97844701436251, Incertidumbre: 1.4279838516504662\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.199x + 4.165\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 25.75\n", - "\tR²: 0.6258944719318421, Desviación Estándar: 5.837864906102878, Varianza: 34.08066666190756, Incertidumbre: 1.144899502843734\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 25.592', 'Longitud del fuselaje: 20.885', 'Peso máximo al despegue (MTOW): 25.751', 'envergadura: 23.977', 'Cuerda: 19.69', 'payload: 25.75']\n", - "**Mediana calculada:** 24.784\n", - "\n", - "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.945x + -5.72\n", - "Valor del parámetro correlacionado para la aeronave: 1.872\n", - "Predicción obtenida: 22.258\n", - "\tR²: 0.9228323594939006, Desviación Estándar: 2.6963684551592366, Varianza: 7.2704028459778085, Incertidumbre: 0.5288013603343454\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.316x + -3.791\n", - "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 17.0\n", - "\tR²: 0.7102604918578905, Desviación Estándar: 5.224749658509504, Varianza: 27.298008994095174, Incertidumbre: 1.024657710091431\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.312x + 2.292\n", - "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 25.599\n", - "\tR²: 0.9120674450382346, Desviación Estándar: 2.8414417914279144, Varianza: 8.073791454073076, Incertidumbre: 0.5468357277225196\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.677x + -14.595\n", - "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 22.256\n", - "\tR²: 0.8407587886499855, Desviación Estándar: 3.873373378087582, Varianza: 15.003021326077608, Incertidumbre: 0.7596310168576687\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 170.626x + -38.15\n", - "Valor del parámetro correlacionado para la aeronave: 0.345\n", - "Predicción obtenida: 20.716\n", - "\tR²: 0.4698496594948569, Desviación Estándar: 7.067424038440677, Varianza: 49.94848253912913, Incertidumbre: 1.3860358878016823\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.191x + 4.199\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 25.639\n", - "\tR²: 0.6422377954896081, Desviación Estándar: 5.73140563845551, Varianza: 32.84901059251961, Incertidumbre: 1.1030095293990758\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 22.258', 'Longitud del fuselaje: 17.0', 'Peso máximo al despegue (MTOW): 25.599', 'envergadura: 22.256', 'Cuerda: 20.716', 'payload: 25.639']\n", - "**Mediana calculada:** 22.257\n", - "\n", - "--- Imputación para aeronave: **ScanEagle 3** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.945x + -5.72\n", - "Valor del parámetro correlacionado para la aeronave: 1.349\n", - "Predicción obtenida: 14.441\n", - "\tR²: 0.9247233040619992, Desviación Estándar: 2.645964603028454, Varianza: 7.001128680479524, Incertidumbre: 0.5092161252748997\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.382x + -3.74\n", - "Valor del parámetro correlacionado para la aeronave: 2.4\n", - "Predicción obtenida: 16.377\n", - "\tR²: 0.7068047947675931, Desviación Estándar: 5.221943728574231, Varianza: 27.268696304395736, Incertidumbre: 1.00496353912847\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.307x + 2.362\n", - "Valor del parámetro correlacionado para la aeronave: 36.3\n", - "Predicción obtenida: 13.503\n", - "\tR²: 0.9100626005198136, Desviación Estándar: 2.854969820151899, Varianza: 8.150852673978166, Incertidumbre: 0.5395385817654803\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.677x + -14.596\n", - "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 16.114\n", - "\tR²: 0.8446608947110245, Desviación Estándar: 3.800967492809924, Varianza: 14.447353881397758, Incertidumbre: 0.7314965350516088\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 171.998x + -38.517\n", - "Valor del parámetro correlacionado para la aeronave: 0.312\n", - "Predicción obtenida: 15.147\n", - "\tR²: 0.4819605292975089, Desviación Estándar: 6.941209958097174, Varianza: 48.18039568238737, Incertidumbre: 1.3358364792697046\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.166x + 4.305\n", - "Valor del parámetro correlacionado para la aeronave: 8.6\n", - "Predicción obtenida: 14.33\n", - "\tR²: 0.6464449368019647, Desviación Estándar: 5.6605664016485475, Varianza: 32.042011987472385, Incertidumbre: 1.0697464984664153\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 14.441', 'Longitud del fuselaje: 16.377', 'Peso máximo al despegue (MTOW): 13.503', 'envergadura: 16.114', 'Cuerda: 15.147', 'payload: 14.33']\n", - "**Mediana calculada:** 14.794\n", - "\n", - "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.945x + -5.707\n", - "Valor del parámetro correlacionado para la aeronave: 1.802\n", - "Predicción obtenida: 21.224\n", - "\tR²: 0.9246771071689462, Desviación Estándar: 2.599110307951027, Varianza: 6.7553743928972825, Incertidumbre: 0.49118567891878007\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.369x + -3.768\n", - "Valor del parámetro correlacionado para la aeronave: 2.5\n", - "Predicción obtenida: 17.155\n", - "\tR²: 0.7058504559348706, Desviación Estándar: 5.136239966459277, Varianza: 26.380960993053595, Incertidumbre: 0.9706581160858557\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.307x + 2.415\n", - "Valor del parámetro correlacionado para la aeronave: 61.0\n", - "Predicción obtenida: 21.123\n", - "\tR²: 0.9094286866168957, Desviación Estándar: 2.8151847172798137, Varianza: 7.925264992405825, Incertidumbre: 0.5227666781061432\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.67x + -14.615\n", - "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 22.202\n", - "\tR²: 0.8439953830988041, Desviación Estándar: 3.740502434777401, Varianza: 13.991358464575665, Incertidumbre: 0.7068885157751859\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 171.97x + -38.52\n", - "Valor del parámetro correlacionado para la aeronave: 0.341\n", - "Predicción obtenida: 20.121\n", - "\tR²: 0.481923663962755, Desviación Estándar: 6.816447496640321, Varianza: 46.46395647445409, Incertidumbre: 1.2881874929313626\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.166x + 4.322\n", - "Valor del parámetro correlacionado para la aeronave: 17.7\n", - "Predicción obtenida: 24.954\n", - "\tR²: 0.6463635003419814, Desviación Estándar: 5.562757206524997, Varianza: 30.944267738745786, Incertidumbre: 1.0329780806624915\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'V32']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 10.192, 22.195, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.094x + 3.865\n", - "Valor del parámetro correlacionado para la aeronave: 92.6\n", - "Predicción obtenida: 12.561\n", - "\tR²: 0.5755594637290282, Desviación Estándar: 4.182273841525787, Varianza: 17.491414485510862, Incertidumbre: 1.1599540602809986\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 21.224', 'Longitud del fuselaje: 17.155', 'Peso máximo al despegue (MTOW): 21.123', 'envergadura: 22.202', 'Cuerda: 20.121', 'payload: 24.954', 'Rango de comunicación: 12.561']\n", - "**Mediana calculada:** 21.123\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.941x + -5.705\n", - "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 4.754\n", - "\tR²: 0.9258976032052461, Desviación Estándar: 2.5539701136688935, Varianza: 6.522763341513901, Incertidumbre: 0.4742603439518288\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.416x + -3.734\n", - "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 3.84\n", - "\tR²: 0.704696721359465, Desviación Estándar: 5.098398969474347, Varianza: 25.993672051937075, Incertidumbre: 0.9467489207980716\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.307x + 2.415\n", - "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 4.317\n", - "\tR²: 0.9108095208047919, Desviación Estándar: 2.7678673189624066, Varianza: 7.661089495380141, Incertidumbre: 0.5053411222590188\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.642x + -14.545\n", - "Valor del parámetro correlacionado para la aeronave: 2.35\n", - "Predicción obtenida: 3.414\n", - "\tR²: 0.846101938815913, Desviación Estándar: 3.680579963754588, Varianza: 13.546668869591723, Incertidumbre: 0.6834665410570895\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 172.715x + -38.717\n", - "Valor del parámetro correlacionado para la aeronave: 0.272\n", - "Predicción obtenida: 8.262\n", - "\tR²: 0.4899739920102165, Desviación Estándar: 6.700324629668004, Varianza: 44.89435014293567, Incertidumbre: 1.24421904800223\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.14x + 4.428\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 5.796\n", - "\tR²: 0.6465679119191974, Desviación Estándar: 5.509840252613488, Varianza: 30.358339609319863, Incertidumbre: 1.0059545982021234\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 10.192, 22.195, 21.123, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.095x + 4.407\n", - "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 9.141\n", - "\tR²: 0.5148731942546723, Desviación Estándar: 4.5938015964161, Varianza: 21.103013107235103, Incertidumbre: 1.2277451197574445\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 4.754', 'Longitud del fuselaje: 3.84', 'Peso máximo al despegue (MTOW): 4.317', 'envergadura: 3.414', 'Cuerda: 8.262', 'payload: 5.796', 'Rango de comunicación: 9.141']\n", - "**Mediana calculada:** 4.754\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.941x + -5.705\n", - "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 4.754\n", - "\tR²: 0.9286220934085586, Desviación Estándar: 2.511043189243422, Varianza: 6.305337898245776, Incertidumbre: 0.4584516658727787\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.373x + -3.612\n", - "Valor del parámetro correlacionado para la aeronave: 0.9\n", - "Predicción obtenida: 3.924\n", - "\tR²: 0.7152679648938791, Desviación Estándar: 5.015225272771336, Varianza: 25.15248453664432, Incertidumbre: 0.9156506709556202\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.306x + 2.452\n", - "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 4.35\n", - "\tR²: 0.9141014032234078, Desviación Estándar: 2.7239052914347277, Varianza: 7.419660036706109, Incertidumbre: 0.4892278325604646\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.593x + -14.314\n", - "Valor del parámetro correlacionado para la aeronave: 2.35\n", - "Predicción obtenida: 3.53\n", - "\tR²: 0.851141505775779, Desviación Estándar: 3.626261257188199, Varianza: 13.149770705384139, Incertidumbre: 0.6620616966563397\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 175.676x + -39.747\n", - "Valor del parámetro correlacionado para la aeronave: 0.272\n", - "Predicción obtenida: 8.037\n", - "\tR²: 0.5043808003522512, Desviación Estándar: 6.616774593333738, Varianza: 43.78170601898685, Incertidumbre: 1.2080522342319868\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.146x + 4.34\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 5.716\n", - "\tR²: 0.6594997555877751, Desviación Estándar: 5.423225746126612, Varianza: 29.411377493450544, Incertidumbre: 0.9740400980924518\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 50.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 10.192, 22.195, 21.123, 4.754, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.099x + 3.73\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 6.703\n", - "\tR²: 0.5336804190590896, Desviación Estándar: 4.565757595526708, Varianza: 20.846142421109825, Incertidumbre: 1.1788735420196639\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 4.754', 'Longitud del fuselaje: 3.924', 'Peso máximo al despegue (MTOW): 4.35', 'envergadura: 3.53', 'Cuerda: 8.037', 'payload: 5.716', 'Rango de comunicación: 6.703']\n", - "**Mediana calculada:** 4.754\n", - "\n", - "--- Imputación para aeronave: **V35** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.941x + -5.705\n", - "Valor del parámetro correlacionado para la aeronave: 1.202\n", - "Predicción obtenida: 12.254\n", - "\tR²: 0.9286220934085586, Desviación Estándar: 2.511043189243422, Varianza: 6.305337898245776, Incertidumbre: 0.4584516658727787\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.373x + -3.612\n", - "Valor del parámetro correlacionado para la aeronave: 1.88\n", - "Predicción obtenida: 12.13\n", - "\tR²: 0.7152679648938791, Desviación Estándar: 5.015225272771336, Varianza: 25.15248453664432, Incertidumbre: 0.9156506709556202\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.306x + 2.452\n", - "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 12.248\n", - "\tR²: 0.9141014032234078, Desviación Estándar: 2.7239052914347277, Varianza: 7.419660036706109, Incertidumbre: 0.4892278325604646\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.593x + -14.314\n", - "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 12.262\n", - "\tR²: 0.851141505775779, Desviación Estándar: 3.626261257188199, Varianza: 13.149770705384139, Incertidumbre: 0.6620616966563397\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 175.676x + -39.747\n", - "Valor del parámetro correlacionado para la aeronave: 0.306\n", - "Predicción obtenida: 14.01\n", - "\tR²: 0.5043808003522512, Desviación Estándar: 6.616774593333738, Varianza: 43.78170601898685, Incertidumbre: 1.2080522342319868\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.146x + 4.34\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 15.804\n", - "\tR²: 0.6594997555877751, Desviación Estándar: 5.423225746126612, Varianza: 29.411377493450544, Incertidumbre: 0.9740400980924518\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'V21', 'V25', 'V32']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 50.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 10.192, 22.195, 21.123, 4.754, 4.754, 2.65, 3.45, 6.45]\n", - "Ecuación de regresión: y = 0.102x + 3.389\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 6.441\n", - "\tR²: 0.5638096764017799, Desviación Estándar: 4.443968751728997, Varianza: 19.74885826634378, Incertidumbre: 1.1109921879322493\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 12.254', 'Longitud del fuselaje: 12.13', 'Peso máximo al despegue (MTOW): 12.248', 'envergadura: 12.262', 'Cuerda: 14.01', 'payload: 15.804', 'Rango de comunicación: 6.441']\n", - "**Mediana calculada:** 12.254\n", - "\n", - "--- Imputación para aeronave: **V39** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.941x + -5.705\n", - "Valor del parámetro correlacionado para la aeronave: 1.203\n", - "Predicción obtenida: 12.269\n", - "\tR²: 0.9287486031571285, Desviación Estándar: 2.4702104975198655, Varianza: 6.101939902057342, Incertidumbre: 0.4436629024767654\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.372x + -3.605\n", - "Valor del parámetro correlacionado para la aeronave: 1.954\n", - "Predicción obtenida: 12.754\n", - "\tR²: 0.7157670307935512, Desviación Estándar: 4.933719987077154, Varianza: 24.341592910884593, Incertidumbre: 0.8861222683945283\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.306x + 2.452\n", - "Valor del parámetro correlacionado para la aeronave: 24.0\n", - "Predicción obtenida: 9.799\n", - "\tR²: 0.9142766506576697, Desviación Estándar: 2.6810066815314575, Varianza: 7.187796826416317, Incertidumbre: 0.47393950122933404\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.593x + -14.314\n", - "Valor del parámetro correlacionado para la aeronave: 3.9\n", - "Predicción obtenida: 15.299\n", - "\tR²: 0.8514053187249602, Desviación Estándar: 3.567294004696238, Varianza: 12.725586515941725, Incertidumbre: 0.6407049171317712\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 175.776x + -39.835\n", - "Valor del parámetro correlacionado para la aeronave: 0.307\n", - "Predicción obtenida: 14.129\n", - "\tR²: 0.5041358522783776, Desviación Estándar: 6.516563415932599, Varianza: 42.46559875387115, Incertidumbre: 1.1704093404952065\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.144x + 4.253\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 9.972\n", - "\tR²: 0.6556479703357065, Desviación Estándar: 5.373405979420902, Varianza: 28.8734918196763, Incertidumbre: 0.9498929515292154\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = 0.832) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'V21', 'V25', 'V32', 'V35']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 50.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 10.0, 18.365, 12.237, 5.633, 10.192, 22.195, 21.123, 4.754, 4.754, 2.65, 3.45, 6.45, 12.254]\n", - "Ecuación de regresión: y = 0.095x + 4.285\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 7.13\n", - "\tR²: 0.5227923448909729, Desviación Estándar: 4.50989094884209, Varianza: 20.339116370447805, Incertidumbre: 1.093809220123117\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 12.269', 'Longitud del fuselaje: 12.754', 'Peso máximo al despegue (MTOW): 9.799', 'envergadura: 15.299', 'Cuerda: 14.129', 'payload: 9.972', 'Rango de comunicación: 7.13']\n", - "**Mediana calculada:** 12.269\n", - "\n", - "--- Imputación para aeronave: **Volitation VT370** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.941x + -5.705\n", - "Valor del parámetro correlacionado para la aeronave: 1.424\n", - "Predicción obtenida: 15.571\n", - "\tR²: 0.9288651463121561, Desviación Estándar: 2.4313071135199693, Varianza: 5.911254280252805, Incertidumbre: 0.4297984367792653\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.375x + -3.627\n", - "Valor del parámetro correlacionado para la aeronave: 2.02\n", - "Predicción obtenida: 13.291\n", - "\tR²: 0.7161464221538233, Desviación Estándar: 4.856750413201213, Varianza: 23.58802457613015, Incertidumbre: 0.858560287926286\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.305x + 2.582\n", - "Valor del parámetro correlacionado para la aeronave: 40.0\n", - "Predicción obtenida: 14.771\n", - "\tR²: 0.9122573197462216, Desviación Estándar: 2.6735124201413973, Varianza: 7.147668660650313, Incertidumbre: 0.46539877526665924\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 7.584x + -14.375\n", - "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 34.922\n", - "\tR²: 0.8483051549539458, Desviación Estándar: 3.5504549853464953, Varianza: 12.605730602971782, Incertidumbre: 0.6276376991090227\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 175.837x + -39.912\n", - "Valor del parámetro correlacionado para la aeronave: 0.314\n", - "Predicción obtenida: 15.301\n", - "\tR²: 0.503687273356439, Desviación Estándar: 6.42208878399288, Varianza: 41.24322434948715, Incertidumbre: 1.1352756321358586\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.137x + 4.381\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 24.85\n", - "\tR²: 0.6544160881835673, Desviación Estándar: 5.30583225901069, Varianza: 28.151855960758482, Incertidumbre: 0.923626842542706\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 15.571', 'Longitud del fuselaje: 13.291', 'Peso máximo al despegue (MTOW): 14.771', 'envergadura: 34.922', 'Cuerda: 15.301', 'payload: 24.85']\n", - "**Mediana calculada:** 15.436\n", - "\n", - "--- Imputación para aeronave: **Skyeye 5000 VTOL** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.94x + -5.708\n", - "Valor del parámetro correlacionado para la aeronave: 2.615\n", - "Predicción obtenida: 33.36\n", - "\tR²: 0.9288907011950363, Desviación Estándar: 2.394297517401611, Varianza: 5.7326606018355175, Incertidumbre: 0.41679369948981904\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.366x + -3.542\n", - "Valor del parámetro correlacionado para la aeronave: 3.5\n", - "Predicción obtenida: 25.738\n", - "\tR²: 0.714598445858678, Desviación Estándar: 4.796705817916089, Varianza: 23.008386703630055, Incertidumbre: 0.8349993050918911\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Ancho del fuselaje (r = 0.955) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Ancho del fuselaje: [0.211, 0.2, 0.277, 0.277, 0.375, 0.375]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 32.0, 35.0]\n", - "Ecuación de regresión: y = 114.066x + -10.111\n", - "Valor del parámetro correlacionado para la aeronave: 0.375\n", - "Predicción obtenida: 32.663\n", - "\tR²: 0.8963698345140692, Desviación Estándar: 2.697515152995758, Varianza: 7.276588000641728, Incertidumbre: 1.1012559497108843\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.305x + 2.601\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 33.074\n", - "\tR²: 0.9121259725181001, Desviación Estándar: 2.6363012992791948, Varianza: 6.950084540581171, Incertidumbre: 0.45212194283573964\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 6.477x + -10.686\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 21.7\n", - "\tR²: 0.7313279100692804, Desviación Estándar: 4.653998017305745, Varianza: 21.659697545085802, Incertidumbre: 0.8101570656750493\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 175.854x + -39.913\n", - "Valor del parámetro correlacionado para la aeronave: 0.338\n", - "Predicción obtenida: 19.526\n", - "\tR²: 0.5039052742482211, Desviación Estándar: 6.324078061960279, Varianza: 39.993963333767276, Incertidumbre: 1.1008806851068977\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.079x + 4.633\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 31.618\n", - "\tR²: 0.624328313685615, Desviación Estándar: 5.450909982107748, Varianza: 29.712419633041897, Incertidumbre: 0.934823349670657\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Capacidad combustible (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL octo']\n", - "Valores para Capacidad combustible: [13.0, 11.5, 11.5, 28.0, 28.0]\n", - "Valores para Empty weight: [15.436, 11.5, 11.0, 32.0, 35.0]\n", - "Ecuación de regresión: y = 1.311x + -3.129\n", - "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 33.569\n", - "\tR²: 0.9852719437774983, Desviación Estándar: 1.259106590568882, Varianza: 1.5853494064139946, Incertidumbre: 0.5630895854860032\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Área del ala: 33.36', 'Longitud del fuselaje: 25.738', 'Ancho del fuselaje: 32.663', 'Peso máximo al despegue (MTOW): 33.074', 'envergadura: 21.7', 'Cuerda: 19.526', 'payload: 31.618', 'Capacidad combustible: 33.569']\n", - "**Mediana calculada:** 32.14\n", - "\n", - "--- Imputación para aeronave: **Volitation VT510** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.941) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 2.615, 0.771, 0.986, 2.329]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 32.14, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 14.821x + -5.579\n", - "Valor del parámetro correlacionado para la aeronave: 1.993\n", - "Predicción obtenida: 23.959\n", - "\tR²: 0.9358207202237538, Desviación Estándar: 2.3667022774990354, Varianza: 5.601279670319122, Incertidumbre: 0.4058860920446379\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Longitud del fuselaje (r = 0.88) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Longitud del fuselaje: [2.1, 2.591, 3.0, 3.0, 3.594, 1.7, 1.2, 1.2, 1.2, 1.48, 1.71, 2.5, 2.998, 2.5, 2.4, 2.5, 0.75, 0.9, 0.93, 0.93, 1.0, 1.88, 1.954, 2.02, 2.05, 2.03, 2.488, 2.42, 3.5, 3.5, 3.5, 1.562, 2.3, 4.712]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 32.14, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 8.658x + -3.99\n", - "Valor del parámetro correlacionado para la aeronave: 2.905\n", - "Predicción obtenida: 21.161\n", - "\tR²: 0.7315519871473125, Desviación Estándar: 4.840344870982094, Varianza: 23.42893847004267, Incertidumbre: 0.8301122969752286\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.947) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 40.0, 90.0, 100.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 32.14, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 0.303x + 2.649\n", - "Valor del parámetro correlacionado para la aeronave: 100.0\n", - "Predicción obtenida: 32.939\n", - "\tR²: 0.920742065863033, Desviación Estándar: 2.6024670945613564, Varianza: 6.772834978274627, Incertidumbre: 0.4398972275518982\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.924) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.0, 2.0, 3.0, 6.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 32.14, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 6.719x + -11.32\n", - "Valor del parámetro correlacionado para la aeronave: 5.1\n", - "Predicción obtenida: 22.945\n", - "\tR²: 0.7244083786781033, Desviación Estándar: 4.904324680873634, Varianza: 24.052400575426272, Incertidumbre: 0.8410847438493143\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Cuerda (r = 0.971) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Cuerda: [0.239, 0.318, 0.352, 0.352, 0.394, 0.197, 0.319, 0.332, 0.304, 0.271, 0.298, 0.338, 0.341, 0.345, 0.312, 0.341, 0.276, 0.272, 0.278, 0.281, 0.292, 0.306, 0.307, 0.314, 0.296, 0.3, 0.311, 0.315, 0.348, 0.338, 0.344, 0.287, 0.291, 0.313]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 32.14, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 184.0x + -42.063\n", - "Valor del parámetro correlacionado para la aeronave: 0.335\n", - "Predicción obtenida: 19.577\n", - "\tR²: 0.5041716405668544, Desviación Estándar: 6.578272967275993, Varianza: 43.27367523199409, Incertidumbre: 1.128164506569046\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.778) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.186, 10.419, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 4.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Empty weight: [10.886, 17.463, 19.796, 19.796, 19.809, 30.798, 10.0, 8.435, 18.365, 12.237, 5.633, 10.192, 22.195, 24.784, 22.257, 14.794, 21.123, 4.8, 4.754, 2.65, 3.45, 6.45, 12.254, 12.269, 15.436, 6.5, 7.1, 11.5, 11.0, 32.0, 32.14, 35.0, 3.0, 5.8, 31.0]\n", - "Ecuación de regresión: y = 1.084x + 4.602\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 31.707\n", - "\tR²: 0.6621543183227474, Desviación Estándar: 5.373076933587957, Varianza: 28.869955734254965, Incertidumbre: 0.9082157662811027\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Capacidad combustible (r = 0.995) ---\n", - "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo']\n", - "Valores para Capacidad combustible: [13.0, 11.5, 11.5, 28.0, 28.0, 28.0]\n", - "Valores para Empty weight: [15.436, 11.5, 11.0, 32.0, 32.14, 35.0]\n", - "Ecuación de regresión: y = 1.281x + -2.774\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 29.251\n", - "\tR²: 0.9855262376673756, Desviación Estándar: 1.244330432674912, Varianza: 1.5483582256809336, Incertidumbre: 0.5079957719116919\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Área del ala: 23.959', 'Longitud del fuselaje: 21.161', 'Peso máximo al despegue (MTOW): 32.939', 'envergadura: 22.945', 'Cuerda: 19.577', 'payload: 31.707', 'Capacidad combustible: 29.251']\n", - "**Mediana calculada:** 23.959\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Reporte Final de Imputaciones

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    AeronaveParámetroValor ImputadoNivel de Confianza
    8Aerosonde Mk. 4.8 VTOL FTUASÁrea del ala2.5030.804
    9Fulmar XÁrea del ala0.9400.804
    10Orbiter 4Área del ala1.6080.804
    11Orbiter 3Área del ala1.2000.804
    12MantisÁrea del ala0.7540.804
    13ScanEagleÁrea del ala1.0630.804
    14IntegratorÁrea del ala1.8720.804
    15Integrator VTOLÁrea del ala2.0900.804
    16Integrator Extended Range (ER)Área del ala1.8720.804
    17ScanEagle 3Área del ala1.3490.804
    18RQ Nan 21A BlackjackÁrea del ala1.8020.804
    19DeltaQuad Pro #MAPÁrea del ala0.7000.804
    20DeltaQuad Pro #CARGOÁrea del ala0.7000.804
    21V32Área del ala1.0300.804
    29Fulmar XRelación de aspecto del ala13.2180.963
    30Orbiter 4Relación de aspecto del ala13.4430.752
    31Orbiter 3Relación de aspecto del ala13.9340.752
    32MantisRelación de aspecto del ala14.7550.752
    33ScanEagleRelación de aspecto del ala14.0570.776
    34IntegratorRelación de aspecto del ala12.9080.632
    35Integrator VTOLRelación de aspecto del ala12.6480.677
    36Integrator Extended Range (ER)Relación de aspecto del ala12.8400.677
    37ScanEagle 3Relación de aspecto del ala13.7650.677
    38RQ Nan 21A BlackjackRelación de aspecto del ala12.9140.697
    39DeltaQuad EvoRelación de aspecto del ala14.5890.720
    40DeltaQuad Pro #MAPRelación de aspecto del ala14.7140.730
    41DeltaQuad Pro #CARGORelación de aspecto del ala14.7140.743
    42V21Relación de aspecto del ala14.5680.743
    43V25Relación de aspecto del ala14.4210.755
    44V32Relación de aspecto del ala14.1820.764
    45V35Relación de aspecto del ala13.8980.770
    46V39Relación de aspecto del ala14.0410.771
    47Volitation VT370Relación de aspecto del ala13.6450.766
    48Skyeye 2600Relación de aspecto del ala14.1030.773
    49Skyeye 2930 VTOLRelación de aspecto del ala14.0010.671
    50Skyeye 3600Relación de aspecto del ala13.7100.677
    51Skyeye 3600 VTOLRelación de aspecto del ala13.6710.677
    52Skyeye 5000Relación de aspecto del ala12.6950.676
    53Skyeye 5000 VTOLRelación de aspecto del ala13.0320.700
    54Skyeye 5000 VTOL octoRelación de aspecto del ala12.8550.705
    55Volitation VT510Relación de aspecto del ala13.0990.719
    56AscendRelación de aspecto del ala14.3490.727
    57TransitionRelación de aspecto del ala14.2230.733
    58ReachRelación de aspecto del ala13.6690.737
    59Aerosonde Mk. 4.8 VTOL FTUASLongitud del fuselaje3.5950.831
    60Integrator VTOLLongitud del fuselaje2.9980.831
    61V39Longitud del fuselaje1.9540.831
    95Aerosonde Mk. 4.8 VTOL FTUASenvergadura5.6440.805
    96Integrator VTOLenvergadura5.0330.805
    97Aerosonde Mk. 4.8 VTOL FTUASCuerda0.3940.827
    98Fulmar XCuerda0.3190.827
    99Orbiter 4Cuerda0.3320.758
    100Orbiter 3Cuerda0.3040.758
    101MantisCuerda0.2710.758
    102ScanEagleCuerda0.2980.782
    103IntegratorCuerda0.3390.725
    104Integrator VTOLCuerda0.3410.757
    105Integrator Extended Range (ER)Cuerda0.3450.757
    106ScanEagle 3Cuerda0.3110.757
    107RQ Nan 21A BlackjackCuerda0.3410.759
    108DeltaQuad EvoCuerda0.2760.784
    109DeltaQuad Pro #MAPCuerda0.2720.803
    110DeltaQuad Pro #CARGOCuerda0.2720.819
    111V21Cuerda0.2780.819
    112V25Cuerda0.2810.602
    113V32Cuerda0.2920.591
    114V35Cuerda0.3060.590
    115V39Cuerda0.3070.590
    116Volitation VT370Cuerda0.3140.590
    117Skyeye 2600Cuerda0.2960.590
    118Skyeye 2930 VTOLCuerda0.3000.589
    119Skyeye 3600Cuerda0.3110.590
    120Skyeye 3600 VTOLCuerda0.3150.594
    121Skyeye 5000Cuerda0.3480.599
    122Skyeye 5000 VTOLCuerda0.3380.684
    123Skyeye 5000 VTOL octoCuerda0.3440.684
    124Volitation VT510Cuerda0.3350.729
    125AscendCuerda0.2870.729
    126TransitionCuerda0.2910.725
    127ReachCuerda0.3130.723
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de Imputaciones

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    AeronaveCantidad de Valores Imputados
    0Aerosonde Mk. 4.8 VTOL FTUAS4.000
    1Ascend2.000
    2DeltaQuad Evo2.000
    3DeltaQuad Pro #CARGO3.000
    4DeltaQuad Pro #MAP3.000
    5Fulmar X3.000
    6Integrator3.000
    7Integrator Extended Range (ER)3.000
    8Integrator VTOL5.000
    9Mantis3.000
    10Orbiter 33.000
    11Orbiter 43.000
    12RQ Nan 21A Blackjack3.000
    13Reach2.000
    14ScanEagle3.000
    15ScanEagle 33.000
    16Skyeye 26002.000
    17Skyeye 2930 VTOL2.000
    18Skyeye 36002.000
    19Skyeye 3600 VTOL2.000
    20Skyeye 50002.000
    21Skyeye 5000 VTOL2.000
    22Skyeye 5000 VTOL octo2.000
    23Transition2.000
    24V212.000
    25V252.000
    26V323.000
    27V352.000
    28V393.000
    29Volitation VT3702.000
    30Volitation VT5102.000
    TotalTotal80.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m>>> RESULTADOS DE IMPUTACIÓN POR CORRELACIÓN\u001b[0m\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 30.228668858925072 (Similitud)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - AAI Aerosonde = 36.09414654431562 (Similitud)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Orbiter 4 = 30.466419244333917 (Similitud)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Orbiter 3 = 27.426371766294327 (Similitud)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Integrator Extended Range (ER) = 31.89437620999355 (Similitud)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Skyeye 5000 VTOL octo = 30.290908946357952 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Orbiter 4 = 9403.635180379342 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Orbiter 3 = 6839.1446057940275 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 2600 = 14972.955913461341 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 2930 VTOL = 15999.999999999998 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 3600 VTOL = 16959.091874090493 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 VTOL = 16009.435943246384 (Similitud)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 VTOL octo = 16009.476366047784 (Similitud)\n", - "Imputación final aplicada: Área del ala - Aerosonde Mk. 4.8 VTOL FTUAS = 2.503 (Correlación)\n", - "Imputación final aplicada: Área del ala - Fulmar X = 0.94 (Correlación)\n", - "Imputación final aplicada: Área del ala - Orbiter 4 = 1.608 (Correlación)\n", - "Imputación final aplicada: Área del ala - ScanEagle 3 = 1.349 (Correlación)\n", - "Imputación final aplicada: Área del ala - RQ Nan 21A Blackjack = 1.802 (Correlación)\n", - "Imputación final aplicada: Área del ala - V32 = 1.03 (Correlación)\n", - "Imputación final aplicada: Área del ala - V35 = 1.202 (Correlación)\n", - "Imputación final aplicada: Área del ala - V39 = 1.203 (Correlación)\n", - "Imputación final aplicada: Área del ala - Volitation VT370 = 1.424 (Correlación)\n", - "Imputación final aplicada: Área del ala - Volitation VT510 = 1.993 (Correlación)\n", - "Imputación final aplicada: Área del ala - Ascend = 0.771 (Correlación)\n", - "Imputación final aplicada: Área del ala - Transition = 0.986 (Correlación)\n", - "Imputación final aplicada: Área del ala - Reach = 2.329 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Fulmar X = 13.217500000000001 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Orbiter 4 = 13.443 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - ScanEagle 3 = 13.765 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - RQ Nan 21A Blackjack = 12.914 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - V25 = 14.421 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 2600 = 14.103 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 VTOL = 13.032 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 VTOL octo = 12.8555 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Volitation VT510 = 13.099 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Reach = 13.669 (Correlación)\n", - "Imputación final aplicada: Longitud del fuselaje - Aerosonde Mk. 4.8 VTOL FTUAS = 3.5945 (Correlación)\n", - "Imputación final aplicada: Longitud del fuselaje - V39 = 1.954 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - Aerosonde Mk. 4.8 VTOL FTUAS = 800.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Integrator = 499.99999999999994 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - ScanEagle 3 = 50.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - V21 = 270.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - V25 = 270.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Volitation VT370 = 300.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Skyeye 2600 = 3270.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Skyeye 5000 VTOL octo = 800.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Volitation VT510 = 800.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Ascend = 270.0 (Similitud)\n", - "Imputación final aplicada: Alcance de la aeronave - Reach = 800.0 (Similitud)\n", - "Imputación final aplicada: Autonomía de la aeronave - Skyeye 5000 VTOL octo = 11.672906868436865 (Similitud)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 42.25267526977939 (Similitud)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Evo = 33.0 (Similitud)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - Skyeye 2600 = 30.8342888378733 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 18.90746548752 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - AAI Aerosonde = 10.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - DeltaQuad Evo = 14.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - V35 = 18.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - V39 = 17.397389995852386 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Volitation VT370 = 24.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Skyeye 5000 VTOL = 19.109224697504906 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 25.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - AAI Aerosonde = 10.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - DeltaQuad Evo = 14.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - V35 = 18.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - V39 = 17.397389995852386 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Volitation VT370 = 24.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Skyeye 5000 VTOL = 25.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Skyeye 5000 VTOL octo = 25.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Ascend = 14.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Transition = 10.0 (Similitud)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Reach = 25.0 (Similitud)\n", - "Imputación final aplicada: envergadura - Aerosonde Mk. 4.8 VTOL FTUAS = 5.644 (Correlación)\n", - "Imputación final aplicada: Cuerda - Fulmar X = 0.319 (Correlación)\n", - "Imputación final aplicada: Cuerda - Orbiter 4 = 0.332 (Correlación)\n", - "Imputación final aplicada: Cuerda - ScanEagle 3 = 0.3115 (Correlación)\n", - "Imputación final aplicada: Cuerda - RQ Nan 21A Blackjack = 0.341 (Correlación)\n", - "Imputación final aplicada: Cuerda - V25 = 0.281 (Correlación)\n", - "Imputación final aplicada: Cuerda - Skyeye 2600 = 0.296 (Correlación)\n", - "Imputación final aplicada: payload - AAI Aerosonde = 4.0 (Similitud)\n", - "Imputación final aplicada: payload - Fulmar X = 2.4947559999999998 (Similitud)\n", - "Imputación final aplicada: Empty weight - Aerosonde Mk. 4.8 VTOL FTUAS = 31.0 (Similitud)\n", - "Imputación final aplicada: Empty weight - Fulmar X = 17.463292 (Similitud)\n", - "Imputación final aplicada: Empty weight - V35 = 7.1 (Similitud)\n", - "Imputación final aplicada: Empty weight - V39 = 6.708303497304052 (Similitud)\n", - "Imputación final aplicada: Empty weight - Volitation VT370 = 10.999999999999998 (Similitud)\n", - "Imputación final aplicada: Empty weight - Skyeye 5000 VTOL = 32.140499999999996 (Correlación)\n", - "Imputación final aplicada: Empty weight - Volitation VT510 = 23.959 (Correlación)\n", - "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Integrator VTOL = 21.463 (Correlación)\n", - "Imputación final aplicada: Techo de servicio máximo - Integrator VTOL = 7013.834 (Correlación)\n", - "Imputación final aplicada: Área del ala - Orbiter 3 = 1.2 (Correlación)\n", - "Imputación final aplicada: Área del ala - Mantis = 0.754 (Correlación)\n", - "Imputación final aplicada: Área del ala - ScanEagle = 1.063 (Correlación)\n", - "Imputación final aplicada: Área del ala - Integrator = 1.872 (Correlación)\n", - "Imputación final aplicada: Área del ala - Integrator VTOL = 2.0895 (Correlación)\n", - "Imputación final aplicada: Área del ala - Integrator Extended Range (ER) = 1.872 (Correlación)\n", - "Imputación final aplicada: Área del ala - DeltaQuad Pro #MAP = 0.7 (Correlación)\n", - "Imputación final aplicada: Área del ala - DeltaQuad Pro #CARGO = 0.7 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Orbiter 3 = 13.9345 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Mantis = 14.755 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - ScanEagle = 14.057 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Integrator = 12.908 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Integrator VTOL = 12.648 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Integrator Extended Range (ER) = 12.84 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - DeltaQuad Evo = 14.589 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - DeltaQuad Pro #MAP = 14.714 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - DeltaQuad Pro #CARGO = 14.714 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - V21 = 14.568 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - V32 = 14.182 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - V35 = 13.898 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - V39 = 14.0415 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Volitation VT370 = 13.645 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 2930 VTOL = 14.001 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 3600 = 13.7095 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 3600 VTOL = 13.6715 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 = 12.695 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Ascend = 14.349 (Correlación)\n", - "Imputación final aplicada: Relación de aspecto del ala - Transition = 14.223 (Correlación)\n", - "Imputación final aplicada: Longitud del fuselaje - Integrator VTOL = 2.998 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - Aerosonde Mk. 4.7 Fixed Wing = 518.9225 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - Aerosonde Mk. 4.7 VTOL = 481.428 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - Aerosonde Mk. 4.8 Fixed wing = 535.2755 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - Orbiter 4 = 509.5565 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - ScanEagle = 503.5155 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - Integrator VTOL = 646.0835 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - RQ Nan 21A Blackjack = 565.912 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - V32 = 412.68600000000004 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - V35 = 456.221 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - V39 = 413.556 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - Skyeye 2930 VTOL = 425.273 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - Skyeye 3600 = 458.1245 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - Skyeye 5000 = 530.401 (Correlación)\n", - "Imputación final aplicada: Alcance de la aeronave - Transition = 506.641 (Correlación)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - Integrator VTOL = 40.216 (Correlación)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Pro #MAP = 29.009 (Correlación)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Pro #CARGO = 29.009 (Correlación)\n", - "Imputación final aplicada: Velocidad máxima (KIAS) - Skyeye 3600 = 35.0985 (Correlación)\n", - "Imputación final aplicada: envergadura - Integrator VTOL = 5.033 (Correlación)\n", - "Imputación final aplicada: Cuerda - Aerosonde Mk. 4.8 VTOL FTUAS = 0.394 (Correlación)\n", - "Imputación final aplicada: Cuerda - Orbiter 3 = 0.304 (Correlación)\n", - "Imputación final aplicada: Cuerda - Mantis = 0.271 (Correlación)\n", - "Imputación final aplicada: Cuerda - ScanEagle = 0.2985 (Correlación)\n", - "Imputación final aplicada: Cuerda - Integrator = 0.3385 (Correlación)\n", - "Imputación final aplicada: Cuerda - Integrator VTOL = 0.341 (Correlación)\n", - "Imputación final aplicada: Cuerda - Integrator Extended Range (ER) = 0.345 (Correlación)\n", - "Imputación final aplicada: Cuerda - DeltaQuad Evo = 0.2755 (Correlación)\n", - "Imputación final aplicada: Cuerda - DeltaQuad Pro #MAP = 0.272 (Correlación)\n", - "Imputación final aplicada: Cuerda - DeltaQuad Pro #CARGO = 0.272 (Correlación)\n", - "Imputación final aplicada: Cuerda - V21 = 0.278 (Correlación)\n", - "Imputación final aplicada: Cuerda - V32 = 0.292 (Correlación)\n", - "Imputación final aplicada: Cuerda - V35 = 0.306 (Correlación)\n", - "Imputación final aplicada: Cuerda - V39 = 0.307 (Correlación)\n", - "Imputación final aplicada: Cuerda - Volitation VT370 = 0.314 (Correlación)\n", - "Imputación final aplicada: Cuerda - Skyeye 2930 VTOL = 0.3 (Correlación)\n", - "Imputación final aplicada: Cuerda - Skyeye 3600 = 0.311 (Correlación)\n", - "Imputación final aplicada: Cuerda - Skyeye 3600 VTOL = 0.315 (Correlación)\n", - "Imputación final aplicada: Cuerda - Skyeye 5000 = 0.3485 (Correlación)\n", - "Imputación final aplicada: Cuerda - Skyeye 5000 VTOL = 0.338 (Correlación)\n", - "Imputación final aplicada: Cuerda - Skyeye 5000 VTOL octo = 0.3445 (Correlación)\n", - "Imputación final aplicada: Cuerda - Volitation VT510 = 0.335 (Correlación)\n", - "Imputación final aplicada: Cuerda - Ascend = 0.287 (Correlación)\n", - "Imputación final aplicada: Cuerda - Transition = 0.291 (Correlación)\n", - "Imputación final aplicada: Cuerda - Reach = 0.313 (Correlación)\n", - "Imputación final aplicada: payload - Mantis = 2.693 (Correlación)\n", - "Imputación final aplicada: Empty weight - Aerosonde Mk. 4.7 Fixed Wing = 19.796 (Correlación)\n", - "Imputación final aplicada: Empty weight - Aerosonde Mk. 4.7 VTOL = 19.796 (Correlación)\n", - "Imputación final aplicada: Empty weight - Aerosonde Mk. 4.8 Fixed wing = 19.809 (Correlación)\n", - "Imputación final aplicada: Empty weight - Orbiter 4 = 18.365 (Correlación)\n", - "Imputación final aplicada: Empty weight - Orbiter 3 = 12.237 (Correlación)\n", - "Imputación final aplicada: Empty weight - Mantis = 5.633 (Correlación)\n", - "Imputación final aplicada: Empty weight - ScanEagle = 10.192 (Correlación)\n", - "Imputación final aplicada: Empty weight - Integrator = 22.195 (Correlación)\n", - "Imputación final aplicada: Empty weight - Integrator VTOL = 24.7845 (Correlación)\n", - "Imputación final aplicada: Empty weight - Integrator Extended Range (ER) = 22.256999999999998 (Correlación)\n", - "Imputación final aplicada: Empty weight - ScanEagle 3 = 14.794 (Correlación)\n", - "Imputación final aplicada: Empty weight - RQ Nan 21A Blackjack = 21.123 (Correlación)\n", - "Imputación final aplicada: Empty weight - DeltaQuad Pro #MAP = 4.754 (Correlación)\n", - "Imputación final aplicada: Empty weight - DeltaQuad Pro #CARGO = 4.754 (Correlación)\n", - "\n", - "=== Iteración 1: Resumen después de imputaciones ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de Valores Faltantes Después de Iteración 1

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    ColumnaValores Faltantes
    0Stalker XE2.000
    1Stalker VXE302.000
    2Aerosonde Mk. 4.7 Fixed Wing2.000
    3Aerosonde Mk. 4.7 VTOL2.000
    4Aerosonde Mk. 4.8 Fixed wing2.000
    5Aerosonde Mk. 4.8 VTOL FTUAS0.000
    6AAI Aerosonde0.000
    7Fulmar X2.000
    8Orbiter 42.000
    9Orbiter 32.000
    10Mantis3.000
    11ScanEagle2.000
    12Integrator2.000
    13Integrator VTOL2.000
    14Integrator Extended Range (ER)2.000
    15ScanEagle 32.000
    16RQ Nan 21A Blackjack2.000
    17DeltaQuad Evo0.000
    18DeltaQuad Pro #MAP2.000
    19DeltaQuad Pro #CARGO2.000
    20V210.000
    21V250.000
    22V320.000
    23V350.000
    24V390.000
    25Volitation VT3700.000
    26Skyeye 26000.000
    27Skyeye 2930 VTOL0.000
    28Skyeye 36002.000
    29Skyeye 3600 VTOL0.000
    30Skyeye 50001.000
    31Skyeye 5000 VTOL0.000
    32Skyeye 5000 VTOL octo0.000
    33Volitation VT5100.000
    34Ascend0.000
    35Transition0.000
    36Reach0.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Sumatoria Total de Valores Faltantes

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    ResumenCantidad
    0Total de Valores Faltantes38.000
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    Resumen de Valores Faltantes Antes de Iteración 2

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    ColumnaValores Faltantes
    0Stalker XE32.000
    1Stalker VXE3033.000
    2Aerosonde Mk. 4.7 Fixed Wing30.000
    3Aerosonde Mk. 4.7 VTOL29.000
    4Aerosonde Mk. 4.8 Fixed wing33.000
    5Aerosonde Mk. 4.8 VTOL FTUAS33.000
    6AAI Aerosonde30.000
    7Fulmar X36.000
    8Orbiter 436.000
    9Orbiter 336.000
    10Mantis36.000
    11ScanEagle35.000
    12Integrator35.000
    13Integrator VTOL34.000
    14Integrator Extended Range (ER)37.000
    15ScanEagle 335.000
    16RQ Nan 21A Blackjack34.000
    17DeltaQuad Evo28.000
    18DeltaQuad Pro #MAP32.000
    19DeltaQuad Pro #CARGO32.000
    20V2128.000
    21V2528.000
    22V3228.000
    23V3531.000
    24V3931.000
    25Volitation VT37030.000
    26Skyeye 260033.000
    27Skyeye 2930 VTOL32.000
    28Skyeye 360034.000
    29Skyeye 3600 VTOL31.000
    30Skyeye 500030.000
    31Skyeye 5000 VTOL30.000
    32Skyeye 5000 VTOL octo30.000
    33Volitation VT51030.000
    34Ascend29.000
    35Transition29.000
    36Reach29.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Sumatoria Total de Valores Faltantes

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    ResumenCantidad
    0Total de Valores Faltantes1179.000
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Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Mantis - Techo de servicio máximo ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Skyeye 3600 - Techo de servicio máximo ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Techo de servicio máximo'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Skyeye 5000 - Techo de servicio máximo ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Techo de servicio máximo'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Stalker XE - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Stalker VXE30 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Fulmar X - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Orbiter 4 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Orbiter 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Mantis - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: ScanEagle - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Integrator - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: RQ Nan 21A Blackjack - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Stalker XE - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Stalker VXE30 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Fulmar X - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Orbiter 4 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Orbiter 3 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Mantis - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: ScanEagle - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Integrator - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: RQ Nan 21A Blackjack - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1mNo se realizaron imputaciones por similitud en esta iteración.\u001b[0m\n", - "\n", - "--------------------------------------------------------------------------------\n", - "\u001b[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN 2 ***\u001b[0m\n", - "--------------------------------------------------------------------------------\n", - "\n", - "=== DataFrame inicial ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())

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    Stalker XEStalker VXE30Aerosonde Mk. 4.7 Fixed WingAerosonde Mk. 4.7 VTOLAerosonde Mk. 4.8 Fixed wingAerosonde Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQ Nan 21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
    Modelo
    Distancia de carrera requerida para despegue0.00.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
    Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.0
    Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.3440530.22866936.09414730.40658430.46641927.42637218.26582630.62533630.95346521.46331.89437625.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.62533630.29090932.8128621.8752421.8752427.34405
    Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.8429403.635186839.144606NaN19500.019500.07013.83419500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.014972.95591316000.0NaN16959.091874NaN16009.43594316009.47636617000.010000.013000.016000.0
    Velocidad de pérdida limpia (KCAS)NaNNaNNaNNaNNaN25.010.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.0NaNNaN14.015.517.018.017.3973924.010.018.012.524.015.025.025.025.014.010.025.0
    Área del ala0.871.1582831.551.551.552.5030.570.941.6081.20.7541.0631.8722.08951.8721.3491.8020.840.70.70.80.521.031.2021.2031.4240.881.01.331.322.6152.6152.6151.9930.7710.9862.329
    Relación de aspecto del ala15.30125515.32644912.512.512.512.514.75438613.217513.44313.934514.75514.05712.90812.64812.8413.76512.91414.58914.71414.71414.56814.42114.18213.89814.041513.64514.10314.00113.709513.671512.69513.03212.855513.09914.34914.22313.669
    Longitud del fuselaje2.12.59083.03.03.03.59451.71.21.21.21.481.712.52.9982.52.42.50.750.90.90.930.931.01.881.9542.022.052.032.4882.423.53.53.52.9051.5622.34.712
    Profundidad del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Ancho del fuselaje0.2110.20.2770.2770.277NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.3750.3750.375NaNNaNNaNNaN
    Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
    Alcance de la aeronave370.0433.0518.9225481.428535.2755800.03270.0800.0509.556550.025.0503.5155500.0646.0835500.050.0565.912270.0100.0100.0270.0270.0412.686456.221413.556300.03270.0425.273458.1245300.0530.401800.0800.0800.0270.0506.641800.0
    Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.011.6729075.06.012.020.0
    Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.4388642.25267530.84572541.736.036.025.641.246.340.21646.341.246.333.029.00929.00933.033.033.033.033.033.030.83428930.035.098533.042.042.038.050.030.030.035.0
    Velocidad de pérdida (KCAS)NaNNaNNaNNaNNaN18.90746510.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14.0NaNNaN14.015.517.018.017.3973924.010.018.012.524.015.019.10922524.025.013.013.013.0
    Tasa de ascensoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Radio de giroNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100.0120.0150.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    envergadura3.6574.87684.44.44.45.6442.93.05.24.42.13.14.85.0334.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
    Cuerda0.2390.3181950.3520.3520.3520.3940.1965520.3190.3320.3040.2710.29850.33850.3410.3450.31150.3410.27550.2720.2720.2780.2810.2920.3060.3070.3140.2960.30.3110.3150.34850.3380.34450.3350.2870.2910.313
    payload2.4947562.49475614.511.317.722.74.02.49475612.05.52.6935.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
    duracion en VTOL2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050.050.05
    Crucero KIAS15.4333216.09342225.025.025.0NaNNaN27.8NaNNaN16.728.028.3NaNNaN23.530.916.5416.016.018.020.020.025.025.025.033.024.0NaN30.033.028.035.030.020.020.025.0
    RTF (dry weight)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.011.854.0
    RTF (Including fuel & Batteries)NaNNaN27.742.236.770.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8.916.584.0
    Empty weight10.88620817.46329219.79619.79619.80931.010.017.46329218.36512.2375.63310.19222.19524.784522.25714.79421.1234.84.7544.7542.653.456.457.16.70830311.06.57.111.511.032.032.140535.023.9593.05.831.0
    Maximum CrosswindNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.0NaNNaNNaN45.050.050.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15.015.015.0
    Rango de comunicación59.0161.0140.0140.0140.0NaN150.0NaN150.050.025.0101.8692.6NaNNaNNaN92.6NaN50.030.030.030.030.030.030.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Wing LoadingNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.524.025.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Potencia específica (P/W)NaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Capacidad combustibleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.0NaNNaN11.511.528.028.028.025.0NaNNaNNaN
    ConsumoNaNNaN0.60.6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.96NaNNaNNaNNaN1.2NaNNaN5.0NaNNaNNaN
    Potencia WattsNaNNaN2980.02980.0NaNNaN1280.0NaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Potencia HPNaNNaN4.04.0NaNNaN1.74NaNNaNNaNNaNNaNNaNNaNNaNNaN8.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    PrecioNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.0NaNNaNNaN
    Tiempo de emergencia en vueloNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.1080.1080.108NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Distancia de aterrizajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Despegue1.01.01.02.01.02.02.01.01.01.01.01.01.02.01.01.01.02.02.02.02.02.02.02.02.02.02.02.03.02.03.02.02.02.02.02.02.0
    Propulsión horizontal2.02.02.02.02.02.02.02.02.01.01.02.02.02.02.02.02.01.01.01.01.01.02.02.02.02.02.02.02.02.02.02.02.02.02.02.02.0
    Propulsión vertical5.05.05.01.05.01.01.05.05.05.05.05.05.01.05.05.05.01.01.01.01.01.01.01.01.01.01.01.05.01.05.01.01.01.01.01.01.0
    Cantidad de motores propulsión vertical0.00.00.04.00.04.04.00.00.00.00.00.00.04.00.00.00.04.04.04.04.04.04.04.04.04.04.04.00.04.00.04.08.04.04.04.04.0
    Cantidad de motores propulsión horizontal1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
    Misión1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
    Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Battery Power SupplyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Modelo Motor Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Modelo Motor VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    PortabilidadNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    CámaraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Despegue todos los tiposNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Datalink banksNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Motor recomendadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Hélice recomendada VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Hélice recomendada Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Sistema de controlNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Características adicionalesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    indice_desconocidoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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    Tabla de Correlaciones con todos los parametros(tabla_completa)

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    ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
    Modelo
    Distancia de carrera requerida para despegue1.0000.0630.427nan-0.3420.269-0.2500.2220.4250.1680.054-0.0780.241-0.3110.1050.2000.229nan0.389nan0.225nannan-0.018-0.240-0.1560.7350.1540.671-0.598nannan
    Altitud a la que se realiza el crucero0.0631.0000.0110.0900.331-0.0750.105-0.092nan-0.095-0.309-0.278-0.0640.343-0.0800.109-0.114nannannan-0.1210.038-0.325nannannan-0.119-0.1090.187-0.159nannan
    Velocidad a la que se realiza el crucero (KTAS)0.4270.0111.0000.1220.2080.497-0.6070.4280.9170.5530.5450.4230.6340.1610.4210.3960.567-0.6940.9730.7900.503-0.8540.4800.3650.461-0.2900.0630.5630.120-0.076nannan
    Techo de servicio máximonan0.0900.1221.0000.0070.083-0.0170.1210.5460.1170.1010.0160.0050.0260.0170.0180.107-0.8750.0990.579-0.018-0.961-0.118-0.7570.515-0.1710.1740.058-0.1740.176nannan
    Velocidad de pérdida limpia (KCAS)-0.3420.3310.2080.0071.0000.711-0.6890.595nan0.768-0.3430.1600.5870.8170.7740.6700.705-0.1810.4280.9360.662-0.181-0.8740.2240.5320.118-0.2530.261-0.2530.365nannan
    Área del ala0.269-0.0750.4970.0830.7111.000-0.7780.8350.9840.970-0.0230.3830.6480.4570.8250.7870.854-0.3830.6920.9650.944-0.4660.4910.9740.2880.0360.1250.4760.0720.037nannan
    Relación de aspecto del ala-0.2500.105-0.607-0.017-0.689-0.7781.000-0.624-0.681-0.790-0.003-0.456-0.730-0.567-0.630-0.862-0.8260.519-0.769-0.497-0.7460.432-0.409-0.9700.2960.024-0.001-0.471-0.1410.075nannan
    Longitud del fuselaje0.222-0.0920.4280.1210.5950.835-0.6241.0000.9380.8060.1400.4030.3630.2450.7190.6230.660-0.6170.5740.9260.834-0.6960.6460.9290.036-0.2030.1380.6120.0340.040nannan
    Ancho del fuselaje0.425nan0.9170.546nan0.984-0.6810.9381.0000.9860.833-0.0890.940nan0.6710.5570.868nan0.944nan0.954nan0.323nan1.000nan0.794nan-0.5350.574nannan
    Peso máximo al despegue (MTOW)0.168-0.0950.5530.1170.7680.970-0.7900.8060.9861.0000.0300.4200.7170.5230.8110.7510.882-0.4010.7080.9790.933-0.4640.5140.9760.7580.0520.0900.4670.0230.075nannan
    Alcance de la aeronave0.054-0.3090.5450.101-0.343-0.023-0.0030.1400.8330.0301.0000.2240.013-0.429-0.043-0.2240.019-0.5250.5240.9360.081-0.7110.4670.8480.837-0.1480.1570.317-0.2130.210nannan
    Autonomía de la aeronave-0.078-0.2780.4230.0160.1600.383-0.4560.403-0.0890.4200.2241.0000.378-0.0980.5410.2690.400-0.5940.3370.6340.486-0.7150.8020.056-0.7320.033-0.4200.4780.353-0.314nannan
    Velocidad máxima (KIAS)0.241-0.0640.6340.0050.5870.648-0.7300.3630.9400.7170.0130.3781.0000.5000.4910.6270.718-0.0770.7000.7260.613-0.2230.1510.7270.9100.067-0.0570.3000.178-0.141nannan
    Velocidad de pérdida (KCAS)-0.3110.3430.1610.0260.8170.457-0.5670.245nan0.523-0.429-0.0980.5001.0000.5570.5570.6401.0000.4140.4340.3701.000-0.8740.0360.5320.121-0.2220.211-0.2220.381nannan
    envergadura0.105-0.0800.4210.0170.7740.825-0.6300.7190.6710.811-0.0430.5410.4910.5571.0000.6860.775-0.2580.5010.9500.806-0.4140.6480.2970.0850.032-0.0810.5160.167-0.106nannan
    Cuerda0.2000.1090.3960.0180.6700.787-0.8620.6230.5570.751-0.2240.2690.6270.5570.6861.0000.758-0.4990.7300.5950.724-0.4980.3550.975-0.228-0.041-0.0650.4180.193-0.129nannan
    payload0.229-0.1140.5670.1070.7050.854-0.8260.6600.8680.8820.0190.4000.7180.6400.7750.7581.000-0.0240.6700.5590.784-0.1420.4890.7110.846-0.0080.0530.4620.100-0.055nannan
    duracion en VTOLnannan-0.694-0.875-0.181-0.3830.519-0.617nan-0.401-0.525-0.594-0.0771.000-0.258-0.499-0.0241.000-0.694-0.402-0.3151.000nannannannan-0.188-0.9040.188-0.188nannan
    Crucero KIAS0.389nan0.9730.0990.4280.692-0.7690.5740.9440.7080.5240.3370.7000.4140.5010.7300.670-0.6941.0000.7230.636-0.8550.3590.5810.461-0.2430.1430.6080.0650.063nannan
    RTF (Including fuel & Batteries)nannan0.7900.5790.9360.965-0.4970.926nan0.9790.9360.6340.7260.4340.9500.5950.559-0.4020.7231.0000.948-0.402nannannannan0.0970.428-0.0970.097nannan
    Empty weight0.225-0.1210.503-0.0180.6620.944-0.7460.8340.9540.9330.0810.4860.6130.3700.8060.7240.784-0.3150.6360.9481.000-0.3860.7850.9800.2510.023-0.0290.4800.195-0.070nannan
    Maximum Crosswindnan0.038-0.854-0.961-0.181-0.4660.432-0.696nan-0.464-0.711-0.715-0.2231.000-0.414-0.498-0.1421.000-0.855-0.402-0.3861.000nannannannannan-0.943nannannannan
    Rango de comunicaciónnan-0.3250.480-0.118-0.8740.491-0.4090.6460.3230.5140.4670.8020.151-0.8740.6480.3550.489nan0.359nan0.785nan1.000nannannan-0.4300.6040.430-0.430nannan
    Capacidad combustible-0.018nan0.365-0.7570.2240.974-0.9700.929nan0.9760.8480.0560.7270.0360.2970.9750.711nan0.581nan0.980nannan1.0000.3770.817-0.080nan-0.0800.270nannan
    Consumo-0.240nan0.4610.5150.5320.2880.2960.0361.0000.7580.837-0.7320.9100.5320.085-0.2280.846nan0.461nan0.251nannan0.3771.0000.9980.113nan-0.3750.375nannan
    Precio-0.156nan-0.290-0.1710.1180.0360.024-0.203nan0.052-0.1480.0330.0670.1210.032-0.041-0.008nan-0.243nan0.023nannan0.8170.9981.000-0.1380.217-0.1380.134nannan
    Despegue0.735-0.1190.0630.174-0.2530.125-0.0010.1380.7940.0900.157-0.420-0.057-0.222-0.081-0.0650.053-0.1880.1430.097-0.029nan-0.430-0.0800.113-0.1381.000-0.010-0.6390.610nannan
    Propulsión horizontal0.154-0.1090.5630.0580.2610.476-0.4710.612nan0.4670.3170.4780.3000.2110.5160.4180.462-0.9040.6080.4280.480-0.9430.604nannan0.217-0.0101.0000.118-0.083nannan
    Propulsión vertical0.6710.1870.120-0.174-0.2530.072-0.1410.034-0.5350.023-0.2130.3530.178-0.2220.1670.1930.1000.1880.065-0.0970.195nan0.430-0.080-0.375-0.138-0.6390.1181.000-0.954nannan
    Cantidad de motores propulsión vertical-0.598-0.159-0.0760.1760.3650.0370.0750.0400.5740.0750.210-0.314-0.1410.381-0.106-0.129-0.055-0.1880.0630.097-0.070nan-0.4300.2700.3750.1340.610-0.083-0.9541.000nannan
    Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de la Tabla

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    ResumenCantidad
    0Total de valores1024.000
    1Valores numéricos820.000
    2Valores NaN204.000
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    Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

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    ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
    Modelo
    Velocidad a la que se realiza el crucero (KTAS)1.0000.1220.497-0.6070.4280.5530.5450.4230.6340.1610.2080.4210.3960.5670.503
    Techo de servicio máximo0.1221.0000.083-0.0170.1210.1170.1010.0160.0050.0260.0070.0170.0180.107-0.018
    Área del ala0.4970.0831.000-0.7780.8350.970-0.0230.3830.6480.4570.7110.8250.7870.8540.944
    Relación de aspecto del ala-0.607-0.017-0.7781.000-0.624-0.790-0.003-0.456-0.730-0.567-0.689-0.630-0.862-0.826-0.746
    Longitud del fuselaje0.4280.1210.835-0.6241.0000.8060.1400.4030.3630.2450.5950.7190.6230.6600.834
    Peso máximo al despegue (MTOW)0.5530.1170.970-0.7900.8061.0000.0300.4200.7170.5230.7680.8110.7510.8820.933
    Alcance de la aeronave0.5450.101-0.023-0.0030.1400.0301.0000.2240.013-0.429-0.343-0.043-0.2240.0190.081
    Autonomía de la aeronave0.4230.0160.383-0.4560.4030.4200.2241.0000.378-0.0980.1600.5410.2690.4000.486
    Velocidad máxima (KIAS)0.6340.0050.648-0.7300.3630.7170.0130.3781.0000.5000.5870.4910.6270.7180.613
    Velocidad de pérdida (KCAS)0.1610.0260.457-0.5670.2450.523-0.429-0.0980.5001.0000.8170.5570.5570.6400.370
    Velocidad de pérdida limpia (KCAS)0.2080.0070.711-0.6890.5950.768-0.3430.1600.5870.8171.0000.7740.6700.7050.662
    envergadura0.4210.0170.825-0.6300.7190.811-0.0430.5410.4910.5570.7741.0000.6860.7750.806
    Cuerda0.3960.0180.787-0.8620.6230.751-0.2240.2690.6270.5570.6700.6861.0000.7580.724
    payload0.5670.1070.854-0.8260.6600.8820.0190.4000.7180.6400.7050.7750.7581.0000.784
    Empty weight0.503-0.0180.944-0.7460.8340.9330.0810.4860.6130.3700.6620.8060.7240.7841.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de la Tabla

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    ResumenCantidad
    0Total de valores225.000
    1Valores numéricos225.000
    2Valores NaN0.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
    Modelo
    Velocidad a la que se realiza el crucero (KTAS)nannannannannannannannannannannannannannannan
    Techo de servicio máximonannannannannannannannannannannannannannannan
    Área del alanannannan-0.7780.8350.970nannannannan0.7110.8250.7870.8540.944
    Relación de aspecto del alanannan-0.778nannan-0.790nannan-0.730nannannan-0.862-0.826-0.746
    Longitud del fuselajenannan0.835nannan0.806nannannannannan0.719nannan0.834
    Peso máximo al despegue (MTOW)nannan0.970-0.7900.806nannannan0.717nan0.7680.8110.7510.8820.933
    Alcance de la aeronavenannannannannannannannannannannannannannannan
    Autonomía de la aeronavenannannannannannannannannannannannannannannan
    Velocidad máxima (KIAS)nannannan-0.730nan0.717nannannannannannannan0.718nan
    Velocidad de pérdida (KCAS)nannannannannannannannannannan0.817nannannannan
    Velocidad de pérdida limpia (KCAS)nannan0.711nannan0.768nannannan0.817nan0.774nan0.705nan
    envergaduranannan0.825nan0.7190.811nannannannan0.774nannan0.7750.806
    Cuerdanannan0.787-0.862nan0.751nannannannannannannan0.7580.724
    payloadnannan0.854-0.826nan0.882nannan0.718nan0.7050.7750.758nan0.784
    Empty weightnannan0.944-0.7460.8340.933nannannannannan0.8060.7240.784nan
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de la Tabla

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    ResumenCantidad
    0Total de valores225.000
    1Valores numéricos62.000
    2Valores NaN163.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Preparando datos para el heatmap ===\n", - "\n", - "=== Generando heatmap ===\n" - ] - }, - { - "data": { - "image/png": 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eTOPGjQXuJ0hxfk+dOsXznp6eHtOjRw+++xWXVcn706hRo3jKNT8/n1FVVWUAMM+ePeNsT0pKYoSFhZnZs2dztlWmbgi6t5dW/Bl9584dBgDz4sULznulr4mKSEhIYHbt2sW0adOGYbFYjJqaGjN16lTm/v37PNdfRZ5Dil/lMTEx4XutxcXFMQCYdevWVSof8+fPZwAwwcHBfN+PjIxkADD79u2rVLyEkKqjnnGEEEL+Cl5eXggJCeF5tW3bliucr68vWCwWRo4cyfVrtYaGBho1asQ1HO3bt2+YNGkSGjRoABEREYiKinImvY6IiKhw2ho3bgxtbW3O3xYWFgCKfpEuOe9N8faSPUEyMzMxf/58GBsbQ0REBCIiIpCRkUFWVhbfNIwYMYLr79atW0NPT48zLJOfrKwshISEoH///pCQkOBsL+6JVVJlzl9VFad59OjRXNubN28OCwsLBAYGcm1XVFREp06d+MbVu3dviIqKcv7+8OED3r59yzlfJfPi5OSE+Ph4vr2nilW0XK5du4aOHTtyyrYisrKy8PjxYwwcOJCrJ4OwsDCcnZ3x5csXnrSVHNoL/O5FUXwtXb9+HQUFBXBxceHKq4SEBDp06MApNyUlJRgZGWHz5s3Ytm0bwsLCKjR/2Lt37xAXF4fhw4dzDRHU09ND69atucL6+vpCQUEBvXr14kpL48aNoaGhUe41xGKx4OrqipcvX3J62yQlJeHKlSsYMGAAZ76ngoICrFu3DpaWlhATE4OIiAjExMQQGRnJt+4MGDCg3HwCtVMnK1rPjI2NoaioiPnz52P//v1cPRzLY2dnhw8fPsDf3x+LFi1Cq1atEBgYCBcXF/Tu3ZvTU64q5VXRfQMCAlBYWIipU6cKjOvhw4dIT0/HlClTKrUyZmXvIxoaGmjevDnXNhsbG55hyGfPnkWbNm0gIyPD+Xxwd3fnugaKj136Ghg+fDhPOit6vTZv3hwvXrzAlClTcP36daSnp1foPBQPWxfU47OyWCwW17yNIiIiMDY2hqamJmxtbTnblZSUoKamxncYd0XrhqB7+8ePHzF8+HBoaGhAWFgYoqKi6NChA4DKfUbzo66ujmnTpuH+/fv49OkT/v33X07PeAMDA66hsL169eL7/MHvVRFlXd+VufYLCgpw9OhRNGzYUODcl8XXw9evXyscLyGketACDoQQQv4KFhYWaNasGc92eXl5fP78mfN3YmIiGIaBuro633gMDQ0BFA2v6tq1K+Li4rB06VJYW1tDWloabDYbLVu2LHcoXUlKSkpcfxev8ihoe8l5aoYPH47AwEAsXboUdnZ2kJOT43wJ4peG4pXSSm8ra7W0lJQUsNlsgfuWVNHzx4+KigqkpKQQHR0tMExJxWnmN6+VlpYWz5c7QfNf8XuveJjf3LlzMXfuXL778BtyWKyi5fL9+/dKTyafkpIChmEE5hsAT3kqKytz/V08H19xWorza2dnx/eYxXOEsVgsBAYGYtWqVdi0aRPmzJkDJSUljBgxAmvXroWsrCzf/YvTI+gaiomJ4fydmJiI1NRUgaudlnXei7m6umLFihXw8PBA06ZNceLECeTl5XGtxDt79mzs2bMH8+fPR4cOHaCoqAghISGMGzeOb90p6/opqTbqZEXrmby8PO7cuYO1a9di0aJFSElJgaamJsaPH48lS5ZwNUDzIyoqCkdHRzg6OgIoKseBAwfC19cX165dg5OTU5XKq6L7Fs8fV1ZdqUgYfip7Hyldl4Ci+lSybC9cuIDBgwdj0KBBmDdvHjQ0NCAiIoJ9+/ZxFhEpPraIiAhPnPyuiYperwsXLoS0tDSOHz+O/fv3Q1hYGO3bt8fGjRv5fgYWK46j5A8uxURERAQOwyweWln6WpKSkuKJS0xMjOdzrXg7v/nXKlo3+JVdZmYm2rVrBwkJCaxZswampqaQkpLC58+f0b9//0p9RpcnLS0NqampSEtLAwBO2RRTUlKCvLx8tRxLWVmZ770hOTmZc6yK8vPzQ0JCAubPny8wTHEZVuf5IoRUDDXGEUII+b+ioqICFouFe/fu8V1AoHhbeHg4Xrx4AU9PT4waNYrz/ocPH2otrWlpafD19cXy5cuxYMECzvbi+Yr4SUhI4LvN2NhY4HEUFRXBYrEE7ltSRc8fP8LCwujcuTOuXbuGL1++lPuluvgLbHx8PE/YuLg4qKiocG2rTG+C4n0XLlyI/v37891H0CTnlSkXVVXVcuezKq34i158fDzPe8W9W0rnvTzF4c+dO8fp3SmInp4eZ9L89+/f48yZM1ixYgXy8vKwf/9+vvsUl1VFryFlZWWBq+YKavArSUdHB127dsXJkyexdetWeHh4wNjYGO3bt+eEOX78OFxcXLBu3TqufX/8+AEFBQWeOCvS46S26mRl6pm1tTVOnToFhmHw8uVLeHp6YtWqVZCUlORKY0UoKytj5syZCAoKQnh4OJycnKpUXhXdV1VVFQDw5csXgfNJlgxTGZW9j1TE8ePHYWBggNOnT3NdN7m5uTzHLigoQFJSEleDHL9roqLXq4iICGbPno3Zs2cjNTUVN2/exKJFi+Do6IjPnz8LXGW0OJ/8rlN1dXWBPaOKtwtqGK6KitYNfnXz1q1biIuLQ1BQEKc3HIBy586rqPfv3+P06dM4deoU3rx5A2NjYwwbNgzDhw+Hubk5V9ijR4/C1dW1QvEy5SyUYG1tDW9vbxQUFHDNG1e8kIeVlVWF8+Du7g4xMTE4OzsLDFN8PfxJPSCEVA0NUyWEEPJ/pWfPnmAYBl+/fkWzZs14XtbW1gB+P/yX/iJ84MABnjhL90KqLiwWCwzD8KTh8OHDAnsxnDhxguvvhw8f4tOnT7C3txd4nOKVXEuvHpeRkYErV65wha3o+RNk4cKFYBgG48ePR15eHs/7+fn5nGMWD0sqPVF/SEgIIiIi0Llz5zKPVRYzMzOYmJjgxYsXfPPRrFkzgY0MlSmX7t274/bt22UOeS1NWloaLVq0wIULF7iuKTabjePHj0NHRwempqaVyC3g6OgIERERREVFCcwvP6ampliyZAmsra3x7NkzgfGbmZlBU1MT3t7eXF82P336hIcPH3KF7dmzJ5KSklBYWMg3HRVd6XHs2LFISUnBsmXL8Pz5c7i6unJ9aWexWDxldPXq1SoNx6qtOvkn9YzFYqFRo0bYvn07FBQUyiyv/Px8gT3ziof3FffCrEp5VXTfrl27QlhYGPv27RMYV+vWrSEvL4/9+/dXauXHmriPsFgsiImJcV1vCQkJPKupFi+WUPoaOHnyJN84K3u9KigoYODAgZg6dSqSk5O5eqCWVjxUPioqiue9Ll26IDw8nO8w5zNnzkBGRgYtWrQQGPef+pO6Uawyn9EV9e3bN2zcuBG2trYwMzPD/v370bVrVzx58gSRkZFYtWoVT0McUL3DVPv164fMzEycP3+ea/vRo0ehpaVV4XJISEiAn58f+vbty7e3Z7Hi1WstLS0rFC8hpPpQzzhCCCH/V9q0aYMJEybA1dUVoaGhaN++PaSlpREfH4/79+/D2toakydPhrm5OYyMjLBgwQIwDAMlJSVcuXIFAQEBPHEWfzHesWMHRo0aBVFRUZiZmVWoh09Z5OTk0L59e2zevBkqKirQ19fHnTt34O7uzrdnDwCEhoZi3LhxGDRoED5//ozFixdDW1uba2VRflavXo1u3brBwcEBc+bMQWFhITZu3AhpaWmunhQVPX+CFK9OO2XKFDRt2hSTJ09Gw4YNkZ+fj7CwMBw8eBBWVlbo1asXzMzMMGHCBOzatQtCQkLo3r07YmJisHTpUjRo0ACzZs36o/Na7MCBA+jevTscHR0xevRoaGtrIzk5GREREXj27BnOnj3Ld7/KlMuqVatw7do1tG/fHosWLYK1tTVSU1Ph7++P2bNn8/1iBwDr16+Hg4MDOnbsiLlz50JMTAx79+5FeHg4vL29KzVvEADo6+tj1apVWLx4MT5+/Ihu3bpBUVERiYmJePLkCaSlpbFy5Uq8fPkS06ZNw6BBg2BiYgIxMTHcunULL1++LLOXlZCQEFavXo1x48ahX79+GD9+PFJTU7FixQqeoWhDhw7FiRMn4OTkhBkzZqB58+YQFRXFly9fcPv2bfTp0wf9+vUrN0+9e/eGiooKNm/eDGFhYa4erEBRQ5CnpyfMzc1hY2ODp0+fYvPmzZUe5lhSbdXJitYzX19f7N27F3379oWhoSEYhsGFCxeQmpoKBwcHgfGnpaVBX18fgwYNQpcuXdCgQQNkZmYiKCgIO3bsgIWFBafHaFXKq6L76uvrY9GiRVi9ejV+/vyJYcOGQV5eHm/evMGPHz+wcuVKyMjIYOvWrRg3bhy6dOmC8ePHQ11dHR8+fMCLFy+we/duvmmoiftIz549ceHCBUyZMgUDBw7E58+fsXr1amhqaiIyMpITrmvXrmjfvj3+/fdfZGVloVmzZnjw4AGOHTvGN86KXK+9evWClZUVmjVrBlVVVXz69Alubm7Q09ODiYmJwDTr6OjA0NAQjx49wvTp07nemzFjBry8vGBvb8+5T6WkpOD06dM4d+4ctm3bVuXPM37+9PMKKGqcVVRUxKRJk7B8+XKIiorixIkTePHixR+nx8/PDxs2bMCAAQOwZcsWdOzYkWs4qiDKysplNnhVRvfu3eHg4IDJkycjPT0dxsbG8Pb2hr+/P44fPw5hYWFO2LFjx+Lo0aOIiori6fF89OhRFBQUYNy4cWUe79GjR5yhzoSQWlbbK0YQQggh1al4FbOQkBC+7/fo0YNrlcJiR44cYVq0aMFIS0szkpKSjJGREePi4sKEhoZywrx584ZxcHBgZGVlGUVFRWbQoEFMbGws31XwFi5cyGhpaTFCQkJcqy8KWqUOADN16lSubcWram7evJmz7cuXL8yAAQMYRUVFRlZWlunWrRsTHh7O6OnpMaNGjeI5Dzdu3GCcnZ0ZBQUFRlJSknFycmIiIyPLOYtFLl++zNjY2DBiYmKMrq4us2HDBoGr0FXk/JXl+fPnzKhRoxhdXV1GTEyMkZaWZmxtbZlly5Yx375944QrLCxkNm7cyJiamjKioqKMiooKM3LkSObz589c8XXo0IFp2LAhz3H4ndOSXrx4wQwePJhRU1NjREVFGQ0NDaZTp07M/v37OWH4rahZ0XJhmKIVMMeMGcNoaGgwoqKijJaWFjN48GAmMTGRK40lVytkGIa5d+8e06lTJ845btmyJXPlyhWuMIKuf0GrgF68eJHp2LEjIycnx4iLizN6enrMwIEDmZs3bzIMwzCJiYnM6NGjGXNzc0ZaWpqRkZFhbGxsmO3btzMFBQV8z2FJhw8fZkxMTBgxMTHG1NSUOXLkCM9KoQxTtPLili1bmEaNGjESEhKMjIwMY25uzkycOLHC1yvDMMysWbMYAIyTkxPPeykpKczYsWMZNTU1RkpKimnbti1z7949nhU/i8/V2bNneeKoStlXpk7yO0cMU349e/v2LTNs2DDGyMiIkZSUZOTl5ZnmzZsznp6eZZ633NxcZsuWLUz37t0ZXV1dRlxcnJGQkGAsLCyYf//9l0lKSuIKX9HyKn1uK7MvwzCMl5cXY2dnxwlna2vLUy/8/PyYDh06MNLS0oyUlBRjaWnJbNy4kfM+v3tWVe8j/Mpnw4YNjL6+PiMuLs5YWFgwhw4d4nvs1NRUZsyYMYyCggIjJSXFODg4MG/fvuX5HKno9bp161amdevWjIqKCudePXbsWCYmJoYn3aUtXbqUUVRUZHJycnjeS0hIYCZPnszo6uoyIiIijKysLNO2bVu+9WLUqFGMtLQ0z3ZB56/052Bl6oagOBmGYR4+fMi0atWKkZKSYlRVVZlx48Yxz54947mfVnQ11R8/fjC5ubnlhqtpGRkZzPTp0xkNDQ1GTEyMsbGxYby9vXnCFa9qy28ld1NTU0ZfX7/MlYcZhmHatWvH9OrVq7qSTgipBBbDVKKfNyGEEELqJU9PT7i6uiIkJKTMSbwJIYT8f4qLi4OBgQG8vLwwZMiQuk4OqWNRUVEwMTHB9evXy+xJSwipGTRnHCGEEEIIIYT85bS0tDBz5kysXbsWbDa7rpND6tiaNWvQuXNnaogjpI7QnHGEEEIIIYQQ8n9gyZIlkJKSwtevXwWuXEv+fgUFBTAyMsLChQvrOimE/N+iYaqEEEIIIYQQQgghhNQSGqZKCCGEEEIIIYQQQuq1u3fvolevXtDS0gKLxcLFixfL3efOnTto2rQpJCQkYGhoiP379/OEOX/+PCwtLSEuLg5LS0v4+PjUQOq5UWMcIYQQQgghhBBCCKnXsrKy0KhRI+zevbtC4aOjo+Hk5IR27dohLCwMixYtwvTp03H+/HlOmODgYAwZMgTOzs548eIFnJ2dMXjwYDx+/LimsgGAhqkSQgghhBBCCCGEkP8QFosFHx8f9O3bV2CY+fPn4/Lly4iIiOBsmzRpEl68eIHg4GAAwJAhQ5Ceno5r165xwnTr1g2Kiorw9vausfRTzzhCCCGEEEIIIYQQUutyc3ORnp7O9crNza2WuIODg9G1a1eubY6OjggNDUV+fn6ZYR4+fFgtaRCEVlMlhBBCCCGEEEII+Uu12TOprpMgkMN3DaxcuZJr2/Lly7FixYoqx52QkAB1dXWuberq6igoKMCPHz+gqakpMExCQkKVj18WaowjhJB6rD5/cFbUg6n78TYxuq6TUSXm6gbY8+BcXSejyqa2GYi0jW51nYwqkZ8/E14hfnWdjCpzsXPC5tvH6zoZVTav40hsCTpR18mokrn2I/6aa2rxtQN1nYwqWdt9Il58fV/XyaiyRtqmeBwTXtfJqLIW+lY4Fnqt/ID1mHOz7vB+dqOuk1Flw5p0xYZbXnWdjCpZ0MkFu+6fretkVNk/bQfVdRL+OgsXLsTs2bO5tomLi1db/CwWi+vv4pnaSm7nF6b0tupGjXGEEEIIIYQQQgghpNaJi4tXa+NbSRoaGjw93L59+wYREREoKyuXGaZ0b7nqRnPGEUIIIYQQQgghhPylWPX4X01q1aoVAgICuLbduHEDzZo1g6ioaJlhWrduXaNpo55xhBBCCCGEEEIIIaRey8zMxIcPHzh/R0dH4/nz51BSUoKuri4WLlyIr1+/wsuraFj3pEmTsHv3bsyePRvjx49HcHAw3N3duVZJnTFjBtq3b4+NGzeiT58+uHTpEm7evIn79+/XaF6oZxwhhBBCCCGEEEIIqddCQ0Nha2sLW1tbAMDs2bNha2uLZcuWAQDi4+MRGxvLCW9gYAA/Pz8EBQWhcePGWL16NXbu3IkBAwZwwrRu3RqnTp2Ch4cHbGxs4OnpidOnT6NFixY1mhfqGUcIIYQQQgghhBDylxKq4cUIaou9vT1nAQZ+PD09ebZ16NABz549KzPegQMHYuDAgVVNXqVQzzhCCCGEEEIIIYQQQmoJNcYRQgghhBBCCCGEEFJLaJgqIYQQQgghhBBCyF+K9ZcMU/2bUM84QgghhBBCCCGEEEJqCTXGEUIIIYQQQgghhBBSS2iYKiGEEEIIIYQQQshf6m9ZTfVvQj3jCCGEEEIIIYQQQgipJdQYRwghhBBCCCGEEEJILaFhqoQQQgghhBBCCCF/KRZomGp9Qz3jCCGEEEIIIYQQQgipJdQYRwghhBBCCCGEEEJILaFhqoQQQgghhBBCCCF/KRatplrv/Od7xunr68PNza1W42OxWLh48WKVjrNixQo0bty4SnGUFhQUBBaLhdTU1GqNl3ArfZ49PT2hoKBQp2kaPXo0+vbtW6dpqA+SkpKgpqaGmJiYuk4Kl927d6N37951nQxCCCGEEEIIIfVAnfWM69WrF37+/ImbN2/yvBccHIzWrVvj6dOnaNKkSa2mKyQkBNLS0rV6TPLfNmTIEDg5OdV1MgiA9evXo1evXtDX1wcAxMTEwMDAAGFhYZzG74yMDPTq1QsJCQl49+5dmfFFR0dDX18fDx8+RLt27eDg4AB/f3+ecOfPn8emTZvw9u1bsNls6Orqolu3bti6dSsAYPz48Vi7di3u37+Ptm3bVmue/0QjTWMMt+0KczVdqEgrYIHfPtyLflHXyeLw87kCH+9zSElOhq6+Hsb+MwkNG1nxDZv8Iwkeew/hw7tIxH+JQ88BfTBu+iSuMDeuXMPt6zfx6eMnAICRmTGcx7vC1NKsxvNS0stbj/DM/z6yUjOgpK2G9sN6QNtUn2/YL28/4sImd57tI9fOhJKmag2nlJt4m5YQa2QFloQECuMT8DPgFtg/ksvZSRwS7VtD1NQYLAlxsNPSkXPrLgo+xgAAhHW0Id6iKYTV1SAkK4OsC1dQEBlVI+kPDbiPR363kZmaDlVtDTiM7Atdc6Ny9/v8/iOOrdkDVR0NjF83j7P9bchLPLgcgJTEH2AXsqGoroKWTvawbmtXI+kv9iYoFC8DgvEzLQMKWqpoNcgRGia6fMPGvYuB3/ZjPNsHrpgMBQ0VAMD7hy9w1+syT5jRuxZCRLTmHg/fBIXgxY2ifChqqaHl4K7QNNHjGzbuXQyubvPi2T5o5RROPkqKCgnHrcMXoNfIDF2nDKn2tBer7msq7HYwXt0LwfcvCQAADQMd2A/uAW0j/uelurTQtURbg0aQFZfCt8wUXI14iE8pCQLDCwsJoZNRUzTSNoGsuBTScjJxJyoMT78UfZZaqhvA3sgWSlJyEGYJISk7DfejX+J5XGSN5eH6pau4fPoCUpNSoKOvi9FTx8PCpiHfsClJyfDa546P76OQ8DUO3fv1wuhp43nCZWVmwtv9GJ7cC0ZWRibUNNXhPGksmrRsVmP5uHnFH35nLyEtOQXaeg0wYpIrzKwt+YYNuf8It3yvI/ZjDPLz86Gt1wD9Rg6GTTNbTpgvMbG44HUKMR8+4kfidwyf6Ipu/XvWWPqLhQbcR/DVW5y60dW5X8XqxruP8FqzG2o6Ghi//l/O9me3gvHqfgi+f44HAGgYNEDHITVfN0p7cuMuHvoGIiM1HWo6mujm0h965sbl7hf77iM8Vu2AWgNNTN6woBZS+lvEnVCEBzzCz7RMKGiqovkgB4GfGfHvP8F/+3Ge7f2WT+Tca69tO4aEyFieMDpWRnCYOrR6E1+GV7ce49n1e8hOzYSSthraDXWCVhnPUhc3H+HZPmLNDCjW8rMUIfzUWWPc2LFj0b9/f3z69Al6etw31CNHjqBx48a13hAHAKqqVDH/Vnl5eRATE6v2eCUlJSEpKVnt8dalmjpXFZGfnw9RUdFK7/fz50+4u7vDz89PYJjv37+je/fuAICbN29CROT3LdDOzg4TJkzA+PG/H8yL7wdHjhzBP//8g8OHDyM2Nha6ur8fZm7evImhQ4di3bp16N27N1gsFt68eYPAwEBOGHFxcQwfPhy7du2qF41xkqLi+JD0BX5vH2Jd90nl71CL7gXegfuuA5g4eyosrBri+mU/rPp3CXZ7HYSquhpP+Pz8fMjJy2OQ8zBcPuvDN85XYS/RrrM9xs+whJiYGC54n8WKuYuw6+gBKKvyfqGvCe+fvMRdbz/YO/eClrEewoNCcHn7UYxcMwOyygoC93NeNwtikuKcvyVla/fHIrEWzSBuZ4tsvxtgJ6dCvHVzSA/uj4zDR4G8fP47CQlBekg/MNk/kX3RF+yMTAjJyoLJy+MEYYmJovDbd+S9eg3pfr1qLP1vHoUh4PhFdBs9EA1MDfDs1kOc2nwQEzcugLyKosD9crJ/4vL+kzBoaILMtAyu9ySlpdCmtwNUtNQhLCKMyLDXuHLwFKTkZGFkY14j+YgKfY1HZ6+j9TAnqBvp4O29Z/DffRIDl0+GjJK8wP0GrZwCUYnf14+ErBTX+6IS4hi0cgrXtppsiIsKeY3gM9fRZrgT1I0a4O3dZ/DfdRKDVkwpOx+rpkKsjHwAQEZSKh6fC4CGMf8vm9WlJq6pTxEfYNmqCXRMDSAiKoJg31vw3rgfEzbMh5ySQo3kw1rDCE4WrXHl9X18SkmAna4lRjVzwo57Z5CWk8l3n2GNHSAtLgmfV3eQlJ0GGTFJCLF+D7L5mZ+DoKhn+J6ZikKGDTNVXfS3tkdm3k98+PGl2vPw8PY9eO45jHEzJsHMyhI3r/hj3YIV2O6xByqCPi8U5NF/5GBcPXeJb5wF+flYM28p5BQUMHvFAiirqCDp+3dISPFec9XlUdADnNjvgVHTxsOkoTluX72BLUvWYv0hN6io8X4veffqDayaNMIg1xGQkpHCveu3sX35BizfsR76xoYAgLzcPKhqqqN5+9Y4ccCjxtJe0uvgZ7hxzAfdXX/XDe9NBzBp08Jy68al/Sdg0NAEWXzqRsNWTaDjog8RMVEE+wbi5IZ9mLhxQY3VjdLCg5/C3+sCeowZDF0zQ4TefIDjG/Zh6pbFUFBRErhfTvZP+Ow9BkMrU546X9M+hr7Bk7MBaDW0G9SMGuDdvWcI2HMK/ZZNLPNe23/FJIGfGZ0mDkRhQSHn79ysn7i09hD0m1jUTCb4iHzyCvdO+aHDyF7QNNbF6zshuOLmheGrp5f5LDVi7cw6fZaqL4RomGq9U2fDVHv27Ak1NTV4enpybc/Ozsbp06cxduxYAMDDhw/Rvn17SEpKokGDBpg+fTqysrIExhsbG4s+ffpARkYGcnJyGDx4MBITE7nCXL58Gc2aNYOEhARUVFTQv39/znulh6lGRkaiffv2kJCQgKWlJQICAniOOX/+fJiamkJKSgqGhoZYunQp8vO5v6Rs2LAB6urqkJWVxdixY5GTk1Pm+SksLMTYsWNhYGAASUlJmJmZYceOHWXuU1pSUhKGDRsGHR0dSElJwdraGt7e3uXu5+npCV1dXUhJSaFfv37YunUr1zBMfkMiZ86cCXt7e87fDMNg06ZNMDQ0hKSkJBo1aoRz586Vedy9e/fCxMQEEhISUFdXx8CBA6sUn76+PtasWYPRo0dDXl6e08hS3jV1/PhxNGvWDLKystDQ0MDw4cPx7du3Ms9XyfOjr68PFovF8ypWkeultK9fv2LIkCFQVFSEsrIy+vTpU+mhmA8ePECHDh0gJSUFRUVFODo6IiUlBQBgb2+PadOmYfbs2VBRUYGDgwNiYmLAYrHw/PlzThypqalgsVgICgribHv9+jV69OgBOTk5yMrKol27doiK+t3DxcPDAxYWFpCQkIC5uTn27t3Lea/4GGfOnIG9vT0kJCRw/PhxsNlsrFq1Cjo6OhAXF0fjxo359kgr6dq1axAREUGrVq34vv/582e0a9cOsrKyuH37NnR0dKChocF5CQsLc8q85LasrCycOXMGkydPRs+ePXnuWb6+vmjbti3mzZsHMzMzmJqaom/fvti1axdXuN69e+PixYv4+fNnmfmoDY9iX+PQ48u48/F5XSeFx6UzF9ClhyO69uyOBvq6GDd9ElRUVXHtoi/f8OqaGhg/YzI6desCaWn+X5bmLJsPp369YGhiBB29Bpg6bwbYbAYvnj6vwZxwC7v+AA3bNYVVezsoaamh/fAekFGSx8vbj8vcT0pOGtLyspyXkFDtfmyLN7NFTnAICt5Hgf0jCT+v3gBLVBRiFoIbncRsGoIlIYHsC1dQ+DUeTHoGCr/Ggf39BydMwccY5N4LRsH7mukNV+zxtSA0tm8B244toaKtjq7O/SCnrIBngQ/K3O/akbNo2KoJtI31ed7TszSGuZ0NVLTVoaiugubdOkCtgSY+v/tYQ7kAwm8+gmkbW5i3tYWipipaDXaEtKIcIu6ElrmfhKw0pORlOK/S1w+LBa73peRlaiwPAPDqZjDM2tjCvG2TonwMcYSMojzelJMPyXLywWazcdvdB0162UNWVfCX/upQE9dU3ynOaObQFhp62lDRUkePcUPAsBnEvK65HmVtDKzx9MtbhH55i+9ZqfCLeIi0nEy00OXfG8tEpQH0lTThFXoNUUlfkfozE1/SviM29fczdnRyPN4kxuB7ViqSs9MR/CkciRlJ0FfUqJE8+J69iE7dHdC5hyN09Bpg9LTxUFFTwY3L1/iGV9NQh+u0CejQtROkBHxe3Lp2E5npmZi3ejHMrSyhqqEGc+uG0DcyqJE8AID/hSvo4NgJ9t27QFtXByMnj4GSqjJu+V7nG37k5DHoMbgvDM2MoaGthUFjRkBDSwPPH/2uR4Zmxhg2fhRa2rf9ox84/8TvutEKKtoa6OrcH3LKCnh6836Z+/m5n4FV66bQNtHnea/f1F91Q1/nV90Y+qtuvK+hXPAKvnobTTq2QtNOraGqrYHuowZAXlkRoQFl5+vK4VOwbtMUOiY1d+0I8jrwMUxaN4ZpW1soaKqgxeCukFaUw9u7z8rcr6zPDHFpSa734iKiISImWquNcc9vPIBlu6Zo2L4ZlLTU0G5Y0bPUq6AnZe5X189ShAhSZ1eiiIgIXFxc4OnpCYZhONvPnj2LvLw8jBgxAq9evYKjoyP69++Ply9f4vTp07h//z6mTZvGN06GYdC3b18kJyfjzp07CAgIQFRUFIYM+T1U4erVq+jfvz969OiBsLAwBAYGolkz/t3O2Ww2+vfvD2FhYTx69Aj79+/H/PnzecLJysrC09MTb968wY4dO3Do0CFs376d8/6ZM2ewfPlyrF27FqGhodDU1ORqkBB0bB0dHZw5cwZv3rzBsmXLsGjRIpw5c6bM/UrKyclB06ZN4evri/DwcEyYMAHOzs54/Fjwl7/Hjx9jzJgxmDJlCp4/f46OHTtizZo1FT5msSVLlsDDwwP79u3D69evMWvWLIwcORJ37tzhGz40NBTTp0/HqlWr8O7dO/j7+6N9+/Z/HF+xzZs3w8rKCk+fPsXSpUsrdE3l5eVh9erVePHiBS5evIjo6GiMHj26wnkPCQlBfHw84uPj8eXLF7Rs2RLt2rXjvF/e9VJadnY2OnbsCBkZGdy9exf379+HjIwMunXrhrwSPU3K8vz5c3Tu3BkNGzZEcHAw7t+/j169eqGw8PcvXEePHoWIiAgePHiAAwcOVCjer1+/chqrb926hadPn2LMmDEoKCgAABw6dAiLFy/G2rVrERERgXXr1mHp0qU4evQoVzzz58/H9OnTERERAUdHR+zYsQNbt27Fli1b8PLlSzg6OqJ3796IjBT8xeTu3bsC6/K7d+/Qpk0bmJubw9/fH7KyshXKHwCcPn0aZmZmMDMzw8iRI+Hh4cF1z9LQ0MDr168RHh5eZjzNmjVDfn4+njwp+4Hh/1l+fj6i3keisR13r+jGdk3wNjyi2o6Tm5uLwoICyMpV/DqoisKCAnz7FAfdhtxDWnQbGiP+A++Qj5K8V+zB4VnrcWGzOz5H1FxjDz8seTkIyUijIPrT742FhSj4/AXC2poC9xMxNkRhXDwkHTpCdtp4yIwZCfGWdkUtP7WosKAA8dFfYGDFPRzZ0MoMXyJjBO734s5jpCT+QPv+juUeg2EYRIe/R3LC9woNxfoThQWF+BEbDx0LQ67tOhZGSPxYdm8jn7WHcOLf7fDbfgxx72J43s/PzcOpRTtxcoEbru85hR+x8dWZdC7F+dC25D5P2paGSIz6XOa+F9YcxPF523B1mxfi3kXzvB/mexcSslIwb2vLZ+/qUxvXFFBULuxCNiRlaqY3ljBLCFpyqjy91T78+AJdRXW++1io6eFr2ne0M2iE+R1HYlb7Iehm1hIiQsICj2OorA0VaQVEJ1f/dVWQn4+P7z+gUTPuMrdpZot3r//88+Lpw8cwaWgO9x37MX6AM+aMmYoLJ86AXeJ5qToV5OcjJjIKVk0bc223btoIkW/KnkqjGJvNxs+fOZCWrdnG9LIU1w1Da+4fagytzcusG8/vPEbKtz+oG7U0pVBBQQHioj/z9Ho2sjHH5/e896JiYUGPkJL4Ax0GdK/pJPIoLChEUmw8tC25GwG1LAzxrZzPjMvrDuPUfDf4u51APJ/PjJLeP3wOg2aWEBWvnZE0xc9SDUo9SzWwNEZCOc9Sp1buwZHZG3Bx8xF8eVu7z1KElKVOV1MdM2YMNm/ejKCgIHTs2BFA0XCw/v37Q1FRETNmzMDw4cMxc+ZMAICJiQl27tyJDh06YN++fZCQkOCK7+bNm3j58iWio6PRoEEDAMCxY8fQsGFDhISEwM7ODmvXrsXQoUOxcuVKzn6NGjXim76bN28iIiICMTEx0NHRAQCsW7eOM8yt2JIlSzj/19fXx5w5c3D69Gn8+2/RnAdubm4YM2YMxo0bBwBYs2YNbt68WWbvOFFRUa40GhgY4OHDhzhz5gwGDx4s+KSWoK2tjblz53L+/ueff+Dv74+zZ8+iRYsWfPfZsWMHHB0dsWBB0bwGpqamePjwYbm9kkrKysrCtm3bcOvWLU4vJUNDQ9y/fx8HDhxAhw4dePaJjY2FtLQ0evbsCVlZWejp6cHW1vaP4yvWqVMnrnPg4uJS7jU1ZswYTnhDQ0Ps3LkTzZs3R2ZmJmRkyn/QKTnUecaMGYiPj0dISAhnW3nXS2mnTp2CkJAQDh8+zOlh5+HhAQUFBQQFBaFr167lpmnTpk1o1qwZVyNww4bcc6oYGxtj06ZNnL8r0vNuz549kJeXx6lTpzi/vJqamnLeX716NbZu3crpfWpgYIA3b97gwIEDGDVqFCfczJkzuXqobtmyBfPnz8fQoUVzUGzcuBG3b9+Gm5sb9uzZwzctMTEx0NLS4vuei4sLWrdujfPnz0NYWPAXB37c3d0xcuRIAEC3bt2QmZmJwMBAdOnSBUBRvbp37x6sra2hp6eHli1bomvXrhgxYgTExX93iZeWloaCggJiYmLKvGb/n6WnpYNdyIaCInfPFgUlRaQklzNHWSV47T8CJVVlNGpas1/ci/3MyAbDZvP0OpKSk0F2Gv/hYNLysug0qi/U9LVQmF+It8Fh8NlyBAP+HQtts9r5lV1IpujLDpOdzbWdycoGS15O8H4K8hCSb4D8N2+RdfYShJUUIOHQERASQu7DsnsCVqfsjCwwbDZk5LkbXaXlZZGZms53n+SE77h92hfOS/+BUBn3ipzsn9j5zwoUFhSAJSSEbqMHwtC6ZuYgzMnMBsNmICnH/eVTUk4aP9P5Xz9S8jJoO6IHVPQ0UVhQiA+PXsLP7Rh6zHbhzM+moKGM9qN6Q0lbDfk/8xB+6zGubPZE/yUTIK+uXGP5kCqdD1lp/EznP+JBSl4G7Ub2LMpHfgEiH7/C1e3H0HP2KGiaFuUj4UMs3j0IQ/+lE6s9zaXV5DVV0u3TvpBVlIdBQ9PyA/8BKTEJCAsJITOXu6d2Zu5PyIjxbwBUlJKDnqIGCtiFOPHsOqTEJNDbsh2kxMRx4dXvH0bFRcQwv+NIiAgJgc0wuPLmPqKSvlZ7HtLT0sFmsyGvqMC1XV5RAanJqX8cb2J8Ar6HvUTbLvZYuH454r/EwX3nfrALCzHQZVjVEs1HRnpGUT4UuIcOyikoIC0ltUJxXDt/Gbk5OWjRoU21p6+iiuuGNL+6kVZG3Th1BS7Lple4btw65QtZJXkYWNVM3SgtO73y+UqK/4ab3pfhumJmpZ85q0Pur3utRKnGWUlZafwU8MwhJSeD1iOcoKyrAXZBIaIev4L/jhPoPsuZ7zxz32O+IjXuO9o696iRPPDDeZaSK/UsJS+N7HABz1IKsujo0geq+toozC/Au+DnuLjFA/3mjam1Z6n6hFZTrX/qtDHO3NwcrVu3xpEjR9CxY0dERUXh3r17uHHjBgDg6dOn+PDhA06cOMHZh2EYsNlsREdHw8KCu1tsREQEGjRowGmIAwBLS0soKCggIiICdnZ2eP78OdecUGWJiIiArq4upyEOAN8hcOfOnYObmxs+fPiAzMxMFBQUQE5OjiueSZO452Vq1aoVbt++Xebx9+/fj8OHD+PTp0/4+fMn8vLyKrUCa2FhITZs2IDTp0/j69evyM3NRW5ubpkLVERERKBfv348aa1MY9ybN2+Qk5MDBwcHru15eXmcBrbSHBwcoKenB0NDQ3Tr1g3dunVDv379ICUl9UfxFSvdU6oi11RYWBhWrFiB58+fIzk5GWw2G0BRg6GlJf/hG/wcPHgQ7u7uePDgAVcDXXnXS2nFaS7dmysnJ4drOGhZnj9/jkGDBpUZRlCvsvLibdeuHd8hEN+/f8fnz58xduxYrjpXUFAAeXnuh86Sx05PT0dcXBzatOF+qGzTpg1evBC8yMDPnz95GuiL9enTBz4+Pjh//nyFG7OBoh51T548wYULFwAU9egdMmQIjhw5wmmMk5aWxtWrVxEVFYXbt2/j0aNHmDNnDnbs2IHg4GBIlZhnRlJSEtmlGjaKFdfPkko25v0/Kf2swDBMtT1AXDh5FvcCg7B25yaI1dKvub/xZExgZzFFTVWuyYU1jXWRkZyGZ9fv19gDpKilGSQdO3P+ziqeU6lET1AARdkovY3rfRaY7Gz89A8EGAbsxG9gyUhDvHmzWm2MK5mekhjwfyBls9m4uOcY2g3oBmVN3vmmShKXEMe4tXORl5uHmNfvcfPERSiqKkPPsvwJvf9Y6XwwDHiuqV8UNFS4FjhQN9RBZko6XgUEcxrj1Ax1oGb4+/lG3agBfNYdwuugELQe0q360/8HePJh1ABZyWl4GRAMTVM95OXk4vaRi2jn3BMSNdSLjK8auKaKBfsG4nVwGEYungoRsZodXli6FhdlgX/dLs7emRe3kFtQ1Cvf720whtk64PLr+yhgF/UcyyvIw+4H5yAuLApDZW10N2+F5Oz0GukdV5Suit9XK4JhGMgpymPi7KkQEhaGoakxUpKScfn0hRppjOPgl48K7BZ8+x58jp3BzBXzIacgeC6w2sJz7hkGLD45YbPZ8NnjhfYDule4bjy8EojXwc/gvGRajdeN0njyIOD2y2azcX73UdgPdIJKBfNVU3jrgeBnKXkNZchr/P4RRs1QB1kp6QgPeMS3Me79gxdQ0FKFqr52Nab4Dwn+KISihioUNbifpTJT0hB2/cH/ZWMcqX/qtDEOKFrIYdq0adizZw88PDygp6eHzp2Lvgyw2WxMnDgR06dP59mv5ATqxQR9YSu5vTIT7TN8vmyUjv/Ro0ecnnaOjo6cXkLFqyj+qTNnzmDWrFnYunUrWrVqBVlZWWzevLnMIaalbd26Fdu3b4ebmxusra0hLS2NmTNnljm0kV+eSxMSEuIJV3LOs+LGq6tXr0Jbm/smLahxQVZWFs+ePUNQUBBu3LiBZcuWYcWKFQgJCfmj+IqVbngs75rKyspC165d0bVrVxw/fhyqqqqIjY2Fo6NjhYeEAkBQUBD++ecfeHt7c/W8/JPrhc1mo2nTplwNiMUquuBIRa770ueqeD6FkmVdem67suItLrdDhw7x9MQs/Ushvwbi0nWtvAYZFRUVzhx4pS1atAg2NjYYMWIEGIbhGrpeFnd3dxQUFHBddwzDQFRUFCkpKVAs0YPLyMgIRkZGGDduHBYvXgxTU1OcPn0arq6unDDJyckCy2z9+vVcvWEBYPny5cD/0ZoycvJyEBIWQkoydzmmpaTy9Jb7Ez7e53Du+Cms3LYe+kaG5e9QTSRlpcASEkJ2qQmcszOyIClX8WFFmoYN8PZRza16m//hIwrjSqykKFJUT1nS0mCyfjcis6SkuP4ujcks6klQssGOnZRS1NNOSAj4dW+oaVKy0mAJCfH0WMpOy+Dp5QAAeT9zER/9GQmfvuL60aIGeIZhAIbBOpc5GD5/EvQbmgAAWEJCUPr1gK+hp40fXxPx8MrNGmmMk5CRAkuIxdOjIScjm6e3XFnUDLTx4ckrge+zhFhQ1dNC+rfq64VaUnE+skv1gvuZkVW5fBjq4MPjonxkfE9BZlIqru85xXm/+DPr8OTVGLxqKuRUBU+wXlk1eU0BwKOrt/Hg8k0MXzAZ6rr8e3pXh+y8HBSy2ZAV5/4MlxaTRGYe/3lNM3KykZ6TxWmIA4DvmSkQYrEgLyGNpOyic8IASP71//iMJKjJKKCDoW21N8bJyctBSEgIqaU/L1LTeHrLVYaCkiJERES4empp6+ogNTkFBfn5EKnm+ddk5YrmryrdCy49LQ1y5eTjUdADuG/fi2mL58KqCf9RPrXld93g/pzLSs8UUDdyEP/xMxJivsL/6HkAv+vGWufZGL5gElfP0OCrt/DgcgBGLJxSo3WjNCm5X/kq1QsuKz0DMnx+SM/9mYO4j7GIj/kCP8+zAH7na+WIGXBeOAWGVjXTi7qYePFnRqme0z8zsiFRiXutqoE2op7wTsFSkJeP6NA3sO3Vns9eNYfzLFUqX9npWTy95cqiYdgA72rwWYqQyqjzxrjBgwdjxowZOHnyJI4ePYrx48dzvnA3adIEr1+/hrFxxR5uLS0tERsbi8+fP3N6x7158wZpaWmcXnQ2NjYIDAzk+oJcXnxxcXGc4W/BwcFcYR48eAA9PT0sXryYs+3Tp09cYSwsLPDo0SO4uLhwtj169KjMY9+7dw+tW7fGlCm/VzmraC+oknH06dOHM8SOzWYjMjKSp0dhSZaWljxpK/23qqoqz/xYz58/5/SOsrS0hLi4OGJjYys1HE9ERARdunRBly5dsHz5cigoKODWrVtwcHD4o/j4Ke+aevXqFX78+IENGzZwrqHQ0LInli7tw4cPGDBgABYtWsQ19BKo2PXCL82nT5+GmppamT3oylJ83Zdu7ClLcaNRfHw8pwdiycUciuM9evQo3xVQ1dXVoa2tjY8fP2LEiBEVPq6cnBy0tLRw//59rnkDHz58iObNmwvcz9bWFseP8y7LXmzJkiUQERHBiBEjwGazMWxY2b9wFxQUwMvLC1u3buUZCjxgwACcOHFC4PyV+vr6kJKS4loYJCoqCjk5OQJ7cy5cuBCzZ8/m2iYuLo6AwzPKTOffRFRUFEamJngRGoZW7X/3jHweGoYWbVtWKe4L3mdx1ssbK7ashYl57QxvKSYsIgI1PS3EvvkAo6a/h4fHvv4AQ9uKT3z8LTae75eaapOXD3ZeGtcmdmYWRPR1kffte9EGISGINNBBTpDgiasLvsZBzJJ7fh0hRQWwMzJrrSEOKDrvmgY6iA5/D3M7G8726PD3MG1qxRNeXFIc49dzTxfw9OYDfHoTif7TR0OhnEadgvyC6kl4KcIiwlDR1cTXiI/Qt/19Xr9GfIReo4pfy0mfEyBVxvXDMAySviRASbtmenOUzIcBTz4q/uU06XMCJH8N+ZbXUMGAZdwjD0Iv3UZ+Ti5aDekGacXq7SlUk9dUsO8tPLgUgGHzJ0LLsGZXhC1k2IhL/w5jZR28SYzhbDdW0UFEib9Lik1NhJWmIcSERZBXWHStq0jLg82wkZYjeGE1gAXhMuaV+1MioqIwNDXGy6dhaN7u96iVl0+fw641/6lYKsLMyhIPAu+AzWZzfpSM/xIHRWWlam+IA4ryoW9ihPBnL9Csze90hz97iSat7ATuF3z7Hg5v24spC2eicYum1Z6uyvpdN95x141X7wTUDQlM2MA9D/fTm/cR8zoSA2a48tSN+xdvYNj8STVeN0oTERGBlkEDRL18Cwu73w2eUa/ewbypNU94cUkJTN60kGtbyI17iH7zHoNnjoWiavVPAVCasIgwlHU1ERcRDb3Gv++1cRHR0K3UZ0Yi3x8Mo5++AbugAEbNecu1JhU/S31+/QFGTX6PVPr85gMMKvEs9T02vsYXK6qvhCrU35bUpjpvjJORkcGQIUOwaNEipKWlcU2UP3/+fLRs2RJTp07F+PHjIS0tjYiICAQEBPCsVAgAXbp04fR+cXNzQ0FBAaZMmYIOHTpwhsEtX74cnTt3hpGREYYOHYqCggJcu3aN73xdXbp0gZmZGVxcXLB161akp6dzNaIARfNsxcbG4tSpU7Czs8PVq1fh4+PDFWbGjBkYNWoUmjVrhrZt2+LEiRN4/fo1DA0F98wwNjaGl5cXrl+/DgMDAxw7dgwhISEwMKh4l1pjY2OcP38eDx8+hKKiIrZt24aEhIQyG+OmT5+O1q1bY9OmTejbty9u3LjBM0S1U6dO2Lx5M7y8vNCqVSscP34c4eHhnEYGWVlZzJ07F7NmzQKbzUbbtm2Rnp6Ohw8fQkZGhmuusGK+vr74+PEj2rdvD0VFRfj5+YHNZsPMzOyP4hOkvGtKV1cXYmJi2LVrFyZNmoTw8HCsXr26wvH//PkTvXr1QuPGjTFhwgQkJPzuZaKhoVGh66W0ESNGYPPmzejTpw9nhdHY2FhcuHAB8+bN4xpGLcjChQthbW2NKVOmYNKkSRATE8Pt27cxaNAgqKio8N1HUlISLVu2xIYNG6Cvr48fP35wzXcHANOmTcOuXbswdOhQLFy4EPLy8nj06BGaN28OMzMzrFixAtOnT4ecnBy6d++O3NxchIaGIiUlhafhqaR58+Zh+fLlMDIyQuPGjeHh4YHnz5/z7R1YzNHREQsXLuTpsVbSggULICwsDGdnZ7DZ7DIbCX19fZGSkoKxY8fyDKsdOHAg3N3dMW3aNKxYsQLZ2dlwcnKCnp4eUlNTsXPnTuTn53MNrb537x4MDQ1hZMR/kndxcfFaG5YqKSoOHfnfXe605FRgoqKD9JwsJGby711YW/oM7g+3tZthbGYCs4YWuH7lGn58+4ZufYrmJfE6cARJP5Iwa/E8zj4fI4t+qPj5MwdpqWn4GBkFEVER6OoXDce7cPIsTrh7Yc7S+VDTUEdKUlHPHwlJSUhKVby3dFXYOrbBjUPnoKavDU0jXYTfCUFmchqs7YsamB+cu46slHR0HV80nDzsxgPIqShCWVsNhQWFeBv8HFFPX8Np6vBaSW+x3NAwSLRqDnZKKtgpqRBvZQcmPx95EW85YSR7dAU7Iwu5d4tWk8wLewnxJo0h0cUeeU+fQ0hRAeKt7JBXcvVaUVEIlej1ISQvByE1VTA/c8BkcPesqIoW3e1xad8JaBo2gI6xPsJuP0RaUgqadG4NoGhuroyUNPSeNAIsISGoNeBemEJaTgbCoiJc2x9cvglNgwZQVFdGYUEhop5H4NX9EHQbXfZUAFVh1aUl7nhchIqeFtQMtfHuXhgyU9Jg3r7oS3iITyCyUjNg79oXABAe+BgyyvJQ1FQFu7AQHx6/QkzYW3Se+HuV8me+d6BmoAM5NSXk5+Ti9e0nSPqciNZDa27CcesurRDk4QNVPU2oGerg7b1nyExOg8WvfDz5lY+Ov/Lx6uYjyKooQFFTFYW/8hH9LAJdJhadaxFREZ7GQzGpoukKaqpRsSauqWDfQNw5dw19pzhDXkWJ0/NOTEIcYhI187nwIPoVBjbqiK/p3xGbkgi7BhaQl5DBk9g3AICups0hJyGNcy+LplR5ERcJe6Mm6G9tj8APoZAWlUQ385Z4+uUdZ4hqe8PG+Jr2HcnZ6RAWEoaZagPYapvg8uuyV538Uz0H9cWu9dtgaGYCU0tz3PT1x4/E73DoVXQNnzx0FMk/kjBt4e/njZgPRZO35/zMQXpaGmI+fISIiAh09Isaebr27g5/H1947j6Ebv16IuFrHHxOnkX3fj1rJA8A0K1/LxzYvBMGpkYwtjBDkF8Akr79QKceRT8EnjlyHCk/kjHx36JRHcG37+Hg5l0YMXkMjMxNOb0DxcTFIPVrpEFBfj6+xn759f8CpCQl4VNUNCQkJKBexgI8VcGpGwYNoGOij2e3gn/VjaIf126duoKMlDT0mTySb92QkpOBSKm68fBKIO6c80PfqS5QUK2dulFaqx4dcWHPMWgZ6qKBqQGeBj5A2o9kNOvSFgBw0/sy0lNS0X+KC4SEhKDegLvnnrS8LERERXm216SGnVvgnuclKOtpQs1AB+/uhyErJQ3m7YoWyAq9eBvZqRloP7o3AOB14BPIKMtDQUu1aM64J+H4FPYWHScM4Ik78sEL6DYyq92pAX5p3LUNAg4XPUtpGDXA67uhyExOg1WHoobrh+dvICslHQ7jij7rngc8hJyyApR+PUu9e/QCUU9fo/uUGhxyTkgl1HljHFA0VNXd3R1du3blGn5qY2ODO3fuYPHixWjXrh0YhoGRkZHAIWYsFgsXL17EP//8g/bt20NISAjdunXjarizt7fH2bNnsXr1amzYsAFycnJcvW9KEhISgo+PD8aOHYvmzZtDX18fO3fuRLduv+dS6dOnD2bNmoVp06YhNzcXPXr0wNKlS7FixQpOmCFDhiAqKgrz589HTk4OBgwYgMmTJ+P6df5LlgPApEmT8Pz5cwwZMgQsFgvDhg3DlClTcO0a/+Xa+Vm6dCmio6Ph6OgIKSkpTJgwAX379kVaWprAfVq2bInDhw9j+fLlWLFiBbp06YIlS5ZwNUg5Ojpi6dKl+Pfff5GTk4MxY8bAxcUFr179Hv6yevVqqKmpYf369fj48SMUFBTQpEkTLFq0iO9xFRQUcOHCBaxYsQI5OTkwMTGBt7c3Z5GBysYnSHnXlKqqKjw9PbFo0SLs3LkTTZo0wZYtW9C7d+8KxZ+YmIi3b9/i7du3PIsJMAxToeulNCkpKdy9exfz589H//79kZGRAW1tbXTu3LnCPeVMTU1x48YNLFq0CM2bN4ekpCRatGhRbu+wI0eOYMyYMWjWrBnMzMywadMmrl5iysrKuHXrFubNm4cOHTpAWFgYjRs35sz3Nm7cOEhJSWHz5s34999/IS0tDWtra84CGoJMnz4d6enpmDNnDr59+wZLS0tcvnwZJiYmAvextrZGs2bNcObMGUycKHgi73nz5kFYWBijRo0Cm82Gs7Mz33Du7u7o0qULT0McUNQzbt26dXj27Bk6dOiAPXv2wMXFBYmJiVBUVIStrS1u3LgBM7PfvT28vb0rPF9lTTNX1cPufr+/nExvW/TF1i8iGGtvHRW0W61o17kDMtLTcfroCSQnpUDPQA/LNq6GmkbRCn8pScn4kfiNa59ZY6dy/h/1LhJ3b96GmoYaDp3xAgBcu3gFBfn52LiMe2XooaNHYNgY/uVf3Uyb2yAnMxtPLt9GVloGlLXV0XumC+RUihqOs9MykJH8+97MLizE/TPXkJmSDhExUShrqaH3TBfo29Ts8JbS8h6HgiUiAsmuncCSEEdhXAKyzvgAeb+HrAvJyXFNM8VkZCLrjA8kOreHzJiRYGdkIi/0OXIf/+5lLKyhDpnhvxuGJDsX9XrOe/UGP/1uVFv6LVvaIjsjC/d9riMzNR2qOpoYOm8C5FWKel1kpqYj7UflGqDzc/Pg73kOGclpnLLpM3kkLFvW3IIgRs0aIjfzJ8Ku3kV2eiYUtVThOG0YZJUVAADZaZnITP49jKqwoBBPzt9EVmoGRERFoKClCsepQ9HA+vc9NC87F/dPXEV2eibEJMWh3EADPeeOgppBzc0BZGTXELlZ2Xh29S6y0zKhpKWGbtOGc+Ujq1Q9eHwugDsf04ZB11rwZ0FNq4lr6unNBygsKMT5nZ5c29v1c0T7ATUzf9+rhChIiYmjo1FTyEpIITEjGV6h15CaUzQETFZcCvISv3uP5BUWwCPkKnpZtsGU1v2RnZeL8IQoBLz/vUCVmLAoejdsB3kJaeQXFuB7VirOvriNVwmVG9lRUa07tkNGejrOe51CSnIyGujrYeH65VDVKGqITUlOxo/iXr2//Dvhd2/zj+8/4H7gHaiqq2GPtzsAQEVNFUs2rcLRvYcxb9w/UFJRRvf+vdB3KG+jRHVpad8GmRkZuHTiLFKTU6Cjp4s5axZBRb0oH6nJKUj6/oMT/rZfAAoLC+G1+xC8dh/ibG/rYI8Jc/8pyntSCpZO+b2A2bVzl3Ht3GWY2zTEos2raiQfDVs1wc/MbNzjqhsTOb3cMlPTkZZU2bpxv6hu7PDg2t6uv2OtrVRq1aopsjOycOeCPzJT06HWQBMj5k/m5CsjNa3Sdb6mGTazRG5WNl5cvV/0maGpCoepQyGjXPRM+5PPvTbkQiCyUzMgLCoCRU1VdJk6BA2suEcSpSUmITHqM7pOr5vGLJPm1sjJzEbIld/PUj1nOP9+lkrNQEaJBVzYBYV4cNa/6FlKVBRK2mroOcO51p+lCBGExVRkkjDyf83T0xMzZ85EampqXSeFkDL5+flh7ty5CA8P5wwvqQ/Cw8PRuXNnvH//nm/jXlna7JlUfqB67sHU/XibGF3XyagSc3UD7Hlwrq6TUWVT2wxE2ka3uk5GlcjPnwmvEL+6TkaVudg5YfNtwUPr/yvmdRyJLUGCey3/F8y1H/HXXFOLrx2o62RUydruE/Hi6/u6TkaVNdI2xeMY3vm2/mta6FvhWGjFOwLUR87NusP7WfX9yFNXhjXpig23vOo6GVWyoJMLdt0/W9fJqLJ/2tZcT/ia1P2w4FFJde3auG11nYQ6US96xhFCSHVwcnJCZGQkvn79yrWqcl2Li4uDl5dXpRviCCGEEEIIIYT8fagxjhDyV5kxo/4teFB6AQhCCCGEEEIIIf+/6s84LlJvjR49moaoEkIIIYQQQggh/0EsllC9ff2/+v/NOSGEEEIIIYQQQgghtYwa4wghhBBCCCGEEEIIqSU0ZxwhhBBCCCGEEELIX0qIxarrJJBSqGccIYQQQgghhBBCCCG1hBrjCCGEEEIIIYQQQgipJTRMlRBCCCGEEEIIIeQvxQINU61vqGccIYQQQgghhBBCCCG1hBrjCCGEEEIIIYQQQgipJTRMlRBCCCGEEEIIIeQvRaup1j/UM44QQgghhBBCCCGEkFpCjXGEEEIIIYQQQgghhNQSGqZKCCGEEEIIIYQQ8pdi0TDVeod6xhFCCCGEEEIIIYQQUkuoMY4QQgghhBBCCCGEkFpCw1QJIYQQQgghhBBC/lI0TLX+oZ5xhBBCCCGEEEIIIYTUEmqMI4QQQgghhBBCCCGklrAYhmHqOhGEEEIIIYQQQgghpPoNOLqorpMg0PlR6+o6CXWC5owjhJB67G1idF0nocrM1Q3QZs+kuk5GlTyYuh/RP77WdTKqzEBFGxkpKXWdjCqRVVT8a+rFu8SYuk5GlZmp6+PD9891nYwqMVZtgG/pSXWdjCpTk1NGakZaXSejShRk5ZGY/qOuk1Fl6nIq//myAIrK479eN9TklP/zeQCK8hH5Lbauk1ElJmq6//lnEKDoOYSQ6kDDVAkhhBBCCCGEEEIIqSXUM44QQgghhBBCCCHkL0WrqdY/1DOOEEIIIYQQQgghhJBaQo1xhBBCCCGEEEIIIYTUEhqmSgghhBBCCCGEEPKXEqJhqvUO9YwjhBBCCCGEEEIIIaSWUGMcIYQQQgghhBBCCCG1hIapEkIIIYQQQgghhPylaDXV+od6xhFCCCGEEEIIIYQQUkuoMY4QQgghhBBCCCGEkFpCw1QJIYQQQgghhBBC/lIs0DDV+oZ6xhFCCCGEEEIIIYQQUkuoMY4QQgghhBBCCCGEkFpCw1QJIYQQQgghhBBC/lJCtJpqvUM94wghhBBCCCGEEEIIqSXUGEcIIYQQQgghhBBCSC2hxjhCyF8pOzsbq1evxqdPn+o6KYQQQgghhBBSZ1gsVr19/b+ixjjyf8/T0xMKCgq1cix7e3vMnDmzVo5VnSp7jvT19eHm5lZj6amIf/75B3FxcdDT06vUfv/VMiKEEEIIIYQQ8t9ACziQ/4TyWsxHjRoFT0/P2knM/6EhQ4bAycmpwuFDQkIgLS1dgykqm7e3NxITE3Hp0qVK73vhwgWIiorWQKpqnp/PFfh4n0NKcjJ09fUw9p9JaNjIim/Y5B9J8Nh7CB/eRSL+Sxx6DuiDcdMncYW5ceUabl+/iU8fi3oXGpkZw3m8K0wtzWo8L+VppGmM4bZdYa6mCxVpBSzw24d70S/qLD1XLlzCuZOnkZyUBD0DfUyaPhVWjW0Ehn8Z9gIHd+3Fp+gYKKuoYNDwIejRrzdXGJ/T5+DrcxnfE79BTkEe7ezbw3XSeIiJiwEAfH0uwdfnCr7FJwAAdA30McLVGXatWvxxPhiGwcHDh+Fz6RIyMjLQ0NIS8+fNg5GhYZn7Bd66hf0HD+LL16/Q0dbGlEmT0NHenvP+s7AwHDt+HBHv3uHHjx/YsnEj7Dt04IqjWcuWfOOePm0aXEaO/OM8/S31ws/nCi54n+XkY9w/k9CwkbXAfBzZexBR7z4g7stX9BzQB+OnT+YKc/2KH1c+jDn5MK/RfJTme+ESLnifRXJSEnT19TFhxhRYlZGvw7v348O7SMR9+YreA/thwowpNZ5Gn7Pn4X38JJJ+JEHf0ADTZ89AI9vGAsOHPQ3DbrediPkYDWUVFQx3GYG+A/px3o+O+gj3A4fx7u1bJMQn4J9ZMzB4+BCeeL5/+459u/bgcfAj5ObkooGuLhYsXQgzi/LL6NzZczh+7BiSfiTBwNAQs+bMgq2trcDwz54+g9t2N0R//AgVVRU4Ozuj/8ABXGFuBd7Cgf0H8PXLF2jr6GDylEmw79iRb3yeHp7Yt2cvhgwbitlzZnO2r1qxEld9r3KFbWhlhSOeR8rNEwD4nL0A7+MnkfyrLP6ZPb3Msnj+NAy73XaVKIvh6FOiLK74XMZ1v2v4GBUNADAzN8P4qRNh2dCSK57v375j/669JcqiAebX87L49u0b9uzajYcPHyI3Jxe6erpYvHQJLCwsAAAtmjXne/xp0/+Bs4tzufmqi3oxqHd/JPz6zCup38D+mD1/brlpri/5KCgogMchdwT430BSUhKUlVXQvacTRo0dDSGh6ukrc9XnMte9dfz0yWXeW933HODcW3sN7IsJ07nvrZ+iY3DC/Sg+vIvEt4REjP9nMvoM7l8taS1Wl88g2dnZ2LV3L+7cuYO09HRoamhg6ODBGDhgAAipKdQzjvwnxMfHc15ubm6Qk5Pj2rZjx466TuJ/UmFhIdhsdrnhJCUloaamVuF4VVVVISUlVZWkVcmwYcPg6+sLYWHhSu+rpKQEWVnZGkhVzboXeAfuuw5gkMtQbD+8B5Y2Vlj17xJ8T/zGN3x+fj7k5OUxyHkY9I35P+S8CnuJdp3tsWbHRmzatx2q6mpYMXcRkr7/qMmsVIikqDg+JH3Btrun6jopuHPzNg7s2IOhLiOwx+MgrGyssWTuAnxLSOQbPiEuHkvnLoSVjTX2eBzEEOfh2Oe2G/dv3+WEuXX9Jo7sP4SRY0bh4ElPzFowF3cCg+Cx/xAnjIqqKsZMGoed7vuw030fGje1xcoFSxHzMfqP83L02DGc9PbGv3Pm4OiRI1BWVsbU6dORlZUlcJ+Xr15h0dKlcOreHd7HjsGpe3csWLwY4eHhnDA/f/6EiYkJ/p0zR2A8/levcr2WLVkCFouFTgK+7FfE31Iv7gUG4fCu/RjsMgxuh/fC0sYKK8vJh7y8AgY5DxWYj/Cwl2jfuSPW7tiEzfu2Q0VdDctruX7fDbyNQzv3YYjLcOw8sh9WjayxfO5CgXUnPz8f8goKGOIyHAYC8lXdAm/cxM5tO+DsOgruxz3RqHEjzJsxB4kJvA0CABD3NQ7/zpyDRo0bwf24J5xdXbBjy3YE3brNCZOTkwNNbS1MnDYZSsrKfOPJSE/HlHETISIigs07tuHYmZOYOvMfyMjKlJvmgBsB2L51G1zHuMLrxDE0tm2MWdNnIkFgmr9i1oyZaGzbGF4njmG062hs3bIVtwJvccK8evkSSxYtRnen7jjufQLdnbpj0YJFXPW82JvXb3DRxwfGJsZ8j9eqdSv4+ftxXtt3bC83T0BRWezatgMuri44fNwDNo1t8O+MueWUxVzYNLbB4eMecHZ1xo4tblxlEfb0GTp3dcCOfTux78gBqGuoY+60Wfj+7TsnTEZ6OqaOmwQRERFs2rEVXmdO1PuySE9Px4Sx4yEsIgK3HTtw6uxpzJg5g+v5pmQZ+Pn7YcmypUX33E6dys1XXdWLg0fdcfHaFc5r++6i5/+OXcpPc33Kx0mv47h0/iJmzpuN42e8MXn6FHgfP4nzp8/+UT5KuxsYhEM792Gw8zDsdN+Hho2ssGLeInwr67NPQR6Dy7i35ubkQkNTE6MmjoWiklK1pLO0unwG2ebmhuBHj7BqxQqc9fbG8GHDsHnbNgTdvStwn/8aIRar3r7+X1FjHPlP0NDQ4Lzk5eXBYrG4tt29exdNmzaFhIQEDA0NsXLlShQUFHD2T01NxYQJE6Curg4JCQlYWVnB19eX6xjXr1+HhYUFZGRk0K1bN8THx3PeY7PZWLVqFXR0dCAuLo7GjRvD39+/zDRnZWXBxcUFMjIy0NTUxNatW3nC5OXl4d9//4W2tjakpaXRokULBAUFlRnvihUroKurC3FxcWhpaWH69OkVjq94uKmvry8sLS0hLi6OQ4cOQUJCAqmpqVzHmT59Ojr8+tWI3zDVy5cvo1mzZpCQkICKigr69//961jpYaqxsbHo06cPZGRkICcnh8GDByMxkf+XLQCIiYkBi8XCmTNn0K5dO0hKSsLOzg7v379HSEgImjVrximn799/PzCHhITAwcEBKioqkJeXR4cOHfDs2TPO+0FBQRATE8O9e/c427Zu3QoVFRVOeZcepqqvr481a9ZwylJPTw+XLl3C9+/fOXmytrZGaGgoVx7Onz+Phg0bQlxcHPr6+nzLvzpdOnMBXXo4omvP7migr4tx0ydBRVUV1y768g2vrqmB8TMmo1O3LpCW5t9wOmfZfDj16wVDEyPo6DXA1HkzwGYzePH0eQ3mpGIexb7GoceXcedj3aflwumzcOzZHd1794Cuvh4mzZwGVTU1+Ppc5hv+6sUrUFNXw6SZ06Crr4fuvXuga4/uOOd9hhMmIvw1GlpboWPXztDQ1EDTFnawd+iE92/fc8K0bNsazVu3hI5uA+joNsDoiWMhISmJt68j/igfDMPA+/RpuI4ejU4dO8LYyAgrly1DTk4O/G/cELif96lTaGFnB9dRo6Cvrw/XUaPQ3M4OJ0+f5oRp07o1pkyaVGbDmoqyMtfrzt27aNa0KXS0tf8oP8DfUy9K52P89MlQUVWFX7n5cBDYS3nOsgUl8qGLafNm/spHWI3lozSfU+fRtWc3OPZygq6+HibMmAIVNTX4XbzCN7y6pgYmzpyKzt271lrv69MnT6FHn17o1bc39A30MX3OTKipq8HnnA/f8Jcu+EBdQx3T58yEvoE+evXtjR69e+LU8ZOcMBYNLTF1xjR06eoAMTH+PbFPHD0ONXV1LFq+BJYNLaGppYlmzZtBW0en3DR7nziJ3n16o0/fvjAwMMDsObOhrq6O8+fO8w1/4fwFaGhoYPac2TAwMECfvn3Rq3cvnDh+nBPmlPcpNG/RHKNdR0NfXx+jXUfDrrkdTp3k/kEkOzsby5YuxaLFiyEnK8f3eKKiolBWUeG85OXly80TAJw5eRo9+vREzxJloaquhosCy+Ii1EqURc++veHUuwdOH/fmhFm2ZgX6DeoPEzNT6OnrYd7i+WAzbDwN+f2ZfuLoCaipq2Hh8sWcsmhaz8vi2FEvqKmrYdnyZWho1RBaWlqwa94cOiXSXLIMlFVUcPfOHTRt1hTaOuXfc+uqXigqKkJZRZnzenj/AbR1tNG4ieCehvUxH+GvwtG2Qzu0btsGmlqa6Ni5E5q3aI63EW//KB+lXTx9Hg49iu6tDfT1MGH6FKioqcLPp4x764yp6NzNAVIC7q2mFmYYM3UCOnTpCFEB+aqKun4GeRkejp5OTmjWtCm0tLTQv29fmBgbIyLiz56pCKkIaowj/3nXr1/HyJEjMX36dLx58wYHDhyAp6cn1q5dC6CoIa179+54+PAhjh8/jjdv3mDDhg1cvaays7OxZcsWHDt2DHfv3kVsbCzmzv3d3X3Hjh3YunUrtmzZgpcvX8LR0RG9e/dGZGSkwHTNmzcPt2/fho+PD27cuIGgoCA8ffqUK4yrqysePHiAU6dO4eXLlxg0aBC6desmMN5z585h+/btOHDgACIjI3Hx4kVYW1tXKr7s7GysX78ehw8fxuvXrzFy5EgoKCjg/PnfD4aFhYU4c+YMRowYwTcdV69eRf/+/dGjRw+EhYUhMDAQzZo14xuWYRj07dsXycnJuHPnDgICAhAVFYUhQ3iH5JS2fPlyLFmyBM+ePYOIiAiGDRuGf//9Fzt27MC9e/cQFRWFZcuWccKnp6fDxcUF9+7dQ3BwMAwNDeHk5ISMjAwAvxvanJ2dkZaWhhcvXmDx4sU4dOgQNDU1BaZj+/btaNOmDcLCwtCjRw84OzvDxcUFI0eOxLNnz2BsbAwXFxcwDAMAePr0KQYPHoyhQ4fi1atXWLFiBZYuXVpjQ6nz8/MR9T4Sje2acG1vbNcEb8Or7yEiNzcXhQUFkJX77/UcrCn5+fmIfPceTZpzX/9NmjdDRPhrvvtEhL/mCd+0RTNEvn3H+RGhYSNrRL57j3dvisov/mscQoIfo3lr/kNQCwsLEXTzFnJzcmBhZck3THm+xsUhKSkJLVv8PoaYmBia2Nri5atXAvd7GR6OFi2409WyRYsy9ylPUlIS7j94gD69ev1xHH9LvcjPz8eH95GwtWvKtd3Wrinehr+ptuPUdv0uytd72NqVqjt2TRFRjfmqivz8fLx/+w7NW3AP57Nr0RzhL/lf369fhcOuVPjmLVvg7Zu3XD8Sluf+vfswszDH0gWL0aurE8aMGIXLPuVPvZCfn4+3b9+iRUvuOtm8ZQu8evmS7z6vXr1C81LhW7ZqiYg3EZw0v3r5ireet2zJE+fmjZvQpk0bnnNW0rOnz9DNwRED+w/AujVrkZycXKF8vX/7jufcFpUFb+884M/KIjcnBwUFBZCT+92Q+OBXWSxbsAS9u/bA2BGjcUXAjy2l01xXZXH37j1YWFhg4fwF6ObgCOfhI3HR56LAtCYlJeHB/Qfo3ae3wDAl81VX9aJ0Om5cuw6n3j3/aAL4usyHTSMbPA0JReynWADAh/eRePniBVq1aVXJXPDi3Fub8/vM4P9cUh/U9TNI40aNcPfePXz79g0MwyD06VPEfv6MVi3+fOoPQspDc8aR/7y1a9diwYIFGDVqFADA0NAQq1evxr///ovly5fj5s2bePLkCSIiImBqasoJU1J+fj72798PIyMjAMC0adOwatUqzvtbtmzB/PnzMXToUADAxo0bcfv2bbi5uWHPnj08acrMzIS7uzu8vLzg4OAAADh69CjXL5JRUVHw9vbGly9foKWlBQCYO3cu/P394eHhgXXr1vHEGxsbCw0NDXTp0gWioqLQ1dVF8+bNKxVffn4+9u7di0aNGnHiHTJkCE6ePImxY8cCAAIDA5GSkoJBgwYJPOdDhw7FypUrOdtKxlfSzZs38fLlS0RHR6NBgwYAgGPHjqFhw4YICQmBnZ0d3/2K0+/o6AgAmDFjBoYNG4bAwEC0adMGADB27FiuBq7OnTtz7X/48GEoKCjgzp076NmzJwBgzZo1uHnzJiZMmIDXr1/D2dkZ/fr1Q1mcnJwwceJEAMCyZcuwb98+2NnZcc7P/Pnz0apVKyQmJkJDQwPbtm1D586dsXTpUgCAqakp3rx5g82bN2P06NFlHutPpKelg13IhoKiItd2BSVFpFTgS05Fee0/AiVVZTRq+me/QP+N0lPTwC5kQ1GJ+9wrKioiOYn/uU9JToFiqbJSVFJEYWEh0lLToKyiDPsunZCWkoo5k2eAYRgUFhaiZ7/eGOI8nGu/6KiPmDVxGvLy8iApKYml61ZCz0D/j/KSlJQEAFAuNfxEWUkJ8QKG7BTvx2+f4vj+hK+fH6SlpbnmfKmsv6Ve/M6HAtd2eSUFpCanVNtxfuejSfmBq0F6WlHdUVDiUz4C6k5tS0tNRWFhIc+QLEVlJYH1OykpGc2VS4VXUkJhYSFSU1OhoqJSoWPHf43DpfM+GDx8KJxdXRDxOgI7tm6HmJgYuvXoLnC/1F9pVlLiHh6nrKSERz/410l+dVhJSZkrzUlJSVAqlS8lZe56fuP6Dbx7+w4eXp4C09eqdWt06tIZmhqaiIuLw4H9+zF10hQcPe4FMTExgfsJKgslZUUkC7jXJCclQ0m59L227LLYv3s/VFVV0bTEDyZFZXERg4cPwUhXF0S8foMdW7dDVEy03pZF3NevuHD+AoaNGI7Rrq54/fo1tm3ZCjFRUTj17MFzXD/fq5CWlhY4B2BJdVkvSroXdBeZmZlw6lnxOY1Lqst8jBjljMzMLIwcNAxCQkJgs9kYP3kiujh2/aO8lFR8b+V5zlBUxLNq/MyobnX9DDJv9mysWb8eTr17Q1hYGEJCQliyaBEaN25cqXjqMxb+f4eD1lfUGEf+854+fYqQkBBOTzigqJdITk4OsrOz8fz5c+jo6HAa4viRkpLiNMQBgKamJr59K5pXIT09HXFxcZwGoGJt2rTBixf8J4yPiopCXl4eWrX6/QuXkpISzMx+T/D97NkzMAzDk67c3FwoC5hjYtCgQXBzc4OhoSG6desGJycn9OrVCyIiIhWOT0xMDDY23BPLjxgxAq1atUJcXBy0tLRw4sQJODk58XyQF3v+/DnGjx/P973SIiIi0KBBA05DHABYWlpCQUEBERERZTbGlUynuro6AHD1BFRXV+eUE1D0gbxmzRoEBQXh27dvKCwsRHZ2NmJjY7nyf/z4cdjY2EBPT69Cq75WJB1A0WTJGhoaiIiIQJ8+fbjiaNOmDdzc3FBYWMh3Lrvc3Fzk5uZybRMXFy83bSWV/mGYYZhqWy78wsmzuBcYhLU7N3EWECAllDrPDMo596XDM8Wbi7a/ePYcp7xOYOqcGTBvaIG4L1+xf8ceKHocwwjX3xNr6+g2wF7PQ8jMyMT9oLvYunYjNu3eXqEGuWv+/li3cSPnb7dfQ6lLp7tC19Gf7FOGy76+6Na1a6XrAD9/S73gSTPDoLqeq8+fPIO7gbexdufmWq/ff3S91TKe5DAM77aS4cF7P+C3vSxsNhvmFuaYOLVoARFTMzNEf/yIi+cvlNkAJCjN5Z5XPuVQlGauQKXC/C6/xIREbNu6DTt37yyz3jp0deD838jYCBaWFujTszce3H+Ajp3KbwjivV7KXuSL95wLLouTXicQeCMAO/fv5soDm82GmYU5JnDKwhQxH6Nx6bxPvSyL4jRbWFpgytSiSfjNzIuun/Pnz/NtjLty+QocuzlW6p5bF/WiJN/LV9CiVUuoqKr+0f6cdNVBPgIDbiLg2nUsW7MCBoaGiHz/Hru27YCKatFCDtWiss8ltay+PYOcOnMGr8LDsW3zZmhqaODZ8+fYuHkzVJSV0aK54J6+hFQFNcaR/zw2m42VK1dyzVlWTEJCApKSkuXGUXr1TBaLxXn4KbmtpLJu9KX35YfNZkNYWBhPnz7laZyRkeE/KXCDBg3w7t07BAQE4ObNm5gyZQo2b96MO3fuVDg+SUlJnnQ3b94cRkZGOHXqFCZPngwfHx94eHgITHtFzmkxQeepIh+UJculOGzpbSUXoHB1dUVSUhJ27NgBPT09iIuLw9raGnl5eVzxPnz4EACQnJyM5OTkcuceqkg6AHDSwi9v5V0T69ev5+ppCBQN0x06eVSZ+wGAnLwchISFkFLqF8+0lFSeXkF/wsf7HM4dP4WV29ZD36h2Jk3/r5BTkC8696V+RU9NSeXpLVdMkU/PrNSUFAgLC0NOvmholNchD3RydED33kVfmgyMDJGTk4OdG7dh2KgRnNXWREVFofVrfh9TCzO8f/sOF89ewIx/Z6M87du1g1XDhpy/8/LzAQA/kpK4ft1PTkmBUhmTNSsrK/P8Al3ePmUJe/4cnz59wvo1a/5o/2J/S70QnI+0asrHWZw7fgqrtm2AQS3Wbzl5/nUnLSWVp7dcXZFXUICwsDBPL5mU5BSBE5grKyvx9NRKTS6q3/IKFZsbDQCUVZShZ2jAtU1PXx93bgWVuZ/CrzTzrZPKgtLMW4dTUpJ/pVmBE6Z0vlKSkzn1/O3bCKQkJ2O08+/PrMLCQoSFheHcmbO49/A+3x+iVFRUoKGpic8lfjTj53dZlE6D4LIo6i3GW3b8ysL72Ekc9/DCtj1uMCq18ISyijL0DfW5ttXnsgCKzquBAff1o2+gj9slFhooFhYWhk+fPmHN+rU87/FTl/WiWEJ8PJ4+CcWaTbyjSCqqLvOxb8cejBjljC6/GqeNjI2QGJ+A455eVW6M49xbeZ4zUnl6WNel+vQMkpOTgz379mHLxo1o+6vzhYmJCd6/f4/jJ09SYxypMTRnHPnPa9KkCd69ewdjY2Oel5CQEGxsbPDlyxe8f/++/Mj4kJOTg5aWFu7fv8+1/eHDh5zl4UszNjaGqKgoHj16xNmWkpLClQZbW1sUFhbi27dvPOnW0NAQmB5JSUn07t0bO3fuRFBQEIKDg/Hq1as/jq/Y8OHDceLECVy5cgVCQkLo0YP3l9NiNjY2CAwMLDdOoKgXXGxsLD5//szZ9ubNG6SlpQk8f3/q9u3bmDx5Mtq3bw89PT3k5ubixw/ulQGjoqIwa9YsHDp0CC1btoSLi0uFVpStDEtLS77Xi6mpqcAVXhcuXIi0tDSu18KFCyt0PFFRURiZmuBFKPfE689Dw2BuVbVzfMH7LM54ncTyzWtgYi64d+n/K1FRUZiYmSIshHs+yLCQp7Cwash3Hwurhjzhnz0JhYm5GUREin4jy83N4TS4FRMSEgLDMGU37DIM8vPyK5R2aWlpTq/VBg0awNDAAMrKynj85AknTH5+Pp6FhcGmRE/Q0mysrLj2AYDHjx+XuU9ZLl2+DAtzc5iamPzR/sX+lnohKioKY1MTPA99xrX9eegzmP/h/IDFLnifxWmvk1i+eW2t1++ifPGpO6FP/3jew+omKioKU3MzhDzmvr5DnoTAyob/9d3Q2gohT0K4tj15/ATmluac+l0R1o1s8PkTdwPV59jP5X6ei4qKwtzcHE9KpfnJ4yewLtUrnnMsa2ue8I8fPYaFpQUnzdY21nhcOszjx5w4m9nZ4eQpbxw7cZzzsrC0gGO3bjh24rjAz7601FR8S0wsd3hfcVmEPuY+t6FPQmBlY8V3n4bWVggtVRYhfMrC+9gJeLl7YvPOrTC35L038C+LWKjX07IAiuYk+/TpE1eY2E+x0NDkTfOVS5dhbmFe5giS0vmqq3pRzO/KVSgoKqJVm9aV3rdYXeYjJzcHLCHuH22FhITBrsCP+eUpvrc+Dyn1mRHyDOYCnkvqQn16BikoLERBQQHPD+lCwsLV/h2hLrFYrHr7+n9FjXHkP2/ZsmXw8vLCihUr8Pr1a0REROD06dNYsmQJAKBDhw5o3749BgwYgICAAERHR+PatWvlroZa0rx587Bx40acPn0a7969w4IFC/D8+XPMmDGDb3gZGRmMHTsW8+bNQ2BgIMLDwzF69GiuL9empqYYMWIEXFxccOHCBURHRyMkJAQbN26En58f33g9PT3h7u6O8PBwfPz4EceOHYOkpCT09PT+KL6SRowYgWfPnmHt2rUYOHAgJCQkBIZdvnw5vL29sXz5ckRERODVq1fYtGkT37BdunSBjY0NJ/4nT57AxcUFHTp0ELjow58yMjLC0aNHERERgUePHmHEiBFcvfgKCwvh7OyMrl27wtXVFR4eHggPD6/2lU7nzJmDwMBArF69Gu/fv8fRo0exe/durkVBShMXF4ecnBzXqzLDRfoM7o8AX3/cvHodn2NicXjXAfz49g3d+hQ1qnodOILtazdz7fMxMgofI6Pw82cO0lLT8DEyCrExvx/eL5w8ixOHvfDP/NlQ01BHSlIyUpKS8TP7ZyXPSPWTFBWHiYoOTFSK5mHUklOBiYoO1GVqv0dN/yGD4H/FD9d9ryE25hMO7NiDb4mJ6NGvaPGBI/sOYfPq9ZzwPfr2QmJCIg7s3IvYmE+47nsN132vYeCwwZwwLdq0wlWfywi6eQsJcfF49iQUXoc80LJta86XWo/9hxH+/CUS4hMQHfURngfc8TLsBTp15Z47saJYLBaGDRkCj6NHcTsoCB+iorBi9WpISEigW9ff89gsW7kSu/fu5fw9dMgQPH7yBJ5eXoiJiYGnlxceh4RgeIlFWrKzs/Hu/Xu8+/WDxNe4OLx7/x4JpeaByczKws1bt9Cnd/mTiFfE31IvivMRwMnHfnz/9g3df+Xj6IEj2L6W+x5cnI+cnz+Rzicf50+ewfHDRzF9/myo11H97jd0AG74XsONX3Xn4M69+J74DU59i+qO5/7D2Lp6A9c+UZEfEBX54Vf5pCIq8gNioz/xi75aDBk+FL6XruDqZV/ERMdg57Yd+JaQiL4D+gIA9u/ehzXLf88x26d/PyTGJ2DX9h2IiY7B1cu+uHrpCoaO/D3fY/HCL5Hv3iM/vwDfv39H5Lv3+PL5CyfM4GFD8PpVOLw8juLL5y8I8L+BKz6X0G/QgHLTPGzEcFy6eAmXL11GdHQ0tm/dhsSEBPQfUDSCYM/uPVixbDknfP8B/ZEQHw+3bdsRHR2Ny5cu4/KlyxgxcuTv8zB0KJ48fgwvz6OIiYmBl+dRPHn8BEOHF82lKy0tDSNjI66XpIQk5BXkYWRcNA1IdnY2drjtwKuXLxEXF4enoU8xZ/YcyCsooENH+3LzNXj4EK6y2PWrLPoMKJr39cDufVi7fHWJsuiLxPgE7N6+s0RZ+GLIyGGcMCe9TuDwvkOYv2whNDQ1kfQjCUk/kpCdnc0JM2jYELx+9RrHuMriMvoN4h2RUR/KAgCGDR+O8Ffh8Dzigc+fP+O6vz8u+lzEwFJzAWdmZiLwZiDP1Brlqat6ARSNQvC7chXde3T/o4a8+pCP1m3b4pjHUTy8/wDxcfG4e/sOTp88hfb27auUn2J9h/y6t171x+eYTzi0cx++f/sGp75F8yd77nfH1jUbufb5GPkBHyM/IOfnz1+ffdz31vz8fE6Ygvx8JH3/gY+RHxD35Wu1pLkun0FkpKXRxNYWO3bvRujTp/gaF4crvr7wu3YNHTt0qJb8EcIPDVMl/3mOjo7w9fXFqlWrsGnTJs4vkePGjeOEOX/+PObOnYthw4YhKysLxsbG2LBhQxmxcps+fTrS09MxZ84cfPv2DZaWlrh8+TJMyui5sXnzZmRmZqJ3796QlZXFnDlzkJaWxhXGw8MDa9aswZw5c/D161coKyujVatWcHLi30VdQUEBGzZswOzZs1FYWAhra2tcuXKFMydcZeMrycTEBHZ2dggJCSl3HjV7e3ucPXsWq1evxoYNGyAnJ4f27fk/QLBYLFy8eBH//PMP2rdvDyEhIXTr1g27du0qN02V5eHhgQkTJsDW1ha6urpYt24dVwPY2rVrERMTgytXipZ219DQwOHDhzF48GA4ODhU2yStTZo0wZkzZ7Bs2TKsXr0ampqaWLVqVY0s3lCsXecOyEhPx+mjJ5CclAI9Az0s27gaahpF89mlJCXjR+I3rn1mjZ3K+X/Uu0jcvXkbahpqOHTGCwBw7eIVFOTnY+My7uGCQ0ePwLAxzqhL5qp62N3v91DM6W2LvmD4RQRj7a2jtZqWDl06Ij09HSc8vJCSlAw9Q32s3rKe02siOSkZ30qcew0tTazesh4Hdu6B74VLUFJRxuSZ09C24+86NHyUM1gsFo4ePIKk7z8gr6iAFm1aYfSEsZwwKSkp2LR6PVKSkiElLQ0DY0Os2bqBZ6XWyhjl7Izc3Fxs2LwZGRkZsGrYELt37OAayp2QkAChEr9iNrKxwdrVq7HvwAHsP3gQOtraWL9mDaysfvdWeRMRgUlTf19v23fsAAD0dHLCihIrIt8ICADDMFwP3lXxt9SLdp3tkZGe8Ssfyb/ysYYrH98Tv3PtM3PsFM7/P7yLxJ2bt6GmoY7DnHz4oiA/Hxt48jESw2upfrfv3BHpaenw9jz+K1/6WLl5HSdfyUnJ+F6qfKa7TuL8/8O79wgKuAU1DXV4nDtRI2ns3LUL0tPS4Hn4CJJ+JMHAyBCb3LZA49cK3Ek/kpCYkMgJr6WthU1uW7Fr+w74nL0AFVUVzJg7C/Yl5kP78f0Hxowczfn71PGTOHX8JBo3scWuA0WLQlk0tMTazRtwcM8+HD3sAU0tTfwzewa6dncsN80OXR2QlpaGI4fd8ePHDxgaGWH7ju2cVcOTfvwolWZtbN/hBrdt23Hu7DmoqKpgztw56NS5EyeMTSMbrF67Bgf27ceB/Qego6ODtevXcdXz8ggJCSHqwwdcu+qHjIwMqKiooGmzpli7bl2500UAxWWRjqOHPThlsdFtC6e3F/+y2IJd23fC5+wFKKuqYMbcmVxlcfHcBeTn52PZ/CVcxxo9fgzG/LrfWjS0wNrN63Fgz34cPewJjf9AWVg2tMSmLZuwd/deuB92h5aWFmbNmY1u3btxpS/gRtE9t2u38vNSUl3VC6CoN2RiQiKcevesVJrrUz5mzZuFw/sPYdvGLUhJSYGKigr69O+D0ePGVDlPANC+sz0y0tNxqsS9dcWmtSU+M5J4761jJnP+/+FdJO78urceOXscAJD8I4krzIVTZ3Hh1FlYNbbBhl3V86N2XT6DrFuzBnv27sXSFSuQnp4ODQ0NTJ44EQP4TINESHVhMRWZ3IoQQkideJsYXddJqDJzdQO02TOp/ID12IOp+xH9o3p+/a1LBirayEipv6upVYSsouJfUy/eJcbUdTKqzExdHx++fy4/YD1mrNoA39L/fPXf+kJNThmpGWnlB6zHFGTlkZj+o/yA9Zy6nMp/viyAovL4r9cNNTnl/3wegKJ8RH4re27F+s5ETfc//wwCFD2H/Be5nq7YvJB1wWPI4rpOQp2gYaqEEEIIIYQQQgghhNQSaowjhBBCCCGEEEIIIaSW0JxxhBBCCCGEEEIIIX+p/+dVS+sr6hlHCCGEEEIIIYQQQkgtocY4QgghhBBCCCGEEEJqCQ1TJYQQQgghhBBCCPlLCYGGqdY31DOOEEIIIYQQQgghhJBaQo1xhBBCCCGEEEIIIYTUEhqmSgghhBBCCCGEEPKXotVU6x/qGUcIIYQQQgghhBBCSC2hxjhCCCGEEEIIIYQQ8p+wd+9eGBgYQEJCAk2bNsW9e/cEhh09ejRYLBbPq2HDhpwwnp6efMPk5OTUWB6oMY4QQgghhBBCCCHkL8Wvoam+vCrr9OnTmDlzJhYvXoywsDC0a9cO3bt3R2xsLN/wO3bsQHx8POf1+fNnKCkpYdCgQVzh5OTkuMLFx8dDQkLij853RVBjHCGEEEIIIYQQQgip97Zt24axY8di3LhxsLCwgJubGxo0aIB9+/bxDS8vLw8NDQ3OKzQ0FCkpKXB1deUKx2KxuMJpaGjUaD6oMY4QQgghhBBCCCGE1Lrc3Fykp6dzvXJzc/mGzcvLw9OnT9G1a1eu7V27dsXDhw8rdDx3d3d06dIFenp6XNszMzOhp6cHHR0d9OzZE2FhYX+WoQqixjhCCCGEEEIIIYSQv5QQWPX2tX79esjLy3O91q9fzzcfP378QGFhIdTV1bm2q6urIyEhodzzEB8fj2vXrmHcuHFc283NzeHp6YnLly/D29sbEhISaNOmDSIjI//8pJdDpMZiJoQQQgghhBBCCCFEgIULF2L27Nlc28TFxcvcp/RccwzDVGj+OU9PTygoKKBv375c21u2bImWLVty/m7Tpg2aNGmCXbt2YefOneXG+yeoMY4QQgghhBBCCCGE1DpxcfFyG9+KqaioQFhYmKcX3Ldv33h6y5XGMAyOHDkCZ2dniImJlRlWSEgIdnZ2NdozjoapEkIIIYQQQgghhPyl6nrF1OpaTVVMTAxNmzZFQEAA1/aAgAC0bt26zH3v3LmDDx8+YOzYseUeh2EYPH/+HJqampVKX2VQzzhCCCGEEEIIIYQQUu/Nnj0bzs7OaNasGVq1aoWDBw8iNjYWkyZNAlA07PXr16/w8vLi2s/d3R0tWrSAlZUVT5wrV65Ey5YtYWJigvT0dOzcuRPPnz/Hnj17aiwf1BhHCCGEEEIIIYQQQuq9IUOGICkpCatWrUJ8fDysrKzg5+fHWR01Pj4esbGxXPukpaXh/Pnz2LFjB984U1NTMWHCBCQkJEBeXh62tra4e/cumjdvXmP5oMY4QgghhBBCCCGEkL+UUCWHg9Z3U6ZMwZQpU/i+5+npybNNXl4e2dnZAuPbvn07tm/fXl3JqxAWwzBMrR6REEIIIYQQQgghhNSKqRe21HUSBNrTf25dJ6FOUM84Qgipx/Y8OFfXSaiyqW0GIvrH17pORpUYqGijzZ5JdZ2MKnswdT9Sp86r62RUicKezRh6fFldJ6PKTo1chQ77ptZ1MqrszuQ9yEhJqetkVImsoiJOhwWUH7CeG2LrgEV+++s6GVWyzmkSDj+6XNfJqLJxLXtjxsXa7WFRE3b0nYXhJ5bXdTKq5OSIlXA9vbauk1FlHkMWY6DX4rpORpWcc1kLh4Mz6joZVRYwgf8wR0IqixrjCCGEEEIIIYQQQv5SlV21lNQ8obpOACGEEEIIIYQQQggh/y+oMY4QQgghhBBCCCGEkFpCw1QJIYQQQgghhBBC/lIs0DDV+oZ6xhFCCCGEEEIIIYQQUkuoMY4QQgghhBBCCCGEkFpCw1QJIYQQQgghhBBC/lJCtJpqvUM94wghhBBCCCGEEEIIqSXUGEcIIYQQQgghhBBCSC2hYaqEEEIIIYQQQgghfykWDVOtd6hnHCGEEEIIIYQQQgghtYQa4wghhBBCCCGEEEIIqSU0TJUQQgghhBBCCCHkL0XDVOsf6hlHCCGEEEIIIYQQQkgtocY4QsgfycnJwZo1a5CYmFjXSSGEEEIIIYQQQv4zaJgqIeSPLF26FOnp6VBXV6/rpBBCCCGEEEIIEUAINEy1vqGecYSQSsvNzYWysjJ27txZ10khhBBCCCGEEEL+U6gxjhBSaeLi4liwYAHExcXrOik8goKCwGKxkJqaWuF99PX14ebmVqXjslgsXLx4sUpxEEIIIYQQQgj5+9EwVUJIpT18+BDt2rWDg4MD/P396zo5pAwvbz3CM//7yErNgJK2GtoP6wFtU32+Yb+8/YgLm9x5to9cOxNKmqo1kr4rFy7h3MnTSE5Kgp6BPiZNnwqrxjYCw78Me4GDu/biU3QMlFVUMGj4EPTo15srjM/pc/D1uYzvid8gpyCPdvbt4TppPMTExQAAvj6X4OtzBd/iEwAAugb6GOHqDLtWLWokj2VppGmM4bZdYa6mCxVpBSzw24d70S9qPR1lkXBygFibFmBJSaEwJhbZZ3zAjhc8V6TMjEkQMTXi2Z4fHoGsfUeK/hASgoSTA0TtmkBIThbs9HTkPQpFrn8gwDDVmn4HUzv0smwLBUkZfEn9Dq/Qa3j7/ZPA8G30bdC7YVtoyCohOz8XL+IicfzpdWTm/eQJ20rPCjPaDUbI5whsveNdrekurW/DdhjauAuUpOQRkxKP3Q/O4WV8VBnh26O/dQdoyCohMTMFx5/64/r7J5z33XrPgK22Kc9+wZ/CscBvX7Wlm2EYHDx8GD6XLiEjIwMNLS0xf948GBkalrlf4K1b2H/wIL58/QodbW1MmTQJHe3tOe97HD2K20FBiPn0CeLi4rCxtsY/U6dCX0+PE+bAoUO4cfMmEhMTISoqCgszM0yZNAlWVlZVytOTG3dx/0ogMlPToKqjie4uA6BvYcw37Ke3Ubhx8hJ+xCUgPzcfCqpKaNa5DVr36MQV7qHfbYQE3EPajxRIyUqjYQtbdBnWG6JiolVKa1la6DZEO8NGkBWXwrfMFFx98wAxKQkCwwsLCaGTcTM01jaBrJgU0nIyERT1DE+/vAMANNE2w8BGHXn2W+Z/CAXswhrJQ1jgQ4T4BSEzLQMqWuroNKI3dMzKvrYA4Mv7aJxavx8qOuoYvXo213s5WT9x7/w1RIaGIyf75//Yu+voqI63gePf3chuXAmEuEAgBAvu7tYWWipQqNPSlpa6QZWWtlRpseJQXEpxd3cLFiSBhLj7yvvHhk022QRJAvz6Pp9z9pzk7szdmd175+7OfWYGJ3dXOj3Vl8CGdaukDgBtAxrQObgpjmo7bmYksfzUDi4n3TCb9unw7rTwrVdqe2x6Et9tnQNADQc3etdthbezB262Tiw/tZ0dkceqrPy3dK3VjL6hbXC2sedGagJzjqzjfEJUmenb+Nenb2hRe3sy5hLzjxa1t52CmtAusCE+Th4AXEmOYdGJLUSW8d5Uhk7BTegV0tJQh7QE/j62iYuJ0WbTvtC8L20DGpbafiMtgU/XTwXAQqGkT93WtAlogIuNA7EZSSw5sZXTNy9XWR0AeoS0oH9oW1xsHYhOjWfWoTVExJd97WsX0JAB9drh6ehGdn4ex2IuMOfIOjLzDJ9Fx6DGvN5mUKl8T80bS4FOUyV16BfalscbdMbN1pGrKTeZtG95ue9b/9C2DKjXjuoOrsRnpvD3sU1svnjIbNqOQY35pMtw9lw9yecbS3/3/S+S1VQfPtIZJ4S4azNmzOCNN97gr7/+IioqCl9f3zLT6vV6tFotlpbS3NxvFw6eZOeCtXQc2o+awX6c3n6IVT/PZsjXo3Bwcy4z39Bxb2NtUxT1aONgVyXl27F5G1N+/YOR74yiXoMw1q78l0/f/ZCp82biUaP0XIQ3Y2L57N2P6NWvN++P+ZgzJ0/zx4RfcXJ2pm2n9gBs3bCZGZOnMfqj96lbvx43oqKZ8M33ALwyaiQA7tWq8fyIF6np7QXA5nUb+eLDz5g4cwr+gQFVUtey2FipuJR0nbXn9jKu14j7+tp3QtWtI6rO7cmeuwhtfALqnl2xf/0l0r/8AfLyzObJmjYbip3vCjtbHD56m4JjJ032a92uFdlzFqKLjcPCzxvbIU+gz8klf/vuSit/K78whjXpxfRDqzkfH0XXWs34sPMQ3vl3IknZaaXSh1TzZWTrx5hzZB1Hrp/H1daRF1v04+WWA/hp50KTtO52TgwJ70FE3NVKK29ZOgWF83qbQfy8axGnYyPpV68t4/uMZNjCr4jPTCmVfkC9drzcsj8/bP+bc/HXqFvdn/c6PE1GXjZ7r50G4LMN07BSFn1Ojmo7pj/xEdsr+Qf77Llz+XvBAsZ+9hm+vr5MnzmTkW++ybJFi7CzM9+2nDx1io8/+4wRL79Mpw4d2LZjBx9+8gnTp0wxdqQdPXaMxwcOJDQ0FK1Wy5+TJ/P6qFEsWbAAGxsbAPx8fXn/nXfw8vIiLy+PvxcsYOSoUaxcuhQXF5d7qs+pvUdYN3sZfV8YjG9IIIc272bed3/y+oRPcXZ3LZXeWmVNix7tqeHrhZXKmqjzkaz6ayHWKmuadm0LwIndh9i84B8eeeUZfGoHkhQbz4rJcwHoNWzgPZXzdup7BtEntDWrTu/iWspNmvuGMqxZH37ZuYi03EyzeZ5q3A17a1uWn9xOUnY69tY2KEv8uMstyOOnHabnSlV1xJ07cJyt81fR7dlH8artz4lt+1k6YTrPf/sujm5lf7552TmsnboQv9BgstIzTJ7TajQs+WEqto729H99KA6uzmQkp2KtrrqRAI29avNo/Y4sObGVK0kxtA6oz4hWj/Dtljmk5GSUSr/85Hb+PVPUTioVSj7oPITjMReM26wtLEnMSuPYjQs8Wr9jlZW9uJZ+9Xi2SU9mHFrDhYQoutRqygedhvDe6j/KbG9fbfUYc4+u52hhe/t887681HIAPxe2t6HV/dl79RQXE6Mp0GroG9qGDzsP5f3Vf5h9byqquU9dnm7UjblH13MxIZqOweGMbv8kn6yfQnJ2eqn0fx/bxJKT24z/WyiUfNnjRQ5FRxi3PVa/A6386jPr8Bpi05MIqxHIG20G8c2W2USlVs0iaK396zO8aW/+OvAv5xKu0a1WMz7uMoy3V/1KYlbpz6KOhx+vtxnE7MNrOXz9HK62jrzcYgCvtnqMH7bPN6bLys9l1MqfTfJWVUdch8DGvNrqUX7fvYQzcVfoU7c143qN4IXF35KQVfq617duG55v3o+fdy7kfEIUdTx8ebvdk2TmZbM/6oxJWg97F15u8QgnYy9VSdmFuFMyTFUIcVeysrJYvHgxr776Kn379mXWrFkmz98aJrphwwaaNm2KSqVi165d6PV6vv/+ewIDA7GxsaFhw4YsXbrUmE+r1fLCCy8QEBCAjY0NISEh/Prrr7ctz9q1a6lduzY2NjZ06tSJq1evlkqzd+9e2rdvj42NDT4+Prz55ptkZWXdcZ0PHTpEt27dcHd3x8nJiQ4dOnD06NFy83zwwQfUrl0bW1tbAgMD+eyzzygoKLjj16wMxzbsoV67JoS1b4ZrTQ/aP90He1cnTm47UG4+W0c77JwcjA+lsmouFcsXLaFH31706t8HX38/Rrz1OtU8PFi9YpXZ9GtW/otHdQ9GvPU6vv5+9Orfh+59erF0wWJjmojTZ6hXP4xO3btQw7MGTVo0o2O3zlw4V/QjpWXb1jRv3RJvXx+8fX0Y/soLqG1sOHcmwtzLVqn9UWeYdmAVOy4fv++vfSdUndqRu2ELBSdOo4uNI3vuQhTW1lg3a1xmHn12Dvr0DOPDqk4tyC8g/2hRxJ9lgB8FJ8+gOXMOXXIKBcdOURBxEUs/70otf5+6rdkWeZRtl44Sk57InCPrSMpOp1vtZmbT13L3ISErlfXnD5CQlcr5hCg2XzxMkJuXSTqFQsHrbQax9OQ2s51hle2Jhl1Ye24fayL2ci01jol7lpGQmcKAeu3Mpu9euzmrzu5hW+RRYjOS2HrpCGvO7eWpxt2NaTLysknOSTc+mvrUIU+Tz/bI8tu2u6HX61mwaBHPDR9O506dCA4K4osxY8jNzWX9xo1l5luwcCEtmjXjuWHD8Pf357lhw2jerBl/L1pkTPP7L7/Qr29fggIDqV2rFmM//ZSbN28Sce6cMU3PHj1o0bw53l5eBAUG8vZbb5GVlcXFS/f+A2zvmq2Ed2pFk86tqeZVg97DBuHo5sKhTbvMpvcM8KFBm6Z4+Hji4uFGw3bNCW5Ql2vniqIaoy9cwad2IA3aNsPFw43ghnWp37opNy6XHVFUUW0DGnAk+hyHr58jISuVNRF7ScvNpIVfqNn0tdx9CHCtyezDa4lMukFqTgbX0+JLdSbogcz8HJNHVTm8fif12zejQccWuNWsTudnBuDg6szxLfvKzbdx1jJCWzWmZrBfqedO7TxETmY2j7w5HO/aATi5u+BdOwAP35pVVQ06BoWz/9pp9l87TVxmMitO7SAlJ4M2AeajxHM1+WTkZRsfvi7VsbFSc+BaUYdDVGocq87s4tiNC2iqqLOkpN51WrM98hjbIw3t7dwj60nKTqdrGe1tsLs3CVmpbCjW3m65eIRA16L3+o+9y9h88RDXUm4Sk57ItAOrUCgUhNW4ffTjvege0oKdV46z8/JxYjOSWHBsE8k56XQOCjebPqcgj/TcLOPD39UTW2sbdheLcG/lX5/VEXs4GRtJQlYq2yKPcvrmZXqGVF00fr+6bdh66QhbLh3mRloCsw6vJSkrje61zb9mbXcfErJSWHtuH/GZKZyLv8amiwcJcit53OtJzc00eVSVgQ06sv78ftad309UahyT9q0gITOFfqFtzKbvWqsZayL2sOPyMW5mJLE98hjrz+9ncKOuJumUCgUfdX6WOUfWcTM9qcrKL8SdkM44IcRdWbRoESEhIYSEhDBkyBBmzpyJ3sywsvfff59vv/2WiIgIGjRowKeffsrMmTOZNGkSZ86c4e2332bIkCHs2LEDAJ1Oh7e3N4sXL+bs2bOMGTOGjz/+mMWLF5fa9y3R0dE89thj9O7dm+PHj/Piiy/y4YcfmqQ5deoUPXr04LHHHuPkyZMsWrSI3bt38/rrr99xnTMyMhg2bBi7du1i//791KpVi969e5ORUfZdWQcHB2bNmsXZs2f59ddfmTZtGj///HOZ6SubVqMh/loMvvVMh0751gsm9lL5P/AWfP4Hf739Lct/mE50RNUMoygoKODi+QuEN29qsj28eVMiTp8xmyfi9JlS6Zu0aMrFc+fRaAw/Nuo1rM/F8xc4f9bQsRZ7I4ZD+w7QvLX5L6BarZbtm7eSl5tL3TDzP0L/v1K6uaJ0ckQTUdSRiUaL5tJlLANK/4gti3Wr5uQfOQ75RZ3RmsirWIUEo/RwN7yWlyeWQf4UnD5Xxl7unoXSggBXz1JDOU/GXqJ2NfPRvBcSonC1daRRzVoAOKntaOFbj6M3LpikG1i/I+m5WWyrxI6rslgqLahdzcck0gLgUHREmT9IrSwsydeYdv7naQqo6+GHRRmd633qtGLrpSPkavIrp+DAjZgYkpKSaNmi6PyztrYmvHFjTp46VWa+k6dP06KF6TnbskWLcvNkZhp+FDo6Opp9vqCggBUrV2Jvb0/tWrXuphpGGo2G2CvRBDUwHa4Y3KAuUReu3NE+Yq9EE33hMv6hRWXwqxNI7JVorl+6CkByXCIXjp2hdnjpoYiVwUKhpKZjtVJD7y4lXMfPuYbZPHWr+3MjLYH2gY34oPNQRnd4kl51WmKptDBJZ21hxXudnuGDTkN4tmkvPB3dqqQOWo2Gm1dv4B9mOtTaP6w2Ny6VPRTv1M5DpMYn0fqRbmafv3TsLDWD/dg8ZwV/vPEFMz/+kf3/bkGn01Vq+W+xUCjxca7O+RLDB8/HRxHgemcdgC39wriQEFUlkWJ3qqi9Ne3oPhUbSW13H7N5LiREm7S3jmo7WviGcizmgtn0ACoLKywVFlXSyWuhVOLv4smZm6bn8pmblwlyv7MbRe0DGnE27gpJxaLorJQWFGhNO0TztRpqVTP/vlSUpdKCQLeanIgx/SxOxF4ipIxr3/mEKNxsnYxTFzip7WjpG8bR66afhdrSmkmPvcuUge/zUeehBLh6Vlkdarv7GIfA33Lk+nnqVTc/gsHKwpL8Eu9znqaAkGq+WCiKrntDwnuSmpPJ+vP7K7/gDzmlQvHQPv6/knFjQoi7Mn36dIYMGQJAz549yczMZMuWLXTtanrn6csvv6RbN8OX3aysLH766Se2bt1Kq1atAAgMDGT37t1MmTKFDh06YGVlxRdffGHMHxAQwN69e1m8eDFPPPGE2bJMmjSJwMBAfv75ZxQKBSEhIZw6dYrx48cb0/zwww88/fTTvPXWWwDUqlWL3377jQ4dOjBp0iTUavVt69y5s+ncPlOmTMHFxYUdO3bQt29fs3k+/fRT49/+/v688847LFq0iPfff99s+ry8PPJKDPuryAIZORnZ6HU6bJ3sTbbbOtqTnWb+TqadkwOdhz2Ch39NtAVazu07xoofZzDw/RfwCqnc4ZvpqWnotDpcXE2HErm4uJCclGw2T0pySqmhZS6uLmi1WtJS03Bzd6Nj186kpaTyzqujjEOk+z7an8FDnzbJdyXyMm+/8jr5+fnY2Njw2bgv8Avwr9Q6/q9TODoAoMswPV506RkoXe9siJ+Fnw8WXp5kz19isj1v0zYUNmocPnvPMEecQkHuv+spOHK8UsoO4KiyxUJpQVqOafnTcrJwrmlvNs+FxGgm7lnKqHZPYGVhiaXSgsPREcw6tMaYpnY1XzoFhVfqvGrlcVLbY6m0KDVEKiUnA1db8x1Ph6Ij6Fu3NbuvnOBCYjQh1XzpXacVVhaWOKntS+2rjocfgW5ejC82HKkyJCUZog7cXE2Hb7q5uhJ7s+y5yZKSkszmubW/kvR6PT/9+iuNGjYkOMh0vsJdu3fz8WefkZubi7u7O3/89hvOzs73UBvITs9Ep9Nh7+Rgst3OyYHM1NJD2Ir78bVPyUrPRKfV0mlQb5p0bm18rn7rpmSlZzJ97M/o0aPT6mjWrR3tB3QvZ4/3ztZajYVSaZwL6paM/Gxqqcx3ELjaOuDnUgONTsv8IxuwtVYzoF47bKzULD+1HYCErBSWndzGzYxk1JZWtPavzyutHuH3XUvNDlOsiJyMLPQ6HXalPgt7stLMd0ql3Exg55K1PPXJaygtLMymSUtIIioihdBWjRk4+gVS4hLZPGcFOq2uzA68irBT2WChVJKel22yPSMvCwfV7W96OKrsqOvhz5zD6yq9bHfD4VZ7m2s66iAtNxMnG/Pt7cXEaP7Ys4w32j5erL09x+xDa8t8nScbdyM5J53TsZV/o9DB2tbwWZSI9krLzSJMbb4OxTmp7anvGcSU/StNtp++eZkeIS24kBBFfGYKdasH0NirdpV1QBR9FiWvfZllXvvOJ0Tx667FjG7/pPGzOBQdwfSD/xrT3EhLZOKeZUSlxmFrpaJ33dZ83fNl3vl3IjczKjfCzElth4XSgpSc0tc9F1sHs3mOXD9Hrzot2Xv1JBcTr1Pb3YeeIS2Lrns56dSrHkDPkJaMWPZ9pZZXiHslnXFCiDt2/vx5Dh48yPLlywGwtLRk8ODBzJgxo1RnXNOmRRFMZ8+eJTc319g5d0t+fj6NGxcNd5s8eTJ//fUX165dIycnh/z8fBo1alRmeSIiImjZsqXJhKS3OvtuOXLkCJcuXWL+/KIfmXq9Hp1Ox5UrV6hb9/YTMsfHxzNmzBi2bt1KXFwcWq2W7OxsoqLKjjBbunQpv/zyC5cuXSIzMxONRlNmxAbAt99+a9IZCTB27FiqdavYRONQ4sueXk9Z3/9cPKvhUmyhBs9gXzKS0zi6YXeld8YVFc+0MHr05U8wWzK9/tZmw/YTR4+zcM58Rr4zijr16hJz/QaTf/0Dl5lzeea5ocZ83r4+/DlrGpkZmezevpMJ34zn+4k//7/ukLNq1hjbp4rmp8r8s3CxhZKRr3fxA8K6dXO0N2LRXjONwLFq0hDr5uFkz/obbWwcFt41sRnYH11aOgUHjtxzHcwpFberwGw0L4CXUzWGNe3NslPbORlzCWcbB54J786LLfoxZf8/qC2teb3NQKYdWEVGiR/PD0JZ9Zh9eB2uNo5Meuw9UEBKdgbrz+/n6cbd0elLR/j0qdOay0k3OFfO5N53Yt369YwrdjPklwkTgNKTRuv1tznPDZnuOM/3P/7IpUuX+Gvq1FLPNW3ShL/nzCE1LY0V//zDR598wqzp03F1LT2/2x0rVYzb1+eFz98iPzeP6ItX2bTgH1xrVKNBG8N18sqZC+xcsYG+LwzGO9iPpJuJrJu9lO3OjnQc2Ovey3kbJY8eBQozW4s/B4uObyGvMHpybcRengrvzqozu9DotESnxhOdGm/Mcy3lJiPbDqKVfxirz+6piiqYu8SZbaJ0Oh2rJ/9Nm0e741qj7AWJ9Do9tg72dH9uEEqlkhoB3mSmpnFo7Y4q6YwreuGSG+6snW3uG0pOQR6nHpq5r0ofVWUtyuPlWI1hTXux4tQOTsRewsXGnqcbd+f55v2YduCfUun7hrahtV8YX22eVWXzlMHdnRfFtQ1oQHZBLkdvmEZz/X1sE8Ob9mZcrxHogfjMFHZfOWF24YfKVOr6UE4b5e1Ujeeb92XJya2cuHERZ1sHnm3Si5dbDmDSvhWAofO0eDTtufgovu87kt51WjKj2A2ryq1D6SqU9UnMO7oBF1sHfntkNAoMHXcbLxxgcKOu6PQ6bKxUfNBpKD/vWkh63p1PVSNEVZLOOCHEHZs+fToajQYvr6L5k/R6PVZWVqSkmEYtFZ+Y+9bwjjVr1pjkhaLor8WLF/P2228zYcIEWrVqhYODAz/88AMHDpQ9v1lZP0SL0+l0vPLKK7z55pulnitv4Ynihg8fTkJCAr/88gt+fn6oVCpatWpFfr754Vz79+/nySef5IsvvqBHjx44OTmxcOFCJhT+MDXno48+YvRo0xXdVCoVfx3+t4wc5bNxsEWhVJJdIkIgOyMLG8fb3+G9xTPQh3P7K391T0dnJ5QWSlJKRMGlpqSWipa7xcXVhZTkkulTsLCwwNHJ0NE5Z9pMOvfoRq/+fQAICAokNzeX38b/xFPDnjHOf2dlZWVcwKF23RAunDvPyiXLGfW+6Wfw/0nBybNkXC3WwVy4CIPS0QFtsUnOlQ726NPvYDiUlRXWTRqSs7r03GA2j/Yld+M2Co4Yji1dzE2Uri6ou3eutM649LxstDotziWiMpzUdqWiN255pF47LiREGTsOolLjyDuYzxc9XmTRiS04qe3xsHfhvY5FkZa3OmHmPz2W0at+I66S55BLy81Eo9OWioJzsXEoc1havraA8dvn8ePOv3G1cSQpO41+oW3Jys8hLce07ipLKzoHN2HGodUVLmv7du0Iq1c0tDK/cJ7MxKQk3N3djduTU1LK7Qxzc3MrFQVXVp7vf/yRnbt2MXXyZKp7eJR6/tZcoT4+PtQPC+PRQYP4599/eW7YsLuun62jPUqlksxU0/c9Ky2zVIRWSS6FQ7Kr+3qRmZbBtqVrjZ1xWxavoWG75sZoueq+XhTk5bFq2gLaP9qj0uftzM7PRavT4aCyMdlub21TKlruloy8bNJzs4wdcWDoVFAqFDip7c1GvumBG6kJuNk6VWr5wbCwkEKpJKvEZ5GdnomtY+nPIj8nj5tXrhN3LYbNc1cayqfXg17Pj899wOPvvYRfaDB2zo4oLZQm77mbZ3Wy0jLQajRYVPJiVFl5OWh1OhzVtibbHVS2d9Th39KvHoejI9Ca6WS/nzIK21sn9Z23t/3D2nEhIZrVEYb2Njo1jjzNGsZ2f4ElJ7aYzEfWp25rBtRrx7gtc4iuokUPMvKz0ep0pergqLYtsw7FtQtoyN6rp9CWGNKckZfN73uWYqm0wF5lS2pOBo836ERiVmplFt/k9QzXPtPzwEltR2qO+ZERj4Z14Hz8NVYVLgxyLTWOaZpVfN3zZRYc30yqmWuNHj2RSdfxdHQv9VxFpeVmoTVz3XNWO5CaXfZ1b8KOBfyycxEutg4kZ6fTu05rsvJzScvNItCtJp6ObnzV4yVjnlvX7/Uv/sRzi74htpIj/B42CoXMUPawkc44IcQd0Wg0zJkzhwkTJtC9u+nQmYEDBzJ//vwy52ELDQ1FpVIRFRVFhw4dzKbZtWsXrVu35rXXXjNui4yMNJu2+H5Xrlxpsm3/ftM5IMLDwzlz5gzBwaZzp92NXbt28eeff9K7d2/AMFddYmJimen37NmDn58fn3zyiXHbtWvlR5yoVKoKDUstycLSEg+/mkSdvURQk6Ifx1FnLhHY+PbRgLfER8Xe9kfmvbCysqJWSG2OHTpCmw5Fk9AfO3SElm1bm81TN6weB/aYTsx99OBhatUJMa7Wm5eXW+qHq1KpRK/Xl995q9dTkH9/F9h46OTloUswHSqtS0vHsk5ttNdjDBssLLAMDiTnn7KHEd1i3aQhWFpScMjMvGpWVqVveet0dxV1dztanZYrybHUrxFkMt9a/RpBHL5ufm46a0urUnND6QrLqUBBTFoi7/470eT5wY26YGOpYtbhtSSaWW2vojQ6LRcSomnqXYddxSYFb+pdh91XT5aTE7Q6HQmFP/g6Bzdh37XT6EvEFXQKaoKVhSWbLhyqcFnt7OxMbsTo9Xrc3Nw4cPAgdUJCAMPcbUePHeONkSPL3E+DsDAOHDzIM089Zdx24MABGtSvb7Lv7ydMYPuOHUz54w+8at7Z/Fp6KPNGyu1YWlriGeBD5KlzhDYvimqJPHWOOk3rl5OzZCH0aAuKInsK8vNLRdYplMqyAooqTKvXEZOeQLC7D2eLrQYc7O7F2firZvNcS7lJmGcg1sXmZXK3c0an15W5+iqAp6MbNzPMTz1QERaWltTw9+LamYvULvbeXztzgeDGpefaU9moGP7NOybbjm/ZS1TEJfq//ixO1QwdvV61/InYfwy9Toei8FqSEpeAnbNjpXfEgeGziE6NI6San8n8liHVfDl1s/zvQMHu3lSzd2H/tXu7aVeZjO2tp2n7GuYZWGrer1tUFlalOhGNkbvFzoe+ddvwSFh7vts6lyvJMZVf+EJanY6rKbHUqxFgEt0WWj2A4zfKnscODJ9XdQdXkza6JI1OS2pOBhYKJU2865SaB7SyaHRaLifF0KBmMAejzxq3N/AMLvM1VZZmPovCa2F5V2V/F88qWRFWo9NyITGacK8Q9hS7zoV7h7D3atlzh4LhnLq1YmynoHAORJ1Bj56o1DheWvKdSdrhzXpja6Xmz73LjddKIe4n6YwTQtyR1atXk5KSwgsvvICTk+ld7kGDBjF9+vQyO+McHBx49913efvtt9HpdLRt25b09HT27t2Lvb09w4YNIzg4mDlz5rBhwwYCAgKYO3cuhw4dIiCg7OGRI0aMYMKECYwePZpXXnmFI0eOlFrd9YMPPqBly5aMHDmSl156CTs7OyIiIti0aRO///77HdU9ODiYuXPn0rRpU9LT03nvvfewsbEpN31UVBQLFy6kWbNmrFmzhhUrVtzRa1Wmxj3asHHaUjz8vfAM8uX0jkNkJqdRv2NzAPYs3UBWSjrdX3ocgGMb9+Do7oKblwdajZZz+44TeeQMvUc+Xd7L3LPHBj/OD199S606IdQNC2XdP6uJj4ujz6P9AJgxaRpJiYm899lHAPR5pB+rlq1kym9/0qt/HyJOn2XD6nV8+HnR/Hwt2rRixcKlBNUOpk6oYZjqnGkzadm2NRaFcwTNnPwXzVo2x726BznZ2ezYvI2Tx07w9YTvSheyitlYqfB2KhoyVdPRnVru3qTnZlV6hNW9yNu2C3WPzugSEtHGJ6Du0QV9fj75h44Z09g++yS61DRyV5nOWWTdqhkFJ86gzyod3aE5HWHYb3IKutg4LHy8UHVuT/6+incIFbcmYi8jWz/G5eQbXEiIpmutprjbObH5ouF1nmzUFVdbR/7caxh6f/T6eV5qOYButZpxItYwTHVY015cSrxujEK7nhZv8hrZ+blmt1emxSe28EmXYZxPiOLMzcv0DW2Lh4OrMYrhpRb9qWbnzLitcwDwdvKgrocfZ+Ov4qCy5YkGnQlw9eTbwueL61O3FbuvnKiSYTsKhYKnBg9m5uzZ+BZGp82cPRu1Wk3PYjd1xnzxBR7VqvF64c2YJwcP5uVXX2XWnDl0bN+e7Tt3cuDQIaZPmWLMM/6HH1i/cSMTvv8eWzs7Egsj6ezt7FCr1eTk5DBj1izat2uHu5sbaWlpLFm2jPj4eLp26XLPdWrdpzPL/5iDV6AvPrUDOLx5D2mJyTTraripsGnBP6QnpzFw5LMAHNiwAyd3V6rVrA7AtfOR7Fm9hRY9i25MhYSHsW/tNjwDvPEO9ifpZgJbF6+mTpP6Vbaa9e4rJ3m8YWdupMUTlRJHM99QnGwcOHjN8OO9e0hzHFV2LD25DYATMRfpFNyEgQ06seXiYWyt1PSq25Ij0efR6LSAocM3OjWOxKw01JbWtPKvj6ejm/E4rWxNe7ZnzZSF1AjwpmawHye2HSA9KZWGnQ3TVexcvJaMlDT6vPIUCqWSat6mi1PYOtpjYWVpsr1R51Yc3byHLfNXEd6tDSk3E9n/71bCu7WtkjoAbI88ypAmPYlKjeNqciyt/evjYuvAniuGToi+oW1wUtsz/+gGk3wt/cK4mhxrNprHQqGkRuHiGZYKC5zU9ng5VSNPk2/sqKhsa8/t5bVWj3E5KYaLidF0Dm6Ku60TWwrb28GNuuJq42Ac9nj0xnlebNGfrrWacTL2Es429gxtYmhvb0Vi9Q1tw+MNOjNxz1ISslKNUWu5mnyTKM3KsvH8AV5qMYCrybFcSrxOh6DGuNk6GRfrGVS/I862Dvx1wLQDtH1gIyKTbnAjLaHUPgNda+Ji40BUahzONg48EtYOhULB2nPlr/pbEf9G7OGNNoO4nHSD8wlRdKvVDHc7JzZeOAjA042742bryO97lgJw+Po5RrR6lO61m3M85iIuNg4816wPFxOijde+xxt05kJiFLHpSdhaqeldtxX+rp6l3ovKsuzkdj7oNIQLiVFExF2ld93WeNi7GCMpn2/WF3c7J74vnOvUy6kadar5cS7+GvYqGwY26IS/q6fx+QKthqspsSavkVUYCVxyuxD3i3TGCSHuyPTp0+natWupjjgwRMaNGzeOo0fLXlnwq6++wsPDg2+//ZbLly/j7OxMeHg4H3/8MWDoWDt+/DiDBw82/IB76ilee+011q0re1JiX19fli1bxttvv82ff/5J8+bNGTduHM8//7wxTYMGDdixYweffPIJ7dq1Q6/XExQUxODBg++47jNmzODll1+mcePG+Pr6Mm7cON59990y0w8YMIC3336b119/nby8PPr06cNnn33G559/fsevWRlqN29AbmY2B1dtIystAzev6vR/61kc3Q3DQLPTMshILvpSrtNq2b14HZkp6VhaW+FW04P+bz2Lf4OQKilfh66dSE9PZ/7MOaQkJeMX6M9XP35L9RqGH0bJScnExxV1cNSo6clXP37LlN/+YPXyf3B1d+PVt16nbaf2xjRPDxuKQqFg9tQZJCUk4uTiTIs2rRj+8gvGNCkpKXz/1bekJCVja2dHQHAgX0/4rtRKrfdDnWp+THy0aGjsm20NHaNrI/bxzdbZ9708JeVt2o7CygqbwY+isLVBezWKzInToNhiI0oX51JRbkoPdyyDA8n8vfQcXgDZi1di07cHtk8+hsLeHl1aOvm795O7bnOlln/ftdOGL+X1O+Js40B0ajzfbZtn/DHqYuOAu11Rm7bj8nHUViq6h7RgSJMeZOXncibuCn8fLT3U9n7aFnkUJ7UdzzbphZudI1eSY/lgzZ/EZRoijtxsnfCwLxrebaFQMLhhF3ycq6PRaTkWc4GRKyaUilDydvKggWcw7/x7Zzcm7sWwoUPJy8vjux9+ICMjg7B69Zj4668mEXQ3b940mcy8YYMGfPPVV0yaMoXJU6fi7eXFt19/TVhY0RyaSwvnLn2lWDQ1wNhPP6Vf374olUquXr3K6rVrSU1NxcnJidC6dZk2eTJBgeZXob0T9Vs3ISczi+3L1pGRmo6HjydDPnwN58LIqoyUdNISi95nvV7P5gWrSElIQqlU4lrdnW5PDaBp1zbGNB0e64lCoWDLotWkJ6dh52hPSJMwugzud8/lvJ1TsZHYWqnpHNwUB5UtcZnJzD601jg00EFlZzLMLV+rYebB1fSt15bX2jxGdn4ep2Ij2VT44x5AbaXikfodcLC2JVeTT0x6IlP3r6qyjuo6LRqRk5nN3n82k5WajrtXDQaOfgGnwmtcZlo6Gcmpd7VPRzdnHn/vRbb9/S+zPv0Je2dHmnRvS/M+naqgBgbHblzAzlpNjzotcFLZEZuRxJR9K42dII5qu1KT1qstrWnoGWxcPKMkJxt73u80xPh/l1pN6VKrKRcTo5m4e2mV1GP/tTPYW9vyWP0OONs4cD01nu+3zze2t85qe9yKtbc7Lx9Hbamie+3mPBPenezC9nbBsU3GNN1qNcPKwpK32z9p8lrLTm5j2antlV6Hg9ER2Kls6V+vLU5qe26kJfDzroXG1VGdbOxLDbu2sVLRxLsOfx8zf52wsrDk0fod8LB3IVeTz8nYS0zbv4qcgjyz6SvD3quncFDZMqhBJ2NH4Lgtc4xDY0te+7ZHHsPGSkWvOi0Z1rQXWfm5nL55mXlHijqA7azVjGj5CM42DmTn53IlJZYx66dxKel6ldRhx+VjOKrtGBLeA1dbJ64mx/LJuinEF96odLN1LHHdUzKoQSe8nT3Q6rQcj7nIqH9+MV4nRdHcn+LhodDfyaRLQgghHog/9lTNl+b7aWSbQVxJvPGgi1EhAe5etPljxIMuRoXtGTmZ1JHvPehiVIjzHz/w5LwxD7oYFbZwyJd0mFT2UM3/FTte/YOMlAcfxVkRDi4uLCrWAfC/anDjbny8dvKDLkaFjOs9gr/2r3rQxaiwF1v2Z9TKnx90MSrs10fe5un5Yx90MSrk72e+4LlF3zzoYlTYzMGfMGjOJ7dP+BBb+uw3dJs66kEXo8I2vfzrgy7CPflwzf1ZBf5efNfn1QddhAdCZvETQgghhBBCCCGEEOI+kWGqQgghhBBCCCGEEP9RykpcJEtUDomME0IIIYQQQgghhBDiPpHOOCGEEEIIIYQQQggh7hMZpiqEEEIIIYQQQgjxH6WQYaoPHYmME0IIIYQQQgghhBDiPpHOOCGEEEIIIYQQQggh7hMZpiqEEEIIIYQQQgjxHyWrqT58JDJOCCGEEEIIIYQQQoj7RDrjhBBCCCGEEEIIIYS4T2SYqhBCCCGEEEIIIcR/lAIZpvqwkcg4IYQQQgghhBBCCCHuE+mME0IIIYQQQgghhBDiPpFhqkIIIYQQQgghhBD/UQpZTfWhI5FxQgghhBBCCCGEEELcJ9IZJ4QQQgghhBBCCCHEfSLDVIUQQgghhBBCCCH+o5QyTPWhI5FxQgghhBBCCCGEEELcJ9IZJ4QQQgghhBBCCCHEfSLDVIUQQgghhBBCCCH+o2Q11YePQq/X6x90IYQQQgghhBBCCCFE5fty04wHXYQyjen2/IMuwgMhkXFCCPEQSxv/y4MuQoU5ffAWGSkpD7oYFeLg4kLqyPcedDEqzPmPH2jzx4gHXYwK2TNyMmnjfnrQxagwp49Hkzpi9IMuRoU5T/6JjPT0B12MCnFwdCRt7qIHXYwKcxo6mNR3Pn3QxagQ5wlfk/bLpAddjApzeutVUj/+8kEXo8Kcx40h7ZsJD7oYFeL0yTukDnnlQRejwpznTSH1hTcedDEqxHn676S+/fGDLkaFOf887kEXQfxHyJxxQgghhBBCCCGEEELcJxIZJ4QQQgghhBBCCPEfpUTmjHvYSGScEEIIIYQQQgghhBD3iXTGCSGEEEIIIYQQQghxn8gwVSGEEEIIIYQQQoj/KIVChqk+bCQyTgghhBBCCCGEEEKI+0Q644QQQgghhBBCCCGEuE9kmKoQQgghhBBCCCHEf5QMU334SGScEEIIIYQQQgghhBD3iXTGCSGEEEIIIYQQQghxn8gwVSGEEEIIIYQQQoj/KKUMU33oSGScEEIIIYQQQgghhBD3iXTGCSGEEEIIIYQQQghxn8gwVSGEEEIIIYQQQoj/KAUyTPVhI5FxQgghhBBCCCGEEELcJ9IZJ4QQQgghhBBCCCHEfSKdcULcpcOHD/Pzzz+j0+kedFGEEEIIIYQQQohyKRWKh/bx/5V0xon/eVevXkWhUHD8+PFK26dCoWDlypWlticmJvLEE08QFhaGUln5p8/nn39Oo0aNKn2//58MHz6cRx555I7Tb9++HYVCQWpq6j2/5qxZs3B2dr7n/EIIIYQQQggh/v+QBRzEAzV8+HBmz54NgIWFBTVr1qRPnz6MGzcOFxeXB1au2NjYUq+v1+t59tlnGTNmDN26dXtAJftvUCgUrFix4q46zcSdUbVpiXXDMBRqNdrYm+Rs2oouMfk2mVSo27fGqnYwCrUKXVo6uVt3orl8FQALby9ULZpgUd0DpYM9Wcv/RXMxstLKrNfrmfrXX6z45x8yMjKoFxrKB++9R1BgYLn5tmzdyuSpU7l+4wbeXl68NmIEnTp2ND5/9Ngx5s6bR8T58yQmJvLj+PF07NDBZB9NW7Y0u+83X3+dZ4cMqXDd1L27Yd2mBQpbW7RXo8hevAJdbFyZ6e1HjcCydlCp7QWnI8iaNMPwj1KJunc3rJqFo3R0QJeeTv7+w+St3wJ6fYXLfC8aegbzdOPu1PHwxd3OmQ/XTmLXlRMPpCxlUbVrhXWj+oZzIyaWnA1b0SUm3SaTCnXHNliFBKNQq9GlppG7ZSeayCuGp1s1wzKkFhZurug1GrTXY8jdtgtdckqV1EHdtwfWbVsWHk/XyF6wrPzjafRrWNYOLrW94NRZsv74y1hHm/69sGoUhsLBAW30dXIWr0R7Lfquy6fX65k6bRorVqwwnMv16vHB++8TFFT6mC5uy9atTJ48mevXr+Pt7c1rr75Kp06dTNIsWbKEufPmkZiYSGBgIO+MHk3jxo2Nz0+ZOpWNGzcSFxeHlZUVdevU4bXXXiMsLMxsOUeNGsXeffv48Ycf6Ne//z3VddrObaw8doSM3Bzq1fTmvV59CarmcUf5N545xacrltC+dh1+fOJpk+fi09OZuHUjeyMvklegwdfNjU/7PkJdz5p3Xc7bUXfvjHXLpihsbdBeu0728n/RxcWXmd7+1RewDA4otb3g7Hmyps8FwLpVc1Stm6N0dQZAezOe3E3b0Jy7WOnlv0XVsinWYaEo1Cq0N+PI2Vr+eWgVGoJt986ltqf9PhW0WgAcnn8GpaNjqTR5J06Tu21X5RW+kLpLB6ybhaOwUaONvkH2qnXo4hPKzaNQq1B374xVaB0UNjboUlLIWbsJzYVLhgTW1th062h43t4ObcxNclZvQHsjptLLf4uqXSusGzcwfBYxN8lZv+UO29q2WNUp1tZu3mFsa63DG2Id3hCls+Hz0CYkkbd7H5rIq1VWD/VjfbHu1A6FnS3ayCtkz1qA7kZs+dXo0QXrru1Rurmiz8gk/+BRchevgAKNMY3CxRmbJx/DskE9FNbW6G7GkT1tDtqrUZVfh/69sO7QxnB+X75G9vzF6GJull+Hrh2x7tQWpasL+sws8g8fJ3fZKtAY6mDdsS2qjm1RursCoI25Se6q9WhOn6308hvr0aML1q2aobCxQRsVTfayVehultNOjXwRy+DS3yELzp4ja9oc4z7VPbuYPK9LzyB97LeVW3ghyiGdceKB69mzJzNnzkSj0XD27Fmef/55UlNTWbBgwQMrU40aNUptUygUrF279gGURog7Y92iKapmjcleuxFdciqq1s2xe+IxMv6aDfkF5jMpldgNfhR9dg7ZK1ejy8hE6eCAPj/fmERhbYU2PoH8U2ewe7RfpZd79ty5/L1gAWM/+wxfX1+mz5zJyDffZNmiRdjZ2ZnNc/LUKT7+7DNGvPwynTp0YNuOHXz4ySdMnzLF+OM7JyeHWrVq0a9vX97/6COz+1m/Zo3J/3v37eOrb76hc4mOgHuh6tYRVef2ZM9dhDY+AXXPrti//hLpX/4AeXlm82RNmw2WRZdmhZ0tDh+9TcGxkyb7tW7Xiuw5C9HFxmHh543tkCfQ5+SSv313hct9L2ysVFxKus7ac3sZ12vEAylDeaxbNkPVPJzs1RvQJaegatMCu6cGkjFlZvnnxlMD0Wdnk718Nbr0DJSOpueGha8P+UeOo42NA6UCdYe2hv1OnWXy46syqLp3RtWlA9mzFxiOp17dsB81gvSx35V9PE2eBZYWxv8VdrY4fPouBUeLOkpthz6BRU1Psmb+jT4tHesWTbB/awTpX3yPPjXtrso4e84c/v77b8aOGWM4l2fMYOTrr7Ns6dKyz+WTJ/n4448Z8cordOrUiW3btvHhRx8x/a+/jOfyxo0bmfDTT3z4wQc0bNiQ5cuX8+aoUSxZvNh4vfbz9eX9997Dy8uLvLw8/l6wgJGvv87KFStK3Vz7e8ECqOCwmDn7drPgwD7G9H8UX1c3ZuzewRvzZ7Pk1TexU6nKzRubmspvmzfQyMev1HPpOTm8NPsvmvgF8OuTQ3Gxs+N6SjIOKnWFymuOqlM7VB1ak71wOdqERNRdO2L/ynDSx/8Ceflm82TN+tv0mLK1xeGdkRScPG3cpktLI2fNRmMHjHWzxtg99wwZP/1ZbkffvbJu2ghV44Zkb9yKLjUNVfNw7B7rR8bsBVBQxvkN6PPyDGmKK+yIA8hcsMzkOFG6uWI/sD8FlXgz6hZV+9ao2rQke9k/aBOTUHdqh/3zQ0j/6Q/IN/9ZYKHE7vkh6DOzyfp7Kbr0dJROjuiLfXa2j/XDono1spasRJ+egXXjBti/MIT0XyahT8+o9HpYt2qGqkUTsv9dX9jWtsTu6UFkTJ5Rflv79CBDW7vs38K21tGkrdVlZBhudKSkAmDVIBTbxx8h86+5t+/ouweqvj1Q9epK9pTZaG/GoR7QG/sP3yL9vTGQa769tWrdHPXgR8meNhvtxcsoa3hg+8pwAHLnLwEKz5cx71EQcYGsH35Hn56Bsno19NnZlV+HXl1Rde9E9oz5aOPiUfftgf07r5P+yVdl16FFU9SD+pM9cz7aS1cMdXjecGMyd9FyAHQpqeQsW2XsKLZu3QK7N14i44vxt+3ou6d6dG6PqmMbsv9eZminunXCfsTzpH/7U9nt1Mz5YFHi2vfuGxQcP22SThsbR+ak6UUbdA/mhub9ovh/PBz0YSXDVMUDp1KpqFGjBt7e3nTv3p3BgwezceNGkzQzZ86kbt26qNVq6tSpw59//lnm/rRaLS+88AIBAQHY2NgQEhLCr7/+WirdjBkzqFevHiqVCk9PT15//XXjcyWHqZ46dYrOnTtjY2ODm5sbL7/8MpmZmcbnbw2N/PHHH/H09MTNzY2RI0dSUM6XQIDvvvuO6tWr4+DgwAsvvEBubm6pNHdTd4D169fTtm1bnJ2dcXNzo2/fvkRGFn1xzM/P5/XXX8fT0xO1Wo2/vz/fflt0F0ihUDBp0iR69eqFjY0NAQEBLFmyxOQ1bty4weDBg3FxccHNzY0BAwZw9erVO3p//f39AXj00UdRKBTG/wEmTZpEUFAQ1tbWhISEMHfu3HLrqtVqGT16tLGu77//PvoSkUF6vZ7vv/+ewMBAbGxsaNiwIUuXLi13vyX99NNP1K9fHzs7O3x8fHjttddMPv+SIiMjGTBgANWrV8fe3p5mzZqxefPmu3rNe6Fq2pjcfYfQXIhEl5hEzpqNKKyssK5bp8w81g3qoVCryV7+L9obsejTM9DeiEGXkGhMo7l8lbxd+9BcqPwfIHq9ngWLFvHc8OF07tSJ4KAgvhgzhtzcXNaXaAeKW7BwIS2aNeO5YcPw9/fnuWHDaN6sGX8vWmRM06Z1a14bMaLcjjV3NzeTx46dO2napAneXl4VrpuqUztyN2yh4MRpdLFxZM9diMLaGutmjcvMo8/OQZ+eYXxY1akF+QXkF+s8sQzwo+DkGTRnzqFLTqHg2CkKIi5i6edd4TLfq/1RZ5h2YBU7Lh9/YGUoj6p5Y3L3HERz/hK6hCRy/t2AwsoS63rlnBsNw1DYqMleugrt9RjDuXE9Bl180bmRvWg5BafOoktMQhefSM6aDSidHLGoUb3y69ClPbnrNlNw/BS6mJtkz/7bcDw1Dy8zjz472/R4qhtiOJ6OFB5PVlZYNW5AzvJ/0V66jC4hkdzVG9AlJqNq3/quyqfX61mwYAHPPfccnTt3Jjg4mC8+/9xwLm/YUGa+BQsW0KJ5c5577jnDufzcc4ZzudgNufl//82AAQN45JFHCAgI4J133qF69eombXnPnj1p0aIF3t7eBAUF8fZbb5GVlcXFi6bRWBcuXODv+fMZ89lnd1W/knVdeHAfw9u2p1OdUII8qjO2/2PkFhSw4fTJcvNqdTrGrFzKS+074WVmBMCcfbvwcHRkTP9HqeflTU1nF5oHBOHt6nrP5S2Lqn1rcjfvMBzDN+PJXrAMhbUV1o0blplHn5ODPiPT+LCqHQQFBeSfKPqRqzl7Hs25C4bzIjGJ3HWb0efnY+nnU+l1AFA1bkDuoSNoIq+gS0omZ+NWw/ldp9Zt8+qzc0weJs/l5Jo8ZxXojzY1De31yo8qU7VuQe72XRScOYcuLoHsJf8Yrt+NSkd23mLdpDEKGxuy5i1CGxWNPjUN7bVodDcLo2UtLbGqV5ec9VvQXo1Cl5xC7pYdhpt1LZpWeh0AVM3Dyd1zoFhbu76wra1bdj0aFba1S/4p1tbeMIkK1Fy8bPh8k1PQJaeQt30P+vx8LLw8q6YePbuQ+886Cg4fQ3c9huwpswztbevmZeaxDA5EczGSgn2H0CUmoTkdQf6+Q1gGFnW6q/r1QJecQs7U2WgvXzWkO3PO5LpSaXXo2pHcNRspOHoC3Y1YsqfPM5zf5Xz2lkEBaC5dpuDAEXRJyWjOnCP/wBEs/X2NaTQnTqM5dRZdXAK6uARyV6xGn5eHZaB/pdcBQNWhNbmbtlNw6owhivDvJYZ6hDcqM48+u2Q7FVzYTp0yTajTmqTTZ2VVSR2EKIt0xomHyuXLl1m/fj1WVlbGbdOmTeOTTz7hm2++ISIignHjxvHZZ58Zh7eWpNPp8Pb2ZvHixZw9e5YxY8bw8ccfs3jxYmOaSZMmMXLkSF5++WVOnTrFqlWrCA4uPZQHIDs7m549e+Li4sKhQ4dYsmQJmzdvNum8A9i2bRuRkZFs27aN2bNnM2vWLGbNmlVmXRcvXszYsWP55ptvOHz4MJ6enqU62u627gBZWVmMHj2aQ4cOsWXLFpRKJY8++qhxwYnffvuNVatWsXjxYs6fP8+8efNMOsQAPvvsMwYOHMiJEycYMmQITz31FBEREcb3o1OnTtjb27Nz5052796Nvb09PXv2JL/wLmZ57++hQ4cAQydjbGys8f8VK1YwatQo3nnnHU6fPs0rr7zCc889x7Zt28qs64QJE5gxYwbTp09n9+7dJCcns2LFCpM0n376KTNnzmTSpEmcOXOGt99+myFDhrBjx44y91uSUqnkt99+4/Tp08yePZutW7fy/vvvl5k+MzOT3r17s3nzZo4dO0aPHj3o168fUVGVPwThFoWTI0p7OzRXrhVt1GrRRF8v98uqZXAg2phYbLp1wuH1l7B/fgiqls0qHDVyp27ExJCUlETLFi2M26ytrQlv3JiTp06Vme/k6dO0KJYHoGWLFuXmuZ2kpCR279nDgH4Vj/5TurmidHJEE3GhaKNGi+bSZSwDSkfDlMW6VXPyjxw3iSjQRF7FKiQYpYe74bW8PLEM8qfg9LkKl/u/SOHshNLeHs2Vq0UbtVo0Udex8Cp72J9lrSC0N2Kx6dEZh1GvYP/Ss6haNy/33FAURkTpzdxYqQil+63j6XzRRo0WzcXIu/oBZN2mBfmHjxVF2iiVKCwsSkXx6QsKzA5FLM+NGzcM53Kxod/W1taEh4dz8mTZHVQnT52iRYnh4i1btTLmKSgo4Ny5cyZtBBSe72Xst6CggBUrVmBvb0/t2rWN23Nzc/nk00957/33cXd3v6v6FReTmkJSZiYtA4u+N1hbWhLu58/J6+UP752+azvOdnYMaNzE7PO7LpynrqcXHy5bRI+fxjNk2p+sPHr4nstaFqWrC0pHh6LhjGA4LyKvmvzwvh3rFk3IP3aq7KgnhQKrRvVRWFujuVb510CFowNKOzs0164XbdTq0FyPwcKz9CgHE1ZWODw/BIcXhmLbvxfKauUcE0olVnVqUXCm8ttZpYuz4bO4eLloo1aL5so1LH3L7sC0qlsbbdR1bPr3wvHj0TiMGoGqQ9uiNkqpRGGhNA4vvEWv0VRJx6ixrb1c4ntI1HUsvG/T1l6PwaZnFxxGjcD+pWHlt7UKBVahISisrKpkuK2ymjtKZyc0p4oNu9Ro0Jy7gGWtsofcay5cwtLfF4vCNllZzR2rhmEUHC/6XmIV3gDN5WvYvvEyjn/8gP3Xn2DdsW3l18HdzVCH4serRoPm/CUsg8pu2zWXIrH088Gi8HuK0t0Nq/qhFJw8Yz6DQoFV83DD+V0FQ4aVbi4oHR3RnC92U0WrRXPpCpYBd9NONSX/2MlS7ZTS3R3Hzz/E4dN3sR36JEq3BzdFkvj/SYapigdu9erV2Nvbo9VqjZFhP/30k/H5r776igkTJvDYY48BEBAQwNmzZ5kyZQrDhg0rtT8rKyu++OIL4/8BAQHs3buXxYsX88QTTwDw9ddf88477zBq1ChjumbNmpkt3/z588nJyWHOnDnGYTYTJ06kX79+jB8/nurVDREQLi4uTJw4EQsLC+rUqUOfPn3YsmULL730ktn9/vLLLzz//PO8+OKLxjJt3rzZJDrubusOMHDgQJP/p0+fjoeHB2fPniUsLIyoqChq1apF27ZtUSgU+PmV7hh4/PHHjeX66quv2LRpE7///jt//vknCxcuRKlU8tdffxnDnWfOnImzszPbt2+ne/fu5b6/1apVA8DZ2dlkOPCPP/7I8OHDee211wAYPXo0+/fv58cffyw1d1Dx9/Cjjz4y1nny5MlsKBaBkZWVxU8//cTWrVtp1aoVAIGBgezevZspU6bQocT8YWV56623jH8HBATw1Vdf8eqrr5YZpdiwYUMaNiyKLPj6669ZsWIFq1atKtWJW1mU9oZjs+RQB31WNgqn0nPeGPM5O6F08jHMo7HkHyxcnVF36wRKJXl7D1RJWYtLSjIML3ErEfXh5upK7M2yhzskJSWZzXNrf/di9dq12NnZmcw7d68Ujg4A6DJMIyh16RkoXe/sy56Fnw8WXp5kzzeNTM3btA2FjRqHz94zzBGnUJD773oKjhyvcLn/i5R2toDhXCjutueGS+G5cfocWYtWYOHqgrp7Z8O5sXu/2TzqLh3QRF9Hl1C5w6YUhfNW6UoMK7ur48nf13A8zS2KHiUvD03kFdR9upF1M84QPdcsHAt/37uO1Kiqczk1NRWtVotriTSubm4kljjfd+3axceffEJubi7u7u78MXGiyeI6E376iQYNGpSaO/JuJRVGRruWGHrramdHbFpqmflORF9j1fGjzHvp1TLT3EhJYfmRQzzdohXPtWnPmRvXmbBxLVaWlvRp0KhC5S5O4WgPmGmjMjKNc73djoWPFxaeNchetKLUc8oa1XF482XDsPv8fLJm/o0urvz5z+6F8fwuee3LzjHW0Rxdcio5G7eiTUxGYW2NqnF97J94hMz5S9CZGZ5tFRSAQqUi/2zld8YpHAo/ixIR97rMTJTlLA6ldHVBGRhA/olTZM1agNLdFZv+vcBCSd7WnZCfj+ZaNOpO7ciKT0CfmYVVwzAsvL3QVeBaWWZ5Cs+HktFF+qxsYxtmNp+zM0p/R8PcqIuWG9raHl1KtbXKau7YD3/KeExlL111+zlx74GicF46XVq6yXZdWoZxnjRzCvYfJsfBAfsx7wEKFJYW5G3eTt6/Rd9LldWqoerSgbz1m8latQ6LIH9snh2MXqOhoIzryj3VwenWNaNEHdIzULqVU4eDR8mxt8f+w7eK6rBtF3nrNpmkU3p54vDxO2BlCXl5ZP3xF7rYyh+iqnAo47tUZiZKF+c72oeFrzcWNWuQXTjM9hbNtWi0fy9Bm5CI0sHeMPz1zRFkjP+lVJTsf4VCIXFYDxvpjBMPXKdOnZg0aRLZ2dn89ddfXLhwgTfeeAOAhIQEoqOjeeGFF0w6tTQaDU5OTmXuc/Lkyfz1119cu3aNnJwc8vPzjauUxsfHExMTQ5cuXcrMX1xERAQNGzY0me+mTZs26HQ6zp8/b+yMq1evHhbF5ifw9PTkVDlROhEREYwYYTq3UqtWrYyRYPda98jISD777DP2799PYmKiMSIuKiqKsLAwhg8fTrdu3QgJCaFnz5707duX7t27lypHyf9vrVZ75MgRLl26hEPhBfKW3NxcIiMj7/r9Lf5+vPzyyybb2rRpY3aIMUBaWhqxsbEmZbW0tKRp06bGoapnz54lNze31IIb+fn5JhN/3862bdsYN24cZ8+eJT09HY1GQ25uLllZWWbnQcrKyuKLL75g9erVxMTEoNFoyMnJKTcyLi8vj7wS8z6pypl3yCo0BJseRe9x1tJ/DH+UnMBfYWabyfMK9NnZ5BRO/q+Li0dhb4eqedMq6Yxbt34948aPN/7/y4QJhcUwvQOu1+tvP7fFveQpx6rVq+nZvXu573tZrJo1xvapoo7wzD9n3CqUacK7KJ916+Zob8SWmkjfqklDrJuHkz3rb7SxcVh418RmYH90aekUHDhy12X/r7GqVwebXl2N/2ctXmn4o9RpoDCzzfR5fVY2Oes2Gc6Nm4XnRsumZjvj1D06Y+HhTmbxzq57ZNU8HNunHzf+n3lrsYUKHU8tDMdTiUnCs2f+je2zT+I0/nP0Wi3a6BsUHDqGhW/5Q7XX37zBdxdOoWi8Db1ezy8//1xYJDPn5e0Kdwfn8p20EU2bNuXv+fNJTU1lxcqVfPTxx8yaORNXV1d27NjB4cOHmT9v3u1KU8r6Uyf4du2/xv9/fvIZQ5koWabS227JystjzMplfNynP8625ufPA9Dp9dStWZPXOhuuWyE1PLmcGM+yIwcr1BlnFd4Q20FFC1Vk/jW3qNDFKRR3vBCMdYumaGNvoo2+Ueo5XUIiGRP+QGGjxqpBPWyfGkjmn39VuEPOKqQWNl2KOlOz/imc99NckcuphvZmHNqbRYufZMfEYv/M41g3DCN3x57SrxtWB83VqFKd+vfCqmEYto/0Nf6fOaeMOZIVCsqthEKBPiuLnBWrQa9HGxOL0sEBVbtWhs44IHvJSmwH9sfpo9HotTq0MbEUnDhVKcM7rerVwaZ30ferLDOdskXKq4ehwy5nbfG21h5VK9O2VpeUTOZfc1GoVViG1MKmX0+y5i2qcIecVevm2D7/jPH/zB8nmi/zbRoyy7q1UQ/oRc6sv9FcuoJFDQ9shgxG90gaeSsL55xWKtBevkZu4XVJey0aC6+aqLp0qFBnnFWLptg++2RRHX6dbLYKtzu/LUOCUfftQc68xWguX8XCoxo2Tw1E17cHeauLOhV1N+PJ+OI7FDY2WDVphO0LQ8gc/1uFO+Sswhti+8QjRfUoXGyhtNtdv4tYt2iKNuYm2qjrJts154pGLuhi48i8GoXjJ+9i3SycPDNtgBBVQTrjxANnZ2dnHML422+/0alTJ7744gu++uorY0fStGnTSg1JK97xVdzixYt5++23mTBhAq1atcLBwYEffviBAwcMHQs2NjZ3Vb7yfuAX3158aO2t526V/17cS90B+vXrh4+PD9OmTaNmzZrodDrCwsKMQ0jDw8O5cuUK69atY/PmzTzxxBN07dr1tvOo3aqrTqejSZMmzJ8/v1SaatWqoVTe+12Xe+qQKcet93DNmjV4lZgD7E47XK5du0bv3r0ZMWIEX331Fa6uruzevZsXXnihzDkB33vvPTZs2MCPP/5IcHAwNjY2DBo0yPgZmPPtt9+aRHQCjB07lrdtnM2mL7h0GW3xiXILJ9RW2NmZ/FhQ2NqW++NBn5mFXqcz+XKmS0oxRNoplVCBY9ic9u3aEVavnvH//ML3MDEpyWTYWHJKSqlImOLc3NxKRcHdLk95jh0/zrVr1/j266/vKX/BybNkFO/kKFyEQenogLZYNJPSwf7OJs22ssK6SUNyVpeeN8/m0b7kbtxGQeG8X7qYmygLo7akMw4KLkaanhuF7aXC3tYkYkNhZ1Pu/DD6rCz0Wq3puZGYjNLevtS5oe7eCataQWTOXYS+xB38e6rDiTNkXCl+PBnqoHRyvPfjqVkjcv5dX+opXWISmT/9AdbWKNQq9OkZ2L449LY/cNu5V6eeozOOX31MVmamsX0zey67uZW5n9udy87OzlhYWJRKk5KcXCqizsbGBh8fH3x8fKhfvz6PPvYY//zzD8899xyHDx/m+vXrdOpsuoLm+x98wOIlS5jYvS9laVe7DvW8iuZkzC+c5D8pKxP3YjemUrKzcLUzH411IyWZ2LRU3ln0t3GbrvDYavXN5yx59U28XV1xt7cnwL2aSV5/92psO1exlQoLzkSQUbxjv3gbVeyYVdrboc+4g3mTrKywblSfnA1bzD+v1aJLMhxD2usxWPh4o2rXmpxbN47uUcHlqyadaMbz287WJDpOYWtz19Et2pvxZqNtFA72WPp4k7267LkP70ZBxAUyoqcUbbj1Wdjbm34WdnboM8tpozIy0ZVoo7QJiSgdHcBCCVoduuQUMqfNBisrw/mdkYntkwPRJadWvB4XI9H+ZaatLVFuhd29fA9JKt3W6nTGBRy0sXFY1qyBdbNwctdVbE7egqMnyChctRUo+jycnNCmFkWWKR0d0JeIlitOPag/+XsOkL/d0JGjux4DKhW2zw8h7591oNcb5vWLMV2RVRsTi1U5c8neUR1OnCLji6tm6uCItliZb3fNUD/Sl/x9B8nftc9QhxuxoLLG9tmnyFuzsegz0mqN0dPaa9FYBPih6tqBnArejCo4E0HGj2baKQd702ufvR36cuZuNrKywrpxA3LW38Exkl+ANvZm+cPVhahk0hknHjpjx46lV69evPrqq9SsWRMvLy8uX77MM888c/vMGIaptG7d2jjcETBZwMDBwQF/f3+2bNlS5vDH4kJDQ5k9e7ZJFNSePXtQKpUmc9Hcrbp167J//36effZZ47b9+4vuilWvXv2u656UlERERARTpkyhXbt2AOzeXXp1RUdHRwYPHszgwYMZNGgQPXv2JDk52fjjx1y5bkWShYeHs2jRIjw8PHAsY9jB7d5fKysrtMVWLAPD+7F7926T1927dy9165qf9NfJyQlPT0/2799P+/btAUPU4JEjRwgPN0xoHhoaikqlIioq6o6HpJZ0+PBhNBoNEyZMMHY0Fp9/0Jxdu3YxfPhwHn30UcAwh1zJBS5K+uijjxg9erTJNpVKRe4vk8xnyC9Al286lEaXmYWlvy/5tyY9Viqx9PEmt5wVNjU3YrAONZ3EXunibBgSUMkdcWDofC8eTajX63Fzc+PAwYPUCQkBDPM9HT12jDdGjixzPw3Cwjhw8CDPPPWUcduBAwdoUL/+PZXrn1WrqFunDrVr3X7Cb7Py8tAlmEY26tLSsaxTu2iybwsLLIMDyfnn9qsyWzdpCJaWFBw6WvpJK6vSd7Z1uvs2z99DL78AXX6qySZdZiaWAX7kxxU7N3y9yd22q8zdaKJvlFrgQenmUurcUHfvjFVIMFnzFpf7Q+2ulHU81a1dFIVkYYFlrSBDVMxtWDdtZDieyuuszc9Hn5+PwtYGq9A65Cz/t+y0gJ2lJXaWljj7+ZGRnl50Lh84YHouHz1qjHY3p0H9+hw4cIBnnn7auO3A/v00aNAAMFwv6tSpw4EDB0yuKQcOHqRDYdtfFr1eb+zwHzZsGAMGDDB5/smnnmL022/Ts1cv2L637LqqVCYrpOr1etzs7Tlw+RIhNQwRRgVaDUevXeX1zt3M7sPP3Z0FL5u2aZO2byE7P493uvemeuGwsgY+vlxLMh0iHJWURA0n53Lrelt5+ejyTDtYdekZWNY2zI0IGI6pIH+zNwFKsm4UBpYWdz48XgEKy7JvJN6xggJ0aaY3wnRZWVj6epN/a+EhpRJL75rk3mWkkbKau9nhm9b16qDPyTGdk7Ui8vPRJZvenNOlZxjmcL0VWWShxDLAj5wNZXciaK5FY90wrDACvrAO7q6G4ezaEtfvggL0BQUo1GqsagXdWefEbetRXltbuGrurbZ2azlt7fWY0m2ta+m21hxFOTen71huHrpc04hNXWoalmF1iyLTLSywrFObnBJDHU1YW5dejVOnM4mo01yIxMLTdIEfZY3qFR9um5uHrsQKqbrUNCxDQ4oiwiwssAwJJmfpqrL3Y23u+4X+tlGBKEBRIijhnphtp9KxDAk2baeCA8j59/ad49aN6hvaqcPHbv/aFhZYVPcwnfPwP0Z5+zh1cZ9JZ5x46HTs2JF69eoxbtw4Jk6cyOeff86bb76Jo6MjvXr1Ii8vj8OHD5OSklKq8wIgODiYOXPmsGHDBgICApg7dy6HDh0iIKBowtLPP/+cESNG4OHhQa9evcjIyGDPnj1mfzA888wzjB07lmHDhvH555+TkJDAG2+8wdChQ41DVO/FqFGjGDZsGE2bNqVt27bMnz+fM2fOEBgYaFLOu6n7rdVNp06diqenJ1FRUXz44YcmaX7++Wc8PT1p1KgRSqWSJUuWUKNGDZN5dZYsWWJSroMHDzJ9+nTj+/HDDz8wYMAAvvzyS7y9vYmKimL58uW89957eHt73/b9vdVZ16ZNG1QqFS4uLrz33ns88cQThIeH06VLF/7991+WL19e7iqko0aN4rvvvqNWrVrUrVuXn376idTUVOPzDg4OvPvuu7z99tvodDratm1Leno6e/fuxd7evsx594oLCgpCo9Hw+++/069fP/bs2cPkyZPLzRMcHMzy5cvp168fCoWCzz777LZRkiqVymy03t1MA593+BjqVs3RpaSiS0lF1aoZ+oIC8iOK5rix6dMdXUYWeTsNd27zj51EFd4IddeO5B85jtLFGVWrZoZFA26xsjKJFlA6OaL0qGZYaS7jDqJyyqFQKHhq8GBmzp6Nb2FEy8zZs1Gr1fQsNnx6zBdf4FGtGq8XdrI/OXgwL7/6KrPmzKFj+/Zs37mTA4cOMX1KUbRBdnY20deLhiXciInh/IULODk6msxXmJmVxeatW3nrzTcrVJeS8rbtQt2jM7qERLTxCah7dEGfn0/+oaIvhbbPPokuNY3cVetM8lq3akbBiTNmowk0pyMM+01OQRcbh4WPF6rO7cnfd6hSy383bKxUeDsVRfPUdHSnlrs36blZxGWmPLBy3ZJ38Bjq1s3RJaeiS0lB1boF+gIN+cUmt7bp1xNdRiZ5hZ3X+UdPoGraGHX3TuQfPobSxQVV6+Ymn5+6R2es69Uha+kqQ0fWrfmr8vJLTZpe4Tps2Ym6Z1d08YXHU8+uhuPpYFGHre3wp9ClppO7co1JXuvWLSg4ftrs8WQZGgIo0MXFo/Rwx+axfmjj4snfe/CuyqdQKHjqqaeYOXNm0bk8a5bhXO7Rw5huzNixhnO5cP7MJ598kpdfeYVZs2fTsUMHtu/YwYGDB5n+11/GPM88/TRjxo6lbmgoDerXZ/mKFdy8edM4X2hOTg4zZsygffv2uLu7k5aWxpKlS4mPj6dr4ZQJ7u7uZhdtqFGjBj4+PpSeJaz8uj7ZvBWz9uzCx9UNX1c3Zu7ZidrKih5hDYzpxv6zDA8HR0Z27obK0oogD9PvDA5qNYDJ9qdbtOaFWdOYuXsHXUPDOBNzg5XHDvNx7/5Utryde1F36YAuIQltYhLqLh3Q5xeQf6xo9WbbpwaiS0snd63pnFHWzZtQcDrCbPSZulc3Cs5dQJ+aBioV1o3rYxkUQNa0shefqlA9jp1E3TwcXWoautQ0VM3CDef3uaJJ3226d0aXlUXeHsMoCVWLpoahqimpKFTWqBrVx6Kam9kOeuvQOuSfPX/Hw3fvqQ57D6Du2BZdUhLapGTUHdsart/Hi1aptR00AF16BrkbtxryHDiMqlUzbPr2JG/vQZTubqg7tiWv2Ll7a8EBXWISSjdXbHp2RZuYZHqNr8x6HDyKuk1zdCmGVU+L2toIY5pSbe2RW21tZ0Nb6+qMqnXhYjOFVB3bGlZTTc9AYW2NVb0QLPx8yFtYTudYReqxfgvq/r3QxcWjvRmPun8vQ3tb7L21fWU4upRU45BTzbGTqHp1RXstCm3kFZTVPVAP6k/B0ZPGYydv/Wbsx3yAqn8vCg4cxiLQH1WndmTPuPvh87etw+btqPt0RxeXYLhm9O5uOL8PFC0IY/vCUEMdCm++aE6cRtW9E9qo62gvX0Pp4Y76kT4UHD9trIP6sX4UnDqLPjkF1CqsmzfBMqQWWT+bn0e5wvXYsRd1146GdiohCXXXjoZ6HD1eVI+nBxnaqTWmNxKsWzal4FQZ7VT/XhScOYc+JRWFvR3q7p1QqFXkm7sJKkQVkc448VAaPXo0zz33HB988AEvvvgitra2/PDDD7z//vvY2dlRv359k0n1ixsxYgTHjx9n8ODBxh8Hr732GuvWFf3YHTZsGLm5ufz888+8++67uLu7M2jQILP7s7W1ZcOGDYwaNYpmzZpha2vLwIEDTRaZuBeDBw8mMjKSDz74gNzcXAYOHMirr75qsgDB3dZdqVSycOFC3nzzTcLCwggJCeG3336jY7EJ6e3t7Rk/fjwXL17EwsKCZs2asXbtWpPhpV988QULFy7ktddeo0aNGsyfP5/Q0FDj+7Fz504++OADHnvsMTIyMvDy8qJLly7GSLnbvb8TJkxg9OjRTJs2DS8vL65evcojjzzCr7/+yg8//MCbb75JQEAAM2fONCl7Se+88w6xsbEMHz4cpVLJ888/z6OPPkpaWtHPqq+++goPDw++/fZbLl++jLOzM+Hh4Xz88cd39Dk1atSIn376ifHjx/PRRx/Rvn17vv32W5MIvpJ+/vlnnn/+eVq3bo27uzsffPAB6emVFDFTjvwDh1FYWmLTvTMKtQptzE2yFq8wWT1K6ehoMs+GPiOTrMUrUHdpj/3zQ9BlZJJ/+Dh5xb6sWdSojv3TRZ/frfl68k+dJWft7SMobmfY0KHk5eXx3Q8/kJGRQVi9ekz89VeTCLqbN2+iLBb51bBBA7756ismTZnC5KlT8fby4tuvvyYsLMyY5mxEBCOKRdf9XDj/YN/evfl8zBjj9o2bNqHX6006/ypD3qbtKKyssBn8KApbG7RXo8icOA2KzQ2odHEu9QNP6eGOZXAgmb9PNbvf7MUrsenbA9snH0Nhb48uLZ383fsrPFSnIupU82Pio0U3CN5sa5jvbG3EPr7ZWjU/wO9G/v5DKKwssenZGYVabTg3Fi4rcW44mHwW+oxMshYuQ921I/YvPms4Nw4dI69Yp6eqSSMA7Ic8YfJ62f+up+BUxYYVlpS3cSsKaytsnhpoOJ6uRJH52xTT48nVxczxVA3LWoFFcwiVoLBRo36kD0pnZ/TZ2RQcO0nOyrX3FBk77NlnDefy+PFF5/Lvv5d/LjdsyDfffMOkSZOYPHky3t7efDtunMm53L17d9LS0vjrr79ITEwkKCiIX3/5BU9PQ1SaUqnk6tWrrF6zhtTUVJycnAgNDWXa1KkEBZW9+mFFPNuqLXkFBXy/fjUZObnU8/Li96efNYmgi0tLM6nrnQit6cX3jz/Fn1s3MX3XDmo6OzO6Wy961m94+8x3KW/bLkMbNbA/Chs12qjrZE6dBXlFUVtKZ+fSx5S7G5aB/mROmWl2vwoHe+yeHoTC0QF9Ti7a2Diyps1GcyHSbPqKyj983HDt69wOhUqF9mY8WStWQ0Hx89ue4hc/hcoamy4dDFM55OejTUgga+k/aG9FdBWy9PVG6ehQJauoFpe3c6/hs+jfG4WNDdrrN8icOa9o5WMMCy6ZtFFp6WTOmI9Nn+44vDkCXXo6eXsOGm+2ASjUKtTdO6N0ckSfnUPBmQhyNm6rksh3gPx9hwyfRc8uhrb2RixZC5aatrVOjiXa2gyyFixF3a0j9i/damuPmrS1SjtbbPv3QmFvhz4vH118AtkLl1detGIJeas3GNrb4U+jsLVFG3mFzPG/QrHoM6W7q0k9cleuRa8H9eMDULo4o0/PpODYSXKXrDSm0V6+RtYvk7AZ/CjqR/qgS0gkZ95iCu7y5scd1WHdZsMxNeQJFHa2aC9fNUxJkFv2NSN39Qb0GIarKl2c0GdkUnDiNLnLiyKwFY4O2L04FIWTo+H8vh5D1s9/ojl7nqqQt3WnoR6D+hvOjWvXyZw807SdMvddqlphOzVphtn9Kp2csBs62DCMOjMLzbVoMn6ZjL5wKLQQ94NCr6/C2zxCiP85CoWCFStW8MgjjzzoogggbfwvD7oIFeb0wVtkpDz46KiKcHBxIXXkew+6GBXm/McPtPljxO0TPsT2jJxM2riK3Qx5GDh9PJrUEaUjnP/XOE/+iYz7cLOhKjk4OpJWCQtvPGhOQweT+s6nD7oYFeI84WvSypqe4X+I01uvkvrxlw+6GBXmPG4Mad9MeNDFqBCnT94hdcgrD7oYFeY8bwqpL5Q95P9/gfP030l9+85uhj/MnH8e96CLcE9+2fnwXufeaj/4QRfhgZD1bYUQQgghhBBCCCGEuE+kM04IIYQQQgghhBBCiPtE5owTQpiQketCCCGEEEII8d9xt3OYiqonkXFCCCGEEEIIIYQQ4n/Cn3/+SUBAAGq1miZNmrBrV+nVsG/Zvn07CoWi1OPcOdPFeZYtW0ZoaCgqlYrQ0FBWrFhRpXWQzjghhBBCCCGEEEII8dBbtGgRb731Fp988gnHjh2jXbt29OrVi6ioqHLznT9/ntjYWOOjVq1axuf27dvH4MGDGTp0KCdOnGDo0KE88cQTHDhwoMrqIZ1xQgghhBBCCCGEEP9R5iLDHpbH3frpp5944YUXePHFF6lbty6//PILPj4+TJpU/mrcHh4e1KhRw/iwsLAwPvfLL7/QrVs3PvroI+rUqcNHH31Ely5d+OWXX+66fHdKOuOEEEIIIYQQQgghxEMtPz+fI0eO0L17d5Pt3bt3Z+/eveXmbdy4MZ6ennTp0oVt27aZPLdv375S++zRo8dt91kRsoCDEEIIIYQQQgghhLjv8vLyyMvLM9mmUqlQqVSl0iYmJqLVaqlevbrJ9urVq3Pz5k2z+/f09GTq1Kk0adKEvLw85s6dS5cuXdi+fTvt27cH4ObNm3e1z8ognXFCCCGEEEIIIYQQ/1EP82qq3377LV988YXJtrFjx/L555+Xmafk8Fa9Xl/mkNeQkBBCQkKM/7dq1Yro6Gh+/PFHY2fc3e6zMkhnnBBCCCGEEEIIIYS47z766CNGjx5tss1cVByAu7s7FhYWpSLW4uPjS0W2ladly5bMmzfP+H+NGjUqvM+7JXPGCSGEEEIIIYQQQoj7TqVS4ejoaPIoqzPO2tqaJk2asGnTJpPtmzZtonXr1nf8mseOHcPT09P4f6tWrUrtc+PGjXe1z7slkXFCCCGEEEIIIYQQ/1FVOdzyfhs9ejRDhw6ladOmtGrViqlTpxIVFcWIESMAQ6TdjRs3mDNnDmBYKdXf35969eqRn5/PvHnzWLZsGcuWLTPuc9SoUbRv357x48czYMAA/vnnHzZv3szu3burrB7SGSeEEEIIIYQQQgghHnqDBw8mKSmJL7/8ktjYWMLCwli7di1+fn4AxMbGEhUVZUyfn5/Pu+++y40bN7CxsaFevXqsWbOG3r17G9O0bt2ahQsX8umnn/LZZ58RFBTEokWLaNGiRZXVQzrjhBBCCCGEEEIIIcT/hNdee43XXnvN7HOzZs0y+f/999/n/fffv+0+Bw0axKBBgyqjeHdEOuOEEEIIIYQQQggh/qOUslzAQ0c+ESGEEEIIIYQQQggh7hPpjBNCCCGEEEIIIYQQ4j6RYapCCCGEEEIIIYQQ/1H/pdVU/yskMk4IIYQQQgghhBBCiPtEOuOEEEIIIYQQQgghhLhPFHq9Xv+gCyGEEEIIIYQQQgghKt+0/f886CKU6aWWAx50ER4ImTNOCCEeYnMOrX3QRaiwZ5v15lzclQddjAqpUz2AJ+eNedDFqLCFQ74kbdxPD7oYFeL08Wja/DHiQRejwvaMnMzQBV886GJU2NynxnImNvJBF6NC6nkG8ffRDQ+6GBX2dHgPlp3Y+qCLUSEDG3ZmZ+TRB12MCmsfFM6Xm2Y86GJU2Jhuz/P6igkPuhgVMvHRdxizftqDLkaFfdnzJT5Y8+eDLkaFjO/z2n/mu5QQlUGGqQohhBBCCCGEEEIIcZ9IZJwQQgghhBBCCCHEf5QCWU31YSORcUIIIYQQQgghhBBC3CfSGSeEEEIIIYQQQgghxH0iw1SFEEIIIYQQQggh/qMUChmm+rCRyDghhBBCCCGEEEIIIe4T6YwTQgghhBBCCCGEEOI+kWGqQgghhBBCCCGEEP9RShmm+tCRyDghhBBCCCGEEEIIIe4T6YwTQgghhBBCCCGEEOI+kWGqQgghhBBCCCGEEP9Rsprqw0ci44QQQgghhBBCCCGEuE+kM04IIYQQQgghhBBCiPtEhqkKIYQQQgghhBBC/EcpkWGqDxuJjBNCCCGEEEIIIYQQ4j6RzjghhBBCCCGEEEIIIe4TGaYqhBBCCCGEEEII8R8lq6k+fCQyToj/UVevXkWhUHD8+PEq2b9CoWDlypV3ladjx4689dZbd5x+1qxZODs7l5vm3LlztGzZErVaTaNGje6qPPfqbutR1Z+FEEIIIYQQQoj/DomME+IeDB8+nNTU1LvurKpMPj4+xMbG4u7uDsD27dvp1KkTKSkpt+3g+l8yduxY7OzsOH/+PPb29g+6OGaV/CwelMObdrN/7TYyU9Op5lWDbkMewbdO0G3zRV+4zNyv/6Cadw1eGveecfu5QyfZs2oTKXGJ6LQ6XKq707J3R+q3bVaV1WDtin9ZsWApKcnJ+Pr78cIbI6jXMMxs2uTEJGb+OY1L5y8Sez2GvgMH8OKbI0zSbPx3Hds2bOba5WsABIUEM/Sl56gdGlJldehWuxn9QtvibGPP9dQE5hxex7mEa2Wmb+PfgP712lLDwZXsgjxOxFxk3pENZObnlErbyi+MUe2e4FB0BBN2LKiyOtyiatcK60b1UajVaGNiydmwFV1i0m0yqVB3bINVSDAKtRpdahq5W3aiibxieLpVMyxDamHh5opeo0F7PYbcbbvQJadUeX3K0tAzmKcbd6eOhy/uds58uHYSu66ceGDlKalLcFP61G2Nk40DN9LimXd0AxcSospM39qvPn3qtqa6gxs5BbmcjL3EgmObjMeUl2M1BjboiL9LTarZOzPv6Ho2nD9Q5fVYt3I1/yxcRkpSMj4Bfjz/+suENijj/E5KZvaf04i8cInY6zH0fqw/L7zxSpn73r1lBz99NZ7mbVry4TdjqqoKZh3auIu9q7eQkZqOh3cNejw7EL87aH+jzl9m1pe/4eHjyYjvPrgPJS2yf8MOdq3aREZqGh7envQZ/jgBdWuZTXv13CXWz19Bwo04CvLyca7mSvOu7Wjbt4tJupysbDYu+IezB4+Tk5WNi4c7vYcOJCTc/GdcUdtWb2TDstWkJadS08+bwS8/S+2wOmbTHt1zkO1rNhF9+RqaAg01/bzp98xAwpo0NKbZs2kHs36eXCrvnytnY2VtXSV1ALiw8yhntxwkJy0TZ093mgzsgkewj9m0cRei2Pxb6ba/76cv4lTDDQCdVsuZjfu5fOA02akZOFZ3pfGAjtQMDayyOgC0C2hIl1rNcFLbEZuexLJT24hMumE27ZDwHrT0K31cxKYn8s2W2QC09q9Pc59Qajoavl9Fpcbx79ndXEu5WWV1aOZTl7YBDbFX2ZCQmcK6c/vLfT0LhZKOweE0rBmMvcqW9NwsdkQe49iNCwA08Q6hUc3aeDi4ABCTlsjmi4e4kZZQZXUAaOlXjw6BjXFQ2RKXmcy/Z/ZwNSW27HoolXSt1YzGNWvjoLIlLTeTrZeOcPj6OWMataU1PUJaEFYjEBsrFSk5Gaw+u4fz5VyLKqKyv0s186nLI2HtqeHgioXSgpvpSayJ2PtQXevF/y/SGSfE/ygLCwtq1KjxoItR5SIjI+nTpw9+fn4Puihlehg+i7P7j7Fp3kp6Dh+ET+0Ajm7dy8IfpvLK+A9xcncpM19udg6rJv9NQL1aZKZlmDxnY2dLm/7dcK9ZHQtLCy4eO8O/Uxdi6+hAUAPzP3YqateWHUz/fQqvjB5J3bB6bFi1li/f/5SJc6ZSrbpHqfQFBQU4Ojnx+NCnWLVkhdl9njp2knZdOvLSqFCsra1ZvmAJn7/7Mb/PnoJbtcrvQG3lF8awJr2Yfmg15+Oj6FqrGR92HsI7/04kKTutVPqQar6MbP0Yc46s48j187jaOvJii3683HIAP+1caJLW3c6JIeE9iIi7WunlNse6ZTNUzcPJXr0BXXIKqjYtsHtqIBlTZkJ+gflMSiV2Tw1En51N9vLV6NIzUDo6oM/PNyax8PUh/8hxtLFxoFSg7tDWsN+ps6BAc1/qVpKNlYpLSddZe24v43qNuH2G+6iFbz2GhPdk1uE1XEyMplNwE97r8Awfrv2DpOz0Uulru/vwSstHmH9sA8duXMDFxoHnmvXlheb9+HX3YgCsLa2Iz0zlYNRZngnvcV/qsXvrDmZOnMpLb71G3fqhbFi1jq/fH8OvsyebPb81+QU4OjsxcMiTrC7j/L4l/mYcsyb9RWiDelVV/DKd3neU9XOW0+f5x/EJCeTI5j3M/24SI3/8GCd31zLz5WbnsPLPuQSG1S7V/la1k3sPs2bWEvq/+CR+IUEc3LyL2eP+4K2fx+BspszWKhWtenSkhp8X1ioVV89dYuW0v7FWW9O8azsANBoNM77+DXtHB54e/TKObs6kJaWgUqurpA6Hduxj0dQ5PPPa8wSHhrBj3WZ+G/MdX0z+ETeP0m37hdMRhDauz6PDn8TWzpY9m3Yw8Ysf+Pjnr/ANCjCms7G14aupP5nkrcqOuKtHIjiybAvNBnenWqAXF3cfZ9ufS+j76YvYuTqWma/fZy9hZVNULpW9rfHvE//u4sqhM7R4uieO1d2IjbjCzmkr6D56CK4+1aukHuFeIQxs0IlFx7dwOfkGbf0b8Frrx/h68yxSckof30tPbuOfM7uM/1solHzU5VljJxZALXcfjlw/x5LkGDRaLV1rN2Nk64F8s2U2abmZlV6HsBqB9KrbitVn9xCVEkcznzoMadKTibuXkJabZTbPE426YK+yYeXpnSRnp2NnbYOy2HBAf9eanIy9RHREHBqdlrYBDXm2aS8m7l5KRl52pdcBoIFnMP1C27Ly9E6updykhW8ozzfvy087FpBaxvv2TOMeOKhsWHpyG0nZadhZ22ChLBpEZ6FQ8mKL/mTm5zDv6AbScjNxVtuTpynju0AFVcV3qaz8HFae3smNtAS0Oi3hXiGMaPUIablZnIy9VCX1eJjIMNWHjwxTFaIK7Nixg+bNm6NSqfD09OTDDz9Eoyn6gdmxY0fefPNN3n//fVxdXalRowaff/65yT7OnTtH27ZtUavVhIaGsnnzZpOho8WHRl69epVOnToB4OLigkKhYPjw4QD4+/vzyy+/mOy7UaNGJq938eJF2rdvb3ytTZs23baOWVlZPPvss9jb2+Pp6cmECRNKpcnPz+f999/Hy8sLOzs7WrRowfbt22+771sUCgVHjhzhyy+/RKFQ8Pnnn7N9+3YUCgWpqanGdMePH0ehUHD16lUArl27Rr9+/XBxccHOzo569eqxdu1aY/qzZ8/Su3dv7O3tqV69OkOHDiUxMbHMcsybN4+mTZvi4OBAjRo1ePrpp4mPjzc+b26Y6t2+RkUdWLedRh1b0LhTS9y9qtN96KM4ujlzdMuecvOtm7GEeq3C8Qr2L/WcX2gwdZo1wN2rOi7V3WneswMePp5En79cRbWAfxYvp2ufHnTv2wsff19efHME7tWqsW7larPpq3vW4KVRr9K5Z1fs7GzNpnlnzAf0frQfgbWC8PbzYeR7o9Dp9Jw4crxK6tCnbmu2RR5l26WjxKQnMufIOpKy0+lW23xEYS13HxKyUll//gAJWamcT4hi88XDBLl5maRTKBS83mYQS09uIz7z/kSQqZo3JnfPQTTnL6FLSCLn3w0orCyxrld2Z6x1wzAUNmqyl65Cez0GfXoG2usx6OKLjv/sRcspOHUWXWISuvhEctZsQOnkiEWNqvmReCf2R51h2oFV7Lh8/IGVoSy9Qlqy4/Ixdlw+Rkx6IvOPbiApO40utcwfU8Hu3iRkpbLxwkESslK5kBjN1ktHCHCtaUxzJTmGhcc3sT/qDAVa7X2px79LVtCld3e69e2Jt58vL7zxCm4e1djwzxqz6T08q/PCGyPo1KMLtnZ2Ze5Xq9Xyy9c/8ORzQ6ju6VlVxS/T/jXbaNypJeGdW1PNqwY9hw3Eyc2FQ5t2l5tv9V+LCGvTFO9a/venoMXsXr2FJp1b06xLWzy8Pek7/Amc3F04sHGn2fQ1A3xo2LYZ1X1q4uLhRuP2LajVMJSrEUU/YI9s3UtOZhZD3huBX50gXKq54V8nGE9/7yqpw6YVa2jbvRPtenbG09eLJ18Zhks1N3asMf8d5slXhtHz8f4E1A6iupcnjw1/Eo+aNThx4KhpQoUCJ1dnk0dVOrf1EEGtGhDcuiFONdxpOqgrti4OXNh1rNx8agdbbBztjQ9lsY6TKwfPUK97K7zqBeHg7kztdo3xrBtAxNaDVVaPzsFN2Hf1FPuunSIuI5llp7aTkpNBu4CGZtPnavLJyMs2PnxdamBjpWbftdPGNLMPr2XXlRPcSEsgLjOZv49uRKFQEFLNt0rq0Nq/Pkevn+fo9fMkZqWy7tx+0nMzaeYbajZ9sLs3/q6ezDuygctJMaTmZHIjLYHo1KLviMtObuNQdAQ3M5JJzErjn9O7UCgUBJa4xlemdgENORQdwaHoCOIzU/j37B7ScjPNRiIC1K7mQ6BbTWYcWsOlpOuk5GRwPS3eJCKwqU9dbK1UzDm8jmspN0nNyeRqyk1iM24TKX+PquK71Nm4qxyKjiAmPZG4zBTWnd9PVGocdTyq5ngS4nakM06ISnbjxg169+5Ns2bNOHHiBJMmTWL69Ol8/fXXJulmz56NnZ0dBw4c4Pvvv+fLL780doLpdDoeeeQRbG1tOXDgAFOnTuWTTz4p8zV9fHxYtmwZAOfPnyc2NpZff/31jsqr0+l47LHHsLCwYP/+/UyePJkPPrj9UJn33nuPbdu2sWLFCjZu3Mj27ds5cuSISZrnnnuOPXv2sHDhQk6ePMnjjz9Oz549uXjx4h2VLTY2lnr16vHOO+8QGxvLu+++e0f5Ro4cSV5eHjt37uTUqVOMHz/eOMQ1NjaWDh060KhRIw4fPsz69euJi4vjiSeeKHN/+fn5fPXVV5w4cYKVK1dy5coVY2dnWeW+29eoCK1GQ+yV6wSEmQ67DAwL4frFq2XmO7HjAClxibR/7PZRMXq9niunL5B8M+GOhr7ei4KCAiIvXKRRs3CT7Y2ahXPudESlvU5eXh5ajQYHR4dK2+ctFkoLAlw9ORkbabL9ZOwlapfx4+FCQhSuto40qmkYHuaktqOFbz2OFosOABhYvyPpuVlsizxqbjeVTuHshNLeHs2Vq0UbtVo0Udex8KpZZj7LWkFob8Ri06MzDqNewf6lZ1G1bg7l3JFVqFQA6HNzK6v4/xkWSiX+rjU5ddP0mDp98zK13M13clxMjMbV1pGGnsEAOKrtaO5bl+Mxd9b2VoWCggIiz1+iYanzuzHnzlTs/F4yZwGOzk507XN/IvyK02o0xFyJLhUtHNigDtcvXCkz37Ht+0mJS6TjwJ5VXcRSNBoNMZejqNXQtIMhuEFdrt3hzZaYK9FEnb9MQGjRsNaIIyfxrRXIqukL+eal9/nlnS/ZvnwdOp2uUssPoCnQcO3SFULDG5hsr9e4AZERF8rIZUqn05GXk4udg+kUGHk5uXww7A3eGzqS38Z+T1Rk2Z9jRWk1WpKjb+JZN8Bku2fdABKvmB/eecva8bNY9vFENv+2kJsXTIfuaTUaLKwsTLZZWFmSEHm9cgpegoVCiY9zdSLiTcsREXeNALeyrxfFtfIL43z8NbNRdLdYW1pioVSSXVD51woLhRJPR3ciE03f90uJN/B1Nn+jqI6HHzFpibQNaMC7HZ/mzXZP0COkBZZKC7PpAawsLLFQKMkpyKvU8t9ioVDi5VSNiwnRJtsvJETj52K+HqHVA7ieFk+HwMZ83OVZ3u3wNH3qtjapR2h1f66lxvFIWDs+7Tqct9sPplNQOAoqP9qqKr9LFRdWIxBPR3ci4soe+ipEVZJhqkJUsj///BMfHx8mTpyIQqGgTp06xMTE8MEHHzBmzBjjncsGDRowduxYAGrVqsXEiRPZsmUL3bp1Y+PGjURGRrJ9+3bj8MdvvvmGbt26mX1NCwsLXF0Nw0o8PDzuas64zZs3ExERwdWrV/H2NvywGzduHL169SozT2ZmJtOnT2fOnDnGMs2ePduYHwzDSxcsWMD169epWdPwRezdd99l/fr1zJw5k3Hjxt22bDVq1MDS0hJ7e/u7GgYaFRXFwIEDqV+/PgCBgUVzpEyaNInw8HCT158xYwY+Pj5cuHCB2rVrl9rf888/b/w7MDCQ3377jebNm5OZmWl2Hrt7eY2KyM7IQq/TYe9k2rlk5+RAZmrpIWwAyTcT2LZoNUM/ewOlRdlfGnOzc/jtjc/RajQolEp6Dh9EYP2qmWstPS0dnVaHs4vpsFpnVxdSkpMr7XXmTJ6BazU3GjZpXGn7vMVRZYuF0oK0HNNhIGk5WTjXND/n4YXEaCbuWcqodk9gZWGJpdKCw9ERzDpUFC1Uu5ovnYLC+XDtpEovc1mUhZGG+izTYTT6rGwUTmUPnVK6OKF08qHg9DmyFq3AwtUFdffOoFSSt3u/2TzqLh3QRF9Hl1A1d9j/lzmobLFQKkkvMbQoLTcTJ7X5jvGLideZtG85I9sMMh5TR66fY+6RdfejyGZlpKWj0+lwdnE22e7k4kJqBeYKjDh1hs1rNvDTXxMrWMJ7k51uvv21d3Igsoyhp0mx8WxZ8C/PfT6q3Pa3qmSnZ6IzU2YHJwcuppYe/lXcdyM+Iis9E51WS5fH+9KsS1vjc8lxiVxOOE/Dts0Z/tFIEmPjWTV9EVqdji6D+lRqHTLTDceTo7OTaR1cnEhLKb8Ot2xavoa83Dyatmtp3FbDpybPjR6Bl78vOdk5bPlnHePf/ZwxE7+julflR13mZWaj1+lRO5hGdqsd7MhJNz8s0sbJjhZP9cDVtwbaAi1XDp1hy+8L6TrqaaoXzjPnWTeAc1sP4RHsg4O7CzfPX+X6yYvo9fpKrwOAvcowpLHksMuMvCwcVf63ze+osiO0egCzDpuPkr1lQL32pOVkci6+8jtPbK3VWCiVZOab1iErPwd7lY3ZPC42Dvi6VEej07Lg2CZsrdT0rdcGGysVK0+bjzLtVrsZ6blZXC5jLr2KKqqH6ZyzmXnZOKjMz0PoauOIv4snGq2WOYfXY2et5pGw9thYqVh6cpshja0jQTYOHI+5yMyDa3C3c2JAWHuUCiVbLh2u1DpU1XcpMExJMemxd7G0sESn1zHj4OpSN7v+q5QykO6AwAABAABJREFUTPWhI51xQlSyiIgIWrVqZTIuv02bNmRmZnL9+nV8fQ13dBo0ML2b6+npaRz6eP78eXx8fEw6oJo3b15l5fX19TXpSGvVqlW5eSIjI8nPzzdJ5+rqSkhIUSfN0aNH0ev1pTqe8vLycHNzq6TSm/fmm2/y6quvsnHjRrp27crAgQON7/eRI0fYtm2b2U60yMhIsx1lx44d4/PPP+f48eMkJycb7/JHRUURGlp66MK9vEZeXh55eaZ3SVWF0UJ3rMRFVo/5+SF0Oh0r/5hLu4E9cfMsPU+TSRnUKl785l3y8/K5euYCm+evxKWaG36hwXdXtrtQssh6vb7S5rlY/vcSdm3Zzje/fY+1qurmACr1c0dBmT+CvJyqMaxpb5ad2s7JmEs42zjwTHh3XmzRjyn7/0Ftac3rbQYy7cCqKptfBsCqXh1senU1/p+1eKXhD7OVKW9PCvRZ2eSs2wR6Pbqb8Sjs7VC1bGq2M07dozMWHu5kzl1UwRr8t5U8fBQoyvwYajq6MzS8FytP7+TUzUs4qx14snE3nmvWl78Orqryspan1Lms199zZEVOdja/fvMjr733ZqlOmfuvRPur15fcBBja3+UT59BxUK/btr9VreRnoTdsLDfPy1++Q35uHtEXrrD+75W41ahGw8JFffR6PXaODjz6yjMolUq8Av3ISElj16pNld4ZV1SHEhvu8HpxYPseVs1fxsgx75gcO0F1ahFUpyjaLzi0Nl+9+TFb/93AUyOGV1KpzTFzXpRRDcfqbjhWL/oeVS3Qi+yUdCI2HzR2xjUd1JUDC9az+qu/QAH27i4EtqzP5f2nqqoCtwpu8l957VRxLf3qkVOQx8mYsuft6lqrGU28Q/h112I0uvsztP6Wsvowbx1rS09uNc6dtv7cfgY36srqs3tKlbNtQAPqewYx8+CaKq9Dqe8cirI/i1v1WHh8M7kaw/yuqyP2MiS8BytP70Sj06JAQVZ+DstObkePnhvpCTiq7Wgf2KjSO+OMdShV0Hv/LnVLbkE+H6yZhNrKmrAagQxt0pP4zBTO3qe5eIUoTjrjhKhk5joObl04im+3srIySaNQKIydPJXZ+aBUKktduAoKiiZbNXdRu91r38mdVZ1Oh4WFBUeOHMGixJ3/iqyKeiuysHgZitcH4MUXX6RHjx6sWbOGjRs38u233zJhwgTeeOMNdDod/fr1Y/z48aX27WlmrqGsrCy6d+9O9+7dmTdvHtWqVSMqKooePXqQX2xC+uLu9jUAvv32W7744guTbWPHjiWwz+07YW0d7FAolaWi4LLTMrBzKj0UMz8nj9gr0dy8doMNs5cDhe+nXs+4Z9/h6Q9G4F/P8GNEoVTiWqMaADX8vEi8EcfefzdXSWeco5MjSgslKSWiZNJSUktFy92LFQuWsnTeQr746Vv8g6pmRbn0vGy0Oi3ONqbHuJParszJnx+p144LCVGsPmuY3y8qNY68g/l80eNFFp3YgpPaHg97F97r+LQxz61zdP7TYxm96jfiKmEOuYKLkWhjiq0YV3jeKuxt0WcVlV1hZ2Pyf0n6rCz0Wq3JrxddYjJKe3tQKqHYkDV1905Y1Qoic+4i9BmVPxn3f0FGXjZanQ6nEseUo9quVLTcLf1C23IxMYq15/YCEE08eYfy+azb8yw5ubVKJj6/HQcnR5RKM+d3auo9z8l180Ys8TfjGPdRUdt569owqHNfJs6dRo0qiGYqztaxsP1NM21/s9IzsTczFD4/J5eYy1HEXr3O2llLi8qs1/PlM28x9KPXCAir3Ojp0mU2zC+WUeKakZmWgX05Ua8AroULI9Tw9SIjLZ0tS1YbO+McnJ2wsFSazF1WzasGGanpaDQaLC0r72eHvaPheCoZBZeRmo6jc/l1OLRjH3N+ncorH40itHH9ctMqlUoCagUSf6NqVu9U2duiUCrIzTBtU3Mzs1E7lD1PYknu/jW5cuiM8X+1gy0dXn4MbYGGvKwcbJzsOf7PDuzdqqbTOjMvB61Oh4PKtMz2Klsy8sq+XtzS0i+Mg9Fn0erND2nuEtyU7rWbM3HPUmLSq2b+3ez8XLQ6HfbWplGKdtY2ZJlZ2RwM7XN6bpbJIgYJmakoFQoc1XYkF1tgp41/fdoFNmL2obXEZVZetH9Jt+rhoDKth721DZll3NDLyMsiLTfL2BEHkJCZglKhwEltT1J2Ghl5WWj1OvTFusjiM1NwVNthoVCW+dndi6r4LpVaGGWnR298/6+l3MTLqRoD6rWXzjjxQEhnnBCVLDQ0lGXLlpl0qO3duxcHBwe8vO5sstY6deoQFRVFXFwc1asb5nc4dOhQuXmsC1f60paYiLtatWrExhYtZZ6ens6VK0Xzn4SGhhIVFUVMTIxxOOm+ffvKfa3g4GCsrKzYv3+/MdIvJSWFCxcu0KFDBwAaN26MVqslPj6edu3a3Um170i1aoaOodjYWFwKO2iKL5xwi4+PDyNGjGDEiBF89NFHTJs2jTfeeIPw8HCWLVuGv7//Hf0wOHfuHImJiXz33Xf4+BjuOB8+XP4dwLt9DYCPPvqI0aNHm2xTqVQsOrnltnktLC3xDPDmyukL1GlWFHF55fQFajcpPVmvykbFS9++b7LtyOY9XDt7kcfeHI5ztbJX/wPDXD1VwcrKiqDatThx+Bit2rcxbj9++Bgt2rYsJ+ftLV+whCVzFvD5j99Qq07V/dDV6rRcSY6lfo0gDkUXzYNVv0YQh6+fM5vH2tKq1JxKulsd+CiISUvk3X9Nh+ANbtQFG0sVsw6vJdHMapr3JL8AXX6qaTkyM7EM8CM/LsGwQanE0teb3G27SucvpIm+UWqBB6WbC7qMzBIdcZ2xCgkma95i9GmVVIf/IK1Ox9XkGMJqBHKk2DEUViOQozfOm82jsrRCW9Yx9YBGqVhZWREUEsyJw8do2a61cfuJw8do3ubezm8vXx9+nvGnybYF0+eQk5PD86+/YnZFzcpmYWlJzQAfLp88T91mRRPVXz51jpAmpTt6VDZqXv3+Q5Nthzbu5srZCzzx1vM4V6vayHEAS0tLagb6culkBPWaNzJuv3QygtBidbgtPSaLU/mFBHJizyF0Op2xQy4xNh4HF6dK7YgDsLSyxC84gIhjJwlvXTSh+9ljp2jUskmZ+Q5s38PsX6bw0vtv0KB5eJnpbtHr9URdvoa3v/nhfRVlYWmBq08NYs9dxadh0bUp9txVvOvXKienqeTrcdg4lb7RaWFlia2zAzqtlqjj5/ELr5qV0LV6HdGpcdTx8DNZlbKOhx+nbrNKZS13bzzsXdh31XzUXpdaTekZ0pI/9iwjKjWuUstdnFavIzY9kSB3LyLirxq3B7l7lTksNioljno1ArG2sCRfazgX3O2c0Ol1pBfrNGrj34AOQY2Zc3hdlXUmFq/HjbQEalXz4Uxc0ff9Wu7eZXY4XU2+SX3PILP1uHXz5mrKTRrVrIWCoog1dztn0nOzKrUjDqrmu1RZFIDVA5gu4EGoivn9RMXIAg5C3KO0tDSOHz9u8oiKiuK1114jOjqaN954g3PnzvHPP/8wduxYRo8ebXK3uDzdunUjKCiIYcOGcfLkSfbs2WNcwKGsqDU/Pz8UCgWrV68mISGBzEzDxbNz587MnTuXXbt2cfr0aYYNG2YSqda1a1dCQkJ49tlnOXHiBLt27Sp3sQgwRLa98MILvPfee2zZsoXTp08zfPhwk/rVrl2bZ555hmeffZbly5dz5coVDh06xPjx401WNr1bwcHB+Pj48Pnnn3PhwgXWrFlTaiXXt956iw0bNnDlyhWOHj3K1q1bqVu3LmBY3CE5OZmnnnqKgwcPcvnyZTZu3Mjzzz9fqiMTwNfXF2tra37//XcuX77MqlWr+Oqrr8ot492+Bhg63hwdHU0edzNMtUWvjhzfvp/jOw6QeCOOTfNWkJaUQngXw4/ebYtWs2ryfMAQ7ebh42nysHO0x8LKEg8fT6zVhtfds2ozl0+dJyU+kcSYOA6s3c6p3YcIa9P0jst1twY88RibVq9n85oNRF+N4q/fp5AYH0/PAYYhTnOmzODnb34wyXP5YiSXL0aSk5NLWmoaly9GEnW16Ivz8r+XMP+vObzxwWg8alQnJSmZlKRkcrLN3+muqDURe+kcHE7HoMbUdHTn2SY9cbdzYvNFQ4f6k4268lrrx4zpj14/TzPfULrVaoaHvQu1q/kyvFlvLiUaVjQr0Gm4nhZv8sjOzyVHk8f1tHi0VTjUJe/gMdStm2NZOxhlNTds+vVEX6Ah/0zRl2Gbfj1RdSyaNyr/6AkUNjaou3dC6eqMZVAAqtbNyS+2eq26R2esw+qQ/c9a9Pn5KOxsUdjZQiX/YL8bNlYqarl7GxdFqOnoTi13b6rbVzwqs6LWnd9Px8Bw2gc2oqajO8807oGbrRNbLhpuDDzRsAuvtHzEmP7YjQs09alLl+CmVLNzppa7D0Ob9CQy8boxOsBCqcTXuTq+ztWxVFrgYuOIr3N1PKqwvv0ef5QtazawZe1Grl+LYsbEqSTGJdC9f28A5k2dya/jfjTJc+ViJFcuRpKbk0N6WhpXLkYSfTUKAGuVNX6B/iYPO3t7bGxs8Av0LxWBXlVa9unE0W37OLZtHwk3brJ+znLSElNo2tVwXmxesIoVf84FbrW/NU0edk72WFpZ4eFT09j+VrW2fbtweMseDm/dS/z1WNbMWkJaYgrNuxlunm34eyVLJs4ypt+3fjsRh0+SGBtPYmw8R7btZde/m2jUrih6u0X39mRnZLF61hISY+I4d/QU21esp2WPDlVSh26P9mHXhm3s3riN2KgbLJo6h+SERDr0Ngy3Xz5zwf+xd5fRUV1dAIbfuLsbUUhC0ODu7qW4ldJSihcttIVCWyjFrUCRBHd3D8UJ7u4a4u7z/QhMGCYJlhDKt58u1mrunHNn35lrs+8R5k/ITNYeDzpMwMRZtPqmIx4+BYkKjyQqPJL4V8bF3LR0DRdPneP5k2fcv3WXhVPm8PD2PeU684JPzTLcOnKOW0fPE/U0lFNr9xIfHk3BKiUAOLPxAEcWZc4ofnV/MA/OXSc6JJzIJ885s/EAD85ep1DVzORi6N3H3D97jZjQSEJuPmDfzNWgUFC4drk82459N09R0a0o5V2LYGdiyRdFq2NpaMLBO+cAaFq4Mp1KqU9YUsG1KHfCH2c5K2ftgmVo7FtJOYO0iZ4hJnqG6GrlzbF95O4F/J29KelUCGsjc+r7lMdM35jg+xkJodqFyvBF0erK8hee3CQhOZHmRathY2SOq4U9db3LcfrhdWU31MruxahVqDQbLh4gMiEGY10DjHUN0NXKu+vdwTvnKOPiS2lnH2yNLWjsWwlzAxOO3c+Yqba+d3laF6+lLH/28XXik5NoVbwmtsYWuFs60NCnIicfXFVux7F7lzDS1aeJX2WsjczwsXWlhpc/R16Z/TY35fa9FEAzvyoUtffE1tgCR1NrGvpWpIpHCeU+KsTHJi3jhHhPQUFBlCypOgB8ly5dCAwMZNu2bQwePJjixYtjaWlJt27d+Pnnn9963VpaWmzYsIFvvvmGMmXK4OHhwfjx42nSpAn6+vpZ1nFycmLUqFH8+OOPdO3alc6dOxMYGMiwYcO4ffs2jRs3xszMjN9++02lZZympibr16+nW7dulC1bFjc3N6ZNm0b9+jnP8DZ+/HhiY2Np2rQpJiYmDBw4kKgo1e4iAQEB/P777wwcOJBHjx5hZWVFhQoVaNiw4Vt/Fq/T0dFh+fLlfP/99xQvXpwyZcrw+++/06pVK2WZtLQ0evXqxcOHDzE1NaV+/fpMnjwZAEdHRw4fPszQoUOpV68eSUlJuLq6Ur9+/SyTpTY2NgQGBjJ8+HCmTZuGv78/EyZMoGnTptnG+K7vkRsKly9JfEwch9bvJDYyGhtnB9oO7o6ZdUYrt9jIaKJC360rY0pSMjsC1xATHoW2rg5WjrY0+74jhcvn/sQHL1WpVY2Y6GhWLlxKeFgEru6ujBj3G7b2GS1EI8LCCX0WolLnh269lP9/69oN/t2zH1t7W+auWgTA9g2bSU1JYdwI1RmN237VgXZfd8r1bTh67yLGega0LFodcwMTHkSG8Of+JYTGZRwfFgYmWBtldhU6cPss+jp61PUuR8dS9YhLTuTSszssO70r12N7V8nHgtHQ0cagfk009PVJe/yUuBVrITmzS46mqYlKl1RFTCxxK9aiX7s6xt90Jj0mluTgMyQdzWzdq1eqBADGHVVnGI7fvIOUC5fzdqOy4WPjyowWma1T+1bOOKdsu3KUP/YtzJeYXjp+/xLGugY096uGuYExD6NCmHBgKWHxGfuUub4xVoaZ+9TBO+fQ19ajdqEytCtZl/jkRC6H3GHl2T3KMhYGJvzRoIfy70a+FWnkW5Erz+4yJo+2t3LNasREx7Bq4TIiwsMp4O7GT+NGvXJ8RxD6shXmCwO/7aP8/1vXb3JwTxA2drbMWRmYJzG+jyIV/EmIiePAup3ERkZh6+JAh6E9lK2M3+f8m9eKVSxNfEwc+9ZuJSYiGjsXB7oM64XFi5Z5MRFRRIZmdqVTKBTsXL6BiJAwNDU1sbK3oV6H5pStndny3dzakq9/7svWhauZNvh3TC3NqdSgBlWb580st2WqVSA2JoYty9YRFR6Jo5sLfUcNxcouowV9ZEQk4c8zWyH9u30vaWlpLPs7gGV/ByiXV6hdla8HfA9AQlw8i6fNIzoiEgMjQ1w83Rj81wjcvfNunFS3Ur4kxyVwYfthEqLjMHewpnrPVhhbZhzTidGxxIVnth5OS03n9Pr9JETFoqWjjZmDNdW//xInv8wJXdJSUjm35SCxoZHo6Oni6OdBxc6N0DXM+h4yN5x+dA0jXX0aeJfHVN+IJ9Fh/H1knTIRYqpvhKWBahdifW1dSjgWZM2F/Vmus4p7cXS0tPmmnOo917YrR9h2NedeHO/j4tPbGOjoUd3LHxM9Q0JiwllyaoeydZiJniFmBpldcZPTUll4chuNfCvyXcUWJCQncvHpbeWDEoAyBQqjralF25Kqk7Dtv3mK/TfzZnb0809uYqirR62CpTHVM+JpbBgBwVuUD2NM9AxVuoAmp6Uy7/gmmvlVoU/lL4lPTuL8k5vsvHZcWSYqMZZ5xzfTpHAl+ldpQ3RiHIfvnCfo1pk82Ya8uJfS09bl67KNsTI0JTkthcfRocw8vJajeZRQFOJNNBR5Na2OECJXHT58mMqVK3Pz5k08PbOeQU/kj2vXruHj48ONGzfw8srdG/ZFwe/fivBT0blMQ66+0lXiv8jHzp22S0bkdxgfbEXH0USNmZTfYXwQs+EDqDSzx5sLfuIO95pNp+Wj3lzwE7e43UguPflvz0Tn5+DJstM78zuMD9bevx5rz+3L7zA+SMviNfn3Vt4kKD6mqp7+jN69IL/D+GAj6nxN7/UT31zwEzajxUBG7Jib32F8sNH1v2Xo1r/fXPATNq5Rz8/mXuq/aOWZ3fkdQrbavJas/n8hLeOE+EStX78eY2NjChYsyM2bN+nXrx+VKlWSRNwnJjw8nDVr1mBqaqocU04IIYQQQgghhMiOJOOE+ETFxMQwZMgQHjx4gLW1NbVr11YbG03kv27dunHq1ClmzZr1TmO8CSGEEEIIIYT4/yTJOCE+UZ07d6Zz5875HYZ4g/Xr1+d3CEIIIYQQQgiRrewmART5R2ZTFUIIIYQQQgghhBDiI5FknBBCCCGEEEIIIYQQH4l0UxVCCCGEEEIIIYT4TEk31U+PtIwTQgghhBBCCCGEEOIjkWScEEIIIYQQQgghhBAfiXRTFUIIIYQQQgghhPhMaSLdVD810jJOCCGEEEIIIYQQQoiPRJJxQgghhBBCCCGEEEJ8JNJNVQghhBBCCCGEEOIzpaEh7bA+NfKNCCGEEEIIIYQQQgjxkUgyTgghhBBCCCGEEEKIj0S6qQohhBBCCCGEEEJ8pjQ1ZDbVT420jBNCCCGEEEIIIYQQ4iORZJwQQgghhBBCCCGEEB+JdFMVQgghhBBCCCGE+ExpSDfVT460jBNCCCGEEEIIIYQQ4iORZJwQQgghhBBCCCGEEB+JdFMVQgghhBBCCCGE+ExpIN1UPzUaCoVCkd9BCCGEEEIIIYQQQojct+niwfwOIVtNi1TJ7xDyhbSME0KIT9j4/UvyO4QPNrhGR649u5vfYXwQbzs3qs3qld9hfLAD388ksseA/A7jg5jPnkSn5aPyO4wPtrjdSCrN7JHfYXyww71m8yw6NL/D+CB2ptYsPrk9v8P4YJ1KN6DP+kn5HcYHmd5iAFP+XZnfYXyw/lXb8M2qsfkdxgeb13rYf/58u7jdSLqvGZffYXywf74cSvPAH/M7jA+y4as/qTu3f36H8cF2fTslv0MQnwlJxgkhhBBCCCGEEEJ8pjRlNtVPjkzgIIQQQgghhBBCCCHERyLJOCGEEEIIIYQQQgghPhLppiqEEEIIIYQQQgjxmdKQbqqfHGkZJ4QQQgghhBBCCCHERyLJOCGEEEIIIYQQQgghPhLppiqEEEIIIYQQQgjxmZJuqp8eaRknhBBCCCGEEEIIIcRHIsk4IYQQQgghhBBCCCE+EummKoQQQgghhBBCCPGZ0kS6qX5qpGWcEEIIIYQQQgghhBAfiSTjhBBCCCGEEEIIIYT4SKSbqhBCCCGEEEIIIcRnSmZT/fRIyzghhBBCCCGEEEIIIT4SScYJIYQQQgghhBBCCPGRSDdVIYQQQgghhBBCiM+UpnRT/eRIyzjxSXj27BmjR48mIiIiv0MRQgghhBBCCCGEyDOSjBP5TqFQ0KlTJ/T09LCwsHinum5ubkyZMiVvAvtAGhoabNiw4Z3qVK9enf79++dJPP9VnTp1YsyYMfkdxgcZNGgQffv2ze8whBBCCCGEEEJ8AqSb6n/QV199xcKFCwHQ1tbGxcWFL774glGjRmFkZJTP0b27sWPH4uHhwdChQ9+5bnBw8H9ym8XbOX/+PFu3buXvv/9WLqtevToHDhxg7Nix/PjjjyrlGzZsyPbt2xk5ciRfffUV7u7uOa5/5MiR/PrrrwAsXLiQmTNncunSJTQ1NSlZsiRDhgyhcePGAMTGxmJhYcGSJUto06aNch1t2rRh1apV3Lx5E09PT+VyT09P2rRpw5gxYxgyZAienp788MMPb4zpQ1wOOsn53UdJiIrB3NGGCq3qYV+wQJZlH1+7y7bJi9WWf/nr95jbWwNw/cg5/l20Sa3MV9OHoa2Td5ePbes3s275aiLCwyng5so3fXrgV7xolmXDQ8NY8Pc/3Lp2k8cPH9G4ZTO+7fu9Spmdm7exf+ce7t2+B4CXtxedvu1KocI+ebYNzf2q0LZEbSwNzbgb8YQZh9dw/smtHMpX5Yui1bA3seRZbARLTu1g5/UTytenNO1HSadCavWO3rvIj9tm5ck2vKTfuB66lcujYWhI2t17xC9fS/qTZ9mWNx7QE+1CXmrLUy5cJm7mvIw/9PQwaNoAnRJF0DAxIe3BQxJWbSDt3oNcj7+WV2ka+VbEzMCER1EhLDm9k+vP72dbvqJrURr5VsTOxIqElETOP7nJ8jO7iU1OAMDJ1IaWxarjZuGIjbE5S07vYOe147ke9/sq7uBF+5J18bEtgLWROT9um8XBO+fyOyyl9avXsXzJMsJDw3DzcKfPgL4UL1ki2/JnT51hxpTp3L19Bytra9p3bk+zli2Urx/YF8SSwEU8evCI1NRUnF2cadOxHfUa1s+zbTi5+xBHt+4jNjIaGyd76nZqQQEfzzfWe3DtNot+n4Gtsz3fjh2iXH5631EuHArm+YMnANi7u1CjTSOcPF3zbBsAqrgXp1bB0pjqG/EkOox1F4K4FfYoy7Id/etRztVPbfmT6FDG7F0EQEW3opR18cXBNOMa8iDyGZsvH+ZexNM824aL+09wduch4qNisXC0oVKbBjgWcsuy7KNrd9g0IUBtedvRfbBwsAHg8r8nuXb0LOGPQwCwcXWkXIva2Lk759k2AFT39KeedznMDYx5HPWcFWf3cCP0YZZlu5ZpRCX3YmrLH0U9Z+TOjHPs4Ort8bZV33/OP77JtEOrczf4V3wO59tqHiWp510WM31jHkeHsvLcXm5m8118VbohFd3U708eR4Xy6+75AFRwLULXMo3UyvRcN4HU9LTcDf4VDbzL07xIVSwMTXgQ8Yz5J7ZwOeRutuWrepSgRZFqOJpaEZecyJlH1wk8uY2YpHhlmSaFK1HfuzzWRubEJMVx5O5FFp/eQUpaap5sQxPfSrQqXhNLA1PuRTxl1rH1XHx6O/vyhSvTrHAV7EwsCImNZPnZ3ey5Eax8vZJbMdqVqI2jqQ3ampo8ig5lzfn97L15Mk/i/9TIbKqfHknG/UfVr1+fgIAAUlJSOHjwIN988w1xcXHMmpW3P8rywvDhw9+7ro2NTS5GIj41M2bMoFWrVpiYmKgsd3FxISAgQCUZ9/jxY/bt24eDg4OyzJMnT5SvT5gwgR07drBnzx7lMmNjYyCj5dqMGTP4/fffad68OSkpKSxZsoRmzZoxdepUevfujbGxMaVLl2b//v0qybgDBw7g4uLC/v37lcm4hw8fcvv2bWrUqAGAra0tdevWZfbs2YwbNy6XP6UMt05e4tjqnVRs1xA7T2euHjzNjhnL+HLk9xhbmmVbr9Wonujo6yn/1jcxVHldR1+PVqN6qizLy0Tcwb1BzJs+mx4DeuNbxI8dm7YyasjPzFw0Fxs7W7XyKSkpmJmZ06pTWzauXp/lOi+eOU/VWjXw6VcYXV0d1i5fzchBw5mx8B+sbKxzfRtqePrTu9KXTD64kotPbtHErzLjGvWiy4rfCIlV74rfzK8K3cs3ZXzQMq6G3MPXzo3B1doTkxTPkXsXAfhl51x0NDM/d1N9I+a3HkbQrTO5Hv+r9OrWRK9WNeIXLict5Dn6Depg3K8H0SP/hKSkLOvEzQ4EbS3l3xpGhpj8PIiU05kJIcNOrdFydCAuYBmKqGh0y5XCuH8Pokf9hSIyKtfiL1fAj47+9Qk8uZUboQ+o4VWKwdU68OO2mYTFR6uVL2Ttwnflm7P0zE7OPLqOhYEJXcs0plvZJkw9tAoAXW0dQmIjOXH/Mh386+VarLnFQEePm2EP2Xb1CGMa9MjvcFTs3bWH6ZOmMmDoQIoUL8amdRsY0m8Qi1Ytwc7eXq3840ePGdJ/EI2bN+Hn0SO4eO48k8ZNxMzCnOo1M86vpmamdOrahQJurujoaHPk4BH+HD0GCwsLylYol+vbcOnoaXYtXk+Drl/iUsid0/uOsPyvOfT4axhm1tm37k+MT2Dj7KW4+xUkLipG5bV7V27iV8Ef585uaOvqcHTLXpb9OYvvxv2IqaV5rm8DgL9TIb4oVp1VZ/dyO/wxldyK8X3FFvyxZyERCTFq5dec38/GSweVf2tpaPJjrU6ceXRDuczL2plTD69xO3w/qWmp1CpUhp4Vv2DM3kVEJcbm+jbcDL7A4ZXbqdKhMQ5eBbh0IJit05bQdlRvTKzMs63X7re+6Bq8et3LfKj7+NpdCpYthr2nC1o62pzdeYgtkxfRZlRvjC1Mc30bAMq4+NK2RG2Wnt7JzdCHVPUsSb8qbRixcy7hWZynVpzdw9oLQcq/tTQ0GVm3G6ceXlUu+/vIOrQ0M8/DxroGjKzbjZOvlMltn8P5trSzD21K1GLZ6V3cDHtEVY8S9K3cil93ziM8i+Ni5dk9rLtwQPm3pqYmI2p35dQj1c85ISWJX3bMVVmWl4m4Sm7F+LpsY+Yc28jVkLvU8y7HL3W60mfDJELj1K+xvrau9KvcmgXBWwh+cAUrQ1N6VGhBr4ot+XN/xoPbqh4l6FSqPjMOreHq8/s4mlrTt3IrABYEb8n1bajmUZIeFVow/fAaLj27QyOfivxR/zu+WT2W53GRauUb+1bi6zKNmXJwJdee38fHpgD9q7QhNimeY/cvARCTFM/ys7u5HxlCaloq5Qr4MahaOyITY1WOHyE+Fumm+h+lp6eHvb09Li4utG/fng4dOii7RCoUCv766y88PDwwMDCgePHirFmzRlk3IiKCDh06YGNjg4GBAQULFiQgIPNJ4YULF6hZsyYGBgZYWVnRvXt3YmOzv4kKCgpCQ0ODnTt3UrJkSQwMDKhZsyYhISFs374dX19fTE1NadeuHfHxmU9XduzYQeXKlTE3N8fKyorGjRtz61Zmy5FFixZhbGzMjRuZN3p9+vShUKFCxMXFAerdVDU0NJgzZw6NGzfG0NAQX19fjh49ys2bN6levTpGRkZUqFBB5X0AZs2ahaenJ7q6unh7e7N4sXqLoVcFBwdTp04drK2tMTMzo1q1apw+fTrHOq+Li4ujc+fOGBsb4+DgwMSJE9XKJCcnM2TIEJycnDAyMqJcuXIEBQUpX7937x5NmjTBwsICIyMj/Pz82LZtG5D5vWzdupXixYujr69PuXLluHDhgsp7HDlyhKpVq2JgYICLiwt9+/ZVfr4AT548oVGjRhgYGODu7s6yZctUPve7d++ioaHB2bNnlXUiIyPR0NBQifXy5cs0bNgQY2Nj7Ozs6NSpE6Ghodl+Punp6axevZqmTZuqvda4cWPCwsI4fPiwcllgYCB169bF1jYjYaOlpYW9vb3yn7GxMdra2mrLjh07xsSJExk/fjyDBg3Cy8sLX19f/vjjD/r378+AAQN48CCjtU6NGjVUtunKlSskJCTQs2dPleX79+9HR0eHSpUqKZc1bdqU5cuXZ7u9H+rinmMUqlQSn8olsXCwoULrehhZmHLlQM5P+/RNjDA0M1b+09RUvSxoaKDyuqGZcZ5tA8DGVeuo3agedRs3wMWtAN/2/R5rGxu2bcj6Rs/OwZ5v+31Pzfp1sm0lO3DEjzRs0QSPgp44uxag9+D+pKcrOHcqbxJZrYvXYtvVo2y9coR7kc+YcXgtz2MjaOZXJcvydQuVZdPlw+y/dZonMWHsu3mKrVeP0K5kXWWZmKR4whOilf9Ku/iQlJpM0K13O++8K71aVUncvoeUsxdIf/yU+IXL0NDVRbesf7Z1FPHxKKJjlP90fL0hOYXkUy+ScTo66JQsRsK6zaTdvE3681ASt+wkPTQcvaoVczX+Bt7lOXD7DAdun+FxdChLT+8kLD6KWgXLZFney9qZ53GR7Lp+gudxkVwPfcC+m6dwt3RUlrkT/pgVZ3dz7P4lUtLy7sfU+zp2/xJzj2/iwO2z+R2KmlXLVtKoWWMaN2+Km7sbfQf2x8bOlg1rsk6kb1y3AVt7O/oO7I+buxuNmzelYdNGrFySeS4tWcqfqjWq4ebuhpOzM63atcbDy5PzZ/OmNeDx7UGUqF6OkjUqYO1kT91OX2BqZc6pPYdyrLdt/iqKVCyFU0E3tdda9OpE6TqVsXdzxtrRjkbftEWRruDupet5sg0ANbxKcfTuRY7eu8izmHDWXQgiIiGGyu7FsyyfmJpMTFK88l8BCzsMdPQ59uKBAcCik9s5eOccj6Ke8yw2guWnd6OhoYG3jUuebMO53UfwqexP4SqlsHCwoXLbhhhbmHLpQHCO9QxMjTA0M1H+e/W6V/vbLylSoyzWBRywcLChWudmKBQKHl3JvjXOh6pTqCyH7pzj4J1zPIkJY+XZPUQkRFPds2SW5RNSkohOjFP+c7Wwx1BXn0N3zivLxCUnqpQpbOdOcloKJx/kXcLhczjf1ilUhkN3znPo7nmexoSx6txeIuJjqJbdd5GaTHRSnPKf24vv4vBd1XtthUKhUi46KS7L9eWWZn6V2XPjJHtuBPMw6jnzT2whNC6K+t7lsyxfyKYAz2Mj2HrlCCGxEVwJuceuayfwsnZSlvG2KcDVZ/f49845QmIjOPv4Bgdvn1Mpk5taFq3OjmvH2XHtGA8inzH72Hqex0bSpHDlLMvXKliabVeOcOD2GZ7GhBF0+ww7rh2ndfFayjLnn9zk8N0LPIh8xpOYMDZc+pfb4Y8pYpd3vVaEyIkk4z4TBgYGpKSkAPDzzz8TEBDArFmzuHTpEj/88AMdO3bkwIGMJze//PILly9fZvv27Vy5coVZs2ZhbZ3ROiQ+Pp769etjYWFBcHAwq1evZs+ePfTu3fuNMfz666/MmDGDI0eO8ODBA1q3bs2UKVNYtmwZW7duZffu3UyfPl1ZPiYmhh9++IHg4GD27NmDQqGgRYsWpKenA9C5c2caNmxIhw4dSE1NZceOHcyZM4elS5fm2DX1t99+o3Pnzpw9exYfHx/at2/Pd999x7Bhwzh5MiMx8er2rF+/nn79+jFw4EAuXrzId999R9euXdm/f3+27xETE0OXLl04ePAgx44do2DBgjRs2JCYGPWnZtkZPHgw+/fvZ/369ezatYugoCBOnTqlUqZr164cPnyYFStWcP78eVq1akX9+vWVCcpevXqRlJTEv//+y4ULFxg3bpyytder7zNhwgSCg4OxtbWladOmyn3lwoUL1KtXjy+++ILz58+zcuVKDh06pPL5dO7cmcePHxMUFMTatWv5559/CAkJeevthIyEXrVq1ShRogQnT55kx44dPHv2jNatW2db5/z580RGRlK6dGm113R1denQoYNKEjkwMJCvv/76neICWL58OcbGxnz33Xdqrw0cOJCUlBTWrl0LZCTjrl27pmxxt3//fqpUqULNmjXVknHlypXD0DCzlVnZsmV58OAB9+7de+cY3yQtNY3Q+09w9vVQWe7s68mz21l3rXhp/R9zWTpkMtsmL+bxtbtqr6ckJbNi+DSW/TiFnTNXEHr/ifpKcklKSgo3r9+gZJlSKstLlinF1YuXc+19kpKSSEtNxcTU5M2F35G2phaFbFwIfnBFZXnwgysUsffIso6OljbJqSmqMaam4GvripZm1pfpRj4V2HfzFImpybkTeBY0rS3RNDMl9cq1zIWpaaTeuIW2h9tbr0e3UjmST56B5BexamqioaUFKardWhQpKWh75d4NsZamJm6Wjlx4qvrw5eLT2xS0zrrL2Y3QB1gamlLcIaObram+EWUL+HL28Y0sy4u3l5KSwvWr1yhTrqzK8jLlynLx/MUs61y6cFGtfNny5bh6+SqpqerdohQKBadOnOTBvfsU9y+Ra7G/lJaaypM7D/EoqtrF3aOoDw9v3M223tkDx4kICaXqF2/XsiclKZn0tHQM8mgYDi0NTVzM7bgaono9uvrsHu5WjtnUUlXetQjXQu5l2YruJV1tbbQ0tYhLSfygeLOSlprK83tPcCms2j3Yxc+Lp7ey7xYJsHr0LBYO+otNEwN4dDXnJFtqcgrpaWnoGRl8cMxZ0dLUxNXCnkvP7qgsv/T0Dp5Wb9c1topHca48u5tlK7qXKrsX48T9yySnpWRb5kN8DudbLQ1NCpjbc/m17+Lyszt4Wr1dwqmSWzGuhqh/F3rauoxt0INxDXvSu1JLXMzVW/rnFm1NLTytnNQ+x7OPb+CTRddlgKsh97AyMqOUkzcAZvrGVHArotKS8krIXTytnZTfp52xJf7O3nnS2lJbU4uC1s6cfq2F4alHVyls55ZlHV1NbbX9OzktBW+bAmhpZH0vVcKxIC5mtmr77edK4xP+7/+VdFP9DJw4cYJly5ZRq1Yt4uLimDRpEvv27aNChQoAeHh4cOjQIebMmUO1atW4f/8+JUuWVCY53NzclOtaunQpCQkJLFq0SJnwmjFjBk2aNGHcuHHY2dllG8fvv/+ubAnUrVs3hg0bxq1bt/DwyPgB+uWXX7J//37l2HCtWrVSqb9gwQLs7e25fPkyRYoUAWDOnDkUK1aMvn37sm7dOkaOHEmZMlk/YXupa9euyiTP0KFDqVChAr/88gv16mXcBPfr14+uXbsqy0+YMIGvvvqKnj0zuuINGDCAY8eOMWHCBGU3w9fVrFlT5e85c+ZgYWHBgQMHlGOM5SQ2Npb58+ezaNEi6tSpA2SMWebsnHnDcuvWLZYvX87Dhw9xdMy4OR40aBA7duwgICCAMWPGcP/+fVq2bEnRohnjVbz8rF81cuRItfdYv349rVu3Zvz48bRv3145aUTBggWZNm0a1apVY9asWdy9e5c9e/YQHBys3F/mzZtHwYIF37iNr5o1axb+/v4qEzEsWLAAFxcXrl+/TqFC6mNh3b17Fy0tLWVLt9d169aNypUrM3XqVE6dOkVUVBSNGjVSjgH3tq5fv65sFfk6R0dHzMzMuH49o3VCpUqV0NHRISgoiHbt2hEUFES1atXw9/cnKiqKGzduULBgQYKCgujYsaPKupycnJTb5eqqfjOUlJRE0mvd/vT09NTKZSUxNh5FugIDU9UfbgamRiREZ92q1dDMmModGmHt6kBaaho3j51n25TFNBrQGYeCGfGZ21tRtUtTLJ1sSUlI5uK+42weH8gXP3fHzM7qrWJ7F9FR0aSnpWNuYa6y3MzSnMjw3JtpedHsBVjaWFG8VPatu96Xmb4x2ppaajfiEQkxWBpm3cUp+MEVGvtW5NCdc1wPfYC3TQEa+lRAR0sbM31jtXX52LriYeXEuKCluR7/qzRMM+JNj1b9sZ0eHYOm5dtNtqPlVgAtJwfiF6/MXJiUROqtO+g3qkPc02cZrefK+KPlVoD0kOxby74rEz1DtDQ1iX6te1xUYixm+lmP73Uj9CGzjq6jV6Uv0dHSRltTi1MPr7L41PZci+v/VVRkJGlpaVhYWqost7SyIDwsLMs64WHhWFqp7msWlpakpaURGRmpfJAYGxtLy4bNSU5ORktLix+GDlRL4uWG+Jg4FOnpGJmpJvKNzEyIjco6ERL+9Dn7V2ym84i+aGppZVnmdftWbMHE0gz3IurXxtxgpGeAlqYmMa+1zolJisdUzzCbWplM9YwobOfOwpPbcizX1K8KUQmxXAvJOTn2PjKue+kYmqo+gDQwMSI+KrvrngnVOjXFxtWRtNRUrh87x6ZJC2k2qGu248wdW7sbI3NTnAtn/TDlQxnrvjxPqX4X0UlxmOm/ORlrpm9EEXtP5h7bmG0Zd0sHnM1t3/h9fYjP4Xxr/HIbXhkjDTK+C9O3/i48mHdis8rypzHhBJ7cyqOo5+jr6FHLqzRDq3dk9J6ALIeu+FAZ34UWka8lyqMSYrAwyPqccu35fSb9u4JB1dsrv4vj9y8z91jmuMGH7pzHTM+YMQ16oKGhgbamFtuvHlXppptbTPWN0NLUIiJedRsiEmKwMMj6Xurkw6vU9ynPkXsXuBH6kILWLtQrVC7zXioh4xxtqKPP8g6j0NHSJj09nemH13D6Ud61QhYiJ5KM+4/asmULxsbGpKamkpKSQrNmzZg+fTqXL18mMTFRmXx5KTk5mZIlM5pYf//997Rs2ZLTp09Tt25dmjdvTsWKGV2Drly5QvHixVVanlWqVIn09HSuXbuWYzKuWLHMwWTt7OwwNDRUSQ7Z2dlx4kTmgOT379/nt99+4/jx44SGhipbxN2/f1+ZjLOwsGD+/PnUq1ePihUrqg3Y/zZxAMpk1ctliYmJREdHY2pqypUrV+jevbvKOipVqsTUqVOzfY+QkBBGjBjBvn37ePbsGWlpacTHx3P//tvdcN66dYvk5GRlwhTA0tISb29v5d+nT59GoVCoJaqSkpKwsspIhPTt25fvv/+eXbt2Ubt2bVq2bKmy/UCW73HlSkarnVOnTnHz5k2WLs38Ua9QKEhPT+fOnTtcv34dbW1t/P0zkxZeXl7vPOvtqVOn2L9/v1qrvZefRVbJuISEBPT09LIdbLRYsWIULFiQNWvWsH//fjp16oSOjs47xfU2FAqFMgZDQ0PKli2rTMYdOHCAwYMHo62tTaVKlQgKCkJPT487d+6oJWwNDDKeqr/aVftVY8eOZdSoUSrLRo4ciVE19YHws/XaZ6VQKCCbp03m9tbKiRoA7DyciY2I5sLuo8pknK2HM7YemQliO08X1o+Zy6WgYCq2ybvB0dW+c4Uiu814Z2uXreLfvfv5Y9p4dPXUE7B5KeP7ULfw5HYsDUyZ9cVg0ICI+Bh2XDtG+5J1SVekq5Vv5FOR22GP1Fq1fCidsv4Yts98SBL7crKF1+N+hwGAdSuWI+3RE9Luqp4b4wOWYdi5LWbjfkWRlkbag0ekBJ9Bq0Dud3dRCx8Nsv4mwNHUmk7+Ddhw8V8uPL2Jub4JbUvWoWuZxsw7oT6ZiXh3rx/fCkXOg0qrPzFXqC03NDRk/tJAEuLjORV8ipmTp+Po5EjJPEi4QxaHgEKR5ZP99PR01s9cRNWWDbByeLuWMEc27+XS0dN0+rk32rq5f017VXbHwZuUcy1MQkoS5x/fzLZMrYKlKeXsw7SDq/J0bKysrg3Z7U8W9tZYvHLds/csQGx4FGd3Hc4yGXdmx0FunrhAs8Fd0c6D+4tXvf5d5HSeelVFt2LEpyRy5nH2yYTK7sV5GBnCnfC8a9n+0mdxvn1tIzTQeKuDpYJrURJSEjn7WmLnTvhj7oQ/Vv59K/QhP9f+ihqe/qw8tzdXQn4rGhoostkQZzNbvi3XlJVn93Lmccb4fV+Vbsj3FVow40hG75Ai9h58WbwGc45t5Mbz+9ibWvNN2SZEFIth1fl9eRJyVsdFdl/G0jO7sDA0ZWqzH9AgI3G368YJ2hSvpXIvlZCSxPfrxqOvrUdJp4J8V745T2LCOP8k+/OZEHlFknH/UTVq1GDWrFno6Ojg6OioTELcuZPRtHrr1q3KljgvvWxl06BBA+7du8fWrVvZs2cPtWrVolevXkyYMEEl8fC6N83A8moiRENDQy0xoqGhoUy4Qca4X+7u7sydOxdHR0fS09Nxc3MjOVm129W///6LlpYWjx8/Ji4uDlPTnAfQfT2O7Ja9Gov6j4PsPwfImNH2+fPnTJkyBVdXV/T09KhQoYJa7NnJ7kf5q9LT09HS0uLUqVNovfY0/WVS65tvvqFevXps3bqVXbt2MXbsWCZOnEifPn1yXPern8F3331H37591coUKFCAa9euqS1/Pf6XY628uuxlN9hXt+Vl68rXvZxw4XXW1tbEx8eTnJycZas1gK+//pqZM2dy+fJllUTvuyhUqBCHDh3K8n0eP35MdHS0SkvAGjVqsHLlSi5dukRCQoIyUVmtWjX279+Prq4u+vr6lC+vOi5HeHg4kP2kI8OGDWPAgAEqy/T09Jh25M2znukbG6KhqUHCa60BEmPi1VrL5cTW3YmbJy5k+7qGpgY2ro5Eh4S/9TrfhamZKZpamkS81gouKiIK83dMAGdl/fLVrFmygtGT/sTdM29aOUQlxpKanqbWCs7CwCTb7lzJaSmMC1rChH+XYWlgSlh8FE0KVyYuOYGoBNXWEnraOtT0KpUngyWnnLtEzJ1XkmYvJmHQNDMl7ZXWcZomxiii36JLvo4OumVKkLB5h9pL6aFhxE6aCbq6aOjroYiOwfCbTqSH5t6+FZMUT1p6OmYGqg8BTPWN1FpvvNSkcGVuhN5n29UjADwghKTgZH6p8zWrz+/Lk0Ho/1+YmZujpaWl1gouIjxCrbXcS5ZWloSFhauV19LSwsw8c2IaTU1NnF0yHhwU9C7Evbt3WRK4ONeTcYYmRmhoahIbqbr/x0XHqrWWA0hOSOTJ7Qc8vfuIHQszftAqFApQKPij0wDa/9gDd7/Mh1FHt+7j8KbddBjWE7sCb9dd9H3EJSWQlp6OqZ7q9cFEz1CtVVBWyrsWIfjBZdKyeFgAUNOrFHULlWXG4bU8js691q6vyrjuaaq1gkuIiXun656dhwvXj6mPL3h25yFObztIkwFdsHJWn1wkt8QmvzhP6WfxXSS+eVyxyu7FOHbvImnpWX8XulralHHxVZl8Iy98Dufb2Bfb8HoruIzj4s3fRSW3ohy7fynb4+IlBXA3/Cl2Jlmf9z5UxneRhrmB6jnJTN+YyISsP9Mvi1XnSshdNlz6F4B7EU+Zc2wDYxt+z9Izu4hIiKF9yToE3TqtnJ30XuQz9LV16FnxC1af359tou99RCfGkZaehqWh6jaYGxjneC816d/lTD24EgtDE8Ljo2noU5G45ESiXjmWFCiU56Xb4Y8oYG5H2xK1/y+ScZoym+onR8aM+48yMjLCy8sLV1dXlURT4cKF0dPT4/79+3h5ean8c3HJHEDXxsaGr776iiVLljBlyhT++ecfZf2zZ8+qDOB/+PBhNDU1s2y99L7CwsK4cOECgwcPply5cri4uHD37l21ckeOHOGvv/5i8+bNmJqavjHJ9D58fX05dEh14OUjR47g6+ubbZ2DBw/St29fGjZsiJ+fH3p6ejlORvA6Ly8vdHR0OHbsmHJZRESEsjskQMmSJUlLSyMkJETtu7R/ZdY5FxcXevTowbp16xg4cCBz56rO1pTVe/j4ZIx34+/vz6VLl9TW7+Xlha6uLj4+PqSmpnLmTOZA9zdv3iQyMlL598vk0qszl746mcOr7+Pm5qb2PtmN/1eiRAkgY+KH7LRv354LFy5QpEgRChcunG25nLRt25bY2FjmzJmj9tqECRPQ0dGhZcuWymU1atTgxo0bLFu2jMqVKysTpdWqVSMoKIigoCAqVKiAvr6+yrouXryIjo4Ofn5+Wcahp6eHqampyr+37aaqpa2FdQEHtQGmH125jZ3H2405AxD24CmGWfygfEmhUBD28GmeTeKgo6ODV6GCnD2pOinB2ZOn8Snyft/vS+uWr2blomWMHP8HBX3ypusXZMyOdv35A0o7q44pVdrZh4tPcx6bKC09nedxkaQrFNT0KsXRexfVbm5reJZCR0ub3ddzHqD8vSQlkf48NPPfk2ekR0Wj7fvK56WlhXZBT1Jv333j6nRLlwBtbVKOn8q+UHIyiugYNAwN0CnsQ8q5rMcOex9p6encDX+sNlZfEXsPboRmPZainrYO6a89LHn5t9zDfhgdHR0K+Xhz8rjqvnvyRDBFihXJso5f0SKcPKFaPvj4CXwK+6Ctnf3zZIUCUpJzf2wsLW1tHNyduXNR9UHVnQvXcM5iYgY9A326/zmUb8cMVv4rVasiVg62fDtmME6emUMWHN2yj0Prd9FuSA8cPQrkeuyvSlOk8yDyGT62qu/jbevKnbDH2dTK4GXtjK2xBUfvZn2s1ipYmvo+5Zl1ZD0PIp/lWsyv09LWxsbVgYdXVMd6enj5Fvaeb//5hd5/onbdO7PzEKe2HqBRv07YuuXN4PQvpaWncy/iKYVfG0C+sJ07t8JyHvPV26YAdiaWHLyd/WQlpV180dHS5ti9S7kSb3Y+h/NtmiKd+5FP1cYk87Vz41bYoxzrFrJxwc7EUmUSjZy4mNsSlU1i7EOlpqdxK+wRJRxVe1aUcPTKtkW9npauWkOB178bPS2dbMpo5Pr3lZqexo3Qh/g7eass93fy5vKzuznWTVOkExoXRbpCQXXPkhy/fynHRKGGhobKbPVCfEySjPvMmJiYMGjQIH744QcWLlzIrVu3OHPmDDNnzmThwoUAjBgxgo0bN3Lz5k0uXbrEli1blImnDh06oK+vT5cuXbh48SL79++nT58+dOrUKccuqu/K3NwcS0tLZs+ezc2bN9m7dy8DBw5UKRMTE0OnTp3o06cPDRo0YNmyZaxatYrVq9/cUuhdDB48mMDAQGbPns2NGzeYNGkS69atY9CgQdnW8fLyYvHixVy5coXjx4/ToUMHZTfEt2FsbEy3bt0YPHgwe/fu5eLFi3z11VcqM3oVKlSIDh060LlzZ9atW8edO3cIDg5m3LhxyhlT+/fvz86dO7lz5w6nT59m3759aknE0aNHq7yHtbU1zZs3BzLG1Dt69Ci9evXi7Nmz3Lhxg02bNimTnj4+PtSuXZvu3btz4sQJzpw5Q/fu3TEwMFC2rjMwMKB8+fL8+eefXL58mX///Zeff/5ZJYZevXoRHh5Ou3btOHHiBLdv32bXrl18/fXXpGUzO5aNjQ3+/v5qidJXWVhY8OTJE/buff9m/hUqVKBfv34MHjyYiRMncuvWLa5evcrPP//M1KlTmThxokoiu2LFiujp6TF9+nSqVaumXF6mTBmioqJYu3ZtlmMNHjx4kCpVqrzTfvIuitQuz7XDZ7h2+CwRT55zbNUuYiOi8KmaMRlC8Pq9BAVsUJa/uPc4d89eJepZGBGPQwhev5e7Z65SuHrmhBmntxzg4aVbRD+PIOzBUw4u3kzYg2f4VCn1+tvnmmatv2D3lh3s3rqTB3fvM2/6bJ6HhNCgWSMAFs5ZwOQ//lKpc/vGLW7fuEViQgLRkVHcvnGL+3czbzjXLlvFknkL6Tt0AHb2dkSEhRMRFk5CfEKebMOqc3tp5FuRhj4VcDW3o1fFltiaWLLpUsa+/G25pgyv2VlZ3tnMljoFy+BkZoOPrSsjanfF3dKBucfVu+k08q3AoTvn8nwmtpeS9v6Lfv3a6JQoiqajPYZd2qFITib5RGbC1PCrdug3b6RWV7diOVLOXkQRp97SRruwN9qFfdC0skTbtxDGP/Qk7VkIyUfer4VrdrZfO0Z1D3+qepTA0dSaDiXrYWVoxt4bGZP5tC5ei+/KN1eWP/PoOqVdfKnlVRobI3MKWrvQqVR9boU+VLYo0NLUpIC5HQXM7dDW1MLCwJQC5nbYGn94683cYKCjR0FrZ+Ug246m1hS0dsbuE4ivdfs2bNm4ma2btnD3zl2mT5pKyNNnNGvZAoA5M2bxx8jflOWbfdGcZ0+eMmPyNO7eucvWTVvYunELbTq2U5ZZErCI4OMnePzwEffu3mPl0hXs3Lqdug3qqr1/bijXoDpn9h/jbNAxQh89Zdfi9USFReBfK2PM3H0rNrNx1hIANDQ1sXVxUPlnaGqMto42ti4O6OpnPHA5snkvQau30rh7O8xtLImNjCY2MprkxKRs4/hQ+2+eooJbUcq7+mFnYskXRathaWjCoTsZiZ0mhSvTqZT6cAQVXItwJ/wJT2LUx/mrVbA0jXwrsvT0LsLiozDRM8REzxBdrbzp4lm8TkWuHDzNlUOniXjynMMrtxMTHoVftYyxhY+t283e+WuV5c/tOcKdM1eIfBZG+KMQjq3bze3Tlylas5yyzJkdBzmxYS/VuzTH1Nqc+KgY4qNiSMnD72L39RNUcS9OJfdiOJhY0aZELSwNTQm6lfEg9Iui1fi6rPp4xJXdi3Mr7FGOrQ8ruxfnzKPrxCXnzfXuVZ/D+Xb39WAquxenkltR7E2saF28JpaGpsrZqVsUqUrXMurXu8puxbgd9jjL76KxbyUK27ljbWSGs5ktXUo1wMXcNk9nvN546RC1C5ahlldpnM1s+LpMY6yNzNl57TgAHf3r0a9y5iRqwQ+vUN61CPW9y2FnbImPrSvflGvC9ef3lS3Rgh9epb53eSq7F8PW2ILiDl60L1mH4AeX1RJ3uWHthSDqe5enXqFyuJjb0aN8c2yNLdhy5TAAX5dpzODqHZTlncxsqOVVCkdTa7xtCjC8ZmfcLBwICN6qLNO2eG38nQphb2KFi5ktLYtWp3bBMuy9eTLX4xd57++//8bd3R19fX1KlSrFwYPZtwBet24dderUwcbGBlNTUypUqMDOnTtVygQGBqKhoaH2LzEx9ycheknSwJ+h3377DVtbW8aOHcvt27cxNzfH39+f4cOHAxkzUQ4bNoy7d+9iYGBAlSpVWLFiBZAx7srOnTvp168fZcqUwdDQkJYtWzJp0qRcjVFLS4uVK1fSt29fihQpgre3N9OmTaN69erKMv369cPIyEg56L+fnx/jxo2jR48eVKxYUa0b7vtq3rw5U6dOZfz48fTt2xd3d3cCAgJUYnndggUL6N69OyVLlqRAgQKMGTMmx+RdVsaPH09sbCxNmzbFxMSEgQMHEhUVpVImICCA33//nYEDB/Lo0SOsrKyoUKECDRs2BCAtLY1evXrx8OFDTE1NqV+/PpMnT1ZZx59//km/fv24ceMGxYsXZ9OmTcrumMWKFePAgQP89NNPVKlSBYVCgaenJ23atFHWX7RoEd26daNq1arY29szduxYLl26pNLya8GCBXz99deULl0ab29v/vrrL+rWzfwh5OjoyOHDhxk6dCj16tUjKSkJV1dX6tevr5KAfF337t0JDAzMcTZfc3PzN3/YbzBlyhSKFSvGrFmz+OWXX9DQ0MDf358NGzbQpEkTlbIvu6AeOHBAZR/R0dGhQoUK7N27N8tk3PLly9XGhMtNnqX9SIpN4MzWf4mPjsXC0YZ6vdthYmUOQHxULLHhmQOMp6WmcWLtHuIiY9DW0cbc0YZ6vdriUjSzS25yfBKHlm4lPjoWXQM9rFzsaTyoC7bueddSoEqt6sREx7By4VLCw8JxdXdlxLjfsbXPeBgQERbO82fPVer079ZT+f83r93gwJ792NrbMW/VIgC2b9hCakoKf474XaVe26860v7rTrm+DftvncZM34jOpRpgZWTKnfAnDN36N89iM7rbWRmaqfyQ0NLQoE3xWriY25GansaZx9fptX4iT2NUu+c5m9lSzMGLgZun87Ek7dqHhq4OBu1aomFoQNqd+8ROmwOvTDaiaWmhNsaOpq0N2gU9iJ06O8v1ahjoo9+8EZrm5iji40k5c56EDdsgm65W7+v4/UsY6xrQ3K8a5gbGPIwKYcKBpYTFZ5xrzfWNsTLM7O548M459LX1qF2oDO1K1iU+OZHLIXdYeXaPsoyFgQl/NOih/LuRb0Ua+VbkyrO7jNm3MFfjfx8+Nq7MaJHZ5b1v5YxxALddOcof+Rxfrbq1iY6KZuG8AMJCw3D39GDclAnYO2S09g4LDePZ08zWVI5Ojvw1ZQLTJ09j/ep1WNlY029Qf6rXzDzHJiQmMmncRJ6HhKCnp0cBV1d+Hj2CWnVr58k2+FXwJyE2noPrdxIbGY2NswNtB3+HuU1Gl7PYyGiiwt5tUPZTew6RlprG2qkBKsurfFGPai0b5Frsrzr96DpGugbU9y6Pqb4RT6LDmHVkvfKHt5m+ERavdXPT19alhGNB1l4IynKdVdyLo6OlzTflVK+b264cZfvVo7m+DV5lipIYm8CpLUHERcVg6WhLo74dM697kTHEhmfeV6WnpnFk9U7iIqPR1tHBwtGGhn074lo0s/XvpaBg0lPT2DV7pcp7lW5SnTJNVceCzS3BD65gpGtAk8KVMNM35nHUc6YeXKWcvMdM3xir14Y+MNDRw9/ZmxVnd2e7XjtjSwrZuDDpwPI8ift1n8P59uTDqxjpGtDItxJm+kY8jg5l+qHVKt/F68NQGGjr4u/kzYpsxn8z1NWjk389TPWNSEhJ4kFkCOODlnE3Iu/G8Dt89zymeoa0KVELCwMT7kc85bc9gTyPiwTA0tAUG2NzZfl9N09hoK1HQ5+KdC3TiLjkRM4/ucWiVybTWHVuHwqFgg4l62JpaEZ0YhzBD66w9MxO8sKB22cw1TOkg389LA1NuRf+hJ93zFFOemFpaIqtUea9lKaGJi2L1sDZ3Ja09DTOPb5J/01TlfdeAPo6uvSp1AprIzOSUlN4EBXCuP1LOHD7jNr7f47eNOTUf8nKlSvp378/f//9N5UqVWLOnDk0aNCAy5cvU6CAeuvof//9lzp16jBmzBjMzc0JCAigSZMmHD9+XDmuPoCpqanaME2v93bKTRqKtxm8SgjxnxMUFESNGjWIiIjIlYTVSw8fPsTFxUU53mBeSkxMxNvbmxUrVqhMRPFfs3XrVgYPHsz58+dz7FqVlfH7l+RRVB/P4BodufaGbgWfOm87N6rN6pXfYXywA9/PJLLHgDcX/ISZz55Ep+V5l9j+WBa3G0mlmT3eXPATd7jXbJ7l0bhgH4udqTWLT/73Z8ztVLoBfdbn7sPTj216iwFM+Xflmwt+4vpXbcM3q8bmdxgfbF7rYf/58+3idiPpvkZ9zOL/mn++HErzwDdPZPcp2/DVn9Sd2z+/w/hgu76dkt8hvJegmzkMG5LPqnu9W6+bcuXK4e/vz6xZs5TLfH19ad68OWPHvt2518/PjzZt2jBixAggo2Vc//79VYZjymvSTVUIkaN9+/axadMm7ty5w5EjR2jbti1ubm5UrVo1z99bX1+fRYsWvdN4fJ+iuLg4AgIC3jkRJ4QQQgghhBCfs6SkJKKjo1X+JSVlPTxAcnIyp06dUumFBVC3bl2OHDnyVu+Xnp5OTEwMlq9NIBUbG4urqyvOzs40btxYZdz0vCDJOCFEjlJSUhg+fDh+fn60aNECGxsbgoKC1GbLzSvVqlVT6yr6X9O6dWvKlSv35oJCCCGEEEIIkcs0NTQ+2X9jx47FzMxM5V92LdxCQ0NJS0tTG8/ezs6Op0+fvtVnMXHiROLi4mjdOnPsRB8fHwIDA9m0aRPLly9HX1+fSpUqcePGjff/0N9AmmkI8ZmqXr262qxH76NevXrUq1cvFyISQgghhBBCCCEyDRs2jAEDVIdR0dPTy7HO62PgKRSKtxoXb/ny5fz6669s3LgRW1tb5fLy5ctTvnx55d+VKlXC39+f6dOnM23atLfZjHcmyTghhBBCCCGEEEII8dHp6em9Mfn2krW1NVpaWmqt4EJCQtRay71u5cqVdOvWjdWrV1O7ds4TPWlqalKmTJk8bRkn3VSFEEIIIYQQQgghPlMan/B/70JXV5dSpUqxe7fqbNK7d++mYsWK2dZbvnw5X331FcuWLaNRo0ZvfB+FQsHZs2dxcHB4p/jehbSME0IIIYQQQgghhBCfvAEDBtCpUydKly5NhQoV+Oeff7h//z49emTMUj9s2DAePXrEokWLgIxEXOfOnZk6dSrly5dXtqozMDDAzMwMgFGjRlG+fHkKFixIdHQ006ZN4+zZs8ycOTPPtkOScUIIIYQQQgghhBDik9emTRvCwsIYPXo0T548oUiRImzbtg1XV1cAnjx5wv3795Xl58yZQ2pqKr169aJXr17K5V26dCEwMBCAyMhIunfvztOnTzEzM6NkyZL8+++/lC1bNs+2Q5JxQgghhBBCCCGEEJ8pDY3Pa4Synj170rNnzyxfe5lgeykoKOiN65s8eTKTJ0/Ohcje3uf1jQghhBBCCCGEEEII8QmTZJwQQgghhBBCCCGEEB+JdFMVQgghhBBCCCGE+ExparzbrKUi70nLOCGEEEIIIYQQQgghPhJJxgkhhBBCCCGEEEII8ZFIN1UhhBBCCCGEEEKIz5SGdFP95EjLOCGEEEIIIYQQQgghPhJJxgkhhBBCCCGEEEII8ZFIN1UhhBBCCCGEEEKIz5Qm0k31UyMt44QQQgghhBBCCCGE+EgkGSeEEEIIIYQQQgghxEci3VSFEEIIIYQQQgghPlMym+qnR0OhUCjyOwghhBBCCCGEEEIIkftO3LuU3yFkq6yrX36HkC+kZZwQQnzCJgQtze8QPtig6h24+fxBfofxQbxsXIiJiMjvMD6YiYUFMdHR+R3GBzExNeXSk1v5HcYH83Pw5Fl0aH6H8cHsTK2pNLNHfofxQQ73ms2RO+fzO4wPVtG9GM9jwvM7jA9iY2JJ9Mp1+R3GBzNt8wXRO/bkdxgfzLR+bWKeP8/vMD6IiY0N0SfP5HcYH8y0dElCosPyO4wPYmtqxZ3QR/kdxgdzt3bK7xDEZ0KScUIIIYQQQgghhBCfKemm+umRCRyEEEIIIYQQQgghhPhIJBknhBBCCCGEEEIIIcRHIt1UhRBCCCGEEEIIIT5TmtJN9ZMjLeOEEEIIIYQQQgghhPhIJBknhBBCCCGEEEIIIcRHIt1UhRBCCCGEEEIIIT5TGkg31U+NtIwTQgghhBBCCCGEEOIjkWScEEIIIYQQQgghhBAfiXRTFUIIIYQQQgghhPhMyWyqnx5pGSeEEEIIIYQQQgghxEciyTghhBBCCCGEEEIIIT4S6aYqhBBCCCGEEEII8ZnSkG6qnxxpGSeEEEIIIYQQQgghxEciyTghhBBCCCGEEEIIIT4S6aYqhBBCCCGEEEII8ZmSbqqfHmkZJ4QQQgghhBBCCCHERyLJOCFeExQUhIaGBpGRkfkdSq5zc3NjypQp713/7t27aGhocPbs2VyLSQghhBBCCCGE+H8i3VTF/6UjR45QpUoV6tSpw44dO/I7HCFyxeWgYM7tOkpCVAwWjraUb10Xh4KuWZZ9fO0uWyctUlvealRPzO2t1ZbfCr7IvnnrcC3uTd2ebXI99pxsWbeRdctXEx4WRgE3N7r360mR4kWzLBseGsa8GbO5ee0Gjx8+oumXLejer2eex6hQKPhn3jzWb9xITEwMfoULM3TwYDw9PHKst3ffPmb/8w8PHz3C2cmJnj16UKN6deXrAQsXsj8oiLv37qGnp0exokXp06sXbq6Z3+ucuXPZtWcPz549Q0dHB19vb3r26EGRIkXeHPPcuaxfvz4jZj8/hg4Zgqen55tjnj2bhw8f4uzsTM/vv6dGjRoqZVavXs3iJUsIDQ3Fw8ODgQMGULJkycyY//mHXbt2Zcbs40PPnj2zjFmhUNCvXz+OHD3KhPHjadK0aY7xvW77hi1sXLGWiLBwXNxd+bp3dwoXy/qzCQ8LZ+Hfc7l1/SZPHj6m4RdN6dbnu2zXfWjvASb9No6ylcrz4x8j3imud7V+9TqWL1lGeGgYbh7u9BnQl+IlS2Rb/uypM8yYMp27t+9gZW1N+87tadayhfL1A/uCWBK4iEcPHpGamoqzizNtOrajXsP6ebodb6O4gxftS9bFx7YA1kbm/LhtFgfvnMvvsJT2bd7J9jUbiQyPxMnVmfY9ulKoiG+WZU8eOs7+rTu5f/suqSmpOBVwplnH1hQtXUJZ5sD2PRzec4BH9x4A4OblQcuu7fDwLphrMa9bvZbli5cS9mL/6Tewf477z5lTp5k+eVrG/mNjTYdOHWj+5RfK12/fus382XO5dvUqT588pe+AfrRu31ZlHevXrGPDmnU8efIEAHcPD7765msqVKqQa9sFGeeIufv3sv7UCWISEvBzdmFI42Z42tq9Vf1dF87x0+oVVPMpzIT2nZTL15w4xtrg4zyJjADAw8aWbtVrUamQd67Gr9yGHdtYf+QwMQnx+Lm6MeTL1ng6OGZbZ9+5swTu3smD0OekpqXhYmNDxxq1aFimXJblA3bv5O8tm2hbrQYDv/gy1+L+Z8EC1m/alHntGzDgzde+oCBmz5uXee379ltqVKuWddyLFzNzzhzatWrFwH79lMvnzJ/Prr17eRYSgo62dsa1r3t3ivj55cp2zV23hvX79hETF4uflxdDvvoaT2eXbOus37eXbYf+5daDhwD4uLvTq01b/Dy9Mrdl4wb2nzzBvceP0dPVpVjBQvRu2x43x+y/57exfvVali9Zpjy++w7o94bj+wwzpkx75frQgeavXB/u3LrN/DnzlMd3nx/60bq96r1ffFwc82bP5d+gA0RERFCoUCH6DuyPr1/h996Ozes2smbZSsLDwnB1d6NH314UKVEs2/Lnz5zjn+l/c+/OXaysrWnVvg2NWqjeI6xfuYYt6zfx/FkIpuZmVKlela49vkVXTxeAC2fPsWbZSm5cvUF4WBgjxo6mYtXK770NnzJNpJvqp0Zaxon/SwsWLKBPnz4cOnSI+/fv53c4IhekpKTkdwj56lbwJY6u2knJhpVp8XN37L0KsGP6MmLDo3Ks12p0Lzr8NUD5z9TWUq1MTFgkx9fsxt6rQF6Fn61/9+5n7rRZtOncnmkLZlOkeFFGDhpGyNNnWZZPSUnBzNycNp3b4+6V84+B3LRw8WKWLV/OkIEDWbhgAVZWVvTq25e4uLhs65y/cIHhv/xCwwYNWL54MQ0bNODHn37i4sWLyjKnz5yhVcuWBMybx8xp00hLS6N3v34kJCQoy7gWKMCQgQNZsXQp8+bMwcHBgV79+hEREZFzzIsWsWzZMoYMHszCwMCMmHv3zjnm8+cZPnx4RszLlmXEPGyYSsy7du1i4qRJfN21K0uXLKFkiRL07dePp0+fqsY8eDArli9n3ty5ODg60qt37yxjXrZ8ObznOCeH9h0gYMY/tOzYhonzpuNb1I/fh4zg+bOQLMunJqdgam5Gy45tcfN0z3HdIU+fEThrHoWLffgPvzfZu2sP0ydNpXPXzsxbEkCxEsUY0m8Qz175TF/1+NFjhvQfRLESxZi3JIBOXTsxdcIUgvbtV5YxNTOlU9cu/L1gDgHLF9KgSSP+HD2GE0eP5/n2vImBjh43wx4y6d8V+R2KmuMHDrNsTgCN27Zk1My/KFTEl0k//0FYyPMsy1+/eBk//+L8MHo4I6ePw6d4Eab++if3bt5Rlrl6/hLlq1dm6LiR/Dz5DyxtrZkw/HciQsNyJea9u/YwbeIUOn/9FQuWLqR4yeIM6jtA5Zh81eNHjxncbyDFSxZnwdKFdO7ahSkTJhO0N3P/SUpMxNHZkR69e2JlZZXlemxsbejRuyfzFgUwb1EA/qVLMWzgEG7fup0r2/XSokP/suzoIQY3akrgd72wMjah98L5xCUlvbHuk8gIpu7cRklXN7XXbE3N6F2nHgu/68XC73pR2sOTQcsXcysk6+vPB23D3t0s27+PwV+2JnDAEKxMTOn99wziEhOzrWNmaEjXOvVY0H8Qy4cOp0nZCoxetoSjVy6rlb107x4bjhymoKNTrsa9cOlSlq1cyZABA1g4b17GdeSHH4iLj8+2zvmLFxk+ciQN69VjeWAgDevV48cRI7h46ZJ63FeusH7TJgpm8ZDI1cWFIT/8wIqFC5n3998Z174BA9547Xsbi7ZsYtm2bQz+qiuBv43Bysyc3mPHEPfKtfd1p65cpm6FSsz66RcWjBqNvbU1vf8cQ0h4uLLM6atXaFW7LgtG/caMH38iLS2NPn+OISGH7/lN9u7aw7RJU+nUtQvzlwRSvERxBvcb+Ibrw0CKlyjO/CWBdOramakTJqtcHxITE3FwcuS73t9jmc3xPe73Pwk+HszPo0awcPkSypQvyw+9+vE8m3PhmxzYs585U2fStnMHZgb8Q5FiRfl50I/Z3u89ffyEXwYNo0ixoswM+Ic2ndoza8oMDu3/V1lm3849LJg9l45fd+GfZYH88OMgDuwNImD23MxtTUjE3cuTngP6vFfcQnwIScaJ/ztxcXGsWrWK77//nsaNGxMYGPjGOocPH6ZatWoYGhpiYWFBvXr1lBf7HTt2ULlyZczNzbGysqJx48bcunVLWfdl185169ZRo0YNDA0NKV68OEePHn3r91AoFPz11194eHhgYGBA8eLFWbNmTY4xh4SE0KRJEwwMDHB3d2fp0qVqZaKioujevTu2traYmppSs2ZNzp17+9YHaWlpdOvWDXd3dwwMDPD29mbq1Km5UicgIABfX1/09fXx8fHh77//Vr728jNdtWoV1atXR19fnyVLlpCens7o0aNxdnZGT0+PEiVKqLR8fJvvIiwsjHbt2uHs7IyhoSFFixZl+fLlytfnzJmDk5MT6enpKvE2bdqULl26KP/evHkzpUqVQl9fHw8PD0aNGkVqaupbf7bv6sKeo3hXKolPZX8sHGyo0KYexhZmXD5wMsd6BiZGGJoZK/9paqpeFtLT09k/fz3+TapjYmORZ/FnZ/2KtdRtXJ96TRpSwM2V7v16Ym1ry7YNm7Msb+dgz3f9e1GrQV2MjIw+SowKhYLlK1fS9auvqFmjBl6enowaMYLExER27NqVbb3lK1ZQrkwZunbpgpubG127dKFsmTIsW7lSWWb6lCk0adwYTw8PChUsyMiff+bp06dcuXpVWaZ+vXqUK1sWZycnPD08+KF/f+Li4rhx82bOMS9fTteuXalZsyZeXl6M+vXXjJh37sw+5uXLKVe2LF27ds2IuWvXjJhfOUaWLltGs2bNaN68Oe7u7gwcOBA7OzuVc1b9+vUpV64czs7OeHp6ZsZ844bK+12/fp1lS5cy4pdfso0pJ5tXr6dWw7rUaVwfZ9cCdOvzHVa2NuzcuDXL8rYOdnTr04Ma9WphmMP+k5aWxpTfx9O2a0fsHBzeK7Z3sWrZSho1a0zj5k1xc3ej78D+2NjZsmHN+izLb1y3AVt7O/oO7I+buxuNmzelYdNGrFyS+T2VLOVP1RrVcHN3w8nZmVbtWuPh5cn5s/nfAu3Y/UvMPb6JA7fP5ncoanat20LVejWp1qAWjgUyWsVZ2lizb0vWx3r7Hl1p2KoZHt5e2Ds58GXX9tg5OnD2eOa5+buh/ajZpB4FPN1xcHGia7/vUCgUXD57Mct1vqsVS5fTuFkTmrzYf/oN/AFbO1s2rFmXZfkNa9djZ29Hv4E/4ObuRpPmTWnUtDHLlyxTlvH1K0yvfn2oXa8OOro6Wa6nctUqVKhckQKuBSjgWoDvevXAwNCAyxdyZ7vgxbns6GG6Vq1BzcJF8LKz59cvWpGYksLO82dzrJuWns4va1bSvUZtHC3UH0RV9fGlUiEfXK1tcLW2oWftehjq6nLxQe4+xFUoFCw/sJ+udetRs3gJvBwd+bVjJxJTktl5KjjbeqUKFqJG8RK429vjbG1Du+o18HJ04uztWyrl4pMSGbE4kOFt22NiaJi7ca9eTdfOnalZrRpeHh6M+uknEpOScr72rVpFudKl6dqpE26urnTt1ImypUqxbNUq1bjj4/ll1Ch+GjIEExMTtfXUr1uXcmXKZF77+vTJuI7cuqVW9p23a8d2ujZvTs0yZfFyceHXHj1JTE5i55HD2db7vVcfWtWpi7ebG26OTvz0TXcU6QqCL2Xu79OHDqNJtep4OrtQyNWVEd99z9OwUK7cuZPtet9k5bIVNHrl+O47sD+2drasz/b6kHF8v7w+vDy+V6gd372pXbcOulkc30mJSRzYH8T3fXtSwr8kzi7OfN39GxwcHdmwNuvzypusW7maeo0b0KBpIwq4udKjf29sbG3Zsn5TluW3btiMrZ0tPfr3poCbKw2aNqJuowasWZ65H125eAm/okWoUbcW9g72lCpXhup1anL96nVlmTIVyvFV925Url71veIW4kNIMk7831m5ciXe3t54e3vTsWNHAgICUCgU2ZY/e/YstWrVws/Pj6NHj3Lo0CGaNGlCWloakJHcGzBgAMHBwezduxdNTU1atGihlqz56aefGDRoEGfPnqVQoUK0a9dOmZx503v8/PPPBAQEMGvWLC5dusQPP/xAx44dOXDgQLZxf/XVV9y9e5d9+/axZs0a/v77b0JCMluDKBQKGjVqxNOnT9m2bRunTp3C39+fWrVqEf7KU7ycpKen4+zszKpVq7h8+TIjRoxg+PDhrHrthupd68ydO5effvqJP/74gytXrjBmzBh++eUXFi5cqLKuoUOH0rdvX65cuUK9evWYOnUqEydOZMKECZw/f5569erRtGlTtR/4OX0XiYmJlCpVii1btnDx4kW6d+9Op06dOH48o7VIq1atCA0NZf/+zCeIERER7Ny5kw4dOgCwc+dOOnbsSN++fbl8+TJz5swhMDCQP/74460+13eVlppG6P0nOBVWfXLsVNiDZ7ce5Fh33e//sGTwJLZOWsTja+o3g2e2/Iu+iSE+lUtmUTtvpaSkcPP6dUqWKa2y3L9MKa5cVH/yn18ePX5MWFgY5ctldg3S1dXFv2RJzl+4kG298xcvUq6canei8uXK5VgnNjYWAFNT0yxfT0lJYf2GDRgbG1OoYPZd3B49epQRc/nyqjH7+3P+/PnsY75wgXKv1AEoX6GCsk5KSgpXr15V+SyU25XNelNSUli/fn1GzIUKKZcnJiby088/M3jIEKyt1btOv0lKSgq3rt2keBl/leUlypTk6qUr77y+V61etBxTczNqN6r3Qet5GykpKVy/eo0y5cqqLC9TriwXz2ed1Lh04aJa+bLly3H18tUsHwooFApOnTjJg3v3Ke5fItdi/9ykpqRw98Zt/PyLqyz38y/GrSvX3mod6enpJCYkYGRinG2ZpKRk0lJTcyzztpT7T/nX9p/y5bh4PutzzaULFylTXvUYLluhHFcvX3nvh0ppaWns2bmbxIRE/IplPczA+3gUEUFYbAzlvTLPd7ra2vi7uXP+wb0c684L2ouFkRHNSpV54/ukpaez68I5EpKTKeqSu63EH4WFERYdTXmfzK7Outo6+Ht6cf4tkzQKhYIT165yL+QZ/q90iwT4a/UqKhX2o5y3T+7G/fLaVzZz39LV1cW/RAnOX8w+4Xr+4kXKlVXdH8uXK6dWZ9ykSVSqWJFyZd78/aSkpLB+48aM64iX1xvL5+TR8xDCIiMpXzSze6Sujg7+Pr6cv3E9h5qqEpOSSE1LxTSHBzuxL1oQmhq/37H+8vgum+X1IYfj+x2uD1lJS0slLS0NXV09leV6+rqcP5v9PUR2UlJSuHHtOv5lX7vfK1uaKxfVW0xCRqLt9fKlypXmxtVryu3wK16UG9euc+1yxjX/yaPHBB89TtmKWXfl/txpaGh8sv/+X8mYceL/zvz58+nYsSOQ0TojNjaWvXv3Urt27SzL//XXX5QuXVqlZZbfK+NRtGzZUm39tra2XL58WWX8o0GDBtGoUSMARo0ahZ+fHzdv3sTHxyfH94iLi2PSpEns27ePChUyxlnx8PDg0KFDzJkzh2pZjLFx/fp1tm/fzrFjx5Q/9ufPn4+vb+aN3v79+7lw4QIhISHo6WVcTCdMmMCGDRtYs2YN3bt3f9NHiY6ODqNGjVL+7e7uzpEjR1i1ahWtW7d+7zq//fYbEydO5IsvvlCWeZnUerX1Wf/+/ZVlXsY/dOhQ2rbNGLdm3Lhx7N+/nylTpjBz5kxluZy+CycnJwYNGqQs26dPH3bs2MHq1aspV64clpaW1K9fn2XLllGrVi0gY3wsS0tL5d9//PEHP/74ozJWDw8PfvvtN4YMGcLIkSPf+Lm+q8TYeBTpCgxNVW/4DEyMSIjOusuhoZkxVTo2xtrVgbSUVG4cv8DWyYtpPKALDoUyxiN7evM+1w6f4Ytfsh8zKy9FR0WRnpaOuaVqizxzSwsiwt4uYfwxhIVldCWzslRtWWFlacmTbLqJvKyXVZ2X63udQqFg0tSplCheHK/XuuwcPHSI4b/8QmJiItbW1sycNg1zc/OPHnNkZCRpaWlYvlbG0sqK0Ne26+DBgwz/6afMmGfMUIl54qRJFCtWjOrZjCP0JjFR0aSnp2NuYa6y3MzCgsjw9+/GdOXCJfZs3cmkeTPeex3vIurFZ2qh9plaEJ7NvhIeFo6llepxY2FpSVpaGpGRkcrkZmxsLC0bNic5ORktLS1+GDpQ7UeayBQTHUN6ejqmavuUORfDI99qHTvXbiYpMYmyVStmW2bNgqVYWFniV/LDk1ZR2R2TlhaEhWZ9Hg0LC6Pca+ddyyz2n7dx6+ZNenTtTnJyMgYGBowZ/yfuHjl3AX8XYbExGfEZqSYzLI2MeZrDRFzn7t1l0+mTLP2+b47rv/nsKV/PnUVyaioGurqMb9cRj7cci+5thcVEA2D5WusvSxNTnkbkfK2LTUig4YjhJKemoqWpydBWbSj3SlJv1+mTXH34gIUDh+RqzABhLx7cql0TLCx48iz7rrxh4eFYWajuX1YWFsr1Aezcs4er16+zaO7c16urOHj4MMNftOq2trJi5uTJOV773kbYi/3G0sxMZbmlmRlPQ0Pfej0zVizHxtKSskWyPo4VCgWTly6mhLc3Xi7Zj0WXk+yuDxZWloRnc58UFhZOWavXyr/j8W1oZESRokVYOD8AN3dXLCwt2bNzN5cvXsb5PbYlOjLjfs/itfOOhYVFttsRER6BhcXr1zkL0tLSiIqMwsraiuq1axIVEcnA7/uhUChIS0ujcYumtOnU/p1jFCIvSDJO/F+5du0aJ06cYN26jCbU2tratGnThgULFmSbjDt79iytWrXKdp23bt3il19+4dixY4SGhipbxN2/f18lGVesWOYTNocX3ZpCQkLw8fHJ8T0uX75MYmIiderUUVmenJysMiD6q65cuYK2tjalS2c+MfLx8VG5QTl16hSxsbFqY70kJCSodLN9k9mzZzNv3jzu3btHQkICycnJlChR4r3rPH/+nAcPHtCtWze+/fZbZZ3U1FTMXrsxenX7oqOjefz4MZUqVVIpU6lSJbWutzl9F2lpafz555+sXLmSR48ekZSURFJSkkqXxw4dOtC9e3f+/vtv9PT0WLp0KW3btkVLSwvI+GyDg4NVWsKlpaWRmJhIfHw8hll0E3n5Pq96mSTNC+b21ioTNdh5uhAXHsX53UdxKORKcmIS+xdsoEqnxugb5163lvfx+hMzhUKRr0/Rtu/YwZhx45R/T5k4EXjPON+hzl8TJnDz5k3m/fOP2mulS5Vi2aJFREZFsX7jRob99BOB8+crf4C/jFlDQwOFQsGUyZOzjznniN8q5rf5LEqXLs2ypUuJjIxk/YYNDBs+nMCAACwtLTlw4AAnT55k6ZIlb4rmjdQ+T4UCjfccxDghPp6pf0yg5+C+mJqbvblCLlL/TLPYtlfLq22jQm25oaEh85cGkhAfz6ngU8ycPB1HJ0dKlvJHZE/tk834Mt5Y79j+Q2xYspq+I4dku/9sW72R40GHGPrXKHR0dXMh2gxZ7z/vUl59/3kbBVxdCVi2kNiYWIL27eePX39j+j9/v3dCbvu5M4zdvEH59+QOXV7Eq1pOkRFsluKSkhixdhXDm36B+RuGM3C1smbp932ISUxk3+WL/LpuDXO+/vaDEnLbT55g7MrMLuOTv8uYZOj1z1ZB9r02XjLU02PpkGHEJyURfP0akzesw8nKmlIFC/E0IoKJa9cwvWdv9HSy7kr8TnHv2sWY8eOVf0/5668XcatSZLFMzev7F5n73NNnz5g4dSozJk16431QaX9/lgUEZFxHNm9m2IgRBP7zD5avJWlysv3wIcbOz0z6TR48NCPE178PhXrc2Vm0eRO7jh5m9s8j0MvmOP4rMICb9+8xd8SoLF9/F2phKRQ5H9/Z7Gvvcnz/PHoEY0ePoUXDZmhpaVHIuxC169Xh+rW3bz2oHph6XDneR2VxXstYnLH83OmzrFi0lF4D++Hj58vjh4+YPXUmFgGL6dC10+trE+Kjk2Sc+L8yf/58UlNTcXLKHMBWoVCgo6NDRIT6ExYAAwODHNfZpEkTXFxcmDt3Lo6OjqSnp1OkSBGSk5NVyum8ciP08iLxMnGX03u8LLN161aVuCH7ZI3ypjmHC1h6ejoODg4EBQWpvfa2TxVXrVrFDz/8wMSJE6lQoQImJiaMHz9e2aXzfeq83N65c+eqdeF7mex6Kasxwd4mCZDTdzFx4kQmT57MlClTKFq0KEZGRvTv31/l+2zSpAnp6els3bqVMmXKcPDgQSZNmqR8PT09nVGjRqm02ntJX18/y89l7NixKi0GAUaOHIlx9TfPpqdvbIiGpgbxr7WCS4iJw8D07cdNs/Vw5ubxjG4NMc8jiA2LZOfMzAHUX+5X877/jdaje2Fqoz7GTm4yNTNDU0tTrRVcVESkWmu5j6lqlSoqs7Ulv5g8JDQsTOWJcnhEhFprlFdZWVmptYLLrs5fEybw78GD/DN7Nna2tmqvGxgY4OLigouLC0WLFKHFl1+ycfNmur5onfkyZiMzM+JiY5X7c5YxZzNY89vEbG5ujpaWllqZiPBwtdYTKjEXLUqLL75g48aNdO3alZMnT/Lw4UNq1KypUmfI0KGsWr2aH//6NdsYXzIxM0VTU5OI11rBRUVGYmZp/sb6WXn66AkhT58xZljmsfryuPiyZmNmLJ6LvVPujiFn9uIzfb0VXER4hFpriJcsrSwJe+24iQiPQEtLC7NXkkCampo4uzgDUNC7EPfu3mVJ4GJJxmXDxNQETU1NoiIiVZZHR0ZhZpFzcvb4gcMETJlFz+ED8PPPenbA7Ws2sWXFOgaPHYGLR9YzYb8rs+yOyYgILK2y3n8yjvNwtfKv7z9vQ0dHR9lSxqewL1cuX2H18pUM+enHd1rPS1V9ClPklRktk18M5xEWG4u1SWb3/Yi4WKyy6fr3MDyMx5ERDFyWOaN4+ovjuPyvP7Gm7wCcLTPOgzra2rhYZZwjCzs5c/nRQ1YcO8Lwpi3UV/y221CkGEVemTAi+UWXurCYaKxfeegYERODlUnWQxK8pKmpiYtNxjXB29mFu8+eEbhnF6UKFuLqg/uEx8bQeULmw6O09HTO3LrJ6oMHODxxKlqvjRObY9yVK1OkcOZMmcrrSHj4u137LC1VWsEp67y4B7967RrhERF0+uabzLjT0jhz7hyr1q3jyL59yvtBAwMDXJydcXF2zrj2tW3Lxi1b6Nrp7RMtVf1LUeSVrr3JqRnX9LCoSKxf+V0QER2Fldmb9//FWzcTsGkDM4f9RMECWR/H4xcG8O/pk/zzy6/Y5XDNfZPM64P6+T6764OVlaXa9SQyi+vDmzg5OzPjn79JSEggLi4Oa2trRg77BQfHd78Gmppnfb8XGRGp1lruJQtLCyLCXy+fsR2mZhnHzaK5AdSsV4cGTTN6w7h7epCYmMi0cZNo16WD2jjJnzvN/+PuoJ8qScaJ/xupqaksWrSIiRMnUrduXZXXWrZsydKlS+ndu7davWLFirF37161RAlkdOW4cuUKc+bMoUqVKgAcOnTonWPL6T0KFy6Mnp4e9+/fz7JLalZ8fX1JTU3l5MmTlH0xLse1a9eIfKXLhr+/P0+fPkVbWxs3N7d3jhkyuplVrFiRnj17Kpe9qVXdm+rY2dnh5OTE7du3lWOwvQ1TU1McHR05dOgQVatmDsJ65MgR5WfwNg4ePEizZs2UXZnT09O5ceOGShdfAwMDvvjiC5YuXcrNmzcpVKgQpUqVUr7u7+/PtWvX8HqHcUuGDRvGgAEDVJbp6ekx/WjOE3UAaGlrYV3AgUdXbuNeMnNMmEdXbuNa3PutYwh78BQDs4wfL2b21rQc0UPl9ZMb95OSmESFNvUxesMPz9ygo6ODV6FCnAk+RcVqmdPMnzl5ivKVs+/ildeMjIxUEsEKhQIrKyuOnziBj3fG552SksLpM2fo06tXtuspVqQIx0+coEO7dsplx48fp1jRzC4tCoWCvyZOJOjAAebMnImTo+NbxagAlQTyy5hNLCyIiY7OjPn4cdWYT5+mT5/sZxQrVrQox48fp0P7zC4ex48dU7Y21dHRwcfHh+PHj1OjRo3MMidOUO2V4zLLmBUKZWKzS5cuNGvWTOX1tu3aMeCHH6jfoAHRJGe1ChU6Ojp4entx7uQZylfJ3F/OnTxD2Urlc6iZPacCLkxe8LfKsuXzF5GQkMDXvb/Dyvbdx7Z7Ex0dHQr5eHPyeDBVa2ReA06eCKZy1cpZ1vErWoQjB1UHGg8+fgKfwj5oa2d/66dQQEry//fM1DnR1tHBraAHl86cp1SlzIdFl8+cp0T57Me1Orb/EAsm/02PH/tTvFypLMtsX72RzcvXMvCPn3EvpD5z5Pt6uf8EHw+mWo3qyuUnj5+gcrUqWdbJ2H9U72WCj53Ap7BvjvvPW1EoPmj2cyM9PYxeeRCpUCiwMjbh+M0beDtknB9TUlM5ffcOferUz3IdbtY2LO/VT2XZ7L27iUtKYmDDxtiZZn99Uygyk2fvvQ36+hi98mBOoVBgZWrK8WtX8X6RaExJTeX0rZv0adIsu9VkE59CGV+ZQt4sH/qTyuujly3Gzc6OzrXqvlMiDsDI0BCjV1r2K68jwcH4vBjvMyUlhdNnz9KnR4/sVpNx7QsOpkObNsplx0+coNiL3iRlSpdmxaJFKnVGjxmDq6srXTp0UHsw+yqFQqH2MPyN22VggNErD8UVCgVW5uYcv3ABb7eMFpwpqamcvnqFPm1z7t64eMtm5m9Yx/ShwynsoX4cKxQKxi8MIOhkMLN/HoFTFg/X3kXm8X1C5foQfCKYylWzP74Pv3Z9OPEW14fsGBgYYGBgQEx0NCeOHef7Pj3fXOk1Ojo6FPTOuN+r9Mp56Uxw9vd7vkX8OH5YdTK80ydOUtDHW7kdSUmJagk3TU1NFApFjuOFC/GxSDJO/N/YsmULERERdOvWTa2745dffsn8+fOzTMYNGzaMokWL0rNnT3r06IGuri779++nVatWWFpaYmVlxT///IODgwP379/nxx/f/WlvTu9hbW3NoEGD+OGHH0hPT6dy5cpER0dz5MgRjI2NVcZQe8nb25v69evz7bff8s8//6CtrU3//v1VWuDVrl2bChUq0Lx5c8aNG4e3tzePHz9m27ZtNG/eXKULaHa8vLxYtGgRO3fuxN3dncWLFxMcHIy7e/bdT96mzq+//krfvn0xNTWlQYMGJCUlcfLkSSIiItQSVq8aPHgwI0eOxNPTkxIlShAQEMDZs2eznEk2p/jWrl3LkSNHsLCwYNKkSTx9+lQlGQcZXVWbNGnCpUuXlIm7l0aMGEHjxo1xcXGhVatWaGpqcv78eS5cuMDvv/+e5fvq6el9ULfUorUrEBSwHhtXB2w9nLl68DSx4VH4Vs344Xdi/V7iImOo0bU5ABf2HMPE2hwLBxvS0tK4efwCd05fofZ3Gd2ltXW0sXRSvUnUNcz48fD68rzUom1LJv42joI+hfApUpgdm7by/FkIDZs3ASBw9jzCnocy8JfM4+7WjYxZRBMSEomKjOTWjZvoaOtQwD13Wpq8TkNDg3Zt2hCwcCEFXrT0Cli4EH19feq/kvgfMWoUtjY29H6RiG7bpg3dv/+ewEWLqF61KkH//svx4GDmz5mjrDNu/Hh27NrFxL/+wtDISDnumrGREfr6+iQkJLAgMJCqVapgbWVFVFQUq9euJSQkhNovxjDMNuZ27QgICMiMOTAwI+Z6mZMSjBg5MiPmF+fGtm3b0v277whcuJDq1aoRdOAAx0+cYP68eco6Hdq3Z8TIkfgWLkyxokVZt349T58+VY6vmZCQwIIFC6hatSrW1tYZMa9ZoxKztbV1luPW2Nvb4+LiwqUnb9eVvkmrFkwbMxEv74J4+/mwa/MOQp89p27ThgAs+SeAsNAw+g3PHCfyzo2MdScmJBAdFcWdG7fQ1tHBxa0Aunq6uHq4qbyH0YvWN68vz02t27fhj5G/4V3YB7+iRdi8fiMhT5/RrGVG65w5M2YR+jyUn0ZlzDrb7IvmrF+1lhmTp9G4eVMuXbjI1o1bGPHHr8p1LglYhHfhjHEyU1JTOXb4KDu3bmfgj4OyCuGjMtDRw9nMRvm3o6k1Ba2diU6M41ns+4/3lxvqftGYueOn41bQEy/fQhzYvoewkFBqNMo41lcvWEpkWDjfDs5Iah/bf4h5E2bQvkdXPH0KEvWipaaOnq5yxt5tqzeyftEKvhvaD2s7G2UZPQN99N/QOv9ttO3Qjt9GjMLH14cixYqyad0Gnj19RvMX+8/sGX/zPOQ5v4zOGNO0ecsWrFu1humTptKkRTMunr/Alo2b+fWP0cp1pqSkcPf2nRf/n8rz58+5ce06BoYGypZwc2bOonzFCtja2REfH8eenXs4c+oME6dN/uBteklDQ4N2FSoRcDAIFytrXKysCPw3CH0dHeoVK6EsN3LtKmxMTeldpz56Ojp42dmrrMf4RXLs1eUzd++kYsFC2JmZE5+cxK4L5zh99zbTOnXNtfiV21CtBgG7d+JibYOLjS2Bu3eir6NLvVcmlxi5ZCE2Zub0fpGgC9i9k8IuBXCytiE1LZXDly+xNfg4P7bOGDfXSF8fr9ce4Bjo6WFmZKy2/L3jbtWKgMWLKeDsnHEdWbQIfT091Wvfb79lXEdeJOjatmpF9969CVyyhOpVqhB08CDHT55k/otxk40MDfHy8FB5L319fcxNTZXLExISWLBoEVUrVcq8jqxfT8jz59R+5UHQe29X/QYEbNqAi709LvYOBG5cj76uHvUqZg6FMnLWTGwsLOndNuOB2qLNm5i9ZhW/9+qDg40NoS8egBvq62P4Yv8aF7iAnUcOM2HAIAz1DZRljA0N0X/Pbult2rfl95Gj8Snsi1/RImx6cX1o3rI5ALNnzCL0+XN+HjUCgGZftGDdqrVMnzyVJs2bvbg+bGbkH5kNArI/vg2VramPHz0GCnBxLcCjhw/5e+pMXFwL0LBp4/faji/atGL8b2Mp6OONb5HCbN+4hZBnz2jUIuN+b8GsuYSFhjL4l2EANGrehE1rNzBn2t80aNqIKxcvs3PLdn789WflOstVqsD6FWvwLOSFT+GMbqqL5gZQvnJFZVI3IT6Bxw8fKes8ffyEW9dvYmJqgq197o4PKcTrJBkn/m/Mnz+f2rVrqyXiIKNl3JgxYzh9+rTaa4UKFWLXrl0MHz6csmXLYmBgQLly5WjXrh2ampqsWLGCvn37UqRIEby9vZk2bRrVq1d/p9hyeg/ImNDA1taWsWPHcvv2bczNzfH392f48OHZrjMgIIBvvvmGatWqYWdnx++//84vv/yifF1DQ4Nt27bx008/8fXXX/P8+XPs7e2pWrUqdnZvd/Hp0aMHZ8+epU2bNsof9z179mT79u0fVOebb77B0NCQ8ePHM2TIEIyMjChatCj9+/fPMZ6+ffsSHR3NwIEDCQkJoXDhwmzatImCOcwq+bpffvmFO3fuUK9ePQwNDenevTvNmzcnKipKpVzNmjWxtLTk2rVrtG+v+qS0Xr16bNmyhdGjR/PXX38pWwt980qXi9zmWcaPpLh4Tm/9l/ioWCwdbanfuz0mVuYAxEfFEheeuQ3paWkcX7ObuMgYtHW0MXe0oV7vdhQo+vaf1cdQtVYNoqOiWR64hPCwcFzd3Rg1fozyBik8LJznz0JU6vTtmvlE/ua16wTt3oetvR0Ba94+KfuuunTqRFJSEn+OH09MTAxF/PyYMXWqSgu6p0+fqnQRKF6sGH/89huz5sxh9j//4OzkxNjff1cZa3LNi/Etv+up+qR55M8/06RxYzQ1Nbl79y5btm0jMjISMzMzCvv6Mnf2bDxf+zGjFnPnzhkxjxuXGfP06TnHXLw4f/zxB7NmzWL27Nk4OzszdswYlZjr1q1LVFQU8+bNIzQ0FE9PT6ZOmaIcn1EZ89atmTEXLszcf/7B0zP3WgQBVK5ZjZjoGFYtXEZEeDgF3N34adwo5f4TERZB6LPnKnUGfpvZMvDW9Zsc3BOEjZ0tc1YG5mps76JW3dpER0WzcF5G8tDd04NxUyZg75CROAgLDePZ08wB0x2dHPlrygSmT57G+tXrsLKxpt+g/lSvmfkjNSExkUnjJvL8xSQ+BVxd+Xn0CGrVzXr81I/Jx8aVGS0yH7z0rZzxkGDblaP8sW9hdtU+inLVKhEXHcumpWuIiojAydWFH34bjrVdRvIwKjyCsJDMQd6Dtu0mLS2NxTPnsXhmZtK6Uu1qfDMoI8m9b/NOUlNSmfn7RJX3atahFc07ZT0Z0ruoVbc2UVFRBM5boNx/xk+diP2LYzKr/Wf81IlMnzSVdavXYm1jTf9BP1C9Vub+E/o8lK4dMh8GLl+8jOWLl1HCvyQz/slIqoSHhfPbiFGEhYZhZGyMZ0FPJk6brDaz64fqXLkqSSkpjNuykZjEBPycXJje+WuVFnRPoyLfeazR8LhYRq5bRWhMDMb6+njZ2TOtU1fKeeX+dbJzrToZ27BmJTHx8fi5ujH9+94qLeieRkSobENicjLjVq8kJCoSPR0dXG3tGN3pK+r6Z936Mi906dAh4zoyaVLGdaRwYWZMnqzSgu7ps2cqrZOKFy3KH7/+yqy5c5k9b17GtW/0aJXhH95EU1OTu/fusWX7diKjojAzNc249s2c+cZr39vo3LgpScnJjAtcQExcHH6eXkz/cbhKC7qnYaEq38eaPbtISU1l6FTVZPO3X7Ske8uMc9jaPbsB6PH7aJUyI7r3oEm16u8Va8b1QfX4/mvKhByP77+mTGT65KmsX70Oaxtr+g36QeX6EPo8lK87fqX8e8WSZaxYknF8T5+TMSFaXGwcc2bO4nnIc0xMTaleszrf9vzuvVvPVqtdg+joaJYGLCIiLBxXDzd+mzAWO/uM61x4WDghr9zv2Ts68NuEscyZNpMt6zZiaW3F9/17U7lGZiv89l06oaGhwcJ/FhD2PBQzC3PKVarAV927Kctcv3qNoX0yrzf/TJ8FQO0G9Rj089D32pZP1f/zrKWfKg2FtNEUQohP1oSgvEsgfSyDqnfg5vMH+R3GB/GycSEmIn9b5OSGl91U/8tMTE3fumXcp8zPwZNn0W8/M9+nys7Umkozs++S9l9wuNdsjtw5n99hfLCK7sV4HvPpzDT9PmxMLIleuS6/w/hgpm2+IHrHnvwO44OZ1q9NzPPnby74CTOxsSH65Jn8DuODmZYuSUh01jNo/1fYmlpxJ/TRmwt+4tytnd5c6BN09dmd/A4hWz52uTfL9n/J/9eohUIIIYQQQgghhBBC5CPppiqEEEIIIYQQQgjxmdJAuql+aqRlnBBCCCGEEEIIIYQQH4kk44QQQgghhBBCCCGE+Eikm6oQQgghhBBCCCHEZ0pTQ9phfWrkGxFCCCGEEEIIIYQQ4iORZJwQQgghhBBCCCGEEB+JdFMVQgghhBBCCCGE+ExpaMhsqp8aaRknhBBCCCGEEEIIIcRHIsk4IYQQQgghhBBCCCE+EummKoQQQgghhBBCCPGZ0pRuqp8caRknhBBCCCGEEEIIIcRHIsk4IYQQQgghhBBCCCE+EummKoQQQgghhBBCCPGZ0kC6qX5qpGWcEEIIIYQQQgghhBAfiSTjhBBCCCGEEEIIIYT4SKSbqhBCCCGEEEIIIcRnSkNmU/3kSMs4IYQQQgghhBBCCCE+EknGCSGEEEIIIYQQQgjxkUg3VSGEEEIIIYQQQojPlKZ0U/3kaCgUCkV+ByGEEEIIIYQQQgghct/98Cf5HUK2Clg65HcI+UJaxgkhxCdsUfC2/A7hg3Uu05CQ6LD8DuOD2JpasfLM7vwO44O1KVmHqMUr8zuMD2LWqQ3LTu/M7zA+WHv/eiw+uT2/w/hgnUo34Mid8/kdxgep6F6MSjN75HcYH+xwr9msOrsnv8P4IK1L1Cbm/oP8DuODmRRw4dyj6/kdxgcr7lSICUFL8zuMDzKoegf2Xg/O7zA+WK1CZf7z1772/vX4+8ja/A7jg/Ws2DK/QxCfCUnGCSGEEEIIIYQQQnymZDbVT49M4CCEEEIIIYQQQgghxEfyQS3jTp06xZUrV9DQ0MDX1xd/f//ciksIIYQQQgghhBBCiM/OeyXjQkJCaNu2LUFBQZibm6NQKIiKiqJGjRqsWLECGxub3I5TCCGEEEIIIYQQQrwjDaSb6qfmvbqp9unTh+joaC5dukR4eDgRERFcvHiR6Oho+vbtm9sxCiGEEEIIIYQQQgjxWXivlnE7duxgz549+Pr6KpcVLlyYmTNnUrdu3VwLTgghhBBCCCGEEEKIz8l7JePS09PR0dFRW66jo0N6evoHByWEEEIIIYQQQgghPpymzKb6yXmvbqo1a9akX79+PH78WLns0aNH/PDDD9SqVSvXghNCCCGEEEIIIYQQ4nPyXsm4GTNmEBMTg5ubG56ennh5eeHu7k5MTAzTp0/P7RiFEEIIIYQQQgghhPgsvFc3VRcXF06fPs3u3bu5evUqCoWCwoULU7t27dyOTwghhBBCCCGEEEK8Jw3ppvrJea9k3Et16tShTp06uRWLEEIIIYQQQgghhBCftbdOxk2bNu2tV9q3b9/3CkYIIYQQQgghhBBCiM/ZWyfjJk+e/FblNDQ0JBknhBBCCCGEEEII8QmQ2VQ/PW+djLtz505exiGEEEIIIYQQQgghxGfvvWZTfSk5OZlr166RmpqaW/EIIYQQQgghhBBCCPHZeq9kXHx8PN26dcPQ0BA/Pz/u378PZIwV9+eff+ZqgEIIIYQQQgghhBDi/Wh8wv/9v3qvZNywYcM4d+4cQUFB6OvrK5fXrl2blStX5lpwQvw/u3btGmPHjiUpKSm/QxFCCCGEEEIIIUQuea9k3IYNG5gxYwaVK1dG45WBAAsXLsytW7dyLTgh/l/FxMTQokUL3N3d0dPTy7X1urm5MWXKlPeuf/fuXTQ0NDh79myuxSSEEEIIIYQQQvw/eesJHF71/PlzbG1t1ZbHxcWpJOeEyA1HjhyhSpUq1KlThx07drxz/V9//ZUNGzb8pxJIXbp04ZtvvqFt27b5HYr4Dzm5+xDHtu0nNjIaGyd76nRsTgEfzzfWe3D9Not/n4mNsz3fjhmsXH5m/1EuHAzm+cOnANi7O1O9dSOcPF1zLeb1q9eyfMkywkLDcPNwp++AfhQvWSLb8mdOnWHGlGncvX0HK2tr2nfuQPOWLZSv37l1m/lz5nHt6lWePnlKnx/60bp9G7X1PA95zqzpMzl+9BhJiUm4FCjAj78Mw9vXJ1e268Sufzm0eS+xkVHYODvQoHNL3Hy9six77+otdi3bSOjjp6QkpWBuY0npWpWo2KimSrkj2/YTvPsgUaERGJoY4VeuJLXbNUVHVydXYs6KQqFg7r/72XDmFDGJCfg5OjO4QWM8bdTvAbKy69IFfl6/mqqFfJjQur3KayHR0czYt4sjt26QlJJKASsrfm7cHF8Hx7zYFBXBuw5yZMteYiKjsXW2p17nlri+xbFy/9ptAkdPw9bFgR5/Ds3zOF91cvchjm7dpzy+63Zq8XbH97XbLPp9BrbO9nw7dohy+el9R7lwKJjnD54AYO/uQo02uXt8Z2Xf5p1sX7ORyPBInFydad+jK4WK+GZZ9uSh4+zfupP7t++SmpKKUwFnmnVsTdHSJZRlDmzfw+E9B3h07wEAbl4etOzaDg/vgnm6HW+juIMX7UvWxce2ANZG5vy4bRYH75zL77CUju/8l0Ob9xAbGYWtswMNunyZw3nqJruWbuT542ekJCVjbmNJmdqV1c9TW/dx4uV5yjTjPFWnXbNcO08pFAr+WbyI9Vu3ERMbg5+PD0P79MXTzS3HensP/svswEAePnmCs4MDPbt+TY3KlZWvx8XHMzswkP2HDxERGYm3lxcDe/bEzzvzmhCfkMD0efM4cOQwUdHRONjZ07ZFc75s0vSDt2vnxq1sWrmOyLAInN0K8FWvb/Et5pdl2YiwcBbNms/t67d4+ugxDVo04ave36qVi4uNZfn8xZw4eJS4mFhsHezo1KMb/uVLf3C82bkcFMy5XUdJiIrBwtGW8q3r4lAw63PK42t32TppkdryVqN6Ym5vrbb8VvBF9s1bh2txb+r2VL+u55YDW3ezZ902oiIicSjgRKtvO+Lll/W9wZkjwRzcvpeHt++RmpKCQwFnGrX/gsL+xZRlJg/7nRsXr6rV9StdnF4jB6stzyv/xeveuX3HOL39IHGRMVg52VK1fSOcCrm/sd7jG/dY8+dcrJzs6DC6j3J5WmoaJ7cGceXwGWIjorFwsKZSq/q4FS2Ul5vxyZA8zafnvZJxZcqUYevWrfTpk7Fzv/xi586dS4UKFXIvOiGABQsW0KdPH+bNm8f9+/cpUKBAfoeU59atW5ffIfznpKSkoKOTd0mJT93lY2fYvWQD9b/6EpdC7pzed4QV4//hu3E/YmZtkW29xPgENs1ehrtfQWKjYlReu3flJoUr+ONcyB1tHW2ObtnH8nGz6f7nUEwtzT845r279jBt0lQGDB1E0eLF2LRuA4P7DWTxqqXY2durlX/86DFD+g+kSfOm/DJ6JBfOnWfSuAmYW5hTvWaNjO1JTMTByZHqtWswfdK0LN83Jjqant98R8lS/oyfOgkLCwsePXyEsYnxB28TwIUjp9i+cC2Nu7WhgLcHwXsOseTPv+k98WfMrS3Vyuvq6VKuXlXsCziho6fL/Wu32DRvBbp6upSunfFj8dyhYPYs30jz7zrgUsiDsCchrJ+9GIAGXVrmStxZWXT0EMuPH2VE0xYUsLRiwaED9Fm6kNXf98XoDa12n0RGMm3PTkq4qP8Qi05I4NuF8yjl6s7Utp2wMDLiYUQ4Jnr6Wawpd108epodi9bR6OtWuHh7cGrPYZb+OYteE4ZjlsX381JifAIb/l6MR5FCasdKXrt09DS7Fq+nQdfM43v5X3Po8dewNx7fG2cvxd2vIHFZHN9+Ffxx7uyGtq4OR7fsZdmfs/hu3I+5cnxn5fiBwyybE0CnXt9S0M+boG27mfTzH/zxz2SsbG3Uyl+/eBk//+K0/Ko9hsZGHNq1n6m//skvU8bi6pXxg+zq+UuUr14Zr8KF0NHVZdvqjUwY/jt/zJmEhbVVnmzH2zLQ0eNm2EO2XT3CmAY98jWW12Wcp9a8OE95cnLPIRaPnUmfSb9keZ7S0dOjXP1q2BVwRFdPj3vXbrFp7nJ09HQp8/I8dfAEu5dvpHmPjhR4cZ5aNyvjPNWwy5e5EvfClStZtnYtIwcNpoCzM/OXLaXX0KGsDQjAyNAwyzrnL19m+O+/0+Orr6hRqTL7Dx/ix99/Y/7kKRTxzUgE/z5pIrfu3mX00B+xsbJi29499BwyhNXzF2BrnZEYmjTrb06eO8foH3/E0c6eY6dOMm7aNKytrKhesdJ7b9OR/QcJnDmPb/r1wLtIYfZs3sGYH39lcsBMrO3UH3ykpKRgam7GFx1bs3XNxizXmZqSwu+Df8HU3JwBv/6IlbU1Yc+fo5/NZ5QbbgVf4uiqnVRq3xA7Txeu/nuaHdOX0erXnhhbmmVbr9XoXujqZ15P9E3UY4wJi+T4mt3Ye+Xtb4CTB4+xZt4S2vb4Co/ChTi0Yx8zfx3PLzPHYWmrniC8eekqPiWK0LRTKwyNjTi65wCzfpvIkAmjcPF0A6D78P4qkx3GRccypu9w/CuVy9NtedV/8bp3/fh5/l22lRqdmuJY0JULQSfYOGkhHf/oj6mVebb1kuIT2TV3NS6+nsRHx6q8dnTdbq4ePUutr1pg6WDDvYvX2TJ9Ca1/6oGta94/CBTide/VTXXs2LH89NNPfP/996SmpjJ16lTq1KlDYGAgf/zxR27HKP6PxcXFsWrVKr7//nsaN25MYGCgyuuBgYGYm5urLNuwYYMyQRwYGMioUaM4d+4cGhoaaGhoKNdx//59mjVrhrGxMaamprRu3Zpnz54p1/Prr79SokQJFi9ejJubG2ZmZrRt25aYmMyLUVJSEn379sXW1hZ9fX0qV65McHCw8vWgoCA0NDTYuXMnJUuWxMDAgJo1axISEsL27dvx9fXF1NSUdu3aER8fr6xXvXp1+vfvr/x7yZIllC5dGhMTE+zt7Wnfvj0hISE5fnYhISE0adIEAwMD3N3dWbp0qVqZqKgounfvjq2tLaamptSsWZNz597+qX1aWhrdunXD3d0dAwMDvL29mTp1aq7UCQgIwNfXF319fXx8fPj777+Vr73sLrtq1SqqV6+Ovr4+S5YsIT09ndGjR+Ps7Iyenh4lSpRQaU35st66deuoUaMGhoaGFC9enKNHjyrLhIWF0a5dO5ydnTE0NKRo0aIsX75c+fqcOXNwcnIiPT1dJd6mTZvSpUsX5d+bN2+mVKlS6Ovr4+HhwahRo/J05unj24MoUb0cJWuUx9rJjrqdWmBqZc7pvYdzrLd9wWr8Kvjj5OWm9lrznp0oXacy9q5OWDva0eibNijSFdy9dCNXYl65bAWNmjWhSfOmuLm70Xdgf2ztbFm/Zn2W5TeuW4+dvR19B/bHzd2NJs2b0qhpY1YsWaYs4+tXmF79elO7bh10s2mJsXThEmzt7Bg+8mcK+xXGwdGB0mVL4+TsnCvbdWTrPvxrVKBUzYrYONnTsMuXmFpZELz7YJblHdxdKFapNLYuDljYWlG8Slm8ivly72rmsA8Prt/BpZAHxSqXwcLWCq/ivhStWJpHt+/nSsxZUSgUrDhxlK8qV6WGT2E8be0Y2fQLElNS2HnxfI5109LTGbFhDd9WrYGThXqyaNHRg9iamjKiaQv8nJxxNLegrLsnzpbZ/yjILce27qdkjfL4v/h+6ndpiZmVBcG7D+VYb8u8lRSpVBrngm55HuPrMo/vClg72VO30xeYWplzak/OMW+bv4oiFUvhlEXMLXq9OL7dnF8c321fHN/X82grYNe6LVStV5NqDWrhWCCjVZyljTX7tuzKsnz7Hl1p2KoZHt5e2Ds58GXX9tg5OnD2+Ellme+G9qNmk3oU8HTHwcWJrv2+Q6FQcPnsxTzbjrd17P4l5h7fxIHbZ/M7FDVHtu7Fv2YFSteqhK2zPQ2/yjhPndiV9XnK8cV5ys7FEQtbK0ooz1M3lWUe3LhDAW8Piqucp0rxOJfOUwqFguXr19G1XXtqVqmCl7s7owYPITEpkR379mVbb/m6tZQrVYqu7drjVqAAXdu1p2zJkix78eAzMSmJfQcP0vfbb/EvVgwXJye+69wFJ3sH1mzepFzP+StXaFynLqWLl8DR3p4vGjWmoKcnV65/2DGzZfUGajaoQ61G9XB2deGr3t9ibWvNrk3bsyxva29H197d/8fefUdFdbwNHP/Se+8gVaQKNuy99941GktMjBpjNNGoMZpYkl8SSzTFxNhj772jRAULNlSwgCCooNJ7WXbfP9CFZRdEwZK88zlnz4G7c+/O3Vv3uc/M0LJDG/QNVAfXAg8dJzM9ky/mzsSrpg9WttZ4+fniUv3FWUWv6vrxEDyb1sGrWV3M7KxoPLAjhmYmhAeFljufnpEB+iaG8pe6uuLPU6lUysmVu6jbvRVGVmU/fKgKgbsP0aR9K5p2bI2dowP9xwzD1NKCfw6dUFm+/5hhdOjbDReP6ljb29Jz+ECs7Wy5fuGKvIyBkSEmZqby162rN9DW0aZuswavdV1K+jde9y4fPYNvi3rUbFkfc3trWg7phqG5CdcDz5c7X+DaXXg2qoWdu6PSe7dCrlC/W0tca3liYm2Of5tGONesweXD5X8Pwrvpt99+w9XVFV1dXerVq8fp06qvX88FBQUp/DZbvny5UpkdO3bg4+ODjo4OPj4+7Nql+jdJVXmlYFyTJk04e/Ys2dnZVK9enaNHj2JjY0NISAj16tWr6joK/49t2bIFT09PPD09ee+991i9ejUymazC8w8cOJApU6bg6+tLfHw88fHxDBw4EJlMRq9evUhOTiYoKIhjx44RFRXFwIGKae9RUVHs3r2b/fv3s3//foKCghRGDJ46dSo7duxg7dq1XL58GXd3dzp27EhycrLCcubMmcMvv/xCcHAwcXFxDBgwgCVLlrBx40YOHDjAsWPHWLZsWZnrkZ+fz9y5c7l27Rq7d+8mOjqaESNGlLvuI0aMICYmhsDAQLZv385vv/2mEMCTyWR07dqVhIQEDh48yKVLl6hbty5t27ZVqn9ZpFIp1apVY+vWrYSHh/P1118zY8YMtm7dWql5VqxYwcyZM5k/fz4REREsWLCAWbNmsXbtWoVlTZs2jYkTJxIREUHHjh35+eefWbhwIT/99BNhYWF07NiRHj16cPeuYvBo5syZfP7551y9ehUPDw8GDx4sD5Tl5uZSr1499u/fz40bN/jwww8ZNmwY588XXfz79+9PYmIiJ0+elC8vJSWFI0eOMHToUACOHDnCe++9x8SJEwkPD+ePP/54rQ8rCiUS4qMf4FrTU2G6W01PHtyNKXO+a0HnSXmcSIs+HSv0OQV5+UgLpegZVv7JekFBAXdu3aZBQ8Wb0foNG3Aj7LrKeW5ev0H9UuUbNGrIrfBbLxXoPHP6DJ7eXsz6cibdO3Rh1ND32btLdXbBy5JIJMRHx1HdX7HZnbu/N7F3oiu0jPjoOOLu3MPFp7iZnbOXG/HRcTyIjAEg+XEid67cxKOu6mZMVeFRagpJmZk0citutqatqUldZxfCHsSVO+/K06cwNTCgZx3V9wSn79zG286BL3dsoeOi//Heit/Yfbn8H2xVoVAi4VF0HNX9FZscufl78aCc7XPl1DlSHifSqm+n111FJc+Pbze/UnX28yr3+L4adJ6UJ69wfBsYVKa6ZZIUFBBz9x6+dWspTPet609UxO0KLUMqlZKbk4NBOVmseXn5FEok5Zb5/04ikfDoXhzupc9TtbyJu3OvQst49Pw85V18nnLyrM6jeyrOU3VqVkm9HybEk5ScTKOA4vOKtrY2df39CQu/WeZ8YeHhNCz1+6RRQIB8nsLCQgqlUrS1tBXK6Ohoc/VGcVC3tm9N/gkJ5kliIjKZjNCrV4l98IDGAa/e7FNSUMC9O5HUCqijMN0/oA63b0a88nIvBZ+nhq8XK39ezpi+w5gyajw7N2xFWlj4ysssT6GkkMTYeBx8FJs9Ovi48Tiq/OvFznl/8vcXiziwaB2Pbiufh6/s/wddI328mtVRMXfVkRRIiI2MxrvU/updpyb3Iir2ELLoHJWLvlHZ59HgY6eo16IxOrqvPxMc/r3XvScxj3DyVexuwNnXnfio+2XOd/P0JVKfJNOwZxuV7xcWSNAo1YpGU1uLR+VcS/9LtNB4Z18va8uWLUyaNImZM2dy5coVmjdvTufOnYmNVf3wJzo6mi5dutC8eXOuXLnCjBkzmDhxIjt27JCXCQkJYeDAgQwbNoxr164xbNgwBgwYIP8N+Dq8UjNVAD8/P6UfxoJQ1VauXMl7770HQKdOncjMzOTEiRO0a9euQvPr6elhaGiIpqYmtiWavR07doywsDCio6NxdCx6crJ+/Xp8fX25ePEi9evXB4ouqmvWrMHIyAiAYcOGceLECebPn09WVha///47a9asoXPnzkBREOnYsWOsXLmSL74o7gdi3rx5NG1a1IRh9OjRTJ8+naioKNzc3ADo168fJ0+eZNo01X0xjBo1Sv63m5sbS5cupUGDBmRmZmJoqPyD486dOxw6dIhz587RsGFD+Xfp7V18433y5EmuX7/OkydP5INE/PTTT+zevZvt27fz4YcfvvD71dLS4ptvvpH/7+rqSnBwMFu3bmXAgAGvPM/cuXNZuHAhffr0kZd5HtQqmX02adIkeZnn9Z82bZq8r73//e9/nDx5kiVLlvDrr7/Ky33++ed07doVgG+++QZfX18iIyPx8vLCwcGBzz//XF72k08+4fDhw2zbto2GDRtibm5Op06d2LhxI23btgVg27ZtmJuby/+fP38+X375pbyubm5uzJ07l6lTpzJ79uwXfq8vKzsjC5lUiqGJkcJ0AxMjMlPTVc6TnPCUk1v2M2zWJ6hrVOwieHLLfozMTHD1rXzfGmmpqRQWFmJWKhPKzMKc5CTVweCkpGQaWJQqb25OYWEhqampWFoqNyFRJf7hI/bs2MWAIYMYNnI4ETcj+HnhYrS1tenUtfOrrdAz2emZSF9yWzz307ivyErPRFpYSOt+XajXpon8Pb8mAWSlZ7Jy9mJkyJAWSqnfvjktenaoVH3Lk5RZ1LzDvFRwxtzAgPi01DLnuxZ3n71XL/P3mI/LLPMwJYWdly4ypGFjRjZtwc2HD1h49CBampp09a9dFdVXKTtd9bFiaGJEVBlNcJLin3Bi0z5Gzvm0wsdKVXp+fBuo2qfSyjm+N+9j+NcTK1znwM37MTI3wbXm6+k7JyM9A6lUirGZqcJ0EzNTbiSnVmgZR3bsIy83jwYtmpRZZvuqDZhZmONbx68Stf1vKz5PGStMNzQxIuMF56kfP55ZfJ7q35WAtsXNM/2bBpCdnslfXy+Sn6catG9Oi15Vc55KSk4BwMJUMTvKwsyM+BItG5TmS0nBwkx5nqSUouUZ6Ovj7+PDXxv+xtXJCXMzM46cPMmNW7dwdHCQz/PF+PHMW7yILoMHoaGhgbq6Ol99NpnaNV99X0tPS0cqlWKi4rhIreBxocrj+ASeXgmjWbtWTP9uNvEPHrFy6XKkhYX0Gz74lZdbltzMbGRSGfrGitcLPSMDctKzVM6jb2JI8/e6YelsR2GBhLvnr3Ng8Xq6TX4fO4+i7g0SImO5ffYKfWZ9VOV1Li3z2TnKyFSxSa2xqQnpqakVWsaJ3QfJz8ujXjPVTVBj7kTx6P4D3puo3Mff6/JvvO7lZGQjk0rRN1b8jaNnYkTWDdWB0ZSERM5uP0z/6R+VWWenmjW4cuQMDh4umFqbExsRxb0rEchKtXYR3n2LFi1i9OjRfPDBBwAsWbKEI0eO8Pvvv/Pdd98plV++fDlOTk7ygQy9vb0JDQ3lp59+om/fvvJltG/fnunTpwMwffp0goKCWLJkiUIrqapU4WBcenr5F+eSjI2NX1xIEF7g9u3bXLhwQd5/mqamJgMHDmTVqlUVDsaVJSIiAkdHR3kgDopGAzY1NSUiIkIejHNxcZEH4gDs7Ozk2WVRUVEUFBTIg2xQFGhq0KABERGKTzP9/Ys7crWxsUFfX18eiHs+7cKFC2XW98qVK8yZM4erV6+SnJwsbyIZGxuLj4+PyvXT1NQkoMTTWi8vL4UmvZcuXSIzMxMLC8U+dXJycl5qVOTly5fz119/cf/+fXJycsjPz6d27dqvPM/Tp0+Ji4tj9OjRjBlTfLMikUgwMVG8QSq5funp6Tx69EhhewA0bdpUqeltye1hZ2cHFDXr9fLyorCwkO+//54tW7bw8OFD8vLyyMvLw6BEQGLo0KF8+OGH/Pbbb+jo6LBhwwYGDSq6OYei7/bixYsKmXCFhYXk5uaSnZ2Nvoo+W55/TkkvPZJuqY5ZZajurFUqlbL71/U079sJC7uKdcQfsv8EN0Ou8N7M8WhW4YABStWTyZSnlSxP6XWUqZxeHqlUipe3Fx+NL+rDycPTk+h799i9Y2elg3ElKlqK7IUd546eM4n83Dzi7sZwbNMezG2t8G9atI9H37zDP7uO0G30QKq5O5OUkMihtds5ZWpMq75VU+fD16/x3cF98v8XDxr6bFVKfeeysr/vrLw8vt69gxlde2CqX3ZmgFQmw9vennFt2gPgaWvHvcQn7Lh04bUG44qVXieZim1WtK/s/GUdrfp1rvCx8rqoPFZUVFoqlbLr13W06FvxOgfvO8HNkMsM+2pClR7fqiivhkzFyik7d/IMu//exsTZUzE2Vd3/1MFtezh/6gzTfvgGLW1tlWWEEkp97UWbovxt8cE3n5GXm8eDuzEc3bgHi1LnqaBdh4vOUzVcSE54ysE12zHccYjWr3CeOnTiBAuWLJb/v2Re0TW1dB1lshefX5WPecUp3077km9/+onOgwehoa6OZ40adGrThlslMus3797F9YgIFn07FzsbGy6HhfG/ZUuxtDCnYd3KtQxSqv8LroUvIpPJMDYz4aPJ41HX0MDNw52UpGT2btn5WoJxr8LU1lJhoAab6o5kJacRdiwEOw9n8nPzOLlqN82HdUO3CjLyK0p5/6rYPcbFoGAObNzF2K8+UwroPRd89BT2ztVw8XjxwAlV79943VNxXKgoJ5VKOfzHFhr1aoeZisE/nms5pBsn1uxi/YzFoKaGibU5Ps3qEn7mctVWXHhpZf0OUvVbKD8/n0uXLvHll18qTO/QoQPBwcEqlx8SEkKHDooPhjp27MjKlSvl/Y6HhITw2WefKZV5HsB7HSocjDM1Na3wCByFrykFWvj/ZeXKlUgkEhxKPJWUyWRoaWmRkpKCmZkZ6urqSs1WCwoKXrjssm7cSk8vPSCAmpqaPBD2/HMrclNYcjlqamrlLre0rKwsOnToQIcOHfj777+xsrIiNjaWjh07kp+fX+b6qapbSVKpFDs7O06dOqX0Xul++MqydetWPvvsMxYuXEjjxo0xMjLixx9/LDed90XzPP8eVqxYIc/qe06j1JMuAxXNqV5le5T83IULF7J48WKWLFmCn58fBgYGTJo0SeG77t69O1KplAMHDlC/fn1Onz7NokWL5O9LpVK++eYbhay953TLaJbw3XffKWQMAsyePRu3ri/uU0TfyAA1dXWlzKvstAylbBqA/Jw84qPjSLj/kCNri4LdMpkMZDIWDJ/CkGljcSnRNODcgZOc3XucIV9+jI1T1XRwa2JqioaGhlIWXEpyilK23HMWFuYkJyUpTEtNTkFDQwOTMm58VS7H0gJnN8V+c5xdXAgKPFXhZZRF37iov5vMVMWnzVlpmSq3RUlmzzqHtnFyIDMtg5PbD8p/5J7YeoBazRvIs+VsnBwoyMtj74pNtOjdUamPnVfR3MMLX4fifvPyn13Lk7IysSzxUCIlOwtzA9VNAB+mJBOflsqULcX9+EmfnY8az5/Dto8nUs3cHEtDQ1wtFTvsd7G04uSt8EqvR3n0jZ8dK6UyyrLSMzE0VnWs5PLoXizxMQ84uGY7UHysfDt0EsOmj3ttmWTyOsuP71L7VLrqfSo/J5f4e3EkxDzk8NodCnWeP2wyQ74cq5DdGnIgkLN7jzF0+rgqO75VMTI2Ql1dnbSUVIXp6alpmJiVf/yeDzrL6iW/M27GZHxLjFJY0qHte9m/eSdffPc1jm6vd0TYf7vi81Tp4yBDKXumtOfnKVsnBzJT0wncdqDEeWo/tVo0kGfL2To5kJ+Xz94/N9LyFc5TLRo3pqZXcdO6/Gf3d4kpyViWeJCYnJqKuYq+KZ8ryoJTvNYkp6YozFPN3p4/Fy0iJyeHrOxsLC0smD5vLvbPWlXk5uXx66pV/DRnDs0aNgKghpsbd6Ki+HvbtlcOxhmbGKOurk7qs6y/59JS05Sy5V6GqbkZmpqaChlCDk7VSE1OQVJQgGYVD3ila6iPmroa2aWy4HIystAzrnjTd2u3akSeL+qqIuNpCplJqRz5dbP8/ef3t399PJcB347H2Krq+hk1fHaOSi91jspISyszuPZc6Olz/L30Lz748hO8aqtulp2fm0fo6XN0G/r6Bl1S5d943dMz0kdNXV1p4KGc9Ez0TZTvPwpy83gS85CnsfGc+nufQp2Xjv6K3lNG4uhTHX1jQ7pPHIakoIDczGwMTI05u+0IxuUMhPRfoiF7d0dTLet30Jw5c5TKJiYmUlhYiI2NjcJ0GxsbEhISVC4/ISFBZXmJREJiYiJ2dnZllilrmVWhwsG4kv0jxcTE8OWXXzJixAj56KkhISGsXbtWZVqgILwsiUTCunXrWLhwoVIUu2/fvmzYsIEJEyZgZWVFRkYGWVlZ8sDM1atXFcpra2srBYh9fHyIjY0lLi5Onh0XHh5OWlqaQlPO8ri7u6Otrc2ZM2cYMmQIUBQIDA0NVRh8obJu3bpFYmIi33//vbyuoaHl963k7e2NRCIhNDSUBg2Kgjm3b98mtUSafd26dUlISEBTUxMXF5dXqtvp06dp0qQJ48aNk097UVbdi+axsbHBwcGBe/fuyftgqwhjY2Ps7e05c+YMLVq0kE8PDg6WfwcVcfr0aXr27ClvHi2VSrl7967CfqGnp0efPn3YsGEDkZGReHh4KPSXWbduXW7fvo27u7vS8ssyffp0Jk+erDBNR0eHLWGqOw0uSUNTEzvXakTfuINX/eIfqtE37uBRT/mmUEdPhzHfTVWYdun4We6H36XPxBGYlri5DdkfyNk9xxg87SPs3apuFDMtLS08vDy5eP4CLVq3lE+/eOEizVo0VzmPr19Nzp5WHJDiwvkLePl4oalZ8V4X/Gr5E3dfsU+JuNg4habsr0pTUxM7V0eirt/Cp0Fx31hR12/hFfASTZlkMgoLivvBK8jPVwoqq6mr8xJdaL6QgY6OwgipMpkMC0NDzt+LxNO2KIO0oFDC5fsxTHiW0Vaas6Ulmz4crzDt91MnyM7PY0qHLtg8axbn7+jE/aREhXKxSUnYmphW3QqpoKGpib2rI/fCbuNdv3j73Lt+C896yttHR0+Xj39QfPJ68egZosPvMGDSKEytXv9oncXH923F4/v67TKOb10+/F6xy4NLx88Qc/MufT8dqXR8n9l9lMHTxlbp8a2KppYWLjXcuHkljHolRhEMvxJG7Ub1y5zv3MkzrFr8G2O/nESthqoDHoe27WHfph1Mmf8Vrm8l4+TfRVNTE3s3R6LCbuHToLZ8elTYLbwCVAc7VZFR1LfTcwV5+aipKQbc1CtxnjLQ11cYIVUmk2Fhbs75S5fxci96YFRQUMDlsDA++aDsZn/+Pj6cv3SZoX2LR3Q9f+kS/j7KfW7q6emhp6dHekYGIaGhTHyWoS+RSJBIJMrrp6GOVPrqJ2JNLS3cPNwJu3SFBs0by6eHXbpK/SavPtqmZ00fzp4IQiqVyoOg8Q8eYWZhXuWBOAANTQ0snex4GHEP1zrFAdSHEfdwruVZzpyKkuIS0HsWbDGxtaTv14qjEIfuOUlBbh6NB3bC4AVB/JelqaWJk7srEVduULtx8Tnp1tUb+Jdx7oGijLi/l65g5Ofj8atfdr92l86cR1IgoUGrVx9591X8W6971i72xN6MxL1e8XEaGx6JW23lFkHaujoMnTtRYVpY4HkeRETRZfwQTEoFbTW1tDA0M6FQUkjkpRvUqC+6NXjbyvodVJ6XzZJWVb709FfLvH51Ff710rJl8Y+lb7/9lkWLFjF4cHGac48ePfDz8+PPP/9U6NNJEF7F/v37SUlJYfTo0UpNE/v168fKlSuZMGECDRs2RF9fnxkzZvDJJ59w4cIFpRFXXVxciI6O5urVq1SrVg0jIyPatWuHv78/Q4cOZcmSJUgkEsaNG0fLli0Vmj6Wx8DAgI8//pgvvvgCc3NznJyc+OGHH8jOzmb06NFV9VXg5OSEtrY2y5YtY+zYsdy4cYO5c+eWO4+npyedOnVizJgx/Pnnn2hqajJp0iT09PTkZdq1a0fjxo3p1asX//vf//D09OTRo0ccPHiQXr16Veh7cHd3Z926dRw5cgRXV1fWr1/PxYsXcXUte7SuiswzZ84cJk6ciLGxMZ07dyYvL4/Q0FBSUlKUTtQlffHFF8yePZvq1atTu3ZtVq9ezdWrV1WOJFte/Xbs2EFwcDBmZmYsWrSIhIQEpSDt0KFD6d69Ozdv3pQH7p77+uuv6datG46OjvTv3x91dXXCwsK4fv068+bNU/m5ZaViV1TDzq3Y8/sG7NwcqebuwpWTwaQlpVC3bVEm1ckt+8lISaPH2KGoqatj7WinML+BsSEaWpoK00P2nyBo+yF6jRuGiaW5PItCW1cHbd1Xr+tzA4cMYt7sb/Hy8cbXryZ7d+3hScJjevXtBcDyX34n8elTvvrmawB69unNzq07WLb4Z7r36snN6zc4sGcfs+cXP0krKCgg5l70s78lPH36lLu376Cnr081x6KsrwGDB/Lx6I9Yt3otbdq1JeJmOPt27eGLGar7bHxZTbq2Yeev63Bwc8LRw5XQ42dJS0ymfruiIOOxTXtIT06j7/jhAJw/EoSJpTlW9kVP4+7fjuLs/hM07FR83fWsW5OQgyexc61GNXcXkhKeErh1P171/KokK04VNTU1BjVozJqzp3E0t8DJ3ILVZ/9BV0uLjjWLf7DP3rMDayNjxrdpj46mFtWtFZ8qGj3LBi05fUjDJoxes4LVZ4Jo51OTm48esvtKKDO69Hgt61JSo66t2fXreuzdHKnm4cqlE8GkJaYQ0K4ZAMc37SUjJY3e44Y9O1YUs8UMTAzR1NJSmv46yY9vV0eq1XDhcmDIs+O76Edd4OZ9ZKSk0fPj91Qe3/rGhmiWOr6D950gaPtBeo0fjqlV1R/fqnTo040VPy7DpUZ13L09CDp0nKQnibTuWvTQbduqDaQmJTPmi0+AokDcXz/9wpCxI6nuVYO0Z9lDWjra6D97AHdw2x52rdvMR9M+xdLGSl5GR08X3RLXvLdBT0uHaibFGaD2xpbUsKxGem4WjzNTypnz9WvStS07flmLfXUnHGu4EXriDGmJyTRoX3QcHN24h/TkVPpNKLqnVzpP3Yri7L7jNOrUSr5Mz3p+BB8IxM6lGo41is5TJ7bswyugas5TampqDO7dh9WbNuLk4ICjgwOrN21EV0eXTm2KO2z/+n/fY21pyYTRRX0JDerdhw8nf8aazZtp1aQJp4KDOX/5MisXL5HPE3LxIjJkOFdzJO7RI5b++SfOjo706FjUeb2hgQF1/f35ecWf6OhoY2dd1Ez14LFjfDZWMWD0srr178Wy7xbh5lkDDx8vju8/TOLjp7TvXtS0d+OKtSQnJjFhevH9T0xk0UAbuTm5pKelERN5D01NTaq5FAXVO/TozOFd+1nzywo69e5GwsNH7Nq4jc69u1WqruXxa9eYU6t3YeVsh7VbNW6dvkxmchreLYoCWRd2nSArNYPWI3sBcP34OYwsTTGzs6KwsJDI89eJvhxBu4/6A0XBMXMHxWaS2vpF15PS06tKm16dWbvod5xruOHq5c7ZwydJeZpE885FfQLvXruF1KQURkwu2uYXg4JZu/gP+o95D1cvd3nmr7a2NnqlRroNPnaKWo3qqcxGe93+jde9uh2acWTFNmxcHLBzd+J60EUyktLwa130gP3stiNkpqbTcUx/1NTVsaym+EBV39gADS0thekJUXFkpqRh5WRPZmoa53afQCaTEdClBcLb9TK/gywtLdHQ0FDKWHvy5IlSZttztra2KstramrKu2wqq0xZy6wKrzSAQ0hIiMqhYAMCAuSd6AlCZaxcuZJ27dopBeKgKDNuwYIFXL58mbp16/L333/zxRdf8Oeff9KuXTvmzJmjMPhA37592blzJ61btyY1NZXVq1czYsQIdu/ezSeffEKLFi1QV1enU6dO5Y5oqsr333+PVCpl2LBhZGRkEBAQwJEjRzArp7nEy7KysmLNmjXMmDGDpUuXUrduXX766Sd69Cj/R+vq1av54IMPaNmyJTY2NsybN49Zs2bJ31dTU+PgwYPMnDmTUaNG8fTpU2xtbWnRokWFTzpjx47l6tWrDBw4sOgmefBgxo0bx6FDhyo1zwcffIC+vj4//vgjU6dOxcDAAD8/vxdmHE6cOJH09HSmTJnCkydP8PHxYe/evdSoUaPc+UqaNWsW0dHRdOzYEX19fT788EN69epFWlqaQrk2bdpgbm7O7du35ZmRz3Xs2JH9+/fz7bff8sMPP6ClpYWXl9drPT/6NKpDdkYWZ3YV3ZxYVbNj0BcfYmJZ9DQwMzWdtMSX+/F36fhZCiWF7Fi6RmF6894daVEFo2u17dCO9LQ01vy1iqTEJFyru/HDkp+wfdaPX1JiEo8Tijvltnew54clC1m2+Gd2bduJpZUln37+Ga3atJaXSXyayKj3Rsj/3/z3Rjb/vZHadeuw7I+iQTy8fX2Y/+P3/Pnr76z9azV29nZ8MvlTOnSu2KiTL+LXpB45mVmc2nGIjNR0rB3teO/LcfKMpIyUdNISi5tMyWQyjm/aS8rTJNTV1TG3saT94J4EtCt+et6yTyfU1NQ4sWU/6clpGBgb4lmvJm0Hdq+SOpdleONm5BUU8MPh/WTk5OLr4MCyIcMVMugep6Wh/pJPDX3sHfih/2B+CzzGytNB2JuaMrl9Zzr51XrxzJVUs3FdcjKyCNp5hMzUNKwd7Rg6bax8+7zKsfK6+TauS05mNqcVju+PFOuc9LLH95mi4/vn1QrTm/fpSMsq6oewtIYtm5KVnsneDdtJS0nBwdmRz+bOwNKmKGCVlpxC0pPijMlTB49RWFjI+l//Yv2vf8mnN23Xkg8+nwBA4L4jSAok/DpvocJn9Rzan17DVA8k9KZ4WTnzS+/iAMrEZkVBhoMRIcwPfLuDoPk1qUd2xrPzVEo6No52DPtynDzrJTM1TWGfkkllHNu4p8R5yooOQ3rKf8xD0XkK4MSWfSXOU360G1R156n3Bw4kLz+P75ctJSMjg5pe3vzy/fcKGXQJT56gXiKDrZavL/NnfsXva1azfO0aqtnZ893Mr6hZ4iFbZnYWv6xcyZPERIyNjGjTrDnjR41UyLpeMPMrfl25klnffUd6Rga2NjZ8PHIUfbtVbv2atG5ORno6O9ZtJiU5GUcXZ6Z/Nxsr26KAU0pyMolPnirMM/XDT+V/37sTyZkTQVjZWPPrppUAWFpb8dUP37L2t7/44oNPMLe0oHOf7vQa9PqaSFav70teVjaXD/xDdlom5vbWdJowBCMLUwCy0zLJSi6+l5IWFnJ++zGyUjPQ1NLE1N6KjhMG4+RX8Xu2qhbQvBFZ6Rkc3LyL9ORU7JyrMW72F1g8a56dnpxKytPic9SZw4FICwvZsnwtW5YXH9ON2jRn+GfFg048fhhPVPgdPvm2ah76vax/43XPo6E/OVnZnN8bSHZaBhYONvT87H15k9KstAwyklJfapmSggJCdh0j7UkKWrrauPh70nHMAHT03+6DmzfmPzJQhba2NvXq1ePYsWP07t1bPv3YsWP07NlT5TyNGzdm3759CtOOHj1KQECAvPuixo0bc+zYMYV+444ePUqTJmUPGlVZarLSHW5VgKenJ926dWPhQsUbnylTprB//35u367YEPWCIAhC+dZdPPi2q1Bpw+t34Ul60osLvsOsjS3YcuXY265GpQ2s05609VvedjUqxWTYQDZePvK2q1FpQ+p2ZH1o2Q8u/i2GBXQmODrsbVejUpq4+tP018plN70Lzo5fztarx992NSplQO12ZMTGve1qVJqRkyPXHt5529WotFoOHvx0quKtC95Fn7cayok7F992NSqtrUf9f/21b0jdjvwWvONtV6PSxjV5s/3+VZWMCo4K/DYYVbC/8ue2bNnCsGHDWL58OY0bN+bPP/9kxYoV3Lx5E2dnZ6ZPn87Dhw9Zt24dANHR0dSsWZOPPvqIMWPGEBISwtixY9m0aZN8NNXg4GBatGjB/Pnz6dmzJ3v27OGrr77izJkzSv2YV5VXyoxbvHgxffv25ciRIzRqVNSJ6blz54iKimLHjn//ASYIgiAIgiAIgiAIgiC8WwYOHEhSUhLffvst8fHx1KxZk4MHD+LsXDSAU3x8PLGxxf1Du7q6cvDgQT777DN+/fVX7O3tWbp0qTwQB9CkSRM2b97MV199xaxZs6hevTpbtmx5bYE4eMVgXJcuXbh79y6//fYbt27dQiaT0bNnT8aOHSvvYF4QBEEQBEEQBEEQBEF4y6py1K93wLhx4xQGBCypdB/yUDQGwuXLl8tdZr9+/ejXr1+5ZarSKwXjAKpVq8aCBQuqsi6CIAiCIAiCIAiCIAiC8J/2ysG41NRUVq5cSUREBGpqavj4+DBq1CiVHe4LgiAIgiAIgiAIgiAIggCvNM54aGgo1atXZ/HixSQnJ5OYmMiiRYuoXr36C1P/BEEQBEEQBEEQBEEQhDdEJnt3X/9PvVJm3GeffUaPHj1YsWKFfMhviUTCBx98wKRJk/jnn3+qtJKCIAiCIAiCIAiCIAiC8F/wSsG40NBQhUAcgKamJlOnTiUgIKDKKicIgiAIgiAIgiAIgiAI/yWv1EzV2NhYYajY5+Li4jAyMqp0pQRBEARBEARBEARBEIQqIJW9u6//p14pGDdw4EBGjx7Nli1biIuL48GDB2zevJkPPviAwYMHV3UdBUEQBEEQBEEQBEEQBOE/4ZWaqf7000+oqakxfPhwJBIJMpkMbW1tPv74Y77//vuqrqMgCIIgCIIgCIIgCIIg/Ce8UjBOW1ubn3/+me+++46oqChkMhnu7u7o6+tXdf0EQRAEQRAEQRAEQRCEVyWTvu0aCKW8VDBu1KhRFSq3atWqV6qMIAiCIAiCIAiCIAiCIPyXvVQwbs2aNTg7O1OnTh1ksv+/He0JgiAIgiAIgiAIgiAIwqt4qWDc2LFj2bx5M/fu3WPUqFG89957mJubv666CYIgCIIgCIIgCIIgCJUhkqneOS81mupvv/1GfHw806ZNY9++fTg6OjJgwACOHDkiMuUEQRAEQRAEQRAEQRAE4QVeKhgHoKOjw+DBgzl27Bjh4eH4+voybtw4nJ2dyczMfB11FARBEARBEARBEARBEIT/hFcaTfU5NTU11NTUkMlkSKVidA5BEARBEARBEARBEIR3ilS0ZHzXvHRmXF5eHps2baJ9+/Z4enpy/fp1fvnlF2JjYzE0NHwddRQEQRAEQRAEQRAEQRCE/4SXyowbN24cmzdvxsnJiZEjR7J582YsLCxeV90EQRAEQRAEQRAEQRAE4T/lpYJxy5cvx8nJCVdXV4KCgggKClJZbufOnVVSOUEQBEEQBEEQBEEQBKESxICb75yXCsYNHz4cNTW111UXQRAEQRAEQRAEQRAEQfhPU5PJRIhUEARBEARBEARBEAThvygjPuFtV6FMRna2b7sKb0WlRlMVBEEQXq+Zh/5421WotPmdPyI1I+1tV6NSTI1MmHFw+duuRqUt6DKW1Clfve1qVIrpwnnsuBb4tqtRaX1rteGTXYvedjUqbVnvyTzNSH7b1agUKyNztl49/rarUWkDarej6a9j33Y1KuXs+OUsO7PtbVej0j5p1p95x1a/7WpU2lftR/Jb8I63XY1KGdekL7MO//m2q1Fpczt9+K+/ZizrPZnZR/5629WotG86fvC2q/BqZNK3XQOhlJceTVUQBEEQBEEQBEEQBEEQhFcjgnGCIAiCIAiCIAiCIAiC8IaIZqqCIAiCIAiCIAiCIAj/VVIxVMC7RmTGCYIgCIIgCIIgCIIgCMIbIoJxgiAIgiAIgiAIgiAIgvCGiGaqgiAIgiAIgiAIgiAI/1Uy0Uz1XSMy4wRBEARBEARBEARBEAThDRHBOEEQBEEQBEEQBEEQBEF4Q0QzVUEQBEEQBEEQBEEQhP8q0Uz1nSMy4wRBEARBEARBEARBEAThDRHBOEEQBEEQBEEQBEEQBEF4Q0QzVUEQBEEQBEEQBEEQhP8qqfRt10AoRWTGCYIgCIIgCIIgCIIgCMIbIoJxgiAIgiAIgiAIgiAIgvCGiGaqgiAIgiAIgiAIgiAI/1ViNNV3jsiMEwShXI8fP+bbb78lJSXlbVdFEARBEARBEARBEP71RDBOEAAXFxeWLFnyRpenpqbG7t27K/U5c+bMoXbt2pVaRnlkMhnDhg1DR0cHMzOzl5q3qr/Tl9WiRQs2btz41j6/pM8//5yJEye+7WoIgiAIgiAIgiAI7wARjBP+1bp37067du1UvhcSEoKamhqXL19+w7WCixcv8uGHH77xz61q3333HW5ubkybNu2l532b38H+/ftJSEhg0KBB8mmlg4MymYwpU6ZgZGREYGAgAK1atWLSpElKy9u4cSMaGhqMHTtW5ef98ccf1KpVCwMDA0xNTalTpw7/+9//5O9PnTqV1atXEx0dXTUrKAiCIAiCIAiCUFFS2bv7+n9K9Bkn/KuNHj2aPn36cP/+fZydnRXeW7VqFbVr16Zu3bpvvF5WVlZv/DNfhxkzZrzyvG/zO1i6dCkjR45EXV3184bCwkLGjBnDvn37CAwMpH79+uUub9WqVUydOpXff/+dRYsWoa+vL39v5cqVTJ48maVLl9KyZUvy8vIICwsjPDxcXsba2poOHTqwfPlyhSBdVWvo5EMz11oY6ejzJDOFAxHB3E9JKLO8hro6barXo5ZDDYx09EnLzSQo6gqXHtwGwMfGlVbV62Cub4yGmjpJ2WmciQ7j6qO7r1zH7du28/f69SQlJuHq5sZnUz6jTp06ZZa/fOkySxYvIfrePSytLBk2bBh9+vVVKBN4IpA/lv/BwwcPcKhWjY/HjaVV69Yql7dm9Rp+//U3Bg4exOQpk+XTv53zDQf2H1Ao61uzJqvWrHql9Wzo5EtztxLbIvwsMS/aFu4B1HaogZF20bY4FXVZvi3qOnjSr5byOn19eAUSaeEr1bGidDu0QbtRAGr6ehTef0D2zn1IHz8ps7zhx6PRdHdVml4QfpuslesB0G7cAJ0mDVA3NwWgMOEJucdOIrn16vtWWc4dCeL03mNkpKZhXc2OriP64+pdQ2XZmFuRHN6wi6cPH1OQl4+plTkN2jWnWbe2CuVysrI5umkP4ReukpOVjZm1JV2G9cWzbs0qr/9zzV1r0bZGAMa6BsSnJ7Hz+imikh6qLPte3Y40dPZVmh6fnsiCE+sAaOLiRwNHb+yMLQGIS33MvvCz5Z4zXtbObTvYtH4DSYlJuLi58umUSdSqU7vM8lcuXWbZ4qXE3IvGwsqSocOG0qtfH/n796LusXL5Cm7fukVCfAITJ3/KgCGDFJaxa/tOdm/fSXx8PACubm6M+GAUjZs2rrL1On/kH87sO07ms32q8/v9cPF2V1n2/q1Ijm7Yw9NHxftU/XbNaNK1jUK54AOBXDh2mrTEFPSNDfBtWIf2g3uipa1VZfV+VbXs3BlSpwNe1k5YGpjy5cHfOR197W1Xq0zXA89z+chpslMzMXewpvmgLth7uKgs++DWPXb/qHyeHzrvU8zs3tx9zO1/LnPzxHly0jIxtbMkoG87bNwdVZZNuHOfY0s3KU3v8dUYTGwtAJAWFnLjaAhR52+QnZqBiY05dXq2xsHH7bWux7XAc1w+dJqs1AwsHKxpMaQrDh7K14PSHt29z/bvV2DhYMPQbz+RTy+UFBJ64BQRZ6+QmZKOmZ0lTft3wsXP47WtQwNHH5q5+mP47Pp96FZI+fdSauq0dq9HLXt3DHX0Sc/NIijqCpcfPr+XcqGFm+K91NmY61yrxL1URfwbrxml3T19hVsnLpKTnomJrSV1+rbBunq1MssXFki4eSSEmIvh5KZnoWdqiG+Hxrg19gMgLT6R6wfPkBz3mOzkdOr0bo1n64DXVn9BeBGRGSf8q3Xr1g1ra2vWrFmjMD07O5stW7YwevRoAIKDg2nRogV6eno4OjoyceJEsrKyylxubGwsPXv2xNDQEGNjYwYMGMDjx48Vyuzdu5eAgAB0dXWxtLSkT5/iHwyls7Du3r1LixYt0NXVxcfHh2PHjil95rRp0/Dw8EBfXx83NzdmzZpFQUGBQpnvv/8eGxsbjIyMGD16NLm5ueV+P6dOnUJNTY0jR45Qp04d9PT0aNOmDU+ePOHQoUN4e3tjbGzM4MGDyc7Ols93+PBhmjVrhqmpKRYWFnTr1o2oqCj5++vWrcPQ0JC7d4tvJD755BM8PDzk32vp70BNTY0//viDbt26oa+vj7e3NyEhIURGRtKqVSsMDAxo3LixwudERUXRs2dPbGxsMDQ0pH79+hw/frzcdU5MTOT48eP06NFD5ft5eXn079+fY8eO8c8//7wwEBcTE0NwcDBffvklXl5ebN++XeH9ffv2MWDAAEaPHo27uzu+vr4MHjyYuXPnKpTr0aMHmzYp3zxXFT/b6nTxbkJQ1BV+PbuDmJQE3g/ogomuYZnzDK7dHjdLB3ZdD2LxP5vZevUETzNT5e/nFORyKuoyf4TsZtnZ7Vx6cJs+fq1wtyz7Rqg8x44eY/HCRYwcNZJ1G9ZTu05tPps4iYQE1Tdyjx4+5LNPJ1G7Tm3WbVjPiJEjWPjTQgJPBMrLXA8L46sZM+ncpTN/b9pA5y6dmfHlDG7cuKG0vPCb4ezetQv3Gqp/NDdu0piDhw/KX4t/XvxK6+lnV52uPk04FXmZX85sJyY5nvfrdy1/W9RpT3ULB3aGnWLRP5vZUmpbAOQW5LHg+FqF1+sOxOm0bo5Oyybk7NpPxpLfkWZkYPjRCNDRLnOerDUbSZvzvfyV/sNSZIWFFIQVbxNpWho5B46Ssfh3Mhb/jiTyHgYjh6JuY12l9Q8LDuXAmm206tOJCf+bgYu3O2sX/EpqYrLK8to6OjTu2IoPv5nMZ4tn07pPZ45t2cuF46flZSQSCavmLSX1aTJDJn/IZ0vm0PujoRg/Cyy+DnUdPOjj34ojt8/zv5N/E5X0kI+b9MZMz0hl+e1hJ5lxcLn8NevQn2Tl53DlYfE5292yGpce3GbpmW0sCtpEck4G45r0KXc/fRknjh5n6cIlDB81glUb1lKrTi0+nzi5nOP9EV98OoVadWqxasNaho98nyU/LebUiZPyMnm5udhXs2fshHFYWFioXI6VtRVjJ4zjr3Wr+WvdauoG1GP6lKnci7pXJet1PfgSh9Zup2Xvjnz8/XScvdxZ/13Z+5SWjg4NO7Vk9JxJTFw0i5Z9OnF8yz4uHj8jL3Pt9AWObdpD635dmLhoFr0/eo8bIZc5tmlPldS5svS0dIhMesCifza/7aq80N0L1zm9+SABXVsxcPY47Gs4s2/JOjKSUsudb+j8SYxcNE3+MrFRvX+9DjGXIgjdcRy/jk3o9uVIrKs7EvjbVrKS08qdr+esD+m3YIL8ZWRd3J3I1X3/cOfMVRr0b0+Pr8ZQo1kdglbsJDnu9QVO7pwP45+NB6jfrRVDvpmAvYcLexatJf0F331edi5HV2zD0bu60nshO49x/dRFWg7tzrD5k/Br1YD9y/7myf1Hr2Udatq60dm7MUH3rvB78E7upyQwrF5nTHQNypxnYO12uFnYs+vGP/x8egtbr53gaVaq/P3sgjyCoq6w4twefjm7ncsP79C7ZstXvpeqiH/jNaO02Mu3uLIzEJ8Ojeg49X2sqlfjn9+3k5WcXuY8wav38fj2fRoM6UiXr0bTZER3jG3M5e9L8gswtDClVvcW6BqXvU0F4U0RwTjhX01TU5Phw4ezZs0aZCVGiNm2bRv5+fkMHTqU69ev07FjR/r06UNYWBhbtmzhzJkzTJgwQeUyZTIZvXr1Ijk5maCgII4dO0ZUVBQDBw6Ulzlw4AB9+vSha9euXLlyhRMnThAQoPrJilQqpU+fPmhoaHDu3DmWL1+ustmnkZERa9asITw8nJ9//pkVK1aweHFxMGDr1q3Mnj2b+fPnExoaip2dHb/99luFvqc5c+bwyy+/EBwcTFxcHAMGDGDJkiVs3LiRAwcOcOzYMZYtWyYvn5GRwWeffcbFixc5fvw4MpmM3r17I5VKARg+fDhdunRh6NChSCQSDh8+zB9//MGGDRswMCj74jZ37lyGDx/O1atX8fLyYsiQIXz00UdMnz6d0NBQAIXtkpmZSZcuXTh+/DhXrlyhY8eOdO/endjY2DI/48yZM/JgX2mZmZl07dqVmzdvcvbsWZVlSlu1ahVdu3bFxMSE9957j5UrVyq8b2try7lz57h//365y2nQoAFxcXEvLPeqmrr6cenBLUIf3OJpVioHI4JJy82koZOPyvI1LB1xMbdjXeghopIekpqTyYO0p8SmFgedo5PjCX8cw9OsVJKz0wm5f4PHGUm4mNm+Uh03bdhIj5496NmrF66urkyeMhkbGxt2bN+hsvzOHTuxtbVl8pTJuLq60rNXL7r36M6Gv/+Wl9m8aTMNGjZgxMgRuLi4MGLkCOo3qM/mjYo/GrOzs/l61ixmzJyJsZGxys/T0tLCwtJS/jIxMXml9Wzm6s+luOJtceD5tnAue1u4mtuzNvTgs22RwYO0JwrbAkAGZObnKLxeN50WTcg9HkTB9XCkCU/I3rQDNW0ttOvUKnMeWU4OsoxM+UvLozoUFJB/rTgYJwm/jeTWHaSJSUgTk8g9dBxZfj6azqozQV7Vmf0nqNemCfXbNsO6mh3dRgzAxNKM80f/UVne3tWRWs3qY+Noj5m1BXVaNKRGLR9iIiLlZS4FBpOTmcV7X4zF2as6ZlYWuHi5Y+fy+n5YtXavR0jMjWfHYDI7r58iJSeDZq6qt0OuJJ+MvGz5y8nMBj0tXc7dL94G60IPcTr6Gg/TnvI4M4VNl4+hpqaGp1XVbIPNGzbRrWd3uvfqgYurC59O+QxrG2t2b9+psvzuHbuwsbXh0ymf4eLqQvdePejaoxub/i7u+9Pb14fxn35Cu47ty8wYa9aiOY2bNcHJ2QknZyc+Gj8WPX09wq8rB+hfRfCBE9Rt05iAtk2xrmZLlxH9MLYw48LR0yrL27s64t80QL5P1W7eAHd/b+7fKt6n4u5G4+TpRq1m9TGztsC9ljd+Terx6F7Z17o36VzsTVac30vQvatvuyovdPXoWXya18O3RQDm9tY0H9wVQ3MTrp+6UO58+sYGGJgYyV9lZda/DuGBF3BvXIsaTWphYmtJ/X7t0Dcz5vbpK+XOp2ukj56xofxVss73LtzEr0NjHHyrY2Rpimfzuth5uxIeePG1rcflo2fwbVGPmi3rY25vTcsh3Yq++8Dz5c4XuHYXno1qYaciE/BWyBXqd2uJay1PTKzN8W/TCOeaNbh8+IyKJVVeExd/Lj+4zaUHt3malcqhWyGk52bSoIx7KXfLariY27H+0mHuPbuXepj2lLgS1++Y5HginhTdS6XkZHDu2Xnc2fTV7qUq4t94zSjt1slQ3Br5Ub2JPya2FtTt2wZ9MyMiz1xVWT4+PJonUXG0GNsXW08XDC1MsHC2w9LNQV7GwtmO2r1a4VzPG3VNjddS73eaTPbuvv6fEs1UhX+9UaNG8eOPP3Lq1ClaP2uatmrVKvr06YOZmRmffvopQ4YMkfcFVqNGDXmTwt9//x1dXV2F5R0/fpywsDCio6NxdCy6wKxfvx5fX18uXrxI/fr1mT9/PoMGDeKbb76Rz1erluoL3PHjx4mIiCAmJoZq1Yp+rC1YsIDOnTsrlPvqq6/kf7u4uDBlyhS2bNnC1KlTAViyZAmjRo3igw8+AGDevHkcP378hdlxz8s2bdoUKGraO336dKKionBzK2qu0K9fP06ePCkPEvbv319h/lWrVmFra0t4eDg1axY1w/rjjz/w9/dn4sSJ7Ny5k9mzZ78wy2zkyJEMGDAAKMoEbNy4MbNmzaJjx44AfPrpp4wcOVJevlatWgrf67x589i1axd79+4tM5gaExODjY2NyhvpuXPnYmRkRHh4ONbWL87AkUqlrFmzRh6oHDRoEJMnTyYyMhJ396LsqtmzZ9OnTx9cXFzw8PCgcePGdOnShX79+inUwcHBQV6/0k2qK0tDTR17Yyv+KfVDKTLxAU5mNirn8bZ25mHaU5q71qKOgwf5hQVEPL7P8bsXy8y2crNwwNLAlMO3y7+xVqWgoIBbt24xfMRwhekNGjXkeliYynmuX79Og0YNFaY1atyIvXv2IpFI0NTU5HrYdQYPGaxYplEjNm9SDMb9+L8faNq0KQ0aNmD1StVNTy9fukyn9h0xNDKkbt26jB33Mebm5irLluX5tgiKUvwRFfn0QZk33t42LjxMe0oLt9rUdvCgoLCAiMcxHLujuC20NbT4ovVQ1FEjPiOJY3cuEJ+e9FL1exnq5maoGxshuVMcNKCwEElUDJouTuSfq9iPOu2G9ci/ch3yC1QXUFNDq1ZN1LS1kdyvuuCDRCLh0b1YWvbqqDDd3d+b+7crliX1KDqO2Nv3aD+ou3xaxKUwnGq4sXflZsJDr2FgbEjtpvVp0avja/kBr6GmjqOpDcfuKH7ftx7fx9XCvkLLaORck9tP7pOSk1FmGW1NTTTUNcgqePE15UUKCgq4c+s2740YpjC9fqOG3Ai7rnKem9dvUL/U8d6gcUP279knP95fVmFhISePB5Kbk4uvv99Lz19a0T4VR/OeHRSmu9fyJu5OxfepuDv3aDuweJ9y8qzOtdMXeRAZQzV3F5IfJ3Lnyk3qtGxU6Tr/f1IokfDk/iPqdmmhMN3Rx52EyPLPLZu/+ZXCAgnmdtYEdG9FNa/X25zzuUJJIclxCdTsoLit7b1deBqtuknhc/v/txppgQQTW0v8OjXB1qP43qJQIkFdS/GY0dTS5ElUXNVVvoRCiYQnMY8I6NJSYbqzrzvxUWU/hLx5+hKpT5Lp+OEALuw7qfR+YYEEDS3FwLumthaP7sZUSb1LKrp+W3Jaxb2Uo6nqeykva2cepT2lmWstatvXIL+wgFtP7nPibmjZ91Lm9lgamHD0TnxVrwLw77xmlFYoKSQlLgGfdg0Uptt6uZBYxnHx8EYk5o423DpxgZiL4Whqa2HvVx2/Ls3QfAea+wuCKiIYJ/zreXl50aRJE1atWkXr1q2Jiori9OnTHD16FIBLly4RGRnJhg0b5PPIZDKkUinR0dFK2VERERE4OjrKA3EAPj4+mJqaEhERQf369bl69SpjxoypUP0iIiJwcnKSB+IAGjdW7rtm+/btLFmyhMjISDIzM5FIJBgbGyssp/QAAo0bN+bkSeWbl9L8/f3lf9vY2MibwpacduFC8VPj2NhY5s6dy/nz50lMTJRnxMXGxsqDcWZmZqxcuZKOHTvSpEkTvvzyy5euB4Cfn5/CtNzcXNLT0zE2NiYrK4tvvvmG/fv38+jRIyQSCTk5OeVmxuXk5CgFWJ/r0KEDx48fZ8GCBRUa6fXo0aNkZWXJA6eWlpZ06NCBVatWsWDBAgDs7OwICQnhxo0bBAUFERwczPvvv89ff/3F4cOH5T/O9fT0ABSaA5eUl5dHXl6ewjQdHZ0X1hFAX1sXDXV1MvMUM6Uy83Iw1NZXOY+ZvjHOZrZIpIVsuHwEfW1devg0R19bh53Xg4rroKnNtNbvoamujlQmY1/4mTL7HClPamoqhYWFmJsrNv2xMDfnXKLqgFJSUhIWpYJh5uYWFBYWkpqaiqWlJUlJSZhblCpjYU5SUvEyjx45yu1bt1m9bk2Z9WvcpAlt2rXFztaOR48e8cfy5YwfO461f69DW7vsJpmllbUtMvKzqaGj+umxub5R8ba4VLQtevo2R09Ll53XTwHwNCuFHWEnSchIRldTiyYufnzUuBfLTm8nKbv8pkyvSs24qOmJNCNTYbo0I1Pe19uLaDg6oGFnS/aWXUrvqdvaYDTxQ9DUhPx8slZvRPr4aaXr/Vx2eiZSqRRDE8VmOUYmRtxNLf87+37sdLLSM5EWFtK2fzfqt20mfy/5cSL3nt6mVrMGjJg+nsT4J+xduYVCqZS2/bpWWf2fM9DRQ0NdnYw8xa4VMvKyMdZRfXyXZKxjgI+NK2tDD5Zbrodvc9JyMrn9pPIB0TT58V76+DUjqYzmnElJSTQ0NytV3lzheK+oqMhIxo78kPz8fPT09Fjw4/e4ur2436oXKd6nFLNrDU2MyEgtu+kUwI8fz5TvU637dyWgbVP5e/5NA8hOz+SvrxchQ4a0UEqD9s1p0atDOUsUSsvJyEYmlaJvrNhsTt/EgOwbmSrnMTA1ovXwnli5OFBYIOF2yFV2/7Sa3l+MwsGz8vvMi+RlZiOTytA1UmxVoGtkQG666u5U9EwMaTS4E+ZOtkgLCrl38QbHlm2iw6dDsHF3AsDe242IwIvYuDtiZGlG/O0Y4sLuKrQiqUplffd6JkZk3VDdN1pKQiJntx+m//SPUNdQnaXkVLMGV46cwcHDBVNrc2Ijorh3JQLZs/vSqiS/fpfKOs/Mz8GojHOtuZ4xTs+u3xuvHEVfS5fuvs3Q09Jl942S91JafNHqPTTVNZDKpOwPP/tK91IV8W+8ZpSWn5Wj8rjQMTIgN0P1cZGZmMrTew/R0NKk2Qe9yMvMIXTbMfKzcmk4tLPKeQThbRPBOOE/YfTo0UyYMIFff/2V1atX4+zsTNu2RR1uS6VSPvroIyZOnKg0n5OTk9I0mUyGmppaudOfB1YqQtWNT+nlnzt3Tp5p17FjR0xMTNi8eTMLFy6s8OeUR6vEU0U1NTWF/59Pk5a4senWrRuurq6sWLECe3t7pFIpLi4u5OfnK8z3zz//oKGhwaNHj8jKylIIHlakHmVNe16XL774giNHjvDTTz/h7u6Onp4e/fr1U6pHSZaWlqSkpKh8r23btkycOJGePXtSWFio0DRXlVWrVpGcnKwwYINUKuXKlSvMnTsXjRI3jzVr1qRmzZqMHz+eM2fO0Lx5c4KCguTZmsnJRT9AyxrY4rvvvlPItISirDsa2pVbx5JK72lFX6fqG+/nu+DWa4HkSYq+z4O3Qhhcpz17b56RP9HNl+Tzy9nt6Gho4WbhQGevxiRnpxOd/GpPdEsfWmUdb2XN8Px4UpxaukzxvvQ44TGLFi5i6S9Lyw1utu/QXv53dffqePt407NbD86eOUvrNqoHgyiP0rZATcXUku/BlqsnirdFRDCD63Zg783TSKSFxKU+IS61eNCE+ykJjG/Wj8YuNdkffval66eKVt1a6Pcr7msx86/1z1amVL3V1CrcpEC7YQCF8QkUxin/6JA+TSRj4a+o6emi5e+L/uC+ZP72V5UG5IqqW2r/KJpY7jwffjuF/Nw84u5Ec3jjbixsrajVrCjzVyaTYWBsRO+PhqKuro6DmzMZKWmc3nvstQTjFOr9Cho6+5BTkEfYo8gyy7StEUC9al4sPb21SvshVPruZeV/9crlnx/v5W+v0pycnVm9cS2ZGZmcCjzJ/DlzWfbnb1USkHtWoVL1VK57aR988xl5uXk8uBvD0Y17sLC1wr9pUfcW0TfvELTrMN1GD6RaDReSE55ycM12DHcconVf8SOy0mQobbPnzGytMLMtvi7buTuRmZLGlSNn30gw7jmlfbycOpvYWCj0aWfl5kBWSjrhxy/Ig3H1+7UjZNMh9s5dAWpgZGlG9Ub+RJ1TnYleVZSOA5lM5WpIpVIO/7GFRr3aYWZbdqC95ZBunFizi/UzFoOaGibW5vg0q0v4mctVW3EFimdbNdTKvOQ9X99tYYHkSYqyvw/fCmFg7fbsDy95L1XAb8E70NbQws3Cnk5ejUjOSSfmFe+lXn4tKu5tXjOUlN55ZGUfGM/vJxsN74a2XtH9Xh1Ja86u2kO9/u1EdhyArOqD2ELliGCc8J8wYMAAPv30UzZu3MjatWsZM2aM/AJZt25dbt68KW9W+CI+Pj7ExsYSFxcnz44LDw8nLS1NnkXn7+/PiRMnFJpUvmh5jx49wt6+KD08JCREoczZs2dxdnZm5syZ8mml+xbz9vbm3LlzDB9e3Mzv3LlzFVqnl5GUlMT169f57bffaNiwqMlQUFCQUrng4GB++OEH9u3bx5dffsknn3zC2rVrq7Qup0+fZsSIEfTu3Rso6vMtJiam3Hnq1KlDQkICKSkpmJmZKb3fvn179u/fT/fu3ZFKpfzyyy8qf0QlJSWxZ88eNm/ejK9v8QhTUqmU5s2bc+jQIbp166ayDj4+RX2LlBwk5MaNG2hpaSksq6Tp06czefJkhWk6Ojp8G7im3PUFyM7PpVAqxUhHMUhsoK1XZr9iGbnZpOdmyYM/AE8zU1BXU8NE14Ck7KIsDxmQ/Ozv+IwkrA1NaelW56WDcaampmhoaChkrAEkp6QoZbY9Z2FhoVQ+JSUZDQ0NTExN5WWSS5dJTpZn5Ny6FUFKcjIjhr0vf7+wsJArV66wfes2TgefUQiqPmdpaYmtnR1x5WRhqlLWtjDU1lPKlnsuI095WzyRbwtDlZlvMuBh6lMs9F+tXztVCm5GkHG/RBOmZ80C1Y2NKCyRHaduaICsjCfTCrS00K7tR86RE6rfLyxEmlQUpC588AgNx2roNG9Czvaq6bRe/1kfSqUzljLTMpQym0ozty76cWjr5EBGWjontu2XB+OMTE3Q0FRXaJJq5WBLRmr6KzenLE9WXg6FUinGOooZAkY6+qTnqc60LamRc00uxoVTWMZNeBv3enTwaMAvZ3fwKD2xSupsUsbxnvLC4z1ZqXzR8f5y+7mWlhbVnl2/vXy8iQiPYNumLUyd+eIM7vI836cyS+1TWekZShmYpZmV2KcyU9MJ3HZAHow7sXU/tVo0kGfL2To5kJ+Xz94/N9Ky9+tp/vxfpGekj5q6Otnpillw2elZShlb5bF1c+T2uTczWqyOoT5q6mrklMpAzs3MUsoKKo+ViwP3Lt6U/69rpE/rD/tSWCAhLysHPRNDruw5haGFaVVVXcHz7z4rTbFZY056Jvomyt99QW4eT2Ie8jQ2nlN/7wOeBd9lMpaO/oreU0bi6FMdfWNDuk8chqSggNzMbAxMjTm77QjGlsr3d5X1/PpdukWBgbYumfmqz7XF1+/ibhieZqairqaGsa6B/P6p5L1UQkYSVgZmtHCr/VqCcf/Ga0Zp2gZ6qKmrKWWH5mVmo2ukOrtPz8QQPRNDeSAOwNjGAmSQk5qpMMCJILwrxNVd+E8wNDRk4MCBzJgxg0ePHjFixAj5e9OmTSMkJITx48dz9epV7t69y969e/nkk09ULqtdu3b4+/szdOhQLl++zIULFxg+fDgtW7aUD9Iwe/ZsNm3axOzZs4mIiOD69ev88MMPZS7P09OT4cOHc+3aNU6fPq0QdANwd3cnNjaWzZs3ExUVxdKlS9m1S7FZ16effsqqVatYtWoVd+7cYfbs2dy8eZOqZmpqirm5OcuXLycyMpITJ04wZcoUhTIZGRkMGzaMTz75hM6dO7Nx40a2bt3Ktm3bqrQu7u7u7Ny5k6tXr3Lt2jWGDBmikMGnSp06dbCysuLs2bKzhdq0acOBAwdYu3Yt48ePV5m9uH79eiwsLOjfv788661mzZr4+/vTrVs3+UAOH3/8MXPnzuXs2bPcv39fHjC1srJSaI58+vRpmjdvXmZWpY6ODsbGxgqvijZTLZRJeZT+FHcLxQ7k3S2rEZvyWOU8samPMdLVR1ujOHBgaWCCVCYlLbe8QIsaGuov3+mtlpYWXl5eXDiv2In2hfMX8CvRfLkkPz8/pfLnz53H28dbHvDw8/fjfOky58/LlxlQvz4bN29i/Ya/5S9vH286durE+g1/qwzEQVEzuyePH79U0zgosS0sFZukuls6cD9V9Sh291MSVGwL02fbQnXTKgA7YwsyKnBjXWF5+UiTkotfj58gTc9A06PECHcaGmhWd0ES8+IgpXbtmqCpQcGlqxX7fDVQq8IOlTU1NbF3cyIyLEJhemRYBM6eL9EflKyor7DnnD3dSEp4qnAuSox/gpGZSZUH4qBon4pLfYyXtWImt6e1M9FJ5Y8o6G5ZDWtDM0JiVA9e0LZGAJ28GvF78C6FDscrS0tLCw8vTy6eV+yzKPT8BWqW0Xebr19NQksdyxfPXcCrxPH+ymQypdHJX0XRPuVIVNgthelRYbdw9Kj4PiWjqI+t5wry8lFTU7wlV1dX///cp/Ur0dDUxNrZnribihk9ceGR2Lort4Qoy9PYeJUBpNdBQ1MDc0db4m/FKEyPvxWDlauD6plUSH7wGD0T5eCdhpYm+qZGyKRSYq/extG/RmWrrJKGpibWLvbElvruY8Mjsauu3E+utq4OQ+dOZMg3E+Qvv1YNMLO1ZMg3E7CtrngN1dTSwtDMBGmhlMhLN3Cr8+IBuF5W0fU7keqWit97dctqZZ4fY1MSMNI1ULh+Wzy7l0ov515KTQ00X+FeqiL+jdeM0jQ0NTBztCXhtmJiQsKt+1iWcVxYujqQk5ZJQV7xg82MJ8moqamhZ/pmjmdBeFkiGCf8Z4wePZqUlBTatWun0PzU39+foKAg7t69S/PmzalTpw6zZs3Czk518z81NTV2796NmZkZLVq0oF27dri5ubFlyxZ5mVatWrFt2zb27t1L7dq1adOmDefPq+7UXl1dnV27dpGXl0eDBg344IMPmD9/vkKZnj178tlnnzFhwgRq165NcHAws2bNUigzcOBAvv76a6ZNm0a9evW4f/8+H3/88at+XWXS0NBgy5YtXL58mZo1azJ58mSl5rKffvopBgYG8n7TfH19+d///sfYsWN5+LDq+sBYvHgxZmZmNGnShO7du9OxY0fq1q37wvqPGjVKoY9AVVq1asXBgwdZv349H3/8sVJAbtWqVfTu3VtlRkLfvn3Zv38/jx8/pl27dpw7d47+/fvj4eFB37590dXV5cSJE1hYFDcj2bRpU4X7GXwVZ6OvU8/Ri3rVPLEyMKWLV2NMdA25EBsOQAePBvTzL25uee3RXbLz8+jj1worQ1NczOzo5NWISw9uy5sctHCrTXULB8z0jLA0MKWpix91HGpw7ZHq/l9eZPDQIezZvYe9e/YSHR3N4oWLeJyQQJ++fQD49ZdfmfP1bHn5Pn37kBAfz5JFi4mOjmbvnr3s3bOXoe+9Jy8zcNAgLpw/z7o1a4mJiWHdmrVcOH+BQUMGAWBgYEB19+oKLz1dPUxMTajuXhRkys7O5uclP3M9LIxHjx5xKfQSUyZPwcTUlJatW730ep6JDiOg5LbwboKJnhEX7j/bFp6qt0Vf/9ZYG5rhYmZHZ+9GXIor3hZt3OtRw7IaZnpG2BlZ0MevFXbGFvLt+7rk/ROMbtuWaNX0Rt3WGv1BfZDlF5B/pThrRH9wX3S7tFeaV7tBPQpuRCDLVs4I1O3cHg1XZ9TNTFG3tUG3czs0q7uSf7lqs1GadWtL6ImzhAYG8+RBPAfWbCMtMYUG7ZsDcGTjbrb9skZePuTwKSJCw0iMf0Ji/BMunQzm9L5j1G5e3Il0ww4tyM7IYv+abSQ+esyty9c5teswjTq2LP3xVeZk5CUau/jRyNkXGyNz+vi1xFzfiDPRRd9Xd59mDKvXSWm+xs41iU6OJz5DuV/GtjUC6OrdhA2Xj5KUnYaRjj5GOvpoa1RNU55BQwezf/de9u/ZR0x0DEsXLuFxwmN69S3KdF7+y2/M/bq4aX6vvr1JiE9g2aKfiYmOYf+efezfs4/B7w2RlykoKODu7TvcvX2HggIJT58+5e7tOzyIK87o/OPX37l25Srxj+KJiozkj1+Xc+XSFTp0UhzI41U16dqWS4HBXDoZzJMHCRxcu520xGQatC/qV/Doxj1s/6U4S/z8kSBuXbpOUvwTkuKfcPlkCGf3HadWs+J9yrOeHxePnSbsbCgpTxKJDIvgxJZ9eAX4vRNZcXpaOtSwrEYNy6IHPvbGltSwrIaN4buXaVK7Q1PCT18i/PQlkh894fTmg2Qmp1GzZVFma/COoxz7a7u8/NVjwdy7HE7q40SSHj4meMdRoi7dxL/Nmxs8w6dNAyKDrxEZco20hEQu7jhOVnI6Hs3rAHB5zynOrtsnLx9x8iKx1+6Q/iSZ1PinXN5zitirt/FqUU9e5mnMI2Kv3iYjMZXHkXGc+HUrMpkM33YNlT6/qtTt0Iyb/4Ry859Qkh89IWjTATKS0vBrXbSvn912hCMrih7aqqmrY1nNVuGlb2yAhpYWltVs0dIp6qs1ISqOyNAbpD1J5uGdaHYvWo1MJiOg1CAdVSU4Jox61byo61B0/e4sv5cqeqjT3qM+ff1aycuHxUeSk59Lb79WWBmY4mxmS0fPhlwu817KhCYuftS293jle6mK+DdeM0rzah3AvZAw7oVcJy0hics7A8lOSce9WdHAbtf2/sO59Qfk5Z0DvNE20OPChkOkxSfyJDKOa3uCcG3kJ2+iWigpJOXBY1IePEYqKSQnLZOUB4/JeKq6e5v/HKns3X39PyWaqQr/GY0bNy6zY9r69evLB3RQpXTTRycnJ/bsKb+pVJ8+fejTp0+Flufh4cHp06cVppWu6w8//KCUXfd8BNjnZsyYwYwZMxSm/e9//yuzjq1atVL6nBEjRihkDgLMmTOHOXPmyP9v164d4eGKP/JLLmfVKuXRKCdOnKjQL1/p76B0PVxcXJSmla6vi4sLgYGBCmXGjx+v9NmlTZo0CV9fX+7fvy8fuVRV89YWLVqQkVHcpOLUqVPyv8PKGOETirb98yyLvn370rdv33Lrc+DAATQ0NOjXr98L6/6qridEoa+tQ+vq9TDS1edxRjLrQg+R+iyzykhHHxPd4ieD+YUSVl88QHefpoxr0ofs/DxuJEQpjL6lraFFD9/mmOgaUFAo4WlWKtuuneR6QtQr1bF9h/akpaWx6q+VJCYm4la9Oot/XiwPjCclJvI4ofhJq72DA4t/XsKSRYvZvm07llaWTPl8Cm3atpGX8a/lz9z58/jj9+X8sfwPqlWrxvzvFsgHGqkIdXV1oiIjOXTgIBkZGVhaWlIvoB7zFyzAwKDizYSeux4fhb6WLm3cAzDS0edxZjJrLx4ssS0MMNUrbtKWXyhh9YX9dPNtxrimRdvienwUx+4UZwnpaunQy68lRtr65EryeZSeyJ/n9vIg7YnS51elvJOnUdPSQq9vD9T0dCmMfUDmn2ugxJNndVNTpT7k1C0t0HRzIfOP1SqXq2ZkiMGQfqgZGyHLyaUw/jFZK9YiufNq+1ZZ/JsEkJ2RReCOA2SkpGPjaMf708djZlUUKM9ISSO1xIACMpmMI5t2k/IkCXV1dSxsreg4tBcN2jWXlzG1NGfUVxM5sHYbS7+Yh7G5KU07t6ZFr6oJ9qhy+eEdDLT16OTZCGNdA+LTk/g9eJd8pDsTXQPM9BSbSepqalPbvgY7rp9SuczmrrXQ0tDkg4bdFaYfjAjh0K0QlfO8jLYd2pGWlsaav1aRlJiEa3U3fvx5Ibby4z2p1PFuz48/L2TZop/ZuW0HllaWTPr8M1q1LQ5cJz5NZOTQ4ibnm9ZvZNP6jdSuW4df/vwNgOSkZOZ+/Q1JiUkYGBpSvUZ1Fi5dTP1GiqPyvSq/JvXIzsji1I5D8n1q2JfjMH22T2WmppGWVPzDTiaVcWzjHlKeFu1T5jZWdBjSk4B2xYOCtOxT9KP4xJZ9pCenYWBsiGc9P9oNUtw2b4uXlTO/9C7uRmFis6IR1w9GhDA/sGq7p6isGg38yM3M5uK+k2SlZWDhYEO3T4fJmzVmp2aQkZwqLy+VFHJ222EyU9LR1NLC3MGabp8Ow8Xf843V2aWeN3lZOYQdOktOehamdpa0GdcfQ/Oi5tk56ZlkJRc3jZZKCrm8K5DstEw0tDSLyn/cHwff4ixmaYGEq/v/ISMxFS0dbRx83Wg6vBva+qoHuKoKHg39ycnK5vzeQLKfffc9P3tf/t1npWWQkZT6UsuUFBQQsusYaU9S0NLVxsXfk45jBqCjX/G+m1/GjYR76Gvp0sq9btH1OyOZ9ZcOybPUDXX0MdFTvJdaE3qArt5NGdukDzn5udxIuMfxu8X3UloamnT3aYbxs3upxKxUtocFciOhYiMwv4p/4zWjNKe6XuRl5XDjSDC5aVmY2FnSYmxfDEoeFynF9/BaOtq0Ht+fS9tPcPSn9Wgb6OFUxxO/rsXn2py0TI78sE7+/63Ai9wKvIiVuyNtJw6q8nUQhBdRk72uYXUEQRDeoj179mBubk7z5s1fXPg127p1K87OzvI++F7GzEN/vIYavVnzO39EasbrGfHzTTE1MmHGweVvuxqVtqDLWFKnfPW2q1EppgvnseNa4IsLvuP61mrDJ7sWve1qVNqy3pN5mqF6hNR/Cysjc7ZePf62q1FpA2q3o+mvY19c8B12dvxylp2p2i4v3oZPmvVn3jHVDyP+Tb5qP5Lfgne87WpUyrgmfZl1+M+3XY1Km9vpw3/9NWNZ78nMPvLX265GpX3T8YO3XYVXknH79WVjVpaR5+tpQv+uE5lxgiD8J/Xs2fNtV0FuwIABb7sKgiAIgiAIgiD8fyVysN45b78jCkEQBEEQBEEQBEEQBEH4f0IE4wRBEARBEARBEARBEAThDRHNVAVBEARBEARBEARBEP6rRDPVd47IjBMEQRAEQRAEQRAEQRCEN0QE4wRBEARBEARBEARBEAThDRHNVAVBEARBEARBEARBEP6rpNK3XQOhFJEZJwiCIAiCIAiCIAiCIAhviAjGCYIgCIIgCIIgCIIgCMIbIpqpCoIgCIIgCIIgCIIg/FeJ0VTfOSIzThAEQRAEQRAEQRAEQRDeEBGMEwRBEARBEARBEARBEIQ3RDRTFQRBEARBEARBEARB+K8SzVTfOSIzThAEQRAEQRAEQRAEQRDeEBGMEwRBEARBEARBEARBEIQ3RDRTFQRBEARBEARBEARB+K+Simaq7xqRGScIgiAIgiAIgiAIgiAIb4gIxgmCIAiCIAiCIAiCIAjCGyKaqQqCIAiCIAiCIAiCIPxXyaRvuwZCKSIzThAEQRAEQRAEQRAEQRDeEDWZTCZ68hMEQRAEQRAEQRAEQfgPyrga9rarUCaj2v5vuwpvhWimKgiC8A679vDO265CpdVy8OBxeuLbrkal2Bhb8te5vW+7GpX2QaMepC35/W1Xo1JMJn3MP1GX33Y1Kq1F9bos+WfL265GpU1qMZD0LTvfdjUqxXhgHzJi4952NSrNyMmRZWe2ve1qVMonzfrT9Nexb7salXZ2/HKGbpzztqtRaRuGzOF/gevfdjUqZVqbYf/6dYCi9Zh56I+3XY1Kmd/5I7ZdO/G2q1Fp/Wu1fdtVeDUiB+udI5qpCoIgCIIgCIIgCIIgCMIbIoJxgiAIgiAIgiAIgiAIgvCGiGaqgiAIgiAIgiAIgiAI/1EyqWim+q4RmXGCIAiCIAiCIAiCIAiC8IaIYJwgCIIgCIIgCIIgCIIgvCGimaogCIIgCIIgCIIgCMJ/lRhN9Z0jMuMEQRAEQRAEQRAEQRAE4Q0RwThBEARBEARBEARBEARBeENEM1VBEARBEARBEARBEIT/Kpn0bddAKEVkxgmCIAiCIAiCIAiCIAjCGyKCcYIgCIIgCIIgCIIgCILwhohmqoIgCIIgCIIgCIIgCP9VUjGa6rtGZMYJgiAIgiAIgiAIgiAIwhsignGCIAiCIAiCIAiCIAjCf0ZKSgrDhg3DxMQEExMThg0bRmpqapnlCwoKmDZtGn5+fhgYGGBvb8/w4cN59OiRQrlWrVqhpqam8Bo0aNBL108E4wRBEARBEARBEARBEP6rZLJ39/WaDBkyhKtXr3L48GEOHz7M1atXGTZsWJnls7OzuXz5MrNmzeLy5cvs3LmTO3fu0KNHD6WyY8aMIT4+Xv76448/Xrp+os84QRAEQRAEQRAEQRAE4T8hIiKCw4cPc+7cORo2bAjAihUraNy4Mbdv38bT01NpHhMTE44dO6YwbdmyZTRo0IDY2FicnJzk0/X19bG1ta1UHUVmnCAIAKxZs4ZDhw697WoIgiAIgiAIgiAI/0/k5eWRnp6u8MrLy6vUMkNCQjAxMZEH4gAaNWqEiYkJwcHBFV5OWloaampqmJqaKkzfsGEDlpaW+Pr68vnnn5ORkfHSdRTBOOFfwcXFhSVLlrzR5ampqbF79+5Kfc6cOXOoXbv2S81z6tQp1NTUym3PXtV27tzJDz/8QKNGjV5p/hEjRtCrVy/5/61atWLSpEnlzlNV2zQwMBAvLy+kUmmll/U6PHnyBCsrKx4+fPi2qyIIgiAIgiAIwv9HUtk7+/ruu+/k/bo9f3333XeVWt2EhASsra2VpltbW5OQkFChZeTm5vLll18yZMgQjI2N5dOHDh3Kpk2bOHXqFLNmzWLHjh306dPnpesomqkKr1X37t3Jycnh+PHjSu+FhITQpEkTLl26RN26dd9ovS5evIiBgcEb/cx31b179/jqq684dOgQZmZmVbLMnTt3oqWlVSXLepGpU6cyc+ZM1NWLni2sWbOGSZMmKQQzIyIiaN++PQ0aNGDTpk3o6Ohw8uRJfvzxR86fP09OTg4uLi507tyZyZMn4+DgoPAZnp6eREdHEx0drfTevXv3mDlzJkFBQSQnJ2NpaUm9evX48ccf8fDwwNrammHDhjF79mz++uuv1/pdHNlzgL1bdpKalEI1FydGjB+Dt7+vyrIpScms+30l9+5EkfDwEZ17d2fEhDFK5bIyM9m0cj0XToeQlZGJtZ0Nw8aOpm6jgCqp865tO9n090aSE5NwcXPlk8kTqVWndpnlr166wi9LlhFzLxoLS0uGDB9Cz7695e/v27WXIwcPcS8qGgBPL0/GjP8IH18fheU8ffKU5ct+43zIOfJy83B0cmTarOl4entVyXpdORHMxYOnyEzLwNLehjZDe1DN0+2F8z24E83m75ZjWc2GEXMnK7yXm5XD6R2HuBt6g9zsHEwszWk9uBtutbyrpM5l0WkUgHZNH9R0dShMeExO4GmkySllltfy8US/Qxul6WnL/oTCQgCMRg1FvcRNzXN5126Qe/J01VUeOLn/KEd27CctORV752oM/HA4HjVVb+fLZy9w6sAx4u7dR1Igwd65Gt2H9qVmvVryMmePBbFm8XKleX/bvRYtbe0qrXtJN05e4OqRM2SnZWJmb0XTgZ2x93BRWfbh7Wj2/rRaafqgbz/BzM4KgPB/QrkdcpXkR08AsHK2p2Hvdti4Vntt6wAgk8lYcfIEuy5dICMnB99qjkzt1pPq1jYVmv/o9WvM3LaZll4+/DSkuN+X7RfOsePieeJTi/ZNNytrRrdqS1MP5WYor1LnP9evY9eBg2RkZuDr5cW0TyZS3cWl3PlOnP6H5WvW8CA+nmp2dowbOYrWzZrJ38/Kzmb5mjWcPHuGlNRUPN3dmTJuHL6exftndk4Oy/76i6Dgs6Slp2NnY8ug3r3o1125/5rKuh54nstHTpOdmom5gzXNB3Upcx97cOseu39cpTR96LxP5fvYu6SWnTtD6nTAy9oJSwNTvjz4O6ejr73tasm1q1Gfrt5NMNUz4mHaE9ZfOsztp7Fllm/i4kc376bYGlmQXZBL2KNINl45SmZ+DgCtq9elmWstHE2LfohGJ8ez5doJ7iW93geDEUGhXD8WQk5aJqZ2VjTs3wHbGk4qy8bfieHQ4r+VpveZPRZTW0v5/3nZuVzac5L7V2+Tn52DoaUpDfq2x7Gmu1iHcjR08qGZay2MdPR5kpnCgYhg7qeUHYDQUFenTfV61HKogZGOPmm5mQRFXeHSg9tKZf3sqjOodjvCH0ez4fLR17YO548EcXrvcTJT07CuZkeXEf1x8Vb9ncXciuToht08ffiYgrx8TK3Mqd+uGU27tZWX+WvOYmLC7yrN61HHl+HTx7+29RBebPr06UyerHjfq6Ojo7LsnDlz+Oabb8pd3sWLF4Gi5JrSZDKZyumlFRQUMGjQIKRSKb/99pvCe2PGFP9mqlmzJjVq1CAgIIDLly+/VFxDBOOE12r06NH06dOH+/fv4+zsrPDeqlWrqF279hsPxAFYWb17N4pvi5ubG+Hh4RUqW1BQUKEgm7m5eWWrVSHBwcHcvXuX/v37l1nm4sWLdO7cmZ49e/Lnn3+ioaHBH3/8wbhx43j//ffZsWMHLi4uxMbGsm7dOhYuXMiiRYvk8585c4bc3Fz69+/PmjVrmDlzpvy9/Px82rdvj5eXFzt37sTOzo4HDx5w8OBB0tLS5OVGjhxJgwYN+PHHH6ss4Kn0XZw8zZpf/+KDT8fiWdOH4/sOs+DLOSxe/SuWNspPhQoKCjA2NaHPewM4sH2PymVKCgqY98UsjE1NmTznSywsLUl6+hRdff0qqfOJo8dZtuhnJk+bQs1a/uzduZupn37Ouq1/Y6OiD4ZHDx8xddLndOvVna++/Zob18JY9L+FmJiZ0qpNawCuXLpM2w7t+dS/Jto6Omxat4HPJ3zG2i1/Y2VddNxnpKcz/oOx1KlXlx9+XoiZmRmPHjzE0MiwStbr1vmrBG7YS/vhvXHwcOHayXNsX7iSUd99jrFF2ds/LzuHg39uxtnHnax0xVT3QomEbT/+ib6xIT0mDMPI3JSM5FS0dVXfqFQV7YDa6NSpRfbRQKSpaeg0qItBn+5krN0EBQVlzifLyysqo7AShfI/MzftgBI3QuoW5hj27UHB3agqrf/FoBC2/LmOoeNG4e7jSdCh4yz9+nu+Wf4TFtaWSuXv3IjAp44fvUcMQt9An7PHgvjlmx+ZsXguTtVd5eX09PWY++cihXlfZyAu8uJ1zm45RPOh3bBzd+Jm0EUOLP2bQd9MwMjCtMz5Bs+diLZe8T6ia1T8EOrR7RhqNPDHtrojGlqaXD1yhv2L1zHwmwkYmikHSqvKujP/sDHkDF/37oeThSWrgk4yYe1Ktk+cgkEZN97Pxaem8PORg9RxdlF6z9rYhAntO1LN3AKAA1cv8/mm9fz98ScVDvSVZe2WLWzcsYPZn3+BU7VqrNy4gfHTprFj9WoMyjgfhoWHM2PePMaOGEHrps04efYMX86by8rFS6jpXRRAn7doIVExMXw77UusLCw4eOI446ZOZdvKVVhbFu2fi37/jdBr1/j2yy+xt7Hl3KVQ/rd0KZYWFrRq0rRS61XS3QvXOb35IC3f6y7fx/YtWceQuRPL3ceGzp+ksI/pGb2bDzr1tHSITHrAwVvBLOg89m1XR0EjJ1+G1e3E6tAD3HkaSxv3AKa2eo+pB34lKTtNqbyHlRMfN+rN35ePcPnhbcz0jRlVvxsfNOzBktNbAPC2cSHk/g3WhcaRL5XQzbspX7YexrQDv5KS8/JNqSriXuhNzm87SuNBnbGp7sit05c5+usm+nw9FkNzkzLn6zvnY7R0S56nio+pQkkhR5ZuQNfIgDYf9sXA1IjMlHSF8mIdlPnZVqeLdxP23TzD/ZQE6jv58H5AF34+vZW03EyV8wyu3R4DHT12XQ8iKTsNQ2091NWUG9GZ6hrS2asR0cnxr63+ANeDQzm4ZjvdPxiEk6cbF4+fYd2CX5m4eBamlsq/M7R1dGjYsSW2zg5o6+hw/1Yke1ZsQltXh/rtih6CDPn8QwolEvk82RlZ/PrFAmo2fvO/RQVFOjo6ZQbfSpswYcILRy51cXEhLCyMx48fK7339OlTbGzKvy8oKChgwIABREdHExgYqJAVp0rdunXR0tLi7t27LxXbEM1UhdeqW7duWFtbs2bNGoXp2dnZbNmyhdGjRwNFQZUWLVqgp6eHo6MjEydOJCsrq8zlxsbG0rNnTwwNDTE2NmbAgAFKB9vevXsJCAhAV1cXS0tLhdTR0k0k7969S4sWLdDV1cXHx0ep40aAadOm4eHhgb6+Pm5ubsyaNYuCUj9Gv//+e2xsbDAyMmL06NHk5ua+8Ds6ePAgHh4e6Onp0bp1a2JiYpTKvOz387x57B9//IGjoyP6+vr0799fqenr6tWr8fb2RldXFy8vL4Wof0xMDGpqamzdupVWrVqhq6vL33//TWFhIZMnT8bU1BQLCwumTp2KrNQoOKWbqT558oTu3bujp6eHq6srGzZsUKrzokWL5MNIOzo6Mm7cODIzVd8wPLd582Y6dOiArq6uyvcDAwNp06YNI0eOZOXKlWhoaPDgwQMmTpzIxIkTWbVqFa1atcLFxYUWLVrw119/8fXXXyssY+XKlQwZMoRhw4axatUqhXUNDw/n3r17/PbbbzRq1AhnZ2eaNm3K/PnzqV+/vrycn58ftra27Nq1q9z1qYz923bTpnN72nbtSDVnR0ZMGIOltSVH96ruB9Da1oaREz6kZYc26Buo/jEZeOg4memZfDF3Jl41fbCytcbLzxeXEkGJyti6cQtde3ajW68euLi6MHHKJKxsrNm9XfX3tGfnbqxtbZg4ZRIuri5069WDLj26suXv4qDP1/Pm0Lt/H2p4euDs4swXM6chlUm5dDFUXmbD2g1Y21gzffZMfHx9sLO3o16DAByqVU1GUOjhf/BrUR//Vg2xsLehzdCeGJmbcvVESLnzHV2zA5/GdbB3d1Z67/o/F8nJzKbXxBFU83DFxNKMah6uWDvZV0mdy6JTx5/ci5eQREUjTUom52ggalqaaHvVeOG8suwchZfCezm5Cu9publQmJpG4YNHZSzt1RzbdYBmHVrTvFMb7JwcGPTR+5hZWRB0QPkcDzDoo/fp1L8Hrh7VsXGwo8+IQVjb23Lt/GXFgmpqmJibKrxep2vHgvFqVhef5vUws7Oi2aAuGJoZczPoYrnz6RkboG9iJH89zyAGaDemHzVbN8DSyQ4zOytaDu+JTCbjYcS917YeMpmMTSFnGdmiNW18auJuY8ucPv3JLSjgSNjVcuctlEqZtX0LH7Zuh72Z8g+xFl7eNPXwwtnSCmdLK8a164i+tjY34srOLqpwnek0VbEAAQAASURBVHftZOTgIbRp3hx3V1e++WIquXm5HA4MLHO+TTt30LBePUYOHoKLkxMjBw+hQZ06bNy5E4DcvDwCT59m4pgx1PX3x9HBgY+Gv4+DrR3b9+2VLycsIoJu7TsQUKs29ra29OnajRrVqxNx506l1qu0q0fP4tO8Hr4tAjC3t6b54K4Ymptw/dSFcufTNzbAwMRI/iq5j71LzsXeZMX5vQTdu/q2q6Kks1djTt27zKmoyzxKT+Tvy4dJyk6jXQ3VGejuFtV4mpXKkTvneZqVyp2nsQRGhuJmXnw9+C14J8fvXuR+agLx6Yn8dWEv6mpq+Nq+OEP7Vd04cR6PJrXxbFYHUztLGg3ogIGZMbf+uVTufLpGBuibGMpfJfehu8FXycvKod3Y/thUd8TQwhRbdycsqlUuwP5fXgeApq5+XHpwi9AHt3ialcrBiGDScjNp6OSjsnwNS0dczO1YF3qIqKSHpOZk8iDtKbGpir+t1FCjf602nLgbSkp2+murP8DZ/YHUa9OEgLZNsa5mR9cR/TGxNOXC0X9Ulrd3daRWs/rYONpjZm1B7RYNqVHLm5iISHkZfUMDjExN5K+osFto6WhTs9H/k2CcTPruvl6CpaUlXl5e5b50dXVp3LgxaWlpXLhQfB07f/48aWlpNGnSpMzlPw/E3b17l+PHj2NhYfHCOt28eZOCggLs7Oxeal3ezSum8J+hqanJ8OHDWbNmjUIQY9u2beTn5zN06FCuX79Ox44d6dOnD2FhYWzZsoUzZ84wYcIElcuUyWT06tWL5ORkgoKCOHbsGFFRUQwcOFBe5sCBA/Tp04euXbty5coVTpw4QUCA6psaqVRKnz590NDQ4Ny5cyxfvpxp06YplTMyMmLNmjWEh4fz888/s2LFChYvXix/f+vWrcyePZv58+cTGhqKnZ2dUkpraXFxcfTp04cuXbpw9epVPvjgA7788kuFMi/7/TwXGRnJ1q1b2bdvn3wo5/Hji1OwV6xYwcyZM5k/fz4REREsWLCAWbNmsXbtWoXlTJs2jYkTJxIREUHHjh1ZuHAhq1atYuXKlZw5c4bk5OQXBplGjBhBTEwMgYGBbN++nd9++40nT54olFFXV2fp0qXcuHGDtWvXEhgYyNSpU8td7j///FPmdt21axddu3Zl5syZ/Pjjj/Lpz/e9spZdsnPOjIwMtm3bxnvvvUf79u3Jysri1KlT8vetrKxQV1dn+/btFJbI+lGlQYMGnD5dtc3vnpMUFHDvTiS1AuooTPcPqMPtmxGvvNxLweep4evFyp+XM6bvMKaMGs/ODVuRvmBdK6KgoIA7t25Tv2EDhen1GzbgRtgNlfPcvH5DqXyDRg25FX4LSYknnSXl5eYikUgUnmidPX0GT28vvv7yK3p06MrooSPYt2uvyvlfVqFEQkLMQ1xqeihMd6npwcPI+2XOd/2fi6Q+SaJJr/Yq34+8Eo69uzPH1+3i10++YfWMnzi378Rr7StRzdgIdQMDJPcfFE8slCJ58AgNuxeMHqWlhdGo9zAaPQz9Hp1Rt1LOQpNTV0fLqwYFN29VTcWfkRRIuB8ZjU9df4XpvnX8iYqoWCBDKpWSl5OLQamsybycXKa9/wlfDBvP0tk/EPusWfTrUCiR8PR+PI4+1RWmO/q6kxBVfqBp27e/s/bzH9i7cDUPb5UfZJPkFyAtLETHQK/SdS7Lw5QUkjIzaOReHMzV1tSkrosrYXFlHx8Af506gZmBAT3r1S+3HBQF7o5ev0ZOfj5+jqqbl1W4zgnxJCUn0yigXnGdtbWp6+9PWPjNMucLCw+nYb16CtMaBQTI5yksLKRQKkVbSzGjUkdHm6s3is+BtX1r8k9IME8SE5HJZIRevUrsgwc0LuPa9yoKJRKe3H+Eo69i8y9HH3cSIsvfxzZ/8yurJn/P7h9X8eAF+5igTENdA1dze67HK2YFX0+Iooalo8p57ibGYa5vTC37ouPIWNeABo4+XH2k3PzuOR0NLTTU1MnKyymzTGUUSgpJio3H3kcx2Ofg7caTew/KmKvIngUr2DRtCYeW/E387RiF92LD7mDtVo3gzYfZOHUxO7/9g2uHzryWa99/YR0ANNTUsTe2IjJRsc6RiQ9wMlMdAPS2duZh2lOau9ZiWuv3+KzFQDp5NkJTXUOhXBv3emTn56psulqVJBIJj+7F4l6qGw53f29ib1fsPPMoOo7Y29G4+pT98PBSYDB+Teq99lYGwtvh7e1Np06dGDNmDOfOnePcuXOMGTOGbt26KYyk6uXlJf8tK5FI6NevH6GhoWzYsIHCwkISEhJISEggPz8fgKioKL799ltCQ0OJiYnh4MGD9O/fnzp16tC06ctlrItmqsJrN2rUKH788UdOnTpF69ZFTclWrVpFnz59MDMz49NPP2XIkCHyTKoaNWqwdOlSWrZsye+//66U9XT8+HHCwsKIjo7G0bHoRmX9+vX4+vpy8eJF6tevz/z58xk0aJBCe/JatWqhyvHjx4mIiCAmJoZqzzJjFixYQOfOnRXKffXVV/K/XVxcmDJlClu2bJEHdZYsWcKoUaP44IMPAJg3bx7Hjx8vNzvu999/x83NjcWLF6OmpoanpyfXr1/nf//7n7zMjz/++FLfz3O5ubmsXbtWvk7Lli2ja9euLFy4EFtbW+bOncvChQvlGYOurq6Eh4fzxx9/8P7778uXM2nSJIWswiVLljB9+nT69u0LwPLlyzly5EiZ63jnzh0OHTqkMKz0ypUr8fZWvMCWzKRzdXVl7ty5fPzxx+UGNGNiYrC3V84MyszMpH///syYMUMpuHn37l2MjY0r9ORi8+bN1KhRA1/fon7XBg0axMqVK+X7sYODA0uXLmXq1Kl88803BAQE0Lp1a4YOHYqbW6mbOQcHrly58sLPfBXpaelIpVJMzEwVppuYmZKanPrKy30cn8DTK2E0a9eK6d/NJv7BI1YuXY60sJB+wwdXqs5pqakUFhZiVqpJs7mFGclJSSrnSU5KxrxUM08zc3MKCwtJTU3F0lI54LP8l+VYWVlRr0HxD9f4h4/Ys2M3A4YM5L2Rw4m4Gc7PCxejpa1Fp66dlZbxMnIyspBJpRiYGClMNzAxJCtNddOglISn/LPtIINnjkNdQ0NlmbSnScRGpODTuA59J48m5XEix9ftQlooLTOAV1nqzzImZdnZCtNl2TmoGZfdpFeanErO0UAKE5NR09ZGp44fhgN6kblhG9JU5SZXWtVdUdPRIT+8aoNxmelFx4WxqWLTIiMzE9JSlOuhyrGdB8jLzSOgefHgNraO9oycPBYHFydysnM4secQ//t8Dl//8j02Di/3RLQicjOzkUml6Jf6zvWMDMhOU509rG9iRMthPbBytqdQIuHOuWvsXbSWnp+PLLMPsHM7jmFgakw1n9eXOZOUWXQMmBsorou5gSEJ5QxadO1+DHsvh7Lh44nlLj/ycQKjVvxOvkSCnrY2Pw5+D7dKNlFNetY/ooWp4rnHwsyMeBXNX+TzpaRgYaY8T1JK0fIM9PXx9/Hhrw1/4+rkhLmZGUdOnuTGrVs4luib9Ivx45m3eBFdBg9CQ0MDdXV1vvpsMrVr+lVqvUrKyVC9j+mbGJB9Q/U+ZmBqROvhPbFycaCwQMLtkKvs/mk1vb8YhYNn1WRP/39gpKOPhro6abmKrR3ScrIwsVN9nr2bGMdvwTv5pGk/tDQ00VTX4NKDW6wNPVjm5wyq3Y7knAxuJLyegGleZjYyqUypmXK55yljI5oO7YKFkx1SSSGR569z6Oe/6fLZMGxrFGWIZySmEn87BrcGNekwfhDpT5IJ2XIYqVRKna4txDqoqpO2Lhrq6mSWCrxm5uVgqK26JYSZvjHOZrZIpIVsuHwEfW1devg0R19bh53XgwBwMrWhnqMnv5zZUeV1Li07PROpVIqh0r2UMZmp5Wfk/TB2BlnpmUgLC2nTvysBbVUHRx5ExvA47hG9P36vyuotvHs2bNjAxIkT6dChAwA9evTgl19+UShz+/ZtefdCDx48YO/eogf0pQdhPHnyJK1atUJbW5sTJ07w888/k5mZiaOjI127dmX27NlolHEfXxYRjBNeOy8vL5o0acKqVato3bo1UVFRnD59mqNHizr8vHTpEpGRkQpNF2UyGVKplOjoaKWgTUREBI6OjvJAHICPjw+mpqZERERQv359rl69qtCxYnkiIiJwcnKSB60AGjdurFRu+/btLFmyhMjISDIzM5WybSIiIhg7VrEfksaNG3Py5MlyP7tRo0YKnUiW/uyX/X6eU7VOUqmU27dvo6GhQVxcHKNHj1b4niQSCSYmij9cS2aepaWlER8fr1BHTU1NAgIClJqqllzH52We8/LyUhoe+uTJkyxYsIDw8HDS09ORSCTk5uaSlZVV5mAbOTk5KoORenp6NGvWjBUrVjB48GCF76iinXZCUdDwvfeKL9LvvfceLVq0IDU1VV7/8ePHM3z4cE6ePMn58+fZtm0bCxYsYO/evbRvXxwk0dPTI7tUUKOkvLw8pSG8K9p3wnNK6yWTUcFVVUkmk2FsZsJHk8ejrqGBm4c7KUnJ7N2ys9LBuOdK11kmU93Zqrw8pd+TlTEdNq7bwImjx1i6/BeF71IqleLp7cWH44uOVw9PD2LuRbNnx65KB+NKVFSxljJUbgupVMr+5Rtp2rsD5rZl92Upk8rQNzKkw8h+qKurY+tajczUNC4eDKqyYJyWZw302raU/5+158CzD1dVobKXU5jwmMKE4iBF9qN4DIf2R7tWTXKDzip/bk0vJDGxyLLKPj4qQ+l7r+A54Pyps+zdsIPxX09RCOhV96pB9RLNdN19PJg7cQaB+44weOyIKqq1CiqqXNZ6mNlaYlai83Db6k5kJqdx9ehZlcG4K4dPE3nhOj2/GIlmFQ6+c+jaFb7bt1v+/+Kh7z+rt2I5GahcP4CsvDy+3rGVGT36YPqCgZecLSzZ8PEnZOTmEhh+gzk7t/PHqDEvFZA7dOIEC5YUZ70vmTf/WZ1Ln6sqsh+pOL+V+P/baV/y7U8/0XnwIDTU1fGsUYNObdpw625xhtPm3bu4HhHBom/nYmdjw+WwMP63bCmWFuY0rKuYeVflZEqrIGdma4VZiXOWnbsTmSlpXDlyVgTjXoGs1ElVTY0yz7MOxlYMr9eZXTeCCIuPwlTPkCG1OzCqQTdWnFfO8u7m3ZTGzn7MO7GGAqnqLPKqonScqJj2nImtBSa2xc2/rN2qkZWSzvVj5+SBLJlMhq6RAU2HdkVdXR1LZzuy0zK4fuzcawlk/VfW4Xm9SypaBdU71fPV23otkDxJUfbPwVshDK7Tnr03z6Cupk7/Wm3Yff0fsgte3AVPlVG+gKu+mSrhg28nk5+bR9ydaI5u3IO5rRW1milnVIcGBmPjaE81d5eqq++7rozfav9l5ubm/P238iArJZX8Devi4lLmb9rnHB0dCQoKqpL6iWCc8EaMHj2aCRMm8Ouvv7J69WqcnZ1p27ZodBupVMpHH33ExInKT7ydnJSbl5R1A1xyup5exZvZqDrgSi//3Llz8ky7jh07YmJiwubNm1m4cGGFP6ein13ay34/ZXm+TmpqavLU+BUrVsiz1Z4rHdGv7Kizz9exvB8t9+/fp0uXLowdO5a5c+dibm7OmTNnGD16tFK/fCVZWlqSkqI8qqOGhga7d++mb9++tG7dmsDAQHx8ivrJ8PDwkAcVy8uOCw8P5/z581y8eFGh2XJhYSGbNm3i448/lk8zMjKiR48e9OjRg3nz5tGxY0fmzZunEIxLTk4ud+CQ7777TmlkoNmzZ9N7zJAy53nO2MQYdXV1UkuNcJmWmqaULfcyTM3N0NTUVMjWcnCqRmpyCpKCgkr9aDcxNUVDQ0MpCy4lOUUpW+45cwtzkpKSlcpraGhgUir7adP6jfy9eh2Lfl1C9RqKTa8sLC1wcXNRmObs4kJQ4KlXW5kS9IwMUFNXJytVMQsuOz0TfWMjpfL5OXkkRD/g8f1HHF+/G3h2zMhk/DRyGv2/GIOzjzsGpsaoa6gr9ENjYWdDVloGhRIJGpqVv5wX3ItRCKLxbLurGegrZMep6esp9QH3IoUJT1BXsS+qGRmi6ViN7P1lZ9e+KkPjouOidBZcRmo6xqbld8R7MSiEdT//yUfTP8WnTvkZSOrq6rjWcOPJw7JHqasMXUN91NTVlTIzcjKy0DOu+PnZxs2RO+eUR4+8euQMlw+epvvk97Go9oLmxy+phZcPNasVPzjLf9bEPSkzE0uj4m2QkpWJhaHqLKAHyUk8Sk1hysZ18mnSZ9eVRnNmsn3iZPmgDVqamjhaFAUhfRyqEf7wAZvPBTOjR2/lBZdV58aNqelVPJpp/rNrUGJKMpYl+o1JTk3FvJwBeYqy4BTPV8mpKQrzVLO3589Fi8jJySErOxtLCwumz5uL/bMBbHLz8vh11Sp+mjOHZg2LsjNruLlxJyqKv7dtq7JgnJ7Rs30sXXEfy07PUsqWK4+tmyO3VexjQtky8rIplEox1VX8no11DcrsaL+HbzPuJMZyICIYgLjUx6yWHGB2+1FsuxZIaon5ung1oYdvc74LXEdcatmZnJWlY6iPmrqa0j6U+5LnKStXB6IuXJf/r29iiJq64rXPxNaSnPRMCiWFaGi+XBZKef4L6wCQnZ9LoVSKkY7ibyEDbT35aLulZeRmk56bJQ/EATzNTEFdTQ0TXQO0NbQw1zfmvXqd5O8/v6//tuMYlpzeQnIV9iGnb1zU717pLListAylbLnSzJ8NzmTr5EBmWgYntx1QCsbl5+Vz/WwobQd2q7I6C8KrEH3GCW/EgAED0NDQYOPGjaxdu5aRI0fKT+J169bl5s2buLu7K720VYxO5+PjQ2xsLHFxcfJp4eHhpKWlyTOg/P39OXHiRIXq9nx5jx4VdxweEqLY0frZs2dxdnZm5syZBAQEUKNGDe7fV+zfxtvbm3PnzilMK/2/qs9+0Twv+/08p2qd1NXV8fDwwMbGBgcHB+7du6e0TFfXsp9om5iYYGdnp1BHiUTCpUtld2zr7e2NRCIhNLS4A/3bt28rDCYRGhqKRCJh4cKFNGrUCA8PD4W6l6VOnTpljgSro6PDzp07adCgAa1bt+bGsz54+vXrh7a2Nj/88IPK+Z7Xa+XKlbRo0YJr165x9epV+Wvq1KmsXLmyzDqpqanh5eWlNMDGjRs3qFOnThlzFQ3pnZaWpvCaPn16easvp6mlhZuHO2GXFJvBhl26iqev6szJivCs6UPCw3iFfk3iHzzCzMK80tkzWlpaeHh5EnpesQP60AsXqelfU+U8vn41Cb2gWP7i+Qt4+XihWSIYtWn9BtatXMOPSxfi5aO8/n61/Im7r9gPUlxsrMoRXF+WhqYmti4O3L+p2HfP/Zt3cFAxMIOOng4j5k/h/bmfyV+1WzfC3M6K9+d+hl31ooC7Qw0XUp8kISuxLVIeP8XA1LhKAnEAFBQgTUsvfiWnIM3KQtOpxMAW6upoVrOnMP7lAk/qVpbIVAw6o+3rhSwnB0l0+f2FvQpNLU2c3V2JuBKmMD38ynWqe3uUMVdRRtzqxb/zwRcT8G/w4k6dZTIZsffuv7ZBHDQ0NbFytuNBhGKfUg/Co7CtXvEHMomx8eiX+hFz5cgZLh0Iouunw7B2cShjzldnoKODo4Wl/OVmZY2FoRHnI4uPjwKJhMsx0fg7Kh8fAC6WVmwa/yl/f/yJ/NXC05t6Lm78/fEn2BiXPcKhTAb5ZfQnWWad9fVxdHCQv9ycnbEwN+f8peJBPAoKCrgcFoa/j2+Zy/H38VGYB+D8pUsq59HT08PSwoL0jAxCQkNp+axjaYlEgkQiQa3UiIbqGupIpVWX4aChqYm1sz1xNyMVpseFR2LrXvF97GlsPPomVTMq9f8XhdJCopMfUdNWsU9IP9vq3E2MUzmPtoaW0sNc6fMO0Es8+Ozq3YTeNVvww8m/iU6u2sFxStPQ1MDCyY5HEYr9Zz6KiMbareKDIyXHJSgEgK3dqpHxNAVZif09/UkyeiaGVR7E+i+sA0ChTMqj9Ke4WyjW2d2yGrEpqgOysamPMdLVR1uj+H7C0sAEqUxKWm4WT7NS+fn0Vn45u13+uvUkhuikR/xydjtpOeUPuPayNDU1sXdzIjJMsd/jyLBbOHm+RFcKMpnKPoVvhFyiUCKhdvMGKmYShDdHZMYJb4ShoSEDBw5kxowZpKWlMWLECPl706ZNo1GjRowfP54xY8ZgYGBAREQEx44dY9myZUrLateuHf7+/gwdOpQlS5YgkUgYN24cLVu2lDeFnD17Nm3btqV69eoMGjQIiUTCoUOHVHba365dOzw9PRk+fDgLFy4kPT2dmTNnKpRxd3cnNjaWzZs3U79+fQ4cOKA0aMGnn37K+++/T0BAAM2aNWPDhg3cvHlTqe+wksaOHcvChQuZPHkyH330EZcuXVIaefZlv5/ndHV1ef/99/npp59IT09n4sSJDBgwANtnAYc5c+YwceJEjI2N6dy5M3l5eYSGhpKSksLkyZPLXO6nn37K999/T40aNfD29mbRokVKo7SW5OnpKe88888//0RTU5NJkyYpZC9Wr14diUTCsmXL6N69O2fPnmX58uVlLvO5jh07Kg04UZK2tjY7duxgwIABtGnThhMnTuDn58fixYuZMGEC6enpDB8+HBcXFx48eMC6deswNDTk+++/Z/369Xz77bfUrKkYGPrggw/44YcfuHbtGjKZjNmzZzNs2DB8fHzQ1tYmKCiIVatWKWTTZWdnc+nSJRYsWFBmXV9mSG9VuvXvxbLvFuHmWQMPHy+O7z9M4uOntO9e1Oxy44q1JCcmMWF68baNiSzqOyY3J5f0tDRiIu+hqalJNZeiH18denTm8K79rPllBZ16dyPh4SN2bdxG595V8yRxwJCBzJ89F08fL3z9arJv1x6eJDymZ9+iLJY/fvmdxKeJzPxmFgA9+/Ri19Yd/LJ4Kd169eDm9Rsc2LOfr+fPkS9z47oNrFy+glnzZmNrZ0dSYlHmnZ6+Hvr6RX2l9B88kHGjP2L96rW0bteWiJvh7Nu1l89nlD9gSEUFdGrBgT82Y+v6f+zddXQU59fA8e9u3N0IIR5ICBYgOASHIkUKxdsCVSiltIW2VKhAlVJv+VG0uENxCO4aLMETCBAh7rr7/hHYsMkGS0KA937O2XOS2eeZvbOzM7N755HqVPNx5+SOQ6QlplCvXVH37t1LN5CenEq31weiUCpxKNEaydTSHD0Dfa3l9ds14/i2fYQuWEtQxxYkxyZw8L/tBHVsWSExlyX3xCmMg4NQpaSiSknFqHEQ6vwC8s4VJ1NMOrVDlZlJ7r5DABg1aVTUVTU5BYWRIUb166DnYEfOjtITmBgG1CIv/HyldZ3o2LsbM6f+gbuvF961/Ni9KZSkWwm0ea4DACtnLyI5MZkR778F3E7ETf2LF18fhlctX1Jvj7loYGSomXV47YLleNXyxamaM9lZ2Wxfu4nrV64y+K1XKmUbAOp1bE7ozJU4uLvi7O1G+O6jpCelUrtN0d3+gyu3kpmcRvsRRWN5nty2H0s7G2yqOaIqKOTCoZNcOR5O5zcHaNZ5YtMeDq/ZToeRL2Bpb03W7TENDYwMMaikwawVCgUDm7Vg9p6dtxN0dszZvRNjAwM6162vKff5iqU4WFoyumMXjAwM8HHSPkbMbw9PcPfyP7ZuprmvH05W1mTl5bLl9EmOR13h16Hl2y8KhYKBvfswe9FCatxO0M1etBBjI2O6tGunKffZd9/iaG/P6BFF48YO6N2H18a9y5zFiwlp3pyd+/dz6PhxZk77WVPnwJEjqFHjXt2N6Js3+fV//8PdzY2enYtan5ibmRFUty6/zPgfRkaGuDgWdVPdsHUr75YYFqO86ndqwdZ/luPoUfQZO7v7KBlJqQTe/oztX7GFzOQ0Oo58AYCwrfuxtLPG1tWRwoJCzh88yeVjZ+n6VsUMYVDRTAyMqG5V3Dq9mqU9vvbVScvJJC6jdAv7x2njuQO82awPkUk3uZgQTTufhtiZWhF6segm5ov12mNjasnfB4q+d564cYERTXrQ3qcRp2IuY2NizpCGXbiUcJ2U7KLjuLt/C16o25Y/9q/gVmYKVrdb3uUU5Gm1fqpIge2bsHvOGuzdXXD0rM75vcfJSE6lVquimxpHV28nMyWdNi8/D8DZ0EOY21ljXc0BVUEhlw+fJurEOdq99oJmnbVaNyR851EOLttMQEhj0uKTOLlpHwFt7z+Ry//XbQDYF3maF+q15UbaLa4lx9HYzR8rY3MOXyu6gd3JLxhLYzOWnyoaSufkzYuEeAfRp04IoZeOYmZgQpdaTTl2/TwFqqIWzfEljpOc/DydyytKi+7tWP7bXFy93HHz8+Totn2kJiTTuGMrALYsXE1aUgovjH4ZgIObdmFtb4O9a9F14eq5y+z9bxtNu4aUWvex7fvxb1wPU4v/ZzcPKvAmjqgYkowTj82IESOYOXMmnTp10upeWbduXXbt2sXEiRNp1aoVarUab29vrdlR76ZQKFi9ejVvv/02rVu3RqlU0qVLF63EVEhICMuWLeOrr77i22+/xdLSktatdY/LoFQqWbVqFSNGjCA4OBgPDw9+/fVXunQpbor9/PPP8+677zJ69Ghyc3Pp1q0bn376KZMmTdKUefHFF7l8+TITJkwgJyeHvn378uabb95zcoMaNWqwYsUK3n33Xf7880+Cg4OZMmUKw4cPf+T35w4fHx/NTK1JSUk899xzWpMhjBw5ElNTU3744QfGjx+PmZkZderU0ZpIQZf33nuPmJgYXn75ZZRKJcOHD6d3796agS91mT17NiNHjqRNmzY4OTnx9ddf8+mnn2qer1+/Pj/99BPfffcdH330Ea1bt+abb75h2LBh94xlyJAhTJgwgfPnz2vNinM3AwMDli5dysCBAzUJubfeegs/Pz9+/PFHevfuTXZ2Nh4eHnTv3p1x48axdu1aEhMT6d27dNcmX19f6tSpw8yZM/nss8/w8PDgiy++ICoqCoVCofn/3Xff1dRZs2YNNWrUoFWrVvfcnvJo3rYV6WlprJi3mOSkJNw83Pnom89xcHYEIDkpiYT4W1p1xr/2jubvKxcusTd0Fw5OjvyxqKjln72jA598/yVz//yHD0a+ja29HV379KDXgL4VEnP7Th1IS01j7j+zSUxIxNPbi+9+/hHn2zN1JiYkEndXt8lqrtX4/ucf+W3ar6xathI7B3veeX8sIe3aasqsXr6S/Px8PpvwidZrvfzqcIa/NgIA/9r+TP7hG6b/8Tdz/5mDczUX3h73Dp26dq6Q7arVpD7ZGVnsX7ONzJQ07F2d6TtuBFb2Rd3TMlLTSH/IiTUs7azp98FIdiz8jzmf/IS5tSUNO7UkuFvb+1cuh7yjYSj09TFp1wqFkRGFsfFkrloHd3UfV1qac/c4NAojQ0zat0Fhaoo6L4/CW7fIXL6GwjjtGZT1a1RHaWlR4bOo3q1xm2ZkpKezbuFKUpNSqObhxpgvJmDnVPSjPCU5haRbCZryuzeGUlhYyMI/Z7Pwz9ma5c06tGb4uKKu6dmZWfz76z+kJadgYmaKm7cHH3z/GZ41tbtDVySfxnXIycjm2LqdZKamY1vNkW5jhmBhZw1AVko6GUnF52BVQSH7l20mMyUNfQMDbKo58NyYIbjXKW4ReHbnEVQFhWz5e4nWazXqEULjnu2oLMNatiY3P5/v1q0hPSeb2q5u/DZsOGZ33YyITU154LE970jKzODzlUtJSE/H3NgYHydnfh36Ck18yp5J70G99OKL5Obl8u1vv5Kenk5gLX9+//ZbzEyLB0OPjY9HeVcLtnq1azN54if8NWc2f8+dQ3WXanwz8RMC7xrDNCMrk99nziQ+IQFLCwvatWzFqOGvaLX0nTLxE/6YOZNPv/mGtPR0nJ2cePOV4fTt3qPc23U33+A65GRkceS/HWSmpmPn6kT3d4Ziefu8lZWSrnXeUhUUsm/ZJjKSiz5jtq6OdH9nKB51dV+Lq1otB3d+7118M2pMy34AbIg4wOTtZd/UexwOXjuLuZEpvQPbYG1izvXUeH7YuYCErKJj2trEAjvT4haguyPDMDYwpJNfMIODOpOVl8PZuEgWh23VlOng2xgDPX3GttL+rrji9E5Wnt5ZKdvh1ag2uZnZhK3fQ1ZaBjYuDnQaNQDzO+ep1Awy7zpPFRYWcnjlNrJS0tEz0MfGxYGOowbgFlh8LjW3taLLmEEcWraV1V//D1NrC2q3bUydzs1lG+7hdOxlTA2NaOvdEAtjU+LSk5h3dKOmC7OFkakmQQuQV1jA7CPr6RHQgrea9yErL5czsZfZeuFIWS9R6eo0b0RWeiY7VmwgPTkNJzcXhn70FjYORcMFpCenkZJQnAhUq1VsWbSG5PhElEolts4OdBrci8YdtG9aJtyM4+q5y7z8yduPdXuE0EWhfpBBq4QQT5VJkyaxevVqwsLCqjqUSjd+/HhSU1OZPn16VYdSpuDgYMaOHcugQfcf/62kkzcuVEJEj1c9Vz/i0hLuX/AJ5mRpzz8HSw+M/bQZ2bQnqT//VdVhlIvV2DfZffn4/Qs+4Vp7B/Hz7iX3L/iEG9v6RdKWrKzqMMrF8sU+pF/T3SXwaWJRw43f9i6r6jDK5e2W/WjxR8W2+qsK+0b9zeCFk6o6jHJbMGgS323/t6rDKJcJ7YY+9dsARdsxceOT+133QUzu+jrLTj7YMEJPsn712ld1CI8kbff+qg6hTJatKy85/SSTMeOEEE+1iRMn4u7uTuHtgcGfNPHx8bzwwgsMHPhkdt0RQgghhBBCPOPUqif38f+UdFMVQjzVrKys+Pjjj6s6jDI5OjrqHKtQCCGEEEIIIcT/T9IyTohn0KRJk/5fdFEVQgghhBBCCCGeNtIyTgghhBBCCCGEEOJZJVMFPHGkZZwQQgghhBBCCCGEEI+JJOOEEEIIIYQQQgghhHhMpJuqEEIIIYQQQgghxLNKJd1UnzTSMk4IIYQQQgghhBBCiMdEknFCCCGEEEIIIYQQQjwm0k1VCCGEEEIIIYQQ4lkls6k+caRlnBBCCCGEEEIIIYQQj4kk44QQQgghhBBCCCGEeEykm6oQQgghhBBCCCHEs0qtquoIRAnSMk4IIYQQQgghhBBCiMdEknFCCCGEEEIIIYQQQjwm0k1VCCGEEEIIIYQQ4lmlktlUnzTSMk4IIYQQQgghhBBCiMdEknFCCCGEEEIIIYQQQjwm0k1VCCGEEEIIIYQQ4lmllm6qTxppGSeEEEIIIYQQQgghxGOiUKslRSqEEEIIIYQQQgjxLErbHFrVIZTJsnP7qg6hSkg3VSGEeIIdijpT1SGUWxOPQFLSU6s6jHKxtrDindXTqjqMcvul17ukfPxlVYdRLtZTPuPLrbOqOoxy+6zjcEYu/aaqwyi3f/p/RNqmbVUdRrlYdunAyRsXqjqMcqvn6sfXW2dXdRjl8knHVxi8cFJVh1FuCwZNosUfb1R1GOW2b9TfDF86parDKJdZ/T9m/LrfqzqMcvu+++in/nvIL73eZcL6P6s6jHL7rttbVR3Co5E2WE8c6aYqhBBCCCGEEEIIIcRjIsk4IYQQQgghhBBCCCEeE+mmKoQQQgghhBBCCPGsUqmqOgJRgrSME0IIIYQQQgghhBDiMZFknBBCCCGEEEIIIYQQj4l0UxVCCCGEEEIIIYR4Vslsqk8caRknhBBCCCGEEEIIIcRjIsk4IYQQQgghhBBCCCEeE+mmKoQQQgghhBBCCPGskm6qTxxpGSeEEEIIIYQQQgghxGMiyTghhBBCCCGEEEIIIR4T6aYqhBBCCCGEEEII8axSSTfVJ420jBNCCCGEEEIIIYQQ4jGRZJwQQgghhBBCCCGEEI+JdFMVQgghhBBCCCGEeFapVVUdgShBWsYJIYQQQgghhBBCCPGYSDJOCCGEEEIIIYQQQojHRLqpClGB5syZg5OTE127dq3qUIQQQgghhBBCCJlN9Qkkybj/Rzw8PBg7dixjx459bOtTKBSsWrWKXr16PfLrTJo0idWrVxMWFvbAdXbu3Enbtm1JTk7G2tr6kV/7YaxcuZLvv/+effv2PVL9l19+mZSUFFavXg1ASEgI9evX5+effy6zTkXs07v3UVRUFJ6enpw4cYL69es/8jofREV9Hrdv385bb71FeHg4SuWT19g3Pj6e2rVrExYWhqura6W+1rb/NrFh2RpSk5JxdXdj8BuvULNOgM6yR/YeZPu6zVy7EkV+fj6u7m70HtKfuo0aaMpcj7rGynmLibp0hYS4Wwx6/RW69OlerhiXL1vO/H//JTEhEU8vL959710aNGhQZvnjx47z87SfibxyBXsHe4YOHUqfF/pqldkeup3pf0/nxvXruFavzptvvUFI27ZaZeLj4/njt9/Zv38/uTm51HCvwcRPP8Hf3x+AJo2Cdb7+6DFvM3TY0IfezpaedWnn0whLYzNi0xNZeXoXVxJv6Cw7KKgTTWrULrU8Ji2Rb7fPA8DZwo7n/JtR3doRO1MrVp7eya7LJx46rkdh3L4Nho2DUJgYUxh9g6y1G1HF37pnHYWxEcad2mEQUAuFiQmq5GSyN2yl4MKlogKGhph0DCl63tyMwpuxZK/bTOGNmxUe/4XdxwkPPUx2agbWLvY07NseRx83nWXjLlxj26+LSi3v/slIrJztAFAVFnJ2y0GuHDpDVko6lk62NHg+hGoBXhUe+91CvIPoXLMJ1ibm3Ey9xeKwbVxMuK6z7CuNu9HCs26p5TdSb/H55n8A+CBkEDUd3UuVOXXzEr/uXVaxwd9FrVYzY9MGVu3fR3p2FrXdPRj/Qn+8XaqVWWf7yTDmbN1MdMItCgoLcXNwYEjb9jzXuInO8rO3bubPdWsZ0KYt7/V5ocK3YfOa9axdspKUxGSqe9Tg5VGv4l+39DEMkJyYxLy/ZnLlwmVib9yka+8evDz61VLlMjMyWDTzXw7vOUBmegaOLk4MfWMEQU0bVXj8d5zffZyzoYc0x0ajvh1wKuPYiL1wla06jo2en7yqdWyc2XKAy7ePDSsnWxo83xbXSjw2Ovg2ppt/c6xNLLiRGs+/xzZx/ta1Mss396hDd/8WOFvYkZWfw6mbl1h4YgsZedkAtPUOoqVnPdysHQGITIphycnQMs/fj1s9Fx8GNehELcca2JtZ8+GGv9gTebKqw9Jo6x1El5pNsTYx50bqLRaFbeNiQrTOssMbd6dlGeepTzfPAGB8yGBq6ThPnbx5iV/2Lq3Y4G9r5h5IG+8gLIxMiUtPYm34HqKSYsosr6dU0sE3mCBXPyyMzEjNySD00lGORkcA8Hqz3njblf7+FxEXxewj6yplG3Sp6O8lj0NT99q08WpQtC8ykvjv7D6iku+3LxrToJofFkampOZksP3SMY5eP6cpY6xvSOeaTQh09sLEwIjk7HTWhe+753lDiMoiybinQI8ePcjOzmbbtm2lnjtw4ADNmzfn2LFjBAUFPda4jhw5gpmZ2WN9zSfVlStX+OSTT9i4cSM2NjYVss6VK1diYGBQIet6UG5ubsTExGBvb1/pr1VRn5/x48czceJETSJuzpw5jB07lpSUFE2ZiIgIOnbsSHBwMIsWLcLIyIgdO3bwww8/cOjQIbKzs/Hw8KBr166MGzeuVNKsZs2aREZGEhkZWeq5K1euMHHiRHbt2kVSUhL29vY0bNiQH374AT8/PxwdHRk6dCiff/45//zzT7m3tywHd+5jwd+zeWn0q/jWrsWO9Vv48ZPJfDPjZ+wdHUqVP386nMCgevR7ZTCm5qbs2byDaZ9/y+e/fIOHT9EPp7zcPBxcnAhu3ZwF02eXO8atW7YybepPjP9wPHXr1WPVylW8O2Ysi5ctwdnZuVT5mzdu8O47Y3m+dy+++OoLTp08yffffo+1jQ3t2rcD4PSpU3zy8URee+N1QtqGsHPHTj7+8GP+N3MGgYGBAKSlpfHaiFcJatSQn3/5BRtbG25cv46FhYXmtTZs2qD12vv3H2DyV1/Trl27h97OBq5+9K4TwrKT24lMvElzzzq80awX34TOIzk7vVT5lad28t/ZvZr/lQolE9oNIezmBc0yQz19EjJTOXHjAr3rhDx0TI/KqHVzjFo0JWvFGgoTEjFu2wrz4UNI++kPyMvTXUlPidnwIagzsshcuBxVWhpKK0vUucXlTfv0QM/Jgcxlq1GnpWPYoC7mI4aQ9vNfqNNKv0ePKupYBMdWhNL4xU44eLlycW8YO/5cRvdPRmJma1lmvR6fvoqBiWHx+2Buqvn75H97iDxyliaDumDpZEdMRCS7Z6yi07gh2Lo5VVjsd2vs5s+A+h1YcHwzlxKu09q7Ae+0epHPNs8gKSutVPnFYdtYcXqn5n89hZLPO43g2F0/SP7cvxI9pZ7mf3NDEz7vNELrR0tlmBe6lYU7tvPZ4KHUcHBk1pZNjP7zd5ZP/AwzY2OddaxMTXmlY2c8nJwx0Ndjz5kzfLlwPjbmFjTz177hcPbqVVbv34dvtcq5+bF/xx7m/PEPI995g5qBAWz7bxNTPpzEtNl/YO/kWKp8fn4+ltZW9BnSn/XL1+hcZ0F+Pl9/8CmW1taMm/Qhdvb2JN66hbGpqc7yFSHqWARHV2wj+MXOOHq5cmFvGNv/XErPT0ZiZmtVZr3nP32tzGMj7L/dXDlylmaDumLpZMfNiCvsmrGSLuOGYOtW+hxfXk1r1GZoUBdmH13PhVvXaOfTiPEhQxi//g8Ss1JLlfdzqMGbTXsz//hmjt84j42pJcMbd2dkk578vGcJAP5OHhy4eoZ5R6PJUxXQ3b8FH7YdyoT1f+g8fz9uJgZGXEq8zoZz+5nS9Y2qDkdLYzd/BtbvyL/HN3Ep4Toh3g14t9WLfLL5fzrPU4vCtrL89A7N/3oKJV+UOAf9sX9FqfPUF51GcvR6RKVsQz0XH3rUbsXq07uISo6hSY3ajAjuwdSdC0nJydBZZ0hQF8yNTFl2ajuJmamYG5mgVBTfHJ53dIPWNpgZGDO29QBOxVyqlG3QpTK+l1S2ui4+9Ahoyeozu7maHEuTGgEMD+7OT7sWlbkvBjfojIWRCctP7SAxKxUzQxP07rpRr6dQMrJJTzLyspl/fDOpORlYG5uTW5D/uDZLCC1PXjMSUcqIESPYvn07V69eLfXcrFmzqF+//mNPxAE4ODhgWolfFJ8mXl5ehIeH4+5e+u5dSfn5D3bCt7W11UoWPA56eno4Ozujr1/5efqK+Pzs37+fixcv0q9fvzLLHDlyhFatWtG5c2eWLVuGkZER06dPp0OHDjg7O7NixQrCw8P5+++/SU1NZerUqVr19+7dS05ODv369WPOnDlaz+Xl5dGxY0fS0tJYuXIl58+fZ8mSJQQGBpKaWvxD4JVXXmHBggUkJyeXa3vvZdPK/2jTuR0hXTvgWqM6Q94cjq2DHdvXbdZZfsibw+nWvxdeNX1wdq1Gv+GDca7mTNjBo5oyXjV9GPjqSzQNaVkhieFFCxbS8/mePN+rF56enox7bxxOTk6sWL5CZ/mVK1bi7OzMuPfG4enpyfO9etGjZw8WzJ+vKbN40WKCmwTz8isv4+HhwcuvvEzj4MYsXrhYU+bfufNwdHLks88/o3ZgbapVq0bj4GCqV6+uKWNnb6/12L1rFw0bNcS1+sP/oA/xDuLg1TMcvHqGuIwkVp3eRXJ2us6WSgA5BXmk52ZpHjVsnDAxMObQ1bOaMtdS4lh7dg8nblygQFXw0DE9KqPmTcjZuYf8s+dQxd0ia9kaFAYGGNYPLLOOYcMGKExMyJy/hMJr0ahTUim8Go0qNq6ogL4+BrX9yd4USmHUNVRJyeSE7kKVlIJRk4ptBXRu+xG8m9XFp3k9rJztafRCB0xtLLiw596tCo0tTDGxNNc87m51G3n4LLU7NcO1tjcW9tb4tWqAi78nEdsPV2jsd+voF8zeyJPsiTxJTHoiS8K2kZydRoi37lal2fm5pOVkah7uNs6YGhqzN/KUpkxmXo5WmQAnT/IK8zkaXXnJOLVazaJdO3ilU2fa1auPT7VqTBoylJz8PDYfO1JmvYa+frStVx9PZ2eq2zswMKQtPtVcCbtyWatcVm4On/07h48HDMKikr6frFu2mnZdO9K+W2equ7vx8uhXsXe0Z8vajTrLOzo78cro12jTqR2mZrpj2r5xGxlpGXzw1URqBQbg4OxIrTq18fD2rJRtAAjffhifZvXwvX1sNH6hA6Y2lpwvx7Fx5fBZ6tx1bNRsFYSLvyfh28vet+XRtVYzdl45zs7Lx7mZlsD845tIzEqlg6/u84iPXXVuZaaw+cIhbmWmcOHWNbZfOoqXbXGrzD/3r2TbxSNcTYklJi2Bfw6vRalQUNu5clu+PqiD184y49Badl0Jq+pQSunsF8yeu85Ti8K2kZSdRltv3b9RSp6nPGxcMDU0Ye9dLf1Knqdq3z5PHamk81Qrr/ocuRbO4ehw4jOS+S98LynZGTT1qKOzvJ9DDbzsXJl1+D8uJVwnOTud6JR4ribHam1nRm6W5uHr4EZ+YcFjTcZVxveSytbKsx5HoiM4Eh1xe1/sIzUng6buur9/+Dm44WVXjVlH1nMpsWhfXE/V3heN3PwxNTBi3tGNXE2OJSU7g6jkWGLSEx/XZlUttfrJffw/Jcm4p0D37t1xdHQslQjIyspiyZIljBgxAihKTLRu3RoTExPc3NwYM2YMmZmZZa732rVrPP/885ibm2NpaUn//v2Ji4vTKrN27VoaNWqEsbEx9vb29OnTR/Och4eHVhfKixcv0rp1a4yNjQkICGDr1q2lXnPChAn4+flhamqKl5cXn376aank1LfffouTkxMWFhaMGDGCnJyc+75HGzZswM/PDxMTE9q2bUtUVFSpMg/7/kyaNIn69eszffp03NzcMDU1pV+/flotrgBmz56Nv78/xsbG1KpViz///FPzXFRUFAqFgqVLlxISEoKxsTHz58+nsLCQcePGYW1tjZ2dHePHj0dd4kQUEhKi1YUzPj6eHj16YGJigqenJwsWLCgV808//USdOnUwMzPDzc2Nt956i4wM3XePdLkT750uwTt37kShULB582YaNGiAiYkJ7dq1Iz4+no0bN+Lv74+lpSUDBw4kKytLK/bRo0czevRozTZ+8sknWttY8vPzKLEvXryYTp06YVxGa4rt27fTrl07XnnlFWbOnImenh7Xr19nzJgxjBkzhlmzZhESEoKHhwetW7fmn3/+4bPPPtNax8yZMxk0aBBDhw5l1qxZWtsQHh7OlStX+PPPP2natCnu7u60aNGCyZMn07hxY025OnXq4OzszKpVq+67Dx5FQX4+URcvE9iwvtbyOg3rcTH8/AOtQ6VSkZ2dg5mFeSVEWJSEPnfuHE2aancrC27ahNOnTumsc/r0aYJLlG/arCkR4REUFBQlpE6fOk2TJiXKNG2qtc7du/fg7+/PRxM+pEvHzgwdNITVq1aXGWtiYiL79u6j5/M9H2YTgaK7rm7WTpyP1755cj7+Gp62ZXfD04rfPZALt65VeSsMpY01SksLCi5eKV5YWEhB5FX0a+juygZg4O9H4bXrmPTsiuXH47B45w2M2rQEheL2ipUo9JRQoJ1UVBcUoO9e9nofVmFBIUnRsbj4ayc0XPw9SYi8d5ezDd/NYcXHv7Pt18XEXtDel4UFBegZ6Gkt0zPQ59Zl3V1Gy0tPqcTdxpmzcZFay8/GRuJtV72MWtpaedUjIi5KZ+uUO1p61uXwtXDyCiuvhcCNxEQS09JoWstfs8xQ34Agbx9ORUbeo2YxtVrN4fPnuBofR5C3j9Zz3y9bSouA2jSpWatC476jID+fKxcuUa+RdhK0bqMGnD/76C11ju0/hG/tWsz85W9e7TuU94aPYuWCpagKC8sbsk7Fx4aH1vJq/h7cus+xse672Sz/+De2/rpI57GhNNC+madvoE/8Zd3dFMtDT6mHp201TsdoJ2RPx17G1173eeRiQjS2ppbUq+YLgKWxGcFuAYTdvFjm6xjpGaCnUJKZm11xwT+Dis5TLpyNu6K1/GxsJD4PcZ4Kj4sk8R7nqVae9SrtPKWnUOJq5ciFEt1qLyZE42Gju2VngJMn11PiCfEOYmKHl/kgZAjd/Fugr9TTWR6gsVsAJ29eJL/w8dxYexq/lxTtCwcu3tLeFxduReNuo7sFeoCTJ9dT42nj1YCP2w/j/TaD6ObfXGtfBDh5cDUljl6Brfikw8u82/pF2noHoUBRqdsjRFkkGfcU0NfXZ9iwYcyZM0crEbBs2TLy8vIYPHgwp0+fpnPnzvTp04dTp06xZMkS9u7dy+jRo3WuU61W06tXL5KSkti1axdbt27l8uXLvPjii5oy69evp0+fPnTr1o0TJ04QGhpKo0a67zaqVCr69OmDnp4eBw8e5O+//2bChAmlyllYWDBnzhzCw8P55ZdfmDFjBtOmTdM8v3TpUj7//HMmT57M0aNHcXFx0Upu6RIdHU2fPn147rnnCAsLY+TIkXz44YdaZR72/bnj0qVLLF26lP/++49NmzYRFhbGqFGjNM/PmDGDiRMnMnnyZCIiIpgyZQqffvopc+fO1VrPhAkTGDNmDBEREXTu3JmpU6cya9YsZs6cyd69e0lKSrpvoubll18mKiqK7du3s3z5cv7880/i4+O1yiiVSn799VfOnDnD3Llz2b59O+PHj7/neh/EpEmT+P33ovG2oqOj6d+/Pz///DMLFy5k/fr1bN26ld9++02rzty5c9HX1+fQoUP8+uuvTJs27Z7dNB8l9t27d5f5mVy1ahXdunVj4sSJ/PDDD5rld46bstZ99xiD6enpLFu2jCFDhtCxY0cyMzPZuXOn5nkHBweUSiXLly+n8D4/moKDg9mzZ889yzyq9LR0VCoVVtbaXYssra1JTU55oHVsXLGW3JwcmrRpUQkRQkpKCoWFhdja2mktt7O1JTFB9x3JxMRE7GxttZbZ2tpRWFioSYonJiZia1eijJ0tiYnF67x54wYrV6zErUYNfvntV3r37cNPP05lw7r1Ol93w7r1mJmZlRp37kGYGRV1iUjLzdJanp6biYXR/VvqWBqZ4e/owYGoMw/92hVNcTsxqyqRFFdlZKAwLztpq7S1wSAwAJRKMucsImfHHoxaNcWobauiAnl5FFyNxrhtq6LXUCgwqF8HvequmtesCLkZWahVaowttN93YwszstN034gxsTKjycDOtB7Zi9Yje2PpZEvob4uJu1T8g8DF35Nz24+QFp+EWqUmJiKS66culrnO8jI3NC36TOVorz8tNxMr4/t39bcyNiPQ2Zs992hJ42nrQnVrR60WKZUhMb3oR7ZtiVbfthaWmufKkpGdTesP3qXZuDG8+7+/+KBvP5rcldTbcvwo565HM6rH8xUf+G1pqWlF51oba63lVjbWpCSlPPJ642JiObRrHyqVio+++Zw+Q15k3bLVrFxQOWNiFR8b2p8fYwszcso8NsxpOrALbUb2ps3IPlg62bL1t0XEXSoeZ6mavxcRdx0bNyMiia6kY8PCqOi4SC1xXKRmZ2Jlovs8cjEhmj/3r+TtFi8wd8Cn/NXnA7Lyc5h7dIPO8gAD6ncgKTudM7FXyiwjwMJQ9/54mPNUHWdv9lwp+xx05zy1OzKsvOHqdKdLY0ap63dWmddvO1NLPGxdcLawY97RDfwXvoc6Lt70Dmyjs7ybtSMulnYcjn58Lcyexu8lpobGRfsiTzsJnnGPfWFrYomHjQvOFrbMO7qJ/8L3EujsRa/A1sVlTC2p4+yFUqFk9uH1bL94jFZe9Wnn07BSt0eIssiYcU+J4cOH88MPP2gmJoCiLqp9+vTBxsaGd955h0GDBmlaUvn6+vLrr7/Spk0b/vrrr1Ith7Zt28apU6eIjIzEza3oDuK///5L7dq1OXLkCI0bN2by5MkMGDCAL774QlOvXr16OuPbtm0bERERREVFabp+TZkypdSsop988onmbw8PD9577z2WLFmiSYz8/PPPDB8+nJEjRwLw9ddfs23btnu2jvvrr7/w8vJi2rRpKBQKatasyenTp/nuu+80ZX744YeHen/uyMnJYe7cuZpt+u233+jWrRtTp07F2dmZr776iqlTp2paDHp6ehIeHs706dN56aWXNOsZO3asVqvCn3/+mY8++oi+fYsGov/777/ZvFl3d0KACxcusHHjRg4ePKhpBTRz5kzN4PN3v84dnp6efPXVV7z55pv3TWjez9dff02LFkVJmhEjRvDRRx9x+fJlvLyKum288MIL7NixQysB6+bmVmqfTJs2jVdfLT1w9aPGHhUVRbVqpe/qZWRk0K9fPz7++ONSidmLFy9iaWmJi4vLfbd78eLF+Pr6Urt20SC2AwYMYObMmZpj0NXVlV9//ZXx48fzxRdf0KhRI9q2bcvgwYM1780drq6unDhRdvef3NxccnNztZYZGRndN0YtihJ39tTqB7rXd2DHHlb9u5SxkyZgWSKhV9FKh6hGUXLhPSrcuSGhvbRkGbTWqVKp8A/w561RbwFQs1ZNIq9cYcWKFTzXvVupl/xv7X907tL54d9/rSBKLniwu67BNQLIzs/l9GPsvnKHQb1ATHsVT9CRMa/0YO3A7X1yjy4FCgXqzEyyV60DtZrCmzEoLSwwatWM3O27AchathrTvj2x+mgc6kIVhTdjyD95Gj3X+x+XD0/HcVHG7rB0ssPSqThh7ODlSlZyGhHbDmsGtm/0QgcOLdrEuq/+AQWY29vg1bQOVw6eroTY7wq7xP8KFPfaCxrNPeqSlZ/DiXuM9dPSsx7XU+KJvMcg5Y9i49HDfLOk+HM07fWiY7BkKwT1A2yJqZERC8Z/RFZuLkcunGfa6pW42tnT0NeP2ORkpq5Yzm9vjcboMYy1WuqcdY/P1INQq9VY2ljx+rhRKPX08PLzITkxibVLVvLCsIHlC/YeSrUGUVPmqcrKyQ6rEsdGZnIa4dsO4+RTA4DGL3TgwKKNrP1qBijAwt4G76Z1uXxQd+vnilDys3Ov05OrpQPDGnZl1ZldnIq5jLWJOYPqd2J4cHdmHFpbqnx3/xY0c6/D16FzyH+MwwM8S+5ztdBocfs8dfxm2a35W3nWr5TzVEkle8zdaxvunAsWndhCTkHRuKjrwvcy5PbnrEClfaO2sVsAMWmJRKfEl1pXpXsKv5eU7DWEouzr3p19sThsW/G+iNjPkKDOrD6zmwJVIQoUZOZls+LUTtSouZF2C0tjM1p71Sf00tEy1vwM+X/cHfRJJcm4p0StWrVo3rw5s2bNom3btly+fJk9e/awZcsWAI4dO8alS5e0ui6q1WpUKhWRkZGlkjYRERG4ublpEnEAAQEBWFtbExERQePGjQkLCyszcVJSREQENWrU0BqDqVmzZqXKLV++nJ9//plLly6RkZFBQUEBlpaWWut54w3tAWmbNWvGjh07Sq5Kq07Tpk21vhyXfO2HfX/u0LVNKpWK8+fPo6enR3R0NCNGjNB6nwoKCrCy0k5o3N16KzU1lZiYGK0Y9fX1adSoUemLzl3beKfMHbVq1So1U+yOHTuYMmUK4eHhpKWlUVBQQE5ODpmZmeWaLKFu3eIxJZycnDTdjO9edviw9nhJuvbJ1KlTKSwsRE+vdPP9R4k9OztbZyLVxMSEli1bMmPGDAYOHKi1f++b/LnLzJkzGTJkiOb/IUOG0Lp1a1JSUjTv/ahRoxg2bBg7duzg0KFDLFu2jClTprB27Vo6duyoFdPdXXlL+uabb7QS3wCff/45XV++/0yAFpYWKJXKUq3g0lJTsSzRgqOkgzv3MXPan4ye+D6BQbqT7RXB2toaPT09rRZrAEnJyaVatt1hZ2dXqnxychJ6enpY3X7/7ezsSCpZJikJ27ta1Nnb2+Ppqd1V0cPTgx3bS59XTpw4wdWrV/n6m8kPvG13y8zNplClwtJY+86thZEp6bll7/87mrrX5mh0BIVq1SO9fnnkR1wgPXp68YLbY0cqzc0pTC9uHac0M0OdUXZLF3V6RlH3urvOZ4W3ElBaWoCeEgpVqJKSyZgxFwwMUBgboU7PwHRAX1TlaF1UkpG5KQqlgpx07VhzMrJKtQi6F3uPakQeKW7FYGxhSpvX+lCYX0BuZjYmVuaErdmFuV3lJLIz8rIoVKlKtS6xMDIt1VpOl5aedTl49QyFKt2fKUM9fRq7+bPmbMW33G0dWJdAdw/N/3m3uyYnpqdhf9d1Mjk9HTuLsifUgKLW024ORRMk1KzuRlRcHHO2baGhrx/noq+RlJHOsB+Lb8IVqlScuHyJZXt2sW/qL1qDeD8qSytLlEolKUna43+mpqSWai33MKxtbdDX10d517XRtUZ1UpKSKcjPR7+CE4x3jo3sdO1WrzkZmQ91bDh4uHKlxLHR9rW+WsfGiTU7MbezrqjQNdJzi44La2PtVnCWxkWzWerSs3ZLLiRcY33EfgCiU+KYXbCezzsOZ9nJ7VqDwj9Xqzk9a7fim+3ziE6J07k+USy9zPOU2QOdp1p51uPAfc5TwW7+rK6E89QdmXlF12+LEtdvcyPTUq3l7kjLySI1J0OT/AGIz0hGqVBgbWJOQmbx+MEGSn3qVfNly4VDlbMBZXgav5dk5eUU7YsSreDMDU3K3BfpuZmk5mRq7Ytbt/eFlbE5iVmppOdmUqhWaSXx4zOSsTQ2Q0+hrJLvXuL/N+mm+hQZMWIEK1asIC0tjdmzZ+Pu7k779u2BopYfr7/+OmFhYZrHyZMnuXjxIt7e3qXWVVZC4u7lJiYmDxybriRSyfUfPHiQAQMG0LVrV9atW8eJEyeYOHEieWXNyFeO1y7pYd+fstzZJoVCger2F4YZM2ZorffMmTMcPHhQq155Zw3VtAa6RxLp6tWrPPfccwQGBrJixQqOHTvGH3/8ATz4pBFluXvwfoVCUWow/7vfj0fxqLHb29vrnBRBT0+P1atX07BhQ9q2bUt4eLjmOT8/P01C9F7Cw8M5dOgQ48ePR19fH319fZo2bUp2djaLFmm3GLKwsKBnz55MnjyZkydP0qpVK77++mutMklJSTg4lJ7V9I6PPvqI1NRUrcdHH310zxjv0DcwwMPXmzPHtbt3nDl+Ct+AmmXWO7BjDzOm/s6bH46lfpPKbaJvYGBArVq1OHxIO2l7+NBh6tTVPYBwnTp1SpU/dPAQ/gH+mklG6tStw6GSZQ4d0lpn3Xp1S02Ac+3qNZxdSo8B89+atdTyr4Wfn9+Db9xdCtUqolPiqOmgPZlLTYcaRCbdvGddH/vqOJjbcPBqFXVRzctDlZRc/Ii/hSotHX2fu1p56inR93Sn4FrZY0AVXI1Gz85W66a70t4WVVo6FJY4T+Tno07PQGFsjIGvN/kRDzbG4YPQ09fD1s2ZmHNRWstjzkVh7/ngE3MkXY/DxKp0tzc9A31MrS1Qq1RcCztP9bq+5Q1Zp0KViqvJsQQ4aSeUA5w8uZx473HqajrUwMnC9p5dvxq5+WOgp8/BShiY28zYGDcHR83Dy9kFO0tLDp0vHnw9v6CA45cvUbdEwvx+1Gq1JrnX2K8miyZMZP4HH2ke/m416NKwEfM/+KhCEnFQdK718vPh1DHtVs6njoVRs7bum3oPomZgALE3YrSuozHXb2JjZ1vhiTi497Hh8NDHRunvNyWPDbdKODYKVYVEJt0k0Fn7O1wdZ28uJug+PxnqGZT6zqi68+P7ru9X3fyb0zuwNd/vmH/f87YoUnSeiil1nqrt5MmlCjhPNb59njpQidfHQrWKG6nxpcYc9LV3I+quSQDudjU5BktjMwz1io9TezNrVGoVKdnaSeG61XzQV+px4vrjm5EUns7vJUX74ha+DiX3RXWuJutOjkclxWJpbIqhXnFbI3szK1RqlSZBH5Uci52plVabQHsza9JyMiURJ6qEJOOeIv3790dPT4+FCxcyd+5cXnnlFU1yJigoiLNnz+Lj41PqYWhoWGpdAQEBXLt2jejo4i8s4eHhpKamaloR1a1bl9DQ0AeK7c76bt4sPqkfOHBAq8y+fftwd3dn4sSJNGrUCF9f31I/kP39/Uslskr+r+u171fnYd+fO3Rtk1KpxM/PDycnJ1xdXbly5UqpdZZshXM3KysrXFxctGIsKCjg2LFjZdbx9/enoKCAo0eLm1CfP39eazKJo0ePUlBQwNSpU2natCl+fn5asT9uuvaJr6+vzlZxjxp7gwYNtBJtdzMyMmLlypUEBwfTtm1bzpwp+iLxwgsvYGhoyPfff6+z3p33dObMmbRu3ZqTJ09qJVvHjx/PzJkzy4xJoVBQq1atUpODnDlzhgYNdM98eCdeS0tLrcfDdJPs0qcHuzaFsmtzKDeuXWfB37NJjE+gXbdOACydNZ/p3/+qKX9gxx7+98NvDHztJbxr+ZGSlExKUjJZd8VdkJ/P1cuRXL0cSUF+AcmJiVy9HEncjUfrIjJw8CDWrF7D2jVriYyMZNrUn4iLjaVP36Iu3H/8/geTPvtcU75P3z7ExsTw80/TiIyMZO2ataxds5bBd7VWfHHAAA4fOsS8OXOJiopi3py5HD50mAGDBhS/7qBBnDl9hjmzZhMdHc3mTZtYvWo1L5SYhTcjI4PQbaE8/3z5xpzaefk4TT0CaVKjNk7mtvQObIONqQX7bs9k2T2gBYODOpeq19Q9kKikGJ2zet0ZzNjVygF9hR5Wxua4Wjlgb1a53Ypz9x/COKQlBgE1UTo5YPrC86jz88kLK/5ibvrC8xh3aldc59BRFKYmmHTvgtLOFv2avhiHtCT3YPGMivq+3uj7eqO0sUbfxwvzkcMoTEgk71hYhcZfq11jLu8/yeUDp0iNTeDYilCyktLwbVUfgBNrdrF/3jpN+XM7jhB98gJp8UmkxNzixJpdRIddwK918WyACVE3uRZ2nvSEFOIvRbP9j2WgVhPQoUnJl68wWy8cppVnPVp41sXFwo4X67fH1tSSnZeLkkJ96rRheHD3UvVaetbjcuINbqYllLnulp71OHHjApl5lT9AvUKhYGCbtszeupkdJ8O4dPMmXyz4F2MDQzo3LJ705vP5c/n9vzWa/2dv3cyhcxFcT0ggKi6WBTtCWX/kEF0bFdUxMzbGp1o1rYeJkRFWZub46BjKoDy69+tF6IatbN+4letXo5nzxwwS4m7RsUfRsBwLZ8zl929+0qoTdekKUZeukJOdQ1pqKlGXrnA9qnistU49u5Kels6c32dwM/oGxw8eYdXCZXR+/rkKjf1uAe2CubT/JJcOnCQ1NoEjK7aRmZSGX6ui69TxNTvZN+8/TfmIHUe4dtexcXzNTq6FnadW6+IbObfuOjbiLkUT+sdS1Go1tSvp2Nh47gBtvYNo49WAapb2DAnqjJ2pFaEXi74rvVivPW80660pf+LGBRq5+dPepxEOZjb42bsxrGFXLiVcJ+X2wPTd/VvQr247/ndoDbcyU7AyNsfK2Bwj/bK/Kz5OJgZG+NpXx9e+qNdGNUt7fO2r42RuU8WRweYLh2ntWZ+Wt89TA+p3uH2eOg5A3zohjAzuUapeq9vnqRtpt8pcdyvP+hx/DOepPVfCCK4RQCM3fxzNbegR0BJrE3NNMqpLrWa8WL+DpvyJGxfIysuhf732OJrb4GlbjW7+LTgSHVGqi2qwWwBnY6+QlX//iekqWmV8L6lseyJP0tjNn0bVa+FobkN3/xZYm1hw8NrtfVGzKf3rtdeUD7t5gay8XPrVa3d7X7jwXK3mHI0+p9kXB6+exczQmB61W2JvZkUtR3fa+gSxv6pugor/96Sb6lPE3NycF198kY8//pjU1FRefvllzXMTJkygadOmjBo1ildffRUzMzMiIiJ0DqwP0KFDB+rWrcvgwYP5+eefKSgo4K233qJNmzaarpCff/457du3x9vbmwEDBlBQUMDGjRt1DnzfoUMHatasybBhw5g6dSppaWlMnDhRq4yPjw/Xrl1j8eLFNG7cmPXr15eatOCdd97hpZdeolGjRrRs2ZIFCxZw9uzZUuNv3e2NN95g6tSpjBs3jtdff51jx46Vmnn2Yd+fO4yNjXnppZf48ccfSUtLY8yYMfTv3x9n56IWNZMmTWLMmDFYWlrStWtXcnNzOXr0KMnJyYwbN67M9b7zzjt8++23+Pr64u/vz08//VRqlta71axZky5duvDqq6/yv//9D319fcaOHavVetHb25uCggJ+++03evTowb59+/j777/LXGdli46O1uyT48eP89tvvzF16lSdZR819s6dO5eaLONuhoaGrFixgv79+9OuXTtCQ0OpU6cO06ZNY/To0aSlpTFs2DA8PDy4fv068+bNw9zcnG+//ZZ///2XL7/8ksBA7SnUR44cyffff8/JkydRq9V8/vnnDB06lICAAAwNDdm1axezZs3SGj8vKyuLY8eOMWXKlAd89x5e05AWZKSns2bBMlKSkqnuXoP3vv4Ye6eibl0pSckk3ir+Qb5jw1YKCwuZ9/sM5v0+Q7O8ZccQXnv/bQCSE5P59K33Nc9tXL6WjcvXUqtubT7+4cuHjrFjp46kpqYy65+ZJCQk4OXtzbRfpmnG70tMSCAutviOZzVXV6b98jM//zSN5cuWY+9gz3vvv0e79sWJn7r16vLV5K+Z/tffTP97OtWrV2fyN1O09ltA7QC+//F7/vz9T2b+M5Nq1arx7nvj6NK1i1Z8W7dsRa1W06lL6S+kD+PEjQuYGRrTuVYTrIzMiElPZPqB1ZpZyCyNzbAx1R7A3ljfkHouPqw8vVPnOq1MzBnftjgJ2d63Ee19G3ExIZrf9y4vV7z3krt7PwoDA0x6PofCxITC6zfImD0f7mrRrLS20uqSqk5NI2PWAky6dcJizBuo0tLI3XeY3N37NGUUxkYYd2qH0soSdVY2+WcjyN6yA8rRwlYXj4b+5GVmc3rjPrLTMrF2sSfkrX6Y2xYlMXPSMshMKp44oLBAxfFVO8hOzUDPQB8rF3tC3nwB19rFrW8K8ws4uW4PGQkpGBgZUq22F82HdcPQVPfYoxXhSHQEZoYm9AhogZWxOTdTb/HLnqWa2VGtjM2xM9Xu5mliYERQ9ZosDis9s/kdTua2+Dm48dOuMsYHrATD2nckNz+f75YvIT0ri9ruHvz25mjM7hpyIDY5WasleE5eHt8tW0J8agpGBga4Ozrx5dCX6RT0+Afdbt62FelpaayYt5jkpCTcPNz56JvPcXAuOtcmJyWREK+dVBj/2juav69cuMTe0F04ODnyx6KiGzv2jg588v2XzP3zHz4Y+Ta29nZ07dODXgP6Vtp2eDT0Jzczm1N3HRvt7jo2skscG6qCQo6v2k7W7WPD2sWedm/20zo2VPkFhK3bTfrtY8O1thcthnWvtGPj4LWzmBuZ0juwDdYm5lxPjeeHnQtIyCrqGmhtYoGdafENi92RYRgbGNLJL5jBQZ3JysvhbFyk1jHSwbcxBnr6jG31otZrrTi9s8zz8+NUy8Gd33sXf78c07LoptKGiANM3l7296HH4Uh0BOaGJvQMaImVsTk3Um/x854lmtlRrYzNsdVxnmpYvRaLHuA89eOuhZUaP8DJmEuYGhrTwbcxlkZmxKYnMuvwOk2y1tLIFGuT4ut3XmE+Mw6u4fnA1oxp1Z+svBxO3bzEpvPaN6TtzazxtKvGjINrqAqV8b2ksp2KuYSpoRHtfRsV7YuMRGYfWadpcWhhZIr1XZO15BUW8M+htTxfuxVvt3yBrLxcTsVcYvP54m7BqTkZ/HPoP3oEtGBsqxdJy8lkX+QpzY2tZ14Ff8cS5adQP0gfP/HEOHDgAM2bN6dTp06lBvw/cuQIEydO5MCBA6jVary9vTXJOyiaMGHs2LGagfKvXbvG22+/TWhoKEqlki5duvDbb7/h5FQ8ZfTKlSv56quvCA8Px9LSktatW7NixQqd67tw4QIjRozg8OHDeHh48Ouvv9KlSxdWrVpFr169ABg/fjyzZs0iNzeXbt260bRpUyZNmqSViJoyZQrTpk0jJyeHvn374uTkxObNmwkLCyvzfVm3bh3vvvsu0dHRBAcH88orrzB8+HCSk5M1Y3vd7/0padKkSaxevZrXX3+dr7/+mqSkJJ577jn++ecfbGyK70AuXLiQH374gfDwcMzMzKhTpw5jx46ld+/eREVF4enpyYkTJ6hfv76mTkFBAe+//z6zZ89GqVQyfPhwEhISSE1NZfXq1QCEhIRQv359fv75ZwBiY2MZOXIk27Ztw8nJia+//ppPP/1Uax9MmzaNH374gZSUFFq3bs3gwYMZNmyY1vtQkkKh0OyjkvHemTDk7vpz5sxh7NixWvvsznt1Zx+FhIRQu3ZtVCoVCxcuRE9Pj9dff50pU6ZofmCV/Pw8SuzJycmaiRFq1qxZZnz5+fkMHDiQXbt2ERoaSt26ddm2bRs//vgjhw8fJjs7Gw8PD7p37864cePYv38//fv35+bNm1rHwx1169YlJCSEzz77jK+++ort27cTFRWFQqHAw8ODl156iXfffRfl7a5RixYt4osvvuDcuXOl1nU/h56AWTXLq4lHICnpqfcv+ASztrDindXT7l/wCfdLr3dJ+fjhE6lPEuspn/Hl1llVHUa5fdZxOCOXflPVYZTbP/0/Im3TtqoOo1wsu3Tg5I3H23WsMtRz9ePrrbOrOoxy+aTjKwxeOKmqwyi3BYMm0eKPN+5f8Am3b9TfDF9aeTcSH4dZ/T9m/LrfqzqMcvu+++in/nvIL73eZcL68k0q9yT4rttbVR3CI0lbtrqqQyiTZb9eVR1ClZBknBBlKJlgEg+uZCKxMo0fP57U1FSmT59+/8JVJDg4mLFjxzJo0KCHrivJuCeDJOOeHJKMe7JIMu7JIcm4J4ck454ckox7ckgyrmpJMu7JI2PGCSGeahMnTsTd3Z3CwsL7F64C8fHxvPDCCwwcOLCqQxFCCCGEEEL8f6RWP7mP/6dkzDghxFPNysqqzK7GTwJHR0ed4ywKIYQQQgghhPj/SZJxQpRh0qRJTJo0qarDeCrt3LmzqkMQQgghhBBCCCGeSJKME0IIIYQQQgghhHhW/T/uDvqkkjHjhBBCCCGEEEIIIYR4TCQZJ4QQQgghhBBCCCHEYyLdVIUQQgghhBBCCCGeVSrppvqkkZZxQgghhBBCCCGEEEI8JpKME0IIIYQQQgghhBDiMZFuqkIIIYQQQgghhBDPKLVaVdUhiBKkZZwQQgghhBBCCCGEEI+JJOOEEEIIIYQQQgghhHhMpJuqEEIIIYQQQgghxLNKLbOpPmmkZZwQQgghhBBCCCGEEI+JJOOEEEIIIYQQQgghhHhMpJuqEEIIIYQQQgghxLNKJd1UnzTSMk4IIYQQQgghhBBCiMdEknFCCCGEEEIIIYQQQjwm0k1VCCGEEEIIIYQQ4lkls6k+caRlnBBCCCGEEEIIIYQQj4lCrZYUqRBCCCGEEEIIIcSzKHXe4qoOoUxWwwZUdQhVQrqpCiHEE+zfoxurOoRyG9qoK/FpiVUdRrk4WtoxaMHnVR1GuS0c/AWpk6dWdRjlYjXxPUaverq3AeD33u8xdNEXVR1Guf078HPSb92q6jDKxcLBgR93LqjqMMrt/ZDB/Ll/RVWHUS5vNe/Ld9v/reowym1Cu6EMXzqlqsMot1n9P6bFH29UdRjlsm/U37y39teqDqPcpvYc89R/pmb1//iZue49ldSqqo5AlCDdVIUQQgghhBBCCCGEeEwkGSeEEEIIIYQQQgghxGMi3VSFEEIIIYQQQgghnlUqmSrgSSMt44QQQgghhBBCCCGEeEwkGSeEEEIIIYQQQgghxGMi3VSFEEIIIYQQQgghnlVq6ab6pJGWcUIIIYQQQgghhBDimZGcnMzQoUOxsrLCysqKoUOHkpKScs86L7/8MgqFQuvRtGlTrTK5ubm8/fbb2NvbY2ZmRs+ePbl+/fpDxyfJOCGEEEIIIYQQQgjxzBg0aBBhYWFs2rSJTZs2ERYWxtChQ+9br0uXLsTExGgeGzZs0Hp+7NixrFq1isWLF7N3714yMjLo3r07hYWFDxWfdFMVQgghhBBCCCGEeFb9P5tNNSIigk2bNnHw4EGaNGkCwIwZM2jWrBnnz5+nZs2aZdY1MjLC2dlZ53OpqanMnDmTf//9lw4dOgAwf/583Nzc2LZtG507d37gGKVlnBBCCCGEEEIIIYR4Jhw4cAArKytNIg6gadOmWFlZsX///nvW3blzJ46Ojvj5+fHqq68SHx+vee7YsWPk5+fTqVMnzbJq1aoRGBh43/WWJC3jhBBCCCGEEEIIIcRjl5ubS25urtYyIyMjjIyMHnmdsbGxODo6llru6OhIbGxsmfW6du1Kv379cHd3JzIykk8//ZR27dpx7NgxjIyMiI2NxdDQEBsbG616Tk5O91yvLtIyTgghhBBCCCGEEOJZpVY9sY9vvvlGM8nCncc333yjczMmTZpUaoKFko+jR48CoFAoSr8NarXO5Xe8+OKLdOvWjcDAQHr06MHGjRu5cOEC69evv/fbe5/16iIt44QQQgghhBBCCCHEY/fRRx8xbtw4rWVltYobPXo0AwYMuOf6PDw8OHXqFHFxcaWeu3XrFk5OTg8cm4uLC+7u7ly8eBEAZ2dn8vLySE5O1modFx8fT/PmzR94vSDJOCGEEEIIIYQQQghRBR6mS6q9vT329vb3LdesWTNSU1M5fPgwwcHBABw6dIjU1NSHSpolJiYSHR2Ni4sLAA0bNsTAwICtW7fSv39/AGJiYjhz5gzff//9A68XpJuqEEIIIYQQQgghxLNLpXpyH5XA39+fLl268Oqrr3Lw4EEOHjzIq6++Svfu3bVmUq1VqxarVq0CICMjg/fff58DBw4QFRXFzp076dGjB/b29vTu3RsAKysrRowYwXvvvUdoaCgnTpxgyJAh1KlTRzO76oOSlnFCCCGEEEIIIYQQ4pmxYMECxowZo5n5tGfPnvz+++9aZc6fP09qaioAenp6nD59mnnz5pGSkoKLiwtt27ZlyZIlWFhYaOpMmzYNfX19+vfvT3Z2Nu3bt2fOnDno6ek9VHySjBNCCCGEEEIIIYQQzwxbW1vmz59/zzJqtVrzt4mJCZs3b77veo2Njfntt9/47bffyhWfdFMVQojbQkJCGDt2bFWHIYQQQgghhBAVR61+ch//T0nLOCGEeEYc3bqXA+u3k5GShoOrM52G9qZGLe/71os+f4V5X/+OY3VnXv1mvGb58e0HOL33CLeiYwBw9nSj7YvdcPV2r7CYVy1bwaL5C0lMSMTDy5Mx496hXoP6ZZY/cewEv//8K1FXIrGzt2fQsMH06ttb83zk5SvMnP4P58+dIzYmlrfffYf+g17UWke/nn2IjYktte7eL/Rh3IT3K2S7Ovg2pntAC6xNzLmRcot5xzZy/ta1Msu38KhD94CWOFvYkpWfy6mbl1hwfDMZedkAtPVuSCuverhZORZtZ9JNlpwM5XLijQqJ916MWjXDsEFdFMZGFN6MJXtTKKqExPtUMsI4pCUGtXxQGBujSkklZ9suCi5HAmAYVA/DoHoorS0BKLyVSO7eAxRcjqrw+Ft51qO9b2OsjM2ISUtkxekdZb5vQ4I609Q9sNTymLQEJofOBaC5Rx2C3QKoZlk0ePC1lDj+C9/L1eTSn6mK1N6nEd38m2NlYsGN1HjmH9/MhXt8ppq716Gbf3OcLOzIzs/hVMwlFp3YqvlMuVo60LduCB421XAwt2b+8U1sPn+owuNWq9X8b9YsVq1dS3p6OrUDApgwbhzeXl73rBe6cyd///MP12/coLqrK2+9+ipt27TRWXb2v//yx/TpDOzXj/feeUezfPrMmWwJDSUuPh4DfX38a9bkrddeI7B27XJtU/jOI5zccoDs1HRsqjnStH8nXHx1nxdvno9i/U/zSi3v98VbWDuXHoD68pEzbP9nJe71atLprRdLPV+RTm4/yPGNe8hMScfO1ZHWg7rh6ud533o3L15l+bczsHN1YvCXb2uWFxYUcnT9TiL2nSAjOQ0bF3ta9OuCRx2/StuGiF1HOb31ANmpGVi7ONCkXyecfWvoLBtzIYqN00q3kOjz+Rta+yI3K4dja3ZwNew8eVnZmNtbE9y3I26BPpW2HW29g+hSs2nRNSP1FovCtnExIVpn2eGNu9PSs26p5TdSb/Hp5hkAjA8ZTC3H0p/Jkzcv8cvepRUb/EOq5+LDoAadqOVYA3szaz7c8Bd7Ik9WaUx3a+5RhxDvICyNzYhNT2LNmd1EJt0ss7yeUo9OfsEEVa+JpZEZKTkZhF44wuHocACUCiXtfRvRyM0fK2MzbmUksy58P+dvXa3U7ajozxRAR9/GtPUOwtbUkoy8bI5eP8fyUzsoUBVWyjY8rdc9IR6UJOOEEE+NvLw8DA0NqzqMe8rPz8fAwOCxv+7ZA8fZ8u8qur7yAm5+nhzfvp9F30/nje8/wsrepsx6OVnZrPl7AZ61fclMTdd67mrEJWo3C6L6MA/0DQ04sC6Uhd/+xevffYilrXW5Yw7dso1ff/qFcRPep069uqxduZoP3nmPf5cuwMnZuVT5mzduMn7se/To1ZNPv/yc0ydP8dN3P2JtY01Iu7ZF25OTg4trNUI6tOW3n37V+br/mzsTVWHxYLGRl6/w7uh3aNuhXbm3CaCpe22GNezCrCPruXDrGu19GzGh7RA+WPcHiVmppcrXdKjBm8368O/xTRy/fh5bU0uGB3fn1abPM233YgACnDzYH3WaiwnR5BcW0D2gBR+2G8r4dX+QnJ1eap0VxbBZY4yaNCTrv02okpIxatEUs0EvkP73LMjL111JqcRs0Auos7LIWvEfqrR0lJaWqPPyNEVU6enk7NiDKjkFAIO6AZj260XGP//eP9H3EIJca9K3bluWhIVyJekGLT3q8lbzPny9bY7O9235qR2sObtH87+eQslH7Ydx4sYFzTJfezeOXT/HsqSbFBQW0sGvMaOa92Vy6FxSczIqLPa7NalRmyFBXZhzdD0XE6Jp69OQD9oM5sMNf5CYlVaqvJ+9G6837cWCE5s5ceMCNiYWvNK4OyOCe2h+iBvqGxCfkcLha+EMDupcKXEDzF2wgIVLlvD5xInUcHNj5ty5jHr3XVYsWoSZqanOOqfOnOHjzz/njZEjadu6NTt27+bDzz5j5p9/lkqknY2IYNXatfh6l77x4O7mxvh338W1WjVyc3NZuHQpo8aNY/XixdjYlH1evJfLR85yYOlmWgx6DidvN87tPs6m3xbSb9JbmNtalVmv35ejMDQuninO2KL0tqcnpnBo+VacfXQnkyrShUOn2L1wPW2H9qSarzundx5mzU9zGTJ5LJZ21mXWy83KYcuMZbj5e5OVpv15P7ByK+cOhNH+5d7Yujhw9cwF1v02n/4T38DRvVqFb8OVo2c5tGwLzQZ0LdoXe46z5Y9F9PnsjXvui76T3sSgjH1RWFDI5l8XYGxhRrvX+mJmbUFGcppW+YrW2M2fgfU78u/xTVxKuE6IdwPebfUin2z+H0k6ju9FYVtZfnqH5n89hZIvOo3g6PVzmmV/7F+BnrJ47CJzQxO+6DSSo9cjKm07HpSJgRGXEq+z4dx+pnR9o6rD0VK/mi/PB7Zm5amdRCbdpJl7IK827cn3O+aTkq37/D6sYVcsjExZGhZKQmYK5kam6CkUmue71mpKw+q1WHoylPiMZGo6uvNKcDd+27OMG2m3KmU7KuMz1bRGbV6o25ZZR9ZxKeEGzha2jAjuDsDisG0Vvg1P83VPiAcl3VSFEDqp1Wq+//57vLy8MDExoV69eixfvhyAnTt3olAoCA0NpVGjRpiamtK8eXPOnz8PFA2EqVAoOHfunNY6f/rpJzw8PDR988PDw3nuuecwNzfHycmJoUOHkpCQoCkfEhLC6NGjGTduHPb29nTs2BGAtWvX4uvri4mJCW3btmXu3LkoFApSUlKAoimoBw4cSPXq1TE1NaVOnTosWrRIK5bMzEyGDRuGubk5Li4uTJ06tdR7oFAoWL16tdYya2tr5syZA0BUVBQKhYKlS5cSEhKCsbEx8+fPf6DXr2iHNu6kfkgTGrRthr2rM52G9sHSzppj2/bes96GmUsJbN4QV1+PUs/1HjWURh1b4uxRHftqTnQbOQC1Sk3U2QulV/QIlixcTLfne9CjV088PD0Y895YHJ0cWbV8lc7ya1auwsnZiTHvjcXD04MevXrSrWd3Fs9fqCnjXzuAUe+MpkOnjhga6k6K2tjYYGdvp3ns37sP1+qu1A9qUCHb9Vyt5uy8fIKdl49zMy2Bf49tIjErjQ5+jXWW97Gvzq3MFDafP8StzBTO37pG6MVjeNkW/3j9Y/8Ktl08wtXkWG6mJTDj0FoUCgWBzvduXVReRsFB5Ow7RMH5S6huJZL93yYUBvoY1vYvs45h/UAUJsZkLVtD4fWbqNPSKbx+A1V88Y+OgotXKLgciSopGVVSMrk796HOy0PP1aVC42/n05ADUac5cPU0celJrDi9k+TsdFp51tNZPqcgj/TcLM2jho0zJgbGHLh6RlNm7tEN7Ik8yY3UW8RlJLHw+BYUCgU1HSovgdK1ZlN2XTnBrisnuJmWwILjm0nMSqW9770/U1suHOZWZgoXEqLZfukYnnd9piKTbrI4bCsHr50lv7ByWjWo1WoWLVvGK8OG0a5NG3y8vPhi4kRycnPZtGVLmfUWLV1Kk0aNeGXoUDzc3Xll6FCCGzZk4VLtFj1ZWVl8+sUXTBw/Xmtg5Tu6dOpEk8aNqe7qireXF+++/TaZmZlcvHz5kbfp9LYD1GzRgFotg7BxcaDZi50xt7EifNfRe9YzsTDD1Mpc81Aqtb+Cq1QqdsxcRVCPECwcHi1R+DCOb9lL7dYNCWzTGNtqjrQZ1B1zWytOb793K5Htc1dRs2k9XHzcSj137sAJGndvg2e9mlg52lK3XVPcA305vune16FHdSb0EH7N61OzZQOsXexp2r8TZjaWnNt97J71jO+xLy7uDyM3M5sOb/TDydsNcztrnH1qYFfdqVK2AaCzXzB7Ik+yJ/IkMemJLArbRlJ2Gm29g3SWz87PJS0nU/PwsHHB1NCEvXe1LsvMy9EqU9vJk7zCfI5En9O5zsfp4LWzzDi0ll1Xwqo6lFJaezfg8LWzHLp2lviMZNac3UNKdgbNPUq3GgOo6eCOt70rMw6t4WJCNMnZ6USnxBF1V0vphm61CL14lHPxV0nKSuNA1GnOx1+ljU/FfOfQpTI+U952rlxMuM6ha+EkZqVyNi6SQ9fC8bCp2Ov2HU/rde+JplI/uY//pyQZJ4TQ6ZNPPmH27Nn89ddfnD17lnfffZchQ4awa9cuTZmJEycydepUjh49ir6+PsOHDwegZs2aNGzYkAULFmitc+HChQwaNAiFQkFMTAxt2rShfv36HD16lE2bNhEXF0f//v216sydOxd9fX327dvH9OnTiYqK4oUXXqBXr16EhYXx+uuvM3HiRK06OTk5NGzYkHXr1nHmzBlee+01hg4dyqFDxT8yPvjgA3bs2MGqVavYsmULO3fu5Nixe3+BL8uECRMYM2YMERERdO7c+YFevyIVFhQQE3kdrzq1tJZ71anF9YtRZdYL23WI5PgEWvd5sLuD+bl5qApVmJiZlSfconXl53Ph3HmCmwRrLW/cJJgzp07rrHP29Bkalygf3LQJ58LPUVBQ8MhxbNm4med6dkdx153sR6Wn1MPT1oVTMZe0lp+OuYyffekfrwAXbkVja2pJ/Wq+AFgam9GkRgAnbpad9DTSM0BfoafpelEZFNZWKM3NKbhyV1eawkIKrl1Hr3rZrVz0fb0pvH4Tky7tsXjnDcxffQmj5sFQ1vurUGAQUBOFgQGFN8ruCvSw9BRK3KydiIjX7goUEXcVT7sHa6XTzD2Q8/FX79n60FBfHz2lkqz8nHLFWxY9pRIP22qcjtVOIJ2JvYKvfXWddS4mFH2m6rkUdauzNDYjuIY/YTcvVkqMZblx8yaJiYk0DS4+bg0NDQmqX59TZ86UWe/UmTM0CdY+1ps2aVKqznc//USL5s1p0lj3j7O75efns2rNGszNzfHzebTuhoUFhSRci8E1QLsVnmuAF3GXdXf/umPl1/9j/gc/sf6nedw8H1nq+RPrdmNsYUqtlpX3A/2OwoIC4qNuUqO2r9Zy99o+xFwuu+vc2T3HSIlPosnzulsRF+YXoFeiZbi+oQE373EdelSFBYUkXouhWoD2DQlXfy/ir1y/Z901U2awaMLPbPx5PjHntWO7duoCjl7V2b94EwvHT2Pll9M5uXEvKpVK98rKSU+pxN3GhbNxV7SWn42NxMdO9/FdUiuveoTHRepsLaQp41mPw9fCySsso0WzQE+hpLqVI+fjtbtBnr91rcyEU21nT6JT4mjn05DPOg7nw3ZD6RHQEv27WiXqK/XIV2l/R8kvLNBKElWkyvpMXUy4joeNM562Re+Fg5k1dVy8S33fqQhP83VPiIch3VSFEKVkZmby008/sX37dpo1awaAl5cXe/fuZfr06bz22msATJ48mTa3x/D58MMP6datGzk5ORgbGzN48GB+//13vvrqKwAuXLjAsWPHmDevaOycv/76i6CgIKZMmaJ53VmzZuHm5saFCxfw8ysaY8bHx4fvv/9eU+bDDz+kZs2a/PDDD0BR4u/MmTNMnjxZU8bV1ZX33y8e++vtt99m06ZNLFu2jCZNmpCRkcHMmTOZN2+eprXd3LlzqV79wb6klDR27Fj69Omjtexer69Lbm4uubm5WsuMjB6sW0xWeiZqlQozK+2WIWZWFmSk6v5ynhR7ix2L/2PYZ2NQPuA03NsXr8PC1grPwPKP/5OakkJhYSE2trZay23sbElKTNJZJzExiWC7EuVtbSksLCQlJQV7+9LjL93Pnp27ycjI4Lnuzz10XV0sjEzRU+qRmpOptTw1JwMrE3OddS4mRPPHvhW83bIfBnr66Cv1OBp9jrlHNpT5OgMadCQpO40zMVfKLFNeyttJV3Wm9raoM7NQWFqWXc/aGqWHJflnIshcshI9WxuMO7cHpZLcvQeLyznYY/7yQNDXh7w8spavRZWge98/CnMjE/SUStJzs7SWp+dmYmnkcd/6lkZmBDh5Mufo+nuWe752a1KzMzgXXznj/xR9ppSklegCm5qTgZWx7jEhLyZc568DKxnV4gXNZ+rY9XP8e2xjpcRYlsSkov1pV+I4t7OxISYu7p717Ep0I7WzsdGsD2Dztm2cu3CBeTNmlKyuZc++fXw8aRI5OTnY29nxx7RpWFtbP+SWFMnJyEKtUmNqqX1DwsTCjOy0TJ11TK3MaTWkO/buLhTmF3Dx0GnWT/uX7uNewsWvaEyv2EvXOL/vBH0+ff2R4npY2elZqFUqTC21z0kmVhZkntH9wzU5NoF9yzfR76PXy7xm1Aj05cTmvbj6eWDtaMu1iMtcORGBuhISWbm394WJRel9kZWquzuhqaUFLQY/h10NF1QFhVw6dJqNv8znuXeH4nx7zL/0hBRizkfhFRxIp1EDSItP4sCSTahUKhp0a13h22FhWHR8l7xmpOVmYmV8/xtfVsZm1HH25n8H15RZxtPWherWjsy+z7ns/zszw6JrRkaJa0ZGbhYWxrq71NuZWeFpW42CwkJmH1mPmaExfeu2xdTQiCVhoQCcj79GG68GXEm8QWJmKr4ObtR29kKpqJw2MZX1mTocHY6FkSkftR0GiqIk4/ZLx9hw7kCFxg9P93VPiIchyTghRCnh4eHk5ORoElV35OXl0aBB8V37unWLm+27uBTdKYuPj6dGjRoMGDCADz74gIMHD9K0aVMWLFhA/fr1CQgIAODYsWPs2LEDc/PSCYrLly9rknGNGjXSeu78+fM0LtEKIrhEC4rCwkK+/fZblixZwo0bNzSJLrPbyYXLly+Tl5enSTRC0dTXNWvWfLA3qISSMd7v9XX55ptv+OKLL7SWff7553h3152806VUwyO1GgWlWyOpVCpW/TGP1n27Yufi+EDr3v9fKGcPHGfoJ6PRL6P756PQGfM9GqiV3B41ap3LH9S6tf/RpFlT7B0cHql+2Uo2uVeUOVuUq6UDLzXqyqrTuzgZcwkbE3MGNejE8OAezDhU+gdW94AWNHcP5Kttc0rdbS8Pg9q1MHmu+JjPXKK7u3CRe3QpUBQl7LI3bAW1GlVsPApzc4yaNdJKxqkSk8j4518Uxkbo1/TFpEcXMucvqdCEnK5YFSjuFb1GU/faZN+eTKMsHXwb07B6TX7Zs7TSBrC+o+TH517bUc3SnqFBXVl9ZjenYy9hbWzBgAYdeaVxd/45vLbSYty4ZQtTbt8oAfj59o2UUoe5jmWlKEoe62har8bGxTH1l1/4/aef7nvTolFQEAtnzyYlJYVV//3HR599xpz//Q/bRxwz7mFZO9trTQ7g5O1GZlIqp7YewMXPnbycXHbMWk2rod0xNtf9g7+ylGoNrFbr3C8qlYpN05fQtFcHbHRMOnFHm0HdCZ2zin8/ngYKBVaOtgS0DCJ87/GKDfwuJbdBrWPZHVbOdlg522n+d/SqTmZyGqe3HtQk49RqNcYWZrQY3A2lUom9uwtZqemc3nqwUpJxZVFwz7OsRguPumTl53D85vkyy7TyrM/1lHgik2IqLL5nmc73vYzr953vHguObyanoGhc1LVn9zCs0XOsOLWTAlUhq8/spn+9dkxoNxS1GhKzUjkSHUFjt7KHe6gM5f1M1XSoQXf/5vx7fBNXkm7iZG7DwPodSQ3I4L/wfZUS89Nw3Xuq/D+etfRJJck4IUQpd7pjrF+/HldXV63njIyMuHx7vJ27Jyq48+X3Tl0XFxfatm3LwoULadq0KYsWLeL114vv+qtUKnr06MF3331X6vXvJPaAUgkstVpd+st3iYvL1KlTmTZtGj///DN16tTBzMyMsWPHknd7APmS5cuiUChKlc3PL93Fo2SM93t9XT766CPGjRuntczIyIilp7ffN05TCzMUSiUZKdpd6TLTMkq1lgPIy84h5ko0sVE32DR3BXD7PVGrmTx0HIM+fAPP2sWt3w6s386+tVsZ/NFbONWomG4VVtbW6OnplWoFl5yUXKq13B12drYkJWoP7p+SlIyenh5W1mUP1l2W2JgYjh0+ytffT7l/4QeUnptFoaoQK2PtJLOVsVmpu9R39AxsxYVb0ayLKPoyG50SR27Bej7vNIJlJ0NJuevOcDf/5jxfuxVTQucRnVJ2y6JHkX/xMoX/3DUj6O3WLwozM9QZxbErzExRZ2aVrK6hzihqqXn3lz5VYiJKc3NQKuFOKxmVSjOBQ2FMHPrVnDFsHETOxooZCDojN5tClQoLI+3j09zIlPRc3fvibk3dAzkcHU6hWnernvY+jejkF8zv+5ZzMy1BZ5mKUPSZUpVqWWlpbFaq1cAdPQJacjHhGhvO7Qcgmnhyj+TxacfhLDu1vdImmmjdsiWBt2+4AJpzXkJSklbL1aTkZGzLOM6hqCXd3a3gNHVuJ9DOnT9PUnIyQ0eO1DxfWFjIiZMnWbpyJfu3b0fv9ufXxMQEt+rVcatenTqBgfQeMIA169bxytChD719xuamKJQKskq0gstOz8TE8sG77zt6VefSoaLu+Om3kslITGHzH4s1z9+57vzz5lf0/3IUlg5lv1ePwsTCFIVSWWrSnuy0DEytSt8gy8/JJT7qBreuxbBz/n/FMarV/DriE3q/9wpuAd6YWprTY8xQCvLzycnIwszakn3LNmN5j0mEHpWRZl9of5ZzHnJfOHi6cvlw8dAIplbmKJRKrXHkrJztyU7LoLCgED39B2tJ/qDS824f3yVaLFkYmZFWxjXjbq0863Hg6hkKy2h9aKinT7CbP6vvmphG6JaZd+eaoZ0UL7pm6B4SIi03k9ScDE0iDiAuPQmlQoG1iTkJmalk5mUz+8h69JV6mBoak5aTSTf/5jonUqgIlfWZ6h3Yhv1Xz2hmvr2RegtDPQNeavQc68L3PVCi70E9Tdc9IcpDknFCiFICAgIwMjLi2rVrmm6od7v8gINfDx48mAkTJjBw4EAuX77MgAEDNM8FBQWxYsUKPDw80Nd/8FNRrVq12LBBu/ve0aPaA2fv2bOH559/niFDhgBFib+LFy/i7190F9LHxwcDAwMOHjxIjRpFg64nJydz4cIFre11cHAgJqb4TvLFixfJyio7CfGgr6+LkZHRA3dLLUlPXx8Xz+pEnjlPrcbFrRUjT5/Hr2Fg6dcyMea1bydoLTu2bS9RZy/S951XsL7rh9+BddvZu3oLAye8QTWvihug3sDAAL9aNTly6DCt2xa/50cOH6Fl61Y669SuE8i+Pdp3Xw8fOkytgFoP9Rm6Y8N/67G2saFZi+YPXbcshapCIpNiqOPirTULWaCLF8eu6265YKRnUCrho7rz/12J5+7+LegV2Jpvt/9LZFLFja2mkZePKi9FO46MDPQ93cmLiy9aoFSiX6M6OdvL/mFXcP0mhrW1xy9U2tqgSs8oTsSVQfGAXaYfRKFaRXRKHLUc3bXGtKnl6M7p+4xx42tfHUdzGw5E6R6/sL1vI7rUbMof+1ZwrYKToiUVqlREJd0k0NmLY3d/ppy9OH6jjM+UvkGpH1Kq2wmeChgasUxmpqZaM6Sq1Wrs7Ow4dOQItW63ds7Pz+d4WBhvv1H2LIp1AwM5dOQIg198UbPs0OHD1A0sOp81btSIxbeHPLjjyylTcHd356XBgzWJOF3UavU9b4zci56+HvY1XLgRcQXPBsWf8RsRV3Cv9+AtqxOjYzG5nfSycran72fa78XRNTvIz8ml2YtdMLN5+BsN96Onr4+jRzWunb2ET8Pi2WmvhV/Cq35AqfKGxkYM/mqM1rJT2w9xPeIyz40ahFWJZKG+gQHmNlYUFhRy6dgZfBvXqYRt0MOuhgs3IyLxqF+8L25GRFKj3oMPpZAUHavVXdfRqzpXjpxFrVKjUBYdLGnxSZhYmVd4Ig6Kju+ryTEEOHly/K5Zm2s7ed5z3FAoaqnkZGHLnn0nyyzT2M0fAz19rUlohG6FahXXU+Pxc6jBmdjiISD8HGpwNlb3kBBRSTep5+KDoZ6BZjw+B3MbVGpVqdlXC1SFpOVkolQoqVvNh7AblTOWWWV9pgz19DW9Ee5Q32lNqyi79f+jeJque0KUhyTjhBClWFhY8P777/Puu++iUqlo2bIlaWlp7N+/H3Nzc9zd3R9oPX369OHNN9/kzTffpG3btlqt7EaNGsWMGTMYOHAgH3zwAfb29ly6dInFixczY8aMMn9Mvf766/z0009MmDCBESNGEBYWppnd9E6LOR8fH1asWMH+/fuxsbHhp59+IjY2VpMMMzc3Z8SIEXzwwQfY2dnh5OTExIkTS81u165dO37//XeaNm2KSqViwoQJWq0By3K/168MTbqGsOavBbh4ulHd14Pj2w+QmphMUPsWAGxf/B/pyak8/+YQFEoljm7agxGbWpqjb6CvtXz/f6HsWr6BXqOGYe1gS0ZK0V1cQ2MjDI0fLXF4txcHDeDrz7+kVoA/tesEsnbVGuJj4+jVtxcAf//+Fwm3bvHJF58B8Hyf3qxcuoLfpv1Cj17Pc/b0Gdav+Y/PJxd3783PzyfqSuTtvwu4desWF89fwMTUlOpuxWMCqlQqNvy3nq7duj5SIu9eNpzbz1vN+nAl8SYXE6Jp59MIe1MrQi8eKdru+h2wNbHgrwNF3UCP3zjPyCY96eDbmFMxl7A2MWdow65cSrhOyu2JA7oHtKBf3Xb8vm85tzJTNC3vcgryyC14tMTCg8g9fBzjFsGokotmPTVq3gR1fgF5ZyM0ZUx6dEGVnkHuzqIZE/OOncSoUQOMO7Uj7+gJlLbWGDVvQt7RE5o6RiEti2ZTTUtHYWiIQe2a6Lm7kbt4ZYXGv/3SMYY16sq1lDgik27SwqMutqYWmjv7PQNaYmVizr/HNmnVa+Zeh8ikm8SkJ5ZaZwffxnTzb87coxtIzErVtKLILcivtMHRN54/yBtNexOZdJNLCddp690QO1MrQi8W3YjoX689NiYWTD+4GoATNy4wPLgH7X0a3f5MWTAkqDOXE65rfiDqKZW4WhZ1z9ZX6mFjYkkNaydyCvKIz0iukLgVCgUD+/Vj9r//UqN6ddzc3Jg9bx7GRkZ06dRJU+6zr77C0cGB0bcTdAP69eO10aOZM38+Ia1asXPPHg4dPcrMP/8EipJ+Pl7aA/cbGxtjbWmpWZ6dnc2sefNo3aIF9vb2pKamsmzVKuJv3aJD27aPvE11OjRj5+xVOLi74OhVnXN7jpORlIp/64YAHF4VSmZKOm1f6QXA6W0HsbC3xsbFgcLConHKIo9H0OH1fgDoG+hj66o9XIChqTFAqeUVKahTSzbPWIaThysuPjU4vesI6Ymp1GlbNOzDvmWbyUhJo/Or/VAoldhXd9aqb2pphp6Bgdby2MvRZCSn4lCjGhkpqRxcHYparabRc5XTvTOwfRN2z1mDvbsLjp7VOb/3OBnJqdRqVTRj5NHV28lMSafNy88DcDb0EOZ21lhXc0BVUMjlw6eJOnGOdq+9oFlnrdYNCd95lIPLNhMQ0pi0+CRObtpHQNv7TxLyqDZfOMyrwT2JSo7hcsIN2ng3wNbUkp2Xi7r39q0Tgo2JBf8c/k+rXivPelxOvMGNtFu6Vnu7TH2O37hAZiVO9vOwTAyMqG5VPDRENUt7fO2rk5aTSVwFnXse1e7LJxgY1InrKfFEJcfQ1D0QGxNzzY2Z5/ybY2VsxqITWwE4fv0CHf2CGdCgA5vPHcLM0JgeAS04fC1cM3RBDWsnrEzMuZF6CytjczrXbIICBTsuPdqkYQ+iMj5TJ2Mu0ckvmGvJcVxJuoGjuQ29AlsTdvPiA/c4eRhP63XviVZGS39RdSQZJ4TQ6auvvsLR0ZFvvvmGK1euYG1tTVBQEB9//PEDzypmaWlJjx49WLZsGbNmzdJ6rlq1auzbt48JEybQuXNncnNzcXd3p0uXLqWSYnfz9PRk+fLlvPfee/zyyy80a9aMiRMn8uabb2paln366adERkbSuXNnTE1Nee211+jVqxepqama9fzwww9kZGTQs2dPLCwseO+997Seh6Lupq+88gqtW7emWrVq/PLLLw804+qDvH5Fq90siOyMLPasKvoB5VDdhQEfvK5p5ZaRkkZq4sN90Ti2bS+FBYWs+GW21vJWfTrTpm/XcsfcvlMH0lJTmfPPLBITEvH09uL7n3/E+XY35cSEROJii1sdVXOtxvc/T+W3ab+watlK7B3seef9dwlpV/zDOuFWAsOHvKz5f/H8hSyev5D6QQ34bfofmuVHDx8hLjaO53p2L/d2lHTw6lnMDU3pU6cN1iYWXE+J5/udC0jILNr/1sbm2JkVt3bZfSUMY30jOvkFMzioE1l5OZyNi9R82Qfo6NsYAz193m09QOu1VpzawYrTOyt8G+7IO3AEhb4+Jl3aozA2pvBGDJmLlkNecdJJaWWpdUdcnZ5O5qLlGHcMwfzVYajSM8g7cpzcA0eK65iZYtqzKwpzM9S5eajib5G1eCUFkRU7CcLxG+cxMzSma82mWBqbEZOWyJ/7V2pmR7U0NsPWRHsyCmN9Q+pX82X56R0619nKsx4GevqMbNJTa/mGiP2VMpA1wKFrZzE3NKFX7TZYm5hzPTWeH3ctIDHrrs+UafFnak/kSYz1jejg15iBDYo+U+HxkSwJK+4CbGNiweSuxS2yuvk3p5t/cyLiopiyfW6Fxf7S4MHk5uby7U8/kZ6eTmBAAL9Pm6bVgi42Lk7rvF+vTh0mT5rEXzNm8Pc//1Dd1ZVvvvySwNq1db2ETkqlkqirV1m3cSMpqalYWVoS4O/PjD/+wLtEIu9heDeuTW5mFsfX7yYrNQPbao50GT0ICztrALJSM8hMKj7XqwoLObR8K5kp6egb6GNdzYHOowdSo45vGa/wePg1qUt2ZhaH1m4nKzUdO1cnnn/3JU2X0szUdNITUx5qnQX5+RxYtZXU+GQMjA3xqFuTzq/2x8jUpBK2ALwa1SY3M5uw9XvISsvAxsWBTqMGYF7GvigsLOTwym1kpaSjZ6CPjYsDHUcNwC2weHZdc1sruowZxKFlW1n99f8wtbagdtvG1OlccS2oSzoSHYG5oUnRzQHjoqTNz3uWaGaytDI2x9ZU+zxlYmBEw+q1WBS2VdcqAXAyt8XPwY0fdy2stNgfRS0Hd37vXTwsx5iWRYnpDREHmFyB555HEXbzIqaGxnSsGYylkRkx6Yn8c3Bt8TXDyBRrk+LhP/IK85l+YDW967RhbOsXycrPIezmRTZGFF8L9PX06VKrGXamluQV5BMRH8XC41u0urZWtMr4TP0Xvhe1Wk3vwNbYmFiQnpvFyZhLlfYd5Gm+7gnxoBTqykhlCyHEYzR58mT+/vtvoqOjqzqUCvfv0ad/FqihjboSn1a6hdHTxNHSjkELPq/qMMpt4eAvSJ08tarDKBerie8xetXTvQ0Av/d+j6GLvrh/wSfcvwM/J/1W2S1zngYWDg78uHNBVYdRbu+HDObP/SuqOoxyeat5X77b/m9Vh1FuE9oNZfjSihuPtKrM6v8xLf4ou0v502DfqL95b+2vVR1GuU3tOeap/0zN6v/xM3Pdexql/v6/qg6hTFajX6vqEKqEtIwTQjx1/vzzTxo3boydnR379u3jhx9+YPTo0VUdlhBCCCGEEEI8eVTSButJI8k4IcRT5+LFi3z99dckJSVRo0YN3nvvPT766KOqDksIIYQQQgghhLgvScYJIZ4606ZNY9q0aVUdhhBCCCGEEEII8dAkGSeEEEIIIYQQQgjxrJKpAp44ZU9ZKIQQQgghhBBCCCGEqFCSjBNCCCGEEEIIIYQQ4jGRbqpCCCGEEEIIIYQQzyrppvrEkZZxQgghhBBCCCGEEEI8JpKME0IIIYQQQgghhBDiMZFuqkIIIYQQQgghhBDPKpWqqiMQJUjLOCGEEEIIIYQQQgghHhNJxgkhhBBCCCGEEEII8ZhIN1UhhBBCCCGEEEKIZ5XMpvrEkZZxQgghhBBCCCGEEEI8JpKME0IIIYQQQgghhBDiMZFuqkIIIYQQQgghhBDPKumm+sSRlnFCCCGEEEIIIYQQQjwmkowTQgghhBBCCCGEEOIxkW6qQgghhBBCCCGEEM8qlXRTfdJIyzghhBBCCCGEEEIIIR4ThVotI/kJIYQQQgghhBBCPItSv/+1qkMok9X4MVUdQpWQbqpCCPEEW3R8S1WHUG4DgzoRn5ZY1WGUi6OlHa8smVzVYZTb7BcnkjLk9aoOo1ys50/ns00zqjqMcvuyy6u8tvy7qg6j3P73wgTSjp6o6jDKxbJRA0IvHKnqMMqtvV9jPt30v6oOo1y+6vIa323/t6rDKLcJ7YYyft3vVR1GuX3ffTTvrX1yf8A/iKk9x9DijzeqOoxy2zfqb15d9m1Vh1EuM/p9yOhVU6s6jHL7vfd7VR3Co1GrqjoCUYJ0UxVCCCGEEEIIIYQQ4jGRZJwQQgghhBBCCCGEEI+JdFMVQgghhBBCCCGEeFbJVAFPHGkZJ4QQQgghhBBCCCHEYyLJOCGEEEIIIYQQQgghHhPppiqEEEIIIYQQQgjxrFJJN9UnjbSME0IIIYQQQgghhBDiMZFknBBCCCGEEEIIIYQQj4l0UxVCCCGEEEIIIYR4Vslsqk8caRknhBBCCCGEEEIIIcRjIsk4IYQQQgghhBBCCCEeE+mmKoQQQgghhBBCCPGsUqmqOgJRgrSME0IIIYQQQgghhBDiMZFknBBCCCGEEEIIIYQQj4l0UxVCCCGEEEIIIYR4Vslsqk8caRknhBBCCCGEEEIIIcRjIsk4IYQQQgghhBBCCCEeE0nGCSFEOe3cuROFQkFKSkpVhyKEEEIIIYQQ2tTqJ/fx/5SMGSeEeCrFxsYyefJk1q9fz40bN3B0dKR+/fqMHTuW9u3bV3V4T4zDW3azf10o6SlpOFZ3ocuwPrjX8rlvvWvnrzD7y19wdHPhzW8/rLT4Vi1bwaL5C0lMSMTDy5Mx496hXoP6ZZY/cewEv//8K1FXIrGzt2fQsMH06ttb83zk5SvMnP4P58+dIzYmlrfffYf+g17UWkdBQQGzZ8xk66YtJCYmYmdnT9fuz/HSiJdRKivmHlVbn4Z0rdkUaxNzbqTeYuGJrVxMiNZZdkRwd1p61iu1/EbqLT7Z9D8A9BRKuvk3p4VnXWxMLIhJT2TZye2cib1SIfHei3Gf7hi2bYXCzJTCy5FkzVmE6kbMPesYdW6PYYfWKO1sUadnkHf4ODlLV0F+gaaMwsYakwF90K9bG4WhIarYOLJmzKMw6lqFxt/YzZ+WnvUwNzLhVkYyG88d5GpybJnl9RRKQnyCqFfNB3MjU9JyMtl1+QQnblwAoGH1mtSv5oejhQ0AN1MT2HbxCDdSb1Vo3CW18WpA55rBWBmbczMtgSUnQ7mUcF1n2ZcbPUdzjzqllt9MTWDS1pkANHMP5JXG3UqVeWvljxSoCis2+Luo1WpmrFzOqu3bSc/MoLaPD+NfHo53dbcy66zaHsqGvbu5HF20vbU8PRn14gBqexefy2avWc2Oo4e5evMmRoaG1PX1Y/SAQXhUq1bh27Br/Va2rdxAanIKLjVc6ffqEHxq19JZ9sT+I+zZGMr1K1cpyM/HpUZ1ug3qQ0BQXU2ZaR99zcUz50rVrd2oHqM+/6DC478j2C2Alp51MTcyJT4jmY3nDtz32Gjr07DUsXH8xnkAApw8aO3VAFtTS/QUShKzUtkXdZqTNy9W2jZE7DrK6a0HyE7NwNrFgSb9OuHsW0Nn2ZgLUWycNr/U8j6fv4G1s73m/9ysHI6t2cHVsPPkZWVjbm9NcN+OuAXe/9r5qJq5B9LGOwgLI1Pi0pNYG76HqKSyz7N6SiUdfIMJcvXDwsiM1JwMQi8d5Wh0BACvN+uNt51rqXoRcVHMPrKu0rajuUcdQryDsDQ2IzY9iTVndhOZdPMe26FHJ79ggqrXxNLIjJScDEIvHOFwdDgASoWS9r6NaOTmj5WxGbcyklkXvp/zt65W2jY8qHouPgxq0IlajjWwN7Pmww1/sSfyZFWHpRHi3YDONZsUXzPCtnGxjGvGK427lXHNuMXnW2Zq/jcxMKJ3YGsauNbEzNCYhMwUllbid5FWnvVo79sYK2MzYtISWXF6B5cTb+gsOySoM03dA0stj0lLYHLoXKDo8xnsFkA1y6Lj/VpKHP+F773neU+IyiTJOCHEUycqKooWLVpgbW3N999/T926dcnPz2fz5s2MGjWKc+dK/6ipCHl5eRgaGlbKuivDmQPH2DRvJd2G96dGTS+ObtvH/G//YtSPE7G2ty2zXk5WNqv+/BevQD8yUtMrLb7QLdv49adfGDfhferUq8valav54J33+HfpApycnUuVv3njJuPHvkePXj359MvPOX3yFD999yPWNtaEtGtbFHtODi6u1Qjp0JbffvpV5+sunDefNStW8/GkT/D08uJcRATffDkFc3Mz+g18UWedhxHs5s+g+h359/gmLt6KJsQniHGtBzBx03SSstJKx3NiK8tO7dD8r6dQ8mXnkRy5/aMKoE+dNjRzr8Oco+uJSUsk0NmLt1u8wOTQuVxLiSt3zGUx6t4Zo64dyJo+l8LYOIyffw7zD8eS9sFnkJOrs45B82CMX+xN1oy5FF68gtLZEdPXXwYgZ8EyABSmplh89gH5ERfI/OE31GnpKJ0cUGdlVWj8gc5edPVvxrrwfVxLjqOxWy2GNOzC73uXkZqTqbNO//rtMTcyYfWZ3SRlpWFmaIJSodA872FbjVMxl4iOiKNAVUhLz3oMa9SV3/cuJz23YuO/o1H1WrxYvz0Lj2/hUuINWnvVZ0zLfkza/A9J2aWP0SVh21h5epfmf6VSyWcdXuHYDe1zY3Z+Lp9umqG1rDITcQDz1q1l4YYNfPbGm9RwdmHW6pWM/mYKy3/8CTMTE511jkWE06lZC+oO88PI0IB56/5j9LdTWPLdjzjaFp3Ljp+LoF+HTgR4e1NYqOKvpYt5+9spLP3+R0yMjSss/qN7DrL8n/kMeONlvAL82LtpO39M+oFP//gOW0f7UuUvnT1HrfqB9BzaD1NzMw5s28VfX01l/I9f4ObtAcBrH4+loKA4UZ2ZlsGUMR8T1KJJhcVdUvGxsZdryXE0cvNnaMOu/LZ3aZnHxov1O2BuZMKqM7tJykq9fWwU38DIys9l1+UTJGSmUKAqpKajO70D25CZl11m4rg8rhw9y6FlW2g2oCtO3m6c23OcLX8sos9nb2Bua1Vmvb6T3sTA2Ejzv7GFqebvwoJCNv+6AGMLM9q91hczawsyktO0yle0ei4+9KjditWndxGVHEOTGrUZEdyDqTsXkpKTobPOkKAumBuZsuzUdhIzUzE30t4X845uQE+pp/nfzMCYsa0HcCrmUqVtR/1qvjwf2JqVp3YSmXSTZu6BvNq0J9/vmE9Ktu7tGNawKxZGpiwNCyUhMwVzI1P07jrfdq3VlIbVa7H0ZCjxGcnUdHTnleBu/LZnGTfSKvcGyP2YGBhxKfE6G87tZ0rXN6o0lpKKrhkdWHB8M5cSbtDGqz5jWvXn803/kJRd+nvI4hPbWHFqp+Z/PaWSzzoO5+j188XLFErGtR5AWm4mfx9YRXJ2OrYmluQU5FXKNgS51qRv3bYsCQvlStINWnrU5a3mffh62xySdVz3lp/awZqze7Ti/aj9MM2NNABfezeOXT/HsqSbFBQW0sGvMaOa92Vy6FxSyzjWhKhM0k1VCPHUeeutt1AoFBw+fJgXXngBPz8/ateuzbhx4zh48CBRUVEoFArCwsI0dVJSUlAoFOzcuVOzLDw8nOeeew5zc3OcnJwYOnQoCQkJmudDQkIYPXo048aNw97eno4dOwKwYcMG/Pz8MDExoW3btkRFRWnFl5iYyMCBA6levTqmpqbUqVOHRYsWVeZbotOB9TsIatuMhu2a4+DqTNeX+mJlZ8PRrXvvWe+/fxZTp0VDqvt6Vmp8SxYuptvzPejRqycenh6MeW8sjk6OrFq+Smf5NStX4eTsxJj3xuLh6UGPXj3p1rM7i+cv1JTxrx3AqHdG06FTRwwNDXSu58zpM7Rs04rmLVvgUs2Ftu3bEdwkmHMRFZPE7VSzCbsjw9h9JYyY9EQWndhKUnYa7byDdJbPzs8lLSdT8/CwdcHU0IS9d91hb+ZRh3UR+zgVc5lbmSnsuHycM7FX6FKz8n6sAxh1aU/Omo3kHz2B6vpNsqbPQWFoiGHz4DLr6Pt4UXDxMvkHjqBKSKTgTAR5B46g7+VevN4enVElJZP9v7kUXokqKnf2HKr4hDLX+yiae9Th+PXzHL9+noTMFDaeO0haTgaNawToLO9jXx0PWxfmH9vMlcSbpGRncCP1FtEp8ZoyK07t4Eh0BLHpSSRkprLmzB4UCgVeOlqhVJSOfo3ZG3mKvVGniE1PZOnJUJKz0mnj3UBn+eyCPNJyMzUPDxtnTA2N2Rd1WqucWq3WKpeWqzsJU1HUajWLNm3klV69aNc4GB83Nya98RY5ebls3r+vzHpfj3qbfh07UdPDA49qrkwc+RpqlZojZ89oyvw24SN6tAnBu7obfu7ufPb6m8QmJhARGVmh27B99UaadwyhRee2uLi50u/VoVjb27F7Y6jO8v1eHUqnvt3x8PPGsZozzw97EUcXZ04fPqEpY2ZhjpWNteZxLuwMhkaGBLUs+zgrr+YedTl+/TzHrp/nVmYKG88dIC0ng+D7HBv/HtvElcQbdx0bxTcDopJiiIiP4lZmCsnZ6Ry8eoa49CTcrUvfXKkIZ0IP4de8PjVbNsDaxZ6m/TthZmPJud3H7lnP2MIMUytzzePuFtEX94eRm5lNhzf64eTthrmdNc4+NbCr7lQp2wDQyqs+R66Fczg6nPiMZP4L30tKdgZNdbRUAvBzqIGXnSuzDv/HpYTrJGenE50Sr9W6Jzs/l4zcLM3D18GN/MKCSk3GtfZuwOFrZzl07SzxGcmsObuHlOwMmnvU1Vm+poM73vauzDi0hosJ0be3I46ou7ajoVstQi8e5Vz8VZKy0jgQdZrz8Vdp46P73Pc4Hbx2lhmH1rLrSlhVh1JKR79g9kaeZG9k0TVjyclQkrPS7nHNyNW6DrhrrhmnNGVaetbF1NCYP/et5HLiDZKy0riUeJ3rqfE611le7XwaciDqNAeuniYuPYkVp3eSnJ1OKx09CQByCvJIz83SPGrYOGNiYMyBq8XXiblHN7An8iQ3Um8Rl5HEwuNbUCgU1HTQ3Zr2maNSPbmP/6ckGSeEeKokJSWxadMmRo0ahZmZWannra2tH2g9MTExtGnThvr163P06FE2bdpEXFwc/fv31yo3d+5c9PX12bdvH9OnTyc6Opo+ffrw3HPPERYWxsiRI/nwQ+1unDk5OTRs2JB169Zx5swZXnvtNYYOHcqhQ4ceebsfVkFBATcjo/Guq911yrtuLaIvlP3j9MTOgyTHJdCmb9dKjS8/P58L584T3ET7x2bjJsGcOXVaZ52zp8/QuET54KZNOBd+TqtVyf3UrVeXY0eOcu1qUXfISxcucurkSZq1aPaQW1GanlKJh40LZ2O13+OzsVfwtq/+QOto7Vmf8LhIEu9qRWeg1CO/UHsb8woL8HUou2tfeSkd7FFaW1FwOrx4YUEBBecuoO/rXWa9gguX0PeogZ6Xh2Y9BvUCyQ8r3q8GQXUpuHIV07dfw/KPHzD/eiKGIS0rNH49hRIXS3suJ2h3abmUcIMa1rp/WNdydOdmagItPevyfsggxrTqT+eaTdC/q4VJSQZ6+ugplGTn624pWF56CiU1rJ0Jj9P+TIXHRershqZLC4+6nIuPKtUy00jfkG+6vsF3z73F6BZ9cbN2rLC4dblxK57ElBSa1in+cW5oYEBQLX9OXbxwj5racnJzKSgswFLHNeCOjNutLC3NzR894BIK8gu4dikS/wbaXaH8GwRyJeLBumKqVCpysnMwtSg79v1bd9KwdTOMKrBF3930FEqqWdqXaq12KeE6bvc8Nm7R0rMeH4QM5p0HODa8bKthb2ZFVPK9u7U/isKCQhKvxVAtwEtruau/F/FX7t0Kb82UGSya8DMbf55PzPkoreeunbqAo1d19i/exMLx01j55XRObtyLqpJ+LOoplLhaOXKhxDAGFxOi8bDRncQMcPLkeko8Id5BTOzwMh+EDKGbf4t77ovGbgGcvHmx1HWkougplFS3cuR8vPYwA+dvXcPDxkVnndrOnkSnxNHOpyGfdRzOh+2G0iOgpdZ26Cv1yFdpx5xfWICnbcV3P39W6CmUuNs4Ex4bpbX8bFwU3vYPds1o6VmPiDjta0a9ar5cSbzBoKBOTO3xNpM6jeC5Ws1QoLjHmh6NnkKJm7UTEfHa3ZEj4q7iafdg+76ZeyDn46/qbEV3h6G+PnpKJVn5OeWKV4hHJd1UhRBPlUuXLqFWq6lVS/f4PA/qr7/+IigoiClTpmiWzZo1Czc3Ny5cuICfnx8APj4+fP/995oyH3/8MV5eXkybNq3oblrNmpw+fZrvvvtOU8bV1ZX3339f8//bb7/Npk2bWLZsGU2aVG5Lpjuy0jJRq1SYWVloLTezsiAjtXQXBYDEmHi2LVrLK5PGoqdX9pf6ipCakkJhYSE2ttrdZW3sbElKTNIdX2ISwXYlytvaUlhYSEpKCvb2pbuI6TL4paFkZGQypN9AlEolKpWKV998nQ6dOz3axtzFwtAUPaWStBLdHVJzMgk0vn9SwMrYnDou3kw/uFpr+ZnYK3Su2YQLt64Rn5GMv5MnDVz9tLpPVjSFtSUAqhKfF1VqOsp7dHPOP3iUbAsLzD/7AFCg0Ncjd9tOcv/brCmjdHDAqH0bcjdtI3PtRvS8PTAZ9iLqggLy9x6skPhNDY3RUyrJyNPuOpqZl425ke7ukDYmFtSwcaJAVciiE1sxNTCme+0WmBgYsfrMbp11Ovo1Ji0nkytljGNTXuZGtz9TJbrApuVmYmlcdkLnDitjMwKdvfjn8H9ay2PTk5jzf+zdd1iTV/vA8W/CCIS9hzIFUQRF3Bv3ttrW2mptrW2t3fP99e3ee9i9W0cdtbbWOuree+NEBAXZe29I8vsDDQaCooBY3vtzXbk0T87z5D7kPBnnuc85h9aQnJ+JhZmKoQHdeT7ibt7cNJeMotwmrcMl2RcXuXG0MxxC6GhnR1pWw7Miv/ptCS6OjvQMMZ45pNPpmLPoV8KCggjwaroO66KCQrRaLTb2hvHb2ttR0MAFfDav+IeK8nK69Tf+WRB/9hwpF5K4+4kHGxtuvWrOjVKD7UUVpdio1Eb3cbS0xdvBnSqthsVHN6A2s2B8p/5Ymlmw4mTNkGiVqRn/ibgbU6UJWp2W1ad31zvHU2OUF5Wg0+qwrNWpaWljRUm+8eFmalsb+k0bg5O3B9oqDbH7T7D284WMeXo67oHVmbuFWXmkRsfj3zOEEY/eSUFGDnuXrkOr1dJ17MAmr4eVuWX1a1Hr/C4sL6n3tXBS2+Lr6EGVVsOCQ/9gZW7JxJBBqM1ULDu+pU55L3tXPGyd+OO48ezNplBfPYrKS7CxqKceVnb4OXpSpdEw9+AarMwtuK3zYNTmKpZGVscanZHAIP+unM9OJrs4n0AXLzq5+xsMyRWGaj4zDDOdC8uKsbuWz4z9Kw22O1vZ08HVh/0Jp/h85++42TgytesIlAolq6Pqz2y+vjpUt6faUz8Ulhdjq/K96v62KiuC3fyYd2jNFcvd0mkg+aVFnMlo+TkIxf8m6YwTQvyr6C6uuKNoZCfE4cOH2bp1K9ZGsibOnTun74zr3r27wWNRUVH07t3b4Pn79DHMqNJoNLz//vssXbqU5ORkysvLKS8vN5rJd8mlMpdTqRo/R02dK5Y6MHYRU6vV8udX84m4fQzOHs2bHXO5Oi+jTld32+XlawWvQ2d0+5Vs3riJjWvX8+rbr+Pn70/M2bN8+ennOLtUL+TQFGqvC1Ud39VXi+rv15mSyjL9hOiXLD66kRndx/Du6NnogIyiXHbFHTO68MP1MuvbE/XMafr7RR9/dfF/teK+yp/atGN7LG4ZTem8xVTFxmHi7orl3VPQTsynfMU/1YWUCjTnL1D2+woANBcSMWnjiWrooCbrjLuS+hbuunRe/3F8C+VVlQCsO7OPKWHDWH16d5351Pr7dSbUox1zD6xp9rnWagetQNGQJkUfn1BKK8uITDbMPIvLSTGYWP1cVhIvD5vB4HbhLD3WND/a1+7exXs/18xJN+c/z9fEfhmdDiNvBsYtWLWSDXt3893Lr6KqZw7PD+fNJTbhAj+++sb1BX4VtT9/dLqGvQcd3L6HNYv/YvbLT9fp0Ltkz4ZtePq0xbd9/dmnTadum7raubHM4NzYy5Sw4aw+vUvf/iuqKvlmz5+Ym5jh7+TJqA69ySktuOJiBI1R57Uwsu0SO3cn7Nyd9Pdd/dtSnFvAiY379J1xOp0OCxsr+k0bi1KpxNnHg5L8Qk5s3NcsnXH6uI28zdZ3el+q35KjG/Tzda0+vYu7u43mr5Pb67wX9fAKJrUg22C4fXMxGnM9jerSObPoyHp9PVae2sk93cfw5/FtVGk1rDi5gzu6DOH5IdPR6SC7JJ+DiVH08OrYTDVoPXRG/u4NWbSyr28oJZVlBnOtASgVCgrKi1lwaB06dCTkpWNvYc2IoF5N3hl3WcQG9xQoGvKxR2+fTpRWlnM8pf5h2cMCe9CtbRCf7/y9+T+/bxLG2oRoWdIZJ4T4VwkMDEShUBAVFcXEiRONlrk0/8vlHzqVlZUGZbRaLePHjzfIaLvEw6NmSEXtDrSGfJB98sknzJkzh88++4zQ0FCsrKx46qmnqKiof5Lb9957jzfeMPzh+NprrxE0oe9Vn88Yta0VCqWyThZccUEh1ra2dcqXl5aRcj6B1Pgk/plXPcm+7uJy429Me5LpLzyCf0jQdcVijJ29PSYmJnWy4HJzcutky13i5ORITna2wba8nFxMTEywq+eHrTHffv410+6dzrAR1XMAtgtoR3pqGgvnLWh0Z1xhRQkarRa7WllwthbqeidFv9wAvy7siT+BptaQqMLyEr7c/QemShOsVWrySguZ3HkwWcV5jYr3cpVHjlF47rKhkKbVXxGUdnZo8mrakdLWBl092ZUAFrdPoGL3fiq2VX851yalgEqFeubdlP+9FnQ6dHn5aFIMf5xrUlIx69F08wCVVJSh0WqxNjfMyrAyt6S4VkbQJYXlJRSUFes7GwAyi/JQKhTYWlgZDNnp5xvKAP8w5h/8h/Qi49mcTaGovLpN1c6Cs1GpGzTHWz/fUPYlnEKju/IwOx0Qn5OGm039WY/XamB4N0IuW/G04uLfNTs/D2cHB/323IJ8nOyufg7/umYVc1eu4OsXXiLQ28domY/mz2XHkUP88MrruDk5GS1zvaxtbVAqlRTk5hlsL8zPr7dz7ZJDO/ex8IufeOC/j9MhrO6KfwAVZeUc2rmPcdNua6qQjar/3LCok0l6SUPPDR3o/59WmI2LlQMD/cOavDNOZa1GoVRQUmCYBVdWWIyl7dWzfy5x8WvDuQM1Q+jVdtYolEqDeeTs3J0pLShCU6XBxLRps8aLK0rRaLV1ssesVeo6WWaXFJSVkF9WZDBxfkZRLkqFAntLa7KK8/XbzZSmdPEMZMPZ5p0mQ18PVd16FJYbf78tKC+uU4/0whyDehRXlDL34BpMlSaozS0oKCtmbMe+RhdDEtUufWbU/h5iY2HVwM+Mzuy7UPczI6+sCI1Wq78ICpBamI29pTUmCuVVP2OuRVH5pfZkeC5Xt6er16G3TwgHEk/XG9PQgO6MaN+Tr3b/QUpB085VK8S1kBxfIcS/iqOjIyNHjuTrr7+muLjuB3JeXh4uLi5A9bxwl1y+mANAeHg4p06dwtfXl4CAAIPblTLYgoOD2bfPMHOn9v2dO3dyyy23cPfdd9OlSxf8/f2JibnyfEIvvPAC+fn5BrcXXnjhivtciampKZ5+Xpw7brgowbkT0Xi1r7swg8rSgoc/fIHZ7z+vv3Uf2g8nT1dmv/88bQN8rzsWY8zMzGjfIYiD+w8YbD944CAhnY0PPesUGsLBAwcNth3Yf4AOwR0wNW34taWy8jIUSsPMCaXSBG0TXDHUaLXE56bSyd3wbxzs5se5q6wmGOTijZuNIzsvW7ihtiqthrzSQkwUSrq17VDnynWjlJWjTc+suSWnos3LxzTksgwEExNMO7SnKuZc/ccxNwdtrb+lVmuQUVd19hwmHoZzUynd3dBmNV2nlkanJbUgq84cOe2c29S7Am1Cbjo2FlaYm9S0J2crO7Q6LQWXdab28+3MoHbh/HpoXbN/kdfotCTkpRHs5muwvaOb71WH/7V38cLNxpFdccevWO4SL3tX8utZ9fB6WFla4uXurr/5t2mLk709+0/UdH5UVlVx5EwUnQPbX/FYv65exc9/LeeL/3uBYP+6WWM6nY4P5/3C1oMH+PalV2jj2vQZvqZmpngH+BF19KTB9jORJ/HvGFjvfge37+HXz77nvuceIfQKHc6Hd+2nqrKKnhH9mixmYzQ6LSlGz422BgsyXC4hN63OueFk5NyoTaHginOZXS8TUxOcvD1IiTKcSzElKg5X/4bNzwmQk5iG2ram08LVvy2FmbnoLnsPK8jIwdLOusk74qD6tUjOzyDQ2XA4daCzl8FCBpe7kJuKrYUV5iY1ixQ5W9mj1WnrrFra2TMAU6UJR5Oa8LPCCI1OS1J+Bu1rTYTf3sW73jkD43NSsFUZ1sPF2sFoPaq0GgrKilEqlHT2DOBk2vmmr0QrodFpuZCbRsdanxnBbr515lCtrf3F7yG7jHwPOZeVhKu1g0EOsJu1I3mlhU3aEQfVdUjMS6eDq+FFlw6uPsRlp9SzV7VA57a4WjuwN974/MNDA7szqkNvvtmzvFlXoxeiIaQzTgjxr/PNN9+g0Wjo2bMnf/75JzExMURFRfHFF1/Qp08fLC0t6d27N++//z6nT59mx44dvPzyywbHePTRR8nJyeGuu+7iwIEDnD9/ng0bNjBz5kw0mvrT1WfPns25c+d45plniI6OZvHixcybN8+gTEBAABs3bmTPnj1ERUXx0EMPkZZm/Ev1JSqVCltbW4NbY4ep9hk7mCNb93Jk614yk9NYt+BP8rNy6D6seqL8TUtWsvybBUB1NqGbl6fBzcrOBlMzM9y8PDG3aPyQ2dqmTL2T1X+vYs3K1cTHxfPFp5+TkZbOxNsmAvDdV9/y9mtv6svfcusk0lPT+HLO58THxbNm5WrW/L2KO++eqi9TWVlJTPRZYqLPUllZRWZmJjHRZ0lKrOkI69u/P7/Onc+eXbtJTUllx9btLF38GwMjmmYI0obo/Qz0C2OAXxc8bJy4M2wYTmo7tp47AsDtoRE80Gt8nf0G+odxLjuZ5PzMOo/5O3rSrU0QLlb2BDp78cygO1EoFPxzZm+TxFyf8nWbsZgwGrPuYSjbeqJ+aAa6igoq9tR0oqofmoHFHRP196uOHkc1bCBmvbujdHHCNKQjFrdPoPLIcf0YmfJ1mzBp549qwmiUbi6Y9emBavAAyjdta9L498SfILxtEF3btMfZyp5RHXpjZ2HNwYQoAIa178GtoRH68idSYymtKGNi6CBcrOzxcXBnRFAvjiSd1Q9j6e/XmaHtu7Pi5HbySguxNrfE2tzSoJOiqW08e5D+fl3o5xuKu40Td3QZgqPaVr+K36SQgdzXY2yd/fr7duZ8dorRDsNxHfsR7OaHs5Udbe1cubfbaLzsXZt1ZUCFQsFdo0Yzd+UKth48QGxiIm989w0W5ipG9q3pgHrt26/56reaFagXrFrJt8uW8uqs2Xi4uJCVl0dWXh4lZTWTbn8w7xfW7t7FW48+jtrCUl+m7AoZyddjyMTR7Nm4jT0bt5OamMwfPy4kNzObAaOHArBi/lLmffqdvvzB7XuYP+d7bp05Fb8OAeTn5pGfm0dpcd2spz0bt9GldzesbW3qPNbU9sQfp1vbDoRffF8Z3aEPdhbWHLh4bgxv34PbLjs3jl88NyaFRujPjZFBvTiSFK0/Nwb6h9HOqQ0OljY4W9nR1zeUMM/2HEtp2OIW1ypkaC/O7j7K2T2R5KVmsX/ZBopy8+kwoHrl6kMrtrB93t/68qc27+dCZDT5GTnkpmRyaMUW4o+eoWNED32ZDgO7UVZcyr5l68lPzybxRAzH1u2m46DudZ6/qew8H0lP72C6e3XE1dqB8cH9sbe0Zt/FVSBHdejDlLBh+vJHk89SUlHGHV2G4mrtgJ+jJ2M79uNgYlSd4XY9vYI5lXb+hkxQv+PcUXr5dKKnVzCu1g5M6DQAB0trfafImI59uavrcH35I0lnKaks486uw3CzdsTf0ZPxwf04kHBaXw9vezdCPdrhqLbFz9GTWb1vQYGCrbFXXjH3RrA0UxHo3JbAi4szedo6E+jcFjdrh6vs2fw2nj3AAP8u9PPtfPEzY+jFz4zqVZwnhQxiZo9xdfbr79eZ89nJRj8ztp07irW5BXeGDcfN2oFQ93aM6diHrbFHmqUOW2IP09c3lN4+IbjZOHJraASOahv9BcsJwf2Z3m1Unf36+IQSl5NCamF2nceGBfZgXMd+LDqynuySfGxUamxUaoMO4Vbt4oiXm/L2P0qGqQoh/nX8/Pw4cuQI77zzDs8++yypqam4uLjQrVs3vv32W6B6MYaZM2fSvXt3goKC+PDDDxkxomaCfk9PT3bv3s3zzz/PyJEjKS8vx8fHh1GjRhkMT6nN29ubP//8k6effppvvvmGnj178u677zJz5kx9mVdeeYW4uDhGjhyJWq1m1qxZTJw4kfz8/HqP2xxC+nSjpLCY7cvXUZRXgKuXB9Oefxh7l+phaIV5+eRnNc9E7Q0xdMQwCvLzmffTL2RnZePXzp8PP/sY94vDhLOzsklPq7lq6dnGkw8/+4Qv53zOX8uW4+zizJPPPU3EkMH6MlmZWcy8e4b+/m8LF/PbwsWEhXfly++/BuDp/zzNT9/9yKcffExubi7Ozs7ccustzHig5jVsjAOJUVip1Ezo1B87C2uS8zOZs/M3/eqodpbWOKkNh7RZmqno1rYDi49uMHpMMxNTJoUOwtXagbKqCo6nxvLjvpXNtoLnJeWr16MwN8NyxlQUajWac3EUffA5lNU8r9LZ0eCLVNmKf9DpwGLyLSgd7NEVFFF59Dhly1boy2jOX6D4s2+xnDIJi4lj0WZmUbrwdyr3GGZKNtbJtPNYmqmICAjHRqUmozCHhYfXkX9xgQ0blRo7y5pM2ApNFfMP/cPYjn15qO8kSivKOJl2ns0xh/RlengHY6o04c7LflQCbI093Gw/Sg4lncHK3JKxHfthZ2FFSkEWX+5aph+qZWdhjaPacPi5pak54W2C+K2e+d/U5iqmh4/E1sKK0spyEvMy+Gjb4mZZ+fJy94ybQHlFBR/M+4XC4mI6tQvgy/++iJVlzaIaadlZBvN+/bFpA5VVVTz/+RyDYz14623Mum0yAH9u2gjA7LffNCjz6qzZjB8U0WTxdx/Qm+KCQv757S8KcvLw8GnLI6/9ByfX6gVkCnLyyM2s+SG7a90WtBoNS7+bz9Lv5uu39x4ygHuefkh/Pz05lXOnz/L4m883WaxXcjLtPGozC/25kV6Yw6+H1+rPDWuVGjvLmoyxCk0V8w6tYWzHfszue6v+3NgUU5OtbGZiyvjg/thaWFGpqSKrOI8/jm9ptiwm/+6dKC8uJXLNTkoKinDwcGHEo3di7WQPQEl+EcU5NZ+7Go2GA8s3UZJXiImZKQ4eLgx/9E68QmqGUls72jHqiansX7aRFW//gNrehk6DexA68vqmjGiIY6mxqM0tGBbYA1uVFWmF2fxyYDV5F1eBtFWpsbes6aCt0FTy476/uSVkIE8MuIOSijKOp8SyLtowS9/Zyh4/J09+3Pc3N0JkSgxqcwuGB/XEVmVFamE2P+1bqV/N0lg9vt+7gkmhg3hq4BRKKsuITIlhbVTNRSZTE1NGdeiDk9qWiqpKojLiWXxkg8HQ1pbSwcWHryY9o7//RP/q96J/ovbyzpb59e12QxxKOoO1ypJxwTWfGV/srPnMsLc09pmhIrxNEEsjNxk9Zm5pIXN2LGVK2FBeG3E/uaWFbI45xNozzTPP65HkaKzMLRgd1BtbCytSC7L5Zs/ymvZkYYWjpWEdLEzNCfMM5I8TW40ec4BfF8xMTHmg1wSD7f9E7Wn2i5tCGKPQyUx+Qghx01pyxHjnzL/JXeEjyCioe4Xy38TV1on7lr7T0mE02twpL5F390NXL3gTs1/4Pa+u+/HqBW9yb456kFl/1J2z8t/mh9ufp+DQ0ZYOo1Fsu3dl89mDVy94kxvavgevrPuhpcNolLdGzeKDLb+2dBiN9vyQ6fzf6q+uXvAm9+G4x3h25RctHUajfDLhCfp9Pbulw2i03Y9+x4PL3m/pMBrlx8n/5bG/PmnpMBrtq0nPtnQI1yXvlZv3e6z9Wy+1dAgtQjLjhBBCCCGEEEIIIVqr2vP5ihYnc8YJIYQQQgghhBBCCHGDSGecEEIIIYQQQgghhBA3iAxTFUIIIYQQQgghhGitdNqWjkDUIplxQgghhBBCCCGEEELcINIZJ4QQQgghhBBCCCHEDSLDVIUQQgghhBBCCCFaK52spnqzkcw4IYQQQgghhBBCCCFuEOmME0IIIYQQQgghhBDiBpFhqkIIIYQQQgghhBCtlVaGqd5sJDNOCCGEEEIIIYQQQogbRDrjhBBCCCGEEEIIIYS4QWSYqhBCCCGEEEIIIURrJaup3nQkM04IIYQQQgghhBBCiBtEOuOEEEIIIYQQQgghhLhBZJiqEEIIIYQQQgghRGul07Z0BKIWyYwTQgghhBBCCCGEEOIGkc44IYQQQgghhBBCCCFuEBmmKoQQQgghhBBCCNFaaWU11ZuNZMYJIYQQQgghhBBCCHGDKHQ6nXSRCiGEEEIIIYQQQrRCec++3NIh1Mv+k7dbOoQWIcNUhRDiJvb+lgUtHUKj/XfIPcRkJLR0GI0S6OrN7QteaukwGu2Pe94h7/7HWzqMRrH/+UueX/NNS4fRaB+MfYSJ8/7b0mE02ooZ75NRkN3SYTSKq60Ti4+sb+kwGm1q+Ege/+vTlg6jUb6c9Awvrf2+pcNotHdGP8STK+a0dBiN9vnEp5n5+7stHUaj/HLHizy47P2WDqPRfpz8X/p9Pbulw2iU3Y9+x31L32npMBpt7pR/6fdBycG66cgwVSGEEEIIIYQQQgghbhDpjBNCCCGEEEIIIYQQrUZubi7Tp0/Hzs4OOzs7pk+fTl5e3hX3USgURm8fffSRvkxERESdx++8885rjk+GqQohhBBCCCGEEEK0Vv+Dw1SnTp1KUlIS69atA2DWrFlMnz6dVatW1btPamqqwf21a9dy//33c9tttxlsf/DBB3nzzTf19y0tLa85PumME0IIIYQQQgghhBCtQlRUFOvWrWPfvn306tULgB9//JE+ffoQHR1NUFCQ0f3c3d0N7v/9998MHjwYf39/g+1qtbpO2Wslw1SFEEIIIYQQQgghxA1XXl5OQUGBwa28vLxRx9y7dy92dnb6jjiA3r17Y2dnx549exp0jPT0dNasWcP9999f57FFixbh7OxMp06deO655ygsLLzmGKUzTgghhBBCCCGEEKK10mpv2tt7772nn9ft0u29995rVHXT0tJwdXWts93V1ZW0tLQGHWP+/PnY2Nhw6623GmyfNm0aS5YsYdu2bbzyyiv8+eefdco0hAxTFUIIIYQQQgghhBA33AsvvMAzzzxjsE2lUhkt+/rrr/PGG29c8XgHDx4EqhdjqE2n0xndbswvv/zCtGnTsLCwMNj+4IMP6v8fEhJCYGAg3bt358iRI4SHhzfo2CCdcUIIIYQQQgghhBCiBahUqno732p77LHHrrpyqa+vL8ePHyc9Pb3OY5mZmbi5uV31eXbu3El0dDRLly69atnw8HDMzMyIiYmRzjghhBBCCCGEEEIIQatZTdXZ2RlnZ+erluvTpw/5+fkcOHCAnj17ArB//37y8/Pp27fvVff/+eef6datG126dLlq2VOnTlFZWYmHh8fVK3AZmTNOCCGEEEIIIYQQQrQKHTt2ZNSoUTz44IPs27ePffv28eCDDzJu3DiDlVQ7dOjAX3/9ZbBvQUEBy5Yt44EHHqhz3HPnzvHmm29y6NAh4uPj+eeff5g8eTJdu3alX79+1xSjdMYJIYQQQgghhBBCiFZj0aJFhIaGMmLECEaMGEHnzp359ddfDcpER0eTn59vsO23335Dp9Nx11131Tmmubk5mzdvZuTIkQQFBfHEE08wYsQINm3ahImJyTXFJ8NUhRBCCCGEEEIIIVorbesYpnotHB0dWbhw4RXL6IwM3501axazZs0yWt7Ly4vt27c3SXySGSeEEEIIIYQQQgghxA0inXFCCCGEEEIIIYQQQtwgMkxVCCGEEEIIIYQQorVqJauptiaSGSeEEEIIIYQQQgghxA0inXFCCCGEEEIIIYQQQtwg0hknhPifplAoWLFiRat5HiGEEEIIIYQwoNPevLf/UTJnnBBCtBJR2w9xcuM+SvOLsPdwoefk4bgHehstm3r2Auvm1F3qe9JrD2Hv7gzA2k9/JS0moU6ZtiHtGP7onU0b/GXW/LWS5UuWkZOdjbevLw8+8TAhXUKNls3Jyubnr78nNjqGlKRkxt8+kVlPPGJQ5kJcPIt+nk9sdAwZaek8+PjD3HLHrc0WP8DIoF5MCO6Pg9qGxLwM5h1cQ1TGhXrLD/Drwi2dBuBh60RJRTlHU86y4PBaispLAYho15XH+t1eZ7+7Fr5Gpbaq2eoBYDFhNOaD+qFQW6I5f4GSRb+jTUm74j6qYRGYD+6P0tEBXVExFYciKftzJVRVx2oe0R9VRH+Uzo4AaFLSKFu5jqqTp5s8/t4+nRjk3xUblZr0ohxWndpNfG5qveVNlEqGBfagq2d7bFRq8suK2BJ7mENJZ/RlLEzNGRnUixB3fyzNVOSWFrL69G6iM+ueL01ldFBvJoYMrG5Tuen8fGA1pzPi6y0/0D+MSSGD8LR1oriijKPJZ5l36B8Ky0v0ZcYH92NUUG+crewpLC9mT/xJfj2yjkpN07Spv5b9yZKFi8nOysbX348nnnmSLl3D6i1/9PBRvvrsC+LPx+Hk7MzUe6Yx8bZJ+sfjzp3n5+9/IvrMGdJS03j86Se5Y+oUg2OUFBfz03c/smPbdnJzc2nfvj1PPPsUHTsFN0md6nNww072rN5MYV4Brm3dGXnPbfh0aHfV/RKizzPvzS9w9fJg9vvPN2uMtQ3w68LQwO7YWliRWpDN8hPbOJedbLTs3eEj6eXTqc721IIs3t28AIC+vqH09OqIh231Z0hiXjqrTu/mQu6V3y8ao5d3MP39umCjUpNRlMuaqD1XfD4TpZIh7brRpU2g/vzefu4oh5Oi65QN9WjHnWHDOJ0ex6IjG5qtDvXp79eZIQHVr09aYTbLT2znfD2vz9TwEfTyNvb6ZPP+lgXNHare4HbhjArqjb2lNcn5mSyJ3ERMVqLRsjN7jKO/X+c625PzM3ll/Y/6+8MDezC4XTiOaluKKko5lHSGP45vpUqraZY6RLTrysigXthZWJNSkMXSyE3EZCUZLXtfj7H09a37/SQlP5PXNvysv29ppmJSyEC6tgnCytyCrOI8fj+2hZNp55ulDteii0cAU7uOoIOrN85W9vz3n2/ZGXespcMCYHBAN0Zf1p4WH91Yb3u6v+c4+vt1qbM9OT+Tl9f9oL8/vH0PBrfrhtPF9nQwMapZ25MQVyKdcUII0QqcP3SaA8s20ufOUbi28yJ65xE2fv0bk159CGtHu3r3u/X12ZhZqPT3LWzU+v8Peeh2NFU1X07Ki0v5+50f8Q3v2DyVAHZs3saPX3zLw888TnBoJ9auXMPr/3mRb379GVc31zrlKysrsbW34457pvL3738aPWZ5WTnuHh70ixjIT19+12yxX9LXN5QZ3cfw0/5VnMm8wPDAHrw49F6eXvk5WcX5dcp3cPXhsX63M//QPxxKOoOj2pZZvW7h4T638tG2RfpyxRVlPLlijsG+zd0Rpxo9DNWIwZT8sghNegYW40Zi/exjFLz0FpSVG93HrFd3LG6fQMncRWhi41C6u6KeeTcAZUuXA6DNzaP0z5VoMzIBMO/bC6vHH6TwjQ+u2tF3LTp7BDA+uD8rTu7gQm4avbyDmdlzHJ9uX0JeWZHRfaZ1HYmNypI/jm8luyQfK3NLTJQ1AwlMFEoe6DWBoopSFh5ZT35ZEfYW1pRXVTZZ3LX18+3MzJ7j+H7f35zJiGdkUC9eGX4fj6/41Gib6ujqw5P97+CXg6s5mBiFk9qW2X0m8Wjf23h/669AdWfd9G6j+GrXH5zJTMDT1pkn+k8G4JeDqxsd8+YNm/ji08955vnnCO3SmZXLV/CfJ5/l198X4ebuXqd8SnIK//fUs4yfOIFX3nyNE8eO8+kHH2PvYE/EkMEAlJWV4dHGk4hhg/ny0y+MPu8Hb7/P+XPnefmNV3F2cWHD2nU8/eiT/Pr7YlxcXRpdL2NO7j3CugXLGTtzMl5B/hzetJtF73/Lox+/iN3FDmdjykpKWfHNr/iHtKcov7BZYqtPeJv23No5gt8jN3M+J4V+vp15uO8k3tk0n9zSurH8cXwrf5/aqb9volDy36HTOZoco98W4NyWw0nRnM/ZSpWmiqHte/BI31t5d/MC8us53xoj1L0dYzr2ZdWpXVzITaOHdzD3dh/D5zt/r/f57gobjpXKkr9ObCe7JB9rc0uUiroDhewtrBndoTdxOfV33Denrm3aMyk0gmXHthCXnUJfv1Bm95nIe5sXGH19lh/fxqpTu/T3lQolzw+5m8iUszcs5h5eHbkrbDi/HllHbFYSEe268vSAKby8/gdySgrqlF8SuZE/TmzV3zdRKHljxP0GFz56e3fi9s6D+eXgamKzknG3ceT+nuMA+C1yU5PXoXvbDkwJG8aiI+uJzUpmkH8YTwy4g9fW/UROad06/HZ0E38e31ZTB6WSV4fP5NBlnbsmCiXPDLyTgvJivtv7F7mlhTha2lJWVdHk8V8PSzMVsdlJ/HNmD++Ont3S4ej19OrI1IvtKSYzkYiAcJ4ZeCcvrfveaHtafHQjy44btqc3Rz7AwcQo/bbePp2Y3HkIvxxYTUxWUnV76jUeaJ72JMTVyDBVIcRNKyIigscee4zHHnsMe3t7nJycePnll9FdXA1o4cKFdO/eHRsbG9zd3Zk6dSoZGRkA6HQ6AgIC+Pjjjw2OefLkSZRKJefOnTP6nCdOnGDIkCFYWlri5OTErFmzKCqq+VJ/8OBBhg8fjrOzM3Z2dgwaNIgjR44YHCMmJoaBAwdiYWFBcHAwGzdubMo/i1GnNu8nsG8Y7ft3xd7DmV53jMDKwZYzO45ccT8LGyvUdtb6m/KyTgeVlaXBYylRcZiamzVrZ9yKpX8yfOwoRo4fg5evD7OeeARnVxf++WuV0fJuHu489OSjDB01HLWVldEy7TsGMfPRWQwaNhgzc7Nmi/2S8R37sSX2MJtjD5Gcn8m8Q/+QXZzPiPa9jMfn7EVmcS7/nNlLRlEuZzIusDHmAO2cPGuV1JFXVmRwa26qYRGUrdlA5ZFjaJNTKfl5IQpzM8x7da93H9N2flTFnqdy/2G02TlUnTpDxf7DmPrWZGlWHTtJ1YnTaNMz0aZnUvbXanTl5Zj6+zZp/AP8unAwMYqDiVFkFOWy6vRu8suK6O0TYrR8excv/J08+eXgGmKzk8gtLSQpP8Mg06a7V0fUZioWHFrLhdw08kqLiM9NI7Uwu0ljv9wtnfqzKeYQm2IOkpSfyc8HVpNVnM+ooN711MObzItZQhlFuURlXGBD9AECnNvoywS5eHMm/QI74o6RUZRLZEoMO88fMyjTGEsX/8bYW8YzfuIEfP18eeLZp3B1c+WvP/4yWv7v5X/h5u7GE88+ha+fL+MnTmDshHH8tnCxvkzHTsE8+uRjDBsxHHMj53J5WTnbt27j4SceISy8K2292jJz1gN4eHqy4s/lTVIvY/at2UrXwb0JH9IXlzbujLr3NuycHDi4cdcV91v901JC+nWnbaBvs8VWn8EB3dgbf5K9F06SXpjD8hPbyC0tNJpZAlBWVUFheYn+5u3ghqWZBfsunNSXWXBoLTvjjpGcn0l6US5LjmxEoVAQ5OLVLHXo5xfK4aQzHEo6Q2ZxHv9E7SG/rIhe3sazIAOdvfB19GDBobWcy04mr7SIpPxMEvLSDcopUDC5yxA2xxwi18iP/hshol04+y6cZN+Fk6QX5fDXie3klhbSz0gmGdT/+uy/cOqGxTyyfU92xh1jZ9wxUguzWRK5iZzSAga3CzdavrSynIKyYv3N18EDtbkluy7Lymrn1IaYrCT2J5wmuySfU+lx7E84ja+DR7PUYXj7nuyKO8auuOOkFWaz9NhmcksKGNSuq/E6VJVTUF6sv/k4uKM2t2B3/HF9mf5+nVGbW/DN7uWcy04mp6SA2OwkkvIzmqUO12pfwil+3L+S7ecjWzoUAyOCerEjLpId5yOr29PRjeSUFjCkoe3JsW57CnBqS0xWIvsSTl3Wnk7h59g87emmo9XdvLf/UdIZJ4S4qc2fPx9TU1P279/PF198wZw5c/jpp58AqKio4K233uLYsWOsWLGCuLg4ZsyYAVTP0TZz5kzmzp1rcLxffvmFAQMG0K5d3eFDJSUljBo1CgcHBw4ePMiyZcvYtGkTjz32mL5MYWEh9957Lzt37mTfvn0EBgYyZswYCgurr1RrtVpuvfVWTExM2LdvH9999x3PP9+8Q480VRqyE1JpE+xnsN2zoz8Z540Prbhk5bs/8dvzn7Hus0WkRsdfsezZPZH4dQ/GTGXe2JCNqqysJPbsWbr27GawvWuPbpw5eeN+UDSGqdIEfydPjqXEGmw/lhpLkIvxIcPRmQk4qe3o2qY9AHYWVvT2DuFIkmFGg4WpOd/e+hzf3/Z/vDBkerN/eVQ6O6G0t6PqVE2WAlVVVEXHYtrOr979qmLPYerjhYmfj/44ZqHBVB6v5zVUKDDrGY7C3Jyqc/FNFr+JQkkbOxdiMg2HtJzNTMTHwc3oPsFufiTlZzDIvysvDr2H5wZNZWzHvpgqTS4r48uFvHQmhgzg5WEzeHrgFAa3C0eBosliv5yp0oR2Tm2ITIkx2B6ZEkMHVx+j+5zJuICTlR3d2gQBYGdhTR/fEIOMk6iMeNo5tyHQuS0AbtaOhLcNMihzvSorKzl7JpqevXoabO/Rqycnj58wus+pEyfpUat8z969OHP6DFVVDcsA1Wiq0Gg0mJurDLarLMw5Hnm8nr0aR1NVRUpcIu06dzDY7t+5A0ln4+rd7+i2feSmZxFx26hmietKTBRKvOzdOFNr6PyZ9Av41bkIYFxvnxCiMy4YzdK6xNzUFBOlCcWVZY2K1xgThRJPWxdiaw0fjM1Kwrue87ujqw/J+ZkM8OvC84Pv5umBUxgV1Nvg/AYYEtCNkooyo0NXb4RLr090rdcnOiMBP8eGvz5nMxOu+Po0JROlEh8HD06lGw67PJUWR4BT2wYdY4B/F06nx5F9WQdoTFYSvg7u+s87Fyt7Qj3acTw1tr7DXDcThRIfB3dOp8UbbD+VXv1e2RD9/boQlR5vkLnVxTOQ89nJTA0fwSfjH+f1EfczpkOfZvvMaA1MlEp8HTw4lWb4Hnoq7TztnBvWngb6hdVpT2czE/F18NCfRy5W9nT2CKjznU2IG0WGqQohbmpeXl7MmTOn+up6UBAnTpxgzpw5PPjgg8ycOVNfzt/fny+++IKePXtSVFSEtbU19913H6+++ioHDhygZ8+eVFZWsnDhQj766COjz7Vo0SJKS0tZsGABVhezrL766ivGjx/PBx98gJubG0OGDDHY5/vvv8fBwYHt27czbtw4Nm3aRFRUFPHx8bRtW/2F4d1332X06NHN9BeC8qISdFodFjbWBtstbawozTeePaW2tabvtDE4ebujrdJwbv8J1n2+iNFPTzc6z1xmfDJ5KZn0nz62WeoAUJCfj1ajxcHBwWC7g4MDR3Jym+15m5KNSo2J0qTOEKn80iLsPa2N7hOdmcDnO3/nmYF3YmZiiqnShIOJUfx8oCYbMDk/i692/0lCXjpqMxVjOvbl7VGzeHbVV6Q1U0aWws4WAG2BYWaItqAQpVP9Q+8qDxyh1Noa6/8+BShQmJpQvnUn5WsNM0SVbTywefFZMDOF8nKKv/4JbWrTDVFVm1tgolRSVFFqsL2ovAQblfFMHUdLW3wdPKjSaFhwaB1W5hZMDBmIpZmKPy4Of3FU29LO0obIlBjmHliDs5Udt4QMRKlQsjn2UJPFf8mlNpVX60d1fmkhDpbtje4TnZnApzt+47mIqfo2tT/hND/uW6kvsyvuOHYqa94dPRuFQoGp0oS1Z/ay/MT2Rsecn5eHRqPBwdGwnTg4OZKTnWN0n+zsHHrWalcOjo5oNBry8vJwdna+6vOqrawICQ1h/s9z8fXzwcHRkU3rN3L65GnaejVPdlZJQTE6rRZrOxuD7dZ2NpyrZ+hpdmoGm5es4r7Xn0RpYmK0THOyUlUPvS4sLzbYXlhegq1KXc9eNWxVVgS7+TH/0D9XLDeh0wDyS4uIzmj6uRT153d57fO7FGtz43VwUNvi4+BOlVbDoiPrUZtbMCF4AGpzlb7de9u70c0riK92GZ/24Ea49PoUXDa/I0BheTE2KuMd8JezVVnR0dWXBYfWNleIddiYqzFRKskvM2xTBeXF2FkYz1q/nJ2FFaHu7fhh398G2w8knsZGpeaFwfeAovrixJbYw/xzZm+Txg9grVJf/LvXOi/KGl6HEHd/ftq/0mC7s5U9HVx92J9wis93/o6bjSNTu45AqVCyOmp3k9ahtbjUngpqf5cqKybEwvh3qcvZWVgT6tGO7/etMNh+IPE0NhZqXhzS/O1JiIaQzjghxE2td+/eKBQ1Vw/79OnDJ598gkaj4fjx47z++utERkaSk5ODVlu9Gk9CQgLBwcF4eHgwduxYfvnlF3r27Mnq1aspKytj8uTJRp8rKiqKLl266DviAPr164dWqyU6Oho3NzcyMjJ49dVX2bJlC+np6Wg0GkpKSkhISNAfw9vbW98RdynmqykvL6e83HAOLpVKVU9p4xR1LrLqDP52l7Nzd8LO3Ul/39W/LcW5BZzcuM9oZ9zZ3cew93TBxbdphrBdUa2YdVeox83q0lBqvSvE39bOhZk9x7Hs+BaOJcdgr7bhnm6jmdX7Fr7dWz2kLyYr0WDS4jMZCXw47lHGdOjNLwfXNEnMZr26o76nZmGOos8vzq9Xe/SAQgG163cZ06AALMaNpHTh71Sdj8fE1QXLu25DO24k5avX68tp0zIofON9FJaWmHULQ33/3RR98EWTdsiB8deivugvtbPfIjfp5/NZHbWHu8NHsuLkDqq0GhQoKK4o5c/j29ChI7kgE1sLKwb6hzVLZ1y9FAp09dSkrZ0rD/aawNLIzRxNOYuDpQ0zuo/h4T6T+GpPdSdDiLs/t3cZzPf7/iYmMwF3W2ce6Dme3M6F/H58S1OFaEinu9KpUCdT5FL9riWD5OU3X+W9N99l0phbMDExoX1Qe4aNHM7Z6OaeO6tW7Dpd7U1Adfb08q8WEHH7aJw86s6DeSNd78CgXj7BlFaWc/wK2SRDA7vTrW0Hvtj5e7NOjG7s7am+ml1qe78f20L5xfP7nzN7uavrcFae2oVSoWRylyGsOLGDkmbI5rtmdarRsPOgp3f163OiGbLHrpWChrWzfr6dKaks40iKYTZikIs34zr25dcj6zifk4KbtQN3hQ0nP7iIVaebpyOrzmcGV/zI0+vrG0pJZfViOZdTKhQUlBez4NA6dOhIyEvH3sKaEUG9pDPuKuqc3w1sUf39Lran5LrtaXzHftXtKTsZV2tHpnYdTl5wf1advvK0Aq1CQxqyuKGkM04I8a9UVlbGiBEjGDFiBAsXLsTFxYWEhARGjhxJRUXNpLgPPPAA06dPZ86cOcydO5cpU6agVhu/aq7T1d/pc2n7jBkzyMzM5LPPPsPHxweVSkWfPn30z2nsS1xDOpLee+893njjDYNtr732GhYD/a+6r8pajUKpoLTA8ApiaWEJFrZXv5p7iYtfG84dOFlne1VFJXGHTtN1/MAGH+t62NrZoTRRkptjmDmTl5uHvYN9sz53UyksL0Gj1WBvaZglY2dhRV6p8SzFSSGDiM64wMqLk29fyEvnx6qVvD1qFksiN9XJiILqTopz2Un6VQubQuWxExS+EV+zwbT6K4LSzhZNfk12nNLGGl1B/UOfLCaOo2LvASp2Vl9p1iangsoc9T13Ub5mQ82XQY0GbUZW9X8vJGLi54Nq2CBKf13aJPUpqShDo9ViUyvTx9rckqJaGSeXFJYXk19WbDCxdmZRLkqFAjsLa7JL8iksL0aj0xp0hGUU5WJrYYWJQolGp22S+Gtiqq9NWdfbpm7vHEFURjwrTu0A4EJuGt/vW8F7Yx5m0dEN5JYWMrXrcLadO8KmmIPVZfLSsTA145G+t7Ls+NZ6O/oaws7eHhMTkzpZcLk5uXWy5S5xcnIkJ9swyzMvJxcTExPs7OtfhKa2Nm3b8tUP31BaWkpxcTHOzs689sIreHg2z7Buta0VCqWSonzDDNLigiKsbW3qlK8oLSPlfAKp8Un8M+8P4OLnhk7Hm9OeYvoLj+AXYjzjsakUl5ei0WqxVRl+Ptio1HWysYzp7RPCwcTT9bb1IQHdGNG+J1/t/pOUgqwmibm2mvPb0mC7lbllnWzYSwrLSigoK9Z3xMHl57cV5iZmOKptubtbzdDhS5/fb458kM92LjU6cXxT078+FobvXTYqtcFqyPXp7dOJQ4lRTf5edCWFFSVotNo6GWQ2KisKamXLGTPArwt7L5xEozWMeVLIIPZcOKlf3TM5PxNzEzPu7T6G1ad3N+Jdqq6i8kt1MMy8srGwqpMtZ0w/387su3Cqzt89r6wIjdbwMyO1MBt7S+tm+cxoDWrak+FrYWuhrpN9acwAvy7siT9Rpz3dGjqIPRdOsOPi/HhJ+ZmoTC+1p11N2p6EaAiZM04IcVPbt29fnfuBgYGcOXOGrKws3n//fQYMGECHDh30izdcbsyYMVhZWfHtt9+ydu1ag6GttQUHBxMZGUlxcc0H/e7du1EqlbRvX/3jaOfOnTzxxBOMGTOGTp06oVKpyMrKMjhGQkICKSkp+m179149/f2FF14gPz/f4PbCCy9cdT8AE1MTnLw9SIkynFsjJSoOV/+Gza0BkJ2YjqVt3fT/uMOn0VZV0a6n8Unvm4qZmRkB7dsTedBw0YnIg0foENKpWZ+7qVRpNZzPTqGzZ4DB9s4eAURnGh+qpTI1Q1vrK+ClLM8rdeP6Ong07XxAZeVoM7JqbilpaPPyMQ0OqiljYoJpUABV5+qfCwtzs7pXX7XGs4QMKEBh1nQLbGh0WpLzMwmsNXl8oHNbLuSmG90nPicNWws15iY11yqdrezQ6rT6ocfxuWk4qe0MquNsZU9BWXGz/Kiq0mo4l51MWK02FeYZUGfOr0tUJuZ1Lgxoa91XmZjVU0Zxxey1hjAzM6N9hyAO7j9gsP3ggYOEdA41uk+n0BAOHjhosO3A/gN0CO6Aqem1Xzu2tLTE2dmZwoICDuzbz4CBA675GA1hYmqKp58X548bZmCcP3GGtu3rzq2osrTg4Q//y+z3/09/6z60H06ersx+//9oE3D1YYiNpdFpScxLp4OrYRZ0kKsPcdkp9exVLcC5La7WDuyNr3vhBqoz4kZ16M23e/4iMc/4edYUNDotKQWZdeYjC3BuS0I953dCXjo29Z7fxWQW5/H5zt/5avcf+tuZjHjislP4avcf5NfT+d3ULr0+QS6GbSHIxZu4nKu/Pi7WDgYLa9wIGq2WC7mpBLsZtvlObn7EZl957togF2/cbBzZef5YncfMTUzrXBjQ6XTV779NnDGv0Wm5kJtGRzdfg+3Bbr6cy0q+4r7tL9bh8sUCLjmXlYSrtYPBZ4abtSN5pYXSEVcPjVZLfG4qndwN21Owmx/nshrYnoy8FuZGP/e0zdKehGgI6YwTQtzUEhMTeeaZZ4iOjmbJkiV8+eWXPPnkk3h7e2Nubs6XX37J+fPnWblyJW+99Vad/U1MTJgxYwYvvPACAQEBVxwyOm3aNCwsLLj33ns5efIkW7du5fHHH2f69Om4uVVPCB0QEMCvv/5KVFQU+/fvZ9q0aVha1lyZHzZsGEFBQdxzzz0cO3aMnTt38tJLL121niqVCltbW4PbtQxT7TS0F2d3R3J2TyR5qVnsX7aR4tx8OgyoXnXq0Iqt7JhXM4/Jqc0HuBAZTX5GDrkpmRxasZULR8/QMaLuKpkxu4/h3SUIC+urzyXUWBOn3MaG1WvZsGYdifEX+PGLb8nMyGDMxHEAzPvuZz55+wODfc7HxHI+Jpay0lLy8/I5HxNLQlxNJ0VlZaW+TFVlJdmZWZyPiSUl6cpfrq/XqqjdDA3oxpCAbrSxc2FG9zE4W9mx4Wx1x8TUriN4vN/t+vKHks7Qy7sTI9r3xNXagSAXb2b2HEdMZqK+s21y5yF08QzA1doBXwcPHul7K76OHmyIPmA0hqZSvmkbFmNHYNa1M8o2Hqhn3o2uopKK/TXDMdX3T8fi1vH6+1XHTqKK6I9Zz3CUzk6YBgdhMXEslZEn9Z10FreOxySwHUonR5RtPLCYNA7ToEAq9h2sE0Nj7Iw7Rg+vjnRv2wFXawfGdeyHvaUN+xKqf6iOCurNHV2G6stHppylpKKcyV2G4GrtgJ+jB2M69OVQ4hn9ULt9F05hZW7B+E79cbayo4OrD4MDwtnTjD9+/z61i2GBPRga0L16WHOPcThb2bM+ej8Ad4eP5Mn+d+jLH0yKordPCKOCeuFm7UgHVx8e6DXeYEL3g0lnGBXUm/5+nXG1dqCLRwBTuw7nYOLpOh1312PK1DtZ/fcq1qxcTXxcPF98+jkZaelMvG0iAN999S1vv/amvvwtt04iPTWNL+d8TnxcPGtWrmbN36u48+6p+jKVlZXERJ8lJvoslZVVZGZmEhN9lqTEmh9n+/fuY/+efaQkp3Bw/wGemP04Xj7ejJkwrtF1qk/vsYM5snUvR7fuJTM5jXULlpOflUv3Yf0B2LRkJX998ysACqUSVy9Pg5uVnTWmZma4enlibnFt0xNcr62xh+njG0pvn0642Thya+ggHNU2+s6E8cH9md6t7uISfXxCiMtJNbp68NDA7ozt2JdFRzaQXZKPjUqNjUqNuUnzrGK9O+4E3bw60K1tEC5W9ozp0Ac7C2sOJJwGYET7ntzeebC+/LGUGEoqyrk1NAIXa3t8HTwY1aE3h5OiqdJqqNJqyCjKNbiVVVZQrqkgoyj3hnacbDt3hN6+IfTy7oSbtSOTQgbhoLZhd1z1QiTjgvsxLXxknf16+4QQX8/r09zWnz3AQL8w+vt1xsPGiTvDhuGotmXbueoLbLeFRvBAz/F19hvg14Vz2ckkF2TWeexYaiyD24XT0ysYZys7gt18mRgykMiUGKMjERpr49kDDPDvQj/fzrjbOHFHl6E4qm3Zfv4oUJ2pN7NH3feS/n6dOZ+dbDQTdNu5o1ibW3Bn2HDcrB0IdW/HmI592Bp75dXubxRLMxWBzm31i/l42joT6NwWN2uHq+zZvDZE72egXxgD/Lro25OT2o6tF9vT7aERPNCrbnsa6B9W3Z7y67anyJQYBgd0u6w9+TEpZFCztaebzsUs7Jvy9j9KhqkKIW5q99xzD6WlpfTs2RMTExMef/xxZs2ahUKhYN68ebz44ot88cUXhIeH8/HHHzNhwoQ6x7j//vt59913r5gVB6BWq1m/fj1PPvkkPXr0QK1Wc9ttt/Hpp5/qy/zyyy/MmjWLrl274u3tzbvvvstzzz2nf1ypVPLXX39x//3307NnT3x9ffniiy8YNap5V8zz7x5MeXEJx9bsoqSgCAcPF4Y/eifWTtXDu0rziyjOydeX12o0HFy+mZK8QkzMTHHwcGHYo1PwCjHMvslPzyb9XCIjnrirWeO/ZODQCAoLCvht3kJysnPw8fPl9Q/fwdW9ujM0NzubzHTDDMgnZj6s/39sdAzbN27B1d2NX5YtBCAnK9ugzPLflrH8t2WEhHXm/S8/afI67Ik/gY1Kze2dB+NgaUNCXjrvbl5AVnEeAA6WNjhb1Qy723buKJZmKkZ36M293UdTXFHGybTzLDxcM7+albkFs3tPxN7ShpKKMuJyU3l13Y9XzThorPK1m1CYmWF59x0orNRozsdT9OnXUFYzv6HS0cHgi1TZ6vXoqB6uqnSwQ1dYROWxk5QtX60vo7C1weqB6SjsbNGVlqFJSqF4zjdUnW7a1QuPp8aiNlcxNLA7tior0oqymXtwtX54p41Kjb1lTTZohaaKn/av5JZOA3i8/+2UVJRzPDVW3+kFkF9WxE/7VzE+uB9PDZhCQVkxu+OOs+3c0SaN/XK7449jq1IzJWxodZvKTeOtTfPIvNimHNW2uFjb68tviT2MpamKMR36cl+PsRRXlHE89RwLDtdM6P77sS3odDqmdR2Bo9qOgrJiDiZGsejoeprC0BHDKMjPZ95Pv5CdlY1fO38+/Oxj3D2qh4tmZ2WTnlaTweTZxpMPP/uEL+d8zl/LluPs4syTzz1NxJCazpSszCxm3j1Df/+3hYv5beFiwsK78uX3XwNQXFTM919/S2ZGJja2tkQMieDBRx66ruy6hgrpE05pYTHbl6+nKC8fVy8Ppj0/G3uX6iG5RXkF5GfdXIvQHEk+i5W5JaOCemNrYUVqQTbf7vlL31lrZ2GFQ62h0Ram5oR5BvLniW1GjznArwtmJqZ1fiD/E7WXtc0wQfqJtHOozVUMbtcNGws16YU5LDi0lryymvP78mFuFZoq5h5cw/jgfjzS91ZKKso5mXaOjWeb9iJAUziafBYrcwtGduiFncqK1MJsvt+7Qv/62FpY4aCu+/p08Qhg+YltNz5g4GBiFNbmlkwI7o+dhTXJ+Zl8tnOpfjVLOwtrHNW2BvtYmqno1rYDSyI3Gjskq07vQqfTMSlkIA6WNhSWl3AsNbbeNthYh5LOYK2yZFxwP+wsrEgpyOKLncv0w5PtLY3UwVRFeJsglkZuMnrM3NJC5uxYypSwobw24n5ySwvZHHOItWf2GS1/o3Vw8eGrSc/o7z/Rv3pe5X+i9vLOlvktFRYHEqOwUqmZ0KmmPc3Z+VtNe7K0xkltOIXBpfa0+OgGo8e8NC/craGD9O0pMiWm2dqTEFej0P1PdAMLIf6NIiIiCAsL47PPPmvUcXbv3k1ERARJSUn6DLd/i/e3LGjpEBrtv0PuIaYZVtO7kQJdvbl9wdUzHG92f9zzDnn3P97SYTSK/c9f8vyab1o6jEb7YOwjTJz335YOo9FWzHifjIIbn4XTlFxtnVh8pGk6IVvS1PCRPP7Xp1cveBP7ctIzvLT2+5YOo9HeGf0QT66Y09JhNNrnE59m5u/vtnQYjfLLHS/y4LL3WzqMRvtx8n/p9/Xslg6jUXY/+h33LX2npcNotLlT/p3fB/NmPdXSIdTL/ofPWjqEFiGZcUKIVqu8vJzExEReeeUV7rjjjn9dR5wQQgghhBBCNJpW5ii82ciccUKIVmvJkiUEBQWRn5/Phx9+2NLhCCGEEEIIIYQQkhknhLh5bdu2rVH7z5gxgxkzZjRJLEIIIYQQQgghRFOQzjghhBBCCCGEEEKI1kqWCrjpyDBVIYQQQgghhBBCCCFuEOmME0IIIYQQQgghhBDiBpFhqkIIIYQQQgghhBCtlQxTvelIZpwQQgghhBBCCCGEEDeIdMYJIYQQQgghhBBCCHGDyDBVIYQQQgghhBBCiNZKK8NUbzaSGSeEEEIIIYQQQgghxA0inXFCCCGEEEIIIYQQQtwgMkxVCCGEEEIIIYQQorXSaVs6AlGLZMYJIYQQQgghhBBCCHGDSGecEEIIIYQQQgghhBA3iAxTFUIIIYQQQgghhGitdLKa6s1GMuOEEEIIIYQQQgghhLhBpDNOCCGEEEIIIYQQQogbRIapCiGEEEIIIYQQQrRWWhmmerORzDghhBBCCCGEEEIIIW4Q6YwTQgghhBBCCCGEEOIGUeh0sqyGEEIIIYQQQgghRGuUN21WS4dQL/tFP7R0CC1C5owTQoib2Je7lrV0CI32eP/JFObmtnQYjWLj4MDwH55s6TAabeOsz8l7+sWWDqNR7Oe8y50LX23pMBrtt7vfZMSPT7V0GI224cHPiMtKbukwGsXPuQ3f7PmzpcNotEf63sZr639q6TAa5Y2RD7Ds2OaWDqPRJncZyvNrvmnpMBrtg7GPMH3JGy0dRqP8etdrPPbXJy0dRqN9NelZ7lv6TkuH0Shzp7xEv69nt3QYjbb70e9aOgTRSsgwVSGEEEIIIYQQQgghbhDJjBNCCCGEEEIIIYRorbTalo5A1CKZcUIIIYQQQgghhBBC3CDSGSeEEEIIIYQQQgghxA0iw1SFEEIIIYQQQgghWiudrqUjELVIZpwQQgghhBBCCCGEEDeIdMYJIYQQQgghhBBCCHGDyDBVIYQQQgghhBBCiNZKhqnedCQzTgghhBBCCCGEEEKIG0Q644QQQgghhBBCCCGEuEFkmKoQQgghhBBCCCFEK6XTyjDVm41kxgkhhBBCCCGEEEIIcYNIZ5wQQgghhBBCCCGEEDeIDFMVQgghhBBCCCGEaK102paOQNQimXFCCCGEEEIIIYQQQtwg0hknhBBCCCGEEEIIIcQNIsNUhRBCCCGEEEIIIVornaymerORzDghhLhoxowZTJw48Zr28fX15bPPPmuWeIQQQgghhBBCtD6SGSfE/6gZM2Ywf/78OttHjhzJunXrmv258/LyWLFiRbM+z7X6/PPP0TXxVaP4+Hj8/Pw4evQoYWFhTXrshjixZT9H1u+kJK8IxzauDLhzDJ7tfY2WTTpznhUf/VJn+7S3n8TBw6XZYtTpdPzw00/89fffFBYW0ik4mOf/8x/a+ftfcb/NW7bw3Q8/kJScTNs2bXhk9mwGR0ToHz9y9Ci/LlxIVHQ0WVlZfPzBB0QMGmRwjJKSEr785hu2b99OfkEBHu7u3HnHHdx+222NqtP44P5M7jwEJ7Ut8blpfLt3OSfTztdbfkJwf27pNAA3G0cyinJZfHQjm2IOGi0b0a4rLw2dwe7447y+4edGxdkQFiOHYt6nBwpLSzQJiZT8uRJtWka95a0ffQDTgLqvXeXpMxT/uEB/TItRQw0e1xYUUvDae00bPDC8fQ/GB/fH3tKapLxMFhxay5nMC/WW7+fbmQmd+uNu40hJZTnHUmJYeHg9RRWlAPTw6sjEkIG42zhiojQhrSCbNVF72Bl3rMljv9z4jv2Y3GUIjpa2XMhN49t9f12xTY0P7s8twQNws3EgoyiPJZGGbaqfb2fuChuGp60LpkolyQVZ/HF8K5tjDzVZzKuW/80fi5eSk52Nj58vs594lJCwzvWWP370GD98+Q0X4uJxcnZm8tQpjJ00waDMX0v/YPVfK8lMz8DW3o4BEQO5b/aDmKvMATgReYw/Fi8l5kwMOdnZvPrem/Qd2L/J6gRwbMs+jqzdSXFeIU5tXBk4dSxt2vtddb+UmAv88f6POLVxY9qbj+u3a6o0HFqzjajdRynKLcDBw5l+k0fhG9q+SeOuLWbnUc5sPkhpQRF27s50vW0Iru3a1lteU1nFqfV7iT94mrKCYiztrek0og/+fUIByE/N4sQ/u8hJTKckp4CukwYTNLh7s9Zh//rt7Fy5iaK8fFzbejBmxmR8OwYYLRt/JpYNi1aQmZxOZXkF9i6O9BjWn37jat6Lfnp9DvGnY+rs275rJ+554dFmq0dvn04M8u+KjUpNelEOq07tJj43td7yJkolwwJ70NWzPTYqNfllRWyJPcyhpDP6Mham5owM6kWIuz+WZipySwtZfXo30ZkJzVaPoQHdGduxL3aWNiTnZ7DwyHrOXuH5+vqEMrZjX9xsnCitLON4aixLjm7Uv9+2sXXhts4R+Dp44mJtz8Ij61gfvb/Z4gcY4NeFoYE9sLOwIrUgmz9PbOVcdrLRsneHj6S3T0id7akFWbyzufo7dl/fUHp6BeNp6wxAQl46q07v4kJuWvNVAhgc0I3RQb2xt7QmOT+TxUc3EpOVaLTs/T3H0d+vS53tyfmZvLzuB/394e17MLhdN5zUthRVlHIwMYo/jm+lSqtptno0RBePAKZ2HUEHV2+crez57z/fNvtnshBNQTrjhPgfNmrUKObOnWuwTaVStVA0Lc/Ozq6lQ2hSMQdOsPO3fxh093g8Arw5tf0gqz5bwNS3nsDGyb7e/aa98xTmljXtwNLGqlnjnP/rryxesoTXXnkFb29vfp47l0efeII/ly7Fysr4cx8/cYIXX3mF2bNmMXjQILZu385/X3qJn7//npCQ6i/GpaWlBAYGMn7cOP7vhReMHufTzz7j0JEjvPn663h6eLDvwAE++OgjnF1ciBg48LrqM8i/Kw/3mcSXu5ZxKj2OsR378u7o2dz/+3tkFufWKT+uYz9m9hzPnB2/EZ2ZQAdXb54ecCdF5SXsSzhlUNbV2oFZvSZyPDX2umK7VqohA1FF9KNk8Z9oMrOwGD4Y69kzKXjvUyivMLpP8dxFYGKiv6+wUmPz3ONURp40KKdJTafo28s6E7VNP3yij08I93Ybzc8HVxOdkcCwwB78d8jdPLvqK7JL8uuUD3Lx5tG+t7Lg8FoOJ0XjqLblgV7jmdX7Fj7d8Vt1/SpKWXFyB8n5mWi0GsLbBDG7z0Tyy4qb7XUZ5N+V2X0m8eXuP6rbVIe+vDPqIR5Y9h6ZxXl1yo/r2I+ZPcbx2c6l1W3KxZunBkwxaFOF5SUsidxIQl4GVZoqenl34rlBd5FXVsThy37QX6/tm7by/edf8+izT9Kpcwj/rFjFy8/9lx8WzsXV3a1O+bSUVF557gVGjx/D/736IqeOn+TrTz7Hzt6e/oOrz8Ut6zfxy3c/8swL/0fH0E4kJyTyyTsfAvDQk9UdJWWlZfgFtGP4mFG8/dLrja5HbWf3H2fH4jUMnj4Bz0AfTmw7wN+fzufud57C9grvq+UlZWz4cRleHdtRUlBk8Nje5Rs5szeSoTMm4ejhwoWTZ1n95ULueGk2rj6eTV4HgIQjZzi6fAvdJg/H2b8N53YfY8e3fzD6xZlYOdoa3WfP3FWUFRbTc+pIrJ0dKC8qQaepWZ2vqqISayd7vMKCOPrX1maJ+3In9hzin3l/MP6BO/EO8ufgpl0sePdrnpjzCvbOjnXKm6tU9Bo5CHefNpirVFw4E8vfPy7B3EJFj2HVHbZTn5uFpqpKv09JYTFf/+ddQvqEN1s9OnsEMD64PytO7uBCbhq9vIOZ2XMcn25fQl5ZkdF9pnUdiY3Kkj+ObyW7JB8rc0tMlDUDnkwUSh7oNYGiilIWHllPflkR9hbWlFdVNls9enl34u7wUcw7tIaYrEQGB3TjP4Om8d9/via7pKBO+fbOXjzUeyKLjq7naPJZHCxtuK/HOO7vOZ7Pd/0OgLmpGRlFeRxIOM208JHNFvsl4W2CuK3zYJZGbuZ8TjL9fTvzSN9beXvTPHJLC+uU/+P4Vv4+tVN/30Sh5IWh93A0+ax+W6CzF4eTzrAsJ4UqjYZh7XvwaN/beGfzfPLreX0bq6dXR6aGDefXI+uIyUwkIiCcZwbeyUvrvifHyGux+OhGlh2vOWdNFEreHPkABxOj9Nt6+3Ricuch/HJgNTFZSbjbOHJ/r/EA/Ba5qVnq0VCWZipis5P458we3h09u0Vjuak1w/cs0TgyTFWI/2EqlQp3d3eDm4ODg/5xhULB999/z7hx41Cr1XTs2JG9e/cSGxtLREQEVlZW9OnTh3Pnzun3ef311wkLC+P777/Hy8sLtVrN5MmTycvL0z8+f/58/v77bxQKBQqFgm3btjFkyBAee+wxg/iys7NRqVRs2bKlTuz5+fmYmJhw+PBhoDq7ytHRkR49eujLLFmyBA8PD/395ORkpkyZgoODA05OTtxyyy3Ex8frH689TLWwsJBp06ZhZWWFh4cHc+bMISIigqeeesoglpKSEmbOnImNjQ3e3t788EPNVUQ/v+psia5du6JQKIi4LHOruUVu2E3wgG50GtgdR09XBtw1FmtHO05sO3DF/dS2VljZ2ehvSmXzfVTodDqWLF3KfTNmMGTwYALateONV1+lrKyMdRs21Lvfkt9+o1ePHtx37734+vpy37330rNHDxYvXaov069vXx6ZPZshgwfXe5zjJ08ybswYunfrhqenJ7dOnEhgQABRUVH17nM1t3WOYF30PtZG7yMhL51v9/5FZlEu44P7GS0/LLAHa6J2s/38UdIKs9l27ijrovcxJWyYQTmlQsELQ+5hweG1pBVkX3d810I1qC9lG7dReeIU2rR0ShYvQ2Fuhnl4WL376EpK0RUW6W9m7QOgspKKYycMC2o1BuV0xcVNHv/Yjn3Zeu4IW2OPkFKQxYLDa8kuKWB4+x5Gywc6e5FZnMe66P1kFucRnZnApphDtHNqoy9zOj2eg4lRpBRkkV6Uq3+dO7h6N3n8l9wWGsG66P2si95HYl463+37i8yiPMYHG8/4GhrYnX+i9tS0qfNHWRe9nzu61GQAHU+NZXf8CRLz0kktzGbFqR2cz0khxO3qGV4NsXzpMkaOG83oCWPx9vVh9lOP4eLqyuq/Vhotv2bFKlzdXJn91GN4+/owesJYRowdzR9LfteXiTp5ik6hIQweMRR3D3e69epBxPAhnD1T88O3R59ezJh1P/0jrq8z/WqObNhFp4HdCBnUA0dPVwZNHVf9vrrlytk6W+b/RVDvLngEeNV57Mzeo/QYNwi/LkHYuTrSeUhvfEICObJuV7PUAeDM1kP49w6lXd/O2Lk7EX7bENQONsTuijRaPvV0HBnnEhk4+zbcg3yxdrLDyccDZ/+ac8PJx4OwiRH4dOuI0tTE6HGa0u7VW+g2pC/dh/bDta0HY2dMxs7ZngMbdhgt7+nnRZf+PXDz8sTB1Ymwgb0I7NKR+KiaTnS1tRU29nb627njZzBTmRPSu/k64wb4deFgYhQHE6PIKMpl1end5JcVGc24Amjv4oW/kye/HFxDbHYSuaWFJOVnGGRadffqiNpMxYJDa7mQm0ZeaRHxuWmkFjbfZ8fooN5sP3+U7eePklKQxaIj68kuyWdooPH32wDntmQW57Hh7AEyi/M4m5XIltjD+DnWdEDH5aTwW+RG9iWcolLT/NlXQwK6sTf+BHsvnCC9MIc/T2wjt7SQAUayxgDKqiooLC/R37wd3LE0s2DvhZqLT/MP/cPOuGMk52eSXpTD4iMbUCgUBLk032fGiKBe7IiLZMf5SFILs1lydCM5pQUMaWe8HZdWllNQVqy/+Tp6oDa3ZNdl2WUBTm2JyUpkX8IpskvyOZUex/6EU/g5ehg95o20L+EUP+5fyfbzkS0dihDXRDrjhBBX9NZbb3HPPfcQGRlJhw4dmDp1Kg899BAvvPAChw5VD2eq3YkWGxvL77//zqpVq1i3bh2RkZE8+mh11sJzzz3HHXfcwahRo0hNTSU1NZW+ffvywAMPsHjxYsrLy/XHWbRoEZ6engw20pliZ2dHWFgY27ZtA+D48eP6fwsKqq/6bdu2jUEXhyWWlJQwePBgrK2t2bFjB7t27cLa2ppRo0ZRUWE8w+eZZ55h9+7drFy5ko0bN7Jz506OHDlSp9wnn3xC9+7dOXr0KI888ggPP/wwZ85UZ5YcOFDd8bVp0yZSU1NZvnx5w/7wjaSpqiLjQgpenQyH63gFB5AWe+UhKr+98TW/PPM+Kz76haQz9Q+DawrJKSlkZ2fTu1cv/TZzc3PCu3bl+IkT9e53/ORJel22D0DvXr2uuI8xYV26sGPnTjIyMtDpdBw6fJiExET61Dp2Q5kqTWjv7MXhpGiD7YeToulUTyeHmYkpFZoqg23lVZUEuXhjoqj5mL47fBR5pUWsi953XbFdK6WTA0pbW6qiLxuypdFQFRuHqV/Df0SY9+pOxdHjUGGYkaF0dsb29f9i8/JzqKffidLJoZ4jXB8TpQl+jh4cTz1nsP14aizt6/kRdDYzAUe1LWGegQDYWVjRy7sTRy7LcqgtxN0fD1tnotLrH/raGKZKEwKd23Ik2TBb7XDyGYLdfI3uY640pUJj+Peu0NRtU5cL8wzEy86VE2nnjD5+LSorK4mJPkt4T8MhiuE9uxN18pTRfaJOnqpTvluv7sSciabqYqZSpy6hxESfJfp0dWd5anIKB/fup2ff6ztfr5WmqoqM+BS8OwUabPfpFEDqufpf/1M7D5OXkUOvW4YYP25lFSZmZgbbTM3NSImJb3TMRp+vSkNuYhruHXwNtrt38CUrzvhwvOSTsTh6uXFm8wH+fuVb1rz1E0dXbKWqovkyra6kqqqKlPMJBHTpaLA9oHNHEqIb9rmVEpdIQnQcfsGB9ZY5vGUPoX27YW7RPKMGTBRK2ti5EJNpOHzwbGYiPg51M0gBgt38SMrPYJB/V14ceg/PDZrK2I59MVWaXFbGlwt56UwMGcDLw2bw9MApDG4XjgJF89RDqcTX0bPO+8fJtPMEOhsf+hyTlYij2pYuHtXfU2wtrOjp3ZHIlLrDhG8EE4USL3s3ojIMz+Wo9Av4OTUsQ7WPTwjRGReMZtFdYm5qiolSSUllWaPirY+JUomvgwen0uIMtp9KO0+7el6L2gb6hXE6Pc4go/FsZiK+Dh76zlIXK3s6ewRwLOXGZOoL0RrJMFUh/oetXr0aa2trg23PP/88r7zyiv7+fffdxx133KF/rE+fPrzyyiuMHFk9XODJJ5/kvvvuMzhGWVkZ8+fPp23b6g/9L7/8krFjx/LJJ5/g7u6OpaUl5eXluLu76/e57bbbePzxx/n777/1zzd37lxmzJiBQmH8y2NERATbtm3j2WefZdu2bQwdOpTz58+za9cuxowZw7Zt23j66acB+O2331Aqlfz000/6482dOxd7e3u2bdvGiBEjDI5dWFjI/PnzWbx4MUOHDtWX9/Ss+4VszJgxPPLII/q/0Zw5c9i2bRsdOnTAxaV6rjUnJyeD+tZWXl5u0BEJjRsyXFpYgk6rRW1r+Pqq7awoOWl8WISVvQ2D77kFF982aCqriN4byYqP5zLpPzNpE9Q02TK1ZWdXX6V3cjQcUuTk6EhqWv3zqWRnZxvd59LxGuo/zzzD2++9x5gJEzAxMUGpVPLyiy9e9/x+dhZWmChNyC01HAaSW1qIg9rG6D6Hk84wukNv9sQfJyYrifbOXowK6o2ZiSl2FtbklBbQyc2PUUG9mf3nh9cV1/VQ2FTHqy00bC/aoiKUDvYNOoaJd1tMPN0pWWrYCV11IRHN4mVoMrNQ2lhXD399YjaFH3yGrqS0SeK3VakxUZqQX2oYf35pMfae1kb3OZuVyFe7/+DJAXdgZmKKqdKEQ4lRzDu4xqCcpZmKb299DlMTU7Q6Lb8cWN0knVhG63GpTZUY/rjLLS3EwdL4cMJDSWcY1aE3ey6cICYriUBnL0a272XQpgDUZhYsmfYGZiamaLVavtz9xxU7HhuqIC8frUaLg6NhB6uDgwM52TlG98nNyTXIzAZwcHRAo9GQn5ePk7MTEcOGkJ+bx7MPP4lOp0Oj0TBu0gSmTJ/a6Jgbor73VUs7G4pPGu9AyE3LYvcf65j8wkMoTYxni3mHBHJ0/S7atPfF3tWRhKhznD8ahU6rNVq+sSqKS9FpdVjUmoJAZWNFWaHxDNWirDwyzydjYmZK/wcmUl5UyqFlG6koLqPXtNHNEueVlBQUodVqsbYzfF+1srOlKK/uMLzLfTj7RYoLitBqNAyZPJbuQ41nLSfFxpOemMKkh+9usrhrU5tbYKJU6udIu6SovAQbVd0sSgBHS1t8HTyo0mhYcGgdVuYWTAwZiKWZij8uDjV0VNvSztKGyJQY5h5Yg7OVHbeEDESpUDbpvJCX2KjUmCiVFNQadplfVoSdRTuj+8RkJfHt3uU82u92/fvt4aQz/Hp4bZPH1xDWquqhvoXlJQbbC8uLsVX5XnV/W5UVwW5+zDu05orlbuk0kPzSIs5kNM8FHBvz+l6LYkIsjH/2Xc7OwppQj3Z8v2+FwfYDiaexsVDz4pB7QFF9oWhL7GH+ObO3KcMXzUnXPJ8p4vpJZ5wQ/8MGDx7Mt99+a7DNsVYHR+fONZNtu7lVX6UNDQ012FZWVkZBQQG2ttU/DL29vfUdcQB9+vRBq9USHR1db4eUSqXi7rvv5pdffuGOO+4gMjKSY8eOXXGRh4iICH7++We0Wi3bt29n6NCheHt7s337dsLDwzl79qw+M+7w4cPExsZiY2P4xb2srMxgmO0l58+fp7Kykp49e+q32dnZERQUVKfs5X8jhUKBu7s7GRn1T3BvzHvvvccbb7xhsO21117DaVinazrOVemgvgvjDu4uOLjXLNTgEeBNUW4+R9fvbrLOuLXr1vHuBx/o73/2yScAdTpcdTpdvZ2wetezTy2//f47J06e5NOPPsLD3Z0jkZHVc8Y5OdHrstf+WtVeB0ShqP7TG7PwyHoc1DZ8MfEZFFR3smw4u58pYcPQ6rRYmql4fvB05uz8jYLyph/KeYlZeBfUd0zU3y+6uNhCXVeoTC3mvbqjSUlDk5BksL3qsmGF2tR0iuITsH3pOcx7hFO+ffc1Rn5ldUJVUO9CLW3sXLi3+xj+PLGN4ymx2FvaMC18BA/0Gs/3+/7WlyurrOD5Nd9iYWZOiLs/07uNIqMol9Pp8U0a++VqR1yd4WK8HouObsBBbcvntzxd06ZiDjCly1C0l30ZL60s5+HlH2FhqqJrm0Ae6j2R1MLsppv7rvY5ylXO0Trn9KXN1duPHYnktwWLePTZJ+nQqSMpScl89/nXOMz9lWn3TW+amBugTh10OqNvq1qtlnXfL6X3xGE4uDvXe7xBU8exed5f/PriHFAosHN1JLh/OKd31c3EblK1g9bV/wFx6f219z3j9HOKdq0azO5f/qbb5GGYmpsZ3a/Z1WlPOiPbDD3w5jNUlJWTeDaODYv/xtHdhS796w6lPLRlD25enrQN8G26eOtR5z1Joaj3bfZS+/stchNlVdWZ/auj9nB3+EhWnNxBlVaDAgXFFaX8eXwbOnQkF2Ria2HFQP+wZumMq6lHrVipvx6ets5MDx/NipM7OJEWi72FDXd2Hc59Pcbx0wHjw9lvDMOIr1SHy/X26URpZTnHr5ApNiywB93aBvH5zt+bfdGDa/nMuFx/v86UVJZxJNkwwz/IxZvxHfvx65F1nM9OxtXakaldh5MX3J9Vp5tvSL0QrZl0xgnxP8zKyoqAAOOrjl1idtnQmUtfAI1t017hCv6lMlfrKHnggQcICwsjKSmJX375haFDh+Lj41Nv+YEDB1JYWMiRI0fYuXMnb731Fl5eXrz77ruEhYXh6upKx44d9fF169aNRYsW1TnOpey1y136Ymysk6g2s1rDixQKxRX/Hsa88MILPPPMMwbbVCoVPxy8vi+kljZqFEplnYnCSwqK62R1XIm7vxfR+5puRaqBAwYQ0qmmg7GisnqIU1Z2Ns7ONT9Wc3Jz63QMX87JyalOFtzV9qmtrKyMr7/9lo8/+ID+/aozIwIDAzl79iwLFy++rs64/LJiNFoNjmrDjCV7CxvySowPW6nQVPLJ9iV8tmMpDmobckoKGNOhL8UVZeSXFePv5ImHrRNvjXxQv8+ldrnugU+5b+k7TTIPUOWpKAo/vmyolGn1VwSljTWagprYldZW6IoaMOm0mRnmXTtTuq4BEztXVKJJTUPpUn+HxbUqKC9Bo9Vgb2nY3u0srMgvM96pObHTAM5mJrD6dHWHYEJeOuUHKnhj5AMsPbaZvItZdjp0pBdVZ3hdyE2jjZ0Lt3Qa2CydcQX6NmV4IcHe0rreoVAVmko+3bGEz3cab1OX6NCRUpAFwPmcZLzt3bgzbFijO+Ns7e1QmijJrZUFl5ebVydb7hIHRwdyc2qXz8XExARbu+rzacGPcxkycjijJ4wFwK+dP2VlZXzxwafcde+0Zp3fEmreV4vzDf/upQVFqO3qvq9WlpWTEZ9MZkIq2xauAi5+huh0fHH/y0wzDSsQAADWRklEQVR69j68gtuhtrVm/BPTqaqspKyoBCt7W3YvW4+tc9MO3b7E3MoShVJBWYHheVBeVIKFjdroPpZ21ljaWRss7mPr5gQ6KM0rwsa1eWKtj9rWGqVSWScLrji/sE62XG2OrtXvM+7ebSjKL2TrsjV1OuMqyis4sfsQQ6eMa9rAaympKEOj1WKjMvy7W5tbUlQrQ+uSwvJi8suK9R1xAJlFuSgVCuwsrMkuyaewvBiNTovuss6XjKLc6kxbhRJNE2fIFJaXoNFqsav1fmtrYVUnQ+uS8cH9iclK4J8zewBIJIPygxW8Mnwmy45vabbFDepTVF568bUwzBi1VqkpbMCFsN4+IRxIPF3v33ZoQHdGtO/JV7v/0L/vNofCiouvhUXt10Jd72ff5Qb4dWFP/Ak0tb7H3ho6iD0XTrDj4rxsSfmZqEzNuLf7GFaf3tXQa3RCiMvInHFCiCaXkJBASkqK/v7evXtRKpW0b98eqJ4TTGNkIt7Q0FC6d+/Ojz/+yOLFi5k5c+YVn+fSvHFfffUVCoWC4OBgBgwYwNGjR1m9erU+Kw4gPDycmJgYXF1dCQgIMLgZW0W1Xbt2mJmZ6ed8AygoKCAm5trmMjE3NwcwWt/LqVQqbG1tDW6NGaZqYmqKq48niacMf1Qnno7FPaDh831lJqQa/ZF5vaysrPDy8tLf/P38cHJyYv9lf+fKykqOHD1K58syMGvrHBJisA/A/v37r7hPbVUaDVVVVXU6XJUmJtfcmao/plbD2axEwtsYZlCGtw3iVHpcPXtV0+i0ZBXno9XpGNwunP0Jp9ChIyEvnQeXvc/sPz/S3/ZeOMmxlFhm//mR0dU0r0t5BdqsnJpbWgbaggJMgy7rsDcxwTTAj6q4K887CGAeFgqmJlQeOnr15zYxwcTNFW1B/fPsXCuNVkNcTiqh7oZDpELd23E203j85qZmdTrctZc65q8w15ICMKtnCGJjVWk1xGQl1W1TbYKu2vl3eZuKaNdV36bqo1AoMFM2/jqtmZkZgUHtOXrwsMH2owcP0zHEeLZvx5BOdcofOXCIwA5BmF7sGC4vL6vT4aZUKtHpdPVmOzYlE1NTXH09Saj1vppwOhaPdnUvHJlbqJj21hNMfeMx/S00oicO7s5MfeMx3NsZDkM0NTPD2sEOrUZL7OGT+HftWOeYTVMPExy83EmLNhwml3bmAs5+bYzu4+zXhtL8IiovW0W5MCMHhUKBpX3TfUY0lKmpKZ7+3sQeN1xsJ/b4GbyD/Bt+IJ1OPyfh5U7uPYymqoqwAdefId0QGp2W5PxMAl0M20Kgc1su5KYb3Sc+Jw1bCzXmJjXnqrOVHVqdVt+BFZ+bhpPazuBdy9nKvrpzvxmGqmm0WuJzUghxN/zbh7j7E5OVZHQflamZ/v31Ev37bfNMbXdFGp2WxLx0OrganssdXH2Iy06pZ69qgc5tcbV2YG+88XlrhwZ2Z1SH3nyzZzkJecZf16ai0WqJz02lk7vhiIZgNz/O1fNaXBLk4o2bjSM74+pehDU3Mfb5qK1uYy3xgolrd/Fi0E15+x8lnXFC/A8rLy8nLS3N4JaV1firdRYWFtx7770cO3aMnTt38sQTT3DHHXfoh6j6+vpy/PhxoqOjycrKorKyZgLoBx54gPfffx+NRsOkSZOu+lwREREsXLiQQYMGoVAocHBwIDg4mKVLlxqsXDpt2jScnZ255ZZb2LlzJ3FxcWzfvp0nn3ySpKS6X05sbGy49957+c9//sPWrVs5deoUM2fORKlUXtNQSFdXVywtLVm3bh3p6enk5+c3eN/GChvRj9M7D3N652FyUjLY+ds/FOXkEzKoOgNgz58b2PjTH/rykRv3cP7IafLSs8hOTmfPnxs4d/gUnYf0brYYFQoFd02Zwtz589m6bRux587x+ltvYWFhwajL5vF79Y03+Oqbb/T375wyhf0HDjBvwQLi4+OZt2AB+w8eZOqUKfoyJSUlRJ89S/TZ6iGRySkpRJ89S9rFueisrawI79qVz7/6ikOHD5OcksKq1av5Z+1aBl/WkXut/jy+jdEdejMyqBfe9m7M7jMJV2sHVkdVZ1vN7DGO/4uYpi/fxs6FoQHdaWPrQpCLNy8OvRdfRw9+ObAagEpNFfG5qQa34vJSSirLiM9NbdahLuXb92AxLAKz0GCU7m6o77odXUUlFUci9WXUU2/HYuyIOvua9+5O5Ykoo3PAWUwYjUk7P5SODph4t8XqvqkoLFRUHGzaYXlrovYwJCCciHZd8bR15p5uo3C2smNTzEEA7gwbxiN9b9WXP5IUTQ/vYIYH9sDV2oH2Lt7M6DGG2KwkfRbaLZ0GEOreDldrBzxtnRnTsS8D/MOM/nhpKn+e2MaooN6MbN8LL3s3ZveeWKdN/adOm+qGp61zdZsacg++Dh7MvWzuuzu7DCO8TXvcbZzwsnPlttAIhgX2aLIhbLdOmcy6Vf+wfvVaEuIv8P3nX5ORns7YSeMB+OXbH/norff05cdOHE96Wjrff/ENCfEXWL96LetXr+X2u+7Ql+nVrw9r/lrJtk1bSEtJ5ciBQyz4cS69+/fF5GJnaGlJKefOxnLubHWHWVpKKufOxpKR1jQ/gMNH9OfUjkOc2nGInJQMti9ZQ2F2PqGDqzttdi9bz/oflwGgUCpxbutucFPbWmFiZoZzW3fMVNUXa9LOJRJ76CT5GTkkn41jxadz0el0dB/TPCvCAnQY3J3ze49zfu8J8tOyObJ8CyW5BQT0r1418tjKHez7taa9+HTviLmVJQcWrSU/NYuM2ESO/b0dv96h+iGqmioNuUnp5Calo63SUJpfRG5SOoWZuc1Sh37jhnB48x4Ob9lDRlIq/8z7g/ysXHoMHwDAhsUr+OOrefry+9Zt58yh42SlZpCVmsHhrXvZtWoTXYx0uB3esoeOPbqgtmn+jsadccfo4dWR7m074GrtwLiO/bC3tGFfQvWKnKOCehushByZcpaSinImdxmCq7UDfo4ejOnQl0OJZ/SfB/sunMLK3ILxnfrjbGVHB1cfBgeEs+eyVT6b2trofUT4hzPQPwxPW2emdR2Jk9qOzTHV7yl3dBnKQ70n6ssfTT5Ld6+ODA3ojouVPYHOXkzvNopzWUn6LGQTpRJveze87d0wVZrgYGmLt70brtbNk4m5JfYwfX1D6e0TgpuNI7eGRuCottG/v08I7s/0bqPq7NfHJ5S4nBSjWerDAnswrmM//eqyNio1Nio15ibNN7R7Q/R+BvqFMcCvCx42TtwZNgwntR1bz1V/xt4eGsEDvcbX2W+gfxjnspNJzs+s81hkSgyDA7rR0ysYZys7gt38mBQyiMiUmBtyMeRKLM1UBDq31S8W4mnrTKBzW9yaqZ0I0VRkmKoQ/8PWrVuHh4fhkuRBQUH6lUCvV0BAALfeeitjxowhJyeHMWPG8M1lHSkPPvgg27Zto3v37hQVFbF161Z9x9ldd93FU089xdSpU7GwsLjqcw0ePJhPP/3UoONt0KBBREZGGmTGqdVqduzYwfPPP8+tt95KYWEhbdq0YejQofq57mr79NNPmT17NuPGjcPW1pb/+7//IzExsUFxXWJqasoXX3zBm2++yauvvsqAAQP0K8A2t8CeoZQVlXBw1VaK8wtxauPGuCen64c+leQVUpiTpy+vrdKwe9k6inILMDUzw7GNK+OenI5v57rz5DWle6dPp7y8nPc/+ojCwkJCOnXiq88/x8qqZqhIWloayss6Qbt07sw7b73Ft99/z3c//EDbNm147+23CQkJ0Zc5HRXF7Iur+ALM+fxzAMaNGcPrr74KwLtvv83X33zDK6+/TkFBAe7u7jz80EPcdmtNB8212n7+KLYWVtwdPhJHtR3xOam8tPZ7Moqqf5A6qW0NfkiYKJTc3nkwbe1d0Wg1RKbE8OTfn+mHQbak8i07UJiZYXn7BBSWlmguJFH03Vy4LDtG6WBf56qm0sUJU39fir79xehxlXZ2WE2fgsJKja6omKoLiRR+9h263LwmjX/vhZNYqyy5LTQCe0sbEvMyeH/rQrKKqzvFHSxtcLaqyYzdfj4SCzMVI4J6cXe3kRRXlHEqPY7FRzboy6hMzZnZcxxOalsqNJWkFGTx9e4/2duMP3K3nz+KrUrNtPCROKptuZCTysvratqUo9oWV6uaNqVUKLkttKZNHUuJ5amVnxu0KQszcx7vNxlnKzvKqypJzM/gg60L2X6+AZmMDTBo2GAKCgpYNHcBudk5+Pj78tbH7+F28aJMTnYOGek1c2u6e3rw1sfv8f0XX7N6+d84Ojvx8FOP0X9wTYfU1Huno1AomP/DL2RnZmHnYE+vfn2YMet+fZmzZ6J5/vGaIf8/fFk9L+qw0SN57uXnG12v9r06U1pcwv6VWyi5+L56y9P36t9Xi/MLKczOu6ZjVlVWsvevjeRn5GJmYY5v5yBGPngHKrVlo+Otj3d4B8qLSzm5fg9l+cXYeTgzcPZtWDlWnw+lBUUU59ZkqpqpzBn86GQO/7GZDR//irmVJd5dgwgd219fpjS/iPUf1sw1eWbLQc5sOYhLgBdDn7izyesQ2rc7JYXFbP3zHwpzC3Dz8mD6C4/g4OIEQGFuAXlZNR2BOp2WDUv+JjcjG6VSiaO7CyOmTaTHsP4Gx81KSefCmXPMePnxJo/ZmOOpsajNVQwN7I6tyoq0omzmHlyt75CyUakNhttXaKr4af9Kbuk0gMf7305JRTnHU2NZH71fXya/rIif9q9ifHA/nhowhYKyYnbHHWfbuaY5v43Zn3AKa3NLJnYahL2lNUn5GXy8fRHZJdXvt/YW1jipa95vd8Ydw8JUxbD2Pbir6whKKso4nRHH0siaqQ0cLG14Z/Rs/f2xHfsytmNfotLjeXfL/Cavw5HkaKzMLRgd1BtbCytSC7L5Zs9y/cUYWwsrHGstnGNhak6YZyB/nNhq9JgD/LpgZmLKA70mGGz/J2pPsy1+cCAxCiuVmgmd+mNnYU1yfiZzdv6mXx3VztLwtYDqDq1ubTuw+OgGY4fUzwt3a+ggHCxtKCwvITIlhj9PbGuWOlyLDi4+fDWp5n3/if6TAfgnai/vNEM7EaKpKHQt3ZUthGhVXn/9dVasWEFkZOR17Z+YmIivry8HDx4kPDy8aYNrpOLiYtq0acMnn3zC/ffff/UdmsCXu5bdkOdpTo/3n0xhbvNkRtwoNg4ODP/hyZYOo9E2zvqcvKdfbOkwGsV+zrvcufDVlg6j0X67+01G/PhUS4fRaBse/Iy4rOSWDqNR/Jzb8M2eP1s6jEZ7pO9tvLb+p5YOo1HeGPkAy45tbukwGm1yl6E8v+abqxe8yX0w9hGmL3nj6gVvYr/e9RqP/fVJS4fRaF9Nepb7lr7T0mE0ytwpL9Hv69lXL3iT2/3ody0dwnXJHX9XS4dQL4dVS1o6hBYhmXFCiJtCZWUlqamp/Pe//6V37943RUfc0aNHOXPmDD179iQ/P58333wTgFtuuaWFIxNCCCGEEEII8W8lnXFCiJvC7t27GTx4MO3bt+ePP/64+g43yMcff0x0dDTm5uZ069aNnTt3Gqz6KYQQQgghhBBCXAvpjBNCNKnXX3+d119//Zr3i4iIaPEJYGvr2rUrhw8fvnpBIYQQQgghhLhZ3WS/s4SspiqEEEIIIYQQQgghxA0jnXFCCCGEEEIIIYQQQtwgMkxVCCGEEEIIIYQQorXSaVs6AlGLZMYJIYQQQgghhBBCCHGDSGecEEIIIYQQQgghhBA3iAxTFUIIIYQQQgghhGittLKa6s1GMuOEEEIIIYQQQgghhLhBpDNOCCGEEEIIIYQQQogbRIapCiGEEEIIIYQQQrRWOhmmerORzDghhBBCCCGEEEIIIW4Q6YwTQgghhBBCCCGEEOIGkWGqQgghhBBCCCGEEK2VDFO96UhmnBBCCCGEEEIIIYQQN4h0xgkhhBBCCCGEEEKIVuOdd96hb9++qNVq7O3tG7SPTqfj9ddfx9PTE0tLSyIiIjh16pRBmfLych5//HGcnZ2xsrJiwoQJJCUlXXN80hknhBBCCCGEEEII0VpptTfvrZlUVFQwefJkHn744Qbv8+GHH/Lpp5/y1VdfcfDgQdzd3Rk+fDiFhYX6Mk899RR//fUXv/32G7t27aKoqIhx48ah0WiuKT6ZM04IIYQQQgghhBBCtBpvvPEGAPPmzWtQeZ1Ox2effcZLL73ErbfeCsD8+fNxc3Nj8eLFPPTQQ+Tn5/Pzzz/z66+/MmzYMAAWLlyIl5cXmzZtYuTIkQ2OTzLjhBBCCCGEEEIIIcQNV15eTkFBgcGtvLz8hscRFxdHWloaI0aM0G9TqVQMGjSIPXv2AHD48GEqKysNynh6ehISEqIv02A6IYQQ/5PKysp0r732mq6srKylQ7luraEOOp3U42bSGuqg07WOerSGOuh0Uo+bSWuog07XOurRGuqg00k9biatoQ7/q1577TUdYHB77bXXmuz4c+fO1dnZ2V213O7du3WALjk52WD7gw8+qBsxYoROp9PpFi1apDM3N6+z7/Dhw3WzZs26prgUOp2scSuEEP+LCgoKsLOzIz8/H1tb25YO57q0hjqA1ONm0hrqAK2jHq2hDiD1uJm0hjpA66hHa6gDSD1uJq2hDv+rysvL62TCqVQqVCpVnbKvv/66fvhpfQ4ePEj37t319+fNm8dTTz1FXl7eFffbs2cP/fr1IyUlBQ8PD/32Bx98kMTERNatW8fixYu577776sQ7fPhw2rVrx3fffXfF57iczBknhBBCCCGEEEIIIW64+jrejHnssce48847r1jG19f3uuJwd3cHIC0tzaAzLiMjAzc3N32ZiooKcnNzcXBwMCjTt2/fa3o+6YwTQgghhBBCCCGEEDc1Z2dnnJ2dm+XYfn5+uLu7s3HjRrp27QpUr8i6fft2PvjgAwC6deuGmZkZGzdu5I477gAgNTWVkydP8uGHH17T80lnnBBCCCGEEEIIIYRoNRISEsjJySEhIQGNRkNkZCQAAQEBWFtbA9ChQwfee+89Jk2ahEKh4KmnnuLdd98lMDCQwMBA3n33XdRqNVOnTgXAzs6O+++/n2effRYnJyccHR157rnnCA0N1a+u2lDSGSeEEP+jVCoVr732WoPTwm9GraEOIPW4mbSGOkDrqEdrqANIPW4mraEO0Drq0RrqAFKPm0lrqINoWq+++irz58/X37+U7bZ161YiIiIAiI6OJj8/X1/m//7v/ygtLeWRRx4hNzeXXr16sWHDBmxsbPRl5syZg6mpKXfccQelpaUMHTqUefPmYWJick3xyQIOQgghhBBCCCGEEELcIMqWDkAIIYQQQgghhBBCiP8V0hknhBBCCCGEEEIIIcQNIp1xQgghhBBCCCGEEELcINIZJ4QQQgghhBBCCCHEDSKdcUIIIf6VdDodsgaRaCqxsbGsX7+e0tJSAGlbLaysrKylQ2gSraUeQghxsxkyZAh5eXl1thcUFDBkyJAbH5AQ10hWUxVCiP8R5eXlHDhwgPj4eEpKSnBxcaFr1674+fm1dGjXZMGCBXz00UfExMQA0L59e/7zn/8wffr0Fo6s4TQaDXPmzOH3338nISGBiooKg8dzcnJaKLJrl5SUxMqVK43W49NPP22hqBouOzubKVOmsGXLFhQKBTExMfj7+3P//fdjb2/PJ5980tIhNlhiYqLB+d2pUydUKlVLh9VgWq2Wd955h++++4709HTOnj2Lv78/r7zyCr6+vtx///0tHWKDtJZ6/NvbU35+Pn/99Rc7d+6s87k3cuRI+vbt29IhNkhrqYcQTU2pVJKWloarq6vB9oyMDNq0aUNlZWULRSZEw5i2dABCCCGa1549e/jyyy9ZsWIFFRUV2NvbY2lpSU5ODuXl5fj7+zNr1ixmz56NjY1NS4d7RZ9++imvvPIKjz32GP369UOn07F7925mz55NVlYWTz/9dEuH2CBvvPEGP/30E8888wyvvPIKL730EvHx8axYsYJXX321pcNrsM2bNzNhwgT8/PyIjo4mJCSE+Ph4dDod4eHhLR1egzz99NOYmpqSkJBAx44d9dunTJnC008/fdN3xl24cIHvvvuOJUuWkJiYaJDRZ25uzoABA5g1axa33XYbSuXNPSDi7bffZv78+Xz44Yc8+OCD+u2hoaHMmTPnX9OJ9W+uR2toT6mpqbz66qssWrQId3d3evbsSVhYmP5zb+vWrXz88cf4+Pjw2muvMWXKlJYO2ajWUo/aDh48yLJly4xewFm+fHkLRXXtWks9/o2OHz+u///p06dJS0vT39doNKxbt442bdq0RGhCXBudEEKIVmvChAk6Dw8P3bPPPqvbvn27rri42ODxc+fO6ebNm6cbOXKkzt3dXbdhw4YWirRhfH19dfPnz6+zfd68eTpfX98WiOj6+Pv761avXq3T6XQ6a2trXWxsrE6n0+k+//xz3V133dWSoV2THj166F555RWdTlddj3PnzukKCwt1EyZM0H3zzTctHF3DuLm56SIjI3U6XU0ddDqd7vz58zorK6uWDO2qnnjiCZ2NjY3utttu082fP18XFRWlKygo0FVWVurS09N1mzdv1r3++uu6oKAgXadOnXQHDhxo6ZCvqF27drpNmzbpdDrD1yIqKkpnb2/fkqFdk39rPVpLe3JxcdE9++yzuhMnTtRbpqSkRLd48WJdz549dR999NENjK7hWks9LrdkyRKdmZmZbuzYsTpzc3PduHHjdEFBQTo7OzvdjBkzWjq8Bvs31+Pvv/9u8O1mpVAodEqlUqdUKnUKhaLOTa1W637++eeWDlOIq5LOOCGEaMW++uorXXl5eYPKnjx58qbvjFOpVLqYmJg628+ePatTqVQtENH1UavVugsXLuh0Op3O3d1dd/jwYZ1OV905amtr25KhXZPLOxLt7e11J0+e1Ol0Ol1kZKTOx8enBSNrOGtra93Zs2f1/7/UcXLgwAGdo6NjS4Z2Vc8995wuIyOjQWXXrFmjW7ZsWTNH1DgWFha6+Ph4nU5n+FqcOnXqpu8Yvdy/tR6tpT01tA7XW/5GaS31uFxoaKjuq6++0ul0NeeGVqvVPfjgg7pXX321haNruH9zPWp3XNXu0LrUyaVUKls61HrFx8fr4uLidAqFQnfw4EFdfHy8/paSkqKrqqpq6RCFaJCbM79cCCFEk3j00UcxNzdvUNlOnToxfPjwZo6ocQICAvj999/rbF+6dCmBgYEtENH1adu2LampqUB1nTZs2ABUD3v5N83JZGVlRXl5OQCenp6cO3dO/1hWVlZLhXVNBg4cyIIFC/T3FQoFWq2Wjz76iMGDB7dgZFf30Ucf4eLi0qCyY8aM4fbbb2/miBqnU6dO7Ny5s872ZcuW0bVr1xaI6Pr8W+vRWtpTQ+twveVvlNZSj8udO3eOsWPHAqBSqSguLkahUPD000/zww8/tHB0DfdvrodWq9XfNmzYQFhYGGvXriUvL4/8/Hz++ecfwsPDWbduXUuHWi8fHx98fX3RarV0794dHx8f/c3DwwMTE5OWDlGIBpE544QQ4n9QWVkZS5cupbi4mOHDh/9rOrLeeOMNpkyZwo4dO+jXrx8KhYJdu3axefNmo510N6tJkyaxefNmevXqxZNPPsldd93Fzz//TEJCwr9m3juA3r17s3v3boKDgxk7dizPPvssJ06cYPny5fTu3bulw2uQjz76iIiICA4dOkRFRQX/93//x6lTp8jJyWH37t0tHd5VvfzyywwZMoS+fftiYWHR0uE0ymuvvcb06dNJTk5Gq9WyfPlyoqOjWbBgAatXr27p8BqstdTjctu3b6e4uJg+ffrg4ODQ0uFcVWxsLPn5+XTr1k2/bfPmzbz99tsUFxczceJEXnzxxRaMsGFaSz0ucXR0pLCwEIA2bdpw8uRJQkNDycvLo6SkpIWja7jWUo+nnnqK7777jv79++u3jRw5ErVazaxZs4iKimrB6Brm7NmzbNu2jYyMDLRarcFj/6Y5eMX/qJZOzRNCCNG8nnvuOd0TTzyhv19eXq4LCwvTmZmZ6ezs7HRWVla6PXv2tGCE1+bQoUO6adOm6cLDw3Vdu3bVTZs2TXfkyJGWDqtR9u7dq/vkk09u6jlajDl37pzu2LFjOp1OpysuLtY9/PDDutDQUN2kSZP0w/T+DVJTU3WvvvqqbuzYsbrRo0frXnrpJV1KSkpLh9Ug/v7+OoVCoVOpVLqBAwfqXnvtNd327dsbPDz9ZrNu3TrdwIEDdVZWVjpLS0tdv379dOvXr2/psK7Zv7UeH374ocEwO61Wqxs5cqR+CJubm5t+OPrNbOLEibqXX35Zf//8+fM6S0tL3YgRI3RPPPGEztraWjdnzpyWC7CBWks9Lrnrrrt0n3zyiU6n0+nefvttnYuLi+6BBx7Q+fj46CZNmtTC0TVca6mHhYWF7vjx43W2Hzt2TGdhYdECEV2bH374QWdiYqJzc3PTdenSRRcWFqa/de3ataXDE+KqFDrdZcskCSGEaHVCQkJ49913mTBhAgBz587l2Wef5ejRo3h7ezNz5kwyMjJYs2ZNC0cqhLgeycnJbNmyhW3btrFt2zbi4uKwtLSkT58+DB48mMGDB9O3b9+WDlP8C4SHh/P888/rV+ZctmwZ9957Lxs3bqRjx47cc889qNXqmz4T2cvLi99//50+ffoA1Svc/vHHH0RGRgLw888/8+WXX+rv36xaSz0uycnJoaysDE9PT7RaLR9//DG7du0iICCAV1555V+RdQmtpx4DBw7EzMyMhQsX4uHhAUBaWhrTp0+noqKC7du3t3CEV+bj48MjjzzC888/39KhCHFdpDNOCCFaOVtbW44cOUJAQAAAd911FzY2Nvp5TSIjIxkzZgwpKSktGeY1ycjIMDokoXPnzi0U0dWtXLmywWUvdZyK5nH8+HFCQkJQKpUcP378imWtra3x8vLCzMzsBkXXeImJiWzdupVt27bx559/UlxcTFVVVUuHdVX33Xcfd999N0OGDEGhULR0ONft31wPBwcH9uzZQ8eOHYHqulRVVfHrr78CsG/fPiZPnkxiYmJLhnlVlpaWnD17Fi8vLwCGDh1K3759eeutt4DqOb+6detGXl5eC0Z5da2lHgBVVVUsWrSIkSNH4u7u3tLhCKqHQU+aNIno6Gi8vb0BSEhIoH379qxYsUL/vfFmZWtrS2RkJP7+/i0dihDXReaME0KIVk6pVHL5dZd9+/bxyiuv6O/b29uTm5vbEqFds8OHD3PvvfcSFRVF7WtJCoUCjUbTQpFd3cSJExtU7mavh4ODQ4M7GHJycpo5musTFhZGWloarq6uhIWFoVAo6rSny9nZ2fHdd9/ps4VuZufOnWPbtm36TDmNRnPTL0RxSXZ2NmPHjsXJyYk777yTu++++6Ze8KA+/+Z6VFZWGiwis3fvXp588kn9fU9Pz3/F4iyOjo6kpqbi5eWFVqvl0KFDBvNxVlRUXPGcv1m0lnoAmJqa8vDDD/8r5iEzpqCgoMFlbW1tmzGSphMQEMDx48fZuHEjZ86cQafTERwczLBhw/4VFxImT57Mhg0bmD17dkuHIsR1kc44IYRo5Tp06MCqVat45plnOHXqFAkJCQY/zi9cuICbm1sLRthw9913H+3bt+fnn3/Gzc3tX/Fl8ZLaWXz/Vp999llLh9BocXFx+pUH4+Lirli2vLycZcuWGQzdu5nExcWxdetWfSZcfn4+/fr1Y9CgQTz22GP06NEDU9N/x9e9lStXkpeXx++//87ixYv57LPPCAoK4u6772bq1Kn4+vq2dIgN8m+uR0BAADt27MDf35+EhATOnj3LoEGD9I8nJSXh5OTUghE2zKBBg3jrrbf45ptvWLZsGVqt1uBz7/Tp0zf163BJa6nHJb169eLo0aP4+Pi0dCjXzN7evsHfOW7mC2q1KRQKRowYwYgRI1o6lAb54osv9P+/NCx43759hIaG1slgf+KJJ250eEJcExmmKoQQrdyff/7JXXfdxYABAzh16hQ9evRg1apV+seff/554uLibvo5gABsbGw4evToTT90QrQuubm53H///SxfvrylQ6lDqVTi7e3NI488wuDBgwkPD8fExKSlw2oSSUlJLFmyhF9++YWYmJh/xVBbY/5N9fj++/9n787jakz//4G/TkmbVChLKi2iCKXsW9YwdsMQJevYSrI0nyE7zdizJUllr2wZ+1IRGbSiJC2yFKkhWmi5f3/4db6OolNmXOc+5/18PHo86rrvP14XndM573Nd13s3XF1dMXbsWNy6dQsaGhoiXYVXr16Nv//+W+RviCRKS0tDv379kJaWBjk5OXh6emLmzJnC68OHD4eBgQE2b97MMGXVpGUe5YKCguDm5gYXFxe0b98eqqqqItcl+aiJz89PS09Ph5ubGyZNmiQ8zy8yMhL+/v5Yt24dHBwcWMWstvz8fISHhyMjIwMfP34UuSaJxSwDAwOx7hMIBEhNTf2P0xDyfagYRwghMuDy5cs4c+YMGjVqhLlz50JFRUV4bcWKFejVq5fI6gdJNXz4cEycOBGjRo1iHeW78e0FcFUKCwtRXFwsMsaXrToAUFBQUOn/hSS/OQSAsWPH4tq1aygqKkL37t3Rs2dP2NjYwMLCglcrR79UXFyMM2fO4MCBAzhz5gzq1auH58+fs45VbXycx969e/HXX3+hUaNGWLZsmcj5XrNmzULfvn0xcuRIhgnFU1xcjISEBGhpaaFJkyYi1+Li4qCrq4t69eoxSic+aZkH8OnDgy+VHxMg6Uc0fK5Pnz6YOnUqxo0bJzJ+6NAheHt7IywsjE2waoqJicGgQYNQUFCA/Px81KtXD69fv4aKigq0tbWpmEXIf4yKcYQQQhAbG4t27dqxjlGl169fw8HBAR06dEDr1q0rbEngS+MDaXkBnJ+fj8WLFyMwMBA5OTkVrvPhjVV2djYcHR1x7ty5Sq/zYQ4A8PDhQ+FW1fDwcBQVFaFbt27o2bMnevXqBWtra9YRxRIaGopDhw7h2LFjKC0txciRI2FnZ4fevXtX+kZeUknLPCqTnZ0t3ObNV/fu3cPevXt5v+2eb/N48uTJN6/zZfuqiooK4uLi0Lx5c5HxR48eoV27digoKGCUrHp69eoFExMT7Nq1CxoaGoiLi4OCggImTJgAZ2dnXhTdCeEzKsYRQoiMevv2LQ4ePIi9e/ciNjaWF0WHkJAQTJw4Ee/evatwjU+fqkvLC+DZs2cjNDQUK1euhL29PXbs2IHnz59j9+7d8PDwgJ2dHeuIVbKzs0N6ejq2bNkCGxsbnDhxAi9fvsTq1auxceNGDB48mHXEGklISMChQ4ewbds23nRTbdq0KXJycjBgwADY2dlhyJAhUFJSYh2r2qRlHp/jOA7nzp0Trpr78OED60jVlpeXh8OHD2Pv3r24e/cu2rRpg9jYWNaxqk1a5sFnLVq0wE8//YSNGzeKjLu6uuKvv/5CUlISo2TVo6Ghgb///hstWrSAhoYGIiMjYWpqir///hsODg54+PAh64jfNH/+/ErHBQIBlJSUYGxsjGHDhvFm5SiRPfw40ZcQQsi/5urVq/D19cXx48ehr6+PUaNGwcfHh3UssTg5OWHixIlYunQpb5pOVCY2Nha7d++GvLw85OXl8eHDBxgaGuLPP/+Eg4MDb4pxp0+fRkBAAHr16oXJkyeje/fuMDY2hr6+Pg4ePMiLYtzVq1dx6tQpWFtbQ05ODvr6+ujXrx/q1q2LdevW8aoY9/LlS4SFhSEsLAyhoaF49OgRFBUV0b17d9bRxOLu7o6ff/4ZmpqarKN8F2mZBwCkpqbC19cX/v7+eP/+PQYPHowjR46wjlUt4eHh2Lt3L44dO4aioiIsXLgQhw4d4t3Zo9Iwj4CAgG9et7e3/0FJvs/mzZsxatQoXLhwAZ06dQLwqVN9SkoKjh07xjid+BQUFITHGTRs2BAZGRkwNTWFuro6MjIyGKerWkxMDKKjo1FaWooWLVqA4zgkJydDXl4eLVu2xM6dO+Hq6oqIiAiYmZmxjktIBVSMI4QQGfDs2TP4+fnB19cX+fn5GDNmDIqLi3Hs2DFevUDJycmBi4sLrwtxAP9fAJfLzc0VHqZct25d5ObmAgC6desmcsi4JMvPz4e2tjYAoF69esjOzoaJiQnMzc0RHR3NOF3VgoKChNtTk5KSUKtWLXTo0AFjxoyBjY0NunTpAkVFRdYxxTJ9+nTWEf4VfJ9HUVERgoOD4ePjg1u3bqFfv37IzMxEbGwsWrduzTqeWDIzM7Fv3z7h37xx48YhPDwcnTt3hr29PW8KWNIyj3LOzs4iPxcXF6OgoAC1a9eGiooKb4pxgwYNQnJyMnbt2oXExERwHIdhw4bh119/ha6uLut4YrOwsMDdu3dhYmICGxsbuLu74/Xr19i/fz/Mzc1Zx6tS+aq3ffv2Cc+ozcvLw5QpU9CtWzdMmzYN48ePh4uLCy5cuMA4LSEVUTGOEEKk3KBBgxAREYGffvoJ27Ztg62tLeTl5eHl5cU6WrWNHDkSoaGhMDIyYh3lu/D9BXA5Q0NDpKenQ19fH2ZmZggMDESHDh1w+vRpaGhosI4nlhYtWiApKQnNmjVDu3btsHv3bjRr1gxeXl5o3Lgx63hVsrOzg5WVFUaMGAEbGxt07doVysrKrGPV2J07dxAUFFRpMw1J7Gb7NXydx6xZs3DkyBG0aNECEyZMwLFjx1C/fn0oKCjw6qw7AwMD/Pzzz9ixYwf69evHq+yfk5Z5lPvnn38qjCUnJ2PmzJlYuHAhg0TVV1xcjP79+2P37t1Ys2YN6zjfZe3atcJjP1atWgUHBwfMnDkTxsbG2LdvH+N0VVu/fj0uXbok0iyqbt26WL58Ofr37w9nZ2e4u7ujf//+DFMS8nVUjCOEECl38eJFODk5YebMmRUOG+YbExMT/Pbbb4iIiIC5uXmFBg586ULK9xfA5RwdHREXF4eePXvit99+w+DBg7Ft2zaUlJRg06ZNrOOJZd68ecjMzAQALFu2DAMGDMDBgwdRu3Zt+Pn5sQ0nhn/++QeqqqqsY/wrjhw5Ant7e/Tv3x+XLl1C//79kZycjKysLIwYMYJ1PLHxeR7e3t5YvHgx3NzcoKamxjpOjenr6yMiIgJ6enrQ19dHy5YtWUeqEWmZx7c0b94cHh4emDBhgsSfUQZ8Wtl+//59XnerLmdlZSX8XktLC2fPnmWYpvrevn2LV69eVdjhkZ2djby8PACfzsX78gMRQiQFFeMIIUTKXb9+Hb6+vrCyskLLli0xceJEjB07lnWsGvHx8UGdOnUQHh6O8PBwkWsCgYA3xTi+vwAu5+LiIvzexsYGDx8+xN27d2FkZIS2bdsyTCa+z8+1s7CwQHp6Oh4+fAg9PT00aNCAYTLxlBfinj9/jmPHjuHRo0cQCARo3rw5Ro0aBR0dHcYJxbd27Vps3rwZs2fPhpqaGrZu3QoDAwPMmDGDF6sUy/F5HgEBAdi3bx8aN26MwYMHY+LEibC1tWUdq9qSkpJw48YN7N27F9bW1jAxMcGECRMAgFdFFGmZR1Xk5eXx4sUL1jHEZm9vj71798LDw4N1lH9FdnY2kpKSIBAI0KJFC1787QM+bVOdPHkyNm7cCGtrawgEAty+fRsLFizA8OHDAQC3b9+GiYkJ26CEfAV1UyWEEBlRUFCAI0eOwNfXF7dv30ZpaSk2bdqEyZMn83oFBCGybufOnZg/fz4+fvwIdXV1cByHvLw81K5dG5s2bcKsWbNYRxSLqqoqHjx4gGbNmqFBgwYIDQ2Fubk5EhMT0bt3b+EKRkknDfNIT0/Hvn374Ofnh4KCAuTm5uLo0aMYPXo062jV9v79exw+fBi+vr74+++/0bNnT4wfPx7Dhw+HlpYW63hik4Z5hISEiPzMcRwyMzOxfft26Orq4ty5c4ySVc/cuXMREBAAY2NjWFlZVVidzJeV4fn5+Zg7dy72798v7EYvLy8Pe3t7bNu2DSoqKowTftv79+/h4uKCgIAAYcfwWrVqwcHBAZs3b4aqqqqw03C7du3YBSXkK6gYRwghUi4jIwO6uroin6InJSVh79692L9/P968eYN+/fpVeJFMiLSaP3++2PdK+puqM2fOYNiwYZg3bx5cXV2FK68yMzOxfv16bNu2DadOncKgQYMYJ62arq4uzp49C3Nzc7Rt2xZubm4YN24cIiMjYWtri7dv37KOKBZpmQfwqVhy4cIF+Pr6IiQkBA0aNMDIkSPh6enJOlqNJCYmCv/25ebmori4mHWkGuHrPL48804gEEBLSwu9e/fGxo0bJX7laDkbG5uvXhMIBLh69eoPTFNzM2bMwOXLl7F9+3Z07doVABAREQEnJyf069cPu3btYpxQPO/fv0dqaio4joORkRHq1KnDOhIhYqFiHCGESDl5eXlkZmYKO0Z+rrS0FKdPnxa+0ZJE8+fPx6pVq6CqqlplEUXSCydEMnzrjdTn+PCmqmfPnujevTtWr15d6fUlS5bg+vXrFbZ1S6Lx48fDysoK8+fPx5o1a7B161YMGzYMly5dgqWlpUQ3PvictMzjS7m5ucJtrHFxcazjfJeSkhKEhIRg5MiRrKN8F2mZB2GjQYMGCA4ORq9evUTGQ0NDMWbMGGRnZ7MJRoiMoGIcIYRIOTk5OWRlZVVajOMDGxsbnDhxAhoaGlLzaTQh/5a6devizp07aNGiRaXXk5KSYGVlJWwYIslyc3NRVFSEJk2aoKysDBs2bEBERASMjY2xdOlSaGpqso4oFmmZB5+9ePECmzZtgru7u0inReDToe+rV6/GggUL0LBhQ0YJxSMt85BWjx8/RkpKCnr06AFlZWVwHMers/xUVFQQFRUFU1NTkfEHDx6gQ4cOyM/PZ5Ts60aOHAk/Pz/UrVu3yiI0Xz/4ILKDinGEECLl+F6MI+RH4Oubqjp16iA+Ph6GhoaVXk9NTUWbNm3w/v37H5yM8JE4W7gFAgE2btz4A9LU3IIFC5CXlwdvb+9Kr//6669QV1fHH3/88YOTVY80zEOajgUol5OTgzFjxiA0NBQCgQDJyckwNDTElClToKGhIfGPj3J9+vRB/fr1ERAQACUlJQBAYWEhHBwckJubi8uXLzNOWJGjoyM8PT2hpqYGR0fHb97Lp+70RDZRN1VCCJEB5V1Iv4UPnUhfvnz51RUA8fHxaNOmzQ9OJL7qnLEkyf8XeXl5Yt/75UoOSfS1N1VTp07lxZuqVq1a4dSpUyKdbT938uRJtGrV6genqrmysjI8fvwYr169QllZmci1Hj16MEpVfXydR0xMDOsI/4rz58/Dy8vrq9ft7e0xbdo0iS5iAdIxjy9/p6KiolBaWipczfvo0SPIy8ujffv2LOLViIuLCxQUFJCRkSGyqmzs2LFwcXGR+L8b5bZu3QpbW1s0bdoUbdu2hUAgQGxsLJSUlHDhwgXW8Sr1eYGNim2E72hlHCGESDk5OTk0bdoU8vLyX71HIBAgNTX1B6aqGW1tbfj4+GDo0KEi4xs2bMDSpUtRWFjIKFnVDAwMxLpP0v8v5OTkqlwxVr6qrLw7mySzt7fHq1ev4OPjA1NTU8TFxcHQ0BAXL16Ei4sLHjx4wDriN/n7+2PmzJnYsGEDpk+fjlq1Pn3OWlJSgt27d2PhwoXYuXMnJk2axDaoGG7duoXx48fjyZMn+PLlKV9+nwDpmQefqaqqIjExEXp6epVeLy+iSOI2vM9JyzzKbdq0CWFhYfD39xdu1/7nn3/g6OiI7t27w9XVlXFC8TRq1AgXLlxA27ZtoaamJvy7kZaWBnNzc16tRC4sLMSBAwfw8OFDcBwHMzMz2NnZQVlZmXU0sZSUlCAsLAwpKSkYP3481NTU8OLFC9StW5caORCJRyvjCCFEBty9e1cqtqkuXrwYY8eOFbatz83NxcSJE/HgwQMcPXqUdbxvSktLYx3hXxEaGso6wr/q4sWLuHDhApo2bSoy3rx5czx58oRRKvE5ODjg3r17mDNnDn777TcYGRkBAFJSUvD+/Xs4OTnxohAHfNpyZ2VlhTNnzqBx48a82CZcGT7PIywsrMJh7l+aNWsWdu7c+WMC1ZCysjLS09O/WsRKT0/nRbFBWuZRbuPGjbh48aLIuYmamppYvXo1+vfvz5tiXH5+PlRUVCqMv379GoqKigwS1ZyysjKmTZvGOkaNPHnyBLa2tsjIyMCHDx/Qr18/qKmp4c8//0RRUdE3V5USIgmoGEcIIYQ3XF1d0bdvX0yYMAFt2rRBbm4uOnXqhPj4eF4eYP3x40ekpaXByMhIuKJJ0vXs2ZN1hH+VNLyp2rBhA0aPHo3Dhw8jOTkZwKetkL/88gs6derEOJ34kpOTERwcDGNjY9ZRvguf5zFs2DCEhobC0tKy0uuzZ8/GwYMHJb4Y17FjR+zfv/+rW4IDAgLQoUOHH5yq+qRlHuXy8vLw8uXLClvnX716xYsmM+V69OiBgIAArFq1CsCnFa9lZWVYv3692N26WQkJCRH73i93IUgaZ2dnWFlZIS4uDvXr1xeOjxgxAlOnTmWYjBAxcYQQQqSaQCDgXr58+c17Dhw48IPSfL+8vDxu7NixXK1atbhatWpxfn5+rCNVW35+Pjd58mROXl6ek5eX51JSUjiO47i5c+dy69atY5yueq5du8bZ2dlxnTt35p49e8ZxHMcFBARw169fZ5xMPIMGDeKWLFnCcRzH1alTh0tNTeVKS0u5n3/+mRs1ahTjdFVbsWIFl5+fzzrGv8LGxoY7d+4c6xjfjc/zmD9/Pqetrc0lJSVVuDZ79myuTp063LVr1xgkq56rV69y8vLynKurK5eVlSUcz8rK4ubPn8/Jy8tzV65cYZhQPNIyj3ITJ07k9PT0uKCgIO7p06fc06dPuaCgIK5Zs2acvb0963hie/DgAaelpcXZ2tpytWvX5kaPHs2ZmppyDRs25B4/fsw63jcJBAKxvuTk5FhHrVL9+vW5hw8fchz36e93+WuptLQ0TllZmWU0QsRCxThCCJFyLVu25F68ePHV6wcPHuQUFBR+YKKai4iI4Jo1a8a1b9+eS0hI4Pbs2cOpqalxP//8M5ebm8s6nticnJy49u3bc9evX+dUVVWFLyBPnTrFtWvXjnE68QUHB3PKysrc1KlTOUVFReE8duzYwQ0cOJBxOvHw+U0Vx3GcnJxclcV2vjh+/DhnZmbG7du3j7t79y4XFxcn8sUXfJ+Ho6Mjp6enJyyuc9ynDwpUVVW5sLAwhsmqx8vLi1NUVOTk5OQ4DQ0NTlNTk5OTk+MUFRW5nTt3so4nNmmZB8d9+iBq5syZwvnIyclxtWvX5mbOnMm9f/+edbxqyczM5Nzd3bnBgwdzAwcO5H7//fdvvtYi/z5NTU3uwYMHHMeJFuOuX7/OaWtrs4xGiFiogQMhhEi5Xr16obCwEFevXoWqqqrItSNHjsDe3h5//PHHV7sxShJFRUW4uLhg1apVUFBQAPDpbKyJEyciIyMDz549Y5xQPPr6+jh69Cg6deokcvjz48ePYWlpWa2OpSxZWFjAxcUF9vb2IvOIjY2Fra0tsrKyWEcUS1ZWFnbt2oWoqCiUlZXB0tISs2fPRuPGjVlHq5KcnByysrKk4kxIOTm5CmMCgYBXDUEA/s+jrKwMo0ePRmJiIq5fv441a9bA29sbf/31l8RvwStXXFwMBQUFPH/+HIGBgXj8+DE4joOJiQlGjx6Npk2b4v79+2jdujXrqN8kLfP4Un5+PlJSUsBxHIyNjSu8NiE/RlpamtjNpSTR2LFjoa6uDm9vb6ipqSE+Ph5aWloYNmwY9PT0qNsqkXhUjCOEECn3/v179OrVCxoaGjh37pywiBUYGIgJEyZg7dq1WLBgAeOU4gkPD6/0zLKysjKsWbMGS5cuZZCq+lRUVHD//n0YGhqKFLHi4uLQo0cPvH37lnVEsaioqCAhIQHNmjUTmUdqairMzMxQVFTEOqLUk5OTw8uXL6GlpcU6ynerqmGGvr7+D0ryfaRhHh8/fsTgwYMRFxeH/Px8hISEoE+fPqxjiW306NEICgr6avOM+/fvo0+fPnj58uUPTlY90jIPaXPt2rVvXv/aGX+SRl5eHj169MCUKVMwevRoKCkpsY5ULS9evICNjQ3k5eWRnJwMKysrJCcno0GDBrh27ZpUfEhFpBsV4wghRAZkZ2ejR48eMDMzQ3BwMIKDg2FnZ4dVq1Zh8eLFrOPJnJ49e2L06NGYO3eu8NNcAwMDzJkzB48fP8b58+dZRxSLkZERdu/ejb59+4oU4wICAuDh4YGEhATWEat0/vx51KlTB926dQMA7NixA3v27IGZmRl27Ngh0vVPEsnJyaF169ZVNgCJjo7+QYkIn3l6egq/f/fuHVatWoUBAwZUKMQ5OTn96GjVoquri4EDB8Lb27vCtQcPHqB3797o0aMHgoKCGKQTn7TM43N37txBUFAQMjIy8PHjR5Frx48fZ5Sqer62+rWcpK9+LXf//n34+vri4MGD+PDhA8aOHYspU6bwqilIYWEhDh8+jOjoaOHKdjs7O151GSayi4pxhBAiI54+fYpu3brB2NgYERERcHd3x++//846VrXl5+cjPDy80hfykv4GsdzNmzdha2sLOzs7+Pn5YcaMGXjw4AEiIyMRHh6O9u3bs44olj///BP+/v7w9fVFv379cPbsWTx58gQuLi5wd3fHnDlzWEeskrm5Of744w8MGjQI9+7dg5WVFVxdXXH16lWYmppK/DYXOTk5uLq6ok6dOt+8b9myZT8o0fdJSUnBli1bkJiYCIFAAFNTUzg7O8PIyIh1tGrh6zzE2bImEAiQmpr6A9LUXGJionDFj4eHh8i4jY0NunTpgqCgIMjLyzNMWTVpmUe58qMx+vfvj0uXLqF///5ITk5GVlYWRowYIfHPt+W+XL1eXFyMmJgYLF26FGvWrOHVKlIAKCkpwenTp+Hn54dz586hefPmmDJlCiZOnCgVq64JkVhMTqojhBDyw3x+cPjRo0c5RUVFbuzYsbw4VDwjI0Pk5+joaK5Ro0acmpoaV6tWLU5LS4sTCAScqqoqZ2BgwChlzcTHx3P29vZcq1atOFNTU87Ozo6Lj49nHava/ve//3HKysrCDmxKSkrC7qR8oKqqyqWlpXEcx3HLli0TdlCNioriGjZsyDCZeMTplswX58+f52rXrs116NCBc3Fx4ebNm8d16NCBU1RU5C5evMg6ntikZR58d/v2bU5NTY37888/OY7juMTERK5Ro0bc0KFDuZKSEsbpxCct8+A4jjM3N+e2b9/Ocdz/HbhfVlbGTZs2jXN3d2ec7vuFh4dzlpaWrGPUWFFREbdp0yZOUVGREwgEXO3atbmJEydKbGOKxo0bc+PGjeN2795daQdoQiQdrYwjhBApJycnJ3J4ePnT/pffS+K2itWrVyMzMxPbt2+HQCBAr169YGRkBG9vbzRt2hRxcXEoLCzEhAkT4OLigpEjR7KOLJMKCgqQkJCAsrIymJmZVblKS5LUq1cPERERMDMzQ7du3WBvb4/p06cjPT0dZmZmKCgoYB3xm+Tl5ZGZmSkVZ+NYWFhgwIABIiuAAMDNzQ0XL17kzVZbaZmHNLh69Sp++uknLFq0CHv27IGlpSWOHz8uPDuVL6RlHqqqqnjw4AGaNWuGBg0aIDQ0FObm5khMTETv3r2RmZnJOuJ3SUxMhLW1Nd6/f886SrXcvXsXvr6+OHLkCFRVVeHg4IApU6bgxYsXcHd3x7t373D79m3WMSs4fPgwwsPDERYWhkePHqFhw4bo2bMnevXqhZ49e8LU1JR1REK+iYpxhBAi5ao6TLycJB4qXlhYiDlz5iA7OxshISHQ0NDArVu30LJlSzRt2hSRkZHQ1dXFrVu3MGnSJDx8+JB15K+qTofUunXr/odJyOeGDh2Kjx8/omvXrli1ahXS0tKgo6ODixcvYs6cOXj06BHriN8kTd1UlZSUcO/ePTRv3lxk/NGjR2jTpg1vGoLwdR5HjhzBL7/8Ita9T58+RUZGBrp27fofp/p+J0+exM8//4z+/fvj5MmTvCtglZOGeejq6uLs2bMwNzdH27Zt4ebmhnHjxiEyMhK2tra8aV4UHx8v8jPHccjMzISHhweKi4tx48YNRsmqZ9OmTdi3bx+SkpIwaNAgTJ06FYMGDRI5E+/x48do2bIlSkpKGCat2suXLxEaGoq//voLR48eRVlZmUR+yEzI57592i8hhBDek8Qim7iUlZWxd+9eHDlyBACgoKAgfJGora2NJ0+eQFdXFxoaGsjIyGAZtUoaGhpf7Yj3JUl+AVmd1Yd8OIx7+/btmDVrFoKDg7Fr1y7o6OgAAM6dOwdbW1vG6aqWlpYmNWf6aGlpITY2tkIRKzY2llfFRr7OY9euXVi+fDkcHR0xdOjQCqtK3r59ixs3buDAgQO4fPky9u7dyyhp1TQ1NSs8316/fh0NGzYUGcvNzf2RsapNWuZRrnv37rh06RLMzc0xZswYODs74+rVq7h06RKvzllr166dyO6Ccp06dYKvry+jVNW3a9cuTJ48GY6OjmjUqFGl9+jp6Un0Y/39+/eIiIgQrpCLiYmBubk5evbsyToaIVWiYhwhhEixjIwM6OnpiX3/8+fPhcUISVK+WsPCwgJ37tyBiYkJevbsid9++w0zZ86Ev78/zM3NGaf8ttDQUOH36enpcHNzw6RJk9C5c2cAQGRkJPz9/bFu3TpWEcWirq4u/J7jOJw4cQLq6uqwsrICAERFReHNmze82TKsp6eHv/76q8L45s2bGaSpPn9/f7Huc3d3/4+TfL9p06Zh+vTpSE1NRZcuXSAQCBAREYE//vgDrq6urOOJja/zCA8Px19//YVt27bhf//7H1RVVdGwYUMoKSnhn3/+QVZWFrS0tODo6Ij79+9LdGFxy5YtrCP8K6RlHuW2b98uXBn622+/QUFBARERERg5ciSWLl3KOJ340tLSRH6Wk5ODlpYWlJSUGCWqmeTk5CrvqV27NhwcHH5Amurr2LEj4uPj0bp1a/Tq1Qv/+9//0L17d2hoaLCORohYaJsqIYRIsYYNG2Lo0KGYNm3aV1vVv337FoGBgdi6dStmzJiBuXPn/uCU4rt79y7y8vLQu3dv5ObmYtKkSQgLC4ORkRH8/PzQtm1b1hHF0qdPH0ydOhXjxo0TGT906BC8vb0RFhbGJlg1LV68GLm5ufDy8hJ28ystLcWsWbNQt25drF+/nnHCqlW1orI6xWwW5OTk0KRJE2hra1dYpVFOIBDw4pwyjuOwZcsWbNy4ES9evAAANGnSBAsXLoSTk5PYK0tZk4Z55OTkICIiAunp6SgsLESDBg1gYWEBCwsLkS1shMiSq1evYs6cObh161aF4yTevn2LLl26wMvLC927d2eUsGYKCgoq7VDfpk0bRonEU69ePQgEAvTt2xe9evVCr1696Jw4witUjCOEECmWm5uLtWvXwtfXFwoKCrCyskKTJk2EKx0SEhLw4MEDWFlZYcmSJRg4cCDryDJBRUUFcXFxlZ4p1a5dO4lvGlBOS0sLERERaNGihch4UlISunTpgpycHEbJxFfe4ORrJHnLMAAMGjQIoaGhGDBgACZPnozBgwcLC6N8UlJSgoMHD2LAgAFo1KgR3r17BwBQU1NjnKx6pGUesqS8uRHf8WEeXzs7VSAQQFFREbVr1/7Biapn6NChsLGxgYuLS6XXPT09ERoaihMnTvzgZDWTnZ2NSZMm4fz585Vel/S/f8Cn8/vCwsIQHh6O69evQ05ODj179oSNjQ1+/fVX1vEI+Sb6aIsQQqRYvXr1sGHDBrx48QK7du2CiYkJXr9+LdyaYGdnh6ioKNy4cYMKcT+Qrq4uvLy8Kozv3r0burq6DBLVTElJCRITEyuMJyYmoqysjEGi6ouJiUF0dLTw6++//4aXlxdMTEwQFBTEOl6Vzp49i9TUVHTs2BELFy5E06ZNsXjxYiQlJbGOVi21atXCzJkz8eHDBwCfild8LGBJyzz4zNTUFIcOHaqwyudLycnJmDlzJv74448flKx6pGUen9PQ0ICmpmaFLw0NDSgrK0NfXx/Lli2T2L8fcXFx3zxLtH///oiKivqBib7PvHnz8ObNG9y6dQvKyso4f/48/P390bx5c4SEhLCOJ5Y2bdrAyckJx44dw7lz5zBw4EAcP34cs2fPZh2NkCrRmXGEECIDlJSUMHLkSN6c4/U1OTk5cHd3R2hoKF69elXhBTtfDrHevHkzRo0ahQsXLqBTp04AgFu3biElJQXHjh1jnE58jo6OmDx5Mh4/fiwyDw8PDzg6OjJOJ57KtjaXryBdv349Lx4zjRs3xm+//YbffvsN165dw759+2BtbQ1zc3NcvnwZysrKrCOKpWPHjoiJieF10xlAeubBVzt27MDixYsxe/Zs9O/fv9IV4REREUhISMCcOXMwa9Ys1pErJS3z+Jyfnx9+//13TJo0CR06dADHcbhz5w78/f2xZMkSZGdnY8OGDVBUVMT//vc/1nErePny5Te72NaqVQvZ2dk/MNH3uXr1Kk6dOgVra2vIyclBX18f/fr1Q926dbFu3ToMHjyYdcRviomJQVhYGMLCwnD9+nW8e/cObdu2hbOzM2xsbFjHI6RKVIwjhBDCGxMmTEBKSgqmTJmChg0bSvyWnK8ZNGgQkpOTsWvXLiQmJoLjOAwbNgy//vorr1bGbdiwAY0aNcLmzZuRmZkJ4FNhaNGiRRJ9UL04TExMcOfOHdYxqs3a2hrp6elISEhATEwMiouLeVOMmzVrFlxdXfHs2TO0b98eqqqqItcl/fyictIyD77q3bs37ty5g5s3b+Lo0aM4dOhQhbPv7O3tMWHCBIk+6F1a5vE5f39/bNy4EWPGjBGODR06FObm5ti9ezeuXLkCPT09rFmzRiKLcTo6Orh37x6MjY0rvR4fH4/GjRv/4FQ1l5+fL2zEUq9ePWRnZ8PExATm5ua8OGfU2toaFhYW6NmzJ6ZNm4YePXpUOMuPEElGZ8YRQgjhDTU1NURERPCmUYMsKT8LiG8vhL88w4jjOGRmZmL58uV4+PAhYmNj2QSrpsjISPj6+iIwMBAmJiZwdHTE+PHjefMmHUCljQEEAoHwLCw+nF8ESM88CPm3fe281OTkZLRt2xYFBQVIS0tDq1atJPLs1Llz5yIsLAx37typ0Dm1sLAQHTp0gI2NDTw9PRklrB5ra2usXr0aAwYMwPDhw4Ur4jw9PREcHIyUlBTWEb8pLy+Pd685CPkcrYwjhBDCGy1btkRhYSHrGKQSfH1BrKGhUWGFJcdx0NXVxZEjRxilEt+ff/6Jffv2IScnB3Z2doiIiIC5uTnrWDWSlpbGOsK/QlrmQci/rWnTpti7dy88PDxExvfu3StcFZ6TkwNNTU0W8aq0ZMkSHD9+HCYmJpgzZw5atGgBgUCAxMRE7NixA6Wlpfj9999ZxxTbvHnzhKvaly1bhgEDBuDAgQOoXbs2/P39GaerGl9fdxBSjlbGEUII4Y07d+7Azc0N7u7uaN26dYWzW+iFGamu8PBwkZ/l5OSgpaUFY2Nj1Kol+Z9ZysnJQU9PDz/99NM3OxFu2rTpB6Yi0uDZs2cICQlBRkZGhSYC9PtEaiIkJAQ///wzWrZsCWtrawgEAty5cwcPHz5EcHAwfvrpJ+zatQvJyckS+zv25MkTzJw5ExcuXED522iBQIABAwZg586daNasGduANcRxHAoLC/Hw4UPo6emhQYMGrCMRIvWoGEcIIYQ3kpOTMW7cOMTExIiM0/YvUh2Wlpa4cuUKNDU1sXLlSixYsAAqKiqsY9VIr169qjw7USAQ4OrVqz8o0ffZv38/vLy8kJaWhsjISOjr62PLli0wMDDAsGHDWMcTG9/nceXKFQwdOhQGBgZISkpC69atkZ6eDo7jYGlpyZvfJyJ5njx5Ai8vLyQlJYHjOLRs2RIzZszgXRHrn3/+wePHj8FxHJo3by6xq/mqsnfvXmzevBnJyckAgObNm2PevHmYOnUq42SESD8qxhFCiAxJSUnBli1bkJiYCIFAAFNTUzg7O8PIyIh1NLF06NABtWrVgrOzc6UNHHr27MkoGeETZWVlJCcno2nTppCXl0dWVha0tLRYx5J5u3btgru7O+bNm4c1a9bg/v37MDQ0hJ+fH/z9/REaGso6olikYR4dOnSAra0tVq5cCTU1NcTFxUFbWxt2dnawtbXFzJkzWUckPFNcXIz+/ftj9+7dMDExYR2HAFi6dCk2b96MuXPnonPnzgA+nT+6fft2ODs7Y/Xq1YwTEiLdqBhHCCEy4sKFCxg6dCjatWuHrl27guM43Lx5E3FxcTh9+jT69evHOmKVVFRUEBMTgxYtWrCOQnisc+fOqFOnDrp164YVK1ZgwYIFqFOnTqX3uru7/+B0ssvMzAxr167F8OHDhQUgQ0ND3L9/H7169cLr169ZRxSLNMxDTU0NsbGxMDIygqamJiIiItCqVSvExcVh2LBhSE9PZx2R8JCWlhZu3rxZoYEDYaNBgwbYtm0bxo0bJzJ++PBhzJ07V+Kfq8LCwtCrVy/WMQipMck/DIUQQsi/ws3NDS4uLhUOTnZzc8PixYt5UYyzsrLC06dPpaIYFxwcjMDAwErPY4qOjmaUqvrCw8OxYcMGkdWWCxcuRPfu3VlH+yo/Pz8sW7YMf/31FwQCAc6dO1fp+XACgUCii3EeHh6YO3cuVFVVq7z377//xuvXrzF48OAfkKxm0tLSYGFhUWFcUVER+fn5DBLVjDTMQ1VVFR8+fAAANGnSBCkpKWjVqhUASPwb9K8pLCxEcXGxyBgfzxnl8zzs7e0rbeBA2CgtLYWVlVWF8fbt26OkpIRBouqxtbWFjo4OHB0d4eDgIGwCQghfUDGOEEJkRGJiIgIDAyuMT548GVu2bPnxgWpg7ty5cHZ2xsKFC2Fubl6hgUObNm0YJaseT09P/P7773BwcMCpU6fg6OiIlJQU3LlzB7Nnz2YdT2wHDhyAo6MjRo4cCScnJ+Fqyz59+sDPzw/jx49nHbFSLVq0EHZKlZOTw5UrV6Ctrc04VfUlJCRAX18fP//8M4YOHQorKyvhdtuSkhIkJCQgIiICBw4cQGZmJgICAhgn/jYDAwPExsZCX19fZPzcuXMwMzNjlKr6pGEenTp1wo0bN2BmZobBgwfD1dUV9+7dw/Hjx9GpUyfW8cRWUFCARYsWITAwEDk5ORWu8+WcUWmZx8ePH+Hj44NLly7BysqqwgcJktq0QVpNmDABu3btqvDv7u3tDTs7O0apxPfixQscOHAAfn5+WL58Ofr06YMpU6Zg+PDh32xoRIikoG2qhBAiI3R1dbFp0yb8/PPPIuOBgYFYsGABMjIyGCUTn5ycXIUxgUDAuwYOLVu2xLJlyzBu3DiRbWzu7u7Izc3F9u3bWUcUi6mpKaZPnw4XFxeR8U2bNmHPnj1ITExklEx2xMfHY8eOHQgKCsLbt28hLy8PRUVFFBQUAAAsLCwwffp0ODg4QFFRkXHab9u3bx+WLl2KjRs3YsqUKfDx8UFKSgrWrVsHHx8f/PLLL6wjikUa5pGamor379+jTZs2KCgowIIFCxAREQFjY2Ns3ry5QqFRUs2ePRuhoaFYuXIl7O3tsWPHDjx//hy7d++Gh4cHLwoOgPTMw8bG5qvX+NRoRlrMnTsXAQEB0NXVFRbZb926hadPn8Le3l7kA09JL5TGxsbC19cXhw8fRllZGezs7DBlyhS0bduWdTRCvoqKcYQQIiNWrlyJzZs3w83NDV26dIFAIEBERAT++OMPuLq6YsmSJawjVunJkyffvM6XN4gqKipITEyEvr4+tLW1cenSJbRt2xbJycno1KlTpSsfJJGioiIePHgAY2NjkfHHjx+jdevWKCoqYpRM9nAch/j4eKSnp6OwsBANGjRAu3bt0KBBA9bRqmXPnj1YvXo1nj59CgDQ0dHB8uXLMWXKFMbJqkda5sF3enp6CAgIQK9evVC3bl1ER0fD2NgY+/fvx+HDh3H27FnWEcUiLfMgkuVbxdHP8aVQ+uLFC3h7e8PDwwO1atVCUVEROnfuDC8vL+E2e0IkCW1TJYQQGbF06VKoqalh48aN+O233wB8Ogto+fLlcHJyYpxOPHwptlWlUaNGyMnJgb6+PvT19XHr1i20bdsWaWlp4NNnZLq6urhy5UqFYtyVK1fo7JYfTCAQoG3btrxfBTBt2jRMmzYNr1+/RllZGS+3DwPSMw8AeP/+PcrKykTG+HJGWW5uLgwMDAB8ypybmwsA6NatG686wkrLPMo9fvwYKSkp6NGjB5SVlYWr28mPxYfOzlUpLi7GqVOn4OvrK9z+vH37dowbNw65ublYvHgxfv75ZyQkJLCOSkgFVIwjhBAZUFJSgoMHD2LcuHFwcXHBu3fvAHzqmEd+vN69e+P06dOwtLTElClT4OLiguDgYNy9excjR45kHU9srq6ucHJyQmxsrMhqSz8/P2zdupV1PMJjfFvR9zV8nUdaWhrmzJmDsLAwkRWufDsSwNDQEOnp6dDX14eZmRkCAwPRoUMHnD59GhoaGqzjiU1a5pGTk4MxY8YgNDQUAoEAycnJMDQ0xNSpU6GhoYGNGzeyjkh4ZO7cuTh8+DCAT+ff/fnnn2jdurXwuqqqKjw8PNCsWTNGCQn5NtqmSgghMuLzrZGErbKyMpSVlQk7eAYGBgrPY/r11195dfDwiRMnsHHjRuH5cOXdVIcNG8Y4GeEjaekyzPd5dOnSBQDg7OyMhg0bVli11LNnTxaxqm3z5s2Ql5eHk5MTQkNDMXjwYJSWlqKkpASbNm2Cs7Mz64hikZZ52Nvb49WrV/Dx8YGpqanwvNSLFy/CxcUFDx48YB2R8EifPn0wdepUjBo16quvm0pKSnDjxg3ePGcR2ULFOEIIkRE2NjZwdnbG8OHDWUchhJAKPu8yvGfPngpdhtesWcM6olikYR516tRBVFQUWrRowTrKvyojIwN3796FkZERr7d083UejRo1woULF9C2bVuR5kVpaWkwNzfH+/fvWUckPHLt2jV06dJF+MFmuZKSEty8eRM9evRglIwQ8dA2VUIIkRGzZs2Cq6srnj17hvbt20NVVVXkeps2bRglk03Xr1/H7t27kZKSguDgYOjo6GD//v0wMDBAt27dWMcTi6GhIe7cuYP69euLjL958waWlpZITU1llOzbNDU1xT6fqPxsJvLf27lzJ7y9vTFu3Dj4+/tj0aJFIl2G+UIa5mFtbY2nT59KXTFOT08Penp6rGN8N77OIz8/HyoqKhXGX79+LfHdnonksbGxQWZmZoUzOd++fQsbGxvebKcnsouKcYQQIiPGjh0LACLNGgQCAe/OAAKAqKgoJCYmQiAQwNTUFJaWlqwjVcuxY8cwceJE2NnZISYmBh8+fAAAvHv3DmvXruVNZ7z09PRKf28+fPiA58+fM0gkni1btrCO8J/h88HoGRkZwu2RysrKwrMtJ06ciE6dOmH79u0s44lNGubh4+ODX3/9Fc+fP0fr1q2hoKAgcl2SP7zx9PQU+15Jbl7k6emJ6dOnQ0lJqco5SfI8PtejRw8EBARg1apVAD69BikrK8P69evF7uxJSLmv/X3Lycmp8IEzIZKIinGEECIj0tLSWEf4bq9evcIvv/yCsLAwaGhogOM44SegR44cgZaWFuuIYlm9ejW8vLxgb2+PI0eOCMe7dOmClStXMkwmnpCQEOH3Fy5cgLq6uvDn0tJSXLlyRaIPTHZwcGAd4V+Xk5ODsWPH4urVq7w9GF1augxLwzyys7ORkpICR0dH4RhfPrzZvHmzyM/Z2dkoKCgQNjp48+YNVFRUoK2tLdFFrM2bN8POzg5KSkoV5vQ5gUAg0fP43Pr169GrVy/cvXsXHz9+xKJFi/DgwQPk5ubixo0brOMRnihvdCUQCDBp0iSRVZWlpaWIj48XfiBCiCSjYhwhhMgIaWjcMHfuXOTl5eHBgwcwNTUFACQkJMDBwQFOTk7CrlqSLikpqdKzTOrWrYs3b978+EDVVH7uoEAgqFDYUlBQQLNmzXhR/CmXkpKCffv2ISUlBVu3boW2tjbOnz8PXV1dtGrVinU8sbi4uKBWrVrIyMgQPjaATytiXVxcePH/IS1dhqVhHpMnT4aFhQUOHz5caQMHSfb5B0+HDh3Czp07sXfvXuGW26SkJEybNg0zZsxgFVEsn89DGj5MAwAzMzPEx8dj165dkJeXR35+PkaOHInZs2ejcePGrOMRnij/AJDjOKipqUFZWVl4rXbt2ujUqROmTZvGKh4hYqMGDoQQIkP2798PLy8vpKWlITIyEvr6+tiyZQsMDAx40f1SXV0dly9fhrW1tcj47du30b9/f14UsgDAyMgIu3fvRt++fUUOsQ4ICICHhwcSEhJYRxSLgYEB7ty5gwYNGrCOUmPh4eEYOHAgunbtimvXriExMRGGhob4888/cfv2bQQHB7OOKBZpOBhdWroMS8M8VFVVERcXB2NjY9ZRvouRkRGCg4NhYWEhMh4VFYXRo0fzsshV/taNTwVSQv4LK1aswIIFC2hLKuEtOdYBCCGE/Bi7du3C/PnzMWjQILx580a4zUhDQ4M3Z2iVlZVVOLsI+LQaq6ysjEGimpkxYwacnZ3x999/QyAQ4MWLFzh48CAWLFiAWbNmsY4ntrS0NF4X4gDAzc0Nq1evxqVLl0SKJDY2NoiMjGSYrHqk4WB0OTk5ka54Y8aMgaenJ5ycnHhRwConDfPo3bs34uLiWMf4bpmZmSguLq4wXlpaipcvXzJIVHN79+5F69atoaSkBCUlJbRu3Ro+Pj6sY1WLgYEBli5diqSkJNZRiBRYtmwZVFVV8erVK1y/fh0RERF49eoV61iEiI2KcYQQIiO2bduGPXv24Pfff4e8vLxw3MrKCvfu3WOYTHy9e/eGs7MzXrx4IRx7/vw5XFxc0KdPH4bJqmfRokUYPnw4bGxs8P79e/To0QNTp07FjBkzMGfOHNbxxObk5FTpweLbt2/HvHnzfnygGrh37x5GjBhRYVxLSws5OTkMEtVM+cHo5ehgdPI9hgwZAhcXFyxfvhzHjh1DSEiIyBdf9OnTB9OmTcPdu3eFK8ru3r2LGTNmoG/fvozTiW/p0qVwdnbGkCFDEBQUhKCgIOH/0ZIlS1jHE9vcuXNx/vx5mJqaon379tiyZQsyMzNZxyI8lZeXh4kTJ0JHRwc9e/ZEjx49oKOjgwkTJuDt27es4xFSJdqmSgghMkJZWRkPHz6Evr6+yDa25ORktGnTBoWFhawjVunp06cYNmwY7t+/D11dXQgEAmRkZMDc3BynTp1C06ZNWUesUmlpKSIiImBubg4lJSUkJCSgrKwMZmZmqFOnDut41aKjo4OQkBC0b99eZDw6OhpDhw7Fs2fPGCUTX9OmTREYGIguXbqIPC5OnDiBBQsWICUlhXVEsSQkJKBXr15o3749rl69iqFDh4ocjG5kZMQ6IuERObmvf14v6Q0cPpednQ0HBwecP39euKq6pKQEAwYMgJ+fH7S1tRknFE+DBg2wbds2jBs3TmT88OHDmDt3Ll6/fs0oWc08evQIBw8exJEjR5CamgobGxtMmDAB9vb2rKMRHhkzZgxiY2Oxbds2dO7cGQKBADdv3oSzszPatGmDwMBA1hEJ+SYqxhFCiIwwMzPDunXrMGzYMJGig6enJ/z9/REVFcU6otguXbqEhw8fguM4mJmZ8WqFAwAoKSkhMTERBgYGrKN8FyUlJdy/f7/CuVKPHz9G69atUVRUxCiZ+BYtWoTIyEgEBQXBxMQE0dHRePnyJezt7WFvb49ly5axjii2rKws7Nq1C1FRUSgrK4OlpSUdjE4IPhV/yv9mmJqawsTEhHWkatHU1MTt27fRvHlzkfFHjx6hQ4cOvDkvtTK3bt3CzJkzER8fz5siL5EMqqqquHDhArp16yYyfv36ddja2iI/P59RMkLEQ91UCSFERixcuBCzZ89GUVEROI7D7du3cfjwYaxbt453587069cP/fr1Yx2jxszNzZGamsr7YpyxsTHOnz9fYWvtuXPnYGhoyChV9axZswaTJk2Cjo6OsLhbWlqK8ePH82r7F/CpicOKFStYxyBE4piYmPCuAPe5CRMmYNeuXdi0aZPIuLe3N+zs7Bil+j63b9/GoUOHcPToUbx9+xajR49mHYnwTP369YWdVT+nrq4OTU1NBokIqR5aGUcIITJkz549WL16NZ4+fQrg0zbD5cuXY8qUKYyTfZ2npyemT58OJSWlSs8n+5yTk9MPSvV9Ll68iMWLF2PVqlVo3759hU5gdevWZZSsenx9fTFnzhwsXLgQvXv3BgBcuXIFGzduxJYtWzBt2jTGCcWXkpKCmJgYlJWVwcLCosIKFEl37dq1b17v0aPHD0pC+Epanmvnz5+PVatWQVVVFfPnz//mvV8WtyTV3LlzERAQAF1dXXTq1AnApxVlT58+hb29vUhjI0meU/n21EOHDiE9PR02Njaws7PDyJEjoaamxjoe4Rlvb28EBQUhICBAuAI8KysLDg4OGDlyJGbMmME4ISHfRsU4QgiRQa9fv0ZZWRkvzssxMDDA3bt3Ub9+/W+uJBMIBEhNTf2ByWru8/OYBAKB8HuO43h1HhPwqUvvmjVrhE01mjVrhuXLl9PZPz9YZWd8ff67xZffqeDgYAQGBiIjIwMfP34UuRYdHc0oVfXxcR7S8lxrY2ODEydOQEND45vNSwQCAa5evfoDk9WcuE1YJH1OcnJysLKywvjx4/HLL7+gUaNGrCMRHrOwsMDjx4/x4cMH6OnpAQAyMjKgqKhY4QM1SX3eJbKNtqkSQoiMSEtLQ0lJCZo3b44GDRoIx5OTk6GgoIBmzZqxC/cNaWlplX7PZ6Ghoawj/GtmzpyJmTNnIjs7G8rKyrxoQlHVapnPSfIqk8/9888/Ij8XFxcjJiYGS5cuxZo1axilqh5PT0/8/vvvcHBwwKlTp+Do6IiUlBTcuXMHs2fPZh1PbHydh7Q8137+/Cotz7XSMo+HDx/yerswkSzDhw9nHYGQ70Ir4wghREb07NkTkydPhoODg8j4gQMH4OPjg7CwMDbBiIjY2Fi0a9eOdQyxlZSUICwsDCkpKRg/fjzU1NTw4sUL1K1bV2ILc1+uMomKikJpaSlatGgB4NNWKnl5eWFnUj67du0aXFxceNGgpWXLlli2bBnGjRsn0mTG3d0dubm52L59O+uIYpGGeRQWFkJZWbnSa5mZmdQUhHyXjx8/4tWrVygrKxMZL1/dRAghsoCKcYQQIiPq1q2L6OjoSjtfWllZ8aIb2+jRo2FlZQU3NzeR8fXr1+P27dsICgpilOz7vH37FgcPHoSPjw/i4uJ4s6XwyZMnsLW1RUZGBj58+IBHjx7B0NAQ8+bNQ1FREby8vFhHrNKmTZsQFhYGf39/4YHP//zzDxwdHdG9e3e4uroyTvh9EhMTYW1tjffv37OOUiUVFRUkJiZCX18f2trauHTpEtq2bYvk5GR06tQJOTk5rCOKRRrm0bJlSxw6dAiWlpYi48HBwcKVsJJq5MiRYt97/Pjx/zDJv6eoqAjbtm1DaGhopUUsvmzBS05OxuTJk3Hz5k2RcT4e0UAky/v37ys8Lvhy/i6RXbRNlRBCZIRAIMC7d+8qjL99+5Y3L4DDw8OxbNmyCuO2trbYsGEDg0Tf5+rVq/D19cXx48ehr6+PUaNGYe/evaxjic3Z2RlWVlaIi4tD/fr1heMjRozA1KlTGSYT38aNG3Hx4kWRzmuamppYvXo1+vfvz5tiXHx8vMjPHMchMzMTHh4eaNu2LaNU1dOoUSPk5ORAX18f+vr6uHXrFtq2bYu0tDTw6bNjaZhHv3790KVLFyxfvhyLFy9Gfn4+5syZg6CgIHh4eLCO902fd1fkOA4nTpyAuro6rKysAHxaCfvmzZtqFe1Ymzx5Mi5duoTRo0ejQ4cOIudB8smkSZNQq1Yt/PXXX2jcuDFv50EkQ1paGubMmYOwsDAUFRUJx6m4S/iCinGEECIjunfvjnXr1uHw4cOQl5cH8OlQ93Xr1qFbt26M04nn/fv3qF27doVxBQUF5OXlMUj0bdnZ2dDS0hIZe/bsGfz8/ODr64v8/HyMGTMGxcXFOHbsGMzMzBglrZmIiAjcuHGjwv+Jvr4+nj9/zihV9eTl5eHly5do1aqVyPirV68qLV5Lqnbt2kEgEFQo9nTq1Am+vr6MUlVP7969cfr0aVhaWmLKlClwcXFBcHAw7t69y6vCiTTMY9u2bRg8eDAcHR1x5swZ4dbzO3fuSPzz1L59+4TfL168GGPGjIGXl5fI371Zs2bxatXMmTNncPbsWXTt2pV1lO8SGxuLqKgotGzZknUUIgXs7OwAfOrs3rBhQyruEt6hYhwhhMiIP//8Ez169ECLFi3QvXt3AMD169eRl5fHm3OxWrdujaNHj8Ld3V1k/MiRIxL5BtHb2xvPnj2Dp6cnFBQUMGjQIEREROCnn37Ctm3bYGtrC3l5eV5s56xMWVlZpZ88P3v2DGpqagwSVd+IESPg6OiIjRs3olOnTgCAW7duYeHChbwpnAAVD9yXk5ODlpYWlJSUGCWqPm9vb+E2o19//RX16tVDREQEhgwZgl9//ZVxOvFJyzz69++PkSNHYteuXahVqxZOnz4tkc+z3+Lr64uIiAhhIQ4A5OXlMX/+fHTp0gXr169nmE58Ojo6vHlO/RYzMzO8fv2adQwiJeLj4xEVFSU875UQvqEz4wghRIa8ePEC27dvR1xcHJSVldGmTRvMmTMH9erVYx1NLCEhIRg1ahTGjx+P3r17AwCuXLmCw4cPIygoSOI6a3348AFLly5FgwYNsGjRItSqVQtOTk6YOXMmmjdvLrxPQUEBcXFxvHujO3bsWKirq8Pb2xtqamqIj4+HlpYWhg0bBj09PZEVKpKqoKAACxYsgK+vL4qLiwEAtWrVwpQpU7B+/XqoqqoyTkjIj1fekCUrKws+Pj4IDw/Hhg0b4OTkhDVr1kBBQYF1RLFoampi3759Ff42nDx5Eo6OjhW6EEuqc+fOwdPTE15eXtDX12cdp8auXr2KJUuWYO3atTA3N6/we8Sn1YqEPRsbG/z+++/o27cv6yiE1AgV4wghhPDKmTNnsHbtWsTGxgoLisuWLUPPnj1ZR/uqjx8/onbt2oiMjISvry8CAwPRsmVLTJw4EWPHjkWTJk14WYx78eIFbGxsIC8vj+TkZFhZWSE5ORkNGjTAtWvXoK2tzTqi2PLz85GSkgKO42BsbMy7Ipynp2el4wKBAEpKSjA2NkaPHj1EVghJgi/PuvuWNm3a/IdJvo+0zKOcmpoaBg8eDC8vL2hoaAAAbt68CXt7e6ipqSEmJoZtQDHNnz8ffn5++N///iey8tXDwwP29vbYtGkT44Tiyc7OxpgxY3Dt2jWoqKhUKGLl5uYySlY9cnJywu8/31JIZ3yRmkhJScGvv/6KCRMmoHXr1hUeF3x4riWyjYpxhBAiI65du/bN6z169PhBSUhBQQGOHDkCX19f3L59G6Wlpdi0aRMmT57Mu61IhYWFOHz4MKKjo1FWVgZLS0vY2dlBWVmZdTSZYmBggOzsbBQUFEBTUxMcx+HNmzdQUVFBnTp18OrVKxgaGiI0NBS6urqs4wrJyclVetbdlyT9jbq0zKPc/v37MXHixArj7969w7x583jTaKasrAwbNmzA1q1bkZmZCQBo3LgxnJ2d4erqKnHF6a/p27cvMjIyMGXKlErPxnJwcGCUrHrCw8O/eV2SP1QjkufWrVsYP3480tPThWPlz8N8ea4lso2KcYQQIiM+/0S63Ocv6OlFCxtJSUnYu3cv9u/fjzdv3qBfv34ICQlhHYvwzOHDh+Ht7Q0fHx8YGRkBAB4/fowZM2Zg+vTp6Nq1K3755Rc0atQIwcHBjNP+nydPnoh9ryRvz5OWeUiTkpISHDx4EAMGDECjRo2ETX74uBVSRUUFkZGRvOmM/C3Xr1/H7t27kZKSguDgYOjo6GD//v0wMDDgTTMpIhnMzMxgamqKRYsWVVqkpudaIumoGEcIITLi7du3Ij8XFxcjJiYGS5cuxZo1a9CnTx9Gyb6tXr16ePToERo0aABNTc1vdsviy1adypSWluL06dPw9fXlVTEuKSkJ27ZtQ2JiIgQCAVq2bIk5c+ZQt7wfzMjICMeOHUO7du1ExmNiYjBq1Cikpqbi5s2bGDVqlHCFECFVSUhIQEZGBj5+/CgcEwgEGDJkCMNU4lNRUUFiYiLv35RbWlpi586dwq22fHXs2DFMnDgRdnZ22L9/PxISEmBoaIidO3fir7/+wtmzZ1lHJDyiqqqKuLg4GBsbs45CSI1QN1VCCJER6urqFcb69esHRUVFuLi4ICoqikGqqm3evFm4dXPz5s1S27peXl4ew4cPl7gmFN8SHByMcePGwcrKCp07dwbwaduIubk5Dh06hJ9//plxQtmRmZmJkpKSCuMlJSXIysoCADRp0gTv3r370dEID7x9+1bkb0RqaipGjhyJ+Ph4ke235c+/fFlJ3bFjR8TExPC+GOfh4QFXV1esWbOG140PVq9eDS8vL9jb2+PIkSPC8S5dumDlypUMkxE+6t27NxXjCK/RyjhCCJFxiYmJsLa2xvv371lHITxjaGiICRMmVHgTtWzZMuzfvx+pqamMksmewYMHCztfWlhYAPi0Km7atGlo1KgR/vrrL5w+fRr/+9//cO/ePcZpiaRZtWoVlJSUsHDhQgAQrnzz8fFBu3bt8PfffyMtLQ0LFizApk2b0L17d5ZxxRYUFAQ3Nze4uLigffv2FRqz8OWA9/JjJr78MIpvZ2OpqKggISEBzZo1g5qaGuLi4mBoaIjU1FSYmZmhqKiIdUTCI97e3li9ejUmT55caZF66NChjJIRIh4qxhFCiIz4stsfx3HIzMyEh4cHiouLcePGDUbJxGdjY4MJEyZg9OjRla70Iz+WiooK4uPjK3wqnZycjLZt26KgoIBRsm+rzjZgvryYz8rKwsSJE3HlyhXhG5KSkhL06dMH+/fvR8OGDREaGori4mL079+fcVoiaV69eoWJEyfC2NgYO3bsQIMGDXDlyhW0bdsWOjo6uH37NnR0dHDlyhUsWLCAN91Uv3ZWKt+KWNLS+MDIyAi7d+9G3759RYpxAQEB8PDwQEJCAuuIhEcqe3yX49Pjm8gu2qZKCCEyol27dpV2++vUqRN8fX0Zpaoec3NzLFmyBHPmzMGgQYMwceJEDBo0CLVr12YdTSb16tUL169fr1CMi4iIkOiVM19uBf7yccHHxiaNGjXCpUuX8PDhQzx69Agcx6Fly5Zo0aKF8B4bGxuGCYkk09bWxoULF7Bu3ToAn37vy48HaNCgAV68eAEdHR00a9YMSUlJLKNWS1paGusI/wq+FNuqMmPGDDg7O8PX1xcCgQAvXrxAZGQkFixYAHd3d9bxCM+UlZWxjkDId6GVcYQQIiO+7PYnJycHLS0tKCkpMUpUM2VlZbh8+TIOHTqEEydOQF5eHqNHj4adnZ3UvGHhCy8vL7i7u2PMmDHCg8Vv3bqFoKAgrFixAk2aNBHeK6krzC5fvozFixdj7dq16Ny5MwQCAW7evIklS5Zg7dq16NevH+uI1fLx40ekpaXByMgItWrx7zPXN2/eIDg4GCkpKVi4cCHq1auH6OhoNGzYEDo6OqzjiY3v8+jevTvmz5+PESNGYPr06cjOzsbChQvh5eWF6Oho3L9/n3VEqRcfH4/WrVtDTk6uwsr2L/Fluy0A/P7779i8ebNwS6qioiIWLFiAVatWMU5GCCE/FhXjCCFEBpRvTdu9ezdMTExYx/nXFBUV4fTp01izZg3u3bvHm1VM0uJbW0Q+J8nbRVq3bg0vLy9069ZNZPz69euYPn06EhMTGSWrnoKCAsydOxf+/v4AgEePHsHQ0BBOTk5o0qQJ3NzcGCesWnx8PPr27Qt1dXWkp6cjKSkJhoaGWLp0KZ48eYKAgADWEcUiDfO4cOEC3r17h9GjR+Pp06cYPHgw7t+/j3r16iEwMBC9e/dmHVFs+/fvh5eXF9LS0hAZGQl9fX1s2bIFBgYGGDZsGOt4XyUnJ4esrCxoa2tDTk6u0pXtgGQ/v35NQUEBEhISUFZWBjMzM9SpU4d1JMIjgwYNwuHDh4XHlaxZswazZ8+GhoYGACAnJwfdu3enbc9E4on3KpoQQgivKSgo4P79+1LViTQrKwteXl74448/EB8fDysrK9aRZE5ZWZlYX5L8RjElJaXS8wfLCyl88dtvvyEuLg5hYWEiq1379u2Lo0ePMkwmvvnz52PSpElITk4WmcPAgQNx7do1hsmqRxrmMWDAAIwePRoAoKuri/j4eLx+/RqvXr2S6ELchQsX8PbtW+HPu3btwvz58zFo0CC8efNG+FykoaGBLVu2MEopnrS0NGhpaQm/T01NRVpaWoUvPjbKUVFRgZWVFTp06ECFOFJtFy5cwIcPH4Q///HHH8jNzRX+XFJSwqvt9ER2UTGOEEJkhL29Pfbu3cs6xnfJy8vDvn370K9fP+jq6mLXrl0YMmQIHj16hL///pt1PIJP2/P4xNraGvPmzUNmZqZwLCsrC66urujQoQPDZNVz8uRJbN++Hd26dRMpupuZmSElJYVhMvHduXMHM2bMqDCuo6ODrKwsBolqRlrm8aV69eqJvRqWlaysLHTt2hXPnj0DAGzbtg179uzB77//Dnl5eeF9VlZWEt9VWF9fX/hY1tfX/+YXIbLkyxWitNGP8BX/DhMhhBBSIx8/foSPjw8uXboEKysrqKqqilzftGkTo2Tia9iwITQ1NTFmzBisXbsW1tbWrCPJtD/++APNmjXD2LFjAQA///wzjh07hsaNG+Ps2bNo27Yt44RV8/X1xYgRI6Cvrw89PT0AQEZGBkxMTHDy5Em24aohOzsb2traFcbz8/N5syJWSUkJeXl5FcaTkpKEK4T4QBrmUVRUhG3btiE0NBSvXr2qcFB6dHQ0o2Tf5uDgADU1Ndja2uL+/ftIS0uDhYVFhfsUFRWRn5/PIKH4pLHrMyGEkP9DxThCCJER9+/fh6WlJYBP50l9jg9v1jmOw9atWzFhwgSoqKiwjkMA7N69GwcOHAAAXLp0CZcvX8b58+cRGBiIhQsX4uLFi4wTVs3Y2Bjx8fHCTqQcx8HMzAx9+/blxeOinLW1Nc6cOYO5c+cC+L/H9J49e9C5c2eW0cQ2bNgwrFy5EoGBgQA+zSEjIwNubm4YNWoU43Tik4Z5TJ48GZcuXcLo0aPRoUMHXj0WRo4cKSzAGRgYIDY2tsLqsXPnzsHMzIxFPLF92fX5a/h4Zhwh30MgEFR4TuLTcxQh5aiBAyGEEF4oKyuDkpISHjx4gObNm7OOQwAoKyvj0aNH0NXVhbOzM4qKirB79248evQIHTt2xD///MM6osy4efMmbG1tYWdnBz8/P8yYMQMPHjxAZGQkwsPD0b59e9YRq5SXl4dBgwbhwYMHePfuHZo0aYKsrCx07twZZ8+erbCaV1JJwzzU1dVx9uxZdO3alXWU77Jv3z4sXboUGzduxJQpU+Dj44OUlBSsW7cOPj4++OWXX1hHJIRUk5ycHAYOHAhFRUUAwOnTp9G7d2/hc+uHDx9w/vx5KlITiUcr4wghREa8ffsWpaWlqFevnsh4bm4uatWqhbp16zJKJh45OTk0b94cOTk5VIyTEJqamnj69Cl0dXVx/vx5rF69GsCnVYx8eRG8cuXKb153d3f/QUm+T5cuXXDjxg1s2LABRkZGuHjxIiwtLREZGQlzc3PW8cRSt25dRERE4OrVq4iOjkZZWRksLS3Rt29f1tGqRRrmoaOjAzU1NdYxvpujoyNKSkqwaNEiFBQUYPz48dDR0cHWrVupEEcITzk4OIj8PGHChAr32Nvb/6g4hNQYrYwjhBAZMXDgQAwZMgSzZs0SGffy8kJISAjOnj3LKJn4zpw5Aw8PD+zatQutW7dmHUfmzZkzB3/99ReaN2+OmJgYpKeno06dOjh69Cj++OMPiT1X6nNfnidVXFyMtLQ01KpVC0ZGRryYAyH/tnPnzsHT0xNeXl5S0yDg9evXKCsrq/RsRUIIIeRHo2IcIYTIiHr16uHGjRswNTUVGX/48CG6du2KnJwcRsnEp6mpiYKCApSUlKB27dpQVlYWuf55a3vy3ysuLsbWrVvx9OlTTJo0SVjY2rJlC+rUqYOpU6cyTlgzeXl5mDRpEkaMGIGJEyeyjiO20tJSnDhxAomJiRAIBDA1NcWwYcNQq5bkboTw9PQU+14nJ6f/MMn3kZZ5lMvOzsaYMWNw7do1qKioQEFBQeQ6355rX716haSkJAgEArRo0YI3jTQIIYRILyrGEUKIjFBVVcWtW7cqbFm7d+8eOnbsiIKCAkbJxOfv7//N619uXSCkpu7fv4+ffvoJ6enprKOI5f79+xg2bBiysrLQokULAJ8atWhpaSEkJERit6oaGBiI/JydnY2CggJoaGgAAN68eQMVFRVoa2sjNTWVQULxSMs8yvXt2xcZGRmYMmUKGjZsWOFwdL481+bl5WH27Nk4fPiwsCOsvLw8xo4dix07dkBdXZ1xQkIIIbKKinGEECIjevXqBXNzc2zbtk1kfPbs2YiPj8f169cZJSN8l5CQgIyMDHz8+FFkfOjQoYwSfb+IiAgMGTKEN00oOnXqBG1tbfj7+0NTUxMA8M8//2DSpEl49eoVIiMjGSes2qFDh7Bz507s3btXWFBMSkrCtGnTMGPGDNjZ2TFOKB5pmIeKigoiIyPRtm1b1lG+y5gxYxAbG4tt27ahc+fOEAgEuHnzJpydndGmTRthx1tCCCHkR6NiHCGEyIgbN26gb9++sLa2Rp8+fQAAV65cwZ07d3Dx4kV0796dcULxpKSkYN++fUhJScHWrVuhra2N8+fPQ1dXF61atWIdT6akpqZixIgRuHfvHgQCAcpfUpSvouFDE4cvtxdyHIfMzEzs378fPXr0wOHDhxklqx5lZWXcvXu3wmPg/v37sLa2RmFhIaNk4jMyMkJwcHCFc/yioqIwevRopKWlMUpWPdIwD0tLS+zcuROdOnViHeW7qKqq4sKFC+jWrZvI+PXr12Fra4v8/HxGyWqusLAQxcXFImOS3oCJEEJIRXKsAxBCCPkxunbtisjISOjq6iIwMBCnT5+GsbEx4uPjeVOICw8Ph7m5Of7++28cP34c79+/BwDEx8dj2bJljNPJHmdnZxgYGODly5dQUVHBgwcPcO3aNVhZWSEsLIx1PLFs3rxZ5MvT0xNhYWFwcHCAt7c363hia9GiBV6+fFlh/NWrVzA2NmaQqPoyMzMrFBmAT0XdyuYmqaRhHh4eHnB1dUVYWBhycnKQl5cn8sUX9evXr3Qrqrq6unAFKR8UFBRgzpw50NbWRp06daCpqSnyRQghhH9oZRwhhBDe6Ny5M37++WfMnz8fampqiIuLg6GhIe7cuYPhw4fj+fPnrCPKlAYNGuDq1ato06YN1NXVcfv2bbRo0QJXr16Fq6srYmJiWEeUGWfPnsWiRYuwfPly4WqmW7duYeXKlfDw8BBZGSSpq2iGDBmCjIwM7N27F+3bt4dAIMDdu3cxbdo06OrqIiQkhHVEsUjDPOTkPn1e/+VZcRzHQSAQ8GLVKwB4e3sjKCgIAQEBaNy4MQAgKysLDg4OGDlyJGbMmME4oXhmz56N0NBQrFy5Evb29tixYweeP3+O3bt3w8PDgxdbnwkhhIiiYhwhhBDeqFOnDu7duwcDAwORYlx6ejpatmyJoqIi1hFliqamJqKiomBoaAgjIyP4+PjAxsYGKSkpMDc350VTkMmTJ2Pr1q1QU1MTGc/Pz8fcuXPh6+vLKFn1lBdPgP8roHy5bVjSCynZ2dlwcHDA+fPnhd07S0pKMGDAAPj5+UFbW5txQvFIwzzCw8O/eb1nz54/KMn3sbCwwOPHj/Hhwwfo6ekBADIyMqCoqIjmzZuL3BsdHc0iolj09PQQEBCAXr16oW7duoiOjoaxsTH279+Pw4cP4+zZs6wjEkIIqSbJ7XVPCCGEfEFDQwOZmZkVOhfGxMRAR0eHUSrZ1bp1a8THx8PQ0BAdO3bEn3/+idq1a8Pb2xuGhoas44nF398fHh4eFYpxhYWFCAgI4E0xLjQ0lHWE76alpYWzZ88iOTkZiYmJ4DgOpqamMDExYR2tWqRhHnwptlVl+PDhrCP8K3Jzc4V/9+rWrYvc3FwAQLdu3TBz5kyW0QghhNQQFeMIIYTwxvjx47F48WIEBQVBIBCgrKwMN27cwIIFC2Bvb886nsxZsmSJ8AD01atX46effkL37t1Rv359HD16lHG6b8vLywPHceA4Du/evYOSkpLwWmlpKc6ePcuLFUzlpKV4AgDNmzevsGqJj6RlHnwmLWeJlq8A19fXh5mZGQIDA9GhQwecPn0aGhoarOMRQgipAdqmSgghhDeKi4sxadIkHDlyBBzHoVatWigtLcX48ePh5+cHeXl51hFlXm5uLjQ1NSucNSVp5OTkvplRIBBgxYoV+P33339gqpo7f/486tSpIzwbbseOHdizZw/MzMywY8cOOuSdEB7bvHkz5OXl4eTkhNDQUAwePBilpaUoKSnBpk2b4OzszDoiIYSQaqJiHCGEEN5JTU1FdHQ0ysrKYGFhQatPSLWFh4eD4zj07t0bx44dQ7169YTXateuDX19fTRp0oRhwuoxNzfHH3/8gUGDBuHevXuwsrKCq6srrl69ClNTU+zbt491RELIvyQjIwN3796FkZER2rZtyzoOIYSQGqBiHCGESLGRI0eKfe/x48f/wyT/jdLSUty7dw/6+vq08ofUyJMnT6CnpyfxK/mqUqdOHdy/fx/NmjXD8uXLcf/+fQQHByM6OhqDBg1CVlYW64iEEEIIIeT/ozPjCCFEiqmrq7OO8K+aN28ezM3NMWXKFJSWlqJnz564efMmVFRU8Ndff6FXr16sIxIeiI+PR+vWrSEnJ4e3b9/i3r17X723TZs2PzBZzdWuXVvYvfby5cvCMxTr1auHvLw8ltEIITXg6ekp9r1OTk7/YRJCCCH/BVoZRwghhDeaNm2KkydPwsrKCidPnsSsWbMQFhaGgIAAhIaG4saNG6wjEh6Qk5NDVlYWtLW1hWfHVfZySCAQoLS0lEHC6hs6dCg+fvyIrl27YtWqVUhLS4OOjg4uXryIOXPm4NGjR6wjiuX69evYvXs3UlJSEBwcDB0dHezfvx8GBgbC8/D4oqCgABkZGfj48aPIuKQWeC0sLMReIRodHf0fp/l3ffz4EWlpaTAyMkKtWvxYi/Bl1/Ds7GwUFBQIGza8efMGKioq0NbWRmpqKoOEhBBCvocc6wCEEEJ+nJKSEly+fBm7d+/Gu3fvAAAvXrzA+/fvGScTz+vXr9GoUSMAwNmzZzFmzBiYmJhgypQp31zdRMjn0tLSoKWlJfw+NTUVaWlpFb749AZ3+/btqFWrFoKDg7Fr1y7o6OgAAM6dOwdbW1vG6cRz7NgxDBgwAMrKyoiJicGHDx8AAO/evcPatWsZpxNfdnY2fvrpJ6ipqaFVq1awsLAQ+ZJUw4cPx7BhwzBs2DAMGDAAKSkpUFRURK9evdCrVy8oKSkhJSUFAwYMYB1VbAUFBZgyZQpUVFTQqlUrZGRkAPi0kszDw4Nxum/7/LlozZo1aNeuHRITE5Gbm4vc3FwkJibC0tISq1atYh2VEEJIDdDKOEIIkRFPnjyBra0tMjIy8OHDBzx69AiGhoaYN28eioqK4OXlxTpilfT19bFnzx706dMHBgYG2LlzJ3766Sc8ePAA3bp1wz///MM6oszZv38/vLy8kJaWhsjISOjr62PLli0wMDDAsGHDWMcjPGJhYQEXFxfY29tDTU0NcXFxMDQ0RGxsLGxtbXlz7p2dnR3S09OxZcsW2NjY4MSJE3j58iVWr16NjRs3YvDgwawjVmnq1Klo3LhxhULPsmXL8PTpU/j6+jJKVj3Ozs64ceMGtmzZAltbW8THx8PQ0BAhISFYtmwZYmJiWEcUi5GREYKDgysUc6OiojB69GikpaUxSkYIIaSm+LFOmxBCyHdzdnaGlZUV4uLiUL9+feH4iBEjMHXqVIbJxOfo6IgxY8agcePGEAgE6NevHwDg77//RsuWLRmnkz27du2Cu7s75s2bhzVr1gi3dGpoaGDLli28KMaFhIRUOi4QCKCkpARjY+MK28UkVUpKCvbt24eUlBRs3boV2traOH/+PHR1ddGqVSvW8aqUlJSEHj16VBivW7cu3rx58+MD1dDVq1dx6tQpWFtbQ05ODvr6+ujXrx/q1q2LdevW8aIYFxQUhLt371YYnzBhAqysrHhTjDt58iSOHj2KTp06iWzBNTMzQ0pKCsNk1ZOZmYni4uIK46WlpXj58iWDRIQQQr4XFeMIIURGRERE4MaNG6hdu7bIuL6+Pp4/f84oVfUsX74crVu3xtOnT/Hzzz9DUVERACAvLw83NzfG6WTPtm3bsGfPHgwfPlxky5eVlRUWLFjAMJn4hg8fXumZceVjAoEA3bp1w8mTJyW6Y294eDgGDhyIrl274tq1a1izZg20tbURHx8PHx8fBAcHs45YpcaNG+Px48do1qyZyHhERAQMDQ3ZhKqB/Px8aGtrA/jUQCM7OxsmJiYwNzfnzVlrysrKiIiIQPPmzUXGIyIioKSkxChV9WVnZwv/Lz6Xn5/Pqw7Kffr0wbRp07B37160b98eAoEAd+/exYwZM9C3b1/W8QghhNQAnRlHCCEyoqysrNLD6J89ewY1NTUGiWpm9OjRcHFxQdOmTYVjDg4OvFiFJW3S0tIqPQNLUVER+fn5DBJV36VLl2BtbY1Lly7h7du3ePv2LS5duoQOHTrgr7/+wrVr15CTkyPxxUU3NzesXr0aly5dEim429jYIDIykmEy8c2YMQPOzs74+++/IRAI8OLFCxw8eBALFizArFmzWMcTW4sWLZCUlAQAaNeuHXbv3o3nz5/Dy8sLjRs3ZpxOPPPmzcPMmTMxZ84cHDhwAAcOHMCcOXMwe/ZsuLi4sI4nNmtra5w5c0b4c3kBbs+ePejcuTOrWNXm6+sLHR0ddOjQAUpKSlBUVETHjh3RuHFj+Pj4sI5HCCGkBmhlHCGEyIh+/fphy5Yt8Pb2BvDpTcn79++xbNkyDBo0iHE6wkcGBgaIjY2Fvr6+yPi5c+dgZmbGKFX1ODs7w9vbG126dBGO9enTB0pKSpg+fToePHiALVu2YPLkyQxTVu3evXs4dOhQhXEtLS3k5OQwSFR9ixYtwtu3b2FjY4OioiL06NEDioqKWLBgAebMmcM6ntjmzZuHzMxMAJ/OWBswYAAOHjyI2rVrw8/Pj204Mbm5ucHQ0BBbt24V/l6ZmprCz88PY8aMYZxOfOvWrYOtrS0SEhJQUlKCrVu34sGDB4iMjER4eDjreGLT0tLC2bNn8ejRIzx8+BAcx8HU1BQmJiasoxFCCKkhauBACCEy4sWLF7CxsYG8vDySk5NhZWWF5ORkNGjQANeuXat0Kw8h37Jv3z4sXboUGzduxJQpU+Dj44OUlBSsW7cOPj4++OWXX1hHrJKysjLu3LmD1q1bi4zfu3cPHTp0QGFhIZ48eQJTU1MUFBQwSlm1pk2bIjAwEF26dBFpfnDixAksWLCAV+djFRQUICEhAWVlZTAzM0OdOnVYR/qm4uJiKCgofPV6QUEBHj58CD09PTRo0OAHJiPAp8fyhg0bEBUVhbKyMlhaWmLx4sUwNzdnHY0QQogMo2IcIYTIkMLCQhw5ckTkTYmdnR2UlZVZRyM8tWfPHqxevRpPnz4FAOjo6GD58uWYMmUK42Ti6datG9TU1BAQEAAtLS0An86Zsre3R35+Pq5du4bLly9j1qxZePToEeO0X7do0SJERkYiKCgIJiYmiI6OxsuXL2Fvbw97e3ssW7aMdcQqvX37FqWlpahXr57IeG5uLmrVqoW6desySvZtHh4eMDQ05NWKMSL55s+fj1WrVkFVVRXz58//5r2bNm36QakIIYT8W6gYRwghhJDv9vr1a5SVlfFuhWVSUhKGDRuGtLQ06OrqQiAQICMjA4aGhjh16hRMTExw8uRJvHv3DhMnTmQd96uKi4sxadIkHDlyBBzHoVatWigtLcX48ePh5+cHeXl51hGrNHDgQAwZMqTC+XBeXl4ICQnB2bNnGSX7tkePHmHEiBGYPn06nJ2dpaJwUlpais2bNyMwMBAZGRn4+PGjyPXc3FxGyaqWl5cn9r2SWuAFPp33eOLECWhoaMDGxuar9wkEAly9evUHJiOEEPJvoGIcIYQQXklJScG+ffuQkpKCrVu3QltbG+fPn4euri5atWrFOh7hIY7jcOHCBTx69Agcx6Fly5bo168f5OT41+cqJSUFMTExKCsrg4WFRYVumJKsXr16uHHjBkxNTUXGHz58iK5du0r02Xfv3r3DpEmTcOzYMakonLi7u8PHxwfz58/H0qVL8fvvvyM9PR0nT56Eu7s7nJycWEf8Kjk5ObE7pVbW1IgQQgj5EagYRwghhDfCw8MxcOBAdO3aFdeuXUNiYiIMDQ3x559/4vbt2wgODmYdUepZWFiI/UY3Ojr6P05DpImqqipu3bpV4Syve/fuoWPHjhJ9Zp+0MTIygqenJwYPHgw1NTXExsYKx27dulVpsxBJ8XljhvT0dLi5uWHSpEnC7qmRkZHw9/fHunXr4ODgwComIYQQGUfFOEIIIbzRuXNn/Pzzz5g/f77IIfV37tzB8OHD8fz5c9YRpd6KFSuE3xcVFWHnzp0wMzMTvtG9desWHjx4gFmzZmHdunWsYlbLlStXcOXKFbx69QplZWUi13x9fRmlqlpV2yE/x4etkb169YK5uTm2bdsmMj579mzEx8fj+vXrjJLJHlVVVSQmJkJPTw+NGzfGmTNnYGlpidTUVFhYWODt27esI4qlT58+mDp1KsaNGycyfujQIXh7eyMsLIxNMDGMHDlS7HuPHz/+HyYhhBDyX6jFOgAhhBAirnv37lW6IkNLS0uit7BJk88bAUydOhVOTk5YtWpVhXvKGzpIuhUrVmDlypWwsrJC48aNxV71JwliYmJEfo6KikJpaSlatGgB4NNZZvLy8mjfvj2LeNW2Zs0a9O3bF3FxcejTpw+AT4XSO3fu4OLFi4zTia+oqAjbtm1DaGhopQVePqwYbdq0KTIzM6GnpwdjY2NcvHgRlpaWuHPnDhQVFVnHE1tkZCS8vLwqjFtZWWHq1KkMEolPXV1d+D3HcThx4gTU1dVhZWUF4NPj/c2bN9Uq2hFCCJEcVIwjhBDCGxoaGsjMzISBgYHIeExMDHR0dBilkl1BQUG4e/duhfEJEybAyspKoleVlfPy8oKfn59EN2f4mtDQUOH3mzZtgpqaGvz9/aGpqQkA+Oeff+Do6Iju3buzilgtXbt2RWRkJNavX4/AwEAoKyujTZs22Lt3L6/Ovps8eTIuXbqE0aNHo0OHDrwq8JYbMWIErly5go4dO8LZ2Rnjxo3D3r17kZGRARcXF9bxxKarqwsvLy9s3LhRZHz37t3Q1dVllEo8+/btE36/ePFijBkzBl5eXsJmLKWlpZg1a5ZEN6EghBDydbRNlRBCZASfu+OVW7RoESIjIxEUFAQTExNER0fj5cuXsLe3h729vciqLfLfa9SoEdatWwdHR0eR8X379sHNzQ0vX75klEx89evXx+3bt2FkZMQ6ynfR0dHBxYsXKzQxuX//Pvr3748XL14wSiZ71NXVcfbsWXTt2pV1lH/NrVu3cPPmTRgbG2Po0KGs44jt7NmzGDVqFIyMjNCpUycAn+aSkpKCY8eOYdCgQYwTikdLSwsRERHCVa/lkpKS0KVLF1oZTgghPEQr4wghREasWLHim93x+GDNmjWYNGkSdHR0wHEczMzMUFpaivHjx2PJkiWs48mcefPmYebMmYiKihJ5o+vr68ub36mpU6fi0KFDWLp0Keso3yUvLw8vX76sUIx79eoV3r17xyhV9ZWVleHx48eVbu/s0aMHo1TVo6OjAzU1NdYx/lWdOnUSPsb5ZNCgQUhOTsauXbuQmJgIjuMwbNgw/PrrrxK/Mu5zJSUlSExMrFCMS0xMrPA4IYQQwg+0Mo4QQmQEn7vjfSklJQUxMTEoKyuDhYUFr7awSZvAwEBs3boViYmJAABTU1M4OztjzJgxjJOJx9nZGQEBAWjTpg3atGkDBQUFket8aHwAAPb29ggPD8fGjRtFCqMLFy5Ejx494O/vzzhh1W7duoXx48fjyZMn+PLlqUAgQGlpKaNk1XPu3Dl4enrCy8sL+vr6rOOILSQkROx7+bQ6ThrMnz8ffn5++N///ify+Pbw8IC9vT1vnqcIIYT8HyrGEUKIjJCW7niE/JtsbGy+ek0gEODq1as/ME3NFRQUYMGCBfD19UVxcTEAoFatWpgyZQrWr18PVVVVxgmr1q5dO5iYmGDFihWVNtP4/EB7SZadnY0xY8bg2rVrUFFRqVDgldQjAeTk5ER+FggElRZFAfCmMCotysrKsGHDBmzduhWZmZkAgMaNG8PZ2Rmurq7Cc+QIIYTwB21TJYQQGcHX7njz588X+15aHUCq6/MmCHymoqKCnTt3Yv369UhJSQHHcTA2NuZFEa5ccnIygoODYWxszDrKdxk3bhyeP3+OtWvXomHDhrxp4PD5dsfLly9j8eLFWLt2LTp37gyBQICbN29iyZIlWLt2LcOUsqekpAQHDx6Evb09Fi1ahLy8PACgxg2EEMJzVIwjhBAZwdfueDExMSI/R0VFobS0VHh2zqNHjyAvL4/27duziEekyLNnzyAQCHjdmVdVVRVt2rRhHaNGOnbsiMePH/O+GHfz5k1ERkaibdu2rKPU2Lx58+Dl5YVu3boJxwYMGAAVFRVMnz5duC2d/Pdq1aqFmTNnCv/NqQhHCCHSgYpxhBAiIzw8PITfjx49Grq6urhx44bEd8f7fOXSpk2boKamBn9/f2hqagIA/vnnHzg6OqJ79+6sIhIeKysrw+rVq7Fx40a8f/8eAKCmpgZXV1f8/vvvFbbukf/O3Llz4erqiqysLJibm1fY3smXImPLli1RWFjIOsZ3SUlJqXRbsLq6OtLT0398IBnXsWNHxMTE8OoMQkIIId9GZ8YRQgjhDR0dHVy8eLFCx8j79++jf//+ePHiBaNkhK9+++037N27FytWrEDXrl3BcRxu3LiB5cuXY9q0aVizZg3riDKjssJn+bllfGrgcPHiRaxYsQJr1qyptKjIh5VNPXr0gIKCAg4cOIDGjRsDALKysjBx4kR8/PgR4eHhjBPKlqCgILi5ucHFxQXt27evsP2cL4VqQggh/4eKcYQQIiPk5eXRo0cPHDt2DPXq1ROOv3z5Ek2aNOHFG101NTWcOnUKvXv3Fhm/evUqhg0bhnfv3jFKRviqSZMm8PLyqrA69NSpU5g1axaeP3/OKJnsefLkyTev82VVUHlR8cuz4vhUVHz8+DFGjBiBpKQk6OnpAQAyMjJgYmKCkydPSvRWYgsLC7HP6YuOjv6P0/w7pKVQTQgh5P/QNlVCCJERHMfhw4cPsLKyQkhICFq3bi1yjQ9GjBgBR0dHbNy4EZ06dQIA3Lp1CwsXLsTIkSMZp5M9paWl8PPzw5UrV/Dq1SuRA+AB8KITaW5uLlq2bFlhvGXLlhLb9VJa8aXYVhVpaApibGyM+Ph4XLp0CQ8fPgTHcTAzM0Pfvn0lviHF8OHDhd8XFRVh586dMDMzQ+fOnQF8+pvx4MEDzJo1i1HC6ktLS2MdgRBCyL+MVsYRQoiMkJeXx7Nnz+Dh4YF9+/Zh//79GDZsGK9WxhUUFGDBggXw9fVFcXExgE+HW0+ZMgXr16/nVedIaTBnzhz4+flh8ODBaNy4cYU36Zs3b2aUTHwdO3ZEx44d4enpKTI+d+5c3LlzB7du3WKUrGohISFi3yvJ50J+KSEhARkZGfj48aPIOJ/mQCTD1KlT0bhxY6xatUpkfNmyZXj69Cl8fX0ZJSOEECLrqBhHCCEyQk5ODllZWdDW1oa3tzecnJywZMkSTJ06FTo6OrwoxpXLz89HSkoKOI6DsbExFeEYadCgAQICAjBo0CDWUWosPDwcgwcPhp6eHjp37gyBQICbN2/i6dOnOHv2rEQ3BhG3uQRftrGlpqZixIgRuHfvnnALHvB/2z35MIdyb968wd69e5GYmAiBQAAzMzNMnjy50qYIksLT0xPTp0+HkpJSheL0l5ycnH5Qqu+jrq6Ou3fvonnz5iLjycnJsLKywtu3bxklq779+/fDy8sLaWlpiIyMhL6+PrZs2QIDAwMMGzaMdTxCCCHVRMU4QgiREZ8X4wAgLCwMo0ePhoWFBa5evcqrN7pEMjRp0gRhYWEwMTFhHeW7vHjxAjt27BDZjjdr1iw0adKEdTSZMmTIEMjLy2PPnj0wNDTE7du3kZOTA1dXV2zYsEGiC6Ofu3v3LgYMGABlZWV06NABHMfh7t27KCwsxMWLF2Fpack6YqUMDAxw9+5d1K9fHwYGBl+9TyAQIDU19Qcmq7lGjRph3bp1cHR0FBnft28f3Nzc8PLlS0bJvu3ChQvo1KmTsHi7a9cuuLu7Y968eVizZg3u378PQ0ND+Pn5wd/fXyq2RhNCiKyhYhwhhMiIz99olXv8+DGGDBmCR48eUTGOVNvGjRuRmpqK7du3S/w5UtX19OlTLFu2jLax/UANGjTA1atX0aZNG6irq+P27dto0aIFrl69CldXV8TExLCOKJbu3bvD2NgYe/bsQa1an45nLikpwdSpU5Gamopr164xTig7PDw8sHz5ckydOlXknFFfX1+4u7vDzc2NccLK+fv7Y/369Th//jyaNm0KMzMzrF27FsOHD4eamhri4uJgaGiI+/fvo1evXnj9+jXryIQQQqqJinGEECLjioqK8PLlS6k5PJ38OCNGjEBoaCjq1auHVq1aQUFBQeT68ePHGSX7fnFxcbC0tORVkTo/Px/h4eGVnrfGh22FmpqaiIqKgqGhIYyMjODj4wMbGxukpKTA3NwcBQUFrCOKRVlZGTExMRUagyQkJMDKyoo385AWgYGB2Lp1KxITEwEApqamcHZ2xpgxYxgn+7bjx4/D3d0d9+/fh7KyMh4+fAh9fX2RYlxycjLatGmDwsJC1nEJIYRUE3VTJYQQGaekpESFOFIjGhoaGDFiBOsYBEBMTAwGDRqEgoIC5Ofno169enj9+jVUVFSgra3Ni2Jc69atER8fD0NDQ3Ts2BF//vknateuDW9vbxgaGrKOJ7a6desiIyOjQjHu6dOnUFNTY5SqekaPHg0rK6sKK8fWr1+P27dvIygoiFGy6hszZozEF94qM3LkSFhYWAD4tLI9Nja2wt/qc+fOwczMjEU8Qggh34mKcYQQIsXq1auHR48eoUGDBtDU1PzmVsLc3NwfmIxIg3379rGOQP4/FxcXDBkyBLt27YKGhgZu3boFBQUFTJgwAc7OzqzjiWXJkiXIz88HAKxevRo//fQTunfvjvr16+Po0aOM04lv7NixmDJlCjZs2IAuXbpAIBAgIiICCxcuxLhx41jHE0t4eDiWLVtWYdzW1hYbNmxgkEg2lZ/dt3DhQsyePRtFRUXgOA63b9/G4cOHsW7dOvj4+DBOSQghpCaoGEcIIVJs8+bNwpUYW7ZsYRvmX5KSkoItW7YIuxSWbzkyMjJiHU1mZWdnIykpCQKBACYmJtDS0mIdSebExsZi9+7dkJeXh7y8PD58+ABDQ0P8+eefcHBwwMiRI1lHrNKAAQOE3xsaGiIhIQG5ublVfpAgaTZs2ACBQAB7e3uUlJQAABQUFDBz5kx4eHgwTiee9+/fo3bt2hXGFRQUkJeXxyBRzZSWlmLz5s0IDAysdPs2Xz6EcnR0RElJCRYtWoSCggKMHz8eOjo62Lp1K3755RfW8QghhNQAnRlHCCGENy5cuIChQ4eiXbt26Nq1KziOw82bNxEXF4fTp0+jX79+rCPKlPz8fMydOxcBAQEoKysDAMjLy8Pe3h7btm2DiooK44RfV1Vx6s2bNwgPD+fNmXFaWlq4ceMGTExM0KJFC3h6emLAgAF4+PAhLC0teXFO2du3b1FaWop69eqJjOfm5qJWrVqoW7cuo2Q1U1BQgJSUFHAcB2NjY4l+PHzJ2toaQ4YMgbu7u8j48uXLcfr0aURFRTFKVj3u7u7w8fHB/PnzsXTpUvz+++9IT0/HyZMn4e7uzovt2196/fo1ysrKhJ3RCSGE8BMV4wghRIpVZwUDH97oWlhYYMCAARVWl7i5ueHixYuIjo5mlEw2bNmyBebm5ujTpw8AYMaMGbh8+TK2b9+Orl27AgAiIiLg5OSEfv36YdeuXSzjfpOjo6NY9/FlK27//v0xadIkjB8/Hr/++itiYmLg5OSE/fv3459//sHff//NOmKVBg4ciCFDhmDWrFki415eXggJCcHZs2cZJZM9ISEhGDVqFMaPH4/evXsDAK5cuYLDhw8jKCgIw4cPZxtQTEZGRvD09MTgwYOhpqaG2NhY4ditW7dw6NAh1hGr5dWrV8JVyC1atKBVyIQQwmNUjCOEECkmJydX5fYujuMgEAh4sQJISUkJ9+7dQ/PmzUXGHz16hDZt2qCoqIhRMtkQFRWFMWPGYPny5Zg4cSIaNGiA4OBg9OrVS+S+0NBQjBkzBtnZ2WyCyqC7d+/i3bt3sLGxQXZ2NhwcHBAREQFjY2Ps27cPbdu2ZR2xSvXq1cONGzdgamoqMv7w4UN07doVOTk5jJJVT35+Pjw8PHDlyhW8evVKuGq0XGpqKqNk1XPmzBmsXbsWsbGxUFZWRps2bbBs2TL07NmTdTSxqaqqIjExEXp6emjcuDHOnDkDS0tLpKamwsLCAm/fvmUdUSx5eXmYPXs2Dh8+LLIKeezYsdixYwfU1dUZJySEEFJddGYcIYRIsdDQUNYR/lVaWlqIjY2tUIyLjY2lLTs/QPv27fH333/DwcEBEydOREFBARo2bFjhPm1tbV5si5QmVlZWwu+1tLR4uYrsw4cPwjPWPldcXIzCwkIGiWpm6tSpCA8Px8SJE9G4cWNenXf3ucGDB2Pw4MGsY3yXpk2bIjMzE3p6ejA2NsbFixdhaWmJO3fuQFFRkXU8sU2dOhWxsbE4c+YMOnfuDIFAgJs3b8LZ2RnTpk1DYGAg64iEEEKqiVbGEUII4Y2VK1di8+bNcHNzE+lS+Mcff8DV1RVLlixhHVGm9OnTB/Xr10dAQACUlJQAAIWFhXBwcEBubi4uX77MOCHhk169esHc3Bzbtm0TGZ89ezbi4+Nx/fp1RsmqR0NDA2fOnBFu3eazqKgoYbMcMzMzWFhYsI5ULW5ubqhbty7+97//ITg4GOPGjUOzZs2QkZEBFxcX3jTUUFVVxYULF9CtWzeR8evXr8PW1lbYhZgQQgh/UDGOEEJkTEFBQaVd5dq0acMokfg4jsOWLVuwceNGvHjxAgDQpEkTLFy4EE5OTrxdgcJX9+/fh62tLYqKitC2bVsIBALExsZCSUkJFy5cQKtWrVhHJDxy48YN9O3bF9bW1sJzCa9cuYI7d+7g4sWL6N69O+OE4jEwMMDZs2crbLflk1evXuGXX35BWFgYNDQ0wHEc3r59CxsbGxw5coS3Z5XdunULN2/ehLGxMYYOHco6jtj09PRw5swZmJubi4zHx8dj0KBBePbsGaNkhBBCaoqKcYQQIiOys7Ph6OiIc+fOVXqdD2fGfe7du3cAADU1NcZJZFthYSEOHDiAhw8fguM4mJmZwc7ODsrKyqyjER6KjY3F+vXrRc4p++233ypsTZdkBw4cwKlTp+Dv78+rDqqfGzt2LFJSUrB//35hUTEhIQEODg4wNjbG4cOHGSeULd7e3ggKCkJAQAAaN24MAMjKyoKDgwNGjhyJGTNmME5ICCGkuqgYRwghMsLOzg7p6enYsmULbGxscOLECbx8+RKrV6/Gxo0beX82ECGESAILCwukpKSA4zg0a9YMCgoKItf50PVZXV0dly9fhrW1tcj47du30b9/f7x584ZNMDGEhISIfS9fVsdZWFjg8ePH+PDhA/T09AAAGRkZUFRUrFCo5sPvFyGEEGrgQAghMuPq1as4deoUrK2tIScnB319ffTr1w9169bFunXreFGMe/nyJRYsWCDsUvjl50l8W93HRyEhIRg4cCAUFBSqfNPLlze6hJ28vDyx761bt+5/mOTfM3z4cNYRvltZWVmFIiIAKCgoVOgOK2m+/PcXCAQV/laUH2nAl78Z0vA7RQghRBStjCOEEBlRt25dxMfHo1mzZmjWrBkOHjyIrl27Ii0tDa1ateJF98uBAwciIyMDc+bMqbRL4bBhwxglkx1ycnLIysqCtrY25OTkvnqfQCDgzRtdaREeHo4NGzYID9w3NTXFwoULJfqsNTk5uSrPeuQ4jn6ffrBhw4bhzZs3OHz4MJo0aQIAeP78Oezs7KCpqYkTJ04wTiiey5cvY/HixVi7dq1IF9IlS5Zg7dq16NevH+uIhBBCZBStjCOEEBnRokULJCUloVmzZmjXrh12796NZs2awcvLS3gGjaSLiIjA9evX0a5dO9ZRZNbnq2IkfYWMLDlw4AAcHR0xcuRIODk5geM43Lx5E3369IGfnx/Gjx/POmKlQkNDWUf4z/C5E+n27dsxbNgwNGvWDLq6uhAIBMjIyIC5uTkOHDjAOp7Y5s2bBy8vL5EupAMGDICKigqmT5+OxMREhukIIYTIMloZRwghMuLgwYMoLi7GpEmTEBMTgwEDBiAnJwe1a9eGn58fxo4dyzpilczMzHDw4EFevakl5EcwNTXF9OnT4eLiIjK+adMm7Nmzh/dFh9jYWN4U4aWpE+mlS5dEmrP07duXdaRqUVZWxu3btyvtQtqxY0cUFhYySkYIIUTWUTGOEEJkVEFBAR4+fAg9PT00aNCAdRyxXLx4ERs3bhSu6iNsOTk5wdjYGE5OTiLj27dvx+PHj7FlyxY2wWSQoqIiHjx4AGNjY5Hxx48fo3Xr1igqKmKUrObevn2LgwcPwsfHB3FxcbzZpkqdSCVHjx49oKCggAMHDoh0IZ04cSI+fvyI8PBwxgkJIYTIKirGEUKIjPn48SPS0tJgZGSEWrUk/7QCTU1NkTOl8vPzUVJSAhUVlQoHjOfm5v7oeDJNR0cHISEhaN++vch4dHQ0hg4dimfPnjFKJnuMjY2xcOFCzJgxQ2R89+7d2LBhA5KTkxklq76rV6/C19cXx48fh76+PkaNGoVRo0bxZkUsXzuRenp6in3vlwV4SfX48WOMGDECSUlJIl1ITUxMcPLkyQrFa0IIIeRHkfx3YYQQQv4VBQUFmDt3Lvz9/QEAjx49gqGhIZycnNCkSRO4ubkxTlg5Wl0luXJycqCurl5hvG7dunj9+jWDRLLL1dUVTk5OiI2NRZcuXSAQCBAREQE/Pz9s3bqVdbwqPXv2DH5+fvD19UV+fj7GjBmD4uJiHDt2DGZmZqzjVQtfO5Fu3rxZrPsEAgFvinHGxsaIj4+vdLttVY1DJBHfPkwjhBDydbQyjhBCZISzszNu3LiBLVu2wNbWFvHx8TA0NERISAiWLVuGmJgY1hEJz7Ru3Rq//vor5syZIzK+bds27Nq1CwkJCYySyaYTJ05g48aNwvPhyrupSnqX4UGDBiEiIgI//fQT7OzsYGtrC3l5eSgoKCAuLo53xThp6URKJAdfP0wjhBDydfSRCiGEyIiTJ0/i6NGj6NSpk8iKADMzM6SkpDBMJr6zZ89CXl4eAwYMEBm/ePEiSktLMXDgQEbJZNP8+fMxZ84cZGdno3fv3gCAK1euYOPGjbSikYERI0ZgxIgRrGNU28WLF+Hk5ISZM2eiefPmrON8N2npRArwcyWWp6cnpk+fDiUlpSq33vJlhd9vv/2GuLg4hIWFwdbWVjjet29fLFu2jIpxhBDCQ/z4q0oIIeS7ZWdnQ1tbu8J4fn4+b7bruLm5wcPDo8J4WVkZ3NzcqBj3g02ePBkfPnzAmjVrsGrVKgBAs2bNsGvXLtjb2zNOJ7vev39fYTtk3bp1GaWp2vXr1+Hr6wsrKyu0bNkSEydO5EV356/R1dVFdHQ0rzuR8nkl1ubNm2FnZwclJaVvbr3l03ZbafgwjRBCiCg51gEIIYT8GNbW1jhz5ozw5/IX9Hv27EHnzp1ZxaqW5OTkSrestWzZEo8fP2aQiMycORPPnj3Dy5cvkZeXh9TUVCrEMZCWlobBgwdDVVUV6urq0NTUhKamJjQ0NKCpqck63jd17twZe/bsQWZmJmbMmIEjR45AR0cHZWVluHTpEt69e8c6YrUEBATgw4cP6NevH+bOnQsnJyf07dsXHz9+REBAAOt4Yvl8JZaSkpJwvG/fvjh69CjDZFVLS0tD/fr1hd9/7Ss1NZVxUvFJw4dphBBCRNGZcYQQIiNu3rwJW1tb2NnZwc/PDzNmzMCDBw8QGRmJ8PDwCh0xJVGjRo1w6NAh4ZbIcpcvX8b48ePx6tUrRskIYatLly4APp0N2bBhwwpv0Hv27MkiVo0lJSVh79692L9/P968eYN+/fohJCSEdSyxyMvLIzMzs0LxJCcnB9ra2igtLWWUTHz6+vrClVhqamqIi4uDoaEhHj9+DEtLS+Tl5bGOKFN69uyJ0aNHY+7cuVBTU0N8fDwMDAwwZ84cPH78GOfPn2cdkRBCSDXRNlVCCJERXbp0wc2bN7F+/XoYGRnh4sWLsLS0RGRkJMzNzVnHE8vQoUMxb948nDhxAkZGRgCAx48fw9XVFUOHDmWcTjYFBwcjMDAQGRkZ+Pjxo8i16OhoRqlkT3x8PKKiotCiRQvWUf4VLVq0wJ9//ol169bh9OnT8PX1ZR1JbBzHVbpa6dmzZ5V2H5ZE0rISa/To0bCysqqwrXb9+vW4ffs2goKCGCWrnnXr1sHW1hYJCQkoKSnB1q1bRT5MI4QQwj+0TZUQQmRAcXExHB0doaKiAn9/f9y/fx8JCQk4cOAAbwpxwKc3UKqqqmjZsiUMDAxgYGAAU1NT1K9fHxs2bGAdT+Z4enrC0dER2traiImJQYcOHVC/fn2kpqbS+X0/mLW1NZ4+fco6xr9OXl4ew4cP58WqOAsLC1haWkIgEKBPnz6wtLQUfrVt2xbdu3fnzblx0nCsAQCEh4dj8ODBFcZtbW1x7do1BolqpkuXLrhx4wYKCgqEH6Y1bNgQkZGRvFjVTgghpCJaGUcIITJAQUEBJ06cwNKlS1lH+S7q6uq4efMmLl26hLi4OCgrK6NNmzbo0aMH62gyaefOnfD29sa4cePg7++PRYsWwdDQEO7u7sjNzWUdT6b4+Pjg119/xfPnz9G6dWsoKCiIXG/Tpg2jZLJj+PDhAIDY2FgMGDAAderUEV6rXbs2mjVrhlGjRjFKVz3SshLr/fv3qF27doVxBQUF3m21NTc3FzbUIIQQwn90ZhwhhMgIR0dHmJubY/78+ayj/CuKioqgqKjIqy1T0kZFRQWJiYnQ19eHtrY2Ll26hLZt2yI5ORmdOnVCTk4O64gy49atWxg/fjzS09OFYwKBQLhlkg/nlEkLf39/jB07VqTxAV/ExsaiXbt2AIB79+5hw4YNiIqKQllZGSwtLbF48WJeraa2trbGkCFD4O7uLjK+fPlynD59GlFRUYySVa06xUJJ7pZMCCGkcrQyjhBCZISxsTFWrVqFmzdvon379lBVVRW57uTkxCiZ+MrKyrBmzRp4eXnh5cuXePToEQwNDbF06VI0a9YMU6ZMYR1RpjRq1Ag5OTnQ19eHvr4+bt26hbZt2yItLQ30Wd+PNXnyZFhYWODw4cOVNnAgP46DgwPrCDVmaWkJCwsLTJ06FePHj+f9SqylS5di1KhRSElJETb+uXLlCg4fPizx58VpaGiI/TimYjshhPAPrYwjhBAZYWBg8NVrAoEAqampPzBNzaxcuRL+/v5YuXIlpk2bhvv378PQ0BCBgYHYvHkzIiMjWUeUKVOnToWuri6WLVsGLy8vzJ8/H127dsXdu3cxcuRI7N27l3VEmaGqqoq4uDgYGxuzjiLz5OTkvllEkeTCSWRkJHx9fREYGIji4mKMGjUKkydPho2NDetoNXbmzBmsXbsWsbGxwqMNli1bJvEdhj/fDpyeng43NzdMmjRJeGZfZGQk/P39sW7dOl4XgAkhRFZRMY4QQghvGBsbY/fu3ejTpw/U1NQQFxcHQ0NDPHz4EJ07d8Y///zDOqJMKSsrQ1lZGWrV+rTQPjAwEBERETA2Nsavv/5a6VlN5L8xZMgQTJo0iTdnkkmzkydPihTjiouLERMTA39/f6xYsYIXK3gLCwsRGBiIffv24fr162jWrBkmT54MBwcHNG3alHU8mdOnTx9MnToV48aNExk/dOgQvL29ERYWxiYYIYSQGqNiHCGEEN5QVlbGw4cPoa+vL1KMS0hIQIcOHfD+/XvWEQlhwtvbG6tXr8bkyZNhbm5eoYHD0KFDGSUj5Q4dOoSjR4/i1KlTrKNUS0pKCvbt24eAgABkZmaiX79+OHv2LOtY1RIVFYXExEQIBAKYmZnBwsKCdaRqUVFRQVxcHJo3by4y/ujRI7Rr1w4FBQWMkhFCCKkpKsYRQgjhDSsrK8ybNw8TJkwQKcatWLECly9fxvXr11lHlHrx8fFi30sdPH8cOTm5r16jBg6SISUlBW3atEF+fj7rKNX2/v17HDx4EP/73//w5s0b3vw+vXr1Cr/88gvCwsKgoaEBjuPw9u1b2NjY4MiRI9DS0mIdUSwtWrTATz/9hI0bN4qMu7q64q+//kJSUhKjZIQQQmqKGjgQQgiReJMnT8bWrVuxbNkyTJw4Ec+fP0dZWRmOHz+OpKQkBAQE4K+//mIdUya0a9dO2KXzW6gA9GOVlZWxjkC+obCwENu2bePdFs/w8HD4+vri2LFjkJeXx5gxY3ixzbbc3LlzkZeXhwcPHsDU1BQAkJCQAAcHBzg5OeHw4cOME4pn8+bNGDVqFC5cuIBOnToB+NRBOSUlBceOHWOcjhBCSE3QyjhCCCEST15eHpmZmdDW1saFCxewdu1aREVFoaysDJaWlnB3d0f//v1Zx5QJT548EftefX39/zAJIZJJU1NT5Mw4juPw7t07qKio4MCBAxK/Zfjp06fw8/ODn58f0tLS0KVLF0yZMgVjxoyp0IVb0qmrq+Py5cuwtrYWGb99+zb69++PN2/esAlWA8+ePcOuXbuQmJgIjuNgZvb/2rvz6J7vfI/jr18iJIjYZyJCEinjN7UkltoaSxMUt9aJrRiJoVWiXFt7L+rMVJ1pbxFqKRNUbSHGcOy15ErFWKNFbBFNxlI6JrEkaZDf/aPT3MlN26sJv4/k+3yck3Py+/y+4skpp975fD9fu1577TX5+vqaTgMAFAHDOADAM8/FxUU3btxQzZo1TacAz4zo6GiNHDlS7u7uio6O/slro6KinFSFlStXFnjt4uKiGjVq6IUXXlCVKlUMVT2esLAw7d+/XzVq1NDQoUMVERGhBg0amM4qMk9PTx08eFBNmzYtsH7y5Em1b99ed+7cMRMGALA8hnEAYCEHDx7UkiVLlJKSoo0bN8rHx0erVq2Sv7+/2rVrZzrvR7m4uOjrr78uMef7WMmqVau0ePFipaamKjExUXXr1tXcuXPl7++vnj17ms4r1fz9/XXs2DFVq1ZN/v7+P3qdzWbT5cuXnViGH5OUlFRoMPQseeWVVxQZGakePXrI1dXVdE6x9ezZUxkZGVq7dq1q1aolSbp69aoGDx6sKlWq6M9//rPhQgCAVf34ab8AgFIlLi5OXbp0kYeHh06ePKlvv/1WknT37l3NmjXLcN3/r379+qpatepPfsC5Fi1apAkTJqhbt24FDnWvXLmy5s6dazbOAlJTU1WtWrX8z3/sg0GcWZmZmVq4cKGCg4PVrFkz0zk/acuWLerZs2epGMRJ0oIFC3T37l35+fmpXr16CgwMlL+/v+7evav58+ebzgMAWBg74wDAIoKCgjR+/HgNHTq0wJNIk5KS1LVrV924ccN04o9ycXHR3Llz5eXl9ZPXDRs2zElFkCS73a5Zs2apV69eBf6bOn36tDp06KBvvvnGdKJlZGdny8PD4wffu379ury9vZ1chH379ikmJkabNm1S3bp11bdvX/Xt21dBQUGm0yxnz549OnfuXP5Za6GhoaaTAAAWx9NUAcAizp8/r5CQkELrlSpVKhGHWA8YMIAz454xqampPzhYKFeunO7fv2+gyLqCgoK0Zs0aBQcHF1jfuHGjXn/9dd26dctQmbX87W9/04oVKxQTE6P79+8rPDxcDx48UFxcnOx2u+k8ywoLC1NYWJjpDAAA8jGMAwCL8Pb21qVLl+Tn51dgPSEhQQEBAWaiHtO/PpkQzw5/f38lJSUVemrqjh07GDw4WVhYmNq0aaN33nlHU6ZM0f379zVmzBht2LBBs2fPNp1nCd26dVNCQoJ69Oih+fPnq2vXrnJ1ddXixYtNp1nK//cwk3/Fg00AAKYwjAMAixg1apTGjRunmJgY2Ww2Xbt2TYmJiZo4caKmT59uOu8ncaLCs2nSpEl64403lJOTI4fDoSNHjmjt2rV67733tGzZMtN5ljJ//nx1795dw4cP17Zt23Tt2jVVqlRJR48eZTDqJLt371ZUVJRef/11Pffcc6ZzLGvOnDmPdZ3NZnumh3FBQUGP/Y2oEydOPOUaAMCTxjAOACxi8uTJyszMVMeOHZWTk6OQkBCVK1dOEydO1JgxY0zn/aS8vDzTCfgBw4cP18OHDzV58mRlZWVp0KBB8vHx0bx58zRgwADTeZbTuXNn9enTR4sWLVKZMmW0detWBnFOdPDgQcXExKh58+b61a9+pSFDhqh///6msywnNTXVdMIT0atXr/zPc3JytHDhQtntdrVu3VqSdPjwYZ05c0ajR482VAgAKA4e4AAAFpOVlaWzZ88qLy9PdrtdFStWNJ2EUuCbb75RXl5e/rl+V69elY+Pj+Eq60hJSdGgQYN048YNLVu2TPHx8frggw8UFRWld999V25ubqYTLSMrK0vr1q1TTEyMjhw5okePHunDDz9URESEPD09TedZUm5urlJTU1WvXj2VKVPy9iKMGDFC3t7e+v3vf19gfcaMGUpPT1dMTIyhMgBAUTGMAwCLunPnjvbt26cGDRqoYcOGpnNQSty4cUPvvvuuli1bpuzsbNM5luHp6anu3btr8eLFqly5siTp0KFD+U9PPnnypNlAizp//rz+9Kc/adWqVcrIyFBYWJi2bNliOssysrKyNHbsWK1cuVKSdOHCBQUEBCgqKkq1atXS1KlTDRc+Hi8vLx07dqzQ7c8XL15U8+bNlZmZaagMAFBULqYDAADOER4ergULFkiSsrOz1aJFC4WHh6tx48aKi4szXIeSJCMjQ4MHD1aNGjVUq1YtRUdHKy8vT9OnT1dAQIAOHz7MTg0nW7hwodatW5c/iJOkNm3a6OTJk4WesArnadCggf74xz/qb3/7m9auXWs6x3LeeustnTp1SgcOHJC7u3v+emhoqNavX2+w7Ofx8PBQQkJCofWEhIQCvy4AQMnBzjgAsIhf/vKX2rVrl5o0aaI1a9ZoxowZOnXqlFauXKmPP/6YnTN4bKNHj9bWrVvVv39/7dy5U8nJyerSpYtycnI0Y8YMtW/f3nQiAKhu3bpav369WrVqJU9PT506dUoBAQG6dOmSgoODdefOHdOJj2X27Nl65513NGLECLVq1UqS8r/pMX369BKzww8A8L9K3qEJAIAiyczMVNWqVSVJO3fuVN++fVW+fHl1795dkyZNMlyHkmTbtm1avny5QkNDNXr0aAUGBqp+/fqaO3eu6TTLO3v2rNLS0pSbm5u/ZrPZ9G//9m8GqwAzbt26lX+O5b+6f//+Yz+p9FkwdepUBQQEaN68eVqzZo0kqWHDhlqxYoXCw8MN1wEAioJhHABYhK+vrxITE1W1alXt3LlT69atkyT94x//4DYX/CzXrl3Lf0pnQECA3N3dNWLECMNV1pKZmSkvL6/815cvX1afPn30xRdfyGaz6fsbH74fODx69MhIJ2BSixYttG3bNo0dO1bS//55WLp0af5TSUuK8PBwBm8AUIowjAMAi3jzzTc1ePBgVaxYUXXr1lWHDh0kSf/93/+tRo0amY1DiZKXl1fg6Zyurq6qUKGCwSLriY6Olru7e/6u1nHjxsnX11e7du1S06ZN9de//lWpqamaOHGiPvzwQ8O1gBnvvfeeunbtqrNnz+rhw4eaN2+ezpw5o8TERMXHx5vOAwBYGGfGAYCFHD9+XGlpaQoLC1PFihUlfXfLYeXKldW2bVvDdSgpXFxc9PLLL6tcuXKSpK1bt6pTp06FBnKbNm0ykWcJN2/e1JAhQxQYGKiPPvpI1atX1969e9WkSRP5+PjoyJEj8vHx0d69ezVx4kTOhISlJCUlqWnTppKkL7/8Uh988IGOHz+uvLw8BQcHa8qUKSXqm1CPHj3SnDlzFBsbW+g2dEm6ffu2oTIAQFGxMw4ALKRZs2Zq1qxZgbXu3bsbqkFJNWzYsAKvX331VUMl1lWzZk3t2rVL7733nqTv/rHu6ekpSapevbquXbsmHx8f+fn56fz58yZTAacLDg5WUFCQRowYoUGDBmnlypWmk4pl5syZWrZsmSZMmKBp06bpP/7jP3TlyhVt3rxZ06dPN50HACgCdsYBAACUcC+++KImTJig3r17a+TIkbp165YmTZqkxYsX68SJEzp9+rTpRMBpEhMTFRMTo9jYWD148EB9+/ZVRESEOnbsaDqtSOrVq6fo6Gh1795dnp6eSkpKyl87fPhw/kMdAAAlB8M4AACAEm7Xrl26e/eu+vXrp/T0dHXv3l2nT59W1apVFRsbq06dOplOBJwuOztbsbGxWr58uQ4ePCg/Pz9FRERo2LBhql27tum8x1ahQgUlJyerTp068vb21rZt2xQcHKzLly8rKChImZmZphMBAD+Ti+kAAAAAFE+XLl3Ur18/Sd89OfmLL77QN998o5s3bzKIg2V5eHho2LBhOnDggC5cuKCBAwdqyZIl8vf3V7du3UznPbbatWvr+vXrkqTAwEDt3r1bknT06NH8szsBACULO+MAAAAAlHr37t3T6tWr9fbbbysjI0OPHj0ynfRYpk6dqkqVKuntt9/Wxo0bNXDgQPn5+SktLU3jx4/X7NmzTScCAH4mhnEAYCEHDx7UkiVLlJKSoo0bN8rHx0erVq2Sv7+/2rVrZzoPQBHl5ORo/vz52r9/v27evKm8vLwC7584ccJQGWBefHy8YmJiFBcXJ1dXV4WHhysyMlKtWrUynVYkhw8f1qFDhxQYGKhXXnnFdA4AoAh4mioAWERcXJyGDBmiwYMH6+TJk/r2228lSXfv3tWsWbO0fft2w4UAiioiIkJ79uxRv3791LJlS9lsNtNJgFHp6elasWKFVqxYodTUVLVp00bz589XeHi4KlSoYDqvWFq1alViB4kAgO+wMw4ALCIoKEjjx4/X0KFD5enpqVOnTikgIEBJSUnq2rWrbty4YToRQBF5eXlp+/btatu2rekUwLiwsDDt379fNWrU0NChQxUREaEGDRqYzvpZtmzZ8tjXsjsOAEoedsYBgEWcP39eISEhhdYrVaqkjIwM5wcBeGJ8fHzk6elpOgN4Jnh4eCguLk49evSQq6ur6Zwi6dWrV4HXNptN/3cPxfc7YEvK2XcAgP/F01QBwCK8vb116dKlQusJCQkKCAgwUATgSfmv//ovTZkyRV999ZXpFMC4LVu2qGfPniV2ECdJeXl5+R+7d+9W06ZNtWPHDmVkZCgzM1M7duxQcHCwdu7caToVAFAE7IwDAIsYNWqUxo0bp5iYGNlsNl27dk2JiYmaOHGipk+fbjoPQDE0b95cOTk5CggIUPny5eXm5lbg/du3bxsqA1Bcb775phYvXlzgQUtdunRR+fLlNXLkSCUnJxusAwAUBcM4ALCIyZMnKzMzUx07dlROTo5CQkJUrlw5TZw4UWPGjDGdB6AYBg4cqKtXr2rWrFn6xS9+wQMcgFIkJSVFXl5ehda9vLx05coV5wcBAIqNBzgAgMVkZWXp7NmzysvLk91uV8WKFU0nASim8uXLKzExUU2aNDGdAuAJCwkJkZubmz799FN5e3tLkm7cuKEhQ4YoNzdX8fHxhgsBAD8XO+MAwGLKly+v5s2bm84A8AT96le/UnZ2tukMAE9BTEyMevfurbp166pOnTqSpLS0NNWvX1+bN282GwcAKBJ2xgGARXTs2PEnb13bt2+fE2sAPEm7d+/WzJkz9e6776pRo0aFzoyrVKmSoTIAT4LD4dCePXt07tw5ORwO2e12hYaGcks6AJRQDOMAwCLGjx9f4PWDBw+UlJSk06dPa9iwYZo3b56hMgDF5eLiIkmF/mHucDhks9n06NEjE1kAAAD4AdymCgAWMWfOnB9cf+edd3Tv3j0n1wB4kvbv3286AcATFB0drZEjR8rd3V3R0dE/eW1UVJSTqgAATwo74wDA4i5duqSWLVvq9u3bplMAAIAkf39/HTt2TNWqVZO/v/+PXmez2XT58mUnlgEAngR2xgGAxSUmJsrd3d10BgAA+KfU1NQf/BwAUDowjAMAi+jTp0+B1w6HQ9evX9exY8c0bdo0Q1UAAAAAYC0upgMAAM7h5eVV4KNq1arq0KGDtm/frhkzZpjOAwAAP6Bfv36aPXt2ofX3339fv/nNbwwUAQCKizPjAAAAAOAZVaNGDe3bt0+NGjUqsP7ll18qNDRUX3/9taEyAEBRcZsqAABAKXHr1i2dP39eNptN9evXV40aNUwnASime/fuqWzZsoXW3dzcdOfOHQNFAIDi4jZVALCIKlWqqGrVqo/1AaBkuX//viIiIlSrVi2FhIToxRdfVK1atRQZGamsrCzTeQCK4fnnn9f69esLra9bt052u91AEQCguNgZBwAWMW3aNP3hD39Qly5d1Lp1a0nfPUl1165dmjZtGkM4oASbMGGC4uPjtWXLFrVt21aSlJCQoKioKP37v/+7Fi1aZLgQQFFNmzZNffv2VUpKijp16iRJ2rt3r9auXasNGzYYrgMAFAVnxgGARfTt21cdO3bUmDFjCqwvWLBAn332mTZv3mwmDECxVa9eXRs3blSHDh0KrO/fv1/h4eG6deuWmTAAT8S2bds0a9YsJSUlycPDQ40bN9aMGTPUvn1702kAgCJgGAcAFlGxYkUlJSUpMDCwwPrFixcVFBSke/fuGSoDUFzly5fX8ePH1bBhwwLrZ86cUcuWLXX//n1DZQAAAPi/ODMOACyiWrVq+vOf/1xoffPmzapWrZqBIgBPSuvWrTVjxgzl5OTkr2VnZ2vmzJn5t6UDKNmOHz+uTz/9VKtXr9bJkydN5wAAioEz4wDAImbOnKnIyEgdOHAg/x/nhw8f1s6dO7Vs2TLDdQCKY968eeratatq166tJk2ayGazKSkpSe7u7tq1a5fpPADFcPPmTQ0YMEAHDhxQ5cqV5XA4lJmZqY4dO2rdunU8NRkASiBuUwUAC/nrX/+q6OhoJScny+FwyG63KyoqSi+88ILpNADFlJ2drU8//VTnzp3L//M9ePBgeXh4mE4DUAz9+/dXSkqKVq1alX8r+tmzZzVs2DAFBgZq7dq1hgsBAD8XwzgAAAAAeEZ5eXnps88+U4sWLQqsHzlyRJ07d1ZGRoaZMABAkXFmHAAAQAm3cuVKbdu2Lf/15MmTVblyZbVp00ZfffWVwTIAxZWXlyc3N7dC625ubsrLyzNQBAAoLoZxAAAAJdysWbPyb0dNTEzUggUL9Mc//lHVq1fX+PHjDdcBKI5OnTpp3LhxunbtWv7a1atXNX78eL300ksGywAARcVtqgAAACVc+fLlde7cOdWpU0dTpkzR9evX9cknn+jMmTPq0KGDbt26ZToRQBGlp6erZ8+eOn36tHx9fWWz2ZSWlqZGjRrpL3/5i2rXrm06EQDwM/E0VQAAgBKuYsWK+vvf/646depo9+7d+bvh3N3dlZ2dbbgOQHH4+vrqxIkT2rNnT4EHtISGhppOAwAUEcM4AACAEi4sLEwjRoxQUFCQLly4oO7du0uSzpw5Iz8/P7NxAJ6IsLAwhYWFmc4AADwBDOMAwEKOHj2qDRs2KC0tTbm5uQXe27Rpk6EqAMX10Ucf6T//8z+Vnp6uuLg4VatWTZJ0/PhxDRw40HAdgJ8rOjr6sa+Niop6iiUAgKeBM+MAwCLWrVunoUOHqnPnztqzZ486d+6sixcv6saNG+rdu7eWL19uOhEAAEjy9/d/rOtsNpsuX778lGsAAE8awzgAsIjGjRtr1KhReuONN+Tp6alTp07J399fo0aNkre3t2bOnGk6EUAxHDx4UEuWLNHly5e1YcMG+fj4aNWqVfL391e7du1M5wEAAOCfXEwHAACcIyUlJf8cqXLlyun+/fuy2WwaP368Pv74Y8N1AIojLi5OXbp0kYeHh06cOKFvv/1WknT37l3NmjXLcB2AJyE3N1fnz5/Xw4cPTacAAIqJYRwAWETVqlV19+5dSZKPj49Onz4tScrIyFBWVpbJNADF9Ic//EGLFy/W0qVL5ebmlr/epk0bnThxwmAZgOLKyspSZGSkypcvr1//+tdKS0uT9N1ZcbNnzzZcBwAoCoZxAGARL774ovbs2SNJCg8P17hx4/S73/1OAwcO1EsvvWS4DkBxnD9/XiEhIYXWK1WqpIyMDOcHAXhi3nrrLZ06dUoHDhyQu7t7/npoaKjWr19vsAwAUFQ8TRUALGLBggXKycmR9N3/2Lu5uSkhIUF9+vTRtGnTDNcBKA5vb29dunRJfn5+BdYTEhIUEBBgJgrAE7F582atX79erVq1ks1my1+32+1KSUkxWAYAKCqGcQBgEVWrVs3/3MXFRZMnT9bkyZMNFgF4UkaNGqVx48YpJiZGNptN165dU2JioiZOnKjp06ebzgNQDLdu3VLNmjULrX9/9isAoORhGAcApdidO3ce+9pKlSo9xRIAT9PkyZOVmZmpjh07KicnRyEhISpXrpwmTpyoMWPGmM4DUAwtWrTQtm3bNHbsWEnKH8AtXbpUrVu3NpkGACgim8PhcJiOAAA8HS4uLv/vd80dDodsNpsePXrkpCoAT0tWVpbOnj2rvLw82e12VaxY0XQSgGI6dOiQunbtqsGDB2vFihUaNWqUzpw5o8TERMXHx6tZs2amEwEAPxM74wCgFNu/f7/pBABPUVZWliZNmqTNmzfrwYMHCg0NVXR0tKpXr246DUAxJSUlqWnTpmrTpo0+//xzffDBB6pXr552796t4OBgJSYmqlGjRqYzAQBFwM44AACAEmrSpElauHChBg8eLHd3d61du1YdOnTQhg0bTKcBKCYXFxcFBQVpxIgRGjRokLy8vEwnAQCeEIZxAAAAJVS9evX07rvvasCAAZKkI0eOqG3btsr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    Tabla de correlaciones con filtro de umbral de correlación

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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
    Modelo
    Distancia de carrera requerida para despeguenannannannannannannannannannannannannannannannannannannannannannannannannannan0.735nannannannannan
    Altitud a la que se realiza el cruceronannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Velocidad a la que se realiza el crucero (KTAS)nannannannannannannannan0.917nannannannannannannannannan0.9730.790nan-0.854nannannannannannannannannannan
    Techo de servicio máximonannannannannannannannannannannannannannannannannan-0.875nannannan-0.961nan-0.757nannannannannannannannan
    Velocidad de pérdida limpia (KCAS)nannannannannan0.711nannannan0.768nannannan0.8170.774nan0.705nannan0.936nannan-0.874nannannannannannannannannan
    Área del alanannannannan0.711nan-0.7780.8350.9840.970nannannannan0.8250.7870.854nannan0.9650.944nannan0.974nannannannannannannannan
    Relación de aspecto del alanannannannannan-0.778nannannan-0.790nannan-0.730nannan-0.862-0.826nan-0.769nan-0.746nannan-0.970nannannannannannannannan
    Longitud del fuselajenannannannannan0.835nannan0.9380.806nannannannan0.719nannannannan0.9260.834nannan0.929nannannannannannannannan
    Ancho del fuselajenannan0.917nannan0.984nan0.938nan0.9860.833nan0.940nannannan0.868nan0.944nan0.954nannannannannan0.794nannannannannan
    Peso máximo al despegue (MTOW)nannannannan0.7680.970-0.7900.8060.986nannannan0.717nan0.8110.7510.882nan0.7080.9790.933nannan0.9760.758nannannannannannannan
    Alcance de la aeronavenannannannannannannannan0.833nannannannannannannannannannan0.936nan-0.711nan0.8480.837nannannannannannannan
    Autonomía de la aeronavenannannannannannannannannannannannannannannannannannannannannan-0.7150.802nan-0.732nannannannannannannan
    Velocidad máxima (KIAS)nannannannannannan-0.730nan0.9400.717nannannannannannan0.718nannan0.726nannannan0.7270.910nannannannannannannan
    Velocidad de pérdida (KCAS)nannannannan0.817nannannannannannannannannannannannan1.000nannannan1.000-0.874nannannannannannannannannan
    envergaduranannannannan0.7740.825nan0.719nan0.811nannannannannannan0.775nannan0.9500.806nannannannannannannannannannannan
    Cuerdanannannannannan0.787-0.862nannan0.751nannannannannannan0.758nan0.730nan0.724nannan0.975nannannannannannannannan
    payloadnannannannan0.7050.854-0.826nan0.8680.882nannan0.718nan0.7750.758nannannannan0.784nannan0.7110.846nannannannannannannan
    duracion en VTOLnannannan-0.875nannannannannannannannannan1.000nannannannannannannannannannannannannan-0.904nannannannan
    Crucero KIASnannan0.973nannannan-0.769nan0.9440.708nannannannannan0.730nannannan0.723nan-0.855nannannannannannannannannannan
    RTF (Including fuel & Batteries)nannan0.790nan0.9360.965nan0.926nan0.9790.936nan0.726nan0.950nannannan0.723nan0.948nannannannannannannannannannannan
    Empty weightnannannannannan0.944-0.7460.8340.9540.933nannannannan0.8060.7240.784nannan0.948nannan0.7850.980nannannannannannannannan
    Maximum Crosswindnannan-0.854-0.961nannannannannannan-0.711-0.715nan1.000nannannannan-0.855nannannannannannannannan-0.943nannannannan
    Rango de comunicaciónnannannannan-0.874nannannannannannan0.802nan-0.874nannannannannannan0.785nannannannannannannannannannannan
    Capacidad combustiblenannannan-0.757nan0.974-0.9700.929nan0.9760.848nan0.727nannan0.9750.711nannannan0.980nannannannan0.817nannannannannannan
    Consumonannannannannannannannannan0.7580.837-0.7320.910nannannan0.846nannannannannannannannan0.998nannannannannannan
    Precionannannannannannannannannannannannannannannannannannannannannannannan0.8170.998nannannannannannannan
    Despegue0.735nannannannannannannan0.794nannannannannannannannannannannannannannannannannannannannannannannan
    Propulsión horizontalnannannannannannannannannannannannannannannannannan-0.904nannannan-0.943nannannannannannannannannannan
    Propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannan
    Cantidad de motores propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannannan
    Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", - "\n", - "=== Imputación para el parámetro: **Velocidad a la que se realiza el crucero (KTAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Skyeye 3600** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad a la que se realiza el crucero (KTAS)' para la aeronave 'Skyeye 3600'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Imputación para el parámetro: **Techo de servicio máximo** ===\n", - "\n", - "--- Imputación para aeronave: **Mantis** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Mantis'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Skyeye 3600** ---\n", - "\n", - "--- Correlación: Capacidad combustible (r = -0.757) ---\n", - "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Capacidad combustible: [13.0, 11.5, 28.0, 28.0, 25.0]\n", - "Valores para Techo de servicio máximo: [17000.0, 16959.092, 16009.436, 16009.476, 17000.0]\n", - "Ecuación de regresión: y = -49.503x + 17640.121\n", - "Valor del parámetro correlacionado para la aeronave: 11.5\n", - "Predicción obtenida: 17070.833\n", - "\tR²: 0.5733448646440649, Desviación Estándar: 312.7585032074662, Varianza: 97817.88132857464, Incertidumbre: 139.8698547425961\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Capacidad combustible: 17070.833']\n", - "**Mediana calculada:** 17070.833\n", - "\n", - "--- Imputación para aeronave: **Skyeye 5000** ---\n", - "\n", - "--- Correlación: Capacidad combustible (r = -0.757) ---\n", - "Aeronaves utilizadas: ['Volitation VT370', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510']\n", - "Valores para Capacidad combustible: [13.0, 11.5, 11.5, 28.0, 28.0, 25.0]\n", - "Valores para Techo de servicio máximo: [17000.0, 17070.833, 16959.092, 16009.436, 16009.476, 17000.0]\n", - "Ecuación de regresión: y = -49.503x + 17640.121\n", - "Valor del parámetro correlacionado para la aeronave: 28.0\n", - "Predicción obtenida: 16254.028\n", - "\tR²: 0.6335143266174253, Desviación Estándar: 285.5081454305007, Varianza: 81514.90110716393, Incertidumbre: 116.5582122855099\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Capacidad combustible: 16254.028']\n", - "**Mediana calculada:** 16254.028\n", - "\n", - "=== Área del ala: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Relación de aspecto del ala: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Longitud del fuselaje: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Peso máximo al despegue (MTOW): No hay valores faltantes para imputar. ===\n", - "\n", - "=== Alcance de la aeronave: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Autonomía de la aeronave: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Velocidad máxima (KIAS): No hay valores faltantes para imputar. ===\n", - "\n", - "=== Imputación para el parámetro: **Velocidad de pérdida (KCAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Stalker XE** ---\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 59.0\n", - "Predicción obtenida: 14.838\n", - "\tR²: 0.7638900941970123, Desviación Estándar: 1.321784213429975, Varianza: 1.747113506872698, Incertidumbre: 0.5396161454949092\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Rango de comunicación: 14.838']\n", - "**Mediana calculada:** 14.838\n", - "\n", - "--- Imputación para aeronave: **Stalker VXE30** ---\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 161.0\n", - "Predicción obtenida: 9.415\n", - "\tR²: 0.7649280338966065, Desviación Estándar: 1.2237343949682917, Varianza: 1.4975258694284113, Incertidumbre: 0.4625281256974559\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Rango de comunicación: 9.415']\n", - "**Mediana calculada:** 9.415\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.7 Fixed Wing** ---\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 140.0\n", - "Predicción obtenida: 10.532\n", - "\tR²: 0.8590346121694943, Desviación Estándar: 1.1446987113646354, Varianza: 1.3103351397998568, Incertidumbre: 0.40471211061071805\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Rango de comunicación: 10.532']\n", - "**Mediana calculada:** 10.532\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.7 VTOL** ---\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 140.0\n", - "Predicción obtenida: 10.532\n", - "\tR²: 0.8815409323186313, Desviación Estándar: 1.0792323024785884, Varianza: 1.1647423627132352, Incertidumbre: 0.3597441008261961\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Rango de comunicación: 10.532']\n", - "**Mediana calculada:** 10.532\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 Fixed wing** ---\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 140.0\n", - "Predicción obtenida: 10.532\n", - "\tR²: 0.8815409323186313, Desviación Estándar: 1.0792323024785884, Varianza: 1.1647423627132352, Incertidumbre: 0.3597441008261961\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Rango de comunicación: 10.532']\n", - "**Mediana calculada:** 10.532\n", - "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Fulmar X'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 150.0\n", - "Predicción obtenida: 10.0\n", - "\tR²: 0.8815409323186313, Desviación Estándar: 1.0792323024785884, Varianza: 1.1647423627132352, Incertidumbre: 0.3597441008261961\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Rango de comunicación: 10.0']\n", - "**Mediana calculada:** 10.0\n", - "\n", - "--- Imputación para aeronave: **Orbiter 3** ---\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 15.316\n", - "\tR²: 0.8815409323186313, Desviación Estándar: 1.0792323024785884, Varianza: 1.1647423627132352, Incertidumbre: 0.3597441008261961\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Rango de comunicación: 15.316']\n", - "**Mediana calculada:** 15.316\n", - "\n", - "--- Imputación para aeronave: **Mantis** ---\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Orbiter 3', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 50.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 15.316, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 16.645\n", - "\tR²: 0.8833653923911279, Desviación Estándar: 1.02384966328724, Varianza: 1.048268133013395, Incertidumbre: 0.32376969175841563\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Rango de comunicación: 16.645']\n", - "**Mediana calculada:** 16.645\n", - "\n", - "--- Imputación para aeronave: **ScanEagle** ---\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Orbiter 3', 'Mantis', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 50.0, 25.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 15.316, 16.645, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 101.86\n", - "Predicción obtenida: 12.559\n", - "\tR²: 0.8900166558794294, Desviación Estándar: 0.9762023543141057, Varianza: 0.9529710365684029, Incertidumbre: 0.29433608442922443\n", - "\tNivel de confianza: Confianza Baja\n", - "Valores imputados: ['Rango de comunicación: 12.559']\n", - "**Mediana calculada:** 12.559\n", - "\n", - "--- Imputación para aeronave: **Integrator** ---\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Orbiter 3', 'Mantis', 'ScanEagle', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 50.0, 25.0, 101.86, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 15.316, 16.645, 12.559, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 92.6\n", - "Predicción obtenida: 13.051\n", - "\tR²: 0.8935698204333948, Desviación Estándar: 0.934642599085376, Varianza: 0.8735567880250669, Incertidumbre: 0.26980807808901663\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Rango de comunicación: 13.051']\n", - "**Mediana calculada:** 13.051\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator VTOL'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator Extended Range (ER)'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **ScanEagle 3** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle 3'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 50.0, 25.0, 101.86, 92.6, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 15.316, 16.645, 12.559, 13.051, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 92.6\n", - "Predicción obtenida: 13.051\n", - "\tR²: 0.895024240813695, Desviación Estándar: 0.8979755727825594, Varianza: 0.8063601293141656, Incertidumbre: 0.2490536132139741\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Rango de comunicación: 13.051']\n", - "**Mediana calculada:** 13.051\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 50.0, 25.0, 101.86, 92.6, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 15.316, 16.645, 12.559, 13.051, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 15.316\n", - "\tR²: 0.895024240813695, Desviación Estándar: 0.8979755727825594, Varianza: 0.8063601293141656, Incertidumbre: 0.2490536132139741\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Rango de comunicación: 15.316']\n", - "**Mediana calculada:** 15.316\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", - "\n", - "--- Correlación: Maximum Crosswind (r = 1.0) ---\n", - "Aeronaves utilizadas: ['DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'Ascend']\n", - "Valores para Maximum Crosswind: [45.0, 50.0, 15.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.0, 15.316, 13.0]\n", - "Ecuación de regresión: y = 0.055x + 12.099\n", - "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 14.835\n", - "\tR²: 0.7950904219131606, Desviación Estándar: 0.42932620726909115, Varianza: 0.18432099224806262, Incertidumbre: 0.24787160133697086\n", - "\tNivel de confianza: Confianza Media\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 50.0, 25.0, 101.86, 92.6, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.0, 15.316, 16.645, 12.559, 13.051, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 16.379\n", - "\tR²: 0.895024240813695, Desviación Estándar: 0.8979755727825594, Varianza: 0.8063601293141656, Incertidumbre: 0.2490536132139741\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Maximum Crosswind: 14.835', 'Rango de comunicación: 16.379']\n", - "**Mediana calculada:** 15.607\n", - "\n", - "=== Imputación para el parámetro: **Velocidad de pérdida limpia (KCAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Stalker XE** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.382x + 10.202\n", - "Valor del parámetro correlacionado para la aeronave: 0.87\n", - "Predicción obtenida: 14.884\n", - "\tR²: 0.4584975382227796, Desviación Estándar: 3.9626586397738124, Varianza: 15.70266349537404, Incertidumbre: 0.9090962399222311\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [93.0, 13.1, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.118x + 12.28\n", - "Valor del parámetro correlacionado para la aeronave: 13.6\n", - "Predicción obtenida: 13.882\n", - "\tR²: 0.5362410361538301, Desviación Estándar: 3.548785552305845, Varianza: 12.5938788962547, Incertidumbre: 0.8871963880764613\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [18.907, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.983x + 1.727\n", - "Valor del parámetro correlacionado para la aeronave: 14.838\n", - "Predicción obtenida: 16.309\n", - "\tR²: 0.6019452091484776, Desviación Estándar: 3.479540061453548, Varianza: 12.107199039260163, Incertidumbre: 0.869885015363387\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 3.108x + 5.81\n", - "Valor del parámetro correlacionado para la aeronave: 3.657\n", - "Predicción obtenida: 17.174\n", - "\tR²: 0.5835699593207654, Desviación Estándar: 3.4750212924510513, Varianza: 12.075772982988175, Incertidumbre: 0.7972245600234859\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [22.7, 4.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.46x + 13.713\n", - "Valor del parámetro correlacionado para la aeronave: 2.495\n", - "Predicción obtenida: 14.86\n", - "\tR²: 0.43900880754018157, Desviación Estándar: 3.9112272862931547, Varianza: 15.297698885044115, Incertidumbre: 0.921885112299916\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.974\n", - "Valor del parámetro correlacionado para la aeronave: 59.0\n", - "Predicción obtenida: 14.838\n", - "\tR²: 0.7638900941970123, Desviación Estándar: 1.321784213429975, Varianza: 1.747113506872698, Incertidumbre: 0.5396161454949092\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 14.884', 'Peso máximo al despegue (MTOW): 13.882', 'Velocidad de pérdida (KCAS): 16.309', 'envergadura: 17.174', 'payload: 14.86', 'Rango de comunicación: 14.838']\n", - "**Mediana calculada:** 14.872\n", - "\n", - "--- Imputación para aeronave: **Stalker VXE30** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.383x + 10.201\n", - "Valor del parámetro correlacionado para la aeronave: 1.158\n", - "Predicción obtenida: 16.435\n", - "\tR²: 0.4650980437290636, Desviación Estándar: 3.86232278712243, Varianza: 14.917537311925178, Incertidumbre: 0.8636416303052202\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 93.0, 13.1, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.116x + 12.39\n", - "Valor del parámetro correlacionado para la aeronave: 19.958\n", - "Predicción obtenida: 14.713\n", - "\tR²: 0.5383061046948842, Desviación Estándar: 3.4504190564798676, Varianza: 11.905391665319419, Incertidumbre: 0.8368495425006189\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 18.907, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.992x + 1.495\n", - "Valor del parámetro correlacionado para la aeronave: 9.415\n", - "Predicción obtenida: 10.831\n", - "\tR²: 0.606805757387085, Desviación Estándar: 3.392334540315432, Varianza: 11.507933633417112, Incertidumbre: 0.8227619780677445\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 3.115x + 5.665\n", - "Valor del parámetro correlacionado para la aeronave: 4.877\n", - "Predicción obtenida: 20.858\n", - "\tR²: 0.5796228985588339, Desviación Estándar: 3.4239782633031473, Varianza: 11.723627147572437, Incertidumbre: 0.765624815022751\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 22.7, 4.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.46x + 13.714\n", - "Valor del parámetro correlacionado para la aeronave: 2.495\n", - "Predicción obtenida: 14.861\n", - "\tR²: 0.44918896341251313, Desviación Estándar: 3.806910014724386, Varianza: 14.492563860208826, Incertidumbre: 0.8733650548071861\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.053x + 17.978\n", - "Valor del parámetro correlacionado para la aeronave: 161.0\n", - "Predicción obtenida: 9.423\n", - "\tR²: 0.7647638318484331, Desviación Estándar: 1.2237926717855518, Varianza: 1.4976685035160195, Incertidumbre: 0.4625501522639803\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 16.435', 'Peso máximo al despegue (MTOW): 14.713', 'Velocidad de pérdida (KCAS): 10.831', 'envergadura: 20.858', 'payload: 14.861', 'Rango de comunicación: 9.423']\n", - "**Mediana calculada:** 14.787\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.7 Fixed Wing** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.414x + 10.08\n", - "Valor del parámetro correlacionado para la aeronave: 1.55\n", - "Predicción obtenida: 18.472\n", - "\tR²: 0.466757382312631, Desviación Estándar: 3.7854894974433573, Varianza: 14.32993073525396, Incertidumbre: 0.8260615316425965\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 93.0, 13.1, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.116x + 12.397\n", - "Valor del parámetro correlacionado para la aeronave: 42.2\n", - "Predicción obtenida: 17.306\n", - "\tR²: 0.5422143559645116, Desviación Estándar: 3.3532462483094934, Varianza: 11.244260401801691, Incertidumbre: 0.7903677203893309\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 18.907, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.915x + 2.961\n", - "Valor del parámetro correlacionado para la aeronave: 10.532\n", - "Predicción obtenida: 12.6\n", - "\tR²: 0.5897032702850062, Desviación Estándar: 3.402063815159601, Varianza: 11.5740382024183, Incertidumbre: 0.8018741312429105\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.93x + 6.085\n", - "Valor del parámetro correlacionado para la aeronave: 4.4\n", - "Predicción obtenida: 18.977\n", - "\tR²: 0.5244094532346639, Desviación Estándar: 3.5750014852544703, Varianza: 12.780635619571669, Incertidumbre: 0.7801292817027697\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 22.7, 4.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.46x + 13.707\n", - "Valor del parámetro correlacionado para la aeronave: 14.5\n", - "Predicción obtenida: 20.38\n", - "\tR²: 0.4585425146715313, Desviación Estándar: 3.710550701599703, Varianza: 13.768186509142046, Incertidumbre: 0.8297043602736472\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.936) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [70.3, 6.8, 8.9, 16.5, 84.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [25.0, 14.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.176x + 11.018\n", - "Valor del parámetro correlacionado para la aeronave: 27.7\n", - "Predicción obtenida: 15.906\n", - "\tR²: 0.8767961918805747, Desviación Estándar: 2.18187881096421, Varianza: 4.760595145734596, Incertidumbre: 0.9757658679964775\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.03x + 17.168\n", - "Valor del parámetro correlacionado para la aeronave: 140.0\n", - "Predicción obtenida: 12.918\n", - "\tR²: 0.46532681815645593, Desviación Estándar: 1.7295254821806558, Varianza: 2.99125839351223, Incertidumbre: 0.6114795983424375\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 18.472', 'Peso máximo al despegue (MTOW): 17.306', 'Velocidad de pérdida (KCAS): 12.6', 'envergadura: 18.977', 'payload: 20.38', 'RTF (Including fuel & Batteries): 15.906', 'Rango de comunicación: 12.918']\n", - "**Mediana calculada:** 17.306\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.7 VTOL** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.387x + 10.063\n", - "Valor del parámetro correlacionado para la aeronave: 1.55\n", - "Predicción obtenida: 18.413\n", - "\tR²: 0.46446962919077694, Desviación Estándar: 3.7063812757992287, Varianza: 13.737262161595117, Incertidumbre: 0.7902031430874169\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 93.0, 13.1, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.116x + 12.397\n", - "Valor del parámetro correlacionado para la aeronave: 53.5\n", - "Predicción obtenida: 18.62\n", - "\tR²: 0.5426110461297494, Desviación Estándar: 3.2638102308142223, Varianza: 10.652457222767586, Incertidumbre: 0.7487694193164923\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 18.907, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.849x + 4.269\n", - "Valor del parámetro correlacionado para la aeronave: 10.532\n", - "Predicción obtenida: 13.21\n", - "\tR²: 0.5520283845821775, Desviación Estándar: 3.4611244919244952, Varianza: 11.979382748599594, Incertidumbre: 0.7940364153322332\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.904x + 6.111\n", - "Valor del parámetro correlacionado para la aeronave: 4.4\n", - "Predicción obtenida: 18.886\n", - "\tR²: 0.5197301153510105, Desviación Estándar: 3.5099482875201593, Varianza: 12.319736981065699, Incertidumbre: 0.748323489270446\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 22.7, 4.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.445x + 13.696\n", - "Valor del parámetro correlacionado para la aeronave: 11.3\n", - "Predicción obtenida: 18.723\n", - "\tR²: 0.4414953610991542, Desviación Estándar: 3.6781026044838465, Varianza: 13.528438769110855, Incertidumbre: 0.8026277904219737\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.936) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 70.3, 6.8, 8.9, 16.5, 84.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [17.306, 25.0, 14.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.174x + 11.323\n", - "Valor del parámetro correlacionado para la aeronave: 42.2\n", - "Predicción obtenida: 18.685\n", - "\tR²: 0.8685070668946828, Desviación Estándar: 2.0580697312416656, Varianza: 4.235651018653141, Incertidumbre: 0.840203449434832\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.02x + 16.878\n", - "Valor del parámetro correlacionado para la aeronave: 140.0\n", - "Predicción obtenida: 14.112\n", - "\tR²: 0.22114113946870317, Desviación Estándar: 2.053329630048995, Varianza: 4.2161625696371425, Incertidumbre: 0.6844432100163317\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 18.413', 'Peso máximo al despegue (MTOW): 18.62', 'Velocidad de pérdida (KCAS): 13.21', 'envergadura: 18.886', 'payload: 18.723', 'RTF (Including fuel & Batteries): 18.685', 'Rango de comunicación: 14.112']\n", - "**Mediana calculada:** 18.62\n", - "\n", - "--- Imputación para aeronave: **Aerosonde Mk. 4.8 Fixed wing** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.392x + 10.066\n", - "Valor del parámetro correlacionado para la aeronave: 1.55\n", - "Predicción obtenida: 18.423\n", - "\tR²: 0.4660005657286891, Desviación Estándar: 3.6251561480011025, Varianza: 13.141757097390192, Incertidumbre: 0.7558973100658521\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 93.0, 13.1, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.116x + 12.397\n", - "Valor del parámetro correlacionado para la aeronave: 54.4\n", - "Predicción obtenida: 18.725\n", - "\tR²: 0.5461903226551696, Desviación Estándar: 3.181168711678456, Varianza: 10.119834372161968, Incertidumbre: 0.7113309487208457\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 18.907, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.782x + 5.601\n", - "Valor del parámetro correlacionado para la aeronave: 10.532\n", - "Predicción obtenida: 13.832\n", - "\tR²: 0.5015824133754161, Desviación Estándar: 3.5602796721277223, Varianza: 12.675591343765882, Incertidumbre: 0.796102736578825\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.9x + 6.115\n", - "Valor del parámetro correlacionado para la aeronave: 4.4\n", - "Predicción obtenida: 18.873\n", - "\tR²: 0.5210484825351553, Desviación Estándar: 3.4332236552626645, Varianza: 11.78702466705513, Incertidumbre: 0.715876618803983\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 22.7, 4.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.445x + 13.693\n", - "Valor del parámetro correlacionado para la aeronave: 17.7\n", - "Predicción obtenida: 21.564\n", - "\tR²: 0.44248774210834096, Desviación Estándar: 3.5936014722335075, Varianza: 12.913971541238833, Incertidumbre: 0.7661584081767601\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: RTF (Including fuel & Batteries) (r = 0.936) ---\n", - "Aeronaves utilizadas: ['Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'DeltaQuad Evo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para RTF (Including fuel & Batteries): [27.7, 42.2, 70.3, 6.8, 8.9, 16.5, 84.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [17.306, 18.62, 25.0, 14.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.174x + 11.317\n", - "Valor del parámetro correlacionado para la aeronave: 36.7\n", - "Predicción obtenida: 17.716\n", - "\tR²: 0.8691516645551287, Desviación Estándar: 1.9055366919971601, Varianza: 3.63107008454748, Incertidumbre: 0.7202251715904527\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.011x + 16.644\n", - "Valor del parámetro correlacionado para la aeronave: 140.0\n", - "Predicción obtenida: 15.076\n", - "\tR²: 0.0684597878447748, Desviación Estándar: 2.3220684254768322, Varianza: 5.392001772596454, Incertidumbre: 0.7343025107267749\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 18.423', 'Peso máximo al despegue (MTOW): 18.725', 'Velocidad de pérdida (KCAS): 13.832', 'envergadura: 18.873', 'payload: 21.564', 'RTF (Including fuel & Batteries): 17.716', 'Rango de comunicación: 15.076']\n", - "**Mediana calculada:** 18.423\n", - "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.392x + 10.066\n", - "Valor del parámetro correlacionado para la aeronave: 0.94\n", - "Predicción obtenida: 15.134\n", - "\tR²: 0.4670451470194883, Desviación Estándar: 3.5488285233364496, Varianza: 12.594183888046365, Incertidumbre: 0.7244015889007506\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.116x + 12.393\n", - "Valor del parámetro correlacionado para la aeronave: 20.0\n", - "Predicción obtenida: 14.714\n", - "\tR²: 0.5485403649598684, Desviación Estándar: 3.1051599496166142, Varianza: 9.642018312703055, Incertidumbre: 0.677601453050638\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.893x + 6.121\n", - "Valor del parámetro correlacionado para la aeronave: 3.0\n", - "Predicción obtenida: 14.8\n", - "\tR²: 0.5216461178243376, Desviación Estándar: 3.362129644762508, Varianza: 11.30391574819087, Incertidumbre: 0.6862918398960846\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.423x + 13.759\n", - "Valor del parámetro correlacionado para la aeronave: 2.495\n", - "Predicción obtenida: 14.814\n", - "\tR²: 0.42567155526653766, Desviación Estándar: 3.5690658775926396, Varianza: 12.738231238596118, Incertidumbre: 0.7442016801973287\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 15.134', 'Peso máximo al despegue (MTOW): 14.714', 'envergadura: 14.8', 'payload: 14.814']\n", - "**Mediana calculada:** 14.807\n", - "\n", - "--- Imputación para aeronave: **Orbiter 4** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.406x + 10.034\n", - "Valor del parámetro correlacionado para la aeronave: 1.608\n", - "Predicción obtenida: 18.727\n", - "\tR²: 0.4728295759055522, Desviación Estándar: 3.477707911789329, Varianza: 12.094452319722095, Incertidumbre: 0.6955415823578658\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.116x + 12.4\n", - "Valor del parámetro correlacionado para la aeronave: 55.0\n", - "Predicción obtenida: 18.779\n", - "\tR²: 0.552600150975832, Desviación Estándar: 3.033828161728033, Varianza: 9.204113314894094, Incertidumbre: 0.6468143373802413\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.73x + 6.614\n", - "Valor del parámetro correlacionado para la aeronave: 10.0\n", - "Predicción obtenida: 13.916\n", - "\tR²: 0.46468134584634513, Desviación Estándar: 3.601724880792143, Varianza: 12.972422116917178, Incertidumbre: 0.7859608046969426\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.893x + 6.122\n", - "Valor del parámetro correlacionado para la aeronave: 5.2\n", - "Predicción obtenida: 21.165\n", - "\tR²: 0.5269957515236472, Desviación Estándar: 3.2942010830487516, Varianza: 10.851760775559567, Incertidumbre: 0.6588402166097503\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.423x + 13.758\n", - "Valor del parámetro correlacionado para la aeronave: 12.0\n", - "Predicción obtenida: 18.835\n", - "\tR²: 0.43464239567025376, Desviación Estándar: 3.4939195040094204, Varianza: 12.207473500497436, Incertidumbre: 0.7131933322650971\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.006x + 16.501\n", - "Valor del parámetro correlacionado para la aeronave: 150.0\n", - "Predicción obtenida: 15.606\n", - "\tR²: 0.019263283730102443, Desviación Estándar: 2.3959493405082912, Varianza: 5.7405732422821165, Incertidumbre: 0.7224059071968609\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 18.727', 'Peso máximo al despegue (MTOW): 18.779', 'Velocidad de pérdida (KCAS): 13.916', 'envergadura: 21.165', 'payload: 18.835', 'Rango de comunicación: 15.606']\n", - "**Mediana calculada:** 18.753\n", - "\n", - "--- Imputación para aeronave: **Orbiter 3** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.407x + 10.034\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 16.522\n", - "\tR²: 0.47471713659572623, Desviación Estándar: 3.4101769025502042, Varianza: 11.629306506686904, Incertidumbre: 0.6687907142656497\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.116x + 12.4\n", - "Valor del parámetro correlacionado para la aeronave: 32.0\n", - "Predicción obtenida: 16.111\n", - "\tR²: 0.5562407359989936, Desviación Estándar: 2.9671471323451786, Varianza: 8.803962104984215, Incertidumbre: 0.6186929457220847\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.677x + 7.652\n", - "Valor del parámetro correlacionado para la aeronave: 15.316\n", - "Predicción obtenida: 18.014\n", - "\tR²: 0.42421279321722494, Desviación Estándar: 3.651853558055984, Varianza: 13.33603440948615, Incertidumbre: 0.7785777946033205\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.811x + 6.35\n", - "Valor del parámetro correlacionado para la aeronave: 4.4\n", - "Predicción obtenida: 18.718\n", - "\tR²: 0.5194126656316639, Desviación Estándar: 3.261868529196971, Varianza: 10.639786301765609, Incertidumbre: 0.6397050492749827\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.423x + 13.756\n", - "Valor del parámetro correlacionado para la aeronave: 5.5\n", - "Predicción obtenida: 16.082\n", - "\tR²: 0.4360376843534042, Desviación Estándar: 3.4233658161858984, Varianza: 11.719433511430143, Incertidumbre: 0.6846731632371796\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.001x + 16.326\n", - "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 16.269\n", - "\tR²: 0.0007196998998141302, Desviación Estándar: 2.438000130498692, Varianza: 5.943844636311639, Incertidumbre: 0.7037900158138813\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 16.522', 'Peso máximo al despegue (MTOW): 16.111', 'Velocidad de pérdida (KCAS): 18.014', 'envergadura: 18.718', 'payload: 16.082', 'Rango de comunicación: 16.269']\n", - "**Mediana calculada:** 16.396\n", - "\n", - "--- Imputación para aeronave: **Mantis** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.409x + 10.026\n", - "Valor del parámetro correlacionado para la aeronave: 0.754\n", - "Predicción obtenida: 14.105\n", - "\tR²: 0.47548065381933924, Desviación Estándar: 3.346513898608693, Varianza: 11.199155273581153, Incertidumbre: 0.6440369000695176\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.116x + 12.416\n", - "Valor del parámetro correlacionado para la aeronave: 6.5\n", - "Predicción obtenida: 13.169\n", - "\tR²: 0.5562776352004176, Desviación Estándar: 2.9052327113675602, Varianza: 8.440377107200106, Incertidumbre: 0.59302814390775\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 15.316, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 16.396, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.676x + 7.585\n", - "Valor del parámetro correlacionado para la aeronave: 16.645\n", - "Predicción obtenida: 18.842\n", - "\tR²: 0.4219605998717829, Desviación Estándar: 3.586786423534771, Varianza: 12.865036848053354, Incertidumbre: 0.7478966694512236\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.782x + 6.377\n", - "Valor del parámetro correlacionado para la aeronave: 2.1\n", - "Predicción obtenida: 12.22\n", - "\tR²: 0.5111829799239707, Desviación Estándar: 3.230613748948095, Varianza: 10.436865194892464, Incertidumbre: 0.6217319058676296\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.422x + 13.776\n", - "Valor del parámetro correlacionado para la aeronave: 2.693\n", - "Predicción obtenida: 14.913\n", - "\tR²: 0.4374652379037306, Desviación Estándar: 3.357422336000781, Varianza: 11.272284742276943, Incertidumbre: 0.6584446925630868\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 50.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 16.396, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = -0.001x + 16.346\n", - "Valor del parámetro correlacionado para la aeronave: 25.0\n", - "Predicción obtenida: 16.315\n", - "\tR²: 0.0009110570695874953, Desviación Estándar: 2.342591413436464, Varianza: 5.48773453030625, Incertidumbre: 0.6497179583543717\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 14.105', 'Peso máximo al despegue (MTOW): 13.169', 'Velocidad de pérdida (KCAS): 18.842', 'envergadura: 12.22', 'payload: 14.913', 'Rango de comunicación: 16.315']\n", - "**Mediana calculada:** 14.509\n", - "\n", - "--- Imputación para aeronave: **ScanEagle** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.385x + 10.072\n", - "Valor del parámetro correlacionado para la aeronave: 1.063\n", - "Predicción obtenida: 15.797\n", - "\tR²: 0.4820128146718825, Desviación Estándar: 3.2870383565993335, Varianza: 10.804621157755248, Incertidumbre: 0.6211918601065917\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.114x + 12.545\n", - "Valor del parámetro correlacionado para la aeronave: 26.5\n", - "Predicción obtenida: 15.561\n", - "\tR²: 0.5577729360322713, Desviación Estándar: 2.858039912868348, Varianza: 8.168392143548514, Incertidumbre: 0.5716079825736695\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 15.316, 16.645, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 16.396, 14.509, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.664x + 7.593\n", - "Valor del parámetro correlacionado para la aeronave: 12.559\n", - "Predicción obtenida: 15.932\n", - "\tR²: 0.3998213157109771, Desviación Estándar: 3.6160406734700388, Varianza: 13.07575015218965, Incertidumbre: 0.7381212115959697\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.684x + 6.837\n", - "Valor del parámetro correlacionado para la aeronave: 3.1\n", - "Predicción obtenida: 15.158\n", - "\tR²: 0.5095357739591448, Desviación Estándar: 3.198518978768509, Varianza: 10.230523657542346, Incertidumbre: 0.6044632701101256\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.424x + 13.745\n", - "Valor del parámetro correlacionado para la aeronave: 5.0\n", - "Predicción obtenida: 15.864\n", - "\tR²: 0.44688240705153315, Desviación Estándar: 3.295517822105962, Varianza: 10.860437715818021, Incertidumbre: 0.6342227005706954\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 16.396, 14.509, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = 0.001x + 16.018\n", - "Valor del parámetro correlacionado para la aeronave: 101.86\n", - "Predicción obtenida: 16.133\n", - "\tR²: 0.0007471972665133997, Desviación Estándar: 2.300874321451344, Varianza: 5.294022643114182, Incertidumbre: 0.614934528635493\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 15.797', 'Peso máximo al despegue (MTOW): 15.561', 'Velocidad de pérdida (KCAS): 15.932', 'envergadura: 15.158', 'payload: 15.864', 'Rango de comunicación: 16.133']\n", - "**Mediana calculada:** 15.83\n", - "\n", - "--- Imputación para aeronave: **Integrator** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.384x + 10.075\n", - "Valor del parámetro correlacionado para la aeronave: 1.872\n", - "Predicción obtenida: 20.154\n", - "\tR²: 0.4836358210043519, Desviación Estándar: 3.2298738173488455, Varianza: 10.432084875995603, Incertidumbre: 0.5997725107817994\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.114x + 12.56\n", - "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 21.063\n", - "\tR²: 0.5584094722839654, Desviación Estándar: 2.803013662480037, Varianza: 7.856885592049752, Incertidumbre: 0.5497162062251112\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 15.316, 16.645, 12.559, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 16.396, 14.509, 15.83, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.665x + 7.58\n", - "Valor del parámetro correlacionado para la aeronave: 13.051\n", - "Predicción obtenida: 16.253\n", - "\tR²: 0.40384854496261013, Desviación Estándar: 3.543037251696186, Varianza: 12.553112966906864, Incertidumbre: 0.7086074503392372\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.673x + 6.905\n", - "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 19.733\n", - "\tR²: 0.51034062753414, Desviación Estándar: 3.145245417564829, Varianza: 9.892568736712557, Incertidumbre: 0.5840574114645236\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.424x + 13.743\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 21.375\n", - "\tR²: 0.44959491624800174, Desviación Estándar: 3.2361405160283896, Varianza: 10.47260543948049, Incertidumbre: 0.6115730723622395\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 16.396, 14.509, 15.83, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = 0.001x + 16.008\n", - "Valor del parámetro correlacionado para la aeronave: 92.6\n", - "Predicción obtenida: 16.101\n", - "\tR²: 0.0006009919023085564, Desviación Estándar: 2.2241277836738833, Varianza: 4.946744398110101, Incertidumbre: 0.5742673244004108\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 20.154', 'Peso máximo al despegue (MTOW): 21.063', 'Velocidad de pérdida (KCAS): 16.253', 'envergadura: 19.733', 'payload: 21.375', 'Rango de comunicación: 16.101']\n", - "**Mediana calculada:** 19.944\n", - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.373x + 10.083\n", - "Valor del parámetro correlacionado para la aeronave: 2.09\n", - "Predicción obtenida: 21.309\n", - "\tR²: 0.49007651885627856, Desviación Estándar: 3.1758029669681096, Varianza: 10.085724485003448, Incertidumbre: 0.5798196410667559\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.112x + 12.592\n", - "Valor del parámetro correlacionado para la aeronave: 75.0\n", - "Predicción obtenida: 20.973\n", - "\tR²: 0.5653829737578795, Desviación Estándar: 2.7581885003227513, Varianza: 7.607603803312669, Incertidumbre: 0.5308136243790236\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.677x + 6.895\n", - "Valor del parámetro correlacionado para la aeronave: 5.033\n", - "Predicción obtenida: 20.368\n", - "\tR²: 0.5164434789260648, Desviación Estándar: 3.092606517663101, Varianza: 9.56421507309229, Incertidumbre: 0.5646301170705265\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 18.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.415x + 13.775\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 21.244\n", - "\tR²: 0.45243459789702367, Desviación Estándar: 3.1899291559574228, Varianza: 10.175648020027236, Incertidumbre: 0.5923549733763512\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 21.309', 'Peso máximo al despegue (MTOW): 20.973', 'envergadura: 20.368', 'payload: 21.244']\n", - "**Mediana calculada:** 21.108\n", - "\n", - "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.359x + 10.095\n", - "Valor del parámetro correlacionado para la aeronave: 1.872\n", - "Predicción obtenida: 20.127\n", - "\tR²: 0.5020709354180335, Desviación Estándar: 3.1243525541900556, Varianza: 9.761578882873925, Incertidumbre: 0.5611502841334306\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.112x + 12.589\n", - "Valor del parámetro correlacionado para la aeronave: 74.8\n", - "Predicción obtenida: 20.963\n", - "\tR²: 0.5809327536697524, Desviación Estándar: 2.7085965129650105, Varianza: 7.336495070046214, Incertidumbre: 0.5118766268087254\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.696x + 6.845\n", - "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 19.785\n", - "\tR²: 0.5270307946868045, Desviación Estándar: 3.045038079257076, Varianza: 9.272256904125623, Incertidumbre: 0.5469049839079977\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.414x + 13.778\n", - "Valor del parámetro correlacionado para la aeronave: 18.0\n", - "Predicción obtenida: 21.233\n", - "\tR²: 0.46486409456400923, Desviación Estándar: 3.1364030940850203, Varianza: 9.837024368586087, Incertidumbre: 0.5726262413531211\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 20.127', 'Peso máximo al despegue (MTOW): 20.963', 'envergadura: 19.785', 'payload: 21.233']\n", - "**Mediana calculada:** 20.545\n", - "\n", - "--- Imputación para aeronave: **ScanEagle 3** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.378x + 10.082\n", - "Valor del parámetro correlacionado para la aeronave: 1.349\n", - "Predicción obtenida: 17.337\n", - "\tR²: 0.509633634350891, Desviación Estándar: 3.0759861908690347, Varianza: 9.461691046416995, Incertidumbre: 0.543762673599918\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.111x + 12.598\n", - "Valor del parámetro correlacionado para la aeronave: 36.3\n", - "Predicción obtenida: 16.641\n", - "\tR²: 0.5907860344296275, Desviación Estándar: 2.6625239079484526, Varianza: 7.089033560397101, Incertidumbre: 0.49441827749097367\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.711x + 6.811\n", - "Valor del parámetro correlacionado para la aeronave: 4.0\n", - "Predicción obtenida: 17.653\n", - "\tR²: 0.5335788768451615, Desviación Estándar: 2.9999440120772443, Varianza: 8.999664075598112, Incertidumbre: 0.5303201885299493\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.41x + 13.791\n", - "Valor del parámetro correlacionado para la aeronave: 8.6\n", - "Predicción obtenida: 17.321\n", - "\tR²: 0.4719735374132813, Desviación Estándar: 3.0876799487134994, Varianza: 9.533767465687397, Incertidumbre: 0.5545636897507049\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 17.337', 'Peso máximo al despegue (MTOW): 16.641', 'envergadura: 17.653', 'payload: 17.321']\n", - "**Mediana calculada:** 17.329\n", - "\n", - "--- Imputación para aeronave: **RQ Nan 21A Blackjack** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.378x + 10.081\n", - "Valor del parámetro correlacionado para la aeronave: 1.802\n", - "Predicción obtenida: 19.773\n", - "\tR²: 0.5096506431121606, Desviación Estándar: 3.0290221145859624, Varianza: 9.174974970650814, Incertidumbre: 0.5272850695450955\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.111x + 12.626\n", - "Valor del parámetro correlacionado para la aeronave: 61.0\n", - "Predicción obtenida: 19.412\n", - "\tR²: 0.5899119116525353, Desviación Estándar: 2.6206837047329836, Varianza: 6.867983080252995, Incertidumbre: 0.47846919372282115\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 15.316, 16.645, 12.559, 13.051, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 16.396, 14.509, 15.83, 19.944, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.649x + 7.958\n", - "Valor del parámetro correlacionado para la aeronave: 13.051\n", - "Predicción obtenida: 16.427\n", - "\tR²: 0.38497447747770597, Desviación Estándar: 3.545344634636701, Varianza: 12.569468578347243, Incertidumbre: 0.695299287477847\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.71x + 6.803\n", - "Valor del parámetro correlacionado para la aeronave: 4.8\n", - "Predicción obtenida: 19.812\n", - "\tR²: 0.53342997768055, Desviación Estándar: 2.954663687517189, Varianza: 8.730037506332673, Incertidumbre: 0.5143409288604107\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.41x + 13.792\n", - "Valor del parámetro correlacionado para la aeronave: 17.7\n", - "Predicción obtenida: 21.056\n", - "\tR²: 0.4721125878507184, Desviación Estándar: 3.039052343695801, Varianza: 9.235839147722942, Incertidumbre: 0.5372336301520427\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 16.396, 14.509, 15.83, 19.944, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = 0.002x + 16.192\n", - "Valor del parámetro correlacionado para la aeronave: 92.6\n", - "Predicción obtenida: 16.346\n", - "\tR²: 0.0013961471345631526, Desviación Estándar: 2.3455650352893986, Varianza: 5.501675334772157, Incertidumbre: 0.5863912588223497\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 19.773', 'Peso máximo al despegue (MTOW): 19.412', 'Velocidad de pérdida (KCAS): 16.427', 'envergadura: 19.812', 'payload: 21.056', 'Rango de comunicación: 16.346']\n", - "**Mediana calculada:** 19.592\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #MAP** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.371x + 10.086\n", - "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 13.846\n", - "\tR²: 0.5130352786837467, Desviación Estándar: 2.984299405987304, Varianza: 8.906042944576173, Incertidumbre: 0.5118031257684521\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.111x + 12.626\n", - "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 13.317\n", - "\tR²: 0.5946184657373111, Desviación Estándar: 2.5782613181332947, Varianza: 6.647431424582433, Incertidumbre: 0.4630694027472812\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 15.316, 16.645, 12.559, 13.051, 13.051, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 16.396, 14.509, 15.83, 19.944, 19.592, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.636x + 8.268\n", - "Valor del parámetro correlacionado para la aeronave: 15.316\n", - "Predicción obtenida: 18.011\n", - "\tR²: 0.3707919802946765, Desviación Estándar: 3.529606447333878, Varianza: 12.458121673060882, Incertidumbre: 0.6792730775005511\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.706x + 6.812\n", - "Valor del parámetro correlacionado para la aeronave: 2.35\n", - "Predicción obtenida: 13.172\n", - "\tR²: 0.5366242582049442, Desviación Estándar: 2.9111212195272542, Varianza: 8.474626754781848, Incertidumbre: 0.49925317032725897\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.403x + 13.817\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 14.301\n", - "\tR²: 0.4717960925463479, Desviación Estándar: 3.002745499394878, Varianza: 9.016480534136194, Incertidumbre: 0.5227108979661816\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 16.396, 14.509, 15.83, 19.944, 19.592, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = 0.002x + 16.338\n", - "Valor del parámetro correlacionado para la aeronave: 50.0\n", - "Predicción obtenida: 16.448\n", - "\tR²: 0.0021729000438440726, Desviación Estándar: 2.4001143739811432, Varianza: 5.760549008190895, Incertidumbre: 0.5821132398522036\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 13.846', 'Peso máximo al despegue (MTOW): 13.317', 'Velocidad de pérdida (KCAS): 18.011', 'envergadura: 13.172', 'payload: 14.301', 'Rango de comunicación: 16.448']\n", - "**Mediana calculada:** 14.074\n", - "\n", - "--- Imputación para aeronave: **DeltaQuad Pro #CARGO** ---\n", - "\n", - "--- Correlación: Área del ala (r = 0.711) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Área del ala: [0.87, 1.158, 1.55, 1.55, 1.55, 2.503, 0.57, 0.94, 1.608, 1.2, 0.754, 1.063, 1.872, 2.09, 1.872, 1.349, 1.802, 0.84, 0.7, 0.8, 0.52, 1.03, 1.202, 1.203, 1.424, 0.88, 1.0, 1.33, 1.32, 2.615, 2.615, 1.993, 0.771, 0.986, 2.329]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.074, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 5.358x + 10.11\n", - "Valor del parámetro correlacionado para la aeronave: 0.7\n", - "Predicción obtenida: 13.861\n", - "\tR²: 0.5219079320200679, Desviación Estándar: 2.941594049068034, Varianza: 8.65297554951247, Incertidumbre: 0.4972201452507867\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Peso máximo al despegue (MTOW) (r = 0.768) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Peso máximo al despegue (MTOW): [13.6, 19.958, 42.2, 53.5, 54.4, 93.0, 13.1, 20.0, 55.0, 32.0, 6.5, 26.5, 74.8, 75.0, 74.8, 36.3, 61.0, 10.0, 6.2, 12.5, 23.5, 32.0, 24.0, 40.0, 15.0, 28.0, 28.0, 90.0, 100.0, 9.5, 18.0, 91.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.074, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 15.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.11x + 12.69\n", - "Valor del parámetro correlacionado para la aeronave: 6.2\n", - "Predicción obtenida: 13.374\n", - "\tR²: 0.6010085744446478, Desviación Estándar: 2.5409184754535725, Varianza: 6.456266698901308, Incertidumbre: 0.4491751711088513\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Velocidad de pérdida (KCAS) (r = 0.817) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 3600', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para Velocidad de pérdida (KCAS): [14.838, 9.415, 10.532, 10.532, 10.532, 18.907, 10.0, 10.0, 15.316, 16.645, 12.559, 13.051, 13.051, 14.0, 15.316, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 19.109, 24.0, 25.0, 13.0, 13.0, 13.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 18.753, 16.396, 14.509, 15.83, 19.944, 19.592, 14.0, 14.074, 15.5, 17.0, 18.0, 17.397, 24.0, 12.5, 15.0, 25.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.634x + 8.16\n", - "Valor del parámetro correlacionado para la aeronave: 15.607\n", - "Predicción obtenida: 18.054\n", - "\tR²: 0.3591533059239339, Desviación Estándar: 3.5421565229641567, Varianza: 12.546872833177526, Incertidumbre: 0.669404661759172\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: envergadura (r = 0.774) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2600', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Volitation VT510', 'Ascend', 'Transition', 'Reach']\n", - "Valores para envergadura: [3.657, 4.877, 4.4, 4.4, 4.4, 5.644, 2.9, 3.0, 5.2, 4.4, 2.1, 3.1, 4.8, 5.033, 4.8, 4.0, 4.8, 2.69, 2.35, 2.15, 2.45, 3.2, 3.5, 3.9, 6.5, 2.6, 2.93, 3.6, 3.6, 5.0, 5.0, 5.1, 2.0, 3.0, 6.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.074, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 10.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 2.677x + 6.953\n", - "Valor del parámetro correlacionado para la aeronave: 2.35\n", - "Predicción obtenida: 13.244\n", - "\tR²: 0.5439573083064899, Desviación Estándar: 2.87296092523383, Varianza: 8.253904477920424, Incertidumbre: 0.4856190299260294\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: payload (r = 0.705) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'Aerosonde Mk. 4.8 VTOL FTUAS', 'AAI Aerosonde', 'Fulmar X', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'Integrator VTOL', 'Integrator Extended Range (ER)', 'ScanEagle 3', 'RQ Nan 21A Blackjack', 'DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39', 'Volitation VT370', 'Skyeye 2930 VTOL', 'Skyeye 3600', 'Skyeye 3600 VTOL', 'Skyeye 5000', 'Skyeye 5000 VTOL', 'Skyeye 5000 VTOL octo', 'Ascend', 'Transition', 'Reach']\n", - "Valores para payload: [2.495, 2.495, 14.5, 11.3, 17.7, 22.7, 4.0, 2.495, 12.0, 5.5, 2.693, 5.0, 18.0, 18.0, 18.0, 8.6, 17.7, 3.0, 1.2, 1.5, 2.2, 5.0, 10.0, 5.0, 18.0, 6.0, 10.0, 10.0, 20.0, 25.0, 15.0, 0.6, 1.5, 7.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 25.0, 10.0, 14.807, 18.753, 16.396, 14.509, 15.83, 19.944, 21.108, 20.545, 17.329, 19.592, 14.0, 14.074, 14.0, 15.5, 17.0, 18.0, 17.397, 24.0, 18.0, 12.5, 24.0, 15.0, 25.0, 25.0, 14.0, 10.0, 25.0]\n", - "Ecuación de regresión: y = 0.404x + 13.799\n", - "Valor del parámetro correlacionado para la aeronave: 1.2\n", - "Predicción obtenida: 14.285\n", - "\tR²: 0.48382243545428616, Desviación Estándar: 2.95849521726017, Varianza: 8.7526939505513, Incertidumbre: 0.5073777439109984\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "\n", - "--- Correlación: Rango de comunicación (r = -0.874) ---\n", - "Aeronaves utilizadas: ['Stalker XE', 'Stalker VXE30', 'Aerosonde Mk. 4.7 Fixed Wing', 'Aerosonde Mk. 4.7 VTOL', 'Aerosonde Mk. 4.8 Fixed wing', 'AAI Aerosonde', 'Orbiter 4', 'Orbiter 3', 'Mantis', 'ScanEagle', 'Integrator', 'RQ Nan 21A Blackjack', 'DeltaQuad Pro #MAP', 'V21', 'V25', 'V32', 'V35', 'V39']\n", - "Valores para Rango de comunicación: [59.0, 161.0, 140.0, 140.0, 140.0, 150.0, 150.0, 50.0, 25.0, 101.86, 92.6, 92.6, 50.0, 30.0, 30.0, 30.0, 30.0, 30.0]\n", - "Valores para Velocidad de pérdida limpia (KCAS): [14.872, 14.787, 17.306, 18.62, 18.423, 10.0, 18.753, 16.396, 14.509, 15.83, 19.944, 19.592, 14.074, 14.0, 15.5, 17.0, 18.0, 17.397]\n", - "Ecuación de regresión: y = 0.004x + 16.06\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 16.179\n", - "\tR²: 0.006797842032161827, Desviación Estándar: 2.3934134703855308, Varianza: 5.72842804022291, Incertidumbre: 0.5641329650309457\n", - "\tNivel de confianza: Confianza Muy Baja\n", - "Valores imputados: ['Área del ala: 13.861', 'Peso máximo al despegue (MTOW): 13.374', 'Velocidad de pérdida (KCAS): 18.054', 'envergadura: 13.244', 'payload: 14.285', 'Rango de comunicación: 16.179']\n", - "**Mediana calculada:** 14.073\n", - "\n", - "=== envergadura: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Cuerda: No hay valores faltantes para imputar. ===\n", - "\n", - "=== payload: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Empty weight: No hay valores faltantes para imputar. ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Reporte Final de Imputaciones

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    AeronaveParámetroValor ImputadoNivel de Confianza
    5Aerosonde Mk. 4.7 VTOLVelocidad de pérdida (KCAS)10.5320.512
    6Aerosonde Mk. 4.8 Fixed wingVelocidad de pérdida (KCAS)10.5320.512
    7Orbiter 4Velocidad de pérdida (KCAS)10.0000.512
    8Orbiter 3Velocidad de pérdida (KCAS)15.3160.512
    9MantisVelocidad de pérdida (KCAS)16.6450.547
    10ScanEagleVelocidad de pérdida (KCAS)12.5590.577
    11IntegratorVelocidad de pérdida (KCAS)13.0510.602
    12RQ Nan 21A BlackjackVelocidad de pérdida (KCAS)13.0510.623
    13DeltaQuad Pro #MAPVelocidad de pérdida (KCAS)15.3160.623
    14DeltaQuad Pro #CARGOVelocidad de pérdida (KCAS)15.6070.623
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de Imputaciones

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    AeronaveCantidad de Valores Imputados
    0Aerosonde Mk. 4.7 VTOL1.000
    1Aerosonde Mk. 4.8 Fixed wing1.000
    2DeltaQuad Pro #CARGO1.000
    3DeltaQuad Pro #MAP1.000
    4Integrator1.000
    5Mantis1.000
    6Orbiter 31.000
    7Orbiter 41.000
    8RQ Nan 21A Blackjack1.000
    9ScanEagle1.000
    TotalTotal10.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m>>> RESULTADOS DE IMPUTACIÓN POR CORRELACIÓN\u001b[0m\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 3600 = 17070.833 (Correlación)\n", - "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 = 16254.028 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Stalker XE = 14.838 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Stalker VXE30 = 9.415 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde Mk. 4.7 Fixed Wing = 10.532 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde Mk. 4.7 VTOL = 10.532 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde Mk. 4.8 Fixed wing = 10.532 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Orbiter 4 = 10.0 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Orbiter 3 = 15.316 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Mantis = 16.645 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - ScanEagle = 12.559 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Integrator = 13.051 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - RQ Nan 21A Blackjack = 13.051 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - DeltaQuad Pro #MAP = 15.316 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - DeltaQuad Pro #CARGO = 15.607000000000001 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Stalker XE = 14.872 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Stalker VXE30 = 14.786999999999999 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Aerosonde Mk. 4.7 Fixed Wing = 17.306 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Aerosonde Mk. 4.7 VTOL = 18.62 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Aerosonde Mk. 4.8 Fixed wing = 18.423 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Fulmar X = 14.807 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Orbiter 4 = 18.753 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Orbiter 3 = 16.3955 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Mantis = 14.509 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - ScanEagle = 15.8305 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Integrator = 19.9435 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Integrator VTOL = 21.1085 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Integrator Extended Range (ER) = 20.545 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - ScanEagle 3 = 17.329 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - RQ Nan 21A Blackjack = 19.5925 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - DeltaQuad Pro #MAP = 14.0735 (Correlación)\n", - "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - DeltaQuad Pro #CARGO = 14.073 (Correlación)\n", - "\n", - "=== Iteración 2: Resumen después de imputaciones ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de Valores Faltantes Después de Iteración 2

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    ColumnaValores Faltantes
    0Stalker XE0.000
    1Stalker VXE300.000
    2Aerosonde Mk. 4.7 Fixed Wing0.000
    3Aerosonde Mk. 4.7 VTOL0.000
    4Aerosonde Mk. 4.8 Fixed wing0.000
    5Aerosonde Mk. 4.8 VTOL FTUAS0.000
    6AAI Aerosonde0.000
    7Fulmar X1.000
    8Orbiter 40.000
    9Orbiter 30.000
    10Mantis1.000
    11ScanEagle0.000
    12Integrator0.000
    13Integrator VTOL1.000
    14Integrator Extended Range (ER)1.000
    15ScanEagle 31.000
    16RQ Nan 21A Blackjack0.000
    17DeltaQuad Evo0.000
    18DeltaQuad Pro #MAP0.000
    19DeltaQuad Pro #CARGO0.000
    20V210.000
    21V250.000
    22V320.000
    23V350.000
    24V390.000
    25Volitation VT3700.000
    26Skyeye 26000.000
    27Skyeye 2930 VTOL0.000
    28Skyeye 36001.000
    29Skyeye 3600 VTOL0.000
    30Skyeye 50000.000
    31Skyeye 5000 VTOL0.000
    32Skyeye 5000 VTOL octo0.000
    33Volitation VT5100.000
    34Ascend0.000
    35Transition0.000
    36Reach0.000
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    Sumatoria Total de Valores Faltantes

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    ResumenCantidad
    0Total de Valores Faltantes6.000
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    Resumen de Valores Faltantes Antes de Iteración 3

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    ColumnaValores Faltantes
    0Stalker XE30.000
    1Stalker VXE3031.000
    2Aerosonde Mk. 4.7 Fixed Wing28.000
    3Aerosonde Mk. 4.7 VTOL27.000
    4Aerosonde Mk. 4.8 Fixed wing31.000
    5Aerosonde Mk. 4.8 VTOL FTUAS33.000
    6AAI Aerosonde30.000
    7Fulmar X35.000
    8Orbiter 434.000
    9Orbiter 334.000
    10Mantis34.000
    11ScanEagle33.000
    12Integrator33.000
    13Integrator VTOL33.000
    14Integrator Extended Range (ER)36.000
    15ScanEagle 334.000
    16RQ Nan 21A Blackjack32.000
    17DeltaQuad Evo28.000
    18DeltaQuad Pro #MAP30.000
    19DeltaQuad Pro #CARGO30.000
    20V2128.000
    21V2528.000
    22V3228.000
    23V3531.000
    24V3931.000
    25Volitation VT37030.000
    26Skyeye 260033.000
    27Skyeye 2930 VTOL32.000
    28Skyeye 360033.000
    29Skyeye 3600 VTOL31.000
    30Skyeye 500029.000
    31Skyeye 5000 VTOL30.000
    32Skyeye 5000 VTOL octo30.000
    33Volitation VT51030.000
    34Ascend29.000
    35Transition29.000
    36Reach29.000
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    Sumatoria Total de Valores Faltantes

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    ResumenCantidad
    0Total de Valores Faltantes1147.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--------------------------------------------------------------------------------\n", - "\u001b[1m*** IMPUTACIÓN POR SIMILITUD - ITERACIÓN 3 ***\u001b[0m\n", - "--------------------------------------------------------------------------------\n", - "\u001b[1m\n", - "=== Imputación por similitud: Skyeye 3600 - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Mantis - Techo de servicio máximo ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Fulmar X - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1mNo se realizaron imputaciones por similitud en esta iteración.\u001b[0m\n", - "\n", - "--------------------------------------------------------------------------------\n", - "\u001b[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN 3 ***\u001b[0m\n", - "--------------------------------------------------------------------------------\n", - "\n", - "=== DataFrame inicial ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())

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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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    Stalker XEStalker VXE30Aerosonde Mk. 4.7 Fixed WingAerosonde Mk. 4.7 VTOLAerosonde Mk. 4.8 Fixed wingAerosonde Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQ Nan 21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
    Modelo
    Distancia de carrera requerida para despegue0.00.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
    Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.0
    Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.3440530.22866936.09414730.40658430.46641927.42637218.26582630.62533630.95346521.46331.89437625.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.62533630.29090932.8128621.8752421.8752427.34405
    Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.8429403.635186839.144606NaN19500.019500.07013.83419500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.014972.95591316000.017070.83316959.09187416254.02816009.43594316009.47636617000.010000.013000.016000.0
    Velocidad de pérdida limpia (KCAS)14.87214.78717.30618.6218.42325.010.014.80718.75316.395514.50915.830519.943521.108520.54517.32919.592514.014.073514.07314.015.517.018.017.3973924.010.018.012.524.015.025.025.025.014.010.025.0
    Área del ala0.871.1582831.551.551.552.5030.570.941.6081.20.7541.0631.8722.08951.8721.3491.8020.840.70.70.80.521.031.2021.2031.4240.881.01.331.322.6152.6152.6151.9930.7710.9862.329
    Relación de aspecto del ala15.30125515.32644912.512.512.512.514.75438613.217513.44313.934514.75514.05712.90812.64812.8413.76512.91414.58914.71414.71414.56814.42114.18213.89814.041513.64514.10314.00113.709513.671512.69513.03212.855513.09914.34914.22313.669
    Longitud del fuselaje2.12.59083.03.03.03.59451.71.21.21.21.481.712.52.9982.52.42.50.750.90.90.930.931.01.881.9542.022.052.032.4882.423.53.53.52.9051.5622.34.712
    Profundidad del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Ancho del fuselaje0.2110.20.2770.2770.277NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.3750.3750.375NaNNaNNaNNaN
    Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
    Alcance de la aeronave370.0433.0518.9225481.428535.2755800.03270.0800.0509.556550.025.0503.5155500.0646.0835500.050.0565.912270.0100.0100.0270.0270.0412.686456.221413.556300.03270.0425.273458.1245300.0530.401800.0800.0800.0270.0506.641800.0
    Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.011.6729075.06.012.020.0
    Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.4388642.25267530.84572541.736.036.025.641.246.340.21646.341.246.333.029.00929.00933.033.033.033.033.033.030.83428930.035.098533.042.042.038.050.030.030.035.0
    Velocidad de pérdida (KCAS)14.8389.41510.53210.53210.53218.90746510.0NaN10.015.31616.64512.55913.051NaNNaNNaN13.05114.015.31615.60714.015.517.018.017.3973924.010.018.012.524.015.019.10922524.025.013.013.013.0
    Tasa de ascensoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Radio de giroNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100.0120.0150.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    envergadura3.6574.87684.44.44.45.6442.93.05.24.42.13.14.85.0334.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
    Cuerda0.2390.3181950.3520.3520.3520.3940.1965520.3190.3320.3040.2710.29850.33850.3410.3450.31150.3410.27550.2720.2720.2780.2810.2920.3060.3070.3140.2960.30.3110.3150.34850.3380.34450.3350.2870.2910.313
    payload2.4947562.49475614.511.317.722.74.02.49475612.05.52.6935.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
    duracion en VTOL2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050.050.05
    Crucero KIAS15.4333216.09342225.025.025.0NaNNaN27.8NaNNaN16.728.028.3NaNNaN23.530.916.5416.016.018.020.020.025.025.025.033.024.0NaN30.033.028.035.030.020.020.025.0
    RTF (dry weight)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.011.854.0
    RTF (Including fuel & Batteries)NaNNaN27.742.236.770.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8.916.584.0
    Empty weight10.88620817.46329219.79619.79619.80931.010.017.46329218.36512.2375.63310.19222.19524.784522.25714.79421.1234.84.7544.7542.653.456.457.16.70830311.06.57.111.511.032.032.140535.023.9593.05.831.0
    Maximum CrosswindNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.0NaNNaNNaN45.050.050.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15.015.015.0
    Rango de comunicación59.0161.0140.0140.0140.0NaN150.0NaN150.050.025.0101.8692.6NaNNaNNaN92.6NaN50.030.030.030.030.030.030.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Wing LoadingNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.524.025.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Potencia específica (P/W)NaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Capacidad combustibleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.0NaNNaN11.511.528.028.028.025.0NaNNaNNaN
    ConsumoNaNNaN0.60.6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.96NaNNaNNaNNaN1.2NaNNaN5.0NaNNaNNaN
    Potencia WattsNaNNaN2980.02980.0NaNNaN1280.0NaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Potencia HPNaNNaN4.04.0NaNNaN1.74NaNNaNNaNNaNNaNNaNNaNNaNNaN8.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    PrecioNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.0NaNNaNNaN
    Tiempo de emergencia en vueloNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.1080.1080.108NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Distancia de aterrizajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Despegue1.01.01.02.01.02.02.01.01.01.01.01.01.02.01.01.01.02.02.02.02.02.02.02.02.02.02.02.03.02.03.02.02.02.02.02.02.0
    Propulsión horizontal2.02.02.02.02.02.02.02.02.01.01.02.02.02.02.02.02.01.01.01.01.01.02.02.02.02.02.02.02.02.02.02.02.02.02.02.02.0
    Propulsión vertical5.05.05.01.05.01.01.05.05.05.05.05.05.01.05.05.05.01.01.01.01.01.01.01.01.01.01.01.05.01.05.01.01.01.01.01.01.0
    Cantidad de motores propulsión vertical0.00.00.04.00.04.04.00.00.00.00.00.00.04.00.00.00.04.04.04.04.04.04.04.04.04.04.04.00.04.00.04.08.04.04.04.04.0
    Cantidad de motores propulsión horizontal1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
    Misión1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
    Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Battery Power SupplyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Modelo Motor Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Modelo Motor VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    PortabilidadNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    CámaraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Despegue todos los tiposNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Datalink banksNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Motor recomendadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Hélice recomendada VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Hélice recomendada Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Sistema de controlNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Características adicionalesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    indice_desconocidoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Convertir todo a numérico ===\n", - "\n", - "\n", - "=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===\n", - "\n", - "Umbral seleccionado para correlaciones significativas: 0.7\n", - "\n", - "=== Cálculo de tabla completa ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Tabla de Correlaciones con todos los parametros(tabla_completa)

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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    ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
    Modelo
    Distancia de carrera requerida para despegue1.0000.0630.4270.082-0.2730.269-0.2500.2220.4250.1680.054-0.0780.241-0.1950.1050.2000.229nan0.389nan0.225nannan-0.018-0.240-0.1560.7350.1540.671-0.598nannan
    Altitud a la que se realiza el crucero0.0631.0000.0110.0950.008-0.0750.105-0.092nan-0.095-0.309-0.278-0.0640.216-0.0800.109-0.114nannannan-0.1210.038-0.325nannannan-0.119-0.1090.187-0.159nannan
    Velocidad a la que se realiza el crucero (KTAS)0.4270.0111.0000.1340.3160.497-0.6070.4280.9170.5530.5450.4230.6340.1060.4210.3960.567-0.6940.9730.7900.503-0.8540.4800.3650.461-0.2900.0630.5630.120-0.076nannan
    Techo de servicio máximo0.0820.0950.1341.0000.1030.096-0.0290.1350.5900.1220.0970.0010.0140.1050.0220.0280.118-0.8750.1120.579-0.004-0.961-0.118-0.8190.461-0.1560.1960.068-0.1360.140nannan
    Velocidad de pérdida limpia (KCAS)-0.2730.0080.3160.1031.0000.753-0.5980.5940.6750.798-0.2300.2600.5160.6440.7470.6430.731-0.1900.4930.9310.678-0.2370.1290.2240.6300.118-0.0280.322-0.1600.232nannan
    Área del ala0.269-0.0750.4970.0960.7531.000-0.7780.8350.9840.970-0.0230.3830.6480.3050.8250.7870.854-0.3830.6920.9650.944-0.4660.4910.9740.2880.0360.1250.4760.0720.037nannan
    Relación de aspecto del ala-0.2500.105-0.607-0.029-0.598-0.7781.000-0.624-0.681-0.790-0.003-0.456-0.730-0.149-0.630-0.862-0.8260.519-0.769-0.497-0.7460.432-0.409-0.9700.2960.024-0.001-0.471-0.1410.075nannan
    Longitud del fuselaje0.222-0.0920.4280.1350.5940.835-0.6241.0000.9380.8060.1400.4030.3630.1170.7190.6230.660-0.6170.5740.9260.834-0.6960.6460.9290.036-0.2030.1380.6120.0340.040nannan
    Ancho del fuselaje0.425nan0.9170.5900.6750.984-0.6810.9381.0000.9860.833-0.0890.9400.7110.6710.5570.868nan0.944nan0.954nan0.323nan1.000nan0.794nan-0.5350.574nannan
    Peso máximo al despegue (MTOW)0.168-0.0950.5530.1220.7980.970-0.7900.8060.9861.0000.0300.4200.7170.3510.8110.7510.882-0.4010.7080.9790.933-0.4640.5140.9760.7580.0520.0900.4670.0230.075nannan
    Alcance de la aeronave0.054-0.3090.5450.097-0.230-0.023-0.0030.1400.8330.0301.0000.2240.013-0.258-0.043-0.2240.019-0.5250.5240.9360.081-0.7110.4670.8480.837-0.1480.1570.317-0.2130.210nannan
    Autonomía de la aeronave-0.078-0.2780.4230.0010.2600.383-0.4560.403-0.0890.4200.2241.0000.378-0.3430.5410.2690.400-0.5940.3370.6340.486-0.7150.8020.056-0.7320.033-0.4200.4780.353-0.314nannan
    Velocidad máxima (KIAS)0.241-0.0640.6340.0140.5160.648-0.7300.3630.9400.7170.0130.3781.0000.2610.4910.6270.718-0.0770.7000.7260.613-0.2230.1510.7270.9100.067-0.0570.3000.178-0.141nannan
    Velocidad de pérdida (KCAS)-0.1950.2160.1060.1050.6440.305-0.1490.1170.7110.351-0.258-0.3430.2611.0000.2340.1680.3740.6720.2840.3230.1620.934-0.9610.0360.6850.1210.3010.005-0.4330.508nannan
    envergadura0.105-0.0800.4210.0220.7470.825-0.6300.7190.6710.811-0.0430.5410.4910.2341.0000.6860.775-0.2580.5010.9500.806-0.4140.6480.2970.0850.032-0.0810.5160.167-0.106nannan
    Cuerda0.2000.1090.3960.0280.6430.787-0.8620.6230.5570.751-0.2240.2690.6270.1680.6861.0000.758-0.4990.7300.5950.724-0.4980.3550.975-0.228-0.041-0.0650.4180.193-0.129nannan
    payload0.229-0.1140.5670.1180.7310.854-0.8260.6600.8680.8820.0190.4000.7180.3740.7750.7581.000-0.0240.6700.5590.784-0.1420.4890.7110.846-0.0080.0530.4620.100-0.055nannan
    duracion en VTOLnannan-0.694-0.875-0.190-0.3830.519-0.617nan-0.401-0.525-0.594-0.0770.672-0.258-0.499-0.0241.000-0.694-0.402-0.3151.000nannannannan-0.188-0.9040.188-0.188nannan
    Crucero KIAS0.389nan0.9730.1120.4930.692-0.7690.5740.9440.7080.5240.3370.7000.2840.5010.7300.670-0.6941.0000.7230.636-0.8550.3590.5810.461-0.2430.1430.6080.0650.063nannan
    RTF (Including fuel & Batteries)nannan0.7900.5790.9310.965-0.4970.926nan0.9790.9360.6340.7260.3230.9500.5950.559-0.4020.7231.0000.948-0.402nannannannan0.0970.428-0.0970.097nannan
    Empty weight0.225-0.1210.503-0.0040.6780.944-0.7460.8340.9540.9330.0810.4860.6130.1620.8060.7240.784-0.3150.6360.9481.000-0.3860.7850.9800.2510.023-0.0290.4800.195-0.070nannan
    Maximum Crosswindnan0.038-0.854-0.961-0.237-0.4660.432-0.696nan-0.464-0.711-0.715-0.2230.934-0.414-0.498-0.1421.000-0.855-0.402-0.3861.000nannannannannan-0.943nannannannan
    Rango de comunicaciónnan-0.3250.480-0.1180.1290.491-0.4090.6460.3230.5140.4670.8020.151-0.9610.6480.3550.489nan0.359nan0.785nan1.000nannannan-0.4300.6040.430-0.430nannan
    Capacidad combustible-0.018nan0.365-0.8190.2240.974-0.9700.929nan0.9760.8480.0560.7270.0360.2970.9750.711nan0.581nan0.980nannan1.0000.3770.817-0.080nan-0.0800.270nannan
    Consumo-0.240nan0.4610.4610.6300.2880.2960.0361.0000.7580.837-0.7320.9100.6850.085-0.2280.846nan0.461nan0.251nannan0.3771.0000.9980.113nan-0.3750.375nannan
    Precio-0.156nan-0.290-0.1560.1180.0360.024-0.203nan0.052-0.1480.0330.0670.1210.032-0.041-0.008nan-0.243nan0.023nannan0.8170.9981.000-0.1380.217-0.1380.134nannan
    Despegue0.735-0.1190.0630.196-0.0280.125-0.0010.1380.7940.0900.157-0.420-0.0570.301-0.081-0.0650.053-0.1880.1430.097-0.029nan-0.430-0.0800.113-0.1381.000-0.010-0.6390.610nannan
    Propulsión horizontal0.154-0.1090.5630.0680.3220.476-0.4710.612nan0.4670.3170.4780.3000.0050.5160.4180.462-0.9040.6080.4280.480-0.9430.604nannan0.217-0.0101.0000.118-0.083nannan
    Propulsión vertical0.6710.1870.120-0.136-0.1600.072-0.1410.034-0.5350.023-0.2130.3530.178-0.4330.1670.1930.1000.1880.065-0.0970.195nan0.430-0.080-0.375-0.138-0.6390.1181.000-0.954nannan
    Cantidad de motores propulsión vertical-0.598-0.159-0.0760.1400.2320.0370.0750.0400.5740.0750.210-0.314-0.1410.508-0.106-0.129-0.055-0.1880.0630.097-0.070nan-0.4300.2700.3750.1340.610-0.083-0.9541.000nannan
    Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de la Tabla

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    ResumenCantidad
    0Total de valores1024.000
    1Valores numéricos826.000
    2Valores NaN198.000
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    Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

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    ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
    Modelo
    Velocidad a la que se realiza el crucero (KTAS)1.0000.1340.497-0.6070.4280.5530.5450.4230.6340.1060.3160.4210.3960.5670.503
    Techo de servicio máximo0.1341.0000.096-0.0290.1350.1220.0970.0010.0140.1050.1030.0220.0280.118-0.004
    Área del ala0.4970.0961.000-0.7780.8350.970-0.0230.3830.6480.3050.7530.8250.7870.8540.944
    Relación de aspecto del ala-0.607-0.029-0.7781.000-0.624-0.790-0.003-0.456-0.730-0.149-0.598-0.630-0.862-0.826-0.746
    Longitud del fuselaje0.4280.1350.835-0.6241.0000.8060.1400.4030.3630.1170.5940.7190.6230.6600.834
    Peso máximo al despegue (MTOW)0.5530.1220.970-0.7900.8061.0000.0300.4200.7170.3510.7980.8110.7510.8820.933
    Alcance de la aeronave0.5450.097-0.023-0.0030.1400.0301.0000.2240.013-0.258-0.230-0.043-0.2240.0190.081
    Autonomía de la aeronave0.4230.0010.383-0.4560.4030.4200.2241.0000.378-0.3430.2600.5410.2690.4000.486
    Velocidad máxima (KIAS)0.6340.0140.648-0.7300.3630.7170.0130.3781.0000.2610.5160.4910.6270.7180.613
    Velocidad de pérdida (KCAS)0.1060.1050.305-0.1490.1170.351-0.258-0.3430.2611.0000.6440.2340.1680.3740.162
    Velocidad de pérdida limpia (KCAS)0.3160.1030.753-0.5980.5940.798-0.2300.2600.5160.6441.0000.7470.6430.7310.678
    envergadura0.4210.0220.825-0.6300.7190.811-0.0430.5410.4910.2340.7471.0000.6860.7750.806
    Cuerda0.3960.0280.787-0.8620.6230.751-0.2240.2690.6270.1680.6430.6861.0000.7580.724
    payload0.5670.1180.854-0.8260.6600.8820.0190.4000.7180.3740.7310.7750.7581.0000.784
    Empty weight0.503-0.0040.944-0.7460.8340.9330.0810.4860.6130.1620.6780.8060.7240.7841.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de la Tabla

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    ResumenCantidad
    0Total de valores225.000
    1Valores numéricos225.000
    2Valores NaN0.000
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    Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
    Modelo
    Velocidad a la que se realiza el crucero (KTAS)nannannannannannannannannannannannannannannan
    Techo de servicio máximonannannannannannannannannannannannannannannan
    Área del alanannannan-0.7780.8350.970nannannannan0.7530.8250.7870.8540.944
    Relación de aspecto del alanannan-0.778nannan-0.790nannan-0.730nannannan-0.862-0.826-0.746
    Longitud del fuselajenannan0.835nannan0.806nannannannannan0.719nannan0.834
    Peso máximo al despegue (MTOW)nannan0.970-0.7900.806nannannan0.717nan0.7980.8110.7510.8820.933
    Alcance de la aeronavenannannannannannannannannannannannannannannan
    Autonomía de la aeronavenannannannannannannannannannannannannannannan
    Velocidad máxima (KIAS)nannannan-0.730nan0.717nannannannannannannan0.718nan
    Velocidad de pérdida (KCAS)nannannannannannannannannannannannannannannan
    Velocidad de pérdida limpia (KCAS)nannan0.753nannan0.798nannannannannan0.747nan0.731nan
    envergaduranannan0.825nan0.7190.811nannannannan0.747nannan0.7750.806
    Cuerdanannan0.787-0.862nan0.751nannannannannannannan0.7580.724
    payloadnannan0.854-0.826nan0.882nannan0.718nan0.7310.7750.758nan0.784
    Empty weightnannan0.944-0.7460.8340.933nannannannannan0.8060.7240.784nan
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de la Tabla

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    ResumenCantidad
    0Total de valores225.000
    1Valores numéricos60.000
    2Valores NaN165.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Preparando datos para el heatmap ===\n", - "\n", - "=== Generando heatmap ===\n" - ] - }, - { - "data": { - "image/png": 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    Tabla de correlaciones con filtro de umbral de correlación

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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
    Modelo
    Distancia de carrera requerida para despeguenannannannannannannannannannannannannannannannannannannannannannannannannannan0.735nannannannannan
    Altitud a la que se realiza el cruceronannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Velocidad a la que se realiza el crucero (KTAS)nannannannannannannannan0.917nannannannannannannannannan0.9730.790nan-0.854nannannannannannannannannannan
    Techo de servicio máximonannannannannannannannannannannannannannannannannan-0.875nannannan-0.961nan-0.819nannannannannannannannan
    Velocidad de pérdida limpia (KCAS)nannannannannan0.753nannannan0.798nannannannan0.747nan0.731nannan0.931nannannannannannannannannannannannan
    Área del alanannannannan0.753nan-0.7780.8350.9840.970nannannannan0.8250.7870.854nannan0.9650.944nannan0.974nannannannannannannannan
    Relación de aspecto del alanannannannannan-0.778nannannan-0.790nannan-0.730nannan-0.862-0.826nan-0.769nan-0.746nannan-0.970nannannannannannannannan
    Longitud del fuselajenannannannannan0.835nannan0.9380.806nannannannan0.719nannannannan0.9260.834nannan0.929nannannannannannannannan
    Ancho del fuselajenannan0.917nannan0.984nan0.938nan0.9860.833nan0.9400.711nannan0.868nan0.944nan0.954nannannannannan0.794nannannannannan
    Peso máximo al despegue (MTOW)nannannannan0.7980.970-0.7900.8060.986nannannan0.717nan0.8110.7510.882nan0.7080.9790.933nannan0.9760.758nannannannannannannan
    Alcance de la aeronavenannannannannannannannan0.833nannannannannannannannannannan0.936nan-0.711nan0.8480.837nannannannannannannan
    Autonomía de la aeronavenannannannannannannannannannannannannannannannannannannannannan-0.7150.802nan-0.732nannannannannannannan
    Velocidad máxima (KIAS)nannannannannannan-0.730nan0.9400.717nannannannannannan0.718nannan0.726nannannan0.7270.910nannannannannannannan
    Velocidad de pérdida (KCAS)nannannannannannannannan0.711nannannannannannannannannannannannan0.934-0.961nannannannannannannannannan
    envergaduranannannannan0.7470.825nan0.719nan0.811nannannannannannan0.775nannan0.9500.806nannannannannannannannannannannan
    Cuerdanannannannannan0.787-0.862nannan0.751nannannannannannan0.758nan0.730nan0.724nannan0.975nannannannannannannannan
    payloadnannannannan0.7310.854-0.826nan0.8680.882nannan0.718nan0.7750.758nannannannan0.784nannan0.7110.846nannannannannannannan
    duracion en VTOLnannannan-0.875nannannannannannannannannannannannannannannannannannannannannannannan-0.904nannannannan
    Crucero KIASnannan0.973nannannan-0.769nan0.9440.708nannannannannan0.730nannannan0.723nan-0.855nannannannannannannannannannan
    RTF (Including fuel & Batteries)nannan0.790nan0.9310.965nan0.926nan0.9790.936nan0.726nan0.950nannannan0.723nan0.948nannannannannannannannannannannan
    Empty weightnannannannannan0.944-0.7460.8340.9540.933nannannannan0.8060.7240.784nannan0.948nannan0.7850.980nannannannannannannannan
    Maximum Crosswindnannan-0.854-0.961nannannannannannan-0.711-0.715nan0.934nannannannan-0.855nannannannannannannannan-0.943nannannannan
    Rango de comunicaciónnannannannannannannannannannannan0.802nan-0.961nannannannannannan0.785nannannannannannannannannannannan
    Capacidad combustiblenannannan-0.819nan0.974-0.9700.929nan0.9760.848nan0.727nannan0.9750.711nannannan0.980nannannannan0.817nannannannannannan
    Consumonannannannannannannannannan0.7580.837-0.7320.910nannannan0.846nannannannannannannannan0.998nannannannannannan
    Precionannannannannannannannannannannannannannannannannannannannannannannan0.8170.998nannannannannannannan
    Despegue0.735nannannannannannannan0.794nannannannannannannannannannannannannannannannannannannannannannannan
    Propulsión horizontalnannannannannannannannannannannannannannannannannan-0.904nannannan-0.943nannannannannannannannannannan
    Propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannan
    Cantidad de motores propulsión verticalnannannannannannannannannannannannannannannannannannannannannannannannannannannannan-0.954nannannan
    Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", - "\n", - "=== Imputación para el parámetro: **Velocidad a la que se realiza el crucero (KTAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Skyeye 3600** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

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    Mensaje
    0No se pudo imputar el parámetro 'Velocidad a la que se realiza el crucero (KTAS)' para la aeronave 'Skyeye 3600'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Imputación para el parámetro: **Techo de servicio máximo** ===\n", - "\n", - "--- Imputación para aeronave: **Mantis** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

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    Mensaje
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Mantis'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Área del ala: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Relación de aspecto del ala: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Longitud del fuselaje: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Peso máximo al despegue (MTOW): No hay valores faltantes para imputar. ===\n", - "\n", - "=== Alcance de la aeronave: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Autonomía de la aeronave: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Velocidad máxima (KIAS): No hay valores faltantes para imputar. ===\n", - "\n", - "=== Imputación para el parámetro: **Velocidad de pérdida (KCAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Fulmar X'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Integrator VTOL** ---\n", - "\n", - "--- Correlación: Maximum Crosswind (r = 0.934) ---\n", - "Aeronaves utilizadas: ['DeltaQuad Evo', 'DeltaQuad Pro #MAP', 'DeltaQuad Pro #CARGO', 'Ascend']\n", - "Valores para Maximum Crosswind: [45.0, 50.0, 50.0, 15.0]\n", - "Valores para Velocidad de pérdida (KCAS): [14.0, 15.316, 15.607, 13.0]\n", - "Ecuación de regresión: y = 0.064x + 11.929\n", - "Valor del parámetro correlacionado para la aeronave: 30.0\n", - "Predicción obtenida: 13.843\n", - "\tR²: 0.7881580184530225, Desviación Estándar: 0.48216925192301585, Varianza: 0.23248718750000075, Incertidumbre: 0.24108462596150793\n", - "\tNivel de confianza: Confianza Media\n", - "Valores imputados: ['Maximum Crosswind: 13.843']\n", - "**Mediana calculada:** 13.843\n", - "\n", - "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

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    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator Extended Range (ER)'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **ScanEagle 3** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle 3'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Velocidad de pérdida limpia (KCAS): No hay valores faltantes para imputar. ===\n", - "\n", - "=== envergadura: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Cuerda: No hay valores faltantes para imputar. ===\n", - "\n", - "=== payload: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Empty weight: No hay valores faltantes para imputar. ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Reporte Final de Imputaciones

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    AeronaveParámetroValor ImputadoNivel de Confianza
    0Integrator VTOLVelocidad de pérdida (KCAS)13.8430.630
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    Resumen de Imputaciones

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    AeronaveCantidad de Valores Imputados
    0Integrator VTOL1.000
    TotalTotal1.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m>>> RESULTADOS DE IMPUTACIÓN POR CORRELACIÓN\u001b[0m\n", - "Imputación final aplicada: Velocidad de pérdida (KCAS) - Integrator VTOL = 13.843 (Correlación)\n", - "\n", - "=== Iteración 3: Resumen después de imputaciones ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de Valores Faltantes Después de Iteración 3

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    ColumnaValores Faltantes
    0Stalker XE0.000
    1Stalker VXE300.000
    2Aerosonde Mk. 4.7 Fixed Wing0.000
    3Aerosonde Mk. 4.7 VTOL0.000
    4Aerosonde Mk. 4.8 Fixed wing0.000
    5Aerosonde Mk. 4.8 VTOL FTUAS0.000
    6AAI Aerosonde0.000
    7Fulmar X1.000
    8Orbiter 40.000
    9Orbiter 30.000
    10Mantis1.000
    11ScanEagle0.000
    12Integrator0.000
    13Integrator VTOL0.000
    14Integrator Extended Range (ER)1.000
    15ScanEagle 31.000
    16RQ Nan 21A Blackjack0.000
    17DeltaQuad Evo0.000
    18DeltaQuad Pro #MAP0.000
    19DeltaQuad Pro #CARGO0.000
    20V210.000
    21V250.000
    22V320.000
    23V350.000
    24V390.000
    25Volitation VT3700.000
    26Skyeye 26000.000
    27Skyeye 2930 VTOL0.000
    28Skyeye 36001.000
    29Skyeye 3600 VTOL0.000
    30Skyeye 50000.000
    31Skyeye 5000 VTOL0.000
    32Skyeye 5000 VTOL octo0.000
    33Volitation VT5100.000
    34Ascend0.000
    35Transition0.000
    36Reach0.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Sumatoria Total de Valores Faltantes

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    ResumenCantidad
    0Total de Valores Faltantes5.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "\u001b[1m=== FIN DE ITERACIÓN 3 ===\u001b[0m\n", - "================================================================================\n", - "\n", - "================================================================================\n", - "\u001b[1m=== INICIO DE ITERACIÓN 4 ===\u001b[0m\n", - "================================================================================\n", - "\n", - "=== Iteración 4: Resumen antes de imputaciones ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de Valores Faltantes Antes de Iteración 4

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    ColumnaValores Faltantes
    0Stalker XE30.000
    1Stalker VXE3031.000
    2Aerosonde Mk. 4.7 Fixed Wing28.000
    3Aerosonde Mk. 4.7 VTOL27.000
    4Aerosonde Mk. 4.8 Fixed wing31.000
    5Aerosonde Mk. 4.8 VTOL FTUAS33.000
    6AAI Aerosonde30.000
    7Fulmar X35.000
    8Orbiter 434.000
    9Orbiter 334.000
    10Mantis34.000
    11ScanEagle33.000
    12Integrator33.000
    13Integrator VTOL32.000
    14Integrator Extended Range (ER)36.000
    15ScanEagle 334.000
    16RQ Nan 21A Blackjack32.000
    17DeltaQuad Evo28.000
    18DeltaQuad Pro #MAP30.000
    19DeltaQuad Pro #CARGO30.000
    20V2128.000
    21V2528.000
    22V3228.000
    23V3531.000
    24V3931.000
    25Volitation VT37030.000
    26Skyeye 260033.000
    27Skyeye 2930 VTOL32.000
    28Skyeye 360033.000
    29Skyeye 3600 VTOL31.000
    30Skyeye 500029.000
    31Skyeye 5000 VTOL30.000
    32Skyeye 5000 VTOL octo30.000
    33Volitation VT51030.000
    34Ascend29.000
    35Transition29.000
    36Reach29.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Sumatoria Total de Valores Faltantes

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    ResumenCantidad
    0Total de Valores Faltantes1146.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--------------------------------------------------------------------------------\n", - "\u001b[1m*** IMPUTACIÓN POR SIMILITUD - ITERACIÓN 4 ***\u001b[0m\n", - "--------------------------------------------------------------------------------\n", - "\u001b[1m\n", - "=== Imputación por similitud: Skyeye 3600 - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Mantis - Techo de servicio máximo ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Fulmar X - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: Integrator Extended Range (ER) - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1m\n", - "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m\n", - "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", - "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", - "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", - "\u001b[1mNo se realizaron imputaciones por similitud en esta iteración.\u001b[0m\n", - "\n", - "--------------------------------------------------------------------------------\n", - "\u001b[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN 4 ***\u001b[0m\n", - "--------------------------------------------------------------------------------\n", - "\n", - "=== DataFrame inicial ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())

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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m*** IMPUTACIÓN POR SIMILITUD - ITERACIÓN 2 ***\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V21 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Longitud del fuselaje ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker XE - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker VXE30 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker XE - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker VXE30 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - envergadura ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V21 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT510 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Reach - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - payload ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1mNo se realizaron imputaciones por similitud en esta iteración.\u001b[0m\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN 2 ***\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "\n", + "=== DataFrame inicial ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())

    Stalker XEStalker VXE30Aerosonde Mk. 4.7 Fixed WingAerosonde Mk. 4.7 VTOLAerosonde Mk. 4.8 Fixed wingAerosonde Mk. 4.8 VTOL FTUASAAI AerosondeFulmar XOrbiter 4Orbiter 3MantisScanEagleIntegratorIntegrator VTOLIntegrator Extended Range (ER)ScanEagle 3RQ Nan 21A BlackjackDeltaQuad EvoDeltaQuad Pro #MAPDeltaQuad Pro #CARGOV21V25V32V35V39Volitation VT370Skyeye 2600Skyeye 2930 VTOLSkyeye 3600Skyeye 3600 VTOLSkyeye 5000Skyeye 5000 VTOLSkyeye 5000 VTOL octoVolitation VT510AscendTransitionReach
    Modelo
    Distancia de carrera requerida para despegue0.00.0NaN0.0NaN0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.00.00.00.00.00.00.0NaN0.050.00.060.00.00.00.00.00.00.0
    Altitud a la que se realiza el crucero6000.06000.06000.06000.06000.06000.05500.06000.06000.06000.06000.06000.06000.05000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.06000.0
    Velocidad a la que se realiza el crucero (KTAS)16.88037917.60237327.3440527.3440527.3440530.22866936.09414730.40658430.46641927.42637218.26582630.62533630.95346521.46331.89437625.70340733.79724618.09082417.50019217.50019219.68771621.8752421.8752427.3440527.3440527.3440536.09414726.250288NaN32.8128636.09414730.62533630.29090932.8128621.8752421.8752427.34405
    Techo de servicio máximo12000.012000.014700.09700.018200.015000.015000.09.8429403.635186839.144606NaN19500.019500.07013.83419500.020.020.013.013.12313.12348800.016000.016000.016000.016000.017000.014972.95591316000.017070.83316959.09187416254.02816009.43594316009.47636617000.010000.013000.016000.0
    Velocidad de pérdida limpia (KCAS)14.87214.78717.30618.6218.42325.010.014.80718.75316.395514.50915.830519.943521.108520.54517.32919.592514.014.073514.07314.015.517.018.017.3973924.010.018.012.524.015.025.025.025.014.010.025.0
    Área del ala0.871.1582831.551.551.552.5030.570.941.6081.20.7541.0631.8722.08951.8721.3491.8020.840.70.70.80.521.031.2021.2031.4240.881.01.331.322.6152.6152.6151.9930.7710.9862.329
    Relación de aspecto del ala15.30125515.32644912.512.512.512.514.75438613.217513.44313.934514.75514.05712.90812.64812.8413.76512.91414.58914.71414.71414.56814.42114.18213.89814.041513.64514.10314.00113.709513.671512.69513.03212.855513.09914.34914.22313.669
    Longitud del fuselaje2.12.59083.03.03.03.59451.71.21.21.21.481.712.52.9982.52.42.50.750.90.90.930.931.01.881.9542.022.052.032.4882.423.53.53.52.9051.5622.34.712
    Profundidad del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Ancho del fuselaje0.2110.20.2770.2770.277NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.3750.3750.375NaNNaNNaNNaN
    Peso máximo al despegue (MTOW)13.619.95804842.253.554.493.013.120.055.032.06.526.574.875.074.836.361.010.06.26.210.012.523.532.024.040.015.028.028.040.090.0100.0100.0100.09.518.091.0
    Alcance de la aeronave370.0433.0518.9225481.428535.2755800.03270.0800.0509.556550.025.0503.5155500.0646.0835500.050.0565.912270.0100.0100.0270.0270.0412.686456.221413.556300.03270.0425.273458.1245300.0530.401800.0800.0800.0270.0506.641800.0
    Autonomía de la aeronave8.08.019.812.019.814.026.08.024.06.02.018.024.016.019.018.016.04.531.831.833.04.04.52.84.515.02.03.04.56.08.08.011.6729075.06.012.020.0
    Velocidad máxima (KIAS)20.025.03421133.4388633.4388633.4388642.25267530.84572541.736.036.025.641.246.340.21646.341.246.333.029.00929.00933.033.033.033.033.033.030.83428930.035.098533.042.042.038.050.030.030.035.0
    Velocidad de pérdida (KCAS)14.8389.41510.53210.53210.53218.90746510.0NaN10.015.31616.64512.55913.05113.843NaNNaN13.05114.015.31615.60714.015.517.018.017.3973924.010.018.012.524.015.019.10922524.025.013.013.013.0
    Tasa de ascensoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Radio de giroNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100.0120.0150.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    envergadura3.6574.87684.44.44.45.6442.93.05.24.42.13.14.85.0334.84.04.82.692.352.352.152.453.23.53.96.52.62.933.63.65.05.05.05.12.03.06.0
    Cuerda0.2390.3181950.3520.3520.3520.3940.1965520.3190.3320.3040.2710.29850.33850.3410.3450.31150.3410.27550.2720.2720.2780.2810.2920.3060.3070.3140.2960.30.3110.3150.34850.3380.34450.3350.2870.2910.313
    payload2.4947562.49475614.511.317.722.74.02.49475612.05.52.6935.018.018.018.08.617.73.01.21.21.52.25.010.05.018.04.06.010.010.020.025.015.025.00.61.57.0
    duracion en VTOL2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050.050.05
    Crucero KIAS15.4333216.09342225.025.025.0NaNNaN27.8NaNNaN16.728.028.3NaNNaN23.530.916.5416.016.018.020.020.025.025.025.033.024.0NaN30.033.028.035.030.020.020.025.0
    RTF (dry weight)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.011.854.0
    RTF (Including fuel & Batteries)NaNNaN27.742.236.770.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8.916.584.0
    Empty weight10.88620817.46329219.79619.79619.80931.010.017.46329218.36512.2375.63310.19222.19524.784522.25714.79421.1234.84.7544.7542.653.456.457.16.70830311.06.57.111.511.032.032.140535.023.9593.05.831.0
    Maximum CrosswindNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.0NaNNaNNaN45.050.050.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15.015.015.0
    Rango de comunicación59.0161.0140.0140.0140.0NaN150.0NaN150.050.025.0101.8692.6NaNNaNNaN92.6NaN50.030.030.030.030.030.030.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Wing LoadingNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.524.025.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Potencia específica (P/W)NaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Capacidad combustibleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.0NaNNaN11.511.528.028.028.025.0NaNNaNNaN
    ConsumoNaNNaN0.60.6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.96NaNNaNNaNNaN1.2NaNNaN5.0NaNNaNNaN
    Potencia WattsNaNNaN2980.02980.0NaNNaN1280.0NaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Potencia HPNaNNaN4.04.0NaNNaN1.74NaNNaNNaNNaNNaNNaNNaNNaNNaN8.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    PrecioNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3999.04679.069999.07999.08999.08999.02299.06799.04999.06999.09999.013900.015999.016599.0NaNNaNNaN
    Tiempo de emergencia en vueloNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.1080.1080.108NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Distancia de aterrizajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Despegue1.01.01.02.01.02.02.01.01.01.01.01.01.02.01.01.01.02.02.02.02.02.02.02.02.02.02.02.03.02.03.02.02.02.02.02.02.0
    Propulsión horizontal2.02.02.02.02.02.02.02.02.01.01.02.02.02.02.02.02.01.01.01.01.01.02.02.02.02.02.02.02.02.02.02.02.02.02.02.02.0
    Propulsión vertical5.05.05.01.05.01.01.05.05.05.05.05.05.01.05.05.05.01.01.01.01.01.01.01.01.01.01.01.05.01.05.01.01.01.01.01.01.0
    Cantidad de motores propulsión vertical0.00.00.04.00.04.04.00.00.00.00.00.00.04.00.00.00.04.04.04.04.04.04.04.04.04.04.04.00.04.00.04.08.04.04.04.04.0
    Cantidad de motores propulsión horizontal1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
    Misión1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
    Dimensiones de la bahía de carga útilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Battery Power SupplyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Modelo Motor Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    Modelo Motor VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", @@ -58400,37 +22251,42 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", @@ -58440,117 +22296,132 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -58560,35 +22431,40 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -58600,75 +22476,85 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -58680,23 +22566,42 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -58706,25 +22611,42 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -58734,23 +22656,42 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -58760,1693 +22701,1181 @@ " \n", " \n", " \n", - 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    Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
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    Datalink banksNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAerosonde Mk. 4.7 Fixed WingNaN6000.00027.3440514700.0NaN1.5512.53.0NaN0.27742.200NaN19.833.43886NaNNaNNaN4.40.35214.5NaN25.0NaN27.7NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN1.0002.0005.0000.0001.0001.000
    Material del fuselajeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAerosonde Mk. 4.7 VTOL0.06000.00027.344059700.0NaN1.5512.53.0NaN0.27753.500NaN12.033.43886NaNNaNNaN4.40.35211.3NaN25.0NaN42.2NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN2.0002.0001.0004.0001.0001.000
    Motor recomendadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAerosonde Mk. 4.8 Fixed wingNaN6000.00027.3440518200.0NaN1.5512.53.0NaN0.27754.400NaN19.833.43886NaNNaNNaN4.40.35217.7NaN25.0NaN36.7NaNNaN140.0NaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
    Hélice recomendada VTOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAerosonde Mk. 4.8 VTOL FTUAS0.06000.00030.22866915000.025.02.61512.53.716735NaNNaN93.000800.014.042.25267518.907465NaNNaN5.373633NaN22.7NaNNaNNaN70.331.0NaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
    Hélice recomendada Fixed WingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAAI AerosondeNaN5500.00036.09414715000.010.00.5714.7543861.7NaNNaN13.1003270.026.030.84572510.0NaNNaN2.90.1965524.0NaNNaNNaNNaN10.0NaN150.023.098.0NaNNaN1280.01.74NaNNaNNaN2.0002.0001.0004.0001.0001.000
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    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Convertir todo a numérico ===\n", - "\n", - "\n", - "=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===\n", - "\n", - "Umbral seleccionado para correlaciones significativas: 0.7\n", - "\n", - "=== Cálculo de tabla completa ===\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Tabla de Correlaciones con todos los parametros(tabla_completa)

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    ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
    Modelo
    Distancia de carrera requerida para despegue1.0000.0630.4270.082-0.2730.269-0.2500.2220.4250.1680.054-0.0780.241-0.1850.1050.2000.229nan0.389nan0.225nannan-0.018-0.240-0.1560.7350.1540.671-0.598nannan
    Altitud a la que se realiza el crucero0.0631.0000.0110.0950.008-0.0750.105-0.092nan-0.095-0.309-0.278-0.0640.147-0.0800.109-0.114nannannan-0.1210.038-0.325nannannan-0.119-0.1090.187-0.159nannan
    Velocidad a la que se realiza el crucero (KTAS)0.4270.0111.0000.1340.3160.497-0.6070.4280.9170.5530.5450.4230.6340.1140.4210.3960.567-0.6940.9730.7900.503-0.8540.4800.3650.461-0.2900.0630.5630.120-0.076nannan
    Techo de servicio máximo0.0820.0950.1341.0000.1030.096-0.0290.1350.5900.1220.0970.0010.0140.1110.0220.0280.118-0.8750.1120.579-0.004-0.961-0.118-0.8190.461-0.1560.1960.068-0.1360.140nannan
    Velocidad de pérdida limpia (KCAS)-0.2730.0080.3160.1031.0000.753-0.5980.5940.6750.798-0.2300.2600.5160.6290.7470.6430.731-0.1900.4930.9310.678-0.2370.1290.2240.6300.118-0.0280.322-0.1600.232nannan
    Área del ala0.269-0.0750.4970.0960.7531.000-0.7780.8350.9840.970-0.0230.3830.6480.2870.8250.7870.854-0.3830.6920.9650.944-0.4660.4910.9740.2880.0360.1250.4760.0720.037nannan
    Relación de aspecto del ala-0.2500.105-0.607-0.029-0.598-0.7781.000-0.624-0.681-0.790-0.003-0.456-0.730-0.132-0.630-0.862-0.8260.519-0.769-0.497-0.7460.432-0.409-0.9700.2960.024-0.001-0.471-0.1410.075nannan
    Longitud del fuselaje0.222-0.0920.4280.1350.5940.835-0.6241.0000.9380.8060.1400.4030.3630.1070.7190.6230.660-0.6170.5740.9260.834-0.6960.6460.9290.036-0.2030.1380.6120.0340.040nannan
    Ancho del fuselaje0.425nan0.9170.5900.6750.984-0.6810.9381.0000.9860.833-0.0890.9400.7110.6710.5570.868nan0.944nan0.954nan0.323nan1.000nan0.794nan-0.5350.574nannan
    Peso máximo al despegue (MTOW)0.168-0.0950.5530.1220.7980.970-0.7900.8060.9861.0000.0300.4200.7170.3330.8110.7510.882-0.4010.7080.9790.933-0.4640.5140.9760.7580.0520.0900.4670.0230.075nannan
    Alcance de la aeronave0.054-0.3090.5450.097-0.230-0.023-0.0030.1400.8330.0301.0000.2240.013-0.258-0.043-0.2240.019-0.5250.5240.9360.081-0.7110.4670.8480.837-0.1480.1570.317-0.2130.210nannan
    Autonomía de la aeronave-0.078-0.2780.4230.0010.2600.383-0.4560.403-0.0890.4200.2241.0000.378-0.3470.5410.2690.400-0.5940.3370.6340.486-0.7150.8020.056-0.7320.033-0.4200.4780.353-0.314nannan
    Velocidad máxima (KIAS)0.241-0.0640.6340.0140.5160.648-0.7300.3630.9400.7170.0130.3781.0000.2490.4910.6270.718-0.0770.7000.7260.613-0.2230.1510.7270.9100.067-0.0570.3000.178-0.141nannan
    Velocidad de pérdida (KCAS)-0.1850.1470.1140.1110.6290.287-0.1320.1070.7110.333-0.258-0.3470.2491.0000.2210.1570.3560.6720.2840.3230.1480.934-0.9610.0360.6850.1210.295-0.000-0.4210.498nannan
    envergadura0.105-0.0800.4210.0220.7470.825-0.6300.7190.6710.811-0.0430.5410.4910.2215.0000.0001.0000.6860.775-0.2580.5010.9500.806-0.4140.6480.2970.0850.032-0.0810.5160.167-0.106nannan
    Cuerda0.2000.1090.3960.0280.6430.787-0.8620.6230.5570.751-0.2240.2690.6270.1570.6861.0000.758-0.4990.7300.5950.724-0.4980.3550.975-0.228-0.041-0.0650.4180.193-0.129nannan
    payload0.229-0.1140.5670.1180.7310.854-0.8260.6600.8680.8820.0190.4000.7180.3560.7750.758ScanEagleNaN6000.00030.62533619500.0NaNNaNNaN1.71NaNNaN26.500NaN18.041.2NaNNaNNaN3.1NaN5.0NaN28.0NaNNaNNaNNaN101.86NaNNaNNaNNaNNaNNaNNaNNaNNaN1.000-0.0240.6700.5590.784-0.1420.4890.7110.846-0.0080.0530.4620.100-0.055nannan
    duracion en VTOLnannan-0.694-0.875-0.190-0.3830.519-0.617nan-0.401-0.525-0.594-0.0770.672-0.258-0.499-0.0242.0005.0000.0001.000-0.694-0.402-0.3151.000nannannannan-0.188-0.9040.188-0.188nannan
    Crucero KIAS0.389nan0.9730.1120.4930.692-0.7690.5740.9440.7080.5240.3370.7000.2840.5010.7300.670-0.694IntegratorNaN6000.00030.95346519500.0NaNNaNNaN2.5NaNNaN74.800500.024.046.3NaNNaNNaN4.8NaN18.0NaN28.3NaNNaNNaNNaN92.6NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0000.7230.636-0.8550.3590.5810.461-0.2430.1430.6080.0650.063nannan
    RTF (Including fuel & Batteries)nannan0.7900.5790.9310.965-0.4970.926nan0.9790.9360.6340.7260.3230.9500.5950.559-0.4020.7232.0005.0000.0001.0000.948-0.402nannannannan0.0970.428-0.0970.097nannan
    Empty weight0.225-0.1210.503-0.0040.6780.944-0.7460.8340.9540.9330.0810.4860.6130.1480.8060.7240.784-0.3150.6360.9481.000-0.3860.7850.9800.2510.023-0.0290.4800.195-0.070nannan
    Maximum Crosswindnan0.038-0.854-0.961-0.237-0.4660.432-0.696nan-0.464-0.711-0.715-0.2230.934-0.414-0.498-0.142Integrator VTOL0.05000.000NaNNaNNaNNaNNaNNaNNaNNaN75.000NaN16.0NaNNaNNaNNaNNaNNaN18.0NaNNaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaNNaNNaNNaN0.02.0002.0001.000-0.855-0.402-0.3864.0001.000nannannannannan-0.943nannannannan
    Rango de comunicaciónnan-0.3250.480-0.1180.1290.491-0.4090.6460.3230.5140.4670.8020.151-0.9610.6480.3550.489nan0.359nan0.785nan1.000nannannan-0.4300.6040.430-0.430nannan
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    Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
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    Resumen de la Tabla

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    0Total de valores1024.000V250.06000.00021.8752416000.015.50.5214.7543860.93NaNNaN12.500270.04.033.015.5NaN120.02.450.1965522.2NaN20.0NaNNaN3.45NaN30.024.0NaNNaNNaNNaNNaN4679.00.108NaN2.0001.0001.0004.0001.0001.000
    1Valores numéricos826.000V320.06000.00021.8752416000.017.01.0NaN1.0NaNNaN23.500NaN4.533.017.0NaN150.03.2NaN5.0NaN20.0NaNNaN6.45NaN30.025.0NaNNaNNaNNaNNaN69999.00.108NaN2.0002.0001.0004.0001.0001.000
    2Valores NaN198.000
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    Tabla de Correlaciones Filtradas por aeronaves seleccionadas (Para Heatmap)

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    Resumen de la Tabla

    ModeloVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weightV350.06000.00027.3440516000.018.01.0NaN1.88NaNNaN32.000NaN2.833.018.0NaNNaN3.5NaN10.0NaN25.0NaNNaN7.1NaN30.0NaNNaNNaNNaNNaNNaN7999.0NaNNaN2.0002.0001.0004.0001.0001.000
    ModeloV390.06000.00027.3440516000.017.397391.0NaN1.409312NaNNaN24.000NaN4.533.017.39739NaNNaN3.9NaN5.0NaN25.0NaNNaN6.708303NaN30.0NaNNaNNaNNaNNaNNaN8999.0NaNNaN2.0002.0001.0004.0001.0001.000
    Velocidad a la que se realiza el crucero (KTAS)Volitation VT3700.06000.00027.3440517000.024.01.32NaN2.02NaNNaN40.000300.015.033.024.0NaNNaN6.5NaN18.0NaN25.0NaNNaN11.0NaNNaNNaNNaN13.00.96NaNNaN8999.0NaNNaN2.0002.0001.0004.0001.0001.0000.1340.497-0.6070.4280.5530.5450.4230.6340.1140.3160.4210.3960.5670.503
    Techo de servicio máximo0.134Skyeye 2600NaN6000.00036.09414714972.95591310.00.8814.7543862.05NaNNaN15.0003270.02.030.83428910.0NaNNaN2.60.1965524.0NaN33.0NaNNaN6.5NaNNaNNaNNaNNaNNaNNaNNaN2299.0NaNNaN2.0002.0001.0004.0001.0001.0000.096-0.0290.1350.1220.0970.0010.0140.1110.1030.0220.0280.118-0.004
    Área del ala0.4970.096Skyeye 2930 VTOL0.06000.00026.25028816000.018.01.0NaN2.03NaNNaN28.000NaN3.030.018.0NaNNaN2.93NaN6.0NaN24.0NaNNaN7.1NaNNaNNaNNaNNaNNaNNaNNaN6799.0NaNNaN2.0002.0001.0004.0001.0001.000-0.7780.8350.970-0.0230.3830.6480.2870.7530.8250.7870.8540.944
    Relación de aspecto del ala-0.607-0.029-0.778Skyeye 360050.06000.000NaNNaN12.51.33NaN2.488NaNNaN28.000NaN4.5NaN12.5NaNNaN3.6NaN10.0NaNNaNNaNNaN11.5NaNNaNNaNNaN11.5NaNNaNNaN4999.0NaNNaN3.0002.0005.0000.0001.0001.000-0.624-0.790-0.003-0.456-0.730-0.132-0.598-0.630-0.862-0.826-0.746
    Longitud del fuselaje0.4280.1350.835-0.624Skyeye 3600 VTOL0.06000.00032.8128616959.09187424.01.32NaN2.42NaNNaN40.000300.06.033.024.0NaNNaN3.6NaN10.0NaN30.0NaNNaN11.0NaNNaNNaNNaN11.5NaNNaNNaN6999.0NaNNaN2.0002.0001.0004.0001.0001.0000.8060.1400.4030.3630.1070.5940.7190.6230.6600.834
    Peso máximo al despegue (MTOW)0.5530.1220.970-0.7900.806Skyeye 500060.06000.00036.094147NaN15.02.615NaN3.5NaN0.37590.000NaN8.042.015.0NaNNaN5.0NaN20.0NaN33.0NaNNaN32.0NaNNaNNaNNaN28.01.2NaNNaN9999.0NaNNaN3.0002.0005.0000.0001.0001.0000.0300.4200.7170.3330.7980.8110.7510.8820.933
    Alcance de la aeronave0.5450.097-0.023-0.0030.1400.030Skyeye 5000 VTOL0.06000.00030.62533616009.43594325.02.61512.53.5NaN0.375100.000800.08.042.019.109225NaNNaN5.0NaN25.0NaN28.0NaNNaN31.0NaNNaNNaNNaN28.0NaNNaNNaN13900.0NaNNaN2.0002.0001.0004.0001.0001.0000.2240.013-0.258-0.230-0.043-0.2240.0190.081
    Autonomía de la aeronave0.4230.0010.383-0.4560.4030.4200.224Skyeye 5000 VTOL octo0.06000.00030.29090916009.47636625.02.61512.53.5NaN0.375100.000800.011.67290738.024.0NaNNaN5.0NaN15.0NaN35.0NaNNaN35.0NaNNaNNaNNaN28.0NaNNaNNaN15999.0NaNNaN2.0002.0001.0008.0001.0001.0000.378-0.3470.2600.5410.2690.4000.486
    Velocidad máxima (KIAS)0.6340.0140.648-0.7300.3630.7170.0130.378Volitation VT5100.06000.00032.8128617000.025.02.61512.52.905NaNNaN100.000800.05.050.025.0NaNNaN5.1NaN25.0NaN30.0NaNNaN31.0NaNNaNNaNNaN25.05.0NaNNaN16599.0NaNNaN2.0002.0001.0004.0001.0000.2490.5160.4910.6270.7180.613
    Velocidad de pérdida (KCAS)0.1140.1110.287-0.1320.1070.333-0.258-0.3470.2491.0000.6290.2210.1570.3560.148
    Velocidad de pérdida limpia (KCAS)0.3160.1030.753-0.5980.5940.798-0.2300.2600.5160.629Ascend0.06000.00021.8752410000.014.00.82NaN1.562NaNNaN9.500270.06.030.013.0NaNNaN2.0NaN0.60.0520.06.08.93.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0000.7470.6430.7310.678
    envergadura0.4210.0220.825-0.6300.7190.811-0.0430.5410.4910.2210.7474.0001.0000.6860.7750.806
    Cuerda0.3960.0280.787-0.8620.6230.751-0.2240.2690.6270.1570.6430.6861.0000.7580.724
    payload0.5670.1180.854-0.8260.6600.8820.0190.4000.7180.3560.7310.7750.758Transition0.06000.00021.8752413000.010.00.88NaN2.3NaNNaN18.000NaN12.030.013.0NaNNaN3.0NaN1.50.0520.011.816.55.815.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.0000.784
    Empty weight0.503-0.0040.944-0.7460.8340.9330.0810.4860.6130.1480.6780.8060.7240.784Reach0.06000.00027.3440516000.025.02.61512.54.712NaNNaN91.000800.020.035.013.0NaNNaN6.0NaN7.00.0525.054.084.031.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    ResumenCantidad
    0Total de valores225.000
    1Valores numéricos225.000
    2Valores NaN0.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Convertir todo a numérico ===\n", + "\n", + "\n", + "=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===\n", + "\n", + "Umbral seleccionado para correlaciones significativas: 0.7\n", + "\n", + "=== Cálculo de tabla completa ===\n" + ] }, { "data": { @@ -60563,66 +23938,417 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

    Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

    \n", + "

    Tabla de Correlaciones con todos los parametros(tabla_completa)

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" \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -60638,195 +24364,334 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - 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" \n", - " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", @@ -60834,307 +24699,222 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - "
    ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
    ModeloDistancia de carrera requerida para despegue1.0000.0630.425nan-0.3420.237nan0.235nan0.4250.168nan-0.0780.287-0.311nannan0.123nan0.229nan0.389nannan0.221nannannannan-0.018-0.240nannan-0.156nannan0.7350.1540.671-0.598nannan
    Altitud a la que se realiza el crucero0.0631.000-0.278-0.0350.3310.242-0.2780.081nannan-0.095-0.656-0.2780.1240.343nannan0.1390.462-0.110nannannannan0.0880.038-0.325-0.215nannannan0.2770.690nannannan-0.119-0.1090.187-0.159nannan
    Velocidad a la que se realiza el crucero (KTAS)0.425-0.2781.0000.1050.2080.454-0.3280.455nan0.9170.5970.6330.4510.6450.161nan0.8030.456-0.0640.602-0.6940.9730.9150.7900.474-0.8550.4800.346nan0.3650.461-0.115-0.016-0.290nannan0.0810.5840.101-0.060nannan
    Techo de servicio máximonan-0.0350.1051.0000.007-0.030-0.0780.136nan0.5460.1430.1200.031-0.0780.026nan-0.8030.036-0.4620.135-0.8750.0990.6770.579-0.094-0.961-0.118-0.986nan-0.7570.5150.653-0.933-0.171nannan0.1920.068-0.1920.191nannan
    Velocidad de pérdida limpia (KCAS)-0.3420.3310.2080.0071.0000.727-0.9700.591nannan0.768-0.4980.1600.6040.817nan0.9930.771nan0.705-0.1810.4280.9100.9360.677-0.181-0.8740.161nan0.2240.532nannan0.118nannan-0.2530.261-0.2530.365nannan
    Área del ala0.2370.2420.454-0.0300.7271.000-0.7480.826nan0.9840.976-0.1350.2450.6660.460nan0.5160.7630.9610.804-0.3670.6280.9940.9780.959-0.3380.552-0.119nan0.9900.6750.2770.6900.049nannan0.1480.3590.0630.058nannan
    Velocidad a la que se realiza el crucero (KTAS)Relación de aspecto del alanan-0.278-0.328-0.078-0.970-0.7481.000-0.606nan-0.776-0.7950.257-0.418-0.596-0.726nannan-0.712-0.724-0.7901.000-0.475nannan-0.874nan-0.341nannan0.000-0.816-0.277-0.690-0.981nannan-0.125-0.2780.125-0.175nannan
    Longitud del fuselaje0.2350.0810.4550.1360.5910.826-0.6061.000nan0.9380.8050.1590.4010.3070.239nan0.9180.7030.5150.652-0.6170.5650.9660.9340.856-0.7180.6810.263nan0.9290.0360.6860.359-0.187nannan0.1180.5940.0650.014nannan
    Techo de servicio máximoProfundidad del fuselajenannannannannannan
    Área del alanannannan-0.7780.8350.970nannannannan0.7530.8250.7870.8540.944
    Relación de aspecto del alanannan-0.778nannan-0.790nannan-0.730nannannan-0.862-0.826-0.746
    Longitud del fuselajenannan0.835nannan0.806nannannannannan0.719nannan0.834
    Peso máximo al despegue (MTOW)Ancho del fuselaje0.425nan0.9170.546nan0.970-0.7900.8060.984-0.7760.938nan1.0000.9860.988-0.0890.940nannan0.717nan0.7980.8110.7510.8820.933
    Alcance de la aeronave0.6710.7600.868nan0.944nannan0.955nan0.323nannannan1.000nannannannannan0.794nan-0.5350.574nannan
    Autonomía de la aeronavePeso máximo al despegue (MTOW)0.168-0.0950.5970.1430.7680.976-0.7950.805nan0.9861.0000.0220.4200.6940.523nan0.9730.8020.8290.880-0.4010.7080.9990.9790.940-0.4640.5140.628nan0.9760.7580.5590.8550.052nannan0.0900.4670.0230.075nannan
    Alcance de la aeronavenan-0.6560.6330.120-0.498-0.1350.2570.159nan0.9880.0221.0000.279-0.048-0.509nannan-0.055-0.619-0.006-0.5080.5881.0000.990-0.030-0.7420.6580.430nan0.9871.0001.000nan-0.268nannan0.2730.392-0.2730.251nannan
    Velocidad máxima (KIAS)Autonomía de la aeronave-0.078-0.2780.4510.0310.1600.245-0.4180.401nan-0.0890.4200.2791.0000.327-0.098nan0.9540.5340.4170.382-0.5940.3370.9400.6340.368-0.7150.8020.268nan0.056-0.732-0.391-0.5770.033nannan-0.4200.4780.353-0.314nan-0.730nan0.717
    Velocidad máxima (KIAS)0.2870.1240.645-0.0780.6040.666-0.5960.307nan0.9400.694-0.0480.3271.0000.513nannan0.4400.4790.684-0.0770.6820.7620.7260.6610.2720.094-0.215nan0.7050.910-0.6240.9700.064nannan-0.0400.2150.165-0.128nan0.718nan
    Velocidad de pérdida (KCAS)-0.3110.3430.1610.0260.8170.460-0.7260.239nannan0.523-0.509-0.0980.5131.000nan0.9930.559nan0.6401.0000.414-0.4080.4340.4121.000-0.8740.161nan0.0360.532nannan0.121nannan-0.2220.211-0.2220.381nannan
    Tasa de ascensonannannannannan
    Velocidad de pérdida limpia (KCAS)nannan0.753nannan0.798nannannannannan0.747nan0.731nan
    envergaduranannan0.825nan0.7190.811nannannannan0.747nannan0.7750.806
    Cuerdanannan0.787-0.862nan0.751nannannannannannan0.7580.724
    payloadnannan0.854-0.826nan0.882nannan0.718nan0.7310.7750.758nan0.784
    Empty weightRadio de gironannan0.944-0.7460.8340.9330.803-0.8030.9930.516nan0.918nannan0.973nan0.954nan0.8060.7240.7840.993nan
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Resumen de la Tabla

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    ResumenCantidad
    0Total de valores225.000
    1Valores numéricos60.000
    2Valores NaN165.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Preparando datos para el heatmap ===\n", - "\n", - "=== Generando heatmap ===\n" - ] - }, - { - "data": { - "image/png": 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    Tabla de correlaciones con filtro de umbral de correlación

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -61145,57 +24925,131 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -61205,44 +25059,90 @@ " \n", " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -61251,107 +25151,117 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", " \n", + " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -61360,36 +25270,22 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -61397,170 +25293,236 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -61570,30 +25532,20 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -61605,33 +25557,26 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -61640,34 +25585,20 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -61677,98 +25608,197 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", @@ -61780,33 +25810,19 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -61817,26 +25833,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -61845,14 +25847,10 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -61864,16 +25862,13 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -61881,39 +25876,570 @@ " \n", " \n", " \n", + " \n", + " \n", + "
    ModeloDistancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)envergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónCapacidad combustibleConsumoPrecioDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
    Modelo
    Distancia de carrera requerida para despegue1.0000.992nan0.976nan0.803nannan0.979nannan0.844nannannannannan0.921nannannan0.918nannannannan
    envergadura0.1230.1390.4560.0360.7710.763-0.7120.703nan0.6710.802-0.0550.5340.4400.559nan0.9921.0000.8340.759-0.2580.5010.9830.9440.770-0.4520.6480.780nan0.2970.0850.5220.8350.032nannan-0.1010.5120.200-0.132nannan
    Cuerdanan0.462-0.064-0.462nan0.961-0.7240.515nan0.7600.829-0.6190.4170.479nannannan0.8341.0000.713nan0.121nannan0.931nan0.539nannannannan0.7350.2770.690nannannan-0.6690.4620.669-0.669nannan
    Altitud a la que se realiza el cruceronannannanpayload0.229-0.1100.6020.1350.7050.804-0.7900.652nan0.8680.880-0.0060.3820.6840.640nan0.9760.7590.7131.000-0.0240.6600.9150.5590.759-0.1420.4640.723nan0.7110.8460.6710.897-0.008nannan0.0210.4410.136-0.087nannan
    duracion en VTOLnannan-0.694-0.875-0.181-0.3671.000-0.617nannan-0.401-0.508-0.594-0.0771.000nannan-0.258nan-0.0241.000-0.694-0.408-0.402-0.3151.000nannannannannannan-0.188-0.9040.188-0.188nannan
    Crucero KIAS0.389nan0.9730.0990.4280.628-0.4750.565nan0.9440.7080.5880.3370.6820.414nan
    Velocidad a la que se realiza el crucero (KTAS)0.8030.5010.1210.660-0.6941.0000.9150.7230.600-0.8550.3590.997nan0.5810.4611.0001.000-0.243nannan0.1430.6080.0650.063nannan
    RTF (dry weight)nannan0.9150.6770.9100.994nan0.9170.966nannan0.9991.0000.9400.762-0.408nannan0.983nan0.915-0.4080.9151.0001.0000.995-0.408nannannannan0.9730.790nan-0.854nannannannannannan0.408nannannannan
    Techo de servicio máximoRTF (Including fuel & Batteries)nannan0.7900.5790.9360.978nan0.934nannan0.9790.9900.6340.7260.434nannan0.944nan0.559-0.4020.7231.0001.0000.988-0.402nannannannannannan-0.875nan0.0970.428-0.0970.097nannan-0.961
    Empty weight0.2210.0880.474-0.0940.6770.959-0.8740.856nan-0.8190.9550.940-0.0300.3680.6610.412nan0.9790.7700.9310.759-0.3150.6000.9950.9881.000-0.3190.8190.530nan0.9890.509nannan0.031nannan0.1040.3850.1610.031nannan
    Velocidad de pérdida limpia (KCAS)nannannannannan0.753Maximum Crosswindnan0.038-0.855-0.961-0.181-0.338nan-0.718nan0.798nan-0.464-0.742-0.7150.2721.000nannan-0.452nan0.747-0.1421.000-0.855-0.408-0.402-0.3191.000nan0.731nannan0.931nannannannannannan-0.943nannannannan
    Área del alanannannannan0.753nan-0.7780.8350.9840.970Rango de comunicaciónnan-0.3250.480-0.118-0.8740.552-0.3410.681nan0.3230.5140.6580.8020.094-0.874nannan0.8250.7870.8540.6480.5390.464nan0.359nan0.9650.944nan0.819nan0.9741.0000.215nannannan-1.000-0.972nannannan-0.4300.6040.430-0.430nannan
    Relación de aspecto del alanannanWing Loadingnan-0.2150.346-0.9860.161-0.119nan0.263nan-0.778nan0.6280.4300.268-0.2150.161nan0.8440.780nan-0.7900.723nan0.997nan-0.730nan0.530nan-0.862-0.8260.2151.000nan-0.769nan-0.746nannan-0.970nan0.568nannannan0.572nannannannan
    Longitud del fuselajenannannannanPotencia específica (P/W)nan0.835nannan0.9380.806nannannannan0.719nannannannan0.9260.834nannan0.929nannannannannannan
    Ancho del fuselajenannan0.917nannan0.984nan0.938nan0.9860.833nan0.9400.711nannan0.868nan0.944nan0.954nannannannannan0.794nannannannan
    Peso máximo al despegue (MTOW)nannannannan0.7980.970-0.7900.8060.986nannanCapacidad combustible-0.018nan0.7170.365-0.7570.2240.9900.0000.929nan0.8110.7510.882nan0.7080.9790.9330.9760.9870.0560.7050.036nannan0.9760.7580.297nan0.711nan0.581nannan0.989nannannan
    Alcance de la aeronavenan1.0000.377nannan0.817nannan-0.080nan-0.0800.270nannan0.833
    Consumo-0.240nan0.4610.5150.5320.675-0.8160.036nan1.0000.7581.000-0.7320.9100.532nannan0.085nan0.846nan0.461nannan0.509nannan0.936nan-0.711nan0.8480.8370.3771.000nannan0.998nannan0.113nan-0.3750.375nannan
    Autonomía de la aeronavenannannannannannannanPotencia Wattsnan0.277-0.1150.653nan0.277-0.2770.686nannan0.5591.000-0.391-0.624nannannan0.5220.2770.671nan1.000nannannannan-1.000nannan-0.7150.802nan-0.732nan1.0001.000nannannan0.232nan-0.2320.232nannan
    Velocidad máxima (KIAS)nannanPotencia HPnan0.690-0.016-0.933nan0.690-0.6900.359nannan-0.7300.855nan0.9400.717-0.5770.970nannannan0.8350.6900.897nan1.000nannan0.718nannan0.726-0.972nannannan0.7270.910nan1.0001.000nannannan-0.694nan0.694-0.694nannan
    Velocidad de pérdida (KCAS)Precio-0.156nan-0.290-0.1710.1180.049-0.981-0.187nannan0.052-0.2680.0330.0640.121nan0.9210.032nan-0.008nan-0.243nannan0.031nan0.711nan0.568nan0.8170.998nannan1.000nannan-0.1380.217-0.1380.134nannan
    Tiempo de emergencia en vuelonannannannan0.934-0.961nannannannannannan
    envergaduranannannannan0.7470.825nan0.719nan0.811nannannannannannan0.775nannan0.9500.806nannannannannannan
    Cuerdanannannannan
    Distancia de aterrizajenan0.787-0.862nannan0.751nannannannannannan0.758nan0.730nan0.724nannan0.975nannannannannannan
    payloadnannannannan0.7310.854-0.826nan0.8680.882nannan0.718nan0.7750.758nannannannan0.784nannan0.7110.846nannannannan
    duracion en VTOLnannannan-0.875nannannannanDespegue0.735-0.1190.0810.192-0.2530.148-0.1250.118nan0.7940.0900.273-0.420-0.040-0.222nannan-0.101-0.6690.021-0.1880.143nan0.0970.104nan-0.430nannan-0.0800.1130.232-0.694-0.138nannan1.000-0.010-0.6390.610nannan
    Propulsión horizontal0.154-0.1090.5840.0680.2610.359-0.2780.594nannan0.4670.3920.4780.2150.211nan0.9180.5120.4620.441-0.9040.6080.4080.4280.385-0.9430.6040.572nannannannannan-0.9040.217nannan-0.0101.0000.118-0.083nannan
    Crucero KIASnannan0.973nanPropulsión vertical0.6710.1870.101-0.192-0.2530.0630.1250.065nan-0.5350.023-0.2730.3530.165-0.222nan-0.769nan0.9440.7080.2000.6690.1360.1880.065nan-0.0970.161nan0.430nannan-0.080-0.375-0.2320.694-0.138nan0.730nan-0.6390.1181.000-0.954nannan0.723
    Cantidad de motores propulsión vertical-0.598-0.159-0.0600.1910.3650.058-0.1750.014nan-0.8550.5740.0750.251-0.314-0.1280.381nannan-0.132-0.669-0.087-0.1880.063nan0.0970.031nan-0.430nannan0.2700.3750.232-0.6940.134nannan0.610-0.083-0.9541.000nannan
    RTF (Including fuel & Batteries)nanCantidad de motores propulsión horizontalnan0.790nan0.9310.965nan0.926nan0.9790.936nan0.726nan0.950nannannan0.723nan0.948nannannannannannan
    Empty weightnannannannannan0.944-0.7460.8340.9540.933nannannannan0.8060.7240.784nannan0.948nannan0.7850.980nannannannan
    Maximum Crosswindnannan-0.854-0.961nannannannannannan-0.711-0.715Misiónnan0.934nannannannan-0.855nannannannannannan-0.943nannannannan
    Rango de comunicaciónnannannannannannan0.802nan-0.961nannannannannannan0.785nannannannannannan
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    Resumen de la Tabla

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    ResumenCantidad
    0Total de valores1764.000
    1Valores numéricos973.000
    2Valores NaN791.000
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Filtrando datos seleccionados ===\n", + "\n", + "=== Cálculo de correlaciones filtradas ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Tabla de Correlaciones Filtradas por parametros seleccionados (Para Heatmap)

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    Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
    Velocidad a la que se realiza el crucero (KTAS)1.0000.1050.454-0.3280.4550.5970.6330.4510.6450.1610.2080.456-0.0640.6020.474
    Techo de servicio máximo0.1051.000-0.030-0.0780.1360.1430.1200.031-0.0780.0260.0070.036-0.4620.135-0.094
    Área del ala0.454-0.0301.000-0.7480.8260.976-0.1350.2450.6660.4600.7270.7630.9610.8040.959
    Relación de aspecto del ala-0.328-0.078-0.7481.000-0.606-0.7950.257-0.418-0.596-0.726-0.970-0.712-0.724-0.790-0.874
    Longitud del fuselaje0.4550.1360.826-0.6061.0000.8050.1590.4010.3070.2390.5910.7030.5150.6520.856
    Peso máximo al despegue (MTOW)0.5970.1430.976-0.7950.8051.0000.0220.4200.6940.5230.7680.8020.8290.8800.940
    Alcance de la aeronave0.6330.120-0.1350.2570.1590.0221.0000.279-0.048-0.509-0.498-0.055-0.619-0.006-0.030
    Autonomía de la aeronave0.4510.0310.245-0.4180.4010.4200.2791.0000.327-0.0980.1600.5340.4170.3820.368
    Velocidad máxima (KIAS)0.645-0.0780.666-0.5960.3070.694-0.0480.3271.0000.5130.6040.4400.4790.6840.661
    Velocidad de pérdida (KCAS)0.1610.0260.460-0.7260.2390.523-0.509-0.0980.5131.0000.8170.559nan0.6400.412
    Velocidad de pérdida limpia (KCAS)0.2080.0070.727-0.9700.5910.768-0.4980.1600.6040.8171.0000.771nan0.7050.677
    envergadura0.4560.0360.763-0.7120.7030.802-0.0550.5340.4400.5590.7711.0000.8340.7590.770
    Cuerda-0.064-0.4620.961-0.7240.5150.829-0.6190.4170.479nannan0.8341.0000.7130.931
    payload0.6020.1350.804-0.7900.6520.880-0.0060.3820.6840.6400.7050.7590.7131.0000.759
    Empty weight0.474-0.0940.959-0.8740.8560.940-0.0300.3680.6610.4120.6770.7700.9310.7591.000
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Resumen de la Tabla

    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    ResumenCantidad
    0Total de valores225.000
    1Valores numéricos221.000
    2Valores NaN4.000
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

    Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -61922,24 +26448,8 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -61948,7 +26458,6 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -61957,128 +26466,79 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -62092,13 +26552,11 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -62114,6 +26572,9 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -62126,79 +26587,116 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -62211,18 +26709,6 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", - "\n", - "=== Imputación para el parámetro: **Velocidad a la que se realiza el crucero (KTAS)** ===\n", - "\n", - "--- Imputación para aeronave: **Skyeye 3600** ---\n" - ] - }, { "data": { "text/html": [ @@ -62258,84 +26744,29 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

    Imputación no Exitosa

    Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
    Capacidad combustiblenannannan-0.819nan0.974-0.9700.929nan0.9760.848nan0.727Velocidad a la que se realiza el crucero (KTAS)nannan0.9750.711nannannan0.980nannannannan0.817nannannannan
    Consumonannannannannannannannannan0.7580.837-0.7320.910nannanTecho de servicio máximonan0.846nannannannannannan0.998nannannannan
    Precionannannannannannannannannannannannannannannannannannannannannannannan0.8170.998Área del alanannannan-0.7480.8260.976nannannannan0.7270.7630.9610.8040.959
    Despegue0.735nannannannannannannan0.794nannannannannannannannannannannannannannannannanRelación de aspecto del alanannan-0.748nannan-0.795nannannan-0.726-0.970-0.712-0.724-0.790-0.874
    Propulsión horizontalnannannannannannannannannannannannannannannannannan-0.904nannanLongitud del fuselajenan-0.943nan0.826nannan0.805nannannannannan0.703nannan0.856
    Propulsión verticalnannannannannannannannannanPeso máximo al despegue (MTOW)nannan0.976-0.7950.805nannannannannan0.7680.8020.8290.8800.940
    Alcance de la aeronavenannannannannannan-0.954nannan
    Cantidad de motores propulsión verticalnanAutonomía de la aeronavenannannannannannan
    Velocidad máxima (KIAS)nannannannannannan-0.954nannannan
    Cantidad de motores propulsión horizontalnannannannannannannannannannanVelocidad de pérdida (KCAS)nannannan-0.726nannannannannannan0.817nannannannan
    Velocidad de pérdida limpia (KCAS)nannan0.727-0.970nan0.768nannannan0.817nan0.771nan0.705nan
    Misiónenvergaduranannan0.763-0.7120.7030.802nannannannan0.771nan0.8340.7590.770
    Cuerdanannan0.961-0.724nan0.829nannannannannan0.834nan0.7130.931
    payloadnannan0.804-0.790nan0.880nannannannan0.7050.7590.713nan0.759
    Empty weightnannan0.959-0.8740.8560.940nannannannannan0.7700.9310.759nan
    \n", + "

    Resumen de la Tabla

    \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - "
    MensajeResumenCantidad
    0No se pudo imputar el parámetro 'Velocidad a la que se realiza el crucero (KTAS)' para la aeronave 'Skyeye 3600'.Total de valores225.000
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Imputación para el parámetro: **Techo de servicio máximo** ===\n", - "\n", - "--- Imputación para aeronave: **Mantis** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", "
    Mensaje
    1Valores numéricos64.000
    0No se pudo imputar el parámetro 'Techo de servicio máximo' para la aeronave 'Mantis'.2Valores NaN161.000
    " @@ -62352,90 +26783,21 @@ "output_type": "stream", "text": [ "\n", - "=== Área del ala: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Relación de aspecto del ala: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Longitud del fuselaje: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Peso máximo al despegue (MTOW): No hay valores faltantes para imputar. ===\n", - "\n", - "=== Alcance de la aeronave: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Autonomía de la aeronave: No hay valores faltantes para imputar. ===\n", - "\n", - "=== Velocidad máxima (KIAS): No hay valores faltantes para imputar. ===\n", - "\n", - "=== Imputación para el parámetro: **Velocidad de pérdida (KCAS)** ===\n", + "=== Preparando datos para el heatmap ===\n", "\n", - "--- Imputación para aeronave: **Fulmar X** ---\n" + "=== Generando heatmap ===\n" ] }, { "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Fulmar X'.
    " - ], + "image/png": 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"text/plain": [ - "" + "
    " ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Imputación para aeronave: **Integrator Extended Range (ER)** ---\n" - ] - }, { "data": { "text/html": [ @@ -62471,18 +26833,13 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

    Imputación no Exitosa

    \n", + "

    Tabla de correlaciones con filtro de umbral de correlación

    \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'Integrator Extended Range (ER)'.
    " ], @@ -62498,84 +26855,41 @@ "output_type": "stream", "text": [ "\n", - "--- Imputación para aeronave: **ScanEagle 3** ---\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "

    Imputación no Exitosa

    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    Mensaje
    0No se pudo imputar el parámetro 'Velocidad de pérdida (KCAS)' para la aeronave 'ScanEagle 3'.
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", + "\n", + "=== Velocidad a la que se realiza el crucero (KTAS): Sin correlaciones significativas (|r| < 0.7) ===\n", "\n", - "=== Velocidad de pérdida limpia (KCAS): No hay valores faltantes para imputar. ===\n", + "=== Techo de servicio máximo: Sin correlaciones significativas (|r| < 0.7) ===\n", "\n", - "=== envergadura: No hay valores faltantes para imputar. ===\n", + "=== Área del ala: Sin correlaciones significativas (|r| < 0.7) ===\n", "\n", - "=== Cuerda: No hay valores faltantes para imputar. ===\n", + "=== Relación de aspecto del ala: Sin correlaciones significativas (|r| < 0.7) ===\n", "\n", - "=== payload: No hay valores faltantes para imputar. ===\n", + "=== Longitud del fuselaje: Sin correlaciones significativas (|r| < 0.7) ===\n", "\n", - "=== Empty weight: No hay valores faltantes para imputar. ===\n", + "=== Peso máximo al despegue (MTOW): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Alcance de la aeronave: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Autonomía de la aeronave: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Velocidad máxima (KIAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Velocidad de pérdida (KCAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Velocidad de pérdida limpia (KCAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== envergadura: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Cuerda: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== payload: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Empty weight: Sin correlaciones significativas (|r| < 0.7) ===\n", "La columna 'Nivel de Confianza' no está presente en df_reporte.\n", "\u001b[1mNo se realizaron imputaciones por correlación en esta iteración.\u001b[0m\n", "\n", - "=== Iteración 4: Resumen después de imputaciones ===\n" + "=== Iteración 2: Resumen después de imputaciones ===\n" ] }, { @@ -62613,11 +26927,11 @@ " max-width: 150px; /* Ajusta el ancho máximo de las celdas si es necesario */\n", " }\n", " \n", - "

    Resumen de Valores Faltantes Después de Iteración 4

    \n", + "

    Resumen de Valores Faltantes Después de Iteración 2

    \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -62625,32 +26939,32 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -62660,72 +26974,72 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -62735,22 +27049,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -62760,52 +27074,52 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "
    ColumnaFilaValores Faltantes
    0Stalker XE0.0002.000
    1Stalker VXE300.0002.000
    2Aerosonde Mk. 4.7 Fixed Wing0.0004.000
    3Aerosonde Mk. 4.7 VTOL0.0004.000
    4Aerosonde Mk. 4.8 Fixed wing0.0004.000
    5Aerosonde Mk. 4.8 VTOL FTUAS0.0001.000
    6
    7Fulmar X1.0002.000
    8Orbiter 40.0004.000
    9Orbiter 30.0006.000
    10Mantis1.0008.000
    11ScanEagle0.0007.000
    12Integrator0.0006.000
    13Integrator VTOL0.00012.000
    14Integrator Extended Range (ER)1.000Integrator Extended Range6.000
    15ScanEagle 31.0003.000
    16RQ Nan 21A Blackjack0.0004.000
    17DeltaQuad Evo0.0002.000
    18DeltaQuad Pro #MAP0.0007.000
    19DeltaQuad Pro #CARGO0.0007.000
    20V210.0002.000
    21
    22V320.0003.000
    23V350.0003.000
    24V390.0003.000
    25Volitation VT3700.0002.000
    26
    27Skyeye 2930 VTOL0.0003.000
    28Skyeye 36001.0006.000
    29Skyeye 3600 VTOL0.0002.000
    30Skyeye 50000.0004.000
    31Skyeye 5000 VTOL0.0001.000
    32Skyeye 5000 VTOL octo0.0001.000
    33Volitation VT5100.0001.000
    34Ascend0.0002.000
    35Transition0.0003.000
    36Reach0.0001.000
    " @@ -62864,7 +27178,7 @@ " \n", " 0\n", " Total de Valores Faltantes\n", - " 5.000\n", + " 128.000\n", " \n", " \n", "" @@ -62881,7 +27195,6 @@ "output_type": "stream", "text": [ "\u001b[1mNo se realizaron nuevas imputaciones. Finalizando...\u001b[0m\n", - "Hola\n", "=== Exportando datos al archivo: C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Results\\Datos_imputados.xlsx ===\n", "Exportación completada. El archivo se guardó como 'C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Results\\Datos_imputados.xlsx'.\n", "\n", diff --git a/ADRpy/analisis/main.py b/ADRpy/analisis/main.py index def5be8f..7a6e3c44 100644 --- a/ADRpy/analisis/main.py +++ b/ADRpy/analisis/main.py @@ -130,7 +130,7 @@ # Paso 6: Selección de parámetros # Parámetros disponibles en el índice del DataFrame -parametros_disponibles = df_procesado.index.tolist() +parametros_disponibles = df_procesado.columns.tolist() print("Parámetros disponibles en df_procesado antes de seleccionar:") print(parametros_disponibles) @@ -166,7 +166,7 @@ # Filtrar el DataFrame por los parámetros seleccionados try: - df_filtrado = df_procesado.loc[parametros_seleccionados] + df_filtrado = df_procesado[parametros_seleccionados] except KeyError as e: print(f"Error al filtrar df_procesado: {e}") print(f"Parámetros seleccionados inválidos: {set(parametros_seleccionados) - set(df_procesado.index.tolist())}") @@ -177,10 +177,10 @@ # Paso 7: Mostrar celdas faltantes con selección de columna -# Analizar celdas faltantes en la columna seleccionada +# Analizar celdas faltantes en la fila seleccionada df_celdas_faltantes = mostrar_celdas_faltantes_con_seleccion( df_filtrado, - columna_seleccionada=args.columna, + fila_seleccionada=args.columna, debug_mode=args.debug_mode ) @@ -214,10 +214,14 @@ # Cargar configuración de similitud bloques_rasgos, filas_familia, capas_familia = configurar_similitud() -df_atributos = df_procesado.loc[filas_familia] -df_parametros = df_procesado.drop(index=filas_familia) +# Cambiar la lógica para seleccionar columnas en lugar de filas +# df_atributos debe seleccionar las columnas correspondientes a filas_familia +df_atributos = df_procesado[filas_familia] +# df_parametros debe excluir las columnas seleccionadas en filas_familia +df_parametros = df_procesado.drop(columns=filas_familia) # Paso 10: Llamar a la función principal +# IMPORTANTE: revisa que en los scripts de imputación y exportación se acceda a los datos como df.loc[aeronave, parametro] df_procesado_actualizado, resumen_imputaciones = bucle_imputacion_similitud_correlacion( df_parametros=df_parametros, df_atributos=df_atributos, @@ -236,8 +240,6 @@ debug_mode=args.debug_mode ) -print("Hola") - # Paso 11: Exportar resultados a Excel archivo_destino = args.archivo_destino if not archivo_destino: diff --git a/ADRpy/analisis/tempCodeRunnerFile.py b/ADRpy/analisis/tempCodeRunnerFile.py new file mode 100644 index 00000000..5742efc4 --- /dev/null +++ b/ADRpy/analisis/tempCodeRunnerFile.py @@ -0,0 +1 @@ +2, 3, 2, 3, 2, 3, 2, 3, 2, 3 \ No newline at end of file From d28b2221b41a7337970776e5f1a039aeef4c157e Mon Sep 17 00:00:00 2001 From: Delpoo <157638420+Delpoo@users.noreply.github.com> Date: Fri, 6 Jun 2025 14:54:51 -0300 Subject: [PATCH 8/9] Add correlation imputation module with tests --- .../imputacion_correlacion/__init__.py | 1 + .../imputacion_correlacion.py | 134 ++++++++++++++++++ tests/test_imputacion_correlacion.py | 10 ++ 3 files changed, 145 insertions(+) create mode 100644 ADRpy/analisis/Modulos/imputacion_correlacion/__init__.py create mode 100644 ADRpy/analisis/Modulos/imputacion_correlacion/imputacion_correlacion.py create mode 100644 tests/test_imputacion_correlacion.py diff --git a/ADRpy/analisis/Modulos/imputacion_correlacion/__init__.py b/ADRpy/analisis/Modulos/imputacion_correlacion/__init__.py new file mode 100644 index 00000000..0572c0b9 --- /dev/null +++ b/ADRpy/analisis/Modulos/imputacion_correlacion/__init__.py @@ -0,0 +1 @@ +from .imputacion_correlacion import imputacion_correlacion diff --git a/ADRpy/analisis/Modulos/imputacion_correlacion/imputacion_correlacion.py b/ADRpy/analisis/Modulos/imputacion_correlacion/imputacion_correlacion.py new file mode 100644 index 00000000..caabdc04 --- /dev/null +++ b/ADRpy/analisis/Modulos/imputacion_correlacion/imputacion_correlacion.py @@ -0,0 +1,134 @@ +import pandas as pd +import numpy as np +from itertools import combinations +from sklearn.linear_model import LinearRegression +from sklearn.preprocessing import PolynomialFeatures +from sklearn.model_selection import LeaveOneOut +from sklearn.metrics import mean_absolute_percentage_error, r2_score + + +def cargar_y_validar_datos(path: str) -> pd.DataFrame: + """Load Excel data from the given path using sheet 'data_frame_prueba'.""" + try: + df = pd.read_excel(path, sheet_name="data_frame_prueba") + except FileNotFoundError: + raise FileNotFoundError(f"Archivo no encontrado: {path}") + except ValueError: + raise ValueError( + "No se pudo leer la hoja 'data_frame_prueba'. Verifique el archivo" + ) + df = df.rename(columns=lambda c: str(c).strip()) + df.replace("nan", np.nan, inplace=True) + return df + + +def seleccionar_predictores_validos(df: pd.DataFrame, objetivo: str) -> list: + """Return numeric predictors with at least 5 non-null values.""" + numericas = df.select_dtypes(include=[np.number]).columns + return [ + col + for col in numericas + if col != objetivo and df[col].notna().sum() >= 5 + ] + + +def generar_combinaciones(predictores: list) -> list: + combos = [] + for r in (1, 2): + combos.extend(list(combinations(predictores, r))) + return combos + + +def entrenar_modelo(df: pd.DataFrame, objetivo: str, predictores: tuple): + df_train = df.dropna(subset=[objetivo, *predictores]) + if len(df_train) < len(predictores) + 1: + return None + X = df_train[list(predictores)] + y = df_train[objetivo] + poly = len(predictores) == 2 + if poly: + pf = PolynomialFeatures(degree=2, include_bias=False) + X_trans = pf.fit_transform(X) + else: + pf = None + X_trans = X + modelo = LinearRegression().fit(X_trans, y) + pred = modelo.predict(X_trans) + mape = mean_absolute_percentage_error(y, pred) * 100 + r2 = r2_score(y, pred) + corr = 0.6 * (r2 / 0.7) + 0.4 * (1 - mape / 15) + return { + "predictores": predictores, + "modelo": modelo, + "pf": pf, + "mape": mape, + "r2": r2, + "corr": corr, + } + + +def filtrar_mejores_modelos(modelos: list, top: int = 2) -> list: + modelos = [m for m in modelos if m is not None] + modelos.sort(key=lambda m: m["corr"], reverse=True) + return modelos[:top] + + +def validar_con_loocv(df: pd.DataFrame, objetivo: str, info: dict) -> float: + df_train = df.dropna(subset=[objetivo, *info["predictores"]]) + if df_train.empty: + return np.inf + X = df_train[list(info["predictores"])] + y = df_train[objetivo] + if info["pf"] is not None: + X = info["pf"].fit_transform(X) + loo = LeaveOneOut() + errores = [] + for train_idx, test_idx in loo.split(X): + m = LinearRegression().fit(X[train_idx], y.iloc[train_idx]) + pred = m.predict(X[test_idx]) + errores.append(abs(y.iloc[test_idx].values[0] - pred[0])) + return float(np.mean(errores)) + + +def imputar_valores(df: pd.DataFrame, objetivo: str, info: dict): + df_res = df.copy() + faltantes = df_res[df_res[objetivo].isna()].index + imputaciones = [] + if not len(faltantes): + return df_res, imputaciones + X_pred = df_res.loc[faltantes, list(info["predictores"])] + if info["pf"] is not None: + X_pred = info["pf"].transform(X_pred) + df_res.loc[faltantes, objetivo] = info["modelo"].predict(X_pred) + for idx in faltantes: + imputaciones.append({"Fila": idx, "Parametro": objetivo, "Valor": df_res.at[idx, objetivo]}) + return df_res, imputaciones + + +def generar_reporte_final(registros: list) -> pd.DataFrame: + return pd.DataFrame(registros) + + +def imputacion_correlacion(path: str = "ADRpy/analisis/Data/Datos_aeronaves.xlsx"): + df = cargar_y_validar_datos(path) + reporte = [] + for objetivo in [c for c in df.columns if df[c].isna().any()]: + predictores = seleccionar_predictores_validos(df, objetivo) + if not predictores: + continue + modelos = [] + for combo in generar_combinaciones(predictores): + modelos.append(entrenar_modelo(df, objetivo, combo)) + mejores = filtrar_mejores_modelos(modelos) + if not mejores: + continue + mejor = min( + ( + (m, validar_con_loocv(df, objetivo, m)) + for m in mejores + ), + key=lambda t: t[1], + )[0] + df, imps = imputar_valores(df, objetivo, mejor) + reporte.extend(imps) + return df, generar_reporte_final(reporte) diff --git a/tests/test_imputacion_correlacion.py b/tests/test_imputacion_correlacion.py new file mode 100644 index 00000000..f7cd8288 --- /dev/null +++ b/tests/test_imputacion_correlacion.py @@ -0,0 +1,10 @@ +import pandas as pd +from ADRpy.analisis.Modulos.imputacion_correlacion import imputacion_correlacion + + +def test_imputacion_correlacion_basica(): + df, reporte = imputacion_correlacion('ADRpy/analisis/Data/Datos_aeronaves.xlsx') + assert not df.isna().any().any(), "Deberia imputar todos los valores faltantes" + # Verificamos que el valor imputado para Potencia en la fila 2 sea cercano al calculo esperado + valor = df.loc[2, 'Potencia'] + assert round(valor, 3) == round(25.8272522839, 3) From 4cfbdfc4caf4f8017b5feff8d2a82714960e36e4 Mon Sep 17 00:00:00 2001 From: Delpoo <157638420+Delpoo@users.noreply.github.com> Date: Fri, 6 Jun 2025 16:53:46 -0300 Subject: [PATCH 9/9] Improve correlation imputation --- .../imputacion_correlacion.py | 136 ++++++++++++++---- tests/test_imputacion_correlacion.py | 3 +- 2 files changed, 107 insertions(+), 32 deletions(-) diff --git a/ADRpy/analisis/Modulos/imputacion_correlacion/imputacion_correlacion.py b/ADRpy/analisis/Modulos/imputacion_correlacion/imputacion_correlacion.py index caabdc04..4c5df757 100644 --- a/ADRpy/analisis/Modulos/imputacion_correlacion/imputacion_correlacion.py +++ b/ADRpy/analisis/Modulos/imputacion_correlacion/imputacion_correlacion.py @@ -1,12 +1,31 @@ import pandas as pd import numpy as np from itertools import combinations +from collections import defaultdict from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures from sklearn.model_selection import LeaveOneOut from sklearn.metrics import mean_absolute_percentage_error, r2_score +def penalizacion_por_k(k: int) -> float: + """Return penalization factor based on sample size.""" + if k > 10: + return 1.0 + return max( + 0, + min( + 1, + 0.00002281 * k ** 5 + - 0.00024 * k ** 4 + - 0.0036 * k ** 3 + + 0.046 * k ** 2 + + 0.0095 * k + + 0.024, + ), + ) + + def cargar_y_validar_datos(path: str) -> pd.DataFrame: """Load Excel data from the given path using sheet 'data_frame_prueba'.""" try: @@ -22,14 +41,28 @@ def cargar_y_validar_datos(path: str) -> pd.DataFrame: return df -def seleccionar_predictores_validos(df: pd.DataFrame, objetivo: str) -> list: - """Return numeric predictors with at least 5 non-null values.""" +def seleccionar_predictores_validos(df: pd.DataFrame, objetivo: str, rango: float = 0.15) -> list: + """Return numeric predictors with enough data and within ±15% range.""" numericas = df.select_dtypes(include=[np.number]).columns - return [ - col - for col in numericas - if col != objetivo and df[col].notna().sum() >= 5 - ] + filas_obj = df[df[objetivo].isna()].index + candidatos = [] + for col in numericas: + if col == objetivo or df[col].notna().sum() < 5: + continue + valido = True + vals_no_nan = df[col].dropna() + if vals_no_nan.empty: + continue + mn, mx = vals_no_nan.min(), vals_no_nan.max() + rango_min, rango_max = mn * (1 - rango), mx * (1 + rango) + for idx in filas_obj: + val = df.at[idx, col] + if pd.isna(val) or not (rango_min <= val <= rango_max): + valido = False + break + if valido: + candidatos.append(col) + return candidatos def generar_combinaciones(predictores: list) -> list: @@ -39,13 +72,15 @@ def generar_combinaciones(predictores: list) -> list: return combos -def entrenar_modelo(df: pd.DataFrame, objetivo: str, predictores: tuple): +def entrenar_modelo( + df: pd.DataFrame, objetivo: str, predictores: tuple, poly: bool +) -> dict | None: + """Train linear or polynomial model and compute metrics.""" df_train = df.dropna(subset=[objetivo, *predictores]) if len(df_train) < len(predictores) + 1: return None X = df_train[list(predictores)] y = df_train[objetivo] - poly = len(predictores) == 2 if poly: pf = PolynomialFeatures(degree=2, include_bias=False) X_trans = pf.fit_transform(X) @@ -57,6 +92,8 @@ def entrenar_modelo(df: pd.DataFrame, objetivo: str, predictores: tuple): mape = mean_absolute_percentage_error(y, pred) * 100 r2 = r2_score(y, pred) corr = 0.6 * (r2 / 0.7) + 0.4 * (1 - mape / 15) + confianza = corr * penalizacion_por_k(len(df_train)) + tipo = ("poly" if poly else "linear") + f"-{len(predictores)}" return { "predictores": predictores, "modelo": modelo, @@ -64,30 +101,45 @@ def entrenar_modelo(df: pd.DataFrame, objetivo: str, predictores: tuple): "mape": mape, "r2": r2, "corr": corr, + "confianza": confianza, + "tipo": tipo, + "n": len(df_train), } def filtrar_mejores_modelos(modelos: list, top: int = 2) -> list: - modelos = [m for m in modelos if m is not None] - modelos.sort(key=lambda m: m["corr"], reverse=True) - return modelos[:top] - - -def validar_con_loocv(df: pd.DataFrame, objetivo: str, info: dict) -> float: + """Return top models per type based on confianza.""" + modelos = [m for m in modelos if m is not None and m["mape"] <= 15 and m["r2"] >= 0.7] + grupos: defaultdict[str, list] = defaultdict(list) + for m in modelos: + grupos[m["tipo"]].append(m) + mejores = [] + for lst in grupos.values(): + lst.sort(key=lambda x: x["confianza"], reverse=True) + mejores.extend(lst[:top]) + return mejores + + +def validar_con_loocv(df: pd.DataFrame, objetivo: str, info: dict) -> tuple: + """Return MAE and R2 from LOOCV validation.""" df_train = df.dropna(subset=[objetivo, *info["predictores"]]) if df_train.empty: - return np.inf + return np.inf, -np.inf X = df_train[list(info["predictores"])] y = df_train[objetivo] if info["pf"] is not None: X = info["pf"].fit_transform(X) + X_vals = X + else: + X_vals = X.values loo = LeaveOneOut() - errores = [] - for train_idx, test_idx in loo.split(X): - m = LinearRegression().fit(X[train_idx], y.iloc[train_idx]) - pred = m.predict(X[test_idx]) - errores.append(abs(y.iloc[test_idx].values[0] - pred[0])) - return float(np.mean(errores)) + preds = np.zeros(len(y)) + for i, (train_idx, test_idx) in enumerate(loo.split(X_vals)): + m = LinearRegression().fit(X_vals[train_idx], y.iloc[train_idx]) + preds[i] = m.predict(X_vals[test_idx])[0] + mae = float(np.mean(np.abs(y - preds))) + r2 = float(r2_score(y, preds)) + return mae, r2 def imputar_valores(df: pd.DataFrame, objetivo: str, info: dict): @@ -99,9 +151,33 @@ def imputar_valores(df: pd.DataFrame, objetivo: str, info: dict): X_pred = df_res.loc[faltantes, list(info["predictores"])] if info["pf"] is not None: X_pred = info["pf"].transform(X_pred) - df_res.loc[faltantes, objetivo] = info["modelo"].predict(X_pred) - for idx in faltantes: - imputaciones.append({"Fila": idx, "Parametro": objetivo, "Valor": df_res.at[idx, objetivo]}) + valores = info["modelo"].predict(X_pred) + df_res.loc[faltantes, objetivo] = valores + vals_no_nan = df[list(info["predictores"])] + advert = "" + mn = vals_no_nan.min() + mx = vals_no_nan.max() + for idx, val in zip(faltantes, valores): + advert = "" + for col in info["predictores"]: + v = df.at[idx, col] + if pd.isna(v) or not (mn[col] * 0.85 <= v <= mx[col] * 1.15): + advert = "Extrapolacion" + break + imputaciones.append( + { + "Fila": idx, + "Parametro": objetivo, + "Valor imputado": val, + "Confianza": info["confianza"], + "Corr": info["corr"], + "k": info["n"], + "Tipo Modelo": info["tipo"], + "Predictores": ",".join(info["predictores"]), + "Penalizacion_k": penalizacion_por_k(info["n"]), + "Advertencia": advert, + } + ) return df_res, imputaciones @@ -118,16 +194,14 @@ def imputacion_correlacion(path: str = "ADRpy/analisis/Data/Datos_aeronaves.xlsx continue modelos = [] for combo in generar_combinaciones(predictores): - modelos.append(entrenar_modelo(df, objetivo, combo)) + for poly in (False, True): + modelos.append(entrenar_modelo(df, objetivo, combo, poly)) mejores = filtrar_mejores_modelos(modelos) if not mejores: continue mejor = min( - ( - (m, validar_con_loocv(df, objetivo, m)) - for m in mejores - ), - key=lambda t: t[1], + ((m, validar_con_loocv(df, objetivo, m)) for m in mejores), + key=lambda t: t[1][0], )[0] df, imps = imputar_valores(df, objetivo, mejor) reporte.extend(imps) diff --git a/tests/test_imputacion_correlacion.py b/tests/test_imputacion_correlacion.py index f7cd8288..5fa131ad 100644 --- a/tests/test_imputacion_correlacion.py +++ b/tests/test_imputacion_correlacion.py @@ -7,4 +7,5 @@ def test_imputacion_correlacion_basica(): assert not df.isna().any().any(), "Deberia imputar todos los valores faltantes" # Verificamos que el valor imputado para Potencia en la fila 2 sea cercano al calculo esperado valor = df.loc[2, 'Potencia'] - assert round(valor, 3) == round(25.8272522839, 3) + assert round(valor, 3) == round(25.9691788448, 3) + assert 'Confianza' in reporte.columns

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